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Recent content on R ViewsHugo -- gohugo.ioen-usRStudio, Inc. All Rights Reserved.Wed, 15 Aug 2018 00:00:00 +0000TokyoR #71
https://rviews.rstudio.com/2018/08/15/tokyor-71/
Wed, 15 Aug 2018 00:00:00 +0000https://rviews.rstudio.com/2018/08/15/tokyor-71/
<p><img src="/post/2018-08-13-TokyoR_files/TokyoR.png" alt="" /></p>
<p>Last month, I was delighted to be invited to speak, along with Hadley Wickham, at the seventy-first meeting of the TokyoR user group in Tokyo, Japan. This day-long mini-conference attracted more than 200 attendees and featured 16 talks that covered a wide range of topics, including two near-real-time analyses of World Cup Soccer games (<a href="http://rpubs.com/Ryo-N7/visualize_world_cup_with_R">here</a> and <a href="http://rpubs.com/Med_KU/404593">here</a>) and an analysis of wind direction with circular data and autogressive processes (<a href="https://nob0r.github.io/TokyoR/tokyor0715.html#/">here</a>). The tone of the talks ranged from light-hearted to business-serious. The slides for most of the presentations can be found <a href="https://tokyor.connpass.com/event/92522/">here</a>. If you scan through these slides, I think you will enjoy the contemporary Japanese aesthetics evident in the color palettes and playful composition of many of the presentations as well as the technical content.</p>
<p>In addition to the technical content, TokyoR was informative in at least three other areas. First of all, conference talks provided some insight into the country-wide R community. I have been doing my best to follow R user groups around the world for several years now, and have been tracking groups in Japan. Nevertheless, it is difficult to get a feel for what is really happening on the ground remotely. In his presentation, the <a href="https://speakerdeck.com/kilometer/71st-tokyo-dot-r-landscape-with-r">Landscape with R – Japanese Rnd. Community</a>, TokyoR organizer Koki Mimura did a great job of presenting the big picture of R in Japan. His talk indicated the breadth of established R user groups in Japan and described something of the evolution of the TokyoR group.</p>
<p><img src="/post/2018-08-13-TokyoR_files/Japan-RUGS.png" alt="" /></p>
<p>A second surprise was to discover that quite a few R books have been published in Japanese. “Recommendation of Reproducibility - Data Analysis and Making a Report using RStudio” by <a href="https://researchmap.jp/read0016899/?lang=english">Ishida Motohiro（石田 基広）</a> and <a href="https://researchmap.jp/kohske/">Kohske Takahashi（高橋 康介）</a> is one prominent recent example.</p>
<p><img src="/post/2018-08-13-TokyoR_files/book.png" alt="" /></p>
<p>You can find this and several more Japanese R and data sceince books by entering Ishida-san’s name, 石田 基広, into Amazon.co.jp.</p>
<p>(As good as Google Translate is, the consequences of having to rely on it are frustrating and occasionally amusing. Entering the book information
とある弁当屋の統計技師(データサイエンティスト) ―データ分析のはじめかた― 単行本 – 2013/9/25 ino Google Translate indicates that a literal translation of the Japanese phrase used to render the concept “data scientist” is “ceremonial statistical technician”. )</p>
<p>I was also very pleased to see that the TokyoR attendees seemed to reflect the diverse background and occupations of R users that one sees world-wide. The business cards I collected included several entrepreneurs, data scientists, software developers, management consultants, a marketing executive from the daily newspaper Asahi Shimbum, an editor from O’Reilly, a scientist from the National Museum of Nature and Science, and at least one researcher from the Department of Musical Creativity and Environment at Tokyo University of the Arts. These diverse roles and backgrounds indicate the great strength and flexibility of the R Community, and, I believe, ensure the continued growth of the R language.</p>
<p>Finally, I would like to thank the TokyoR organizers and participants alike for their gracious hospitality. This was just a fine group of people to hang out with.</p>
<p>Note, for more insight into the technical content discussed at TokyoR, Koki Mimura has made the slides of presentations he has delivered over the years available <a href="https://speakerdeck.com/kilometer">here</a>. Many of these are not only technically compelling, but splendid in their presentation.</p>
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Highcharting Jobs Friday
https://rviews.rstudio.com/2018/08/09/highcharting-jobs-friday/
Thu, 09 Aug 2018 00:00:00 +0000https://rviews.rstudio.com/2018/08/09/highcharting-jobs-friday/
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<p>Today, in honor of last week’s jobs report from the <a href="https://www.bls.gov/">Bureau of Labor Statistics</a> (BLS), we will visualize jobs data with <code>ggplot2</code> and then, more extensively with <code>highcharter</code>. Our aim is to explore <code>highcharter</code> and its similarity with <code>ggplot</code> and to create some nice interactive visualizations. In the process, we will cover how to import BLS data from FRED and then wrangle it for visualization. We won’t do any modeling or statistical analysis today, though it wouldn’t be hard to extend this script into a forecasting exercise. One nice thing about today’s code flow is that it can be refreshed and updated on each BLS release date.</p>
<p>Let’s get to it!</p>
<p>We will source our data from <a href="https://fred.stlouisfed.org/">FRED</a> and will use the <code>tq_get()</code> function from <code>tidyquant</code> which enables us to import many data series at once in tidy, <code>tibble</code> format. We want to get total employment numbers, ADP estimates, and the sector-by-sector numbers that make up total employment. Let’s start by creating a <code>tibble</code> to hold the FRED codes and more intuitive names for each data series.</p>
<pre class="r"><code>library(tidyverse)
library(tidyquant)
codes_names_tbl <- tribble(
~ symbol, ~ better_names,
"NPPTTL", "ADP Estimate",
"PAYEMS", "Nonfarm Employment",
"USCONS", "Construction",
"USTRADE", "Retail/Trade",
"USPBS", "Prof/Bus Serv",
"MANEMP", "Manufact",
"USFIRE", "Financial",
"USMINE", "Mining",
"USEHS", "Health Care",
"USWTRADE", "Wholesale Trade",
"USTPU", "Transportation",
"USINFO", "Info Sys",
"USLAH", "Leisure",
"USGOVT", "Gov",
"USSERV", "Other Services"
)</code></pre>
<p>Now we pass the <code>symbol</code> column to <code>tq_get()</code>.</p>
<pre class="r"><code>fred_empl_data <-
tq_get(codes_names_tbl$symbol,
get = "economic.data",
from = "2007-01-01")</code></pre>
<p>We have our data but look at the <code>symbol</code> column.</p>
<pre class="r"><code>fred_empl_data %>%
group_by(symbol) %>%
slice(1)</code></pre>
<pre><code># A tibble: 15 x 3
# Groups: symbol [15]
symbol date price
<chr> <date> <dbl>
1 MANEMP 2007-01-01 14008
2 NPPTTL 2007-01-01 115437.
3 PAYEMS 2007-01-01 137497
4 USCONS 2007-01-01 7725
5 USEHS 2007-01-01 18415
6 USFIRE 2007-01-01 8389
7 USGOVT 2007-01-01 22095
8 USINFO 2007-01-01 3029
9 USLAH 2007-01-01 13338
10 USMINE 2007-01-01 706
11 USPBS 2007-01-01 17834
12 USSERV 2007-01-01 5467
13 USTPU 2007-01-01 26491
14 USTRADE 2007-01-01 15443.
15 USWTRADE 2007-01-01 5969.</code></pre>
<p>The symbols are the FRED codes, which are unrecognizable unless you have memorized how those codes map to more intuitive names. Let’s replace them with the <code>better_names</code> column of <code>codes_names_tbl</code>. We will do this with a <code>left_join()</code>. (This explains why I labeled our original column as <code>symbol</code> - it makes the <code>left_join()</code> easier.) Special thanks to <a href="https://twitter.com/JennyBryan">Jenny Bryan</a> for pointing out this <a href="http://stat545.com/bit008_lookup.html">code flow</a>!</p>
<pre class="r"><code>fred_empl_data %>%
left_join(codes_names_tbl,
by = "symbol" ) %>%
select(better_names, everything(), -symbol) %>%
group_by(better_names) %>%
slice(1)</code></pre>
<pre><code># A tibble: 15 x 3
# Groups: better_names [15]
better_names date price
<chr> <date> <dbl>
1 ADP Estimate 2007-01-01 115437.
2 Construction 2007-01-01 7725
3 Financial 2007-01-01 8389
4 Gov 2007-01-01 22095
5 Health Care 2007-01-01 18415
6 Info Sys 2007-01-01 3029
7 Leisure 2007-01-01 13338
8 Manufact 2007-01-01 14008
9 Mining 2007-01-01 706
10 Nonfarm Employment 2007-01-01 137497
11 Other Services 2007-01-01 5467
12 Prof/Bus Serv 2007-01-01 17834
13 Retail/Trade 2007-01-01 15443.
14 Transportation 2007-01-01 26491
15 Wholesale Trade 2007-01-01 5969.</code></pre>
<p>That looks much better, but we now have a column called <code>price</code>, that holds the monthly employment observations, and a column called <code>better_names</code>, that holds the more intuitive group names. Let’s change those column names to <code>employees</code> and <code>sector</code>.</p>
<pre class="r"><code>fred_empl_data <-
fred_empl_data %>%
left_join(codes_names_tbl,
by = "symbol" ) %>%
select(better_names, everything(), -symbol) %>%
rename(employees = price, sector = better_names)
head(fred_empl_data)</code></pre>
<pre><code># A tibble: 6 x 3
sector date employees
<chr> <date> <dbl>
1 ADP Estimate 2007-01-01 115437.
2 ADP Estimate 2007-02-01 115527.
3 ADP Estimate 2007-03-01 115647
4 ADP Estimate 2007-04-01 115754.
5 ADP Estimate 2007-05-01 115809.
6 ADP Estimate 2007-06-01 115831.</code></pre>
<p><code>fred_empl_data</code> has the names and organization we want, but it still has the raw number of employees per month. We want to visualize the month-to-month <em>change</em> in jobs numbers, which means we need to perform a calculation on our data and store it in a new column. We use <code>mutate()</code> to create the new column and calculate monthly change with <code>value - lag(value, 1)</code>. We are not doing any annualizing or seasonality work here - it’s a simple substraction. For yearly change, it would be <code>value - lag(value, 12)</code>.</p>
<pre class="r"><code>empl_monthly_change <-
fred_empl_data %>%
group_by(sector) %>%
mutate(monthly_change = employees - lag(employees, 1)) %>%
na.omit()</code></pre>
<p>Our final data object <code>empl_monthly_change</code> is tidy, has intuitive names in the group column, and has the monthly change that we wish to visualize. Let’s build some charts.</p>
<p>We will start at the top and use <code>ggplot</code> to visualize how total non-farm employment (Sorry farmers. Your jobs don’t count, I guess) has changed since 2007. We want an end-user to quickly glance at the chart and find the months with positive jobs growth and negative jobs growth. That means we want months with positive jobs growth to be one color, and those with negative jobs growth to be another color. There is more than one way to accomplish this, but I like to create new columns and then add <code>geoms</code> based on those columns. (Check out <a href="http://lenkiefer.com/2018/03/11/charting-jobs-friday-with-r/">this post</a> by Freddie Mac’s Len Kiefer for another way to accomplish this by nesting <code>ifelse</code> statements in <code>ggplot's</code> aesthetics. In fact, if you like data visualization, check out all the stuff that Len writes.)</p>
<p>Let’s walk through how to create columns for shading by positive or negative jobs growth. First, we are looking at total employment here, so we call <code>filter(sector == "Nonfarm Employment")</code> to get only total employment.</p>
<p>Next, we create two new columns with <code>mutate()</code>. The first is called <code>col_pos</code> and is formed by <code>if_else(monthly_change > 0, monthly_change,...)</code>. That logic is creating a column that holds the value of monthly change if monthly change is positive, else it holds NA. We then create another column called <code>col_neg</code> using the same logic.</p>
<pre class="r"><code>empl_monthly_change %>%
filter(sector == "Nonfarm Employment") %>%
mutate(col_pos =
if_else(monthly_change > 0,
monthly_change, as.numeric(NA)),
col_neg =
if_else(monthly_change < 0,
monthly_change, as.numeric(NA))) %>%
dplyr::select(sector, date, col_pos, col_neg) %>%
head()</code></pre>
<pre><code># A tibble: 6 x 4
# Groups: sector [1]
sector date col_pos col_neg
<chr> <date> <dbl> <dbl>
1 Nonfarm Employment 2007-02-01 85 NA
2 Nonfarm Employment 2007-03-01 214 NA
3 Nonfarm Employment 2007-04-01 59 NA
4 Nonfarm Employment 2007-05-01 153 NA
5 Nonfarm Employment 2007-06-01 77 NA
6 Nonfarm Employment 2007-07-01 NA -30</code></pre>
<p>Have a qucik look at the <code>col_pos</code> and <code>col_neg</code> columns and make sure they look right. <code>col_pos</code> should have only positive and NA values, <code>col_neg</code> shoud have only negative and NA values.</p>
<p>Now we can visualize our monthly changes with <code>ggplot</code>, adding a separate <code>geom</code> for those new columns.</p>
<pre class="r"><code>empl_monthly_change %>%
filter(sector == "Nonfarm Employment") %>%
mutate(col_pos =
if_else(monthly_change > 0,
monthly_change, as.numeric(NA)),
col_neg =
if_else(monthly_change < 0,
monthly_change, as.numeric(NA))) %>%
ggplot(aes(x = date)) +
geom_col(aes(y = col_neg),
alpha = .85,
fill = "pink",
color = "pink") +
geom_col(aes(y = col_pos),
alpha = .85,
fill = "lightgreen",
color = "lightgreen") +
ylab("Monthly Change (thousands)") +
labs(title = "Monthly Private Employment Change",
subtitle = "total empl, since 2008",
caption = "inspired by @lenkiefer") +
scale_x_date(breaks = scales::pretty_breaks(n = 10)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust=0))</code></pre>
<p><img src="/post/2018-08-07-highcharting-jobs-friday_files/figure-html/unnamed-chunk-8-1.png" width="672" /></p>
<p>That plot is nice, but it’s static! Hover on it and you’ll see what I mean.</p>
<p>Let’s head to <code>highcharter</code> and create an interactive chart that responds when we hover on it. By way of brief background, <code>highcharter</code> is an R hook into the fantastic <a href="www.highcharts.com">highcharts</a> JavaScript library. It’s free for personal use but a license is required for commercial use.</p>
<p>One nice feature of <code>highcharter</code> is that we can use very similar aesthetic logic to what we used for <code>ggplot</code>. It’s not identical, but it’s similar and let’s us work with tidy data.</p>
<p>Before we get to the <code>highcharter</code> logic, we will add one column to our <code>tibble</code> to hold the color scheme for our positive and negative monthly changes. Notice how this is different from the <code>ggplot</code> flow above where we create one column to hold our positive changes for coloring and one column to hold our negative changes for coloring.</p>
<p>I want to color positive changes light blue and negative changes pink, and put the <a href="https://www.w3schools.com/colors/colors_picker.asp">rgb</a> codes for those colors directly in the new column. The rgb code for light blue is “#6495ed” and for pink is “#ffe6ea”. Thus we use <code>ifelse</code> to create a column called <code>color_of_bars</code> that holds “#6495ed” (light blue) when <code>monthly_change</code> is postive and “#ffe6ea” (pink) when it’s negative.</p>
<pre class="r"><code>total_employ_hc <-
empl_monthly_change %>%
filter(sector == "Nonfarm Employment") %>%
mutate(color_of_bars = ifelse(monthly_change > 0, "#6495ed", "#ffe6ea"))
head(total_employ_hc)</code></pre>
<pre><code># A tibble: 6 x 5
# Groups: sector [1]
sector date employees monthly_change color_of_bars
<chr> <date> <dbl> <dbl> <chr>
1 Nonfarm Employment 2007-02-01 137582 85 #6495ed
2 Nonfarm Employment 2007-03-01 137796 214 #6495ed
3 Nonfarm Employment 2007-04-01 137855 59 #6495ed
4 Nonfarm Employment 2007-05-01 138008 153 #6495ed
5 Nonfarm Employment 2007-06-01 138085 77 #6495ed
6 Nonfarm Employment 2007-07-01 138055 -30 #ffe6ea </code></pre>
<p>Now we are ready to start the <code>highcharter</code> flow.</p>
<p>We start by calling <code>hchart</code> to pass in our data object. Note the similarity to <code>ggplot</code> where we started with <code>ggplot</code>.</p>
<p>Now, intead of waiting for a call to <code>geom_col</code>, we set <code>type = "column"</code> to let <code>hchart</code> know that we are building a column chart. Next, we use <code>hcaes(x = date, y = monthly_change, color = color_of_bars)</code> to specify our aesthetics. Notice how we can control the colors of the bars from values in the <code>color_of_bars</code> column.</p>
<p>We also supply a <code>name = "monthly change"</code> because we want <code>monthly change</code> to appear when a user hovers on the chart. That wasn’t a consideration with <code>ggplot</code>.</p>
<pre class="r"><code>library(highcharter)
hchart(total_employ_hc,
type = "column",
pointWidth = 5,
hcaes(x = date,
y = monthly_change,
color = color_of_bars),
name = "monthly change") %>%
hc_title(text = "Monthly Employment Change") %>%
hc_xAxis(type = "datetime") %>%
hc_yAxis(title = list(text = "monthly change (thousands)")) %>%
hc_exporting(enabled = TRUE)</code></pre>
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<p>Let’s stay in the <code>highcharter</code> world and visualize how each sector changed in the most recent month, which is July of 2018.</p>
<p>First, we isolate the most recent month by filtering on the last date. We also don’t want the ADP Estimate and filter that out as well.</p>
<pre class="r"><code>empl_monthly_change %>%
filter(date == (last(date))) %>%
filter(sector != "ADP Estimate")</code></pre>
<pre><code># A tibble: 14 x 4
# Groups: sector [14]
sector date employees monthly_change
<chr> <date> <dbl> <dbl>
1 Nonfarm Employment 2018-07-01 149128 157
2 Construction 2018-07-01 7242 19
3 Retail/Trade 2018-07-01 15944 7.1
4 Prof/Bus Serv 2018-07-01 21019 51
5 Manufact 2018-07-01 12751 37
6 Financial 2018-07-01 8568 -5
7 Mining 2018-07-01 735 -4
8 Health Care 2018-07-01 23662 22
9 Wholesale Trade 2018-07-01 5982. 12.3
10 Transportation 2018-07-01 27801 15
11 Info Sys 2018-07-01 2772 0
12 Leisure 2018-07-01 16371 40
13 Gov 2018-07-01 22334 -13
14 Other Services 2018-07-01 5873 -5 </code></pre>
<p>That filtered flow has the data we want, but we have two more tasks. First, we want to <code>arrange</code> this data so that it goes from smallest to largest. If we did not do this, our chart would still “work”, but the column heights would not progress from lowest to highest.</p>
<p>Second, we need to create another column to hold colors for negative and positive values, with the same <code>ifelse()</code> logic as we used before.</p>
<pre class="r"><code>emp_by_sector_recent_month <-
empl_monthly_change %>%
filter(date == (last(date))) %>%
filter(sector != "ADP Estimate") %>%
arrange(monthly_change) %>%
mutate(color_of_bars = if_else(monthly_change > 0, "#6495ed", "#ffe6ea"))</code></pre>
<p>Now we pass that object to <code>hchart</code>, set <code>type = "column"</code>, and choose our <code>hcaes</code> values. We want to label the x-axis with the different sectors and do that with <code>hc_xAxis(categories = emp_by_sector_recent_month$sector)</code>.</p>
<pre class="r"><code>last_month <- lubridate::month(last(empl_monthly_change$date),
label = TRUE,
abbr = FALSE)
hchart(emp_by_sector_recent_month,
type = "column",
pointWidth = 20,
hcaes(x = sector,
y = monthly_change,
color = color_of_bars),
showInLegend = FALSE) %>%
hc_title(text = paste(last_month, "Employment Change", sep = " ")) %>%
hc_xAxis(categories = emp_by_sector_recent_month$sector) %>%
hc_yAxis(title = list(text = "Monthly Change (thousands)"))</code></pre>
<div id="htmlwidget-2" style="width:100%;height:500px;" class="highchart html-widget"></div>
<script type="application/json" data-for="htmlwidget-2">{"x":{"hc_opts":{"title":{"text":"July Employment Change"},"yAxis":{"title":{"text":"Monthly Change (thousands)"},"type":"linear"},"credits":{"enabled":false},"exporting":{"enabled":false},"plotOptions":{"series":{"turboThreshold":0,"showInLegend":false,"marker":{"enabled":true}},"treemap":{"layoutAlgorithm":"squarified"},"bubble":{"minSize":5,"maxSize":25},"scatter":{"marker":{"symbol":"circle"}}},"annotationsOptions":{"enabledButtons":false},"tooltip":{"delayForDisplay":10},"series":[{"group":"group","data":[{"sector":"Gov","date":"2018-07-01","employees":22334,"monthly_change":-13,"color_of_bars":"#ffe6ea","y":-13,"color":"#ffe6ea","name":"Gov"},{"sector":"Financial","date":"2018-07-01","employees":8568,"monthly_change":-5,"color_of_bars":"#ffe6ea","y":-5,"color":"#ffe6ea","name":"Financial"},{"sector":"Other Services","date":"2018-07-01","employees":5873,"monthly_change":-5,"color_of_bars":"#ffe6ea","y":-5,"color":"#ffe6ea","name":"Other Services"},{"sector":"Mining","date":"2018-07-01","employees":735,"monthly_change":-4,"color_of_bars":"#ffe6ea","y":-4,"color":"#ffe6ea","name":"Mining"},{"sector":"Info Sys","date":"2018-07-01","employees":2772,"monthly_change":0,"color_of_bars":"#ffe6ea","y":0,"color":"#ffe6ea","name":"Info Sys"},{"sector":"Retail/Trade","date":"2018-07-01","employees":15944,"monthly_change":7.10000000000036,"color_of_bars":"#6495ed","y":7.10000000000036,"color":"#6495ed","name":"Retail/Trade"},{"sector":"Wholesale Trade","date":"2018-07-01","employees":5981.5,"monthly_change":12.3000000000002,"color_of_bars":"#6495ed","y":12.3000000000002,"color":"#6495ed","name":"Wholesale Trade"},{"sector":"Transportation","date":"2018-07-01","employees":27801,"monthly_change":15,"color_of_bars":"#6495ed","y":15,"color":"#6495ed","name":"Transportation"},{"sector":"Construction","date":"2018-07-01","employees":7242,"monthly_change":19,"color_of_bars":"#6495ed","y":19,"color":"#6495ed","name":"Construction"},{"sector":"Health Care","date":"2018-07-01","employees":23662,"monthly_change":22,"color_of_bars":"#6495ed","y":22,"color":"#6495ed","name":"Health Care"},{"sector":"Manufact","date":"2018-07-01","employees":12751,"monthly_change":37,"color_of_bars":"#6495ed","y":37,"color":"#6495ed","name":"Manufact"},{"sector":"Leisure","date":"2018-07-01","employees":16371,"monthly_change":40,"color_of_bars":"#6495ed","y":40,"color":"#6495ed","name":"Leisure"},{"sector":"Prof/Bus Serv","date":"2018-07-01","employees":21019,"monthly_change":51,"color_of_bars":"#6495ed","y":51,"color":"#6495ed","name":"Prof/Bus Serv"},{"sector":"Nonfarm Employment","date":"2018-07-01","employees":149128,"monthly_change":157,"color_of_bars":"#6495ed","y":157,"color":"#6495ed","name":"Nonfarm Employment"}],"type":"column","pointWidth":20,"showInLegend":false}],"xAxis":{"type":"category","title":{"text":"sector"},"categories":["Gov","Financial","Other Services","Mining","Info Sys","Retail/Trade","Wholesale Trade","Transportation","Construction","Health Care","Manufact","Leisure","Prof/Bus Serv","Nonfarm Employment"]}},"theme":{"chart":{"backgroundColor":"transparent"}},"conf_opts":{"global":{"Date":null,"VMLRadialGradientURL":"http =//code.highcharts.com/list(version)/gfx/vml-radial-gradient.png","canvasToolsURL":"http =//code.highcharts.com/list(version)/modules/canvas-tools.js","getTimezoneOffset":null,"timezoneOffset":0,"useUTC":true},"lang":{"contextButtonTitle":"Chart context menu","decimalPoint":".","downloadJPEG":"Download JPEG image","downloadPDF":"Download PDF document","downloadPNG":"Download PNG image","downloadSVG":"Download SVG vector image","drillUpText":"Back to {series.name}","invalidDate":null,"loading":"Loading...","months":["January","February","March","April","May","June","July","August","September","October","November","December"],"noData":"No data to display","numericSymbols":["k","M","G","T","P","E"],"printChart":"Print chart","resetZoom":"Reset zoom","resetZoomTitle":"Reset zoom level 1:1","shortMonths":["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],"thousandsSep":" ","weekdays":["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"]}},"type":"chart","fonts":[],"debug":false},"evals":[],"jsHooks":[]}</script>
<p>Finally, let’s compare the ADP Estimates to the actual Nonfarm payroll numbers since 2017. We start with filtering again.</p>
<pre class="r"><code>adp_bls_hc <-
empl_monthly_change %>%
filter(sector == "ADP Estimate" | sector == "Nonfarm Employment") %>%
filter(date >= "2017-01-01")</code></pre>
<p>We create a column to hold different colors, but our logic is not whether a reading is positive or negative. We want to color the ADP and BLS reports differently.</p>
<pre class="r"><code>adp_bls_hc <-
adp_bls_hc %>%
mutate(color_of_bars =
ifelse(sector == "ADP Estimate", "#ffb3b3", "#4d94ff"))
head(adp_bls_hc)</code></pre>
<pre><code># A tibble: 6 x 5
# Groups: sector [1]
sector date employees monthly_change color_of_bars
<chr> <date> <dbl> <dbl> <chr>
1 ADP Estimate 2017-01-01 123253. 245. #ffb3b3
2 ADP Estimate 2017-02-01 123533. 280. #ffb3b3
3 ADP Estimate 2017-03-01 123655 122. #ffb3b3
4 ADP Estimate 2017-04-01 123810. 155. #ffb3b3
5 ADP Estimate 2017-05-01 124012. 202. #ffb3b3
6 ADP Estimate 2017-06-01 124166. 154. #ffb3b3 </code></pre>
<pre class="r"><code>tail(adp_bls_hc)</code></pre>
<pre><code># A tibble: 6 x 5
# Groups: sector [1]
sector date employees monthly_change color_of_bars
<chr> <date> <dbl> <dbl> <chr>
1 Nonfarm Employment 2018-02-01 148125 324 #4d94ff
2 Nonfarm Employment 2018-03-01 148280 155 #4d94ff
3 Nonfarm Employment 2018-04-01 148455 175 #4d94ff
4 Nonfarm Employment 2018-05-01 148723 268 #4d94ff
5 Nonfarm Employment 2018-06-01 148971 248 #4d94ff
6 Nonfarm Employment 2018-07-01 149128 157 #4d94ff </code></pre>
<p>And now we pass that object to our familiar <code>hchart</code> flow.</p>
<pre class="r"><code>hchart(adp_bls_hc,
type = 'column',
hcaes(y = monthly_change,
x = date,
group = sector,
color = color_of_bars),
showInLegend = FALSE
) %>%
hc_title(text = "ADP v. BLS") %>%
hc_xAxis(type = "datetime") %>%
hc_yAxis(title = list(text = "monthly change (thousands)")) %>%
hc_add_theme(hc_theme_flat()) %>%
hc_exporting(enabled = TRUE)</code></pre>
<div id="htmlwidget-3" style="width:100%;height:500px;" class="highchart html-widget"></div>
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","weekdays":["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"]}},"type":"chart","fonts":[],"debug":false},"evals":[],"jsHooks":[]}</script>
<p>That’s all for today. Try revisiting this script on September 7th, when the next BLS jobs data is released, and see if any new visualizations or code flows come to mind.</p>
<p>See you next time and happy coding!</p>
<script>window.location.href='https://rviews.rstudio.com/2018/08/09/highcharting-jobs-friday/';</script>
Two Big Ideas from JSM 2018
https://rviews.rstudio.com/2018/08/07/two-big-ideas-from-jsm-2018/
Tue, 07 Aug 2018 00:00:00 +0000https://rviews.rstudio.com/2018/08/07/two-big-ideas-from-jsm-2018/
<p>The Joint Statistical Meetings offer an astounding number of talks. It is impossible for an individual to see more than a small portion of what is going on. Even so, a diligent attendee ought to come away with more than a few good ideas. The following are two big ideas that I got from the conference.</p>
<p><a href="https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215070">Session 149</a>, an invited panel on Theory versus Practice which featured an All-Star team of panelists (Edward George, Trevor Hastie, Elizaveta Levina, John Petkau, Nancy Reid, Richard J Samworth, Robert Tibshirani, Larry Wasserman and Bin Yu), covered a lot of ground and wove a rich tapestry of ideas. A persistent theme among many of the discussions was the worry that the paper publication process was undermining the quality of statistical results. Pressed to “sell” their ideas to journal editors, and constrained by publication space authors are being conditioned to emphasize the evidence for their results and neglect limitations or cases where their methods don’t perform well.</p>
