We knew we were on to something important early on in the process of organizing R / Medicine 2018. Even during our initial attempts to articulate the differences between this conference and R / Pharma 2018, it became clear that the focus on the use of R and statistics in clinical settings was going to be a richer topic than just the design of clinical trials. However, it wasn’t until the conference got underway that we realized there was magic in the mix of attendees.

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September was another relatively slow month for new package activity on CRAN: “only” 126 new packages by my count. My Top 40 list is heavy on what I characterize as “utilities”: packages that either extend R in some fashion or make it easier to do things in R. This month, the packages I selected fall into eight categories: Data, Finance, Machine Learning, Science, Statistics, Time Series, Utilities and Visualization.

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It’s no secret that there are few industries more competitive than the pharmaceutical industry. Big money placed on long-shot bets for block-buster drugs where being first makes all the difference means a constant struggle to gain a competitive edge. So, you might find it surprising that the inaugural R / Pharma Conference held this past August on the Harvard campus in a very classy auditorium was all about collaboration. Some might also find it surprising that data scientists from competitive companies would gather to share information, but this is quite common.

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Package developers relaxed a bit in August.; only 160 new packages went to CRAN that month. Here are my “Top 40” picks organized into seven categories: Data, Machine Learning, Science, Statistics, Time Series, Utilities, and Visualization. Data nsapi v0.1.1: Provides an interface to the Nederlandse Spoorwegen (Dutch Railways) API, allowing users to download current departure times, disruptions and engineering work, the station list, and travel recommendations from station to station.

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I wish this post existed when I was struggling to add interactive plots to my Shiny app. I was mainly focused on recreating functionality found in other “dashboarding” applications. When looking for options, I found that htmlwidgets were the closest to what companies usually expect. However, while they are great for client-side interactivity, I often hit walls with them when I try to add click-through interactivity because the functionality is either not there, is very limited, or is bloated.

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Today, we will look at the GDP data that is released every quarter or so by the Bureau of Economic Analysis (BEA), and get familiar with the BEA API (see the documentation here). For a primer on GDP in general, BEA publishes this guide. To access the BEA API, we will need two packages, httr and jsonlite. library(tidyverse) library(tidyquant) library(httr) library(jsonlite) We also need to know the API address and parameters to get.

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Why is this about trucks? Last month, at the R/Pharma conference that took place on the Harvard Campus, I presented bioWARP, a large Shiny application containing more than 500,000 lines of code. Although several other Shiny apps were presented at the conference, I noticed that none of them came close to being as big as bioWARP. And I asked myself, why? I concluded that most people just don’t need to build them that big!

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In the previous post, we introduced plumber as a way to expose R processes and programs to external systems via REST API endpoints. In this post, we’ll go further by building out an API that powers a Slack slash command, all from within R using plumber. A subsequent post will outline deploying and securing the API. We will create an API built on top of simulated customer call data that powers a slash command.

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July was a big month for submitting new packages to CRAN; by my count, 251 unique and truly new packages were accepted. In addition to quantity, I was pleased to see quality and variety. For instance, tropicalSparse, a package for exploring some abstract mathematics, and eChem, a package for teaching analytical chemistry, exemplify R’s expansion into new fields. Below are my “Top 40” picks organized into ten categories: Computational Methods, Data, Econometrics, Machine Learning, Mathematics, Science, Statistics, Time Series, Utilities, and Visualization

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It all starts with sandboxes. Development sandboxes are dedicated safe spaces for experimentation and creativity. A sandbox is a place where you can go to test and break things, without the ramifications of breaking the real, important things. If you’re an analytic administrator who doesn’t have access or means to get a sandbox, I recommend that you consider advocating to change that. Here are just some of the arguments for why sandboxes are a powerful tool for the R admin that you may find helpful.

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