Pre-programme Assignment 1
Henrik Rettedal Skjaeveland
06.09.2025
The goal is to test your software installation, to demonstrate
competency in Markdown, and in the basics of ggplot.
R and RStudio installation
You should successfully install R and R studio in your computer. We will do all of our work in this class with the open source (and free!) programming language R. However, we will use RStudio software application, an Integrated Development Environment (IDE), which allows us to seamlessly interact with R and write code in a pleasant environment.
Install tidyverse and gapminder
packages
The basic installation of R is known as base R. (If
you haven’t already done so) we need to install a couple of packages,
namely tidyverse and gapminder. Go to the
packages panel in the bottom right of RStudio, click on “Install,” type
tidyverse, and press enter. Once it finishes, install
gapminder. You’ll see a bunch of output in the RStudio
console as all the packages are installed.
You can also just paste and run these two commands
install.packages("tidyverse")install.packages("gapminder")
in the console (bottom left in RStudio) instead of using
the packages panel.
You can find details on R packages here
Practice using Markdown
Written assignments will be submitted using Markdown.
Markdown is a lightweight text formatting language that easily converts
between file formats. It is integrated directly into R Markdown, which combines R
code, output, and written text into a single document
(.Rmd).
There is a very nice Markdwown tutorial that I suggest you go through before working on your assignment. If you want to use a stand-alone Markdown editor Typora is a lightweight Markdown editor that inherently supports pandoc-flavoured Markdown.
Pandoc
Pandocis a program that converts Markdown files into basically anything else. It was created by John MacFarlane, a philosophy professor at the University of California, Berkeley and is widely used as a writing tool and as a basis for publishing workflow. Kieran Healy’s Plain Text Social Science workflow describes how to use Markdown and then convert your Markdown document to HTML, PDF, word, etc.
You should create a file whose name will be your
Name_Surname.Rmd.
Task 1: Short biography written using markdown
You should write within this Rmd file a brief biography of yourself using markdown syntax. I know you have already achieved a lot, but a couple of paragraphs is more than enough.
To achieve full marks, you should include at least 4 of the following elements:
- Headers
- Emphasis (italics or bold)
- Bullet points
- Links
- Embeding images
Please write your short biography after this blockquote.
####An introduction of my me, Henrik
My name is Henrik and I am a 25 year old man from Stavanger, Norway. Growing up, I have been extremely passionate about footbal. I even played competitive for several years dreaming of playing in the Premier League one day. My favorite team is Manchester United. In addition to footbal, I have become increasingly passionate about cooking, and I have the most fun when I make food with my gorlfriend, whom used to go to cooking school in Copenhagen several years ago. My favorite cuisines are: * Italian cuisine * Japanese cuisine, and * Mexican cuisine
“Life is a combination of magic and pasta.” – Federico Fellini
Professionally, I have worked for a top-tier management consulting firm in Norway, Implement Consulting Group. At ICG, I specialised in Strategy and M&A, mostly working for our Private Equity clients doing Commerical due diligences and market studies. Before moving to London for studies, I decided to try something new. Therefore I went to Kukula Capital in Zambia to volunteer and gain more hands-on transaction experience. Now however, I am reading for the Global Master in Management programme at London Business School, looking forward to two years of networking, learning, and adventures!
Task 2: gapminder country comparison
You have seen the gapminder dataset that has data on
life expectancy, population, and GDP per capita for 142 countries from
1952 to 2007. To get a glipmse of the dataframe, namely to see the
variable names, variable types, etc., we use the glimpse
function. We also want to have a look at the first 20 rows of data.
## Rows: 1,704
## Columns: 6
## $ country <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
## $ 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, 40.8…
## $ pop <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …## # A tibble: 20 × 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.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## 11 Afghanistan Asia 2002 42.1 25268405 727.
## 12 Afghanistan Asia 2007 43.8 31889923 975.
## 13 Albania Europe 1952 55.2 1282697 1601.
## 14 Albania Europe 1957 59.3 1476505 1942.
## 15 Albania Europe 1962 64.8 1728137 2313.
## 16 Albania Europe 1967 66.2 1984060 2760.
## 17 Albania Europe 1972 67.7 2263554 3313.
## 18 Albania Europe 1977 68.9 2509048 3533.
## 19 Albania Europe 1982 70.4 2780097 3631.
## 20 Albania Europe 1987 72 3075321 3739.I have created the country_data and
continent_data with the code below.
country_data <- gapminder %>%
filter(country == "Russia") # just choosing Russia, as this is where I come from
continent_data <- gapminder %>%
filter(continent == "Europe")Your task is to produce two graphs of how life expectancy has changed
over the years for the country and the
continent you come from. First, create a plot of life
expectancy over time for the single country you chose. You should use
geom_point() to see the actual data points and
geom_smooth(se = FALSE) to plot the underlying trendlines.
