Tag: categorical

Factors in R: forcats to help

Factors in R: forcats to help

Hugo Toscano wrote a great blog providing an overview of all the helpful functionalities of the R forcats package. The package includes functions that help handling categorical data, by setting a random or fixed order, and by recategorizing or anonymizing. These functions are specifically helpful when visualizing data with R’s ggplot2.

A comprehensive overview is provided in the form of the RStudio forcats cheat sheet, but, on his blog, Hugo demonstrates some of its functionalities using a dataset on suicides and people’s ages:

For instance, you might want to reorder the categories using forcats::fct_reorder.

Via https://toscano84.github.io/2019/05/factors-in-r-forcats-to-help/

Other functions can be used to automatically surpress infrequent categories, to reverse the order of categories, to shuffle or shift categories, to quickly relabel or anonimize categories, and many more…

Have a look at Hugo’s original blog: https://toscano84.github.io/2019/05/factors-in-r-forcats-to-help/

Alternatively, the tidyverse helppage is also a good starting point: https://forcats.tidyverse.org/

A Categorical Spatial Interpolation Tutorial in R

A Categorical Spatial Interpolation Tutorial in R

Timo Grossenbacher works as reporter/coder for SRF Data, the data journalism unit of Swiss Radio and TV. He analyzes and visualizes data and investigates data-driven stories. On his website, he hosts a growing list of cool projects. One of his recent blogs covers categorical spatial interpolation in R. The end result of that blog looks amazing:

This map was built with data Timo crowdsourced for one of his projects. With this data, Timo took the following steps, which are covered in his tutorial:

  • Read in the data, first the geometries (Germany political boundaries), then the point data upon which the interpolation will be based on.
  • Preprocess the data (simplify geometries, convert CSV point data into an sf object, reproject the geodata into the ETRS CRS, clip the point data to Germany, so data outside of Germany is discarded).
  • Then, a regular grid (a raster without “data”) is created. Each grid point in this raster will later be interpolated from the point data.
  • Run the spatial interpolation with the kknn package. Since this is quite computationally and memory intensive, the resulting raster is split up into 20 batches, and each batch is computed by a single CPU core in parallel.
  • Visualize the resulting raster with ggplot2.

All code for the above process can be accessed on Timo’s Github. The georeferenced points underlying the interpolation look like the below, where each point represents the location of a person who selected a certain pronunciation in an online survey. More details on the crowdsourced pronunciation project van be found here, .

Another of Timo’s R map, before he applied k-nearest neighbors on these crowdsourced data. [original]
If you want to know more, please read the original blog or follow Timo’s new DataCamp course called Communicating with Data in the Tidyverse.