The R language has withstood the test of time. Forty years after it was initially developed (in the form of the S language) R is being used by millions of programmers on workflows the inventors of the language could never have imagined. Although base R packages perform well in most settings, workflows can be made more efficient by developing packages with more consistent arguments, inputs and outputs and emphasizing constantly improving code over historical code consistency. The universe of R packages known as the tidyverse, including dplyr, tidyr and others, aim to improve workflows and make data analysis as smooth as possible by applying a set of core programming principles in package development.

The R language has withstood the test of time. Forty years after it was initially developed (in the form of the S language) R is being used by millions of programmers on workflows the inventors of the language could never have imagined. Although base R packages perform well in most settings, workflows can be made more efficient by developing packages with more consistent arguments, inputs and outputs and emphasizing constantly improving code over historical code consistency. The universe of R packages known as the tidyverse, including dplyr, tidyr and others, aim to improve workflows and make data analysis as smooth as possible by applying a set of core programming principles in package development.