Harrison Schramm, CAP, PStat, is a Senior Fellow at the Center for Strategic and Budgetary Assessments.
In this short piece, I’m going to discuss a fun photography project I did over the winter using R. I’m also going to touch on some of the implications of the R license, which underlies our entire ecosystem, but we don’t usually think about that often.
I’ve been a dedicated useR for the past 4 years.
rstudio::conf kicked off the 2020 season for R conferences last week with record attendance somewhere north of twenty-one hundred. Session topics ranged from business to science, marketing to medicine and attracted R users with very varied backgrounds including DevOps professionals, data scientists, journalists, physicians, statisticians, R package developers, Shiny developers and more. Although it is true that the San Francisco Bay Area is home to a large R Community, and that a great deal of planning and promotion went into making rstudio::conf a success, I don’t think that the enthusiasm and energy that permeated the conference was a local phenomenon.
rstudio::conf 2020 got underway today with a huge training event featuring eighteen workshops taught by some of the most experienced and sought after instructors in the R Community.
The workshops covered a wide range of topics including the Tidyverse, machine learning, deep learning, JavaScript, Shiny, R Markdown, package building, geospatial statistics, visualization, teaching R and working as an RStudio professional products administrator.
Tidy Time Series Analysis and Forecasting
R for Excel Users What They Forgot to Teach You about R
One hundred fifty-two packages made it to CRAN in December. Here are my “Top 40” picks in ten categories: Data, Genomics, Machine Learning, Mathematics, Medicine, Science, Statistics, Time Series, Utilities, and Visualization.
Data climate v0.3.0: Provides access to meteorological and hydrological data from OGIMET, University of Wyoming - atmospheric vertical profiling data, and Polish Institute of Meteorology and Water Management - National Research Institute. Look here for more information as well as the vignette.
Pedro Coutinho Silva is a software engineer at Appsilon Data Science.
It is not always possible to create a dashboard that fully meets your expectations or requirements using only existing libraries. Maybe you want a specific function that needs to be custom built, or maybe you want to add your own style or company branding. Whatever the case, a moment might come when you need to expand and organize your code base, and dive into creating a custom solution for your project; but where to start?
Welcome to another installment of Reproducible Finance with R - the blog series that never seems to stop reproducing itself. Today we will explore the new almanac package for working with dates, which sprang forth courtesy of the mad genius behind riingo and furrr. We will be examining rolling returns and daily trading volumes from several ETFs over the past few years and we will use almanac to flag certain dates of interest.
If you are lucky enough to have some extra time for discretionary reading during the holiday season, you may find it interesting (and rewarding) to sample some of the nearly two hundred posts written across the various RStudio blogs.
R Views R Views, our blog devoted to the R Community and the R Language, published over sixty posts in 2019. Many of these were contributed by guest authors from the R Community who volunteered to share some outstanding work.
One hundred forty-four new packages made it to CRAN in November. Here are my picks for the “Top 40” in eight categories: Computational Methods, Data, Genomics, Machine Learning, Statistics, Time Series, Utilities, and Visualization.
Computational Methods calculus v0.1.1: Provides C++ optimized functions for numerical and symbolic calculus including symbolic arithmetic, tensor calculus, Einstein summation convention, Taylor series expansion, multivariate Hermite polynomials and much more.
Jaya v0.1.9: Implements a gradient-free algorithm, without hyperparameters, for solving both constrained and unconstrained optimization problems.
A task common to many machine learning workflows is to compare the performance of several models with respect to some metric such as accuracy or area under the ROC curve. Standard practice is to try out several different algorithms on a training data set and see which works better. Unfortunately, all to often, after this work has been done, model selection comes down to “eyeballing” several different ROC curves. If you find eyeballing a little too informal, then take a look at the tidyposterior package (part of the universe of ‘tidymodels`).
In two previous posts, we explored IPOs and IPO returns by sector and year since 2004 and then examined the returns of portfolios constructed by investing in IPOs each year. In today’s post, we will add a benchmark so that we can compare our IPO portfolios to something besides themselves. Next time, we will delve into return attribution to visualize how individual equities have contributed to portfolios over time.