#data analysis
Where Python has libraries, R has packages
A package is a unit of reproducible code – allowing you to leverage other people’s work, and repeat actions faster
R packages are stored in directories called libraries
Common examples of packages you’d encounter and leverage in R include the base ones that tend to come with R:
base, compiler, datasets, grDevices, graphics, grid, methods, parallel, splines, stats, stats4, tcltk, tools, translations, and utils
Then there’s the recommended set:
KernSmooth, MASS, Matrix, boot, class, cluster, codetools, foreign, lattice, mgcv, nlme, nnet, rpart, spatial, and survival
another common one is the tidyverse
Compared to libraries in other programming languages, R packages must conform to a relatively strict specification. The Writing R Extensions manual specifies a standard directory structure for R source code, data, documentation, and package metadata, which enables them to be installed and loaded using R’s in-built package management tools
Questions? Drop them in the comments