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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.

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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.

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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.

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I’m happy to announce that rwhatsapp is now on CRAN. After being tested by users on GitHub for a year now, I decided it is time to make the package available to a wider audience. The goal of the package is to make working with ‘WhatsApp’ chat logs as easy as possible. ‘WhatsApp’ seems to become increasingly important not just as a messaging service but also as a social network—thanks to its group chat capabilities.

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Today is Valentine’s Day. And since both I and my sweetheart are R enthusiasts, here is how to say “I love you” using a statistical programming language:

library("dplyr")
library("gganimate")
library("ggplot2")

hrt_dat <- data.frame(t = seq(0, 2 * pi, by = 0.01)) %>%
  bind_rows(data.frame(t = rep(max(.$t), 300))) %>% 
  mutate(xhrt = 16 * sin(t) ^ 3,
         yhrt = 13 * cos(t) - 5 * cos(2 * t) - 2 * cos(3 * t) - cos(4 * t),
         frame = seq_along(t)) %>% 
  mutate(text = ifelse(frame > 300, "            J", "")) %>%
  mutate(text = ifelse(frame > 500, "A           J", text)) %>%
  mutate(text = ifelse(frame > 628, "A     +     J", text)) %>% 
  mutate(texty = 0, textx = 0)

ggplot(hrt_dat, aes(x = xhrt, y = yhrt)) +
  geom_line(colour = "#C8152B") +
  geom_polygon(fill = "#C8152B") +
  geom_text(aes(x = textx, y = texty, label = text), 
            size = 18, 
            colour = "white",
            vjust = "center") +
  theme_void() +
  transition_reveal(frame)

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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: 10 June 2020).

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