R Top Packages

By Daniel Nicola in R Posts

May 9, 2022

As of today, R is one of the most popular languages for Data Science. Being so popular, you can find 18872 packages published on CRAN as of today. Not to mention all packages that are being developed and can already be used.

Having mentioned this, you would realise that you can find several packages doing similar things and some being better than others. To make your life easier and thanks to a nice conversation I had with friends/colleagues, here’s a list of the fundamental packages you would need for your day by day Data Science.

  • The tidyverse (check it out here): Not really a package but a collection of packages, including readr to read data files, tidyr to get your data tidy, dplyr to manipulate your data and ggplot to create visualizations. Of course there are more but these might be the most used in your daily work.

  • knitr if you’re thinking on creating reports with RMarkdown. If you’re planning to mix some languages, you can also take a look at quarto (here).

  • Shiny is the way to go if you want to create dashboards and apps. A whole world of applications can be created. You can start with a small app like this (sorry it’s only in spanish). You can also find lots of examples here

  • plotly (check it out here) will allow you to create interactive graphs (you can also start with ggplot and convert these graphs with ggplotly).

  • And last but not least, the tidymodels framework (check it out here) which you can use to prepare data, create models and summarise results, using the tidyverse principles.

Of course there might be some useful packages I’ve forgotten (some are included in the tidyverse) but this list covers most of the things you need to go from zero to hero with data. Start by reading in some data and manipulating it with the tidyverse packages; train, test and get results from models with tidymodels; and create cool visualizations (ggplot and plotly), dashboards (Shiny) and reports (knitr and quarto) to communicate your results.

Posted on:
May 9, 2022
Length:
2 minute read, 343 words
Categories:
R Posts
Tags:
R
See Also:
R en Paralelo (la manera tradicional)
Tu primera Shiny App (?)
My first Mac...