<p>The big idea that really struck me was the notion articulated by Rob Tibshirani that simulation is the practical way to express healthy scientific skepticism that can be incorporated into both theoretical and applied papers without significantly increasing the papers length or complexity. (For the purposes of reproducibility, almost all of the simulation work can be submitted as supplementary material or stashed on a GitHub site.) For theoretical papers, authors could use simulation to examine underlying assumptions and determine which are most important, while authors of applied papers could point out cases where their methods or algorithms don’t work particularly well. Tibshirani noted that every model has its Achilles heel, and went so far as to suggest that every paper ought to have at least one table that exposes the weaknesses of a model or algorithm.</p>
<p>For researchers working in R, including simulations should add no additional burden as Monte Carlo simulation capabilities are built into the core of the language. (If you are new to R, you might find this <a href="https://speakerdeck.com/cjbayesian/introduction-to-simulation-using-r">brief tutorial</a> by Corey Chivers helpful in getting started with simulating from statistical models.)</p>
<p>The second big idea came in <a href="https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215839">Section 271</a>, the Invited Special Presentation: <em>Introductory Overview Lecture: Reproducibility, Efficient Workflows, and Rich Environments]</em>. In her talk, <a href="https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=333047"><em>How Computational Environments Can (Unexpectedly) Influence Statistical Findings</em></a> Victoria Stodden elaborated on the idea that “As statistical research typically takes place in a constructed environment in silico, the findings may not be independent of this environment”. To help establish the pedigree of her ideas, Stodden briefly quoted David Donoho’s famous remark on a scientific paper only being the advertising for scientific work and not the scholarship itself. The paragraph surrounding this remark is illuminating. In his 2010 paper, <a href="https://academic.oup.com/biostatistics/article/11/3/385/257703"><em>An invitation to reproducible computational research</em></a>, Donoho writes:</p>
<blockquote>
<p>I was inspired more than 15 years ago by John Claerbout, an earth scientist at Stanford, to begin practicing reproducible computational science. See <a href="https://library.seg.org/doi/abs/10.1190/1.1822162">Claerbout and Karrenbach (1992)</a>. He pointed out to me, in a way paraphrased in Buckheit and Donoho (1995): “an article about computational result is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the result.” This struck me as getting to the heart of a central problem in modern scientific publication. Most of the work in a modern research project is hidden in computational scripts that go to produce the reported results. If these scripts are not brought out into the open, no one really knows what was done in a certain project…</p>
</blockquote>
<p>Here we have an assertion of the essential scientific value of software and code in a thread that traces the need for reproducible research back quite a few years to a collaboration between scientists and statisticians.</p>
<p>Immediately following Stodden, in his talk on <a href="https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=333049">Living a Reproducible Life</a> Hadley Wickham gave a virtuoso presentation on using modern, R centric reproducible tools. (He even managed to rebase a GitHub repo without calling attention to it).</p>
<p>My main “takeaways” from these two talks were, first of all, an affirmation that the CRAN and <a href="https://www.bioconductor.org/">Bioconductor</a> repositories are themselves extremely valuable contributions to statistics. Not only do they enable the daily practice of statistics for many statisticians, by providing reference implementations (and documentation) for a vast number of models and algorithms they are the repositories of statistical knowledge.</p>
<p>The second takeaway is that reproducible research, long acknowledged to be essential to the scientific process, is now feasible for a large number of practitioners. Using coding tools such as <code>R Markdown</code> along with infrastructure such as GitHub, it is possible to develop reproducible workflows for significant portions of a research process. R-centric reproducible tools are helping to put the science in data science.</p>
<p>Note that both Victoria Stodden and Rob Tibshirani, along with R Core member Michael Lawrence, will be delivering keynote presentations at the inaugural <a href="r-medicine.com">R / Medicine</a> conference coming up September 7th and 8th in New Haven, CT.</p>
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June 2018: Top 40 New Packages
https://rviews.rstudio.com/2018/07/29/june-2018-top-40-new-packages/
Sun, 29 Jul 2018 00:00:00 +0000https://rviews.rstudio.com/2018/07/29/june-2018-top-40-new-packages/
<p>Approximately 144 new packages stuck to CRAN in June. That fact that 31 of these are specialized to particular scientific disciplines or analyses provides some evidence to my hypothesis that working scientists are actively adopting R. Below are my Top 40 picks for June, organized into the categories of Computational Methods, Data, Data Science, Economics, Science, Statistics, Time Series, Utilities and Visualizations. The Data packages, especially <code>rtrek</code> and <code>opensensmapr</code>, look like they have some interesting new data to explore.</p>
<h3 id="computational-methods">Computational Methods</h3>
<p><a href="https://cran.r-project.org/package=nnTensor">nnTensor</a> v0.99.1: Provides methods for n-negative matrix factorization and decomposition. See <a href="doi:10.1002/9780470747278">Cichock et al (2009)</a> for details.</p>
<p><a href="https://cran.r-project.org/package=RcppEigenAD">RcppEigenAD</a> v1.0.0: Provides functions to compile <code>C++</code> code using <code>Rcpp</code>, <code>Eigen</code>, and <code>CppAD</code> to produce first- and second-order partial derivatives, and also provides an implementation of Faa’ di Bruno’s formula to combine the partial derivatives of composed functions. See <a href="arXiv:math/0601149v1">Hardy (2006)</a>.</p>
<p><a href="https://CRAN.R-project.org/package=rcane">rcrane</a> v1.0: Provides optimization algorithms to estimate coefficients in models such as linear regression and neural networks. Includes batch gradient descent, stochastic gradient descent, minibatch gradient descent, and coordinate descent. See [Kiwiel, (2001)](doi:10.1007/PL00011414, <a href="ISBN:1-4020-7553-7">Yu Nesterov (2004)</a>, <a href="doi:10.1080/01621459.1982.10477894">Ferguson (1982)</a>, <a href="arXiv:1212.5701">Zeiler (2012)</a>, and <a href="arXiv:1502.04759">Wright (2015)</a>. The <a href="https://cran.r-project.org/web/packages/rcane/vignettes/rcane.html">vignette</a> introduces the package.</p>
<h3 id="data">Data</h3>
<p><a href="https://cran.r-project.org/package=bjscrapeR">bjscrapeR</a> v0.1.0: Scrapes crime data from the <a href="https://www.bjs.gov/developer/ncvs/methodology.cfm">National Crime Victimization Survey</a>, which tracks personal and household crime in the USA.</p>
<p><a href="https://cran.r-project.org/package=genesysr">genesysr</a> v0.9.1: Implements an API to access data on plant genetic resources from genebanks around the world published on <a href="https://www.genesys-pgr.org">Genesys</a>. The <a href="https://cran.r-project.org/web/packages/genesysr/vignettes/tutorial.html">vignette</a> offers a short tutorial.</p>
<p><a href="https://cran.r-project.org/package=opensensmapropem">opensensmapr</a> v0.4.1: Allows users to download real-time environmental measurements and sensor station metadata from the <a href="https://opensensemap.org/">OpenSenseMap</a> API. There are vignettes for <a href="https://cran.r-project.org/web/packages/opensensmapr/vignettes/osem-history.html">Visualization</a>, <a href="https://cran.r-project.org/web/packages/opensensmapr/vignettes/osem-intro.html">Exploration</a>, and <a href="https://cran.r-project.org/web/packages/opensensmapr/vignettes/osem-serialization.html">Caching Data for Reproducibility</a>.</p>
<p><img src="/post/2018-07-21-June-Top40_files/opensense.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=readabs">readabs</a> v0.2.1: Provides functions to read <code>Excel</code> files from the Australian Bureau of Statistics into Tidy Data Sets. See the <a href="https://cran.r-project.org/web/packages/readabs/vignettes/my-vignette.html">vignette</a>.</p>
<p><a href="https://cran.r-project.org/package=rppo">rppo</a> v1.0: Implements an interface to the <a href="https://www.plantphenology.org/">Global Plant Phenology Data Portal</a>. See the <a href="https://cran.r-project.org/web/packages/rppo/vignettes/rppo-vignette.html">vignette</a>.</p>
<p><a href="https://cran.r-project.org/package=rtrek">rtrek</a> v0.1.0: Provides datasets related to the Star Trek fictional universe, functions for working with the data, and access to real-world datasets based on the televised series and other related licensed media productions. It interfaces with <a href="https://www.wikipedia.org/">Wikipedia</a>, the <a href="http://stapi.co/">Star Trek API (STAPI)</a>, <a href="http://memory-alpha.wikia.com/wiki/Portal:Main">Memory Alpha</a>, and <a href="http://memory-beta.wikia.com/wiki/Main_Page">Memory Beta</a> to retrieve data, metadata, and other information relating to Star Trek. See the <a href="https://cran.r-project.org/web/packages/rtrek/readme/README.html">README</a> for usage information.</p>
<p><img src="/post/2018-07-21-June-Top40_files/rtrek.png" height = "500" width="700"></p>
<p><a href="https://cran.r-project.org/package=skynet">skynet</a> v1.2.2: Implements methods for generating air transport statistics based on publicly available data from the <a href="https://www.transtats.bts.gov/databases.asp?Mode_ID=1&Mode_Desc=Aviation&Subject_ID2=0">U.S. Bureau of Transport Statistics (BTS)</a>. See the <a href="https://cran.r-project.org/web/packages/skynet/vignettes/skynet.html">vignette</a>.</p>
<p><img src="/post/2018-07-21-June-Top40_files/skynet.png" alt="" /></p>
<h3 id="data-science">Data Science</h3>
<p><a href="https://cran.r-project.org/package=AdaSampling">AdaSampling</a> v1.1: Implements the adaptive sampling procedure, a framework for both positive unlabeled learning and learning with class label noise. See <a href="doi:10.1109/TCYB.2018.2816984">Yang et al. (2018)</a> and the <a href="https://cran.r-project.org/web/packages/AdaSampling/vignettes/vignette.html">vignette</a>.</p>
<p><a href="https://cran.r-project.org/package=AROC">AROC</a> v1.0: Provides functions to estimate the covariate-adjusted Receiver Operating Characteristic (AROC) curve and pooled (unadjusted) ROC curve. See <a href="arXiv:1806.00473">de Carvalho and Rodriguez-Alvarez (2018)</a>.</p>
<p><img src="/post/2018-07-21-June-Top40_files/AROC.png" alt="" /></p>
<p><a href="https://CRAN.R-project.org/package=cloudml">cloudml</a> v0.5.1: Provides an interface to the Google Cloud Machine Learning Platform. There is a <a href="https://cran.r-project.org/web/packages/cloudml/vignettes/getting_started.html">Getting Sarted Guide</a> and vignettes on <a href="https://cran.r-project.org/web/packages/cloudml/vignettes/deployment.html">Deploying Models</a>, <a href="https://cran.r-project.org/web/packages/cloudml/vignettes/storage.html">Cloud storage</a>, <a href="https://cran.r-project.org/web/packages/cloudml/vignettes/training.html">Training</a>, and <a href="https://cran.r-project.org/web/packages/cloudml/vignettes/tuning.html">Hyperparameter Tuning</a>.</p>
<p><img src="/post/2018-07-21-June-Top40_files/cloudml.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=reclin">reclin</a> v0.1.0: Provide functions to assist in performing probabilistic record linkage and deduplication: generating pairs, comparing records, em-algorithm for estimating m- and u-probabilities, forcing one-to-one matching. There is an <a href="Introduction to reclin">Introduction</a> and a vignette on <a href="https://cran.r-project.org/web/packages/reclin/vignettes/deduplication.html">Duplication</a>.</p>
<p><a href="https://cran.r-project.org/package=vip">vip</a> v0.1.0: Provides a general framework for constructing variable importance plots from various types of machine learning models, based on a novel approach using partial dependence plots and individual conditional expectation curves as described in <a href="arXiv:1805.04755">Greenwell et al. (2018)</a>. See the <a href="https://cran.r-project.org/web/packages/vip/readme/README.html">README</a> for details and examples.</p>
<p><img src="/post/2018-07-21-June-Top40_files/vip.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=wevid">wevid</a> v0.4.2: Provides functions to quantify the performance of a binary classifier through weight of evidence. These can be used with any test dataset on which you have observed case-control status, and have computed prior and posterior probabilities of case status using a model learned on a training dataset. Look at this <a href="http://www.homepages.ed.ac.uk/pmckeigu/preprints/classify/wevidtutorial.html">website</a> for details and examples.</p>
<p><img src="/post/2018-07-21-June-Top40_files/wevid.png" alt="" /></p>
<h3 id="economics">Economics</h3>
<p><a href="https://CRAN.R-project.org/package=trade">trade</a> v0.5.3: Provides tools for working with trade model, including the ability to calibrate different consumer-demand systems and simulate the effects of tariffs and quotas under different competitive regimes. The <a href="https://cran.r-project.org/web/packages/trade/vignettes/Reference.html">vignette</a> provides details.</p>
<h3 id="science">Science</h3>
<p><a href="https://CRAN.R-project.org/package=linpk">linpk</a> v1.0: Provides functions and a shiny application to generate concentration-time profiles from linear pharmacokinetic (PK) systems. Single or multiple doses may be specified. The <a href="https://cran.r-project.org/web/packages/linpk/vignettes/linpk-intro.html">vignette</a> offers details and examples.</p>
<p><img src="/post/2018-07-21-June-Top40_files/linpk.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=ratematrix">ratematrix</a> v1.0: Provides functions to estimate the evolutionary rate matrix ® using Markov chain Monte Carlo (MCMC), as described in <a href="doi:10.1111/2041-210X.12826">Caetano and Harmon (2017)</a>. There is a vignette on <a href="https://cran.r-project.org/web/packages/ratematrix/vignettes/Set_custom_starting_point.html">Setting a custom starting point</a> and another on <a href="https://cran.r-project.org/web/packages/ratematrix/vignettes/Making_prior_on_ratematrix.html">Using prior distributions</a>.</p>
<p><img src="/post/2018-07-21-June-Top40_files/ratematrix.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=spectralAnalysis">spectralAnalysis</a> v3.12.0: Provides a toolkit for spectral-analysis, enabling users to pre-process, visualize, and analyse process analytical dat, by spectral data measurements made during a chemical process.</p>
<h3 id="statistics">Statistics</h3>
<p><a href="https://cran.r-project.org/package=betaboost">betaboost</a> v1.0.1: Implements boosting beta regression for potentially high-dimensional data <a href="doi:10.1093/ije/dyy093">Mayr et al. (2018)</a> using the same parametrization as <code>betareg</code> <a href="doi:10.18637/jss.v034.i02">Cribari-Neto and Zeileis (2010)</a>. The underlying algorithms are implemented via the R add-on packages <code>mboost</code> <a href="doi:10.1007/s00180-012-0382-5">Hofner et al. (2014)</a> and <code>gamboostLSS</code> <a href="doi:10.1111/j.1467-9876.2011.01033.x">Mayr et al. (2012)</a>. The <a href="https://cran.r-project.org/web/packages/betaboost/vignettes/Using_betaboost_IJE.html">vignette</a> offers examples.</p>
<p><a href="https://cran.r-project.org/package=bfw">bfw</a> v0.1.0: Provides a framework for conducting Bayesian analysis using Markov chain Monte Carlo with the <a href="http://mcmc-jags.sourceforge.net/">JAGS</a> sampler. There are vignettes on <a href="https://cran.r-project.org/web/packages/bfw/vignettes/fit_latent_data.html">Fitting Latent Data</a>, <a href="https://cran.r-project.org/web/packages/bfw/vignettes/fit_observed_data.html">Fitting Observed Data</a>, the <a href="https://cran.r-project.org/web/packages/bfw/vignettes/metric.html">Predict Metric</a>, <a href="https://cran.r-p[roject.org/web/packages/bfw/vignettes/plot_data.html">Plotting</a>, and <a href="https://cran.r-project.org/web/packages/bfw/vignettes/regression.html">Regression</a>.</p>
<p><img src="/post/2018-07-21-June-Top40_files/bfw.png" height = "500" width="700"></p>
<p><a href="https://cran.r-project.org/package=CaseBasedReasoning">CaseBasedReasoning</a> v0.1: Given a large set of problems and their individual solutions, case-based reasoning seeks to solve a new problem by referring to the solution of that problem that is “most similar” to the new problem. See <a href="doi:10.1016/S0167-9473(02)00058-0">Dippon et al. (2002)</a>, the vignette on <a href="https://cran.r-project.org/web/packages/CaseBasedReasoning/vignettes/Distance_Measures.html">Motivation</a>, and examples of case-based reasoning with a <a href="https://cran.r-project.org/web/packages/CaseBasedReasoning/vignettes/Cox-Beta-Model.html">Cox-Beta Model</a> and a <a href="https://cran.r-project.org/web/packages/CaseBasedReasoning/vignettes/RandomForest-Model.html">Random Forest Model</a>.</p>
<p><img src="/post/2018-07-21-June-Top40_files/case.png" alt="" /></p>
<p><a href="https://CRAN.R-project.org/package=coxed">coxed</a> v0.1.1: Provides functions for generating, simulating, and visualizing expected durations and marginal changes in duration from the Cox proportional hazards model. There is a vignette on using the <a href="https://cran.r-project.org/web/packages/coxed/vignettes/coxed.html">coxed() function</a> and another on <a href="https://cran.r-project.org/web/packages/coxed/vignettes/simulating_survival_data.html">simulating survival data</a>.</p>
<p><img src="/post/2018-07-21-June-Top40_files/coxed.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=GLMMadaptive">GLMMadaptive</a> v0.2-0: Provides functions to fit generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule. See <a href="doi:10.1080/10618600.1995.10474663">Pinheiro and Bates (1995)</a> and the vignettes on <a href="https://cran.r-project.org/web/packages/GLMMadaptive/vignettes/Custom_Models.html">Custom Models</a>,
<a href="https://cran.r-project.org/web/packages/GLMMadaptive/vignettes/GLMMadaptive_basics.html">GLMMadaptive Basics</a>, <a href="https://cran.r-project.org/web/packages/GLMMadaptive/vignettes/Methods_MixMod.html">Methods for MixMod Objects</a>, and <a href="https://cran.r-project.org/web/packages/GLMMadaptive/vignettes/ZeroInflated_and_TwoPart_Models.html">Zero-Inflated and Two-Part Mixed Effects Models</a>.</p>
<p><a href="https://cran.r-project.org/package=glmmfields">glmmfields</a> v0.1.0: Implements generalized linear mixed models with robust random fields for spatiotemporal modeling. The <a href="https://cran.r-project.org/web/packages/glmmfields/vignettes/spatial-glms.html">vignette</a> provides examples.</p>
<p><img src="/post/2018-07-21-June-Top40_files/glmmfields.png" height = "500" width="700"></p>
<p><a href="https://cran.r-project.org/package=kendallRandomWalks">kendallRandomWalks</a> v0.9.3: Provides functions for simulating Kendall random walks, continuous-space Markov chains generated by the Kendall generalized convolution. See <a href="arXiv:1412.0220">Jasiulis-Gołdyn (2014)</a> for details and the vignettes <a href="https://cran.r-project.org/web/packages/kendallRandomWalks/vignettes/kendall_rws.html">Kendall Random Walks</a> and <a href="https://cran.r-project.org/web/packages/kendallRandomWalks/vignettes/behaviour.html">Studying the Behavior of Kendall Random Walks</a>.</p>
<p><img src="/post/2018-07-21-June-Top40_files/kendall.png" height = "500" width="500"></p>
<p><a href="https://cran.r-project.org/package=netSEM">netSEM</a> v0.5.0: Provides functions for structural equation modeling. There is an <a href="https://cran.r-project.org/web/packages/netSEM/vignettes/netSEM.html">Introduction</a> and vignettes on <a href="https://cran.r-project.org/web/packages/netSEM/vignettes/Backsheet.html">Backsheet Degradation</a>, <a href="https://cran.r-project.org/web/packages/netSEM/vignettes/Crack.html">Backsheet Cracking</a>, <a href="https://cran.r-project.org/web/packages/netSEM/vignettes/IVfeature.html">Current Voltage Features</a>, and <a href="https://cran.r-project.org/web/packages/netSEM/vignettes/pet.html">Modeling of the Weathering Driven Degradation of Poly(ethylene-terephthalate) Films</a>.</p>
<p><img src="/post/2018-07-21-June-Top40_files/netSEM.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=umap">umap</a> v0.1.0.3: Implements the uniform manifold approximation and projection technique for dimension reduction as described in <a href="arXiv:1802.03426">McInnes and Healy (2018)</a>. The <a href="https://cran.r-project.org/web/packages/umap/vignettes/umap.html">vignette</a> shows how to use the package.</p>
<p><a href="https://cran.r-project.org/package=vimp">vimp</a> v1.0.0:Provides functions to calculate point estimates of, and valid confidence intervals for, non-parametric variable importance measures in high and low dimensions. For information about the methods, see <a href="https://biostats.bepress.com/uwbiostat/paper422/">Williamson et al. (2017)</a>. The <a href="https://cran.r-project.org/web/packages/vimp/vignettes/introduction_to_vimp.html">vignette</a> contains an introduction to the package.</p>
<p><img src="/post/2018-07-21-June-Top40_files/vimp.png" height = "500" width="600"></p>
<p><a href="https://cran.r-project.org/package=vsgoftest">vsgoftest</a> v0.3-2: Implements Vasicek and Song goodness-of-fit tests (based on Kullbach-Leibler divergence) for a family of distributions that include uniform, Gaussian, log-normal, exponential, gamma, Weibull, Pareto, Fisher, Laplace, and beta distributions. See <a href="arXiv:1806.07244">Lequesne and Regnault (2018)</a> for details and the <a href="https://cran.r-project.org/web/packages/vsgoftest/vignettes/vsgoftest_tutorial.pdf">Tutorial</a>.</p>
<h3 id="time-series">Time Series</h3>
<p><a href="https://cran.r-project.org/package=anomaly">anomaly</a> v1.0.0: Implements the CAPA (Collective And Point Anomaly) algorithm of <a href="arXiv:1806.01947">Fisch, Eckley and Fearnhead (2018)</a> for the detection of anomalies in time series data.</p>
<p><a href="https://cran.r-project.org/package=exuber">exuber</a> v0.1.0: Provides functions for testing and dating periods of explosive dynamics (exuberance) in time series using recursive unit root tests as proposed by <a href="doi:10.1111/iere.12132">Phillips et al. (2015)</a>. See the <a href="https://cran.r-project.org/web/packages/exuber/readme/README.html">README</a> to get started.</p>
<p>Simulate a variety of periodically-collapsing bubble models. The estimation and simulation utilizes the matrix inversion lemma from the recursive least squares algorithm, which results in a significant speed improvement.</p>
<h3 id="utilities">Utilities</h3>
<p><a href="https://cran.r-project.org/package=BiocManager">BiocManager</a> v1.30.1: Implements a tool to install and update Bioconductor packages. The <a href="https://cran.r-project.org/web/packages/BiocManager/vignettes/BiocManager.html">vignette</a> shows how to use the package.</p>
<p><a href="https://cran.r-project.org/package=IntervalSurgeon">IntervalSurgeon</a> v1.0: Provides functions for manipulating integer-bounded intervals including finding overlaps, piling, and merging. The <a href="https://cran.r-project.org/web/packages/IntervalSurgeon/vignettes/intro.html">vignette</a> shows how to use the package.</p>
<p><img src="/post/2018-07-21-June-Top40_files/interval.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=pkgbuild">pkgbuild</a> v1.0.0: Provides functions used to build R packages. Locates compilers needed to build R packages on various platforms and ensures the PATH is configured appropriately.</p>
<p><a href="https://cran.r-project.org/package=rqdatatable">rqdatatable</a> v0.1.2: Implements the <code>rquery</code> piped query algebra using <code>data.table</code>. There is a vignette on <a href="https://cran.r-project.org/web/packages/rqdatatable/vignettes/GroupedSampling.html">Grouped Sampling</a> and a <a href="https://cran.r-project.org/web/packages/rqdatatable/vignettes/logisticexample.html">Logistic Example</a>.</p>
<p><a href="https://cran.r-project.org/package=ssh">ssh</a> v0.2: Provides functions to connect to a remote server over SSH to transfer files via SCP, setup a secure tunnel, or run a command or script on the host while streaming stdout and stderr directly to the client. There is a <a href="https://cran.r-project.org/web/packages/ssh/vignettes/intro.html">vignette</a>.</p>
<h3 id="visualization">Visualization</h3>
<p><a href="https://cran.r-project.org/package=mgcViz">mgcViz</a> v0.1.1: An extension of the <code>mgcv</code> package, providing visual tools for Generalized Additive Models (GAMs) that exploit the additive structure of GAMs, scale to large data sets, and can be used in conjunction with a wide range of response distributions. See the <a href="https://cran.r-project.org/web/packages/mgcViz/vignettes/mgcviz.html">vignette</a> for examples.</p>
<p><img src="/post/2018-07-21-June-Top40_files/mgcViz.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=tiler">tiler</a> v0.2.0: Provides functions to create geographic map tiles from geospatial map files or non-geographic map tiles from simple image files. The <a href="https://cran.r-project.org/web/packages/tiler/vignettes/tiler.html">vignette</a> provides an introduction.</p>
<p><img src="/post/2018-07-21-June-Top40_files/tiler.png" height = "500" width="600"></p>
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JSM 2018 Itinerary
https://rviews.rstudio.com/2018/07/25/jsm-2018-itinerary/
Wed, 25 Jul 2018 00:00:00 +0000https://rviews.rstudio.com/2018/07/25/jsm-2018-itinerary/
<p>JSM 2018 is almost here! Usually around this time, I comb through the entire program manually making an itinerary for myself. But this year I decided to try something new – a programmatic way of going through the program, and then building a Shiny app that helps me better navigate the online program.</p>
<p>The end result of the app is below. (I might tweak it a bit further after this post goes live, depending on feedback I receive.) You can interact with the app <a href="https://minecr.shinyapps.io/jsm2018-schedule/">here</a>.</p>
<p>I’m often dissatisfied with conference webpages, and almost always dissatisfied with conference apps, so I thought this was a good opportunity to build one myself. Also, I’m teaching Shiny at JSM and have been wanting to acquaint myself better with the <a href="http://glue.tidyverse.org/">glue</a> package, so I figured spending some time web scraping, text wrangling, and building an app could be fun. (Note to self: Next time do this <em>after</em> you’re done preparing all your presentations!)</p>
<p>This is a three part blog post: (1) the data, (2) the app, and (3) the itinerary.</p>
<p>All relevant source code can be found <a href="https://github.com/mine-cetinkaya-rundel/jsm2018-schedule">here</a>.</p>
<div id="the-data" class="section level2">
<h2>The data</h2>
<p>The data were scraped from the <a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/index.cfm">JSM 2018 Online Program</a>.</p>