You need to remove the comments # from the lines below
for your code to run.
library(ggplot2)
library(dplyr)
library(gapminder)
Norway_data1 <- gapminder %>%
filter(country == "Norway")
Europe_data1 <- gapminder %>%
filter(continent == "Europe")
plot_1 <- ggplot(Norway_data1, aes(x = year, y = lifeExp))+
geom_point() +
geom_smooth(se = FALSE)
print(plot_1)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Next we need to add a title. Create a new plot, or extend plot1,
using the labs() function to add an informative title to
the plot.
plot_2 <- ggplot(Norway_data1, aes(x = year, y = lifeExp))+
geom_point() +
geom_smooth(se = FALSE) +
labs(
subtitle = "Life Expectancy in Norway grew by ~18 years since 1952",
x = "Year", y = "Life Expectancy"
)
print(plot_2)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Secondly, produce a plot for all countries in the continent
you come from. (Hint: map the country variable to the
colour aesthetic).
plot_3 <- ggplot(Europe_data1 ,aes(x = year, y = lifeExp, colour= country))+
geom_line() +
geom_smooth(se = FALSE) +
labs(
subtitle = "Development of Life Expectancy in Europe",
x = "Year",
y = "Life Expectancy"
)
print(plot_3)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Finally, using the original gapminder data, produce a
life expectancy over time graph, grouped (or faceted) by continent. We
will remove all legends, adding the
theme(legend.position = "none") in the end of our
ggplot.
plot_4 <- ggplot(gapminder, aes(x = year , y = lifeExp, colour = country ))+
geom_line() +
geom_smooth(se = FALSE) +
facet_wrap(~continent) +
theme(legend.position="none") #remove all legends
print(plot_4)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Given these trends, what can you say about life expectancy since 1952? Again, don’t just say what’s happening in the graph. Tell some sort of story and speculate about the differences in the patterns.
Type your answer after this blockquote.
####The Global Story Since 1952
From the early 1950s to the 2000s, the world experienced a dramatic rise in life expectancy almost everywhere — but the pace and stability of improvement varied sharply by region.
#####Europe & Oceania: Early Leaders, Steady Gains
Europe and Oceania (Australia & New Zealand) already had relatively high life expectancies in 1952 (mid-60s to 70s).
Their trajectories are smoother and more consistent, presumingly reflecting stable economies, established healthcare systems, lower infant mortality, and strong public health infrastructure.
Small but steady improvements — vaccines, antibiotics, etc. — added years gradually rather than dramatically.
#####Americas: Catching Up
The Americas started lower than Europe but rose quickly, converging somewhat by the 2000s.
Countries in the Americas are catching up, where improvements in sanitation, and declines in infectious diseases probably were key drivers.
Inequalities persisted — some Latin American countries improved more slowly, reflecting political instability and uneven healthcare access.
#####Asia: Rapid Progress with Setbacks
Asia shows remarkable improvement, starting from a low baseline in the 1950s (~40–50 years).
Post-war recovery, widespread vaccination, and later economic growth in East Asia (Japan, South Korea, Singapore, China) drove big leaps.
But there are visible dips — wars (Vietnam, Cambodia, Afghanistan), and later the HIV/AIDS epidemic in parts of Asia most likely slowed improvement for specific countries.
#####Africa: Progress Interrupted
Africa shows the most volatility. Life expectancy climbed from the 1950s through the 1970s but stalled — and in some cases fell sharply in the 1980s–1990s.
The HIV/AIDS crisis devastated southern Africa, cutting decades off life expectancy in some nations during this period.
Political instability, poverty, and limited healthcare access most likely where key drivers for the setbacks. Still, post-2000, there’s a recovery trend as HIV treatment, malaria control, and international aid improves.
####Conclusion: The gap is closing
Globally, the “health gap” narrowed — countries that started far behind made the largest absolute gains. But new regional disparities emerged: while Europe and Oceania pushed into the 80s, many African countries struggled to climb out of the 50s.
Life expectancy is not just biology — it’s shaped by economics, politics, epidemics, wars, and global cooperation.
Task 3: Brexit voting
We will have a quick look at the results of the 2016 Brexit vote in
the UK. First we read the data using read_csv() and have a
quick glimpse at the data
## [1] "C:/Users/henri/AppData/Local/Temp/5ac36198-0921-4292-902d-2603977ab767_pre_programme_assignment-1.zip.767/pre_programme_assignment"## Rows: 632
## Columns: 11
## $ Seat <chr> "Aldershot", "Aldridge-Brownhills", "Altrincham and Sale W…
## $ con_2015 <dbl> 50.592, 52.050, 52.994, 43.979, 60.788, 22.418, 52.454, 22…
## $ lab_2015 <dbl> 18.333, 22.369, 26.686, 34.781, 11.197, 41.022, 18.441, 49…
## $ ld_2015 <dbl> 8.824, 3.367, 8.383, 2.975, 7.192, 14.828, 5.984, 2.423, 1…
## $ ukip_2015 <dbl> 17.867, 19.624, 8.011, 15.887, 14.438, 21.409, 18.821, 21.…
## $ leave_share <dbl> 57.89777, 67.79635, 38.58780, 65.29912, 49.70111, 70.47289…
## $ born_in_uk <dbl> 83.10464, 96.12207, 90.48566, 97.30437, 93.33793, 96.96214…
## $ male <dbl> 49.89896, 48.92951, 48.90621, 49.21657, 48.00189, 49.17185…
## $ unemployed <dbl> 3.637000, 4.553607, 3.039963, 4.261173, 2.468100, 4.742731…
## $ degree <dbl> 13.870661, 9.974114, 28.600135, 9.336294, 18.775591, 6.085…
## $ age_18to24 <dbl> 9.406093, 7.325850, 6.437453, 7.747801, 5.734730, 8.209863…The data comes from Elliott Morris, who cleaned it and made it available through his DataCamp class on analysing election and polling data in R.
