Recent Posts

Also on R-Bloggers

More Posts

My thesis “Troublemakers in the Streets? A Framing Analysis of Newspaper Coverage of Protest in the UK 1992-2017” is available on the website of the University of Glasgow since last week. It scrutinises how mainstream news media in the United Kingdom have framed domestic protest over the last three decades. I will (try to) publish parts of this research for different audiences over the next year. But here I wanted to summarise a few key points.

CONTINUE READING

If you have build your homepage using blogdown, it’s actually quite simple to integrate Javascript snippets in it. While this is mentioned in the book “blogdown: Creating Websites with R Markdown”, it still took me a little bit to undertstand how it works. As an example, let’s make different versions of a simple plot and let the user decide which one to display. First I make the plots and save them in a sub-directory:

CONTINUE READING

R 4.0.0 was released on 2020-04-24. Among the many news two stand out for me: First, R now uses stringsAsFactors = FALSE by default, which is especially welcome when reading in data (e.g., via read.csv) and when constructing data.frames. The second news that caught my eye was that all packages need to be reinstalled under the new version. This can be rather cumbersome if you have collected a large number of packages on your machine while using R 3.

CONTINUE READING

Like last year I wanted to make something special for my wonderful R-Lady. This year the main work was done by the very talented Will Chase, who makes wonderful aRt including the animation this post is based on. All I did to change the original animation was to cut it into a heart shape and carve our initials into the center. Enjoy: library(dplyr) library(poissoned) library(gganimate) # generate points pts <- poisson_disc(ncols = 150, nrows = 400, cell_size = 2, xinit = 150, yinit = 750, keep_idx = TRUE) %>% arrange(idx) # generate heart shape hrt_dat <- data.

CONTINUE READING

Today I was struggling with a relatively simple operation: unnest() from the tidyr package. What it’s supposed to do is pretty simple. When you have a data.frame where one or multiple columns are lists, you can unlist these columns while duplicating the information in other columns if the length of an element is larger than 1. library(tibble) df <- tibble( a = LETTERS[1:5], b = LETTERS[6:10], list_column = list(c(LETTERS[1:5]), "F", "G", "H", "I") ) df ## # A tibble: 5 x 3 ## a b list_column ## <chr> <chr> <list> ## 1 A F <chr [5]> ## 2 B G <chr [1]> ## 3 C H <chr [1]> ## 4 D I <chr [1]> ## 5 E J <chr [1]> library(tidyr) unnest(df, list_column) ## # A tibble: 9 x 3 ## a b list_column ## <chr> <chr> <chr> ## 1 A F A ## 2 A F B ## 3 A F C ## 4 A F D ## 5 A F E ## 6 B G F ## 7 C H G ## 8 D I H ## 9 E J I I came across this a lot while working on data from Twitter since individual tweets can contain multiple hashtags, mentions, URLs and so on, which is why they are stored in lists.

CONTINUE READING

Projects

rwhatsapp

An R package for working with WhatsApp data

LexisNexisTools

LexisNexisTools. An R Package for Working with Files from ‘LexisNexis’

rDNA

Work on rDNA with Philip Leifeld

CV

Download my academic CV (last update: 22 July 2021).