Do you plan to upgrade your server installation from Ubuntu 16.04 to Ubuntu 18.04 LTS ? It is also the best time to migrate to R 3.5 ! By the way, if you always found difficult to install R packages for geographical data on Ubuntu, this time is over. Just follow the guide… Note that this tutorial also works for ...

Let’s welcome the viridis palette into the new version of {ggplot2}! Viri-what ? viridis is one of the favorite color palettes of one of the member of the team (guesswho). The viridis palette was first developed for the python package matplotlib, and has been implemented in R since. The strengths of this palette are that: plots are beautiful (which is ...

Our diligent readers already know how important having well formatted data is for efficient statistical analyses. Here we gathered some advice on how to make a well structured database, in order to perform accurate analyses, and avoid driving your fellow analysts crazy. We are very grateful to Marion Louveaux, bio-image analyst for this translation of french version “Une base de ...

There are several reasons you would want to deploy your own R archive repo: you don’t want to rely on GitHub for your dev packages, you want to use a more “confidential” way, or maybe (and that’s good enough a reason), you’re a nerd and you like the idea of hosting your own repo. So, here’s how to. ...

The object oriented system is a nice and powerful way to program and to build softwares and data in R. Yet, it can be a little bit daunting at first, especially if you’ve alway been coding in R, and with functions. In this blogpost, we’ll have a look at {R6}, one of the most downloaded package from the CRAN, which ...

Because I was tired of typing the same thing over and over. I finally took the time to make myself a little Rstudio snippet. To do this just go to Tools > global Option > code > edit snippet and to add in the list these few lines : snippet aa ${1:dataset} <- ${1:dataset} %>% ${0} This is the Result ...

In the ThinkR Task force, we love playing with H2O in R. Their algorithms for machine learning and artificial intelligence are really powerful. Combined with Apache Spark through Sparkling Water, H2O provides even more powerful data processing workflows, which you can run on your own laptop. Installing Spark and H2O so that they work together within R The H2O documentation ...

In this article, I will introduce the approach we use at ThinkR for Shiny applications we design. This is a relatively strict framework but designed to simplify our life and guarantee to deliver a “prod ready” application (whether it is launched locally, on shinyserver, Rconnect or via ShinyProxy with docker). This framework simplifies application maintenance and collaborative work. Edit: Better read ...