tidyverse

Every day we deal with errors, warnings and messages while writing, debugging or reviewing code. The three types belong to conditions in R. You might hope to see as few of them as possible, but actually they are so helpful when they describe the problem concisely and refer to its source. So if you write functions or code for yourself or others, it is a good practice to spend more time in writing descriptive conditions. [Read More]

Almost a year ago I wrote about my My First Steps into The World of Tidy Eval. At the end I tweeted asking Hadley Wickham and Lionel Henrey whether ggplot2 was compatible with the tidy eval, They said that it was on the todo list. Finally, ggplot2 3.0.0 got released last week with the support of tidy eval, so I thought it was time to write about it! ggplot2 3. [Read More]

A couple of days ago, I passed by Sarah Drasner’s Array Explorer. It was through a retweet by Emily Robinson, who proposed the idea of a similar app for working with strings in R. I thought about giving it a try, and I deployed a preliminary Shiny App Stringr Explorer; which is still under development. In the following sections, I will give a brief about the data extracted from the package documentation to use in the Stringr Explorer app, and I’ll be glad to get better suggestions and contributions. [Read More]

A couple of weeks ago, I was looking for a package, I previously passed by, that prints summary statistics with inline histograms. I checked all my bookmarks and liked tweets, but I couldn’t find it! So I asked on twitter. fortunately Maëlle Salmon read the tweet and guided me to skimr by ropenscilabs, who actually release many useful packages. In this post, I will focus on spark histograms in summary statistics and beyond. [Read More]