Pre-programme Assignment 1
Cynthia Wang
2025/09/06
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.
#install.packages(“tidyverse”) #install.packages(“gapminder”)
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.
About Me
Hello! My name is Cynthia Wang. I am currently
pursuing my master’s degree at London Business School.
I have a strong interest in finance, especially in the
areas of:
- Investment Banking
- Sales & Trading, with a special interest in equity derivatives
- Asset Management
You can check more about my school here: London Business School.
Outside of finance, I enjoy:
- Traveling
- Fitness and yoga
- Exploring art and museums
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.
# plot 1<- ggplot(data = ??, mapping = aes(x = ??, y = ??))+
# geom_??() +
# geom_smooth(se = FALSE)
# print(plot1)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 1<- ggplot(data = ??, mapping = aes(x = ??, y = ??))+
# geom_??() +
# geom_smooth(se = FALSE) +
# labs(?????)
# print(plot1)Secondly, produce a plot for all countries in the continent
you come from. (Hint: map the country variable to the
colour aesthetic).
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.
# ggplot(data = gapminder , mapping = aes(x = , y = , colour= ))+
# geom_??? +
# geom_smooth(se = FALSE) +
# facet_wrap(~continent) +
# theme(legend.position="none") #remove all legendsGiven 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.
country_data <- gapminder %>%
filter(country == "China")
continent_data <- gapminder %>%
filter(continent == "Asia")plot1 <- ggplot(country_data, aes(x = year, y = lifeExp)) +
geom_point() +
geom_smooth(se = FALSE)
plot1
plot1_labs <- ggplot(country_data, aes(x = year, y = lifeExp)) +
geom_point() +
geom_smooth(se = FALSE) +
labs(
title = paste("Life Expectancy Over Time — China"),
subtitle = "Gapminder 1952–2007 • Points = observations; curve = trend",
x = "Year", y = "Life Expectancy (years)"
)
plot1_labs
plot2 <- ggplot(continent_data, aes(x = year, y = lifeExp, colour = country)) +
geom_point() +
geom_smooth(se = FALSE) +
labs(
title = paste("Life Expectancy Over Time — Asia"),
subtitle = "Each colour represents a country within the continent",
x = "Year", y = "Life Expectancy (years)", colour = "Country"
)
plot2
I selected China as my country, and Asia as my continent.
- For China, life expectancy increased significantly from below 45
years in the 1950s to above 70 years by 2007.
- The steep rise from the 1960s onwards reflects improvements in
healthcare, nutrition, and economic growth.
- A dip during the late 1950s/early 1960s corresponds to the Great Famine period.
- The steep rise from the 1960s onwards reflects improvements in
healthcare, nutrition, and economic growth.
- For Asia as a continent, the trend also shows a steady rise.
However, the gap between developed countries (e.g., Japan, South Korea)
and less developed countries (e.g., Afghanistan, Nepal) is visible.
- By 2007, life expectancy in leading Asian countries exceeded 80 years, while some lagged around 40–50 years.
Story:
Overall, the data tells a story of convergence but not full equality.
While Asia as a whole made strong progress in life expectancy since
1952, within-continent inequality persists. The improvements highlight
the role of economic development, public health measures, and political
stability in shaping life outcomes.
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
## 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.
Type your answer after this blockquote.
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()## `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.
Story and hypotheses: Structural segmentation: Areas with lower UK-born shares are typically more urban, diverse, and economically dynamic; they skew Remain. Conversely, constituencies with higher UK-born shares often co-occur with older demographics, more exposure to manufacturing decline, and fewer perceived opportunities—conditions that can increase receptivity to Leave messaging. Perceived threat vs. direct contact: In places with limited day-to-day contact with migrants, attitudes may be shaped more by media framing and perceived risk than by lived experience, reinforcing a pro-Leave stance. Heterogeneity & dispersion: At very high or very low values of UK-born share, the point cloud widens (heteroskedasticity): the average trend is positive, but dispersion grows—signal that omitted covariates (education, income, unemployment, regional effects) matter.
Method notes: This is ecological correlation at constituency level; do not infer individual behavior (avoid ecological fallacy). Results are unweighted by electorate size; weighting by voter counts could shift the magnitude. The linear smoother captures an average slope; non-linear checks (e.g., LOESS) may reveal curvature.
Details
- Who did you collaborate with: All myself
- How much time did you spend on each of the 3 Datacamp chapters and on this preliminary assignment completion: around 1 day for each chapter in Datacamp with overall 2 weeks to complete
- What, if anything, gave you the most trouble: tight deadline given we’re in recruiting season