# Chapter 4 Data analysis with the tidyverse

The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.

For learning how to do data analysis from importing data and tidying it to analyzing it and reporting results, we will use book R for Data Science. You can find most of the exercise solutions there.

## 4.1 Program

my {R Markdown} presentation (also see https://r4ds.had.co.nz/r-markdown.html)

my {ggplot2} presentation + exercises from data visualization with {ggplot2}

tidy data will rationalize the concept of “tidy” data that is used in the tidyverse and that is easier to work with

relational data will give you tools to

*join*information from several datasetsmore if time allows it (see below)

## 4.2 Other chapters from this book

**The other chapters of R for Data Science book are very interesting and you should read them.**
Unfortunately, we won’t have time to cover them in class. A brief introduction of what you could learn:

data import will give you tools to import data (e.g. as a replacement of

`read.table`

)strings will help you work with strings and regular expressions

factors will help you work with factors

dates and times will help you work with dates and times

many models will introduce the concept of

*list-columns*that enable you to store complex objects in a structured way inside a data framedatabases: packages {DBI} and {dbplyr} + RStudio’s webpage

## 4.3 Other resources

package {tidylog} provides verbose feedback about {dplyr} and {tidyr} operations

## 4.4 Other “tidy” packages

analysis of text data: package {tidytext} with the associated book,

analysis of financial data: package {tidyquant},

analysis of time series data: package {tidytime},

a collection of packages for modeling and machine learning using tidyverse principles: package {tidymodels},

a tidy API for graph manipulation: package {tidygraph},

many other packages..