In mvuorre/systemaGlobalis: Gapminder's fact base in R

library(knitr)
opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  warning = FALSE,
  fig.retina = 2
)

systemaGlobalis

systemaGlobalis is an R package that provides quick access to the complete Gapminder data repository.

Installation

You can install the development version of systemaGlobalis from GitHub with:

devtools::install_github("mvuorre/systemaGlobalis")

It is not currently available on CRAN (nor is it likely to be in the near future.)

Description

systemaGlobalis is a minimal R package interface to the complete Systema Globalis data compilation gathered by Gapminder.

library(systemaGlobalis)
library(tidyverse)
theme_set(theme_linedraw() + theme(panel.grid = element_blank()))

It containts the following data frames (tibbles):

datapoints
For each country and year, 532 variables
The main data frame you'll want to work with.

dim(datapoints)
datapoints[1:5, 1:5]  # First five rows and columns

countries
Additional information, such as naming schemes, regions, etc., about the countries included in datapoints

countries

concepts
Descriptions of each of the variables in datapoints, including sources, tags, etc.

concepts

That's it. There are no functions, simply clean datasets that are easily used in analyses.

Examples

Let's first look at a selection of variables containing the word "income":

datapoints %>% 
  select(contains("income")) %>% 
  names(.)

datapoints %>% 
  ggplot(aes(time, income_per_person_long_series)) +
  geom_line(aes(group = geo)) +
  scale_x_continuous(limits = c(1900, 2018))

To find out more about this particular variable, look at the concepts data frame

concepts %>% 
  filter(str_detect(concept, "income")) %>%
  select(1:2) %>% 
  slice(1:4) %>% 
  kable()

We can use the countries data frame to get more information about the countries

datapoints %>% 
  left_join(rename(countries, geo = country)) %>% 
  filter(!is.na(world_4region)) %>% 
  ggplot(aes(time, income_per_person_long_series)) +
  geom_line(aes(group = geo)) +
  scale_x_continuous(limits = c(1800, 2018)) +
  facet_wrap("world_4region")

And, of course, animations! Here's an animated version of the figure on page 26 of Factfulness:

library(gganimate)
p <- datapoints %>% 
  select(geo, 
         time, 
         child = children_per_woman_total_fertility,
         mortality = child_mortality_0_5_year_olds_dying_per_1000_born, 
         pop = population_total) %>% 
  filter(between(time, 1945, 2018)) %>%
  ggplot(aes(child, (1000-mortality)/1000, size = pop)) +
  annotate(geom="rect", fill = NA, col = "gray40", 
           xmin = 8.5, xmax = 5, ymin = .55, ymax = .95) +
  annotate(geom="rect", fill = NA, col = "gray40", 
           xmin = 3.5, xmax = 1.1, ymin = .90, ymax = 1) +
  geom_point(shape = 21, fill = "gray40", alpha = .8) +
  scale_y_continuous("Children surviving to age 5",
                     breaks = 5:10/10,
                     limits = c(.5, 1),
                     labels = scales::percent) +
  scale_x_reverse("Babies per woman",
                  breaks = 1:8) +
  annotate(geom="text", col = "gray40", 
           x = 8.5, y = .55, vjust = 1, hjust = 0,
           label = "Big families and many children die") +
    annotate(geom="text", col = "gray40", 
           x = 3.5, y = .9, vjust = 1, hjust = 0,
           label = "Small families and\nfew children die") +
  theme(legend.position = "none") +
  labs(title = "Year: {frame_time}") +
  # Animation
  transition_time(time)
animate(p, nframes = 100)

Acknowledgements

systemaGlobalis is simply a minimal R package wrapper to the dataset created by Gapminder. As such, please refer to https://www.gapminder.org/ for more information about the data, including sources, and please acknowledge them for any reuse of the data.