<p>Before scraping the data, I checked that the scraping is allowed on this page using <code>robotstxt::paths_allowed()</code>.</p>
<pre class="r"><code>library(robotstxt)
paths_allowed("http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/index.cfm")</code></pre>
<pre><code>##
ww2.amstat.org No encoding supplied: defaulting to UTF-8.</code></pre>
<pre><code>## [1] TRUE</code></pre>
<p>Looks like we’re good to go!</p>
<p>Once you’re on the online program <a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/index.cfm">landing page</a>, you need to click Search without setting any search parameters in order to get to a page with information on all JSM sessions. For convenience, I saved the resulting HTML file for this page in my repo. (You can access it <a href="https://github.com/mine-cetinkaya-rundel/jsm2018-schedule/blob/master/data/jsm2018.html">here</a>.)</p>
<p>The next step is using <a href="https://github.com/hadley/rvest">rvest</a> and the <a href="https://selectorgadget.com/">SelectorGadget</a> to scrape the data. This process allows us to take not-so-tidy data from the web and turn it into a tidy data frame that we can then work with in R.</p>
<div class="figure">
<img src="/post/2018-07-25-jsm-2018-itinerary_files/data-scrape.png" />
</div>
<p>Based on these data, I created two data frames: one for sessions and the other for talks. These two data frames will serve as the source data for the two tabs in the app. The code for data scraping the data and wrangling into these two data frames can be found <a href="https://github.com/mine-cetinkaya-rundel/jsm2018-schedule/blob/master/scrape.R">here</a>.</p>
</div>
<div id="the-app" class="section level2">
<h2>The app</h2>
<p>The app is built with Shiny, using a <code>navbarPage</code> to allow for two separate <code>tabPanel</code>s.</p>
<p>The first panel is the session schedule. This tab allows users to subset sessions based on days and times, as well as session sponsors and types. This is similar to the functionality on the JSM page; however, it’s designed to easily subset for sessions I like having on my radar, and it allows me to subset by time of day.</p>
<div class="figure">
<img src="/post/2018-07-25-jsm-2018-itinerary_files/jsm2018-app-sessions.png" />
</div>
<p>The second tab is designed to navigate talks, as opposed to sessions. You can look for keywords in talk titles. Curious how many talks have “R” in their title? How about “tidy”? Take a guess first, then peek at <a href="https://minecr.shinyapps.io/jsm2018-schedule/">the app</a> to check your answer.</p>
<div class="figure">
<img src="/post/2018-07-25-jsm-2018-itinerary_files/jsm2018-app-talks.png" />
</div>
<p>The source code for the app can be found <a href="https://github.com/mine-cetinkaya-rundel/jsm2018-schedule/blob/master/app.R">here</a>.</p>
</div>
<div id="the-itinerary" class="section level2">
<h2>The itinerary</h2>
<p>Using a combination of the app I build and good ol’ Ctrl-F on the online program, I came up with the following itinerary for JSM. The foci of the sessions I selected are education, data science, computing, visualization, and social responsibility. I obviously won’t make it to all the sessions I list here, but I plan to at least try to get my hands on the slides for the talks I don’t make.</p>
<p>I also plan on stopping by the <a href="https://ww2.amstat.org/meetings/jsm/2018/dataartshow.cfm">JSM Data Art Show</a> at some point!</p>
<p>If you have suggestions for other sessions (in these topics or other) that you think should be on this list, let me know in the comments!</p>
<div id="saturday-jul-28" class="section level3">
<h3>Saturday, Jul 28</h3>
<ul>
<li><p>8:00 AM - 12:00 PM: <a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215972">Shiny Essentials</a> - This morning I will be teaching a half-day workshop on building Shiny apps and dashboards. If you’re interested, you can sign up <a href="https://www.amstat.org/_EventSolution/EventDisplay.aspx?WebsiteKey=26030f62-5b88-4f45-9fe5-e6cf2757ee09&Eventkey=JSM2018&e0fa4633_3294_4cc7_b5d8_eef47e46e6ea=2#e0fa4633_3294_4cc7_b5d8_eef47e46e6ea">here</a>.</p></li>
<li><p>8:00 AM - 4:00 PM: <a href="https://sites.google.com/view/preparetoteach/">Preparing Graduate Students to Teach Statistics and Data Science</a> - This is a workshop designed to prepare graduate students for a role as undergraduate faculty responsible for teaching statistics and data science. I will be teaching two modules in this workshop in the afternoon.</p></li>
</ul>
</div>
<div id="sunday-jul-29" class="section level3">
<h3>Sunday, Jul 29</h3>
<ul>
<li>2:00 PM - 3:50 PM
<ul>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215165">Data Science Education - Successes and Challenges: Stories from the Classroom and Beyond</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215027">Transparency, Reproducibility and Replicability in Work with Social and Economic Data</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215835">Introductory Overview Lecture: The Deep Learning Revolution</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215591">Leading to Quantitative Literacy</a></li>
</ul></li>
<li>4:00 PM - 5:50 PM: <a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215836">Introductory Overview Lecture: Examining What and How We Teach at All Levels: Key Ideas to Ensure the Progress and Relevance of Statistics — Invited Special Presentation</a> - I will be chairing this session and based on what I’ve seen from the fantastic speakers so far, I strongly recommend you not miss it!</li>
</ul>
</div>
<div id="monday-jul-30" class="section level3">
<h3>Monday, Jul 30</h3>
<ul>
<li>7:00 AM - 8:30 AM: <a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=216530">Section on Statistical Education Officers Meeting</a></li>
<li>8:30 AM - 10:20 AM:
<ul>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215215">Visualization and Reproducibility - Challenges and Best Practices</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215677">Curricular Considerations for Statistics and Data Science Education</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215837">Introductory Overview Lecture: Leading Data Science: Talent, Strategy, and Impact</a></li>
</ul></li>
<li>10:30 AM - 12:20 PM
<ul>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215256">Creating and Sustaining an Undergraduate Research Program</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215482">Statistical Computing and Statistical Graphics: Student Paper Award and Chambers Statistical Software Award</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215938">SPEED: Teaching Statistics: Strategies and Applications</a> (Ends at 11:15 AM)</li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215941">SPEED: Data Expo</a> (Starts at 11:35 AM)</li>
</ul></li>
<li>2:00 PM - 3:50 PM: It will be particularly difficult to choose between these three sessions.
<ul>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215487">Late-Breaking Session: Addressing Sexual Misconduct in the Statistics Community</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=214992">An Emerging Ecosystem for Data Science/Statistics Education</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215035">Academic Publication Is Dead, Long Live Academic Publication</a></li>
</ul></li>
<li>4:00 PM - 5:50 PM: <a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215427">ASA President’s Invited Address - Helping to Save the Business of Journalism, One Data Insight at a Time</a></li>
<li>6:00 PM - 8:00 PM: <a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=216569">Statistical Computing and Statistics Graphics Mixer</a></li>
<li>7:00 PM - 8:30 PM: <a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=216813">Public Lecture: Born on Friday the Thirteenth: The Curious World of Probabilities</a></li>
</ul>
</div>
<div id="tuesday-jul-31" class="section level3">
<h3>Tuesday, Jul 31</h3>
<ul>
<li>8:30 AM - 11:30 AM: <a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=216546">ASA DataFest Steering Committee and Information Session</a> - If you’re running an ASA DataFest or if you’re interested running one next year, come join us! We’ll be discussing leads for next year’s dataset, and organization tips.</li>
<li>8:30 AM - 10:20 AM:
<ul>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215839">Introductory Overview Lecture: Reproducibility, Efficient Workflows, and Rich Environments</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215319">Student Outcomes in Undergraduate Courses Using a Simulation-Based Inference Approach to Teaching Statistics</a></li>
</ul></li>
<li>10:30 AM - 12:20 PM:
<ul>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215022">The Future of Spatial and Spatio-Temporal Statistics</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215286">Graphics in Statistical Practice: Saying it with Pictures in the Classroom, Boardroom, or the Consulting Cube</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215809">SPEED: Sports to Fire: Fascinating Applications of Statistics</a> - A variety of interesting applications; there may be some good examples for teaching among them.</li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215744">Data Science</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215887">Late-Breaking Session: Statistical Issues in Application of Machine Learning to High-Stakes Decisions</a></li>
</ul></li>
<li>12:30 PM - 2:00 PM: <a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=216815">2019 SDSS Planning Meeting</a> - I will be the short-course organizer for SDSS 2019. If you have ideas for a short course, either that you might want to teach or that you might want to take, let’s chat at JSM!</li>
<li>2:00 PM - 3:50 PM:
<ul>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215024">Bringing Intro Stats into a Multivariate and Data-Rich World</a> - I will be speaking at this session, hope to see you there!</li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215580">Lead with Statistics: Case Studies and Methods for Learning and Improving Healthcare Through EHRs</a></li>
</ul></li>
<li>5:30 PM - 7:30 PM: <a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=216829">Bayesian Mixer</a></li>
</ul>
</div>
<div id="wednesday-aug-1" class="section level3">
<h3>Wednesday, Aug 1</h3>
<ul>
<li>8:30 AM - 11:30 AM:
<ul>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215099">Getting Shots Inside the Box-Cox</a> - I don’t usually go to sports statistics sessions, but (1) this one is about soccer and (2) that session title is pretty witty!</li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215252">Worldwide Statistics Without Borders Projects: Statistics, Data Visualization, and Decision Making</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215533">Innovative and Effective Teaching for Large-Enrollment Statistics and Data Science Courses</a></li>
</ul></li>
<li>10:30 AM - 12:20 PM:
<ul>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215217">The Potential for Web-Scraping in the Production of Official Statistics: An Opportunity for Statistics to Lead?</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215333">Cloud and Distributed Computing for Statisticians</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215674">Fresh Approaches to Statistical Pedagogy</a></li>
</ul></li>
<li>2:00 PM - 3:50 PM:
<ul>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215679">A Mixed Bag of Graphical Delights</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215089">The State of Peer-Review and Publication in Statistics and the Sciences</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215131">Innovations in Teaching Undergraduate Probability</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215260">Staying Statistically Relevant: Keep Your Skills Sharp!</a></li>
</ul></li>
<li>4:00 PM - 5:50 PM: <a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215426">COPSS Awards and Fisher Lecture - The Future: Stratified Micro-Randomized Trials with Applications in Mobile Health</a></li>
<li>6:00 PM - 7:30 PM: <a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=216529">Section on Statistical Education Business Meeting</a> - Come celebrate the section turning 70, we’ll have cake!</li>
</ul>
</div>
<div id="thursday-aug-2" class="section level3">
<h3>Thursday, Aug 2</h3>
<ul>
<li>8:30 AM - 10:20 AM
<ul>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215033">Foundation or Backdrop? - the Role of Statisticians in Academic Data Science Initiatives</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215525">GAISEing into Introductory Service Courses in Light of Analytics/Data Scienc</a></li>
</ul></li>
<li>10:30 AM - 12:20 PM
<ul>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215338">Expanding the Tent: Undergraduate Majors in Data Science</a> - I’ll be a discussant in this session, very much looking forward to hearing others’ ideas on curricular approaches for data science education.</li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215026">Data Science for Social Good</a></li>
<li><a href="http://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215317">The ‘Ergonomics’ of Statistics and Data Science</a></li>
<li><a href="https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/ActivityDetails.cfm?SessionID=215097">Statistical Computing on Parallel Architectures</a></li>
</ul></li>
</ul>
</div>
</div>
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REST APIs and Plumber
https://rviews.rstudio.com/2018/07/23/rest-apis-and-plumber/
Mon, 23 Jul 2018 00:00:00 +0000https://rviews.rstudio.com/2018/07/23/rest-apis-and-plumber/
<p>Moving R resources from development to production can be a challenge, especially when the resource isn’t something like a <a href="http://shiny.rstudio.com"><code>shiny</code> application</a> or <a href="https://rmarkdown.rstudio.com"><code>rmarkdown</code> document</a> that can be easily published and consumed. Consider, as an example, a customer success model created in R. This model is responsible for taking customer data and returning a predicted outcome, like the likelihood the customer will churn. Once this model is developed and validated, there needs to be some way for the model output to be leveraged by other systems and individuals within the company.</p>
<p>Traditionally, moving this model into production has involved one of two approaches: either running customer data through the model on a batch basis and caching the results in a database, or handing the model definition off to a development team to translate the work done in R into another language, such as Java or Scala. Both approaches have significant downsides. Batch processing works, but it misses real-time updates. For example, if the batch job runs every night and a customer calls in the next morning and has a heated conversation with support, the model output will have no record of that exchange when the customer calls the customer loyalty department later the same day to cancel their service. In essence, model output is served on a lag, which can sometimes lead to critical information loss. However, the other option requires a large investment of time and resources to convert an existing model into another language just for the purpose of exposing that model as a real-time service. Neither of these approaches is ideal; to solve this problem, the optimal solution is to expose the existing R model as a service that can be easily accessed by other parts of the organization.</p>
<p><a href="https://www.rplumber.io"><code>plumber</code></a> is an R package that allows existing R code to be exposed as a web service through special decorator comments. With minimal overhead, R programmers and analysts can use <code>plumber</code> to create REST APIs that expose their work to any number of internal and external systems. This solution provides real-time access to processes and services created entirely in R, and can effectively eliminate the need to perform batch operations or technical hand-offs in order to move R code into production.</p>
<p>This post will focus on a brief introduction to RESTful APIs, then an introduction to the <code>plumber</code> package and how it can be used to expose R services as API endpoints. In subsequent posts, we’ll build a functioning web API using <code>plumber</code> that integrates with <a href="https://slack.com">Slack</a> and provides real-time customer status reports.</p>
<div id="web-apis" class="section level2">
<h2>Web APIs</h2>
<p>For some, <a href="https://en.wikipedia.org/wiki/Application_programming_interface">APIs (Application Programming Interface)</a> are things heard of but seldom seen. However, whether seen or unseen, APIs are part of everyday digital life. In fact, you’ve likely used a web API from within R, even if you didn’t recognize it at the time! Several R packages are simply wrappers around popular web APIs, such as <a href="https://walkerke.github.io/tidycensus/"><code>tidycensus</code></a> and <a href="https://github.com/r-lib/gh"><code>gh</code></a>. Web APIs are a common framework for sharing information across a network, most commonly through <a href="https://en.wikipedia.org/wiki/Hypertext_Transfer_Protocol">HTTP</a>.</p>
<div id="http" class="section level3">
<h3>HTTP</h3>
<p>To understand how HTTP requests work, it’s helpful to know the players involved. A <em>client</em> makes a request to a <em>server</em>, which interprets the request and provides a response. An HTTP request can be thought of simply as a packet of information sent to the server, which the server attempts to interpret and respond to. Every time you visit a URL in a web browser, an HTTP request is made and the response is rendered by the browser as the website you see. It is possible to inspect this interaction using the development tools in a browser.</p>
<div class="figure">
<img src="/post/2018-07-17-blair-plumber-intro-files/devtools-screenshot-request.png" />
</div>
<p>As seen above, this request is composed of a URL and a request method, which in the case of a web browser accessing a website, is GET.</p>
<div id="request" class="section level4">
<h4>Request</h4>
<p>There are several components of an <a href="https://www.w3.org/Protocols/rfc2616/rfc2616-sec5.html">HTTP request</a>, but here we’ll mention on only a few.</p>
<ul>
<li>URL: the address or endpoint for the request</li>
<li>Verb / method: a specific method invoked on the endpoint (GET, POST, DELETE, PUT)</li>
<li>Headers: additional data sent to the server, such as who is making the request and what type of response is expected</li>
<li>Body: data sent to the server outside of the headers, common for POST and PUT requests</li>
</ul>
<p>In the browser example above, a GET request was made by the web browser to www.rstudio.com.</p>
</div>
<div id="response" class="section level4">
<h4>Response</h4>
<p>The API response mirrors the request to some extent. It includes headers that contain information about the response and a body that contains any data returned by the API. The headers include the HTTP status code that informs the client how the request was received, along with details about the content that’s being delivered. In the example of a web browser accessing www.rstudio.com, we can see below that the response headers include the status code (200) along with details about the response content, including the fact that the content returned is HTML. This HTML content is what the browser renders into a webpage.</p>
<div class="figure">
<img src="/post/2018-07-17-blair-plumber-intro-files/devtools-screenshot-response.png" />
</div>
</div>
</div>
<div id="httr" class="section level3">
<h3>httr</h3>
<p>The <a href="http://httr.r-lib.org/index.html"><code>httr</code></a> package provides a nice framework for working with HTTP requests in R. The following basic example demonstrates some of what we’ve already learned by using <code>httr</code> and <a href="http://httpbin.org/">httpbin.org</a>, which provides a playground of sorts for HTTP requests.</p>
<pre class="r"><code>library(httr)
# A simple GET request
response <- GET("http://httpbin.org/get")
response</code></pre>
<pre><code>## Response [http://httpbin.org/get]
## Date: 2018-07-23 14:57
## Status: 200
## Content-Type: application/json
## Size: 266 B
## {"args":{},"headers":{"Accept":"application/json, text/xml, application/...</code></pre>
<p>In this example we’ve made a GET request to httpbin.org/get and received a response. We know our request was successful because we see that the status is 200. We also see that the response contains data in JSON format. The <a href="http://httr.r-lib.org/articles/quickstart.html"><em>Getting started with httr</em></a> page provides additional examples of working with HTTP requests and responses.</p>
</div>
<div id="rest" class="section level3">
<h3>REST</h3>
<p><a href="https://en.wikipedia.org/wiki/Representational_state_transfer">Representational State Transfer (REST)</a> is an architectural style for APIs that includes specific constraints for building APIs to ensure that they are consistent, performant, and scalable. In order to be considered truly RESTful, an API must meet each of the following six constraints:</p>
<ul>
<li>Uniform interface: clearly defined interface between client and server</li>
<li>Stateless: state is managed via the requests themselves, not through reliance on an external service</li>
<li>Cacheable: responses should be cacheable in order to improve scalability</li>
<li>Client-Server: clear separation of client and server, each with it’s on distinct responsibilities in the exchange</li>
<li>Layered System: there may be intermediaries between the client and the server, but the client should be unaware of them</li>
<li>Code on Demand: the response can include logic executable by the client</li>
</ul>
<p>We could spend a lot of time diving further into each of these specifications, but that is beyond the scope of this post. More detail about REST can be found <a href="https://www.restapitutorial.com">here</a>.</p>
</div>
</div>
<div id="plumber" class="section level2">
<h2>Plumber</h2>
<p>Creating RESTful APIs using R is straightforward using the <code>plumber</code> package. Even if you have never written an API, <code>plumber</code> makes it easy to turn existing R functions into API endpoints. Developing <code>plumber</code> endpoints is simply a matter of providing specialized R comments before R functions. <code>plumber</code> recognizes both <code>#'</code> and <code>#*</code> comments, although the latter is recommended in order to avoid potential conflicts with <a href="https://github.com/yihui/roxygen2"><code>roxygen2</code></a>. The following defines a <code>plumber</code> endpoint that simply returns the data provided in the request query string.</p>
<pre class="r"><code>library(plumber)
#* @apiTitle Simple API
#* Echo provided text
#* @param text The text to be echoed in the response
#* @get /echo
function(text = "") {
list(
message_echo = paste("The text is:", text)
)
}</code></pre>
<p>Here we’ve defined a simple function that takes a parameter, <code>text</code>, and returns it with some additional comments as part of a list. By default, <code>plumber</code> will serialize the object returned from a function into JSON using the <a href="https://github.com/jeroen/jsonlite"><code>jsonlite</code></a> package. We’ve provided specialized comments to inform <code>plumber</code> that this endpoint is available at <code>api-url/echo</code> and will respond to GET requests.</p>
<p>There are a few ways this <code>plumber</code> script can be run locally. First, assuming the file is saved as <code>plumber.R</code>, the following code would start a local web server hosting the API.</p>
<pre class="r"><code>plumber::plumb("plumber.R")$run(port = 5762)</code></pre>
<p>Once the web server has started, the API can be interacted with using any set of HTTP tools. We could even interact with it using <code>httr</code> as demonstrated earlier, although we would need to open a separate R session to do so since the current R session is busy serving the API.</p>
<p>The other method for running the API requires a recent <a href="https://www.rstudio.com/products/rstudio/download/preview/">preview build</a> of the RStudio IDE. Recent preview builds include features that make it easier to work with <code>plumber</code>. When editing a <code>plumber</code> script in a recent version of the IDE, a “Run API” icon will appear in the top right hand corner of the source editor. Clicking this button will automatically run a line of code similar to the one we ran above to start a web server hosting the API. A <a href="https://swagger.io">swagger</a>-generated UI will be rendered in the Viewer pane, and the API can be interacted with directly from within this UI.</p>
<div class="figure">
<img src="/post/2018-07-17-blair-plumber-intro-files/swagger-screenshot.png" />
</div>
<p>Now that we have a running <code>plumber</code> API, we can query it using <code>curl</code> from the command line to investigate it’s behavior.</p>
<pre class="bash"><code>$ curl "localhost:5762/echo" | jq '.'
{
"message_echo": [
"The text is: "
]
}</code></pre>
<p>In this case, we queried the API without providing any additional data or parameters. As a result, the <code>text</code> parameter is the default empty string, as seen in the response. In order to pass a value to our underlying function, we can define a query string in the request as follows:</p>
<pre class="bash"><code>$ curl "localhost:5762/echo?text=Hi%20there" | jq '.'
{
"message_echo": [
"The text is: Hi there"
]
}</code></pre>
<p>In this case, the <code>text</code> parameter is defined as part of the query string, which is appended to the end of the URL. Additional parameters could be defined by separating each key-value pair with <code>&</code>. It’s also possible to pass the parameter as part of the request body. However, to leverage this method of data delivery, we need to update our API definition so that the <code>/echo</code> endpoint also accepts POST requests. We’ll also update our API to consider multiple parameters, and return the parsed parameters along with the entire request body.</p>
<pre class="r"><code>library(plumber)
#* @apiTitle Simple API
#* Echo provided text
#* @param text The text to be echoed in the response
#* @param number A number to be echoed in the response
#* @get /echo
#* @post /echo
function(req, text = "", number = 0) {
list(
message_echo = paste("The text is:", text),
number_echo = paste("The number is:", number),
raw_body = req$postBody
)
}</code></pre>
<p>With this new API definition, the following <code>curl</code> request can be made to pass parameters to the API via the request body.</p>
<pre class="bash"><code>$ curl --data "text=Hi%20there&number=42&other_param=something%20else" "localhost:5762/echo" | jq '.'
{
"message_echo": [
"The text is: Hi there"
],
"number_echo": [
"The number is: 42"
],
"raw_body": [
"text=Hi%20there&number=42&other_param=something%20else"
]
}</code></pre>
<p>Notice that we passed more than just <code>text</code> and <code>number</code> in the request body. <code>plumber</code> parses the request body and matches any arguments found in the R function definition. Additional arguments, like <code>other_param</code> in this case, are ignored. <code>plumber</code> can parse the request body if it is URL-encoded or JSON. The following example shows the same request, but with the request body encoded as JSON.</p>
<pre class="bash"><code>$ curl --data '{"text":"Hi there", "number":"42", "other_param":"something else"}' "localhost:5762/echo" | jq '.'
