This is code accompanying an article about Data Storytelling in the German IT magazine iX 1/2022. The figures in this article were created as follows. Note: Because the data is partly from a German source, the data description is in German as well. iX 1/2022 library(readr) library(tidyverse) library(stringr) library(lubridate) Set colorblind-friendly palette cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#999999", "#0072B2", "#D55E00", "#CC79A7") ggplot <- function(...) ggplot2::ggplot(...) + scale_color_manual(values = cbp2) + scale_fill_manual(values = cbp2) + # note: needs to be overridden when using continuous color scales theme_bw() library(plotrix) sliceValues <- rep(10, 8) # each slice value=10 for proportionate slices ( p <- pie3D(sliceValues, explode=0, theta = 1.
Alright, this is it: I am officially back! :-D I have not written any blogposts for over a year. Last year, I had the cutest baby boy and ever since then, I did not get around to doing much coding. One of the reasons was that, unfortunately, we did not have the easiest of starts with the little one. Feeding problems led to weight gain problems, so we had to weigh him regularly.
Working in Data Science, I often feel like I have to justify using R over Python. And while I do use Python for running scripts in production, I am much more comfortable with the R environment. Basically, whenever I can, I use R for prototyping, testing, visualizing and teaching. But because personal gut-feeling preference isn’t a very good reason to give to (scientifically minded) people, I’ve thought a lot about the pros and cons of using R.