Our main outcome variable (or y) is leave_share, which
is the percent of votes cast in favour of Brexit, or leaving the EU.
Each row is a UK parliament
constituency.
To get a sense of the spread of the data, plot a histogram and a density plot of the leave share in all constituencies.
ggplot(brexit_results, aes(x = leave_share)) +
geom_histogram(binwidth = 2.5) +
labs(title = "Constituencies voting to leave the EU",
subtitle = "Columns = number of observations",
x = "Share of constituencies voting to leave EU", y = "Number of constituencies")
ggplot(brexit_results, aes(x = leave_share)) +
geom_density() +
labs(title = "Constituencies are positive to leaving the EU",
subtitle = "Thin line = density",
x = "Share of constituencies voting to leave EU", y = "Density of leave share")
One common explanation for the Brexit outcome was fear of immigration
and opposition to the EU’s more open border policy. We can check the
relationship between the proportion of native born residents
(born_in_uk) in a constituency and its
leave_share. To do this, let us get the correlation between
the two variables:
## leave_share born_in_uk
## leave_share 1.0000000 0.4934295
## born_in_uk 0.4934295 1.0000000The correlation is almost 0.5, which shows that the two variables are positively correlated.
We can also create a scatterplot between these two variables using
geom_point. We also add the best fit line, using
geom_smooth(method = "lm").
ggplot(brexit_results, aes(x = born_in_uk, y = leave_share)) +
geom_point(alpha=0.3) +
geom_smooth(method = "lm") +
theme_bw() +
labs(title = "UK-born Constituencies vote to leave the EU",
subtitle = "Thin line = trend; black dots = observations",
x = "Share of constituencies born in the UK", y = "Share of constituencies voting for brexit")## `geom_smooth()` using formula = 'y ~ x'
You have the code for the plots, I would like you to revisit all of
them and use the labs() function to add an informative
title, sub-title, and axes titles to all plots.
What can you say about the relationship shown above? Again, don’t just say what’s happening in the graph. Tell some sort of story and speculate about the differences in the patterns.
Type your answer after this blockquote.
Looking at the data from the 2016 election, it is supporting the claim that constituencies with a large share of UK born citizens vote for Brexit. The correlation between the two variables is ~0.5, and looking at the graphs we can see that the trend line is positive. The larger the share of UK born citizens in a constituency, the larger share vote for brexit. One explanation can definitely be fear of immigration and opposition to the EU’s more open border policy. That is the easy explanation at least.
One can assume that the reason constituencies with a larger share of UK-born members are more positive to brexit, can be that they believe number of immigrants in an area correlates with criminality in that area. It could be that the handling of immigrants coming to the UK have not been working well. That immigrants have a hard time being integrated in the UK culture, finding a job, and that getto’s quickly tend to form.
Another possible explanation can be that immigrants tend to “steal jobs from UK-born citizens”. This hostile mindset is very common in the US at least, and could be an explanation for why UK-born constituency elects are positive to brexit, and therefore implicitly negative to immigration.
Submit the assignment
Knit the completed R Markdown file as ah HTML or pdf document (use the “Knit” button at the top of the script editor window) and upload it to Canvas.
Rubric
Check minus: Name_Surname.Rmd just has a paragraph or
two of plain text with no formatting, bullet points, links, etc. You
used the hashtag #, which is a chapter, and the
>, which is a blockquote, inappropriately.
Check: something in between.
Check plus: Name_Surname.Rmd provides a proper
introduction of student to the class. It also demonstrates
experimentation with 4 or more aspects of the Markdown syntax. Examples:
section headers, links, bold, italic, bullet points, image embed, etc.
The student offers a few reflections on their experience with Markdown
(e.g. did you manage to knit both to HTML and pdf?).
Details
If you want to, please answer the following
- Who did you collaborate with: TYPE NAMES HERE
- How much time did you spend on each of the 3 Datacamp chapters and on this preliminary assignment completion: ANSWER HERE
- What, if anything, gave you the most trouble: ANSWER HERE