{
"message_echo": [
"The text is: Hi there"
],
"number_echo": [
"The number is: 42"
],
"raw_body": [
"{\"text\":\"Hi there\", \"number\":\"42\", \"other_param\":\"something else\"}"
]
}</code></pre>
<p>While these examples are fairly simple, they demonstrate the extraordinary facility of <code>plumber</code>. Thanks to <code>plumber</code>, it is now a fairly straightforward process to expose R functions so they can be consumed and leveraged by any number of systems and processes. We’ve only scratched the surface of its capabilities and, as mentioned, future posts will walk through the creation of a Slack app using <code>plumber</code>. Comprehensive documentation for <code>plumber</code> can be found <a href="https://www.rplumber.io/docs/">here</a>.</p>
</div>
<div id="deploying" class="section level2">
<h2>Deploying</h2>
<p>Up until now, we’ve just been interacting with our APIs in our local development environment. That’s great for development and testing, but when it comes time to expose an API to external services, we don’t want our laptop held responsible (at least, I don’t!). There are several <a href="https://www.rplumber.io/docs/hosting.html">deployment methods</a> for <code>plumber</code> outlined in the documentation. The most straightforward method of deployment is to use <a href="https://www.rstudio.com/products/connect/">RStudio Connect</a>. When editing a <code>plumber</code> script in recent versions of the RStudio IDE, a blue publish button will appear in the top right-hand corner of the source editor. Clicking this button brings up a menu that enables the user to publish the API to an instance of RStudio Connect. Once published, API access and performance can be configured through RStudio Connect and the API can be leveraged by external systems and processes.</p>
</div>
<div id="conclusion" class="section level2">
<h2>Conclusion</h2>
<p>Web APIs are a powerful mechanism for providing systematic access to computational processes. Writing APIs with <code>plumber</code> makes it easy for others to take advantage of the work you’ve created in R without the need to rely on batch processing or code rewriting. <code>plumber</code> is exceptionally flexible and can be used to define a wide variety of endpoints. These endpoints can be used to integrate R with other systems. As an added bonus, downstream consumers of these APIs require no knowledge of R. They only need to know how to properly interact with the API via HTTP. <code>plumber</code> provides a convenient and reliable bridge between R and other systems and/or languages used within an organization.</p>
</div>
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CVXR: A Direct Standardization Example
https://rviews.rstudio.com/2018/07/20/cvxr-a-direct-standardization-example/
Fri, 20 Jul 2018 00:00:00 +0000https://rviews.rstudio.com/2018/07/20/cvxr-a-direct-standardization-example/
<p>In our <a href="https://rviews.rstudio.com/2017/11/27/introduction-to-cvxr/">first blog post</a>, we introduced <code>CVXR</code>, an R package for disciplined convex optimization, and showed how to model and solve a non-negative least squares problem using its interface. This time, we will tackle a non-parametric estimation example, which features new atoms as well as more complex constraints.</p>
<div id="direct-standardization" class="section level2">
<h2>Direct Standardization</h2>
<p>Consider a set of observations <span class="math inline">\((x_i,y_i)\)</span> drawn non-uniformly from an unknown distribution. We know the expected value of the columns of <span class="math inline">\(X\)</span>, denoted by <span class="math inline">\(b \in {\mathbf R}^n\)</span>, and want to estimate the true distribution of <span class="math inline">\(y\)</span>. This situation may arise, for instance, if we wish to analyze the health of a population based on a sample skewed toward young males, knowing the average population-level sex, age, etc.</p>
<p>A naive approach would be to simply take the empirical distribution that places equal probability <span class="math inline">\(1/m\)</span> on each <span class="math inline">\(y_i\)</span>. However, this is not a good estimation strategy when our sample is unbalanced. Instead, we will use the method of <strong>direct standardization</strong> (Fleiss, Levin, and Paik 2003, 19.5): we solve for weights <span class="math inline">\(w \in {\mathbf R}^m\)</span> of a weighted empirical distribution, <span class="math inline">\(y = y_i\)</span> with probability <span class="math inline">\(w_i\)</span>, which rectifies the skewness of the sample. This can be posed as the convex optimization problem</p>
<p><span class="math display">\[
\begin{array}{ll} \underset{w}{\mbox{maximize}} & \sum_{i=1}^m -w_i\log w_i \\
\mbox{subject to} & w \geq 0, \quad \sum_{i=1}^m w_i = 1,\quad X^Tw = b.
\end{array}
\]</span></p>
<p>Our objective is the total entropy, which is concave on <span class="math inline">\({\mathbf R}_+^m\)</span>. The constraints ensure <span class="math inline">\(w\)</span> is a probability vector that induces our known expectations over the columns of <span class="math inline">\(X\)</span>, i.e., <span class="math inline">\(\sum_{i=1}^m w_iX_{ij} = b_j\)</span> for <span class="math inline">\(j = 1,\ldots,n\)</span>.</p>
</div>
<div id="an-example-with-simulated-data" class="section level2">
<h2>An Example with Simulated Data</h2>
<p>As an example, we generate <span class="math inline">\(m = 1000\)</span> data points <span class="math inline">\(x_{i,1} \sim \mbox{Bernoulli}(0.5)\)</span>, <span class="math inline">\(x_{i,2} \sim \mbox{Uniform}(10,60)\)</span>, and <span class="math inline">\(y_i \sim N(5x_{i,1} + 0.1x_{i,2},1)\)</span>. We calculate <span class="math inline">\(b_j\)</span> to be the mean over <span class="math inline">\(x_{.,j}\)</span> for <span class="math inline">\(j = 1,2\)</span>. Then we construct a skewed sample of <span class="math inline">\(m = 100\)</span> points that over-represent small values of <span class="math inline">\(y_i\)</span>, thus biasing its distribution downwards.</p>
<p>Using <code>CVXR</code>, we construct the direct standardization problem. We first define the variable <span class="math inline">\(w\)</span>.</p>
<pre class="r"><code>w <- Variable(m)</code></pre>
<p>Then, we form the objective function by combining <code>CVXR</code>’s library of operators and atoms.</p>
<pre class="r"><code>objective <- Maximize(sum(entr(w)))</code></pre>
<p>Here, <code>entr</code> is the element-wise entropy atom; the S4 object <code>entr(w)</code> represents an <span class="math inline">\(m\)</span>-dimensional vector with entries <span class="math inline">\(-w_i\log(w_i)\)</span> for <span class="math inline">\(i=1,\ldots,m\)</span>. The <code>sum</code> operator acts exactly as expected, forming an expression that is the sum of the entries in this vector. (For a full list of atoms, see the <a href="http://cvxr.rbind.io/post/cvxr_functions/">function reference</a> page).</p>
<p>Our next step is to generate the list of constraints. Note that, by default, the relational operators apply over all entries in a vector or matrix.</p>
<pre class="r"><code>constraints <- list(w >= 0, sum(w) == 1, t(X) %*% w == b)</code></pre>
<p>Finally, we are ready to formulate and solve the problem.</p>
<pre class="r"><code>prob <- Problem(objective, constraints)
result <- solve(prob)
weights <- result$getValue(w)</code></pre>
<p>Using our optimal <code>weights</code>, we can then re-weight our skewed sample and compare it to the population distribution. Below, we plot the density functions using linear approximations for the range of <span class="math inline">\(y\)</span>.</p>
<pre class="r"><code>## Approximate density functions
dens1 <- density(ypop)
dens2 <- density(y)
dens3 <- density(y, weights = weights)
yrange <- seq(-3, 15, 0.01)
d <- data.frame(x = yrange,
True = approx(x = dens1$x, y = dens1$y, xout = yrange)$y,
Sample = approx(x = dens2$x, y = dens2$y, xout = yrange)$y,
Weighted = approx(x = dens3$x, y = dens3$y, xout = yrange)$y)
## Plot probability distribution functions
plot.data <- gather(data = d, key = "Type", value = "Estimate", True, Sample, Weighted,
factor_key = TRUE)
ggplot(plot.data) +
geom_line(mapping = aes(x = x, y = Estimate, color = Type)) +
theme(legend.position = "top")</code></pre>
<pre><code>## Warning: Removed 300 rows containing missing values (geom_path).</code></pre>
<div class="figure"><span id="fig:unnamed-chunk-6"></span>
<img src="/post/2018-07-20-cvxr-a-direct-standardization-example_files/figure-html/unnamed-chunk-6-1.png" alt="Probability distribution functions: population, skewed sample and reweighted sample" width="672" />
<p class="caption">
Figure 1: Probability distribution functions: population, skewed sample and reweighted sample
</p>
</div>
<pre class="r"><code>## Return the cumulative distribution function
get_cdf <- function(data, probs, color = 'k') {
if(missing(probs))
probs <- rep(1.0/length(data), length(data))
distro <- cbind(data, probs)
dsort <- distro[order(distro[,1]),]
ecdf <- base::cumsum(dsort[,2])
cbind(dsort[,1], ecdf)
}
## Plot cumulative distribution functions
d1 <- data.frame("True", get_cdf(ypop))
d2 <- data.frame("Sample", get_cdf(y))
d3 <- data.frame("Weighted", get_cdf(y, weights))
names(d1) <- names(d2) <- names(d3) <- c("Type", "x", "Estimate")
plot.data <- rbind(d1, d2, d3)
ggplot(plot.data) +
geom_line(mapping = aes(x = x, y = Estimate, color = Type)) +
theme(legend.position = "top")</code></pre>
<div class="figure"><span id="fig:unnamed-chunk-7"></span>
<img src="/post/2018-07-20-cvxr-a-direct-standardization-example_files/figure-html/unnamed-chunk-7-1.png" alt="Cumulative distribution functions: population, skewed sample and reweighted sample" width="672" />
<p class="caption">
Figure 2: Cumulative distribution functions: population, skewed sample and reweighted sample
</p>
</div>
<p>As is clear from the plots, the sample probability distribution peaks around <span class="math inline">\(y = 2.0\)</span>, and its cumulative distribution is shifted left from the population’s curve, a result of the downward bias in our sampled <span class="math inline">\(y_i\)</span>. However, with the direct standardization weights, the new empirical distribution cleaves much closer to the true distribution shown in red.</p>
<p>We hope you’ve enjoyed this demonstration of <code>CVXR</code>. For more examples, check out our <a href="http://cvxr.rbind.io">official site</a> and recent presentation <a href="https://www.youtube.com/watch?v=MyglbtnmQ8A">“Disciplined Convex Optimization with CVXR”</a> at useR! 2018.</p>
</div>
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Monte Carlo Shiny: Part Three
https://rviews.rstudio.com/2018/07/18/monte-carlo-shiny-part-3/
Wed, 18 Jul 2018 00:00:00 +0000https://rviews.rstudio.com/2018/07/18/monte-carlo-shiny-part-3/
<p>In previous posts, we covered how to <a href="https://rviews.rstudio.com/2018/06/05/monte-carlo/">run a Monte Carlo simulation</a> and <a href="https://rviews.rstudio.com/2018/06/13/monte-carlo-part-two/">how to visualize the results</a>. Today, we will wrap that work into a Shiny app wherein a user can build a custom portfolio, and then choose a number of simulations to run and a number of months to simulate into the future.</p>
<p>A link to that final Shiny app is <a href="http://www.reproduciblefinance.com/shiny/monte-carlo-simulation/">here</a> and here is a snapshot:</p>
<p><a href="http://www.reproduciblefinance.com/shiny/monte-carlo-simulation/"><img src="/post/2018-07-17-monte-carlo-shiny-part-3_files/app-snapshot.png" alt="monte carlo app" /></a></p>
<p>We will use RMarkdown to build our Shiny application by inserting <code>runtime: shiny</code> into the yaml header. This will alert the server (or our laptop) that this is an interactive document. The yaml header also gives us a space for the title and to specify the format as <code>flexdashboard</code>. This is what the yaml header looks like for the app.</p>
<pre class="r"><code>---
title: "Monte Carlo"
runtime: shiny
output:
flexdashboard::flex_dashboard:
orientation: rows
source_code: embed
---</code></pre>
<p>Note that when using RMarkdown and <code>runtime: shiny</code> we do no need to worry about UI and server logic, just inputs and outputs.</p>
<p>Our first code chunk is the <code>setup</code>, wherein we can load packages or data, just as we do with an R Notebook or static RMarkdown file.</p>
<pre class="r"><code># This is the setup chunk
library(tidyverse)
library(highcharter)
library(tidyquant)
library(timetk)</code></pre>
<p>Our first substantive task is to build an <code>input sidebar</code> where users choose five stocks and weights, a starting date, the number of months to be simulated and the number of simulations to be run.</p>
<p>The sidebar looks like this</p>
<div class="figure">
<img src="/post/2018-07-17-monte-carlo-shiny-part-3_files/input-sidebar.png" />
</div>
<p>The code for building that sidebar starts with <code>textInput("stock1",...))</code> to create a space where the user can type a stock symbol and then <code>numericInput("w1",...)</code> to create a space where the user can enter a numeric weight. We want those entry spaces to be on the same line so we will nest them inside of a call to <code>fluidRow()</code>.</p>
<p>Since we have five stocks and weights, we repeat this five times. Notice that the stock symbol field uses <code>textInput()</code> because the user needs to enter text, and the weight field uses <code>numericInput()</code> because the user needs to enter a number.</p>
<pre class="r"><code>fluidRow(
column(6,
textInput("stock1", "Stock 1", "SPY")),
column(5,
numericInput("w1", "Portf. %", 25,
min = 1, max = 100))
)
# Repeat this fluidRow() four more times, changing names to
# stock2, stock3, stock4, stock5 and w2, w3, 4, w5</code></pre>
<p>Let’s dissect one of those fluid rows line-by-line.</p>
<p><code>fluidRow()</code> creates the row.</p>
<p><code>column(6...)</code> creates a column for our stock ticker input with a length of 6.</p>
<p><code>textInput("stock1", "Stock 1", "SPY"))</code> creates our first text input field.</p>
<p>We assigned it <code>stock1</code> which means it will be referenced in downstream code as <code>input$stock1</code>. We labeled it with “Stock 1”, which is what the end user will see when viewing the app.</p>
<p>Finally, we set “SPY” as the default initial value. If the user does nothing, the value will be this default.</p>
<p>We also include a row where the user can choose a start date with <code>dateInput(...)</code>.</p>
<pre class="r"><code>fluidRow(
column(7,
dateInput("date",
"Starting Date",
"2013-01-01",
format = "yyyy-mm-dd"))
)</code></pre>
<p>Next, we create the <code>numericInput</code> fields for the number of months to simulate and the number of simulations to run.</p>
<pre class="r"><code>fluidRow(
column(5,
numericInput("sim_months", "Months", 120,
min = 6, max = 240, step = 6)),
column(5,
numericInput("sims", "Sims", 51,
min = 31, max = 101, step = 10))
)</code></pre>
<p>We now have all the inputs from the user and are almost ready to start calculating. Before we do so, let’s ask the user to click <code>submit</code>.</p>
<pre class="r"><code>actionButton("go", "Submit")</code></pre>
<p>The ‘submit’ button is very important because it enables the use of <code>eventReactive()</code> to control our computation. An <code>eventReactive()</code> is a reactive function that will not start until it observes some event. Without, our reactives would start firing each time a user changed an input.</p>
<p>In the next code chunk (and our subsequent calculation chunks as well), we tell <code>prices</code> to wait for <code>input$go</code> by calling <code>eventReactive(input$go...)</code>. When the user clicks, the reactive <code>inputs</code> get passed to our function.</p>
<pre class="r"><code>prices <- eventReactive(input$go, {
symbols <- c(input$stock1, input$stock2, input$stock3, input$stock4, input$stock5)
getSymbols(symbols, src = 'yahoo', from = input$date,
auto.assign = TRUE, warnings = FALSE) %>%
map(~Ad(get(.))) %>%
reduce(merge) %>%
`colnames<-`(symbols)
})</code></pre>
<p>From here, we pass the prices and weights to a portfolio returns code flow, which should look familiar from the first post. The only difference is that we are passing in the reactively chosen prices instead of the statically defined prices when we constructed the portfolio ourselves.</p>
<pre class="r"><code>portfolio_returns_tq_rebalanced_monthly <- eventReactive(input$go, {
prices <- prices()
w <- c(input$w1/100, input$w2/100, input$w3/100, input$w4/100, input$w5/100)
portfolio_returns_tq_rebalanced_monthly <-
prices %>%
to.monthly(indexAt = "last", OHLC = FALSE) %>%
tk_tbl(preserve_index = TRUE, rename_index = "date") %>%
gather(asset, returns, -date) %>%
group_by(asset) %>%
mutate(returns = (log(returns) - log(lag(returns)))) %>%
tq_portfolio(assets_col = asset,
returns_col = returns,
weights = w,
col_rename = "returns",
rebalance_on = "months")
})</code></pre>
<p>We now have a reactive object called <code>portfolio_returns_tq_rebalanced_monthly</code> and need to use it to find the mean and standard deviation of returns. Those are the parameters we need for the simulation.</p>
<pre class="r"><code>mean_port_return <- eventReactive(input$go, {
portfolio_returns_tq_rebalanced_monthly <-
portfolio_returns_tq_rebalanced_monthly()
mean(portfolio_returns_tq_rebalanced_monthly$returns)
})
stddev_port_return <- eventReactive(input$go, {
portfolio_returns_tq_rebalanced_monthly <-
portfolio_returns_tq_rebalanced_monthly()
sd(portfolio_returns_tq_rebalanced_monthly$returns)
})</code></pre>
<p>Next, we define one of our simulation functions that we discussed in the <a href="https://rviews.rstudio.com/2018/06/05/monte-carlo/">previous post</a>.</p>
<pre class="r"><code>simulation_accum_1 <- function(init_value, N, mean, stdev) {
tibble(c(init_value, 1 + rnorm(N, mean, stdev))) %>%
`colnames<-`("returns") %>%
mutate(growth =
accumulate(returns, function(x, y) x * y)) %>%
select(growth)
}</code></pre>
<p>Then, we call <code>eventReactive()</code> to run the simulation following the same logic as we did above.</p>
<pre class="r"><code>sims <- eventReactive(input$go, {input$sims})
monte_carlo_sim <- eventReactive(input$go, {
sims <- sims()
starts <-
rep(1, sims) %>%
set_names(paste("sim", 1:sims, sep = ""))
map_dfc(starts, simulation_accum_1,
N = input$sim_months, mean = mean_port_return(),
stdev = stddev_port_return()) %>%
mutate(month = seq(1:nrow(.))) %>%
select(month, everything()) %>%
`colnames<-`(c("month", names(starts))) %>%
gather(sim, growth, -month) %>%
group_by(sim) %>%
mutate_all(funs(round(., 2)))
})</code></pre>
<p>We now have a reactive object called <code>monte_carlo_sim()</code> which holds our 51 simulations of the custom portfolio. We can visualize with <code>highcharter()</code>, exactly as we did in the <a href="https://rviews.rstudio.com/2018/06/13/monte-carlo-part-two/">visualization post</a>. We pass the reactive object directly to <code>highcharter</code> by calling <code>hchar(monte_carlo_sim()...)</code>. Note that we begin the chunk with <code>renderHighchart()</code>. That alerts the file that the visualization is a reactively defined plot, and not a statically defined plot. If this were a <code>ggplot</code> visualization, we would start the call with <code>renderPlot()</code>.</p>
<pre class="r"><code>renderHighchart(
hchart(monte_carlo_sim(),
type = 'line',
hcaes(y = growth,
x = month,
group = sim)) %>%
hc_title(text = paste(sims(),
"Simulations",
sep = " ")) %>%
hc_xAxis(title = list(text = "months")) %>%
hc_yAxis(title = list(text = "dollar growth"),
labels = list(format = "${value}")) %>%
hc_add_theme(hc_theme_flat()) %>%
hc_exporting(enabled = TRUE) %>%
hc_legend(enabled = FALSE)
)</code></pre>
<p>And finally, we isolate the minimum, median and maximum simulations for visualization and pass them to <code>highcharter</code>.</p>
<pre class="r"><code>renderHighchart({
sim_summary <-
monte_carlo_sim() %>%
summarise(final = last(growth)) %>%
summarise(
max = max(final),
min = min(final),
median = median(final))
mc_max_med_min <-
monte_carlo_sim() %>%
filter(
last(growth) == sim_summary$max ||
last(growth) == sim_summary$median ||
last(growth) == sim_summary$min)
hchart(mc_max_med_min,
type = 'line',
hcaes(y = growth,
x = month,
group = sim)) %>%
hc_title(text = "Min Max Median Simulations") %>%
hc_xAxis(title = list(text = "months")) %>%
hc_yAxis(title = list(text = "dollar growth"),
labels = list(format = "${value}")) %>%
hc_add_theme(hc_theme_flat()) %>%
hc_exporting(enabled = TRUE) %>%
hc_legend(enabled = FALSE)
})</code></pre>
<p>That wraps our Monte Carlo application and this showcases a powerful use for Shiny (we could have written any simulation function and then displayed the results). The end user sees the charts and we, as the R coders, can run whatever functions we wish under the hood. Thanks for reading and happy coding!</p>
<script>window.location.href='https://rviews.rstudio.com/2018/07/18/monte-carlo-shiny-part-3/';</script>
Solver Interfaces in CVXR
https://rviews.rstudio.com/2018/07/09/solver-interfaces-in-cvxr/
Mon, 09 Jul 2018 00:00:00 +0000https://rviews.rstudio.com/2018/07/09/solver-interfaces-in-cvxr/
<h2 id="introduction">Introduction</h2>
<p>In our <a href="https://rviews.rstudio.com/2017/11/27/introduction-to-cvxr/">previous blog
post</a>, we
introduced <code>CVXR</code>, an R package for disciplined convex
optimization. The package allows one to describe an optimization
problem with <a href="http://dcp.stanford.edu">Disciplined Convex Programming</a>
rules using high level mathematical syntax. Passing this problem
definition along (with a list of constraints, if any) to the <code>solve</code>
function transforms it into a form that can be handed off to
a solver. The default installation of <code>CVXR</code> comes with two (imported)
open source solvers:</p>
<ul>
<li><a href="https://github.com/embotech/ecos">ECOS</a> and its mixed integer
cousin <code>ECOS_BB</code> via the CRAN package
<a href="https://cloud.r-project.org/package=ECOSolveR">ECOSolveR</a></li>
<li><a href="https://github.com/cvxgrp/scs">SCS</a> via the CRAN package
<a href="https://cloud.r-project.org/package=scs">scs</a>.
<br /></li>
</ul>
<p><code>CVXR</code> (version 0.99) can also make use of several other open source
solvers implemented in R packages:</p>
<ul>
<li>The linear and mixed integer programming package
<a href="http://lpsolve.sourceforge.net/5.5/"><code>lpSolve</code></a> via the
<a href="https://cloud.r-project.org/package=lpSolveAPI"><code>lpSolveAPI</code></a> package</li>
<li>The linear and mixed integer programming package <a href="https://www.gnu.org/software/glpk/"><code>GLPK</code></a> via the
<a href="https://cloud.r-project.org/package=Rglpk"><code>Rglpk</code></a> package.
<br /></li>
</ul>
<h2 id="about-solvers">About Solvers</h2>
<p>The real work of finding a solution is done by solvers, and writing
good solvers is hard work. Furthermore, some solvers work particularly
well for certain types of problems (linear programs, quadratic
programs, etc.). Not surprisingly, there are commercial vendors who
have solvers that are designed for performance and scale. Two
well-known solvers are <a href="https://www.mosek.com">MOSEK</a> and
<a href="https://www.gurobi.com">GUROBI</a>. R packages for these solvers are
also provided, but they require the problem data to be constructed in
a specific form. This necessitates a bit of work in the current version of
<code>CVXR</code> and is certainly something we plan to include in future versions.
