Blog Archives

R Shiny for beginners: annotated starter code

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...

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Tidyverse evolutions: curly-curly operator and pivoting (feat. tidytuesday data & leaflet visuals)

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...

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The best of both worlds: R meets Python via reticulate

The best of both worlds: R meets Python via reticulate

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...

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Tidy evaluation in R: Part 2 – Complex use cases (feat. facet zoom)

Tidy evaluation in R: Part 2 – Complex use cases (feat. facet zoom)

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:...

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Tidy evaluation in R: Part 2 – Complex use cases (feat. facet zoom)

Tidy evaluation in R: Part 2 – Complex use cases (feat. facet zoom)

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...

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Data flow visuals – alluvial vs ggalluvial in R

Data flow visuals – alluvial vs ggalluvial in R

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...

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Data flow visuals – alluvial vs ggalluvial in R

Data flow visuals – alluvial vs ggalluvial in R

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...

Read more »

Tidy evaluation in R – Simple Examples

Tidy evaluation in R – Simple Examples

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...

Read more »

Tidy evaluation in R – Simple Examples

Tidy evaluation in R – Simple Examples

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...

Read more »

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