Cleaning the data to make it work with your data visualization toolkit is 90% of the work; to learn a new plotting technique take the sample code, print out the dataset and and write your code to you create the identically structured dataset

Pontifications

And it’s not fun work but it is rewarding work because then you can easily plot using ggplot2 or whatever you use!
In fact it’s not fun but data cleaning is also an act of judgement and analysis because it’s almost always a judgment call as to what an ‘outlier’ is and what outliners to throw out!
I’m far from the first to observe this of course :-)
And ggplot2 is great fun because you can easily create many many types of graphs with that 1 cleaned dataset with minimal lines of code.
An example was the previous post on the Operating System graph for Firefox Desktop questions: e.g. what is ‘linux’? should I track every distro? You can see my arbitrary-but-hopefully-mostly-correct choices in the ruby code to generate the dataset:

case os
  when /^Windows 7/i
    os = "Windows 7"
  when /^Windows 10/i
    os = "Windows 10"
  when /^Windows 8/i
    os = "Windows 8"
  when /^Windows XP/i
    os = "Windows XP"
  when /^Mac OS/i, /^macos/i
    os = "Mac OS"
  when /^Linux/i, /^ubuntu/i, /^centos/i, /^arch/i, /^lfs/i, /^fedora/i 
    os = "Linux"
  else 
    logger.debug "SETTING os:" + os + " to other"
    os = "Other"
end