Category: R

Sarah Dutkiewicz figures out how to get R Tools for Visual Studio to stop having R files open in Notepad:

As I have been going through my courses – which use swirl() – I have been looking at how things work, comparing RStudio to RTVS.  One of the things that was maddening for me was going through one of the courses in RTVS and having R files open in Notepad.  Notepad?!?  RStudio wasn’t doing this, so I was even more frustrated.  I could also open R files with Visual Studio right from the file system, so the file association was already in place.  This didn’t make sense.  However… RTVS is an open source project, as is swirl().  So I spent tonight looking at code in GitHub.

Read on for the answer.

Bob Muenchen notes a structural conflict between R and its most common set of packages:

There’s a common theme in many of the sections above: a task that is hard to perform using base a R function is made much easier by a function in the dplyr package. That package, and its relatives, are collectively known as the tidyverse. Its functions help with many tasks, such as selecting, renaming, or transforming variables, filtering or sorting observations, combining data frames, and doing by-group analyses. dplyr is such a helpful package that Rdocumentation.org shows that it is the single most popular R package (as of 3/23/2017.) As much of a blessing as these commands are, they’re also a curse to beginners as they’re more to learn. The main packages of dplyr, tibble, tidyr, and purrr contain a few hundred functions, though I use “only” around 60 of them regularly. As people learn R, they often comment that base R functions and tidyverse ones feel like two separate languages. The tidyverse functions are often the easiest to use, but not always; its pipe operator is usually simpler to use, but not always; tibbles are usually accepted by non-tidyverse functions, but not always; grouped tibbles may help do what you want automatically, but not always (i.e. you may need to ungroup or group_by higher levels). Navigating the balance between base R and the tidyverse is a challenge to learn.

Interesting read.  As Bob notes in the comments, he’s still a fan of the tidyverse, but it’s important to recognize that there are pain points there.

Adnan Fiaz reviews conference abstracts for patterns:

Certainly an interesting graph! It may have been better to show the proportions instead of counts as the number of abstracts in each category are not equal. Nevertheless, the conclusion remains the same. The words “r” and “data” are clearly the most common. However, what is more interesting is that abstracts in the “yes” category use certain words significantly more often than abstracts in the “no” category and vice versa (more often because a missing bar doesn’t necessarily mean a zero observation). For example, the words “science”, “production” and “performance” occur more often in the “yes” category. Vice versa, the words “tools”, “product”, “package” and “company(ies)” occur more often in the “no” category. Also, the word “application” occurs in its singular form in the “no” category and in its plural form in the “yes” category. Certainly, at EARL we like our applications to be plural, it is in the name after all.

Granted, this is only abstracts for one conference, but it’s an interesting idea.

Shahrokh Mortazavi announces that R Tools for Visual Studio 1.0 is officially out:

RTVS builds on Visual Studio, which means you get numerous features for free: from using multiple languages to word-class Editing and Debugging to over 7,000 extensions for every need:

• A polyglot IDE – VS supports R, Python, C++, C#, Node.js, SQL, etc. projects simultaneously.

• Editor – complete editing experience for R scripts and functions, including detachable/tabbed windows, syntax highlighting, and much more.

• IntelliSense – (aka auto-completion) available in both the editor and the Interactive R window.

• R Interactive Window – work with the R console directly from within Visual Studio.

• History window – view, search, select previous commands and send to the Interactive window.

• Variable Explorer – drill into your R data structures and examine their values.

• Plotting – see all of your R plots in a Visual Studio tool window.

• Debugging – breakpoints, stepping, watch windows, call stacks and more.

• R Markdown – R Markdown/knitr support with export to Word and HTML.

• Git – source code control via Git and GitHub.

• Extensions – over 7,000 Extensions covering a wide spectrum from Data to Languages to Productivity.

• Help – use ? and ?? to view R documentation within Visual Studio.

I’ve been using it for a little while and it’s pretty snazzy for integrating with SQL Server R Services.  R Studio is still more feature-rich, but RTVS is definitely catching up.

Tomaz Kastrun uses the igraph package to graph package dependencies in R:

With importing package tools, we get many useful functions to find additional information on packages.

Function package.dependencies() parses and check dependencies of a package in current environment. Function package_dependencies()  (with underscore and not dot) will find all dependent and reverse dependent packages.

This probably tilts more toward “fun” than “practical,” but this will let you see the full set of dependencies for a package if, for example, you need to grab all of these packages for upgrading an offline instance.