Why am I here?

Welcome to the second iteration of the Humans Learning lessons. As a reminder, each lesson is designed as a 5 - 10 minute virtual session conducted for EnCompass staff to expand their skills with data, and the means of learning is the R programming language. Each lesson will have learning objectives, some example code and explanation to demonstrate a technique or skill, and an open code chunk at the end for you to have some fun. Each lesson is then captured in an html file for online access. This is all in the service of humans learning. Enjoy!

Learning objectives

For this session, the learning objectives are to:

Group data to calculate summary statistics
Make your first line plot

Install and load packages

In your R script, you will use the install.packages() and library() functions to install and load the two packages Tidyverse and Gapminder.

Tidyverse provides a suite of compatible data wrangling and visualization tools. The workhorse of data visualization is the ggplot2 package. With ggplot2 the sky is the limit! From basic bar plots to animated graphics to interactive charts and tables connected by a common data source, ggplot2 and its extension packages can do it all. And once again, Gapminder provides a dataset extracted from the global trend data maintained by, https://www.gapminder.org/.

#Installs the package on your system.
install.packages(c("tidyverse", "gapminder"))

Warning: packages 'tidyverse', 'gapminder' are in use and will not be installed

#load the libraries so you can use them
library(tidyverse)
library(gapminder)

View the data

Just as in the first session, the first step is to view the data. To look at just the first six rows so you can see the variable names and structure of the data pass gapminder to head() as in the code below.

# assign gapminder to df 
# this is required, but it makes life easier
# don't we all want life to be easier

df <- gapminder

# look at the gapminder dataset
head(df)

# A tibble: 6 × 6
  country     continent  year lifeExp      pop gdpPercap
  <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
1 Afghanistan Asia       1952    28.8  8425333      779.
2 Afghanistan Asia       1957    30.3  9240934      821.
3 Afghanistan Asia       1962    32.0 10267083      853.
4 Afghanistan Asia       1967    34.0 11537966      836.
5 Afghanistan Asia       1972    36.1 13079460      740.
6 Afghanistan Asia       1977    38.4 14880372      786.

tail(df)

# A tibble: 6 × 6
  country  continent  year lifeExp      pop gdpPercap
  <fct>    <fct>     <int>   <dbl>    <int>     <dbl>
1 Zimbabwe Africa     1982    60.4  7636524      789.
2 Zimbabwe Africa     1987    62.4  9216418      706.
3 Zimbabwe Africa     1992    60.4 10704340      693.
4 Zimbabwe Africa     1997    46.8 11404948      792.
5 Zimbabwe Africa     2002    40.0 11926563      672.
6 Zimbabwe Africa     2007    43.5 12311143      470.

Make a line plot

As fun as looking at data is, we probably want to do more. Since our objective is to make a line plot, we should do that next. The structure of ggplot requires that we pass it an object (df), the type of geom_* we want to make (in this case a line plot), and the aesthetics or the variables we want to plot.

#make a plot
ggplot(data = df) +
  geom_line(aes(x = year, y = gdpPercap, color = continent))

Yeeeeesh!! That did not work at all. Looks like we forgot to group_by() and summarize() to create summary statistics that would provide us a single datapoint to follow across the years.

Let’s us df to create a new object, df1. df1 will group the data by continent and year and create a variable for the mean GDP per capita. This will provide a single data point for each continent, for each year so that we can make useful lines for the mean GDP per capita over time.

#| class-output: pre


df1 <- df |> 
  group_by(continent, year) |> 
  summarize(avg_gdpPercap = mean(gdpPercap))

`summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.

Now we just have to remember the how ggplot works and make our line plot. It takes three components.

The data object, df1
The geom, geom_line()
The aesthetics, year (x-axis), avg_gdpPercap (y-axis), and continent (color).

#| class-output: pre


ggplot(df1) +
  geom_line(aes(x = year, y = avg_gdpPercap, color = continent))

Have fun!

Now it’s your turn practice! Below is a fully functioning code editor with starting code in place. Feel free to experiment with different grouping variables in the group_by() call or to adjust the summary statistic in summarize(). Then, have fun with the plot!