Intro to the Tidyverse

June 22, 2025

A Data Science Workflow


The Tidyverse

  • The Tidyverse is a collection of data science packages
  • It is also considered a dialect of R
  • In this class, we will be using many Tidyverse packages
    • readr for reading data
    • tidyr for data tidying
    • dplyr for data manipulation
    • ggplot2 for data visualization
  • Click here for a full list

Working with Tidyverse packages


  • At first we will load the packages independently, e.g. library(ggplot2)
  • Later we will load them all at once with library(tidyverse)
  • Another way to call a package is with ::, e.g. ggplot2::ggplot()

Reading Data into R


  • Let’s use the readr package to read in a dataset
library(readr)

dem_summary <- read_csv("data/dem_summary.csv")

Let’s Look at the Data


One way to do this is with the base R head() function

head(dem_summary)
# A tibble: 6 × 5
  region         polyarchy gdp_pc  flfp women_rep
  <chr>              <dbl>  <dbl> <dbl>     <dbl>
1 The West           0.871  37.9   53.0      28.1
2 Latin America      0.637   9.61  48.1      21.3
3 Eastern Europe     0.539  12.2   50.5      18.0
4 Asia               0.408   9.75  50.3      14.5
5 Africa             0.393   4.41  56.7      17.4
6 Middle East        0.246  21.1   26.6      10.2

Use View()


Another way to look at the data is with View(). Or click on the name of the data frame in the Environment pane.

View(dem_summary)

Using glimpse() from dplyr


Another way to look at the data is with glimpse() from the dplyr package.

library(dplyr)

glimpse(dem_summary)
Rows: 6
Columns: 5
$ region    <chr> "The West", "Latin America", "Eastern Europe", "Asia", "Afri…
$ polyarchy <dbl> 0.8709230, 0.6371358, 0.5387451, 0.4076602, 0.3934166, 0.245…
$ gdp_pc    <dbl> 37.913054, 9.610284, 12.176554, 9.746391, 4.410484, 21.134319
$ flfp      <dbl> 52.99082, 48.12645, 50.45894, 50.32171, 56.69530, 26.57872
$ women_rep <dbl> 28.12921, 21.32548, 17.99728, 14.45225, 17.44296, 10.21568

Your Turn!


  • Read in the dem_summary.csv file
  • Use the three methods we discussed to view the data
05:00

A Few More Basic dplyr Functions


Use select() to choose columns.

dem_summary_abbr <- dem_summary |>
  select(polyarchy, gdp_pc)

glimpse(dem_summary_abbr)
Rows: 6
Columns: 2
$ polyarchy <dbl> 0.8709230, 0.6371358, 0.5387451, 0.4076602, 0.3934166, 0.245…
$ gdp_pc    <dbl> 37.913054, 9.610284, 12.176554, 9.746391, 4.410484, 21.134319

A Few More Basic dplyr Functions

Use filter() to choose rows.

dem_summary_abbr <- dem_summary |>
  filter(gdp_pc > 10)

glimpse(dem_summary_abbr)
Rows: 3
Columns: 5
$ region    <chr> "The West", "Eastern Europe", "Middle East"
$ polyarchy <dbl> 0.8709230, 0.5387451, 0.2458892
$ gdp_pc    <dbl> 37.91305, 12.17655, 21.13432
$ flfp      <dbl> 52.99082, 50.45894, 26.57872
$ women_rep <dbl> 28.12921, 17.99728, 10.21568

Note

Using the same name for the data frame results in overwriting the original data frame. If you want to keep the original data frame, use a different name.

A Few More Basic dplyr Functions

Use mutate() to create new columns.

dem_summary_abbr <- dem_summary |>
  mutate(gdp_pc_thousands = gdp_pc * 1000)

glimpse(dem_summary_abbr)
Rows: 6
Columns: 6
$ region           <chr> "The West", "Latin America", "Eastern Europe", "Asia"…
$ polyarchy        <dbl> 0.8709230, 0.6371358, 0.5387451, 0.4076602, 0.3934166…
$ gdp_pc           <dbl> 37.913054, 9.610284, 12.176554, 9.746391, 4.410484, 2…
$ flfp             <dbl> 52.99082, 48.12645, 50.45894, 50.32171, 56.69530, 26.…
$ women_rep        <dbl> 28.12921, 21.32548, 17.99728, 14.45225, 17.44296, 10.…
$ gdp_pc_thousands <dbl> 37913.054, 9610.284, 12176.554, 9746.391, 4410.484, 2…

Your Turn!


  • Use your new dplyr verbs to manipulate the data
  • Select columns, filter rows, and create new columns
05:00

Basic Data Viz with ggplot2


  • ggplot2 is a powerful data visualization package
  • It is based on the grammar of graphics
  • We will talk about this more in depth later

Basic Data Viz with ggplot2

  • For now, let’s make a simple column chart
Code 
library(ggplot2)

ggplot(data = dem_summary, aes(x = region, y = polyarchy)) +
  geom_col(fill = "dodgerblue")

Your Turn!


  • Use ggplot2 to make a simple column chart
  • Choose a different variable to plot
  • Change the color of the bars
05:00