4-8_tidyverse.qmd

# Tidyverse {.unnumbered}

## Introduction

The so called tidyverse is a collection of connected R-packages that are created by the same people that also made R-studio. You have already seen one of these packages: ggplot. Like ggplot, many of these packages do things that could, in principle, also be done in base R but in a may that some people find more intuitive. Because these packages are so and general popular it may well well happen that when you try to find something R-related on the internet you find a solution using the tidyverse. For this reason we give a short introduction to the tidyverse here.

## tibble

The package `tibble` provides the `tibble` class and some functions to work with this class. A `tibble` is an alternative to a `data.frame` and is mostly (but not completely) compatible with this class. The most important differences are the following: Unlike a data.frame a `tibble` does not use row names. When a single column is selected from a `data.frame` using `[,]` the result is converted to a vector. This does not happen with a `tibble`. Finally, when a `tibble` is printed only a few rows and columns are shown. `tibble` is an important package because most other tidyverse packages use `tibble`s.

## dplyr

The `dplyr` package provides new methods to do data manipulation. It also provides the so-called pipe operator `%>%` that takes the output of a function on the left-hand-side and uses it as input for a function on the right (by default as the fist argument). This often can make long sequences of transformations much more readable.

```{r dplyr example}
library(dplyr)
starwars %>% 
  select(mass, height, gender, homeworld) %>% 
  group_by(gender, homeworld) %>% 
  filter(mass > mean(mass, na.rm = TRUE)) %>% 
  summarise(min_height=min(height,na.rm = TRUE), n=n())

```

## readr

A package to read text files, mostly much faster than base R does.

## purrr

The package `purrr` makes functional programming with R easier. That is it contains methods that apply functions to the elements of lists or otherwise work with lists and functions. An example is shown below:

```{r}
library(dplyr)
library(purrr)
data("ToothGrowth")
ToothGrowth %>% 
  split(.$supp) %>% 
  map(function(x) lm(len ~ dose, data=x)) %>% 
  map(coef) %>% 
  bind_rows()

```

## forcats

This package makes working with `factor`s easier.

## stringr

The `stringr` package helps you ro work with character data (strings). It does things like merging and splitting and otherwise manipulating character variables and finding and selecting substrings in larger strings.

## tidyr

The `tidyr` package can be used to make `data.frame`s or `tibble`s 'tidy'. That is make sure it is organised in a way so that each variable is in a column of its own and each observation has a separate row. This is the format that is expected by most functions in `R`. However many data sets (for example from excel) do not have this structure. The package also contains functions to reshape data (from long to wide format or vice-versa) or work with nested data, that is `tibble`s that are stored in single cells of an other table.