4.1 Get the data

We are using the gapminder dataset (https://www.gapminder.org/data) that has been put into an R package by Bryan (2017) so we can load it with library(gapminder).

library(tidyverse)
library(gapminder)

glimpse(gapminder)

## Rows: 1,704
## Columns: 6
## $ country   <fct> Afghanistan, Afghanistan, Afghanistan, Afghanistan, Afghani…
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia,…
## $ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997,…
## $ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.…
## $ pop       <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 1…
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134,…

The dataset includes 1704 observations (rows) of 6 variables (columns: country, continent, year, lifeExp, pop, gdpPercap). country, continent, and year could be thought of as grouping variables, whereas lifeExp (life expectancy), pop (population), and gdpPercap (Gross Domestic Product per capita) are values.

The years in this dataset span 1952 to 2007 with 5-year intervals (so a total of 12 different years). It includes 142 countries from 5 continents (Asia, Europe, Africa, Americas, Oceania).

You can check that all of the numbers quoted above are correct with these lines:

library(tidyverse)
library(gapminder)
gapminder$year %>% unique()
gapminder$country %>% n_distinct()
gapminder$continent %>% unique()

Let’s create a new shorter tibble called gapdata2007 that only includes data for the year 2007.

gapdata2007 <- gapminder %>% 
  filter(year == 2007)

gapdata2007

## # A tibble: 142 x 6
##    country     continent  year lifeExp       pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>     <int>     <dbl>
##  1 Afghanistan Asia       2007    43.8  31889923      975.
##  2 Albania     Europe     2007    76.4   3600523     5937.
##  3 Algeria     Africa     2007    72.3  33333216     6223.
##  4 Angola      Africa     2007    42.7  12420476     4797.
##  5 Argentina   Americas   2007    75.3  40301927    12779.
##  6 Australia   Oceania    2007    81.2  20434176    34435.
##  7 Austria     Europe     2007    79.8   8199783    36126.
##  8 Bahrain     Asia       2007    75.6    708573    29796.
##  9 Bangladesh  Asia       2007    64.1 150448339     1391.
## 10 Belgium     Europe     2007    79.4  10392226    33693.
## # … with 132 more rows

The new tibble - gapdata2007 - now shows up in your Environment tab, whereas gapminder does not. Running library(gapminder) makes it available to use (so the funny line below is not necessary for any of the code in this chapter to work), but to have it appear in your normal Environment tab you’ll need to run this funny looking line:

# loads the gapminder dataset from the package environment
# into your Global Environment
gapdata <- gapminder

Both gapdata and gapdata2007 now show up in the Environment tab and can be clicked on/quickly viewed as usual.

References

Bryan, Jennifer. 2017. Gapminder: Data from Gapminder. https://CRAN.R-project.org/package=gapminder.