Discover Tidyverse

Today I would like to tell you about an incredibly useful R package for data analysis: Tidyverse. If you’re a regular R user, you’ve probably already heard of it, but if not, it’s time to rectify that because it’s so important!

What is the Tidyverse package

The Tidyverse package is a set of R packages created by Hadley Wickham to facilitate the processing, manipulation and visualization of data. It groups together a series of individual packages that all share the same design philosophy. This design philosophy focuses on simplifying common tasks by using a consistent, clear and intuitive syntax.

The Tidyverse is therefore a package designed to work with clean and organized data, i.e. data that is organized in “tables” (or “dataframes”).

Why use the Tidyverse Package?

The Tidyverse Package is a powerful and convenient solution because it combines several packages into one, and saves you from having to load several packages in addition to having a lot of functions. It offers tools to simplify the most common data analysis tasks, such as importing and manipulating data, visualizing data, statistical modeling, creating graphs and much more. Using Tidyverse, you can perform complex tasks quickly and easily, eliminating the need to hard-code solutions at every step.

The packages included in the Tidyverse

There are a total of 18 packages included in the Tidyverse, that’s a lot. In the main ones we find :

• dplyr: a package for data manipulation that provides functions to filter, sort and group data;
• ggplot2: a package for data visualization that offers an elegant syntax to create high quality graphs;
• tidyr: a data formatting package that provides functions for rotating, stacking and combining data;
• purrr: a package for functional programming that provides functions to apply functions to data using functional programming techniques.

Here is the list of all packages organized by type:

1. ggplot2: for data visualization
2. dplyr: for data manipulation
3. tidyr: for data formatting
4. readr: for data import
5. purrr: for functional programming
6. tibble: for manipulation of data as tibbles (an enhanced version of data frames)
7. stringr: for string manipulation
8. forcats: for manipulation of factors (categorical variables)
9. lubridate: for manipulation of dates and times
10. rvest: for web scraping
11. xml2: for analysis of XML data
12. httr: for HTTP requests
13. jsonlite: for manipulation of JSON data
14. broom: for conversion of statistical models to tabular formats
15. tidyr: for data manipulation
16. modelr: for data modeling
17. readxl: for importing Excel data
18. haven: for importing data from other statistical software such as SAS, SPSS, and Stata

Each of these packages brings specific functionalities that integrate perfectly with the other packages of the Tidyverse, allowing users to work efficiently and consistently with their data.