Two weeks ago, I was fortunate to attend my very first Rstudio conference. Spoiler: it was an amazing event - packed to the brim with new ideas, tools, impressions, and most important of all: smart and kind people. This was the first time that San F...
In my first post of the year I will provide a gentle introduction to web scraping with the tidyverse package rvest. As the package name pun suggests, web scraping is the process of harvesting, or extracting, data from websites. The extraction process is greatly simplified by the fact that websites are predominantly built using HTML (= Hyper Text Markup...
This week I decided to get started with the R shiny package for interactive web applications. As an absolute beginner, I want to document my learning journey in the hope that it will be useful for other first-time shiny users. This post assumes some basic familiarity with R and the tidyverse, but no prior knowledge of shiny is required. The...
The tidyverse ecosystem is steadily growing and adapting to the needs of its users. As part of this evolution, existing tools are being replaced by new and better methods. As useful as this flexibility is to the strength of the system, sometimes it can be hard to keep track of all the changes. This blogpost will deal with two...
As far as rivalries go, R vs Python can almost reach the levels of the glory days of Barca vs Madrid, Stones vs Beatles, or Sega vs Nintendo. Almost. Just dare to venture onto Twitter asking which language is best for data science to witness two tightly entrenched camps. Or at least that’s what seemingly hundreds of Medium articles...
In an earlier post I gave a gentle introduction to tidy evaluation in the R tidyverse using simple examples. I covered quoting with enquo and unquoting with !! in brief dplyr and ggplot2 snippets. Today, I aim to build a collection of more complex use cases involving additional tools. Those are our libraries: libs % head(5) ## # A tibble:...
In an earlier post I gave a gentle introduction to tidy evaluation in the R tidyverse using simple examples. I covered quoting with enquo and unquoting with !! in brief dplyr and ggplot2 snippets. Today, I aim to build a collection of more complex use cases involving additional tools. Those are our libraries: libs % head(5) ## # A tibble: 5...
I have long been a fan of creative data visualisation techniques. For me, the choice of visual representation is driven by both the type of data and the kind of question one wants to examine. The power of its visualisation tools has been a major strength of the R language well before the ggplot2 package and the tidyverse burst onto...
I have long been a fan of creative data visualisation techniques. For me, the choice of visual representation is driven by both the type of data and the kind of question one wants to examine. The power of its visualisation tools has been a major strength of the R language well before the ggplot2 package and the tidyverse burst onto...
The tidyverse philosophy introduced by Hadley Wickham has been a game changer for the R community. It is based on intuitive rules of what a tidy data set should look like: each variable is a column, each observation is a row (Wickham 2014). At its core, the tidyverse collection of R packages is powered by a consistent grammar of...
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