Task 1: Short biography written using markdown

Please delete all the intro tet I wrote from line 22 to line 69 and start writing your short biography after this blockquote.

Suzie Melkonyan

Bio

I am Suize (Syuzanna) Melkonyan and am a current MFA student at London Business School. I was born and raised in Armenia but later moved to the United States for my undergraduate studies. In 2019, I received my Bachelors degree in Management - Finance at University of Massachusetts Boston. I have always been passionate about the world of finance and have always wanted to deepen my understanding and knowledge of it. Having received this unique opportunity to pursue Masters in Financial Analysis at one of the top-ranked universities in the world makes me even more willing to learn as much as I can and reach my career goals.

Besides gaining knowledge and developing skills in finance, I am willing to expand my network and be part of the diverse LBS community. To make that happen, I have joined a number of clubs at LBS and am looking forward to meet other students who come from so many different backgrounds and cultures. A few of these clubs are:

  • Finance Club
  • Entrepreneurship Clun
  • Golf Club
  • Mindfulness and Yoga Club
  • Middle Eastern club

Further details on my education and experience are available on my LinkedIn profile

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.

glimpse(gapminder)
## Rows: 1,704
## Columns: 6
## $ country   <fct> Afghanistan, Afghanistan, Afghanistan, Afghanistan, Afgha...
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asi...
## $ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 199...
## $ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 4...
## $ pop       <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372,...
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.113...
head(gapminder, 20) # look at the first 20 rows of the dataframe
## # A tibble: 20 x 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.

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.

I have created the country_data and continent_data with the code below.

country_data <- gapminder %>% 
            filter(country == "United States") 

continent_data <- gapminder %>% 
            filter(continent == "Americas")

#I am originally from Armenia. However, seems like gapminder does not include data for Armenia, therefore I have completed the task with the example of the US, which is were I lived for the past years.

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.

plot1 <- ggplot(data = country_data, mapping = aes(x = year, y = lifeExp)) +
  geom_point() +
  geom_smooth(se = FALSE) +
  NULL 

 plot1
## `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.

plot1 <- ggplot(data = country_data, mapping = aes(x = year, y = lifeExp)) +
  geom_point() +
  geom_smooth(se = FALSE) +
labs(title = "Life expectancy in the US 1952-2007",
     x = "Year",
     y = "Life Expectancy") +
     NULL


print(plot1)
## `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).

ggplot(data = continent_data , mapping = aes(x = year, y =  lifeExp, colour = country))+
  geom_point() + 
  geom_smooth(se = FALSE) +
  NULL
## `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.

ggplot(data = gapminder , mapping = aes(x = year , y = lifeExp , colour = continent))+
   geom_point() + 
   geom_smooth(se = FALSE) +
   facet_wrap(~continent) +
   theme(legend.position="none") +
  NULL
## `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.

Since 1952 life expectancy has been consistently increasing in all 5 continents. A few factors have resulted in the gradual upward trend throughout the past decades. First, the development of societies and improved socio-economic conditions are contributing factors. In a more developed world people have become more conscious about personal hygiene, which in turn lowered mortality rate over time. Next, innovations in healthcare and medicine have