However, it is also true that these commercial solvers expose a much
richer API to Python programmers than to R programmers. How, then, do we
interface such solvers with R as quickly as possible, at least
in the short term?</p>
<h2 id="reticulate-to-the-rescue">Reticulate to the Rescue</h2>
<p>The current version of <code>CVXR</code> exploits the
<a href="https://cran.r-project.org/package=reticulate"><code>reticulate</code></a> package
for commercial solvers such as MOSEK and GUROBI. We took the Python solver interfaces in <a href="http://www.cvxpy.org/versions/0.4.11/index.html"><code>CVXPY</code> version
0.4.11</a>, edited them
suitably to make them self-contained, and hooked them up to <code>reticulate</code>.</p>
<p>This means that one needs two prerequisites to use these commercial solvers in the current version of <code>CVXR</code>:</p>
<ul>
<li>A Python installation</li>
<li>The <a href="https://cran.r-project.org/package=reticulate"><code>reticulate</code></a> R
package.</li>
</ul>
<h2 id="installing-mosek-gurobi">Installing MOSEK/GUROBI</h2>
<p>Both <a href="https://www.mosek.com">MOSEK</a> and
<a href="https://www.gurobi.com">GUROBI</a> provide academic versions
(registration required) free of charge. For example,
<a href="https://www.anaconda.com">Anaconda</a> users can install MOSEK with the command:</p>
<pre><code class="language-bash, eval=FALSE">conda install -c mosek mosek
</code></pre>
<p>Others can use the <code>pip</code> command:</p>
<pre><code class="language-bash, eval = FALSE">pip install -f https://download.mosek.com/stable/wheel/index.html Mosek
</code></pre>
<p>GUROBI is handled in a similar fashion. The solvers must be activated using a
license provided by the vendor.</p>
<p>Once activated, one can check that <code>CVXR</code> recognizes the solver;
<code>installed_solvers()</code> should list them.</p>
<pre><code>> installed_solvers()
[1] "ECOS" "ECOS_BB" "SCS" "MOSEK" "LPSOLVE" "GLPK" "GUROBI"
</code></pre>
<h2 id="further-information">Further information</h2>
<p>More information on these solvers, along with a number of tutorial examples are
available on the <a href="https://cvxr.rbind.io">CVXR</a> site. If you are
attending <a href="https://user2018.r-project.org">useR! 2018</a>, you can catch
Anqi’s <code>CVXR</code> talk on Friday, July 13.</p>
<script>window.location.href='https://rviews.rstudio.com/2018/07/09/solver-interfaces-in-cvxr/';</script>
A First Look at NIMBLE
https://rviews.rstudio.com/2018/07/05/a-first-look-at-nimble/
Thu, 05 Jul 2018 00:00:00 +0000https://rviews.rstudio.com/2018/07/05/a-first-look-at-nimble/
<p>Writing a domain-specific language (DSL) is a powerful and fairly common method for extending the R language. Both <code>ggplot2</code> and <code>dplyr</code>, for example, are DSLs. (See Hadley’s <a href="http://adv-r.had.co.nz/dsl.html">chapter in Advanced R</a> for some elaboration.) In this post, I take a first look at <a href="https://CRAN.R-project.org/package=nimble"><code>NIMBLE</code></a> (Numerical Inference for Statistical Models using Bayesian and Likelihood Estimation), a DSL for formulating and efficiently solving statistical models in general, and Bayesian hierarchical models in particular. The latter comprise a class of interpretable statistical models useful for both inference and prediction. (See Gelman’s 2006 <a href="http://www.stat.columbia.edu/~gelman/research/published/multi2.pdf">Technographics paper</a> for what these models can and cannot do.)</p>
<p>Most of what I describe here can be found in the comprehensive and the very readable <a href="https://arxiv.org/pdf/1505.05093.pdf">paper</a> by Valpine et al., or the extensive <a href="https://r-nimble.org/manuals/NimbleUserManual.pdf">NIMBLE User Manual</a>.</p>
<p>At the risk of oversimplification, it seems to me that the essence of the NIMBLE is that it is a system for developing models designed around two dichotomies. The first dichotomy is that NIMBLE separates the specification of a model from the implementation of the algorithms that express it. A user can formulate a model in the <a href="http://www.openbugs.net/w/FrontPage"><code>BUGS</code></a> language, and then use different NIMBLE functions to solve the model. Or, looking at things the other way round, a user can write a NIMBLE function to implement some algorithm or another efficiently, and then use this function in different models.</p>
<p>The second dichotomy separates model setup from model execution. NIMBLE programming is done by writing <code>nimbleFunctions</code> (see <a href="https://r-nimble.org/manuals/NimbleUserManual.pdf">Chapter 11 of the Manual</a>), a subset of the R Language that is augmented with additional data structures and made compilable. <code>nimbleFunctions</code> come in two flavors. <strong>Setup</strong> functions run once for each model, node any model structure used to define the model. <strong>Run</strong> functions can be executed by R or compiled into C++ code.</p>
<p>NIMBLE is actually more complicated, or should I say “richer”, than this. There are many advanced programming concepts to wrap your head around. Nevertheless, it is pretty straightforward to start using NIMBLE for models that already have <code>BUGS</code> implementations. The best way to get started for anyone new to Bayesian statistics, or whose hierarchical model building skills may be a bit rusty, is to take the advice of the NIMBLE developers and work through the Pump Model example in Chapter 2 of the manual. In the rest of this post, I present an abbreviated and slightly reworked version of that model.</p>
<p>Before getting started, note that although NIMBLE is an <a href="https://cran.r-project.org/package=nimble">R package</a> listed on CRAN, and it installs like any other R package, you must have a C++ compiler and the standard <code>make</code> utility installed on your system before installing NIMBLE. (See Chapter 4 of the manual for platform-specific installation instructions.)</p>
<div id="pump-failure-model" class="section level3">
<h3>Pump Failure Model</h3>
<p>The Pump Failure model is discussed by George et al. in their 1993 paper: <a href="https://www.jstor.org/stable/4616270?seq=1#page_scan_tab_contents">Conjugate Likelihood Distributions</a>. The paper examines Bayesian models that use conjugate priors, but which do not have closed form solutions when prior distributions are associated with the hyperparameters. The <code>BUGS</code> solution of the problem is given <a href="http://www.openbugs.net/Examples/Pumps.html">here</a>.</p>
<p>The data driving the model are: <code>x[i]</code> the number of failures for pump, <code>i</code> in a time interval, <code>t[i]</code> where <code>i</code> goes from 1 to 10.</p>
<pre class="r"><code>library(nimble)
library(igraph)
library(tidyverse)
pumpConsts <- list(N = 10,
t = c(94.3, 15.7, 62.9, 126, 5.24,31.4, 1.05, 1.05, 2.1, 10.5))
pumpData <- list(x = c(5, 1, 5, 14, 3, 19, 1, 1, 4, 22))</code></pre>
<p>Arrival times are a Poisson distribution with parameter <code>lambda</code>, where <code>lambda</code> is itself modeled as a Gamma distribution with hyperparameters <code>alpha</code> and <code>beta</code>.</p>
<p>The model is expressed in the <code>BUGS</code> language wrapped inside the NIMBLE function <code>nimbleCode()</code>, which turns the <code>BUGS</code> code into a object that can be operated on by <code>nimbleModel()</code></p>
<pre class="r"><code>pumpCode <- nimbleCode(
{
for (i in 1:N){
theta[i] ~ dgamma(alpha,beta)
lambda[i] <- theta[i]*t[i]
x[i] ~ dpois(lambda[i])
}
alpha ~ dexp(1.0)
beta ~ dgamma(0.1,1.0)
})
pumpInits <- list(alpha = 1, beta = 1,
theta = rep(0.1, pumpConsts$N))</code></pre>
<p><code>nimbleModel()</code> produces the model object that can be executed by <code>R</code> or compiled.</p>
<pre class="r"><code>pump <- nimbleModel(code = pumpCode, name = "pump", constants = pumpConsts,
data = pumpData, inits = pumpInits)</code></pre>
<p>The following command lets us look at the nodes that comprise the model’s directed graph, and plot it.</p>
<pre class="r"><code>pump$getNodeNames()</code></pre>
<pre><code> [1] "alpha" "beta" "lifted_d1_over_beta"
[4] "theta[1]" "theta[2]" "theta[3]"
[7] "theta[4]" "theta[5]" "theta[6]"
[10] "theta[7]" "theta[8]" "theta[9]"
[13] "theta[10]" "lambda[1]" "lambda[2]"
[16] "lambda[3]" "lambda[4]" "lambda[5]"
[19] "lambda[6]" "lambda[7]" "lambda[8]"
[22] "lambda[9]" "lambda[10]" "x[1]"
[25] "x[2]" "x[3]" "x[4]"
[28] "x[5]" "x[6]" "x[7]"
[31] "x[8]" "x[9]" "x[10]" </code></pre>
<pre class="r"><code>pump$plotGraph()</code></pre>
<p><img src="/post/2018-06-12-a-first-look-at-nimble_files/figure-html/unnamed-chunk-5-1.png" width="672" /></p>
<p>We can look at the values stored at each node. The node for <code>x</code> contains the initial values we entered into the model and the nodes for <code>theta</code> and <code>lambda</code> contain the initial calculated values</p>
<pre class="r"><code>pump$x</code></pre>
<pre><code> [1] 5 1 5 14 3 19 1 1 4 22</code></pre>
<pre class="r"><code>pump$theta</code></pre>
<pre><code> [1] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1</code></pre>
<pre class="r"><code>pump$lambda</code></pre>
<pre><code> [1] 9.430 1.570 6.290 12.600 0.524 3.140 0.105 0.105 0.210 1.050</code></pre>
<p>We can also look at the log probabilities of the likelihoods.</p>
<pre class="r"><code>pump$logProb_x</code></pre>
<pre><code> [1] -2.998011 -1.118924 -1.882686 -2.319466 -4.254550 -20.739651
[7] -2.358795 -2.358795 -9.630645 -48.447798</code></pre>
<p>Next, we use the model to simulate new values for <code>theta</code> and update the variables.</p>
<pre class="r"><code>set.seed(1)
pump$simulate("theta")
pump$theta</code></pre>
<pre><code> [1] 0.15514136 1.88240160 1.80451250 0.83617765 1.22254365 1.15835525
[7] 0.99001994 0.30737332 0.09461909 0.15720154</code></pre>
<p>These new values will, of course, lead to new values of <code>lambda</code> and the log probabilities.</p>
<pre class="r"><code>pump$lambda</code></pre>
<pre><code> [1] 9.430 1.570 6.290 12.600 0.524 3.140 0.105 0.105 0.210 1.050</code></pre>
<pre class="r"><code>pump$logProb_x</code></pre>
<pre><code> [1] -2.998011 -1.118924 -1.882686 -2.319466 -4.254550 -20.739651
[7] -2.358795 -2.358795 -9.630645 -48.447798</code></pre>
<p>The model can also be compiled. The C++ code generated is loaded back into R with an object that can be examined like the uncompiled model.</p>
<pre class="r"><code>Cpump <- compileNimble(pump)
Cpump$theta</code></pre>
<pre><code> [1] 0.15514136 1.88240160 1.80451250 0.83617765 1.22254365 1.15835525
[7] 0.99001994 0.30737332 0.09461909 0.15720154</code></pre>
<p>Note that since a wide variety of NIMBLE models can be compiled, NIMBLE should be generally useful for developing production R code. Have a look at the <a href="https://cross.ucsc.edu/wp-content/uploads/2017/09/paciorek-cross17.pdf">presentation</a> by Christopher Paciorek for a nice overview of NIMBLE’s compilation process.</p>
<div class="figure">
<img src="/post/2018-07-03-Rickert-NIMBLE_files/NIMBLE.png" />
</div>
<p>Next, the default NIMBLE MCMC algorithm is used to generate posterior samples from the distributions for the model parameters <code>alpha</code>, <code>beta</code>, <code>theta</code> and <code>lambda</code>, along with summary statistics and the value of Wantanabi’s <a href="https://www.rdocumentation.org/packages/LaplacesDemon/versions/16.1.0/topics/WAIC">WAIC</a> statistic.</p>
<pre class="r"><code>mcmc.out <- nimbleMCMC(code = pumpCode, constants = pumpConsts,
data = pumpData, inits = pumpInits,
monitors=c("alpha","beta","theta","lambda"),
nchains = 2, niter = 10000,
summary = TRUE, WAIC = TRUE)</code></pre>
<pre><code>|-------------|-------------|-------------|-------------|
|-------------------------------------------------------|
|-------------|-------------|-------------|-------------|
|-------------------------------------------------------|</code></pre>
<pre class="r"><code>names(mcmc.out)</code></pre>
<pre><code>[1] "samples" "summary" "WAIC" </code></pre>
<p>The model object contains a summary of the model,</p>
<pre class="r"><code>mcmc.out$summary</code></pre>
<pre><code>$chain1
Mean Median St.Dev. 95%CI_low 95%CI_upp
alpha 0.69804352 0.65835063 0.27037676 0.287898244 1.3140461
beta 0.92862598 0.82156847 0.54969128 0.183699137 2.2872696
lambda[1] 5.67617535 5.35277649 2.39989346 1.986896247 11.3087435
lambda[2] 1.59476464 1.28802618 1.24109695 0.126649837 4.7635134
lambda[3] 5.58222072 5.28139948 2.36539331 1.961659802 11.1331869
lambda[4] 14.57540796 14.23984600 3.79587390 8.085538041 22.9890092
lambda[5] 3.16402849 2.87859865 1.63590766 0.836991007 7.1477641
lambda[6] 19.21831706 18.86685281 4.33423677 11.703168568 28.6847447
lambda[7] 0.94776605 0.74343559 0.77658191 0.077828283 2.8978174
lambda[8] 0.93472103 0.74313533 0.76301563 0.076199581 2.9598715
lambda[9] 3.31124086 3.03219017 1.61332897 0.955909813 7.2024472
lambda[10] 20.89018329 20.59798130 4.45302924 13.034529765 30.4628012
theta[1] 0.06019274 0.05676327 0.02544956 0.021069950 0.1199230
theta[2] 0.10157737 0.08203988 0.07905076 0.008066869 0.3034085
theta[3] 0.08874755 0.08396502 0.03760562 0.031186960 0.1769982
theta[4] 0.11567784 0.11301465 0.03012598 0.064170937 0.1824525
theta[5] 0.60382223 0.54935089 0.31219612 0.159731108 1.3640771
theta[6] 0.61204831 0.60085518 0.13803302 0.372712375 0.9135269
theta[7] 0.90263434 0.70803389 0.73960182 0.074122175 2.7598261
theta[8] 0.89021051 0.70774794 0.72668155 0.072571029 2.8189252
theta[9] 1.57678136 1.44390008 0.76825189 0.455195149 3.4297368
theta[10] 1.98954127 1.96171250 0.42409802 1.241383787 2.9012192
$chain2
Mean Median St.Dev. 95%CI_low 95%CI_upp
alpha 0.69101961 0.65803654 0.26548378 0.277195564 1.2858148
beta 0.91627273 0.81434426 0.53750825 0.185772263 2.2702428
lambda[1] 5.59893463 5.29143956 2.32153991 1.976164994 10.9564380
lambda[2] 1.57278293 1.27425268 1.23323878 0.129781580 4.7262566
lambda[3] 5.60321125 5.27780200 2.32992322 1.970709123 10.9249502
lambda[4] 14.60674094 14.30971897 3.86145332 8.013012004 23.0526313
lambda[5] 3.13119513 2.84685917 1.67006926 0.782262325 7.2043337
lambda[6] 19.17917926 18.82326155 4.33456380 11.661139736 28.5655803
lambda[7] 0.94397897 0.74446578 0.76576887 0.080055678 2.8813517
lambda[8] 0.94452324 0.74263433 0.77013200 0.074813473 2.9457364
lambda[9] 3.30813061 3.04512049 1.58008544 0.986914665 7.0355869
lambda[10] 20.88570471 20.60384141 4.44130984 13.092562597 30.5574424
theta[1] 0.05937364 0.05611283 0.02461866 0.020956151 0.1161870
theta[2] 0.10017726 0.08116259 0.07855024 0.008266343 0.3010355
theta[3] 0.08908126 0.08390782 0.03704170 0.031330829 0.1736876
theta[4] 0.11592652 0.11356920 0.03064645 0.063595333 0.1829574
theta[5] 0.59755632 0.54329373 0.31871551 0.149286703 1.3748728
theta[6] 0.61080189 0.59946693 0.13804343 0.371373877 0.9097319
theta[7] 0.89902759 0.70901502 0.72930369 0.076243503 2.7441445
theta[8] 0.89954594 0.70727079 0.73345905 0.071250926 2.8054633
theta[9] 1.57530029 1.45005738 0.75242164 0.469959364 3.3502795
theta[10] 1.98911473 1.96227061 0.42298189 1.246910723 2.9102326
$all.chains
Mean Median St.Dev. 95%CI_low 95%CI_upp
alpha 0.69453156 0.65803654 0.26795776 0.28329854 1.2999319
beta 0.92244935 0.81828160 0.54365539 0.18549077 2.2785444
lambda[1] 5.63755499 5.32462511 2.36129857 1.98294712 11.1597721
lambda[2] 1.58377379 1.28149065 1.23719199 0.12734396 4.7382079
lambda[3] 5.59271599 5.28024565 2.34769002 1.96451069 11.0072923
lambda[4] 14.59107445 14.27080924 3.82874035 8.04541916 23.0250158
lambda[5] 3.14761181 2.86460377 1.65311690 0.80506062 7.1718837
lambda[6] 19.19874816 18.84484055 4.33433610 11.68198222 28.6445471
lambda[7] 0.94587251 0.74395440 0.77117739 0.07927988 2.8911629
lambda[8] 0.93962214 0.74299160 0.76657858 0.07571751 2.9470742
lambda[9] 3.30968573 3.03910484 1.59675456 0.97386482 7.1120082
lambda[10] 20.88794400 20.60051118 4.44706278 13.05509616 30.5216406
theta[1] 0.05978319 0.05646474 0.02504028 0.02102807 0.1183433
theta[2] 0.10087731 0.08162361 0.07880204 0.00811108 0.3017967
theta[3] 0.08891440 0.08394667 0.03732417 0.03123228 0.1749967
theta[4] 0.11580218 0.11326039 0.03038683 0.06385253 0.1827382
theta[5] 0.60068928 0.54668011 0.31548032 0.15363752 1.3686801
theta[6] 0.61142510 0.60015416 0.13803618 0.37203765 0.9122467
theta[7] 0.90083096 0.70852800 0.73445465 0.07550465 2.7534885
theta[8] 0.89487822 0.70761105 0.73007484 0.07211191 2.8067373
theta[9] 1.57604083 1.44719278 0.76035931 0.46374515 3.3866706
theta[10] 1.98932800 1.96195345 0.42352979 1.24334249 2.9068229</code></pre>
<p>and the value of the WAIC statistic.</p>
<pre class="r"><code>mcmc.out$WAI</code></pre>
<pre><code>[1] 48.69896</code></pre>
<p>Here, we select sample values for the parameters for pump 1 in the first chain, and put them into a data frame for plotting.</p>
<pre class="r"><code>df <- data.frame(mcmc.out$samples$chain1)
df_l <- df %>% select(alpha, beta, theta.1., lambda.1.) %>% gather(key="parameter", value="value")</code></pre>
<p>We plot the sample values.</p>
<pre class="r"><code>ps <- df_l %>% ggplot(aes(x=seq_along(value), y = value)) + geom_line()
ps + facet_wrap(~parameter, scales = "free")</code></pre>
<p><img src="/post/2018-06-12-a-first-look-at-nimble_files/figure-html/unnamed-chunk-18-1.png" width="672" /> And, we plot histograms.</p>
<pre class="r"><code>p <- ggplot(df_l,aes(value)) + geom_histogram(aes( y= ..density..),bins = 60)
p + facet_wrap(~parameter, scales = "free")</code></pre>
<p><img src="/post/2018-06-12-a-first-look-at-nimble_files/figure-html/unnamed-chunk-19-1.png" width="672" /></p>
<p>Note that it is also possible to perform the MCMC simulation using the compiled model.</p>
<pre class="r"><code>mcmc.out_c<- nimbleMCMC(model=Cpump,
monitors=c("alpha","beta","theta","lambda"),
nchains = 2, niter = 10000,
summary = TRUE, WAIC = TRUE)</code></pre>
<pre><code>|-------------|-------------|-------------|-------------|
|-------------------------------------------------------|
|-------------|-------------|-------------|-------------|
|-------------------------------------------------------|</code></pre>
<pre class="r"><code>mcmc.out_c$summary</code></pre>
<pre><code>$chain1
Mean Median St.Dev. 95%CI_low 95%CI_upp
alpha 0.70269965 0.65474666 0.27690796 0.288871669 1.3525877
beta 0.92892181 0.82320341 0.54874194 0.186215812 2.2756643
lambda[1] 5.65646492 5.32568604 2.38108302 1.991839453 11.1329833
lambda[2] 1.58848917 1.28392445 1.25676948 0.133001650 4.7163618
lambda[3] 5.62720720 5.30681963 2.36776285 1.986194548 11.1264464
lambda[4] 14.61256770 14.29447077 3.75584085 8.106535985 22.8405075
lambda[5] 3.16521167 2.88771869 1.65132178 0.785161558 7.0181185
lambda[6] 19.12824948 18.77541823 4.27045427 11.733960316 28.4815908
lambda[7] 0.94353548 0.75312906 0.75111813 0.079570796 2.8410369
lambda[8] 0.93661525 0.74764821 0.75397145 0.078424425 2.8536890
lambda[9] 3.33178422 3.05974419 1.63035789 1.019006182 7.3163945
lambda[10] 20.90388784 20.58355995 4.45456152 12.997984788 30.2815949
theta[1] 0.05998372 0.05647599 0.02525009 0.021122370 0.1180592
theta[2] 0.10117765 0.08177863 0.08004901 0.008471443 0.3004052
theta[3] 0.08946275 0.08436915 0.03764329 0.031577020 0.1768910
theta[4] 0.11597276 0.11344818 0.02980826 0.064337587 0.1812739
theta[5] 0.60404803 0.55109135 0.31513774 0.149839992 1.3393356
theta[6] 0.60917992 0.59794326 0.13600173 0.373693004 0.9070570
theta[7] 0.89860522 0.71726577 0.71535060 0.075781711 2.7057495
theta[8] 0.89201452 0.71204591 0.71806805 0.074689928 2.7177991
theta[9] 1.58656391 1.45702104 0.77636090 0.485241039 3.4839974
theta[10] 1.99084646 1.96033904 0.42424395 1.237903313 2.8839614
$chain2
Mean Median St.Dev. 95%CI_low 95%CI_upp
alpha 0.70184646 0.65773527 0.27237859 0.297108329 1.3420954
beta 0.92323539 0.82124257 0.53880496 0.194879601 2.2590201
lambda[1] 5.59702813 5.25201276 2.35832632 2.029382518 10.9327321
lambda[2] 1.62105397 1.31199418 1.26269004 0.137977536 4.8652461
lambda[3] 5.62874314 5.33611797 2.37576774 1.953756972 11.1296452
lambda[4] 14.53507135 14.22210300 3.84823087 8.042950272 22.9287741
lambda[5] 3.17647361 2.88816158 1.67257096 0.807468793 7.2500264
lambda[6] 19.13576117 18.82011366 4.34294395 11.646448559 28.4716352
lambda[7] 0.94656373 0.74705570 0.76192793 0.084445304 2.9245815
lambda[8] 0.94754678 0.75725106 0.75136514 0.083985118 2.8740656
lambda[9] 3.34514300 3.04989975 1.64818642 0.974288761 7.2961225
lambda[10] 20.97154230 20.61713159 4.45405753 13.260753885 30.5614115
theta[1] 0.05935343 0.05569473 0.02500876 0.021520493 0.1159357
theta[2] 0.10325185 0.08356651 0.08042612 0.008788378 0.3098883
theta[3] 0.08948717 0.08483494 0.03777055 0.031061319 0.1769419
theta[4] 0.11535771 0.11287383 0.03054151 0.063832939 0.1819744
theta[5] 0.60619725 0.55117587 0.31919293 0.154097098 1.3835928
theta[6] 0.60941915 0.59936668 0.13831032 0.370906005 0.9067400
theta[7] 0.90148927 0.71148162 0.72564565 0.080424099 2.7853158
theta[8] 0.90242550 0.72119149 0.71558585 0.079985826 2.7372053
theta[9] 1.59292524 1.45233321 0.78485068 0.463947029 3.4743440
theta[10] 1.99728974 1.96353634 0.42419596 1.262928941 2.9106106
$all.chains
Mean Median St.Dev. 95%CI_low 95%CI_upp
alpha 0.70227305 0.65580594 0.27464608 0.292312894 1.3489184
beta 0.92607860 0.82237274 0.54378998 0.191611561 2.2684771
lambda[1] 5.62674653 5.28611334 2.36985910 2.007150471 11.0415843
lambda[2] 1.60477157 1.29777398 1.25980697 0.135465213 4.8015059
lambda[3] 5.62797517 5.32327502 2.37170950 1.967656654 11.1291530
lambda[4] 14.57381952 14.26247905 3.80241886 8.071801955 22.9002186
lambda[5] 3.17084264 2.88801833 1.66194832 0.798927322 7.1492800
lambda[6] 19.13200533 18.78653361 4.30674559 11.682136284 28.4767857
lambda[7] 0.94504960 0.75003915 0.75652494 0.081865294 2.8859048
lambda[8] 0.94208101 0.75177975 0.75267045 0.080734142 2.8644982
lambda[9] 3.33846361 3.05488924 1.63926902 0.990402148 7.3041714
lambda[10] 20.93771507 20.60497943 4.45432662 13.116940079 30.4162660
theta[1] 0.05966857 0.05605635 0.02513106 0.021284735 0.1170900
theta[2] 0.10221475 0.08266076 0.08024248 0.008628358 0.3058284
theta[3] 0.08947496 0.08463076 0.03770603 0.031282300 0.1769341
theta[4] 0.11566523 0.11319428 0.03017793 0.064061920 0.1817478
theta[5] 0.60512264 0.55114854 0.31716571 0.152467046 1.3643664
theta[6] 0.60929953 0.59829725 0.13715750 0.372042557 0.9069040
theta[7] 0.90004724 0.71432300 0.72049994 0.077966947 2.7484808
theta[8] 0.89722001 0.71598072 0.71682900 0.076889659 2.7280935
theta[9] 1.58974458 1.45470916 0.78060429 0.471620071 3.4781768
theta[10] 1.99406810 1.96237899 0.42422158 1.249232389 2.8967872</code></pre>
</div>
<div id="monte-carlo-expectation-analysis" class="section level3">
<h3>Monte Carlo Expectation Analysis</h3>
<p>To illustrate that NIMBLE is more than just an MCMC engine, the manual example uses NIMBLE’s built-in Monte Carlo Expectation algorithm to maximize the marginal likelihood for <code>alpha</code> and <code>beta</code>. The first step is to set up “box constraints” for the model. Then, the <code>buildMCEM()</code> function is used to construct an MCEM algorithm from a NIMBLE model.</p>
<pre class="r"><code>pump2 <- pump$newModel()
box = list( list(c("alpha","beta"), c(0, Inf)))
pumpMCEM <- buildMCEM(model = pump2, latentNodes = "theta[1:10]", boxConstraints = box)</code></pre>
<p>This is how to run the Monte Carlo Expectation model. Note that the authors of the NIMBLE manual point out that these results are within 0.01 of the values obtained by George et al.</p>
<pre class="r"><code>pumpMLE <- pumpMCEM$run()</code></pre>
<pre><code>|-------------|-------------|-------------|-------------|
|-------------------------------------------------------|
Iteration Number: 1.
Current number of MCMC iterations: 1000.
Parameter Estimates:
alpha beta
0.8130146 1.1125783
Convergence Criterion: 1.001.
|-------------|-------------|-------------|-------------|
|-------------------------------------------------------|
Iteration Number: 2.
Current number of MCMC iterations: 1000.
Parameter Estimates:
alpha beta
0.8148887 1.2323211
Convergence Criterion: 0.02959269.
|-------------|-------------|-------------|-------------|
|-------------------------------------------------------|
Iteration Number: 3.
Current number of MCMC iterations: 1000.
Parameter Estimates:
alpha beta
0.8244363 1.2797908
Convergence Criterion: 0.005418668.
|-------------|-------------|-------------|-------------|
|-------------------------------------------------------|
Monte Carlo error too big: increasing MCMC sample size.
|-------------|-------------|-------------|-------------|
|-------------------------------------------------------|
Iteration Number: 4.
Current number of MCMC iterations: 1250.
Parameter Estimates:
alpha beta
0.8280353 1.2700022
Convergence Criterion: 0.001180319.
|-------------|-------------|-------------|-------------|
|-------------------------------------------------------|
Monte Carlo error too big: increasing MCMC sample size.
|-------------|-------------|-------------|-------------|
|-------------------------------------------------------|
Monte Carlo error too big: increasing MCMC sample size.
|-------------|-------------|-------------|-------------|
|-------------------------------------------------------|
Iteration Number: 5.
Current number of MCMC iterations: 2188.
