When creating syntax, one has to ask themselves about the naming scheme: should I make the functions short for typing efficiency, or long for increased readability? Ruby has the former benefit, but sometimes the methods can be difficult to remember (e.g. is it len or length? Is it swapcase or swap_case?), as there isn’t a consistent naming scheme–however, some functions have synonyms to help those from other programming languages learn Ruby faster (e.g. reduce and inject do the same thing). On the other hand, the stringr library has a consisent naming scheme for its functions, but does not have synonyms, so you are forced to learn the stringr way. Thirdly, R does not have concatenation operator (only functions) like in Ruby and BASIC, which is odd, as many situations require concatenation; so using the paste/paste0() functions can make code less readable. As such, I am introducing a new package to take these considerations into account: stringops, a library consisting of tools for processing strings in R.

What this package brings are (1) a consistent naming-scheme for functions, (2) synonyms for said functions, and (3) a concatenation operator. The first item benefits users of all skill levels, as it makes certain functions easier to remember while making use of RStudio’s predictive text. The second item is useful when one tires of typing string_cull(), for example, and wishes to use a shorthand to simplify the code (in this case, the shorthand would be cull()). The third item’s benefit is more readable code by avoiding the function syntax of paste/paste0(). Ultimately, these items will hopefully make processing strings in R more fun for the user!

This package currently is only available on GitHub–there are no plans to submit this package to CRAN at this time. As such, please use the devtools library to install stringops.

# install.packages('devtools')

devtools::install_github('robertschnitman/stringops')

library(stringops)