Interesting to compare R on these specific pain points. I’m ignoring 5,6 & 8 because they are silly (you have to learn the language to use it at the end of the

Interesting to compare R on these specific pain points. I’m ignoring 5,6 & 8 because they are silly (you have to learn the language to use it at the end of the day). 1. Versions. R gets updated quite frequently, and most users just update regularly. It is rare for there to be breaking changes. Packages often require the latest versions of R, so that is an issue. 2. Installation. Well on linux distros R often has the same problem, you install R with the package manager and it isn’t clear which version it is. Some additional config is required to make sure you get the latest version. This is explained on the R core website. On windows and Mac, this isn’t an issue. 3. Syntax. If you don’t like indents then R is your friend. The syntax is pretty flexible compared to python. For data science this is a plus in my book. 4. Includes. In R, for larger projects the issue is dealt with by turning your code into a package, which is quite neat and tidy. 7. Pass by object reference. R functions default to pass by value. You can still mess around with global state if you want to complicate your life.