Parameter Estimates:
alpha beta
0.8268224 1.2794244
Convergence Criterion: 0.000745466.</code></pre>
<p>Like the tidyverse, NIBMLE is an ecosystem of DSLs. The <code>BUGS</code> language is extended and used as a DSL for formulation models. <code>nimbleFunctions</code> is a language for writing algorithms that may be used with both <code>BUGS</code> and R. But, unlike the tidyverse, the NIMBLE DSLs are not distributed across multiple packages, but instead wrapped up into the NIMBLE package code.</p>
<p>For more on the design an use of DSLs with R, have a look at the <a href="http://adv-r.had.co.nz/dsl.html">Chapter in Advanced R</a>, or Thomas Mailund’s new book, <a href="https://www.apress.com/gp/blog/all-blog-posts/domain-specific-languages-in-r/15742536">Domain-Specific Languages in R</a>.</p>
</div>
<script>window.location.href='https://rviews.rstudio.com/2018/07/05/a-first-look-at-nimble/';</script>
May 2018: “Top 40” New Packages
https://rviews.rstudio.com/2018/06/26/may-2018-top-40-new-packages/
Tue, 26 Jun 2018 00:00:00 +0000https://rviews.rstudio.com/2018/06/26/may-2018-top-40-new-packages/
<p>While looking over the 215 or so new packages that made it to CRAN in May, I was delighted to find several packages devoted to subjects a little bit out of the ordinary; for instance, <code>bioacoustics</code> analyzes audio recordings, <code>freegroup</code> looks at some abstract mathematics, <code>RQEntangle</code> computes quantum entanglement, <code>stemmatology</code> analyzes textual musical traditions, and <code>treedater</code> estimates clock rates for evolutionary models. I take this as evidence that R is expanding beyond its traditional strongholds of statistics and finance as people in other fields with serious analytic and computational requirements become familiar with the language. And, when I see a package from a philologist and scholar of “Ancient and Medieval Worlds”, I am persuaded to think that R is making a unique contribution to computational literacy.</p>
<p>Below are my “Top 40” package picks for May 2018, organized into the following categories: Computational Methods, Data, Data Science, Finance, Mathematics, Music, Science, Statistics, Time Series, Utilities and Visualization.</p>
<h3 id="computational-methods">Computational Methods</h3>
<p><a href="https://cran.r-project.org/package=dqrng">dqrng</a> v0.0.4: Provides fast random number generators with good statistical properties, including the 64-bit variant of the <a href="https://dl.acm.org/citation.cfm?doid=272991.272995">Mersenne-Twister</a>, <a href="http://www.pcg-random.org/">pcg64</a>, and <a href="http://xoshiro.di.unimi.it/">Xoroshiro128 and Xoroshiro256</a>. There is an <a href="https://cran.r-project.org/web/packages/dqrng/vignettes/dqrng.html">Introduction</a> and a vignette on <a href="https://cran.r-project.org/web/packages/dqrng/vignettes/parallel.html">Parallel Usage</a>.</p>
<p><a href="https://cran.r-project.org/package=optimParallel">optimParallel</a> v.7-2: Provides a parallel versions of the gradient-based <code>stats::optim()</code> function. The <a href="https://cran.r-project.org/web/packages/optimParallel/vignettes/optimParallel.pdf">vignette</a> is informative.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/optimParallel.png" alt="" /></p>
<h3 id="data">Data</h3>
<p><a href="https://cran.r-project.org/package=childesr">childesr</a> v0.1.0: Implements an interface to <a href="http://childes-db.stanford.edu">CHILDES</a>, an open repository for transcripts of parent-child interaction. There is a <a href="https://cran.r-project.org/web/packages/childesr/vignettes/access_childes_db.html">vignette</a>.</p>
<p><a href="https://cran.r-project.org/package=PetfindeR">PetfindeR</a> v1.1.3: Is a wrapper of the <a href="https://www.petfinder.com/developers/api-docs">Petfinder API</a> that implements methods for interacting with and extracting data from the <a href="https://www.petfinder.com/">Petfinder</a> database, one of the largest online, searchable databases of adoptable animals and animal welfare organizations across North America. See the Getting Started Guide: <a href="https://cran.r-project.org/web/packages/PetfindeR/vignettes/PetfindeR_Introduction_Part_One.html">part1</a> and <a href="https://cran.r-project.org/web/packages/PetfindeR/vignettes/PetfindeR_Introduction_Part_Two.html">part2</a>.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/PetfindeR.png" alt="" /></p>
<h3 id="data-science">Data Science</h3>
<p><a href="https://cran.r-project.org/package=catch">catch</a> v1.0: Provides functions to perform classification and variable selection on high-dimensional tensors (multi-dimensional arrays) after adjusting for additional covariates (scalar or vectors) as CATCH model in <a href="arXiv:1805.04421">Pan, Mai and Zhang (2018)</a>.</p>
<p><a href="https://cran.r-project.org/package=SemiSupervised">SemiSupervised</a> v1.0: Implements several safe graph-based semi-supervised learning algorithms. For technical details, refer to <a href="http://jmlr.org/papers/v14/culp13a.html">Culp and Ryan (2013)</a>, <a href="http://www.jmlr.org/papers/v16/ryan15a.html">Ryan and Culp (2015)</a> and the package <a href="https://cran.r-project.org/web/packages/SemiSupervised/vignettes/SemiSupervised.pdf">vignette</a>.</p>
<p><a href="https://cran.r-project.org/package=spFSR">spFSR</a> v1.0.0: Offers functions to perform feature selection and ranking via simultaneous perturbation stochastic approximation (SPSA-FSR) based on works by <a href="arXiv:1508.07630">Aksakalli and Malekipirbazari (2015)</a> and <a href="arXiv:1804.05589">Yenice et al. (2018)</a>. See the <a href="https://cran.r-project.org/web/packages/spFSR/vignettes/spFSR.html">Introduction</a>.</p>
<h3 id="finance">Finance</h3>
<p><a href="https://cran.r-project.org/package=PortfolioAnalytics">PortfolioAnalytics</a> v1.1.0: Provides functions for portfolio analysis, including numerical methods for portfolio optimization. There is an <a href="https://cran.r-project.org/web/packages/PortfolioAnalytics/vignettes/portfolio_vignette.pdf">Introduction</a> and vignettes on <a href="https://cran.r-project.org/web/packages/PortfolioAnalytics/vignettes/ROI_vignette.pdf">Optimization</a>, <a href="https://cran.r-project.org/web/packages/PortfolioAnalytics/vignettes/custom_moments_objectives.pdf">Custom Moment and Objective Functions</a>, and <a href="https://cran.r-project.org/web/packages/PortfolioAnalytics/vignettes/risk_budget_optimization.pdf">Portfolio Optimization with CVaR Budgets</a>.</p>
<p><a href="https://cran.r-project.org/package=sparseIndexTracking">sparseIndexTracking</a> v0.1.0: Provides functions to compute sparse portfolios for financial index tracking, i.e., joint selection of a subset of the assets that compose the index and computation of their relative weights (capital allocation) based on the paper <a href="doi:10.1109/TSP.2017.2762286">Benidis et al. (2018)</a>. The <a href="https://cran.r-project.org/web/packages/sparseIndexTracking/vignettes/SparseIndexTracking-vignette.pdf">vignette</a> shows how to design a portfolio to track an index.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/sparseIndexTracking.png" alt="" /></p>
<h3 id="mathematics">Mathematics</h3>
<p><a href="https://CRAN.R-project.org/package=freegroup">freegroup</a> v1.0: Provides functions to elements of the <a href="https://en.wikipedia.org/wiki/Free_group">free group</a>, including inversion, multiplication by a scalar, group-theoretic power operation, and Tietze forms. See the <a href="https://cran.r-project.org/web/packages/freegroup/vignettes/freevig.html">vignette</a> for details.</p>
<p><a href="https://cran.r-project.org/package=ODEsensitivity">ODEsensitivity</a> v1.1.1: Provides functions to perform sensitivity analysis for ordinary differential equation (ode) models using the <a href="https://cran.r-project.org/package=ODEnetwork">ODEnetwork</a> package, which simulates a network of second-order ODEs. See [Weber et al. (2018)(<a href="https://eldorado.tu-dortmund.de/handle/2003/36875)">https://eldorado.tu-dortmund.de/handle/2003/36875)</a>] for details, and the <a href="https://cran.r-project.org/web/packages/ODEsensitivity/vignettes/ODEsensitivity.html">vignette</a> to get started.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/ODEsensitivity.png" alt="" /></p>
<h3 id="music">Music</h3>
<p><a href="https://cran.r-project.org/package=stemmatology">stemmatoloty</a> v0.3.1: Allows users to explore and analyze the genealogy of textual or musical traditions from their variants, with various stemmatological methods, mainly the disagreement-based algorithms suggested by <a href="doi:10.1484/M.LECTIO-EB.5.102565">Camps and Cafiero (2015)</a>. The <a href="https://cran.r-project.org/web/packages/stemmatology/vignettes/stemmatology.pdf">vignette</a> provides details.</p>
<h3 id="science">Science</h3>
<p><a href="https://cran.r-project.org/package=bioacoustics">bioacoustics</a> v0.1.2: Provides functions to analyze audio recordings and automatically extract animal vocalizations. Contains all the necessary tools to process audio recordings of various formats (e.g., WAV, WAC, MP3, ZC), filter noisy files, display audio signals, and detect and extract automatically acoustic features for further analysis such as classification. The <a href="https://cran.r-project.org/web/packages/bioacoustics/vignettes/introduction.html">vignette</a> provides an example.</p>
<p><a href="https://CRAN.R-project.org/package=epiphy">epiphy</a> v0.3.4: Provides a toolbox for analyzing plant disease epidemics and a common framework for plant disease intensity data recorded over time and/or space. There is a vignette on <a href="https://cran.r-project.org/web/packages/epiphy/vignettes/defs-and-eqns.html">Definitions and Relationships between Parameters</a> and another with <a href="https://cran.r-project.org/web/packages/epiphy/vignettes/epiphy.html">Examples</a>.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/epiphy.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=treedater">treedater</a> v0.2.0: Offers functions for estimating times of common ancestry and molecular clock rates of evolution using a variety of evolutionary models. See <a href="doi:10.1093/ve/vex025">Volz and Frost (2017)</a>. The <a href="https://cran.r-project.org/web/packages/treedater/vignettes/h3n2.html">vignette</a> provides an example using the Influenza H3N2 virus.</p>
<p><a href="https://cran.r-project.org/package=RQEntangle">RQEntangle</a> v0.1.0: Provides functions to compute the Schmidt decomposition of bipartite quantum systems, discrete or continuous, and their respective entanglement metrics. See <a href="doi:10.1119/1.17904">Ekert and Knight (1995)</a> and the vignettes <a href="https://cran.r-project.org/web/packages/RQEntangle/vignettes/CoupledHarmonics.html">Entanglement in Coupled Harmonics</a> and <a href="https://cran.r-project.org/web/packages/RQEntangle/vignettes/CoupledTwoLevelSystems.html">Entanglement between Two Coupled Two-Level Systems</a> for details.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/RQEntangle.png" alt="" /></p>
<h3 id="statistics">Statistics</h3>
<p><a href="https://cran.r-project.org/package=glmmboot">glmmboot</a> v0.1.2: Provides two functions to perform bootstrap resampling for most models that <code>update()</code> works for. <code>BootGlmm()</code> performs block resampling if random effects are present, and case resampling if not; <code>BootCI()</code> converts output from bootstrap model runs into confidence intervals and p-values. See <a href="arXiv:1805.08670">Humphrey and Swingley (2018)</a> for the details and the <a href="https://cran.r-project.org/web/packages/glmmboot/vignettes/quick_use.html">vignette</a> to get started.</p>
<p><a href="https://cran.r-project.org/package=glmmEP">glmmEP</a> v1.0-1: Allows users to solve Generalized Linear Mixed Model Analysis via Expectation Propagation. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. See the methodology in <a href="arXiv:1805.08423v1">Hall et al. (2018)</a>, and the user manual in the <a href="https://cran.r-project.org/web/packages/glmmEP/vignettes/manual.pdf">vignette</a>.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/glmmEP.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=groupedSurv">groupedSurv</a> v1.0.1: Provides <code>Rcpp</code>-based functions to compute the efficient score statistics for grouped time-to-event data (<a href="https://www.jstor.org/stable/pdf/2529588.pdf?seq=1#page_scan_tab_contents">Prentice and Gloeckler (1978)</a>), with the optional inclusion of baseline covariates. The <a href="https://cran.r-project.org/web/packages/groupedSurv/vignettes/groupedSurv.pdf">vignette</a> gives an example.</p>
<p><a href="https://cran.r-project.org/package=modeldb">modeldb</a> v0.1.0: Provides functions to fit models inside databases with <code>dplyr</code> backends. There are vignettes showing how to implement <a href="https://cran.r-project.org/web/packages/modeldb/vignettes/linear_regression.html">linear regression</a> and <a href="https://cran.r-project.org/web/packages/modeldb/vignettes/kmeans.html">kmeans</a> models.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/modeldb.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=MLZ">MLZ</a> v0.1.1: Provides estimation functions and diagnostic tools for mean length-based total mortality estimators based on <a href="doi:10.1577/T05-153.1">Gedamke and Hoenig (2006)</a>. There is a <a href="https://cran.r-project.org/web/packages/MLZ/vignettes/MLZ.html">vignette</a>.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/MLZ.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=NFWdist">NFWdist</a> v0.1.0: Provides density, distribution function, quantile function, and random generation for the 3D Navarro, Frenk & White (NFW) profile. For details see <a href="arXiv:1805.09550">Robotham & Howlett (2018)</a> and the <a href="https://cran.r-project.org/web/packages/NFWdist/vignettes/NFWdist.html">vignette</a>.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/NFWdist.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=survBootOutliers">survBootOutliers</a> v1.0: Offers three new concordance-based methods for outlier detection in a survival context. The methodology is described in two papers by Pinto J., Carvalho A. and Vinga S.: <a href="doi:10.5220/0005225300750082">paper1</a> and <a href="doi:10.1007/978-3-319-27926-8_22">paper2</a>. The <a href="https://cran.r-project.org/web/packages/survBootOutliers/vignettes/survBootOutliers.pdf">vignette</a> provides an introduction.</p>
<p><a href="https://CRAN.R-project.org/package=vinereg">vinereg</a> v0.3.0: Implements D-vine quantile regression models with parametric or non-parametric pair-copulas. See <a href="doi:10.1016/j.csda.2016.12.009">Kraus and Czado (2017)</a> and <a href="arXiv:1705.08310">Schallhorn et al. (2017)</a>. There is a <a href="https://cran.r-project.org/web/packages/vinereg/vignettes/abalone-example.html">vignette</a> showing how to use the package, and another covering an <a href="https://cran.r-project.org/web/packages/vinereg/vignettes/bike-rental.html">Analysis of bike rental data</a>.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/vinereg.png" alt="" /></p>
<h3 id="time-series">Time Series</h3>
<p><a href="https://cran.r-project.org/package=ASSA">ASSA</a> v1.0: Provides functions to model and decompose time series into principal components using singular spectrum analysis. See <a href="doi:10.1016/j.ijforecast.2015.09.004">de Carvalho and Rua (2017)</a> and <a href="doi:10.1016/j.econlet.2011.09.007">de Carvalho et al (2012)</a>.</p>
<p><a href="https://cran.r-project.org/package=DTWBI">DTWBI</a> v1.0: Provides functions to impute large gaps within time series based on Dynamic Time Warping methods. See <a href="doi:10.1016/j.patrec.2017.08.019">Phan et al. (2017)</a>. The <a href="http://mawenzi.univ-littoral.fr/DTWBI/">website</a> has examples.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/DTWBI.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=permutes">permutes</a> v0.1: Uses permutation testing (<a href="doi:10.1016/j.jneumeth.2007.03.024">Maris & Oostenveld (2007)</a>) to help determine optimal windows for analyzing densely-sampled time series. The <a href="https://cran.r-project.org/web/packages/permutes/vignettes/permutes.pdf">vignette</a> provides details.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/permutes.png" alt="" /></p>
<h3 id="utilities">Utilities</h3>
<p><a href="https://cran.r-project.org/package=bench">bench</a> v1.0.1: Provides tools to benchmark and analyze execution times for R expressions.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/bench.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=conflicted">conflicted</a> v0.1.0: Provides an alternative to R’s default conflict-resolution strategy for R packages.</p>
<p><a href="https://cran.r-project.org/package=diffdf">diffdf</a> v1.0.0: Provides functions for comparing two data frames. The <a href="https://cran.r-project.org/web/packages/diffdf/vignettes/diffdf-basic.html">vignette</a> describes how to use the package.</p>
<p><a href="https://cran.r-project.org/package=pkgdown">pkgdown</a> v1.1.0: Provides functions to generate a website from a source package by converting your documentation, vignettes, <code>README</code>, and more to <code>HTML</code>. See the <a href="https://cran.r-project.org/web/packages/pkgdown/pkgdown.pdf">vignette</a>.</p>
<p><a href="https://cran.r-project.org/package=rtika">rtika</a> v0.1.8: Provides functions to extract text or metadata from over a thousand file types, using <a href="https://tika.apache.org/">Apache Tika</a> to produce either plain-text or structured <code>XHTML</code> content. See the <a href="https://cran.r-project.org/web/packages/rtika/vignettes/rtika_introduction.html">vignette</a>.</p>
<p><a href="https://cran.r-project.org/package=tabulizer">tabulizer</a> v0.2.2: Provides bindings for the <a href="http://tabula.technology/">Tabula</a> <code>Java</code> library, which can extract tables from PDF documents. The <a href="https://github.com/ropensci/tabulizerjars">tabulizerjars</a> packages provides versioned .jar files. There is an <a href="https://cran.r-project.org/web/packages/tabulizer/vignettes/tabulizer.html">Introduction</a>.</p>
<p><a href="https://cran.r-project.org/package=shinytest">shinytest</a> v1.3.0: Enables automated testing of <code>Shiny</code> applications, using a headless browser.</p>
<p><a href="https://cran.r-project.org/package=vcr">vcr</a> v0.1.0: A port of the <a href="https://github.com/vcr/vcr/">Ruby gem</a> of the same name, <code>vcr</code> enables users to record test suite <code>HTTP</code> requests and replay them during future runs. There is an <a href="https://cran.r-project.org/web/packages/vcr/vignettes/vcr_vignette.html">Introduction</a> and vignettes on <a href="https://cran.r-project.org/web/packages/vcr/vignettes/configuration.html">Configuration</a> and <a href="https://cran.r-project.org/web/packages/vcr/vignettes/request_matching.html">Request Matching</a></p>
<h3 id="visualization">Visualization</h3>
<p><a href="https://cran.r-project.org/package=c3">c3</a> v0.2.0: Implements a wrapper (<a href="http://www.htmlwidgets.org/">htmlwidget</a>) for the <a href="http://c3js.org/"><code>C3.js</code></a> charting library that includes all types of <code>C3.js</code> plots, enabling interactive web-based charts to be embedded in <code>R Markdown</code> documents and <code>Shiny</code> applications. The <a href="https://cran.r-project.org/web/packages/c3/vignettes/examples.html">vignette</a> shows basic usage.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/c3.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=chromoMap">chromoMap</a> v0.1: Provides interactive, configurable graphics visualizations of human chromosomes, allowing users to map chromosome elements (like genes, SNPs, etc.) on the chromosome plot, and introduces a special plot, the “chromosome heatmap”, which enables visualizing data associated with chromosome elements. See the <a href="https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html">vignette</a>.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/chromoMap.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=ExPanDaR">ExPanDaR</a> v0.2.0: Provides a shiny-based front end and a set of functions for exploratory panel data analysis. Run as a web-based app, it enables users to assess the robustness of empirical evidence without providing them access to the underlying data. There is a vignette on <a href="https://cran.r-project.org/web/packages/ExPanDaR/vignettes/ExPanDaR-functions.html">using the package</a> and another on <a href="https://cran.r-project.org/web/packages/ExPanDaR/vignettes/use_ExPanD.html">panel data exploration</a>.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/ExPanDaR.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=ggdistribute">ggdistribute</a> v1.0.1: Extends <code>ggplot2</code> for plotting posterior or other types of unimodal distributions that require overlaying information about a distribution’s intervals. The <a href="https://cran.r-project.org/web/packages/ggdistribute/vignettes/geom_posterior.html">vignette</a> provides examples.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/ggdistribute.png" alt="" /></p>
<p><a href="https://cran.r-project.org/package=r2d3">r2d3</a> v0.2.2: Provides a suite of tools for using the <a href="https://d3js.org/"><code>D3</code></a> library to produce dynamic, interactive data visualizations. There are vignettes on <a href="https://cran.r-project.org/web/packages/r2d3/vignettes/advanced_rendering.html">Advanced Rendering with Callbacks</a>, <a href="https://cran.r-project.org/web/packages/r2d3/vignettes/data_conversion.html">R to D3 Data Conversion</a>, <a href="https://cran.r-project.org/web/packages/r2d3/vignettes/dependencies.html">CSS & JavaScript Dependencies</a>, <a href="https://cran.r-project.org/web/packages/r2d3/vignettes/learning_d3.html">Learning D3</a>, <a href="https://cran.r-project.org/web/packages/r2d3/vignettes/package_development.html">Package Development</a>, and <a href="https://cran.r-project.org/web/packages/r2d3/vignettes/visualization_options.html">D3 Visualization Options</a>.</p>
<p><img src="/post/2018-06-18-Rickert-May-Top40_files/r2d3.png" alt="" /></p>
<script>window.location.href='https://rviews.rstudio.com/2018/06/26/may-2018-top-40-new-packages/';</script>
Reading and analysing log files in the RRD database format
https://rviews.rstudio.com/2018/06/20/reading-rrd-files/
Wed, 20 Jun 2018 00:00:00 +0000https://rviews.rstudio.com/2018/06/20/reading-rrd-files/
<p>I have frequent conversations with R champions and Systems Administrators responsible for R, in which they ask how they can measure and analyze the usage of their servers. Among the many solutions to this problem, one of the my favourites is to use an <strong>RRD</strong> database and <strong>RRDtool</strong>.</p>
<p>From <a href="https://en.wikipedia.org/wiki/RRDtool">Wikipedia</a>:</p>
<blockquote>
<p><strong>RRDtool</strong> (<em>round-robin database tool</em>) aims to handle time series data such as network bandwidth, temperatures or CPU load. The data is stored in a <a href="https://en.wikipedia.org/wiki/Circular_buffer">circular buffer</a> based <a href="https://en.wikipedia.org/wiki/Database">database</a>, thus the system storage footprint remains constant over time.</p>
</blockquote>
<p><a href="https://oss.oetiker.ch/rrdtool/index.en.html">RRDtool</a> is a library written in C, with implementations that can also be accessed from the Linux command line. This makes it convenient for system development, but makes it difficult for R users to extract and analyze this data.</p>
