R and ggplot2 barcode of Firefox questions using no for loop just map functions, gain is questionable :-) but worth it in the long run, learned lots from learn to purrr

(please refer to my previous post: R and ggplot2 barcode of Firefox questions using a for loop; functional version in next post)
Was it worth it? Is code without for loops better?
My answer is no, not for short programs but in the large probably! I learned a lot from Rebecca Barter’s Learn to purrr from 2018 in the process! such as:
1. The tilde-dot shorthand for functions will come handy in the future! Like ruby blocks, it is more compact to use this for simple functions instead having to write out a function and decide on a parameter name instead of just using .x

~{colours_array[(abs(digest::digest2int(.x)) %% 657) + 1]})

2. map functions like map_chr() can’t be passed in a whole data frame; they need to be passed a specific column.
Here’s the actual code from no-for-loop-ff-question-barcode.R without the “kludgy” for loop:

library(tidyverse)
colours_array <- colours()

getOSColour <- function(.x) { 
  os_colour <- "purple"
  macos_str <- "mac-os|os-x|osx|macos"
  windows_str <- "windows-7|windows-8|windows-10"
  linux_str <- "linux|ubuntu|redhat|debian"
  if(str_detect(.x, macos_str)) os_colour <- "blue"
  if(str_detect(.x, linux_str)) os_colour <- "green"
  if(str_detect(.x, windows_str)) os_colour <- "hotpink"
  return(os_colour)
}

csv <- read.csv("https://raw.githubusercontent.com/rtanglao/rt-kits-api3/main/2020/2020-10-20-2020-10-20-firefox-creator-answers-desktop-all-locales.csv")
# see https://stackoverflow.com/questions/39975317/how-to-reverse-the-order-of-a-dataframe-in-r
reverse_csv <- csv %>% map_df(rev)
length <- nrow(reverse_csv)
xintercept <- seq.int(by = 10, length = length * 2, from = 0)
size <- rep(c(4,2), length = length * 2)
reverse_csv_with_title_plus_content <- reverse_csv %>% 
  mutate(tplusc = paste(title, content))
os_colours_vector <- map_chr(reverse_csv$tags, getOSColour)
question_colours_vector <- map_chr(reverse_csv_with_title_plus_content$tplusc,
  ~{colours_array[(abs(digest::digest2int(.x)) %% 657) + 1]})
colours_vector <- c(rbind(os_colours_vector, question_colours_vector))
p <- ggplot() + theme_void()
p <- p + geom_vline(col = colours_vector, 
                    xintercept = xintercept, size = size)

Output

Here’s the output (which is the same as the for loop output of course!)