Explore global development with R

In this exercise, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks within this script.

Submit to Github this script with solutions and its knitted version.
Submit to Brightspace a document with the (1) URL of this script and its knitted version in Github (2) answer of 250 words and a (3) visualisation of the Final Question

Get the necessary packages

First, start with installing and activating the relevant packages tidyverse, gganimate, and gapminder if you do not have them already. Pay attention to what warning messages you get when installing gganimate, as your computer might need other packages than gifski and av

#install.packages("gganimate")
#install.packages("gifski")
#install.packages("av")
#install.packages("gapminder")
#Installing Tidyverse:)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.2.0     ✔ readr     2.2.0
## ✔ forcats   1.0.1     ✔ stringr   1.6.0
## ✔ ggplot2   4.0.2     ✔ tibble    3.3.1
## ✔ lubridate 1.9.5     ✔ tidyr     1.3.2
## ✔ purrr     1.2.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

#Installing packages
library(gganimate)
library(gifski)
library(av)
library(gapminder)

Look at the data and tackle the tasks

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

str(gapminder)

## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...

unique(gapminder$year)

##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007

head(gapminder)

## # 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.

data(gapminder)

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(color = continent)) +
  scale_x_log10() +
  ggtitle("World Development in 1952")

library(ggplot2)
library(scales)

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
## 
##     discard

## The following object is masked from 'package:readr':
## 
##     col_factor

library(dplyr)

# Filter data for 1952
gapminder_1952 <- gapminder %>% filter(year == 1952)

ggplot(gapminder_1952, aes(x = gdpPercap, y = lifeExp, size = pop/1000000, color = continent)) +
  geom_point(alpha = 0.7) +
  scale_x_log10(labels = comma) +
  scale_size(labels = comma) +
    labs(
    title = "Figure 01: Life Expectancy vs GDP per Capita (1952)",
    x = "GDP per Capita (USD, log scale)",
    y = "Life Expectancy (years)",
    size = "Population (millions)",
    color = "Continent"
  ) +
  theme_minimal()

We see an interesting spread with an outlier to the right. Explore who it is so you can answer question 2 below!

Next, you can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() +
  ggtitle("Figure 02")

# Filter data for 2007
gapminder_2007 <- gapminder %>% filter(year == 2007)

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp, size = pop, color = continent)) +
  geom_point(alpha = 0.7) +
  scale_x_log10(labels = comma) +
  scale_size(labels = comma) +
    labs(
    title = "Life Expectancy vs GDP per Capita (2007)",
    x = "GDP per Capita (USD, log scale)",
    y = "Life Expectancy (years)",
    size = "Population",
    color = "Continent"
  ) +
  theme_minimal()

library(dplyr)

# Filter data for 2007
gapminder_2007 <- gapminder %>% filter(year == 2007)

# Find the top 5 countries with the highest GDP per capita
top5_richest_2007 <- gapminder_2007 %>%
  arrange(desc(gdpPercap)) %>%
  slice_head(n = 5) %>%
  select(country, gdpPercap)

print(top5_richest_2007)

## # A tibble: 5 × 2
##   country       gdpPercap
##   <fct>             <dbl>
## 1 Norway           49357.
## 2 Kuwait           47307.
## 3 Singapore        47143.
## 4 United States    42952.
## 5 Ireland          40676.

…

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Questions for the static figures:

Answer: why does it make sense to have a log10 scale (scale_x_log10()) on the x axis? (hint: try to comment it out and observe the result)

Using scale_x_log10() makes sense for three clear reasons: 1. GDP varies extremely widelygdpPercap spans several orders of magnitude (from ~\(300\) to ~\(100,000\)).On a normal (linear) axis:Poor countries get squashed together on the left.Rich countries take up almost the entire graph.→ You lose the underlying structure of the data. 2. The log scale “unfolds” low valuesA \(log_{10}\) scale:Spreads out the small values.Compresses the large values.→ You can actually see the differences between poor and middle-income countries. 3. The relationship becomes more linearThe relationship between GDP and life expectancy is not linear—it levels off at high income levels.On a log scale:The curve becomes closer to a straight line.→ This makes it easier to analyze and interpret.

Answer: In Figure 1: Who is the outlier (the richest country in 1952) far right on the x axis? Outlieren er Kuwait. I 1952 havde Kuwait ekstremt høj gdpPercap pga. olieindtægter, hvilket placerer landet langt ude til højre i Figure 1 sammenlignet med alle andre lande.
Fix Figures 1 and 2: Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”. You want to eliminate it.)
Answer: What are the five richest countries in the world in 2007?

country gdpPercap 1 Norway 49357. 2 Kuwait 47307. 3 Singapore 47143. 4 United States 42952. 5 Ireland 40676.

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.

Also, there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

library(ggplot2)
library(gganimate)
library(scales)
library(dplyr)

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(color=continent)) +
  scale_x_log10(labels = scales::comma) + #adds commas to divide the large numbers on the x-axis
  scale_size_continuous(labels = scales::comma)+ #adds commas to the population legend
  ggtitle("The World in {frame_time}") +
   labs(x = "GDP Per Capita USD", y = "Life Expectancy", size="Population (Millions)")+
  theme(text=element_text(size=14))+
  transition_time(year)

anim

…

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.

anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

…

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smooths the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim+
  transition_time(year)

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages

Tasks for the animations:

Can you add a title to one or both of the animations above that will change in sync with the animation? (Hint: search labeling for transition_states() and transition_time() functions respectively)

#Done (see code above)

Can you made the axes’ labels and units more readable? Consider expanding the abbreviated labels as well as the scientific notation in the legend and x axis to whole numbers. Also, differentiate the countries from different continents by color