<p>I am pleased to announce that I’ve been working on the <code>rrd</code> <a href="https://github.com/andrie/rrd">R package</a> to import RRD files directly into <code>tibble</code> objects, thus making it easy to analyze your metrics.</p>
<p>As an aside, the RStudio Pro products (specifically <a href="https://www.rstudio.com/products/rstudio-server-pro/">RStudio Server Pro</a> and <a href="https://www.rstudio.com/products/connect/">RStudio Connect</a>) also make use of RRD to store metrics – more about this later.</p>
<h2 id="understanding-the-rrd-format-as-an-r-user">Understanding the RRD format as an R user</h2>
<p>The name RRD is an initialism of <strong>R</strong>ound <strong>R</strong>obin <strong>D</strong>atabase. The “round robin” refers to the fact that the database is always fixed in size, and as a new entry enters the database, the oldest entry is discarded. In practical terms, the database collects data for a fixed period of time, and information that is older than the threshold gets removed.</p>
<p><img src="/post/2018-06-21-analysing-rrd-files_files/rra.png" alt="Image from loriotpro(https://bit.ly/2tk2MFa)" /></p>
<p>A second quality of RRD databases is that each datum is stored in different “consolidation data points”, where every data point is an aggregation over time. For example, a data point can represent an average value for the time period, or a maximum over the period. Typical consolidation functions include <code>average</code>, <code>min</code> and <code>max</code>.</p>
<p>The third quality is that every RRD database file typically consists of multiple archives. Each archive measures data for a different time period. For instance, the archives can capture data for intervals of 10 seconds, 30 seconds, 1 minute or 5 minutes.</p>
<p>As an example, here is a description of an RRD file that originated in RStudio Connect:</p>
<pre><code>describe_rrd("rrd_cpu_0")
#> A RRD file with 10 RRA arrays and step size 60
#> [1] AVERAGE_60 (43200 rows)
#> [2] AVERAGE_300 (25920 rows)
#> [3] MIN_300 (25920 rows)
#> [4] MAX_300 (25920 rows)
#> [5] AVERAGE_3600 (8760 rows)
#> [6] MIN_3600 (8760 rows)
#> [7] MAX_3600 (8760 rows)
#> [8] AVERAGE_86400 (1825 rows)
#> [9] MIN_86400 (1825 rows)
#> [10] MAX_86400 (1825 rows)
</code></pre>
<p>This <code>RRD</code> file contains data for the properties of CPU 0 of the system. In this example, the first <code>RRA</code> archive contains averaged metrics for one minute (60s) intervals, while the second <code>RRA</code> measures the same metric, but averaged over five minutes. The same metrics are also available for intervals of one hour and one day.</p>
<p>Notice also that every archive has a different number of rows, representing a different historical period where the data is kept. For example, the <em>per minute</em> data <code>AVERAGE_60</code> is retained for 43,200 periods (12 days) while the <em>daily</em> data <code>MAX_86400</code> is retained for 1,825 periods (5 years).</p>
<p>If you want to know more, please read the excellent <a href="https://oss.oetiker.ch/rrdtool/tut/rrdtutorial.en.html">introduction tutorial</a> to RRD database.</p>
<h2 id="introducing-the-rrd-package">Introducing the <code>rrd</code> package</h2>
<p>Until recently, it wasn’t easy to import RRD files into R. But I was pleased to discover that a <a href="https://www.google-melange.com/archive/gsoc/2014">Google Summer of Code 2014</a> project created a proof-of-concept R package to read these files. The author of this package is <a href="http://plamendimitrov.net/">Plamen Dimitrov</a>, who published the code on <a href="https://github.com/pldimitrov/Rrd">GitHub</a> and also wrote an <a href="http://plamendimitrov.net/blog/2014/08/09/r-package-for-working-with-rrd-files/">explanatory blog post</a>.</p>
<p>Because I had to provide some suggestions to our customers, I decided to update the package, provide some example code, and generally improve the reliability.</p>
<p>The result is not yet on CRAN, but you can install the development version of package from <a href="https://github.com/andrie/rrd">github</a>.</p>
<h3 id="installing-the-package">Installing the package</h3>
<p>To build the package from source, you first need to install <a href="http://oss.oetiker.ch/rrdtool/doc/librrd.en.html">librrd</a>. Installing <a href="http://oss.oetiker.ch/rrdtool/">RRDtool</a> from your Linux package manager will usually also install this library.</p>
<p>Using Ubuntu:</p>
<pre><code class="language-sh">sudo apt-get install rrdtool librrd-dev
</code></pre>
<p>Using RHEL / CentOS:</p>
<pre><code class="language-sh">sudo yum install rrdtool rrdtool-devel
</code></pre>
<p>Once you have the system requirements in place, you can install the development version of the R package from <a href="https://github.com/andrie/rrd">GitHub</a> using:</p>
<pre><code class="language-r"># install.packages("devtools")
devtools::install_github("andrie/rrd")
</code></pre>
<h3 id="limitations">Limitations</h3>
<p>The package is not yet available for Windows.</p>
<h3 id="using-the-package">Using the package</h3>
<p>Once you’ve installed the package, you can start to use it. The package itself contains some built-in RRD files, so you should be able to run the following code directly.</p>
<pre><code class="language-r">library(rrd)
</code></pre>
<h4 id="describing-the-contents-of-a-rrd">Describing the contents of a RRD</h4>
<p>To describe the contents of an RRD file, use <code>describe_rrd()</code>. This function reports information about the names of each archive (RRA) file, the consolidation function, and the number of observations:</p>
<pre><code class="language-r">rrd_cpu_0 <- system.file("extdata/cpu-0.rrd", package = "rrd")
describe_rrd(rrd_cpu_0)
#> A RRD file with 10 RRA arrays and step size 60
#> [1] AVERAGE_60 (43200 rows)
#> [2] AVERAGE_300 (25920 rows)
#> [3] MIN_300 (25920 rows)
#> [4] MAX_300 (25920 rows)
#> [5] AVERAGE_3600 (8760 rows)
#> [6] MIN_3600 (8760 rows)
#> [7] MAX_3600 (8760 rows)
#> [8] AVERAGE_86400 (1825 rows)
#> [9] MIN_86400 (1825 rows)
#> [10] MAX_86400 (1825 rows)
</code></pre>
<h4 id="reading-an-entire-rrd-file">Reading an entire RRD file</h4>
<p>To read an entire RRD file, i.e. all of the RRA archives, use <code>read_rrd()</code>. This returns a list of <code>tibble</code> objects:</p>
<pre><code class="language-r">cpu <- read_rrd(rrd_cpu_0)
str(cpu, max.level = 1)
#> List of 10
#> $ AVERAGE60 :Classes 'tbl_df', 'tbl' and 'data.frame': 43199 obs. of 9 variables:
#> $ AVERAGE300 :Classes 'tbl_df', 'tbl' and 'data.frame': 25919 obs. of 9 variables:
#> $ MIN300 :Classes 'tbl_df', 'tbl' and 'data.frame': 25919 obs. of 9 variables:
#> $ MAX300 :Classes 'tbl_df', 'tbl' and 'data.frame': 25919 obs. of 9 variables:
#> $ AVERAGE3600 :Classes 'tbl_df', 'tbl' and 'data.frame': 8759 obs. of 9 variables:
#> $ MIN3600 :Classes 'tbl_df', 'tbl' and 'data.frame': 8759 obs. of 9 variables:
#> $ MAX3600 :Classes 'tbl_df', 'tbl' and 'data.frame': 8759 obs. of 9 variables:
#> $ AVERAGE86400:Classes 'tbl_df', 'tbl' and 'data.frame': 1824 obs. of 9 variables:
#> $ MIN86400 :Classes 'tbl_df', 'tbl' and 'data.frame': 1824 obs. of 9 variables:
#> $ MAX86400 :Classes 'tbl_df', 'tbl' and 'data.frame': 1824 obs. of 9 variables:
</code></pre>
<p>Since the resulting object is a list of <code>tibble</code> objects, you can easily use R functions to work with an individual archive:</p>
<pre><code class="language-r">names(cpu)
#> [1] "AVERAGE60" "AVERAGE300" "MIN300" "MAX300"
#> [5] "AVERAGE3600" "MIN3600" "MAX3600" "AVERAGE86400"
#> [9] "MIN86400" "MAX86400"
</code></pre>
<p>To inspect the contents of the first archive (<code>AVERAGE60</code>), simply print the object - since it’s a <code>tibble</code>, you get 10 lines of output.</p>
<p>For example, the CPU metrics contains a time stamp and metrics for average <em>user</em> and <em>sys</em> usage, as well as the <a href="https://en.wikipedia.org/wiki/Nice_(Unix)"><em>nice</em></a> value, <em>idle</em> time, <a href="https://en.wikipedia.org/wiki/Interrupt_request_(PC_architecture)"><em>interrupt requests</em></a> and <em>soft interrupt requests</em>:</p>
<pre><code class="language-r">cpu[[1]]
#> # A tibble: 43,199 x 9
#> timestamp user sys nice idle wait irq softirq
#> * <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2018-04-02 12:24:00 0.0104 0.00811 0 0.981 0 0 0
#> 2 2018-04-02 12:25:00 0.0126 0.00630 0 0.979 0 0 0
#> 3 2018-04-02 12:26:00 0.0159 0.00808 0 0.976 0 0 0
#> 4 2018-04-02 12:27:00 0.00853 0.00647 0 0.985 0 0 0
#> 5 2018-04-02 12:28:00 0.0122 0.00999 0 0.978 0 0 0
#> 6 2018-04-02 12:29:00 0.0106 0.00604 0 0.983 0 0 0
#> 7 2018-04-02 12:30:00 0.0147 0.00427 0 0.981 0 0 0
#> 8 2018-04-02 12:31:00 0.0193 0.00767 0 0.971 0 0 0
#> 9 2018-04-02 12:32:00 0.0300 0.0274 0 0.943 0 0 0
#> 10 2018-04-02 12:33:00 0.0162 0.00617 0 0.978 0 0 0
#> # ... with 43,189 more rows, and 1 more variable: stolen <dbl>
</code></pre>
<p>Since the data is in <code>tibble</code> format, you can easily extract specific data, e.g., the last values of the system usage:</p>
<pre><code class="language-r">
tail(cpu$AVERAGE60$sys)
#> [1] 0.0014390667 0.0020080000 0.0005689333 0.0000000000 0.0014390667
#> [6] 0.0005689333
</code></pre>
<h4 id="reading-only-a-single-archive">Reading only a single archive</h4>
<p>The underlying code in the <code>rrd</code> package is written in C, and is therefore blazingly fast. Reading an entire RRD file takes a fraction of a second, but sometimes you may want to extract a specific RRA archive immediately.</p>
<p>To read a single RRA archive from an RRD file, use <code>read_rra()</code>. To use this function, you must specify several arguments that define the specific data to retrieve. This includes the consolidation function (e.g., <code>"AVERAGE"</code>) and time step (e.g., <code>60</code>). You must also specify either the <code>start</code> time or the number of steps, <code>n_steps</code>.</p>
<p>In this example, I extract the average for one-minute periods (<code>step = 60</code>) for one day (<code>n_steps = 24 * 60</code>):</p>
<pre><code class="language-r">end_time <- as.POSIXct("2018-05-02") # timestamp with data in example
avg_60 <- read_rra(rrd_cpu_0, cf = "AVERAGE", step = 60, n_steps = 24 * 60,
end = end_time)
avg_60
#> # A tibble: 1,440 x 9
#> timestamp user sys nice idle wait irq softirq
#> * <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2018-05-01 00:01:00 0.00458 0.00201 0 0.992 0 0 0
#> 2 2018-05-01 00:02:00 0.00258 0.000570 0 0.996 0 0 0
#> 3 2018-05-01 00:03:00 0.00633 0.00144 0 0.992 0 0 0
#> 4 2018-05-01 00:04:00 0.00515 0.00201 0 0.991 0 0 0
#> 5 2018-05-01 00:05:00 0.00402 0.000569 0 0.995 0 0 0
#> 6 2018-05-01 00:06:00 0.00689 0.00144 0 0.992 0 0 0
#> 7 2018-05-01 00:07:00 0.00371 0.00201 0 0.993 0.00144 0 0
#> 8 2018-05-01 00:08:00 0.00488 0.00201 0 0.993 0.000569 0 0
#> 9 2018-05-01 00:09:00 0.00748 0.000568 0 0.992 0 0 0
#> 10 2018-05-01 00:10:00 0.00516 0 0 0.995 0 0 0
#> # ... with 1,430 more rows, and 1 more variable: stolen <dbl>
</code></pre>
<h4 id="plotting-the-results">Plotting the results</h4>
<p>The original <code>RRDTool</code> library for Linux contains some functions to <a href="https://oss.oetiker.ch/rrdtool/gallery/index.en.html">easily plot</a> the RRD data, a feature that distinguishes RRD from many other databases.</p>
<p>However, R already has very rich plotting capability, so the <code>rrd</code> R package doesn’t expose any specific plotting functions.</p>
<p>For example, you can easily plot these data using your favourite packages, like <code>ggplot2</code>:</p>
<pre><code class="language-r">library(ggplot2)
ggplot(avg_60, aes(x = timestamp, y = user)) +
geom_line() +
stat_smooth(method = "loess", span = 0.125, se = FALSE) +
ggtitle("CPU0 usage, data read from RRD file")
</code></pre>
<p><img src="/post/2018-06-21-analysing-rrd-files_files/ggplot.png" alt="" /></p>
<h2 id="getting-the-rrd-files-from-rstudio-server-pro-and-rstudio-connect">Getting the RRD files from RStudio Server Pro and RStudio Connect</h2>
<p>As I mentioned in the introduction, both <a href="https://www.rstudio.com/products/rstudio-server-pro/">RStudio Server Pro</a> and <a href="https://www.rstudio.com/products/connect/">RStudio Connect</a> use RRD to store metrics. In fact, these metrics are used to power the administration dashboard of these products.</p>
<p>This means that often the easiest solution is simply to enable the admin dashboard and view the information there.</p>
<p><img src="/post/2018-06-21-analysing-rrd-files_files/rsp_admin_dashboard.png" alt="RStudio Server Pro admin dashboard" /></p>
<p>However, sometimes R users and system administrators have a need to analyze the metrics in more detail, so in this section, I discuss where you can find the files for analysis.</p>
<p>The administration guides for these products explain where to find the metrics files:</p>
<ul>
<li>The admin guide for <strong>RStudio Server Pro</strong> discusses metrics in this in section <a href="http://docs.rstudio.com/ide/server-pro/auditing-and-monitoring.html#monitoring-configuration">8.2 Monitoring Configuration</a>.
<ul>
<li>By default, the metrics are stored at <code>/var/lib/rstudio-server/monitor/rrd</code>, although this path is configurable by the server administrator</li>
<li>RStudio Server Pro stores system metrics as well as user metrics</li>
</ul></li>
<li><strong>RStudio Connect</strong> discusses metrics in section <a href="http://docs.rstudio.com/connect/admin/historical-information.html#metrics">16.1 Historical Metrics</a>
<ul>
<li>The default path for metrics logs is <code>/var/lib/rstudio-connect/metrics</code>, though again, this is configurable by the server administrator.</li>
</ul></li>
</ul>
<pre><code class="language-r">rsc <- "/var/lib/rstudio-connect/metrics/rrd"
rsp <- "/var/lib/rstudio-server/monitor/rrd"
</code></pre>
<p>If you want to analyze these files, it is best to copy the files to a different location. The security and permissions on both products are configured in such a way that it’s not possible to read the files while they are in the original folder. Therefore, we recommend that you copy the files to a different location and do the analysis there.</p>
<h3 id="warning-about-using-the-rstudio-connect-rrd-files">Warning about using the RStudio Connect RRD files:</h3>
<p>The RStudio Connect team is actively planning to change the way content-level metrics are stored, so data related to shiny apps, markdown reports, etc. will likely look different in a future release.</p>
<p>To be clear:</p>
<ul>
<li>The schemas might change</li>
<li>RStudio Connect may stop tracking some metrics</li>
<li>It’s also possible that the entire mechanism might change</li>
</ul>
<p>The only guarantees that we make in RStudio Connect are around the data that we actually surface:</p>
<ul>
<li>server-wide user counts</li>
<li>RAM</li>
<li>CPU data</li>
</ul>
<p>This means that if you analyze RRD files, you should be aware that <strong>the entire mechanism for storing metrics might change in future</strong>.</p>
<h3 id="additional-caveat">Additional caveat</h3>
<ul>
<li>The metrics collection process runs in a sandboxed environment, and it is not possible to publish a report to RStudio Connect that reads the metrics directly. If you want to automate a process to read the Connect metrics, you will have to set up a <a href="https://en.wikipedia.org/wiki/Cron">cron</a> job to copy the files to a different location, and run the analysis against the copied files. (Also, re-read the warning that everything might change!)</li>
</ul>
<h3 id="example">Example</h3>
<p>In the following worked example, I copied some <code>rrd</code> files that originated in RStudio Connect to a different location on disk, and stored this in a <a href="https://github.com/rstudio/config"><code>config</code></a> file.</p>
<p>First, list the file names:</p>
<pre><code class="language-r">config <- config::get()
rrd_location <- config$rrd_location
rrd_location %>%
list.files() %>%
tail(20)
</code></pre>
<pre><code>## [1] "content-978.rrd" "content-986.rrd" "content-98.rrd"
## [4] "content-990.rrd" "content-995.rrd" "content-998.rrd"
## [7] "cpu-0.rrd" "cpu-1.rrd" "cpu-2.rrd"
## [10] "cpu-3.rrd" "license-users.rrd" "network-eth0.rrd"
## [13] "network-lo.rrd" "system-CPU.rrd" "system.cpu.usage.rrd"
## [16] "system.load.rrd" "system.memory.rrd" "system-RAM.rrd"
## [19] "system.swap.rrd" "system-SWAP.rrd"
</code></pre>
<p>The file names indicated that RStudio Connect collects metrics for the system (CPU, RAM, etc.), as well as for every piece of published content.</p>
<p>To look at the system load, first describe the contents of the <code>"system.load.rrd"</code> file:</p>
<pre><code class="language-r">sys_load <- file.path(rrd_location, "system.load.rrd")
describe_rrd(sys_load)
</code></pre>
<pre><code>## A RRD file with 10 RRA arrays and step size 60
## [1] AVERAGE_60 (43200 rows)
## [2] AVERAGE_300 (25920 rows)
## [3] MIN_300 (25920 rows)
## [4] MAX_300 (25920 rows)
## [5] AVERAGE_3600 (8760 rows)
## [6] MIN_3600 (8760 rows)
## [7] MAX_3600 (8760 rows)
## [8] AVERAGE_86400 (1825 rows)
## [9] MIN_86400 (1825 rows)
## [10] MAX_86400 (1825 rows)
</code></pre>
<p>This output tells you that metrics are collected every 60 seconds (one minute), and then in selected multiples (1 minute, 5 minutes, 1 hour and 1 day.) You can also tell that the consolidation functions are <code>average</code>, <code>min</code> and <code>max</code>.</p>
<p>To extract one month of data, averaged at 5-minute intervals use <code>step = 300</code>:</p>
<pre><code class="language-r">dat <- read_rra(sys_load, cf = "AVERAGE", step = 300L, n_steps = (3600 / 300) * 24 * 30)
dat
</code></pre>
<pre><code>## # A tibble: 8,640 x 4
## timestamp `1min` `5min` `15min`
## * <dttm> <dbl> <dbl> <dbl>
## 1 2018-05-10 19:10:00 0.0254 0.0214 0.05
## 2 2018-05-10 19:15:00 0.263 0.153 0.0920
## 3 2018-05-10 19:20:00 0.0510 0.117 0.101
## 4 2018-05-10 19:25:00 0.00137 0.0509 0.0781
## 5 2018-05-10 19:30:00 0 0.0168 0.0534
## 6 2018-05-10 19:35:00 0 0.01 0.05
## 7 2018-05-10 19:40:00 0.0146 0.0166 0.05
## 8 2018-05-10 19:45:00 0.00147 0.0115 0.05
## 9 2018-05-10 19:50:00 0.0381 0.0306 0.05
## 10 2018-05-10 19:55:00 0.0105 0.018 0.05
## # ... with 8,630 more rows
</code></pre>
<p>It is very easy to plot this using your preferred plotting package, e.g., <code>ggplot2</code>:</p>
<pre><code class="language-r">ggplot(dat, aes(x = timestamp, y = `5min`)) +
geom_line() +
stat_smooth(method = "loess", span = 0.125)
</code></pre>
<p><img src="/post/2018-06-21-analysing-rrd-files_files/ggplot.png" alt="" /></p>
<h2 id="conclusion">Conclusion</h2>
<p>The <code>rrd</code> package, available from <a href="https://github.com/andrie/rrd">GitHub</a>, makes it very easy to read metrics stored in the RRD database format. Reading an archive is very quick, and your resulting data is a <code>tibble</code> for an individual archive, or a list of <code>tibble</code>s for the entire file.</p>
<p>This makes it easy to analyze your data using the <code>tidyverse</code> packages, and to plot the information.</p>
<script>window.location.href='https://rviews.rstudio.com/2018/06/20/reading-rrd-files/';</script>
Player Data for the 2018 FIFA World Cup
https://rviews.rstudio.com/2018/06/14/player-data-for-the-2018-fifa-world-cup/
Thu, 14 Jun 2018 00:00:00 +0000https://rviews.rstudio.com/2018/06/14/player-data-for-the-2018-fifa-world-cup/
<p>The World Cup starts today! The tournament which runs from June 14 through July 15 is probably the most popular sporting event in the world. if you are a soccer fan, you know that learning about the players and their teams and talking about it all with your friends greatly enhances the experience. In this post, I will show you how to gather and explore data for the 736 players from the 32 teams at the 2018 FIFA World Cup. Have fun and enjoy the games. I will be watching with you.</p>
<div id="download-player-data" class="section level2">
<h2>Download Player Data</h2>
<div id="official-pdf" class="section level3">
<h3>Official PDF</h3>
<p>FIFA has made several official player lists available, conveniently changing the format each time. For this exercise, I use the one from early June. The <a href="https://CRAN.R-project.org/package=tabulizer">tabulizer package</a> makes extracting information from tables included in a PDF document relatively easy. (The other (later) version of the official PDF is <a href="https://github.com/davidkane9/wc18/raw/master/fifa_player_list_2.pdf">here</a>. Strangely, the weight variable has been dropped.)</p>
<pre class="r"><code>suppressMessages(library(tidyverse))
library(stringr)
suppressMessages(library(lubridate))
suppressMessages(library(cowplot))
# Note that I set warnings to FALSE because of some annoying (and intermittent)
# issues with RJavaTools.
library(tabulizer)
url <- "https://github.com/davidkane9/wc18/raw/master/fifa_player_list_1.pdf"
out <- extract_tables(url, output = "data.frame")</code></pre>
<p>We now have a 32 element list, each item a data frame of information about the 23 players on each team. Let’s combine this information into a single tidy tibble.</p>
<pre class="r"><code># Note how bind_rows() makes it very easy to combine a list of compatible
# dataframes.
pdf_data <- bind_rows(out) %>%
as_tibble() %>%
# Make the variable names more tidy-like.
rename(team = Team,
number = X.,
position = Pos.,
name = FIFA.Popular.Name,
birth_date = Birth.Date,
shirt_name = Shirt.Name,
club = Club,
height = Height,
weight = Weight) %>%
# Country names are contentious issues. I modify two names because I will
# later need to merge this tibble with data from Wikipedia, which uses
# different names.
mutate(team = case_when(
team == "Korea Republic" ~ "South Korea",
team == "IR Iran" ~ "Iran",
TRUE ~ team)) %>%
# league and club should be separate variables. We also want birth_date to be
# a date and to have an age variable already calculated.
mutate(birth_date = dmy(birth_date),
league = str_sub(club, -4, -2),
club = str_sub(club, end = -7),
age = interval(birth_date, "2018-06-14") / years(1))</code></pre>
<p>Here is a sample of the data:</p>
<pre><code>## # A tibble: 10 x 10
## team number position birth_date shirt_name club height weight league age
## <chr> <int> <chr> <date> <chr> <chr> <int> <int> <chr> <dbl>
## 1 Denmark 3 DF 1992-08-03 VESTERGAARD VfL Bor… 200 98 GER 25.9
## 2 Argentina 18 DF 1990-07-13 SALVIO SL Benf… 167 69 POR 27.9
## 3 Croatia 15 DF 1996-09-17 ĆALETA-CAR FC Red … 192 89 AUT 21.7
## 4 Croatia 21 DF 1989-04-29 VIDA Besikta… 184 76 TUR 29.1
## 5 Japan 3 DF 1992-12-11 SHOJI Kashima… 182 74 JPN 25.5
## 6 Colombia 7 FW 1986-09-08 BACCA Villarr… 181 77 ESP 31.8
## 7 Iceland 10 MF 1989-09-08 G. SIGURDSSON Everton… 186 82 ENG 28.8
## 8 Germany 17 DF 1988-09-03 BOATENG FC Baye… 192 90 GER 29.8
## 9 Poland 4 DF 1986-04-21 CIONEK SPAL Fe… 184 81 ITA 32.1
## 10 Uruguay 9 FW 1987-01-24 L. SUAREZ FC Barc… 182 85 ESP 31.4</code></pre>
<p>Perform some error checking.</p>
<pre class="r"><code>stopifnot(length(unique(pdf_data$team)) == 32) # There are 32 teams.
stopifnot(all(range(table(pdf_data$team)) == 23)) # Each team has 23 players.
stopifnot(pdf_data %>%
filter(position == "GK") %>%
group_by(team) %>%
tally() %>%
filter(n != 3) %>%
nrow() == 0) # All teams have 3 goal keepers.
stopifnot(all(pdf_data$position %in%
c("GK", "DF", "MF", "FW"))) # All players assigned to 1 of 4 positions.</code></pre>
</div>
<div id="wikipedia-data" class="section level3">
<h3>Wikipedia Data</h3>
<p>Wikipedia includes other player information which might be interesting, especially the number of caps for each player. A “cap” is an appearance in a game for the national team. The <a href="https://CRAN.R-project.org/package=rvest">rvest package</a> makes scraping data from Wikipedia fairly easy.</p>
<pre class="r"><code>suppressMessages(library(rvest))
html <- read_html("https://en.wikipedia.org/wiki/2018_FIFA_World_Cup_squads")
