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")
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

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

?gapminder
head(gapminder_unfiltered)

## # 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.

view(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.

library(scales)

## 
## Attaching package: 'scales'

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

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

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

options(scipen=999)

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp,size=pop/1000000)) +
  geom_point(aes(color=continent)) +
  scale_x_log10() +
  ggtitle("World in 1952")+
  labs(x= "GDP per capita",
       y= "Life expectancy") + 
  theme(text = element_text(size=18))

gapminder %>%
  filter(year==1952)%>%
  slice_max(gdpPercap,n=1)

## # A tibble: 1 × 6
##   country continent  year lifeExp    pop gdpPercap
##   <fct>   <fct>     <int>   <dbl>  <int>     <dbl>
## 1 Kuwait  Asia       1952    55.6 160000   108382.

…

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/1000000)) +
  geom_point(aes(color=continent)) +
  scale_x_log10() +
  ggtitle("World in 2007")+
  labs(x= "GDP per capita",
       y= "Life expectancy") + 
  theme(text = element_text(size=18))

…

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) It makes sense to have log10scale in the function, because the gdpPercapita in different countries varies from from under 1000 to over 100.000. By using the log10scale, the spread of the gdpPercapita in the different countries will be compressed and the graphs patterns will be easier to see
Answer: In Figure 1: Who is the outlier (the richest country in 1952) far right on the x axis? By using the code gapminder %>% filter(year == 1952) %>% slice_max(gdpPercap, n = 1), we obtain a table showing the country with the highest GDP per capita in 1952. The result is Kuwait, located in Asia, with a GDP per capita of 108,382.4.
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.) The commands to fixing the color, axis labels and units are these:

Color = geom_point(aes(color=continent)) Axis labels = ggtitle(“World in 1952”) + labs(x= “GDP per capita”, y= “Life expectancy”) + theme(text = element_text(size=18)) Units = options(scipen=999)

Answer: What are the five richest countries in the world in 2007?

By using the code gapminder %>% filter(year == 2007) %>% slice_max(gdpPercap, n = 5), we obtain a table showing the five countries with the highest GDP per capita in 2007. The result from nr 1 to 5 Norway, Kuwait, Singapore, United States and Ireland. .

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

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop/1000000)) +
  geom_point(aes(color=continent)) +
  scale_x_log10() +
  ggtitle("World from 1952 - 2007")+
  labs(x= "GDP per capita",
       y= "Life expectancy") + 
  theme(text = element_text(size=18))
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)+
  ggtitle("World in {closest_state}")

…

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.

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop/1000000)) +
  geom_point(aes(color=continent)) +
  scale_x_log10() + 
  labs(x= "GDP per capita",
       y= "Life expectancy") + 
  transition_time(year) +
  ggtitle("World in {frame_time}")+
  theme(text=element_text(size=14,face="bold"))
anim2

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) Yes, you can with this function: anim + transition_states(year, transition_length = 1, state_length = 1)+ ggtitle(“World in {closest_state}”)
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 By using the code options(scipen = 999), you get much more readable numbers on the x-axis, as this code converts the values into whole numbers instead of using scientific notation with letters and plus signs.

To divide the countries into different colors based on which continent they belong to, you can use the code geom_point(aes(color = continent)). This provides a better overview of which countries belong to the different continents.

Final Question

Is the world a better place today than it was in the year you were born? Answer this question using the gapminder data. Define better either as more prosperous, more free, more healthy, or suggest another measure that you can get from gapminder. Submit a 250 word answer with an illustration to Brightspace. Include a URL in your Brightspace submission that links to the coded solutions in Github. [Hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset or download more historical data at https://www.gapminder.org/data/ ]

The gapminder data used in this are only available to the year 2007. This is the reason as to why we are comparing the data from 2002 to the data from 2007. There are several methods to search for data that can answer the question “Is the world a better place today than it was in the year you were born?” We have chosen this method by doing this command: gapminder_unfiltered%>% filter(year %in% c(2002,2007)) %>% group_by(year)%>% summarise(weighted_lifeExp=weighted.mean(lifeExp,pop)) This function calculates the global life expectancy, where every country is weighted with its population for the years 2002 and 2007. The results are: 2002: 67.7 2007: 68.8 Even after 5 years, the life expectancy on a global level has risen by 1.2 years, which indicates that factors that impact life expectancy such as, child mortality, welfare, medicine has improved and the average people are therefore more healthy another useful function is this: gapminder_unfiltered %>% filter(year %in% c(2002, 2007)) %>% group_by(year) %>% summarise(weighted_gdpPercap = weighted.mean(gdpPercap, pop)) This code calculates the global populated-weighted GDP per capita for the years 2002 and 2007 The results are: 2002: 7998.131 2007: 9353.027 Again, after 5 years the global populated-weighted GDP per capita has risen by 1354,89, which says that the average people have a higher income in the year and therefore are more prosperous. With the use of these codes, the conclusion is that the world has become a better place from the year 2002 to 2007.

library(ggplot2)
#
gapminder_unfiltered%>%
 filter(year %in% c(2002,2007)) %>%
  group_by(year)%>%
  summarise(weighted_lifeExp=weighted.mean(lifeExp,pop))

## # A tibble: 2 × 2
##    year weighted_lifeExp
##   <int>            <dbl>
## 1  2002             67.8
## 2  2007             68.8

gapminder_unfiltered %>%
  filter(year %in% c(2002, 2007)) %>%
  group_by(year) %>%
  summarise(weighted_gdpPercap = weighted.mean(gdpPercap, pop))

## # A tibble: 2 × 2
##    year weighted_gdpPercap
##   <int>              <dbl>
## 1  2002              7998.
## 2  2007              9353.

#illustrastions 

compare_year <- gapminder_unfiltered%>%
 filter(year %in% c(2002,2007)) %>%
  group_by(year)%>%
  summarise(weighted_lifeExp=weighted.mean(lifeExp,pop))
ggplot(compare_year,aes(factor(year),weighted_lifeExp))+
  geom_col(fill="darkblue")+
  labs(
    title= "Global life expectancy 2002-2007",
    x="Year",
    y="Life expectancy"
  ) + 
  coord_cartesian(ylim = c(65, 70))

Make Countries Move

Valdemar

19/3/2026