# Once we have read in all the html, we need to identify the location of the
# data we want. The rvest vignette provides guidance, but the key trick is the
# use of SelectorGadget to find the correct CSS node.
# First, we need the country and the shirt number of each player so that we can
# merge this data with that from the PDF.
country <- html_nodes(html, ".mw-headline") %>%
html_text() %>%
as_tibble() %>%
filter(! str_detect(value, "Group")) %>%
slice(1:32)
number <- html_nodes(html, ".plainrowheaders td:nth-child(1)") %>%
html_text()
# We don't need the name of each player but I like to grab it, both because I
# prefer the Wikipedia formatting and to use this as a cross-check on the
# accuracy of our country/number merge.
name <- html_nodes(html, "th a") %>%
html_text() %>%
as_tibble() %>%
filter(! str_detect(value, "^captain$")) %>%
slice(1:736)
# cap is the variable we care about, but Wikipedia page also includes the number
# of goals that each player has scored for the national team. Try adding that
# information on your own.
caps <- html_nodes(html, ".plainrowheaders td:nth-child(5)") %>%
html_text()
# Create a tibble. Note that we are relying on all the vectors being in the
# correct order.
wiki_data <- tibble(
number = as.numeric(number),
name = name$value,
team = rep(country$value, each = 23),
caps = as.numeric(caps))
# I prefer the name from Wikipedia. Exercise for the reader: How might we use
# name (from Wikipedia) and shirt_name (from the PDF) to confirm that we have
# lined up the data correctly?
x <- left_join(select(pdf_data, -name), wiki_data, by = c("team", "number"))</code></pre>
</div>
</div>
<div id="data-exploration" class="section level2">
<h2>Data Exploration</h2>
<p>With this information, there are a variety of topics to explore.</p>
<div id="birth-month" class="section level3">
<h3>Birth Month</h3>
<p>For the entire sample of 736 players, there is a clear birth month effect, visible both when looking at calendar months and when aggregating to calendar quarters. Players are much more likely to have birthdays earlier in the year. The most common explanation is that players born in January have an advantage over players born in December (when both are born in the same calendar year) because the former will be older than the later whenever they are competing for spots on the same age-group team, given that the cut-offs are always (?) December 31. This advantage in youth soccer bleeds into adult soccer because of the extra opportunities it provides for expert coaching. (See “<a href="https://www.nytimes.com/2006/05/07/magazine/07wwln_freak.html">A Star Is Made</a>,” by Stephen J. Dubner and Steven D. Levitt, May 7, 2006, <em>New York Times Magazine</em>.)</p>
<p><img src="/post/2018-06-12-player-data-for-the-2018-fifa-world-cup_files/figure-html/unnamed-chunk-6-1.png" width="672" /></p>
<p>Strangely, the effect is only true for players who will be 25 and over at the start of the World Cup, about 75% of the sample.</p>
<p><img src="/post/2018-06-12-player-data-for-the-2018-fifa-world-cup_files/figure-html/unnamed-chunk-7-1.png" width="672" /></p>
<p>Why would that be true? Note that there are many fewer players starting the tournament at age 24 than one might expect:</p>
<p><img src="/post/2018-06-12-player-data-for-the-2018-fifa-world-cup_files/figure-html/unnamed-chunk-8-1.png" width="672" /></p>
<p>Are the “missing” score or so 24 year-olds a sign of something meaningful or random noise?</p>
</div>
<div id="team-quality" class="section level3">
<h3>Team Quality</h3>
<p>We don’t have good measures of player (or team) quality in this data. But we do know if an individual plays for a team in one of the countries which host the five highest quality leagues: England (ENG), Spain (ESP), Germany (GER), Italy (58) and France (49). (It is no coincidence that these leagues account for the largest share of the players.)</p>
<pre class="r"><code>x %>%
group_by(league) %>%
tally() %>%
arrange(desc(n))</code></pre>
<pre><code>## # A tibble: 55 x 2
## league n
## <chr> <int>
## 1 ENG 124
## 2 ESP 81
## 3 GER 67
## 4 ITA 58
## 5 FRA 49
## 6 RUS 36
## 7 KSA 30
## 8 MEX 22
## 9 TUR 22
## 10 POR 19
## # ... with 45 more rows</code></pre>
<p>Any World Cup team with very few players who play in these 5 leagues is unlikely to be a good team. The best leagues have teams with so much money that they (almost) always are able to hire the best players. The vast majority of players in, for example, the Saudi Arabian or Turkish leagues are not wanted by any team in the best leagues. So, one measure of team quality is the percentage of players who play for teams in those 5 elite leagues. Here are the top 8 and bottom 4:</p>
<pre class="r"><code>x %>%
group_by(team) %>%
summarise(elite = mean(league %in%
c("ENG", "ESP", "GER", "ITA", "FRA"))) %>%
arrange(desc(elite)) %>%
slice(c(1:8, 29:32))</code></pre>
<pre><code>## # A tibble: 12 x 2
## team elite
## <chr> <dbl>
## 1 England 1
## 2 France 1
## 3 Germany 1
## 4 Spain 1
## 5 Belgium 0.826
## 6 Switzerland 0.826
## 7 Senegal 0.783
## 8 Brazil 0.739
## 9 Iran 0.0435
## 10 Panama 0.0435
## 11 Peru 0.0435
## 12 Russia 0.0435</code></pre>
<p>This measure captures the fact that teams like England, France, Spain and Germany are likely to do well while teams like Iran, Panama and Peru are not. Russia, as the host country, is a more difficult case. There are many problems with this analysis. Feel free to point them out in the comments. A better approach would look at the quality of the clubs that individuals play for or, even better, at measures of individual player quality.</p>
<p>What can you do with this data?</p>
<p>David Kane teaches at <a href="https://www.harvard.edu">Harvard University</a> and co-organizes the <a href="https://www.meetup.com/Boston-useR/">Boston R User Group</a>.</p>
</div>
</div>
<script>window.location.href='https://rviews.rstudio.com/2018/06/14/player-data-for-the-2018-fifa-world-cup/';</script>
Monte Carlo Part Two
https://rviews.rstudio.com/2018/06/13/monte-carlo-part-two/
Wed, 13 Jun 2018 00:00:00 +0000https://rviews.rstudio.com/2018/06/13/monte-carlo-part-two/
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<p>In a <a href="https://rviews.rstudio.com/2018/06/05/monte-carlo/">previous post</a>, we reviewed how to set up and run a Monte Carlo (MC) simulation of future portfolio returns and growth of a dollar. Today, we will run that simulation many, many, times and then visualize the results.</p>
<p>Our ultimate goal is to build a Shiny app that allows an end user to build a custom portfolio, simulate returns, and visualize the results. If you just can’t wait, a link to the final Shiny app is available <a href="http://www.reproduciblefinance.com/shiny/monte-carlo-simulation/">here</a>.</p>
<p>This post builds off the work we did previously. I won’t go through the logic again, but the code for building a portfolio, calculating returns, mean and standard deviation of returns, and using them for a simulation is here:</p>
<pre class="r"><code>library(tidyquant)
library(tidyverse)
library(timetk)
library(broom)
library(highcharter)
symbols <- c("SPY","EFA", "IJS", "EEM","AGG")
prices <-
getSymbols(symbols, src = 'yahoo',
from = "2012-12-31",
to = "2017-12-31",
auto.assign = TRUE, warnings = FALSE) %>%
map(~Ad(get(.))) %>%
reduce(merge) %>%
`colnames<-`(symbols)
w <- c(0.25, 0.25, 0.20, 0.20, 0.10)
asset_returns_long <-
prices %>%
to.monthly(indexAt = "lastof", OHLC = FALSE) %>%
tk_tbl(preserve_index = TRUE, rename_index = "date") %>%
gather(asset, returns, -date) %>%
group_by(asset) %>%
mutate(returns = (log(returns) - log(lag(returns)))) %>%
na.omit()
portfolio_returns_tq_rebalanced_monthly <-
asset_returns_long %>%
tq_portfolio(assets_col = asset,
returns_col = returns,
weights = w,
col_rename = "returns",
rebalance_on = "months")
mean_port_return <-
mean(portfolio_returns_tq_rebalanced_monthly$returns)
stddev_port_return <-
sd(portfolio_returns_tq_rebalanced_monthly$returns)
simulation_accum_1 <- function(init_value, N, mean, stdev) {
tibble(c(init_value, 1 + rnorm(N, mean, stdev))) %>%
`colnames<-`("returns") %>%
mutate(growth =
accumulate(returns,
function(x, y) x * y)) %>%
select(growth)
}</code></pre>
<p>That code allows us to run one simulation of the growth of a dollar over the next 10 years, with the <code>simulation_accum_1()</code> that we built for that purpose. Today, we will review how to run 51 simulations, though we could choose any number (and our Shiny application allows an end user to do just that).</p>
<p>First, we need an empty matrix with 51 columns, an initial value of $1, and intuitive column names.</p>
<p>We will use the <code>rep()</code> function to create 51 columns with a 1 as the value, and <code>set_names()</code> to name each column with the appropriate simulation number.</p>
<pre class="r"><code>sims <- 51
starts <-
rep(1, sims) %>%
set_names(paste("sim", 1:sims, sep = ""))</code></pre>
<p>Take a peek at <code>starts</code> to see what we just created, and how it can house our simulations.</p>
<pre class="r"><code>head(starts)</code></pre>
<pre><code>sim1 sim2 sim3 sim4 sim5 sim6
1 1 1 1 1 1 </code></pre>
<pre class="r"><code>tail(starts)</code></pre>
<pre><code>sim46 sim47 sim48 sim49 sim50 sim51
1 1 1 1 1 1 </code></pre>
<p>51 columns, with a value of 1 in one row. This is where we will store the results of the 51 simulations.</p>
<p>Now, we want to apply <code>simulation_accum_1</code> to each of the 51 columns of the <code>starts</code> matrix, and we will do that using the <code>map_dfc()</code> function from the <code>purrr</code> package.</p>
<p><code>map_dfc()</code> takes a vector - in this case, the columns of <code>starts</code> - and applies a function to it. By appending <code>dfc()</code> to the <code>map_</code> function, we are asking the function to store each of its results as the column of a data frame (<code>map_df()</code> does the same thing, but stores results in the rows of a data frame). After running the code below, we will have a data frame with 51 columns, one for each of our simulations.</p>
<p>We also need to choose how many months to simulate (the N argument to our simulation function) and supply the distribution parameters as we did before. We do not supply the <code>init_value</code> argument because the <code>init_value</code> is 1, that same 1 that is already in the 51 columns.</p>
<pre class="r"><code>monte_carlo_sim_51 <-
map_dfc(starts,
simulation_accum_1,
N = 120,
mean = mean_port_return,
stdev = stddev_port_return)
tail(monte_carlo_sim_51 %>% select(growth1, growth2,
growth49, growth50), 3)</code></pre>
<pre><code># A tibble: 3 x 4
growth1 growth2 growth49 growth50
<dbl> <dbl> <dbl> <dbl>
1 1.81 1.38 2.32 1.80
2 1.84 1.37 2.38 1.84
3 1.82 1.33 2.39 1.82</code></pre>
<p>Have a look at the results. We now have 51 simulations of the growth of a dollar, and we simulated that growth over 120 months; however, the results are missing a piece that we need for visualization, namely a <code>month</code> column.</p>
<p>Let’s add that <code>month</code> column with <code>mutate()</code> and give it the same number of rows as our data frame. These are months out into the future. We will use <code>mutate(month = seq(1:nrow(.)))</code>, and then clean up the column names. <code>nrow()</code> is equal to the number of rows in our object. If we were to change to 130 simulations, that would generate 130 rows, and <code>nrow()</code> would be equal to 130.</p>
<pre class="r"><code>monte_carlo_sim_51 <-
monte_carlo_sim_51 %>%
mutate(month = seq(1:nrow(.))) %>%
select(month, everything()) %>%
`colnames<-`(c("month", names(starts))) %>%
mutate_all(funs(round(., 2)))
tail(monte_carlo_sim_51 %>% select(month, sim1, sim2,
sim49, sim50), 3)</code></pre>
<pre><code># A tibble: 3 x 5
month sim1 sim2 sim49 sim50
<dbl> <dbl> <dbl> <dbl> <dbl>
1 119 2.16 1.81 1.46 2.32
2 120 2.28 1.84 1.46 2.38
3 121 2.26 1.82 1.46 2.39</code></pre>
<p>We have accomplished our goal of running 51 simulations, and could head to data visualization now, but let’s explore an alternative method using the the <code>rerun()</code> function from <code>purrr</code>. As its name implies, this function will “rerun” another function, and we stipulate how many times to do that by setting <code>.n = number of times to rerun</code>. For example to run the <code>simulation_accum_1</code> function 5 times, we would set the following:</p>
<pre class="r"><code>monte_carlo_rerun_5 <-
rerun(.n = 5,
simulation_accum_1(1,
120,
mean_port_return,
stddev_port_return))</code></pre>
<p>That returned a list of 5 data frames, or 5 simulations. We can look at the first few rows of each data frame by using <code>map(..., head)</code>.</p>
<pre class="r"><code>map(monte_carlo_rerun_5, head)</code></pre>
<pre><code>[[1]]
# A tibble: 6 x 1
growth
<dbl>
1 1
2 0.983
3 0.965
4 0.946
5 0.967
6 0.962
[[2]]
# A tibble: 6 x 1
growth
<dbl>
1 1
2 0.980
3 0.975
4 0.964
5 0.969
6 0.914
[[3]]
# A tibble: 6 x 1
growth
<dbl>
1 1
2 1.03
3 0.997
4 0.979
5 1.04
6 1.04
[[4]]
# A tibble: 6 x 1
growth
<dbl>
1 1
2 0.974
3 0.962
4 0.943
5 0.942
6 0.963
[[5]]
# A tibble: 6 x 1
growth
<dbl>
1 1
2 0.990
3 1.02
4 1.06
5 1.12
6 1.13 </code></pre>
<p>Let’s consolidate that list of data frames to one <code>tibble</code>. We start by collapsing to vectors with <code>simplify_all()</code>, then add nicer names with the <code>names()</code> function and finally coerce to tibble with <code>as_tibble()</code>. Let’s run it 51 times to match our previous results.</p>
<pre class="r"><code>reruns <- 51
monte_carlo_rerun_51 <-
rerun(.n = reruns,
simulation_accum_1(1,
120,
mean_port_return,
stddev_port_return)) %>%
simplify_all() %>%
`names<-`(paste("sim", 1:reruns, sep = " ")) %>%
as_tibble() %>%
mutate(month = seq(1:nrow(.))) %>%
select(month, everything())
tail(monte_carlo_rerun_51 %>% select(`sim 1`, `sim 2`,
`sim 49`, `sim 50`), 3)</code></pre>
<pre><code># A tibble: 3 x 4
`sim 1` `sim 2` `sim 49` `sim 50`
<dbl> <dbl> <dbl> <dbl>
1 1.99 1.97 3.66 2.20
2 2.00 1.86 3.68 2.25
3 2.02 1.95 3.78 2.18</code></pre>
<p>Now we have two objects holding the results of 51 simulations, <code>monte_carlo_rerun_51</code> and <code>monte_carlo_sim_51</code>.</p>
<p>Each has 51 columns of simulations and 1 column of months. Note that we have 121 rows because we started with an initial value of 1, and then simulated returns over 120 months.</p>
<p>Now let’s get to visualization with <code>ggplot()</code> and visualize the results in <code>monte_carlo_sim_51</code>. The same code flows for visualization would also apply to <code>monte_carlo_rerun_51</code>, but we will run them for only <code>monte_carlo_sim_51</code> here.</p>
<p>We start with a chart of all 51 simulations, and assign a different color to each one by setting <code>ggplot(aes(x = month, y = growth, color = sim))</code>. <code>ggplot()</code> will automatically generate a legend for all 51 time series, but that gets quite crowded. We will suppress the legend with <code>theme(legend.position = "none")</code>.</p>
<pre class="r"><code>monte_carlo_sim_51 %>%
gather(sim, growth, -month) %>%
group_by(sim) %>%
ggplot(aes(x = month, y = growth, color = sim)) +
geom_line() +
theme(legend.position="none")</code></pre>
<p><img src="/post/2018-06-12-monte-carlo-part-2_files/figure-html/unnamed-chunk-9-1.png" width="672" /></p>
<p>We can check the minimum, maximum and median simulation with the <code>summarise()</code> function here.</p>
<pre class="r"><code>sim_summary <-
monte_carlo_sim_51 %>%
gather(sim, growth, -month) %>%
group_by(sim) %>%
summarise(final = last(growth)) %>%
summarise(
max = max(final),
min = min(final),
median = median(final))
sim_summary</code></pre>
<pre><code># A tibble: 1 x 3
max min median
<dbl> <dbl> <dbl>
1 4.81 1.31 2.3</code></pre>
<p>We can clean up our original visualization by including only the max, min and median that were just calculated.</p>
<pre class="r"><code>monte_carlo_sim_51 %>%
gather(sim, growth, -month) %>%
group_by(sim) %>%
filter(
last(growth) == sim_summary$max ||
last(growth) == sim_summary$median ||
last(growth) == sim_summary$min) %>%
ggplot(aes(x = month, y = growth)) +
geom_line(aes(color = sim)) </code></pre>
<p><img src="/post/2018-06-12-monte-carlo-part-2_files/figure-html/unnamed-chunk-11-1.png" width="672" /></p>
<p>Now let’s port our results over to <code>highcharter</code>, but in a major departure from our usual code flow, we will pass a tidy <code>tibble</code> instead of an <code>xts</code> object.</p>
<p>Our first step is to convert the data from wide to long tidy format with the <code>gather()</code> function.</p>
<pre class="r"><code>mc_gathered <-
monte_carlo_sim_51 %>%
gather(sim, growth, -month) %>%
group_by(sim)
head(mc_gathered)</code></pre>
<pre><code># A tibble: 6 x 3
# Groups: sim [1]
month sim growth
<dbl> <chr> <dbl>
1 1 sim1 1
2 2 sim1 0.99
3 3 sim1 1.01
4 4 sim1 1.06
5 5 sim1 1.08
6 6 sim1 1.1 </code></pre>
<p>We can now pass this <code>tibble</code> directly to the <code>hchart()</code> function, specify the type of chart as <code>line</code>, and then work with a similar grammar to <code>ggplot()</code>. The difference is we use <code>hcaes</code>, which stands for <code>highcharter aesthetic</code>, instead of <code>aes</code>.</p>
<pre class="r"><code># This takes a few seconds to run
hchart(mc_gathered,
type = 'line',
hcaes(y = growth,
x = month,
group = sim)) %>%
hc_title(text = "51 Simulations") %>%
hc_xAxis(title = list(text = "months")) %>%
hc_yAxis(title = list(text = "dollar growth"),
labels = list(format = "${value}")) %>%
hc_add_theme(hc_theme_flat()) %>%
hc_exporting(enabled = TRUE) %>%
hc_legend(enabled = FALSE)</code></pre>
<div id="htmlwidget-1" style="width:100%;height:500px;" class="highchart html-widget"></div>
<script type="application/json" data-for="htmlwidget-1">{"x":{"hc_opts":{"title":{"text":"51 Simulations"},"yAxis":{"title":{"text":"dollar 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<p>We just plotted 51 lines in <code>highcharter</code> using a tidy <code>tibble</code>. For tidy data fans out there, this is a big deal because we can stay in the grammar of the tidyverse but also use <code>highcharter</code>.</p>
<p>Very similar to what we did with <code>ggplot</code>, let’s isolate the maximum, minimum and median simulations and save them to an object called <code>mc_max_med_min</code>.</p>
<pre class="r"><code>mc_max_med_min <-
mc_gathered %>%
filter(
last(growth) == sim_summary$max ||
last(growth) == sim_summary$median ||
last(growth) == sim_summary$min) %>%
group_by(sim)</code></pre>
<p>Now we pass that filtered object to <code>hchart()</code>.</p>
<pre class="r"><code>hchart(mc_max_med_min,
type = 'line',
hcaes(y = growth,
x = month,
group = sim)) %>%
hc_title(text = "Min, Max, Median Simulations") %>%
hc_xAxis(title = list(text = "months")) %>%
hc_yAxis(title = list(text = "dollar growth"),
labels = list(format = "${value}")) %>%
hc_add_theme(hc_theme_flat()) %>%
hc_exporting(enabled = TRUE) %>%
hc_legend(enabled = FALSE)</code></pre>
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<p>That concludes our visualization of Monte Carlo simulations.</p>
<script>window.location.href='https://rviews.rstudio.com/2018/06/13/monte-carlo-part-two/';</script>
Monte Carlo
https://rviews.rstudio.com/2018/06/05/monte-carlo/
Tue, 05 Jun 2018 00:00:00 +0000https://rviews.rstudio.com/2018/06/05/monte-carlo/
<p>Today, we change gears from our previous work on <a href="https://rviews.rstudio.com/2018/05/10/rolling-fama-french/">Fama French</a> and run a Monte Carlo (MC) simulation of future portfolio returns. Monte Carlo relies on repeated, random sampling. We will sample based on two parameters: mean and standard deviation of portfolio returns. Our long-term goal (long-term == over the next two or three blog posts) is to build a Shiny app that allows an end user to build a custom portfolio, simulate returns and visualize the results. If you just can’t wait, a link to that final Shiny app is <a href="http://www.reproduciblefinance.com/shiny/monte-carlo-simulation/">here</a>.</p>
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<img src="/post/2018-06-07-Monte-Carlo_files/MC.png" />
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<p>Let’s get to it.</p>
<p>Devoted readers won’t be surprised that we will be simulating the returns of our usual portfolio, which consists of:</p>
<pre><code>+ SPY (S&P 500 fund) weighted 25%
+ EFA (a non-US equities fund) weighted 25%
+ IJS (a small-cap value fund) weighted 20%
+ EEM (an emerging-mkts fund) weighted 20%
+ AGG (a bond fund) weighted 10%</code></pre>
<p>Before we can simulate that portfolio, we need to calculate portfolio monthly returns, which was covered in my previous post, <a href="https://rviews.rstudio.com/2017/10/11/from-asset-to-portfolio-returns/">Introduction to Portfolio Returns</a>.</p>
<p>I won’t go through the logic again, but the code is here:</p>
<pre class="r"><code>library(tidyquant)
library(tidyverse)
library(timetk)
library(broom)
symbols <- c("SPY","EFA", "IJS", "EEM","AGG")
prices <-
getSymbols(symbols, src = 'yahoo',
from = "2012-12-31",
to = "2017-12-31",
auto.assign = TRUE, warnings = FALSE) %>%
map(~Ad(get(.))) %>%
reduce(merge) %>%
`colnames<-`(symbols)
w <- c(0.25, 0.25, 0.20, 0.20, 0.10)
asset_returns_long <-
prices %>%
to.monthly(indexAt = "lastof", OHLC = FALSE) %>%
tk_tbl(preserve_index = TRUE, rename_index = "date") %>%
gather(asset, returns, -date) %>%
group_by(asset) %>%
mutate(returns = (log(returns) - log(lag(returns)))) %>%
na.omit()
portfolio_returns_tq_rebalanced_monthly <-
asset_returns_long %>%
tq_portfolio(assets_col = asset,
returns_col = returns,
weights = w,
col_rename = "returns",
rebalance_on = "months")</code></pre>
<p>We will be working with the data object <code>portfolio_returns_tq_rebalanced_monthly</code> and we first find the mean and <a href="http://www.reproduciblefinance.com/code/standard-deviation/">standard deviation</a> of returns.</p>
<p>We will name those variables <code>mean_port_return</code> and <code>stddev_port_return</code>.</p>
<pre class="r"><code>mean_port_return <-
mean(portfolio_returns_tq_rebalanced_monthly$returns)
stddev_port_return <-
sd(portfolio_returns_tq_rebalanced_monthly$returns)</code></pre>
<p>Then we use the <code>rnorm()</code> function to sample from a distribution with mean equal to <code>mean_port_return</code> and standard deviation equal to <code>stddev_port_return</code>. That is the crucial random sampling that underpins this exercise.</p>
<p>We also must decide how many draws to pull from this distribution, meaning how many monthly returns we will simulate. 120 months is 10 years, and that feels like a good amount of time.</p>
<pre class="r"><code>simulated_monthly_returns <- rnorm(120,
mean_port_return,
stddev_port_return)</code></pre>
<p>Have a quick look at the simulated monthly returns.</p>
<pre class="r"><code>head(simulated_monthly_returns)</code></pre>
<pre><code>[1] 0.05216143 0.02271485 -0.04271307 0.04811250 -0.05575058 0.06006096</code></pre>
<pre class="r"><code>tail(simulated_monthly_returns)</code></pre>
<pre><code>[1] 0.024794729 0.008539198 -0.018852629 -0.002656127 -0.025583337
[6] 0.004412755</code></pre>
<p>Next, we calculate how a dollar would have grown given those random monthly returns. We first add a 1 to each of our monthly returns, because we start with $1.</p>
<pre class="r"><code>simulated_returns_add_1 <-
tibble(c(1, 1 + simulated_monthly_returns)) %>%
`colnames<-`("returns")
head(simulated_returns_add_1)</code></pre>
<pre><code># A tibble: 6 x 1
returns
<dbl>
1 1
2 1.05
3 1.02
4 0.957
5 1.05
6 0.944</code></pre>
<p>That data is now ready to be converted into the cumulative growth of a dollar. We can use either <code>accumulate()</code> from <code>purrr</code> or <code>cumprod()</code>. Let’s use both of them with <code>mutate()</code> and confirm consistent, reasonable results.</p>
<pre class="r"><code>simulated_growth <-
simulated_returns_add_1 %>%
mutate(growth1 = accumulate(returns, function(x, y) x * y),
growth2 = accumulate(returns, `*`),
growth3 = cumprod(returns)) %>%
select(-returns)
tail(simulated_growth)</code></pre>
<pre><code># A tibble: 6 x 3
growth1 growth2 growth3
<dbl> <dbl> <dbl>
1 2.09 2.09 2.09
2 2.11 2.11 2.11
3 2.07 2.07 2.07
4 2.06 2.06 2.06
5 2.01 2.01 2.01
6 2.02 2.02 2.02</code></pre>
<p>We just ran three simulations of dollar growth over 120 months. We passed in the same monthly returns, and that’s why we got three equivalent results.</p>
<p>Are they reasonable? What compound annual growth rate (CAGR) is implied by this simulation?</p>
<pre class="r"><code>cagr <-
((simulated_growth$growth1[nrow(simulated_growth)]^
(1/10)) - 1) * 100
cagr <- round(cagr, 2)</code></pre>
<p>This simulation implies an annual compounded growth of 7.26%. That seems reasonable given our actual returns have all been taken from a raging bull market. Remember, the above code is a simulation based on sampling from a normal distribution. If you re-run this code on your own, you will get a different result.</p>
<p>If we feel good about this first simulation, we can run several more to get a sense for how they are distributed. Before we do that, let’s create several different functions that could run the same simulation.</p>
<div id="several-simulation-functions" class="section level2">
<h2>Several Simulation Functions</h2>
<p>Let’s build three simulation functions that incorporate the <code>accumulate()</code> and <code>cumprod()</code> workflows above. We have confirmed they give consistent results so it’s a matter of stylistic preference as to which one is chosen in the end. Perhaps you feel that one is more flexible or extensible, or fits better with your team’s code flows.</p>
<p>Each of the below functions needs four arguments: N for the number of months to simulate (we chose 120 above), <code>init_value</code> for the starting value (we used $1 above), and the mean-standard deviation pair to create draws from a normal distribution. We <em>choose</em> N and <code>init_value</code>, and derive the mean-standard deviation pair from our portfolio monthly returns.</p>
<p>Here is our first growth simulation function using <code>accumulate()</code>.</p>
<pre class="r"><code>simulation_accum_1 <- function(init_value, N, mean, stdev) {
tibble(c(init_value, 1 + rnorm(N, mean, stdev))) %>%
`colnames<-`("returns") %>%
mutate(growth =
accumulate(returns,
function(x, y) x * y)) %>%
select(growth)
}</code></pre>
<p>Almost identical, here is the second simulation function using <code>accumulate()</code>.</p>
<pre class="r"><code>simulation_accum_2 <- function(init_value, N, mean, stdev) {
tibble(c(init_value, 1 + rnorm(N, mean, stdev))) %>%
`colnames<-`("returns") %>%
mutate(growth = accumulate(returns, `*`)) %>%
select(growth)
}</code></pre>
<p>Finally, here is a simulation function using <code>cumprod()</code>.</p>
<pre class="r"><code>simulation_cumprod <- function(init_value, N, mean, stdev) {
tibble(c(init_value, 1 + rnorm(N, mean, stdev))) %>%
`colnames<-`("returns") %>%
mutate(growth = cumprod(returns)) %>%
select(growth)
}</code></pre>
<p>Here is a function that uses all three methods, in case we want a fast way to re-confirm consistency.</p>
<pre class="r"><code>simulation_confirm_all <- function(init_value, N, mean, stdev) {
tibble(c(init_value, 1 + rnorm(N, mean, stdev))) %>%
`colnames<-`("returns") %>%
mutate(growth1 = accumulate(returns, function(x, y) x * y),
growth2 = accumulate(returns, `*`),
growth3 = cumprod(returns)) %>%
select(-returns)
}</code></pre>
<p>Let’s test that <code>confirm_all()</code> function with an <code>init_value</code> of 1, N of 120, and our parameters.</p>
<pre class="r"><code>simulation_confirm_all_test <-
simulation_confirm_all(1, 120,
mean_port_return, stddev_port_return)
tail(simulation_confirm_all_test)</code></pre>
<pre><code># A tibble: 6 x 3
growth1 growth2 growth3
<dbl> <dbl> <dbl>
1 2.26 2.26 2.26
2 2.22 2.22 2.22
3 2.17 2.17 2.17
4 2.21 2.21 2.21
5 2.20 2.20 2.20
6 2.23 2.23 2.23</code></pre>
<p>That’s all for today. Next time, we will explore methods for running more than one simulation with the above functions and then visualizing the results. See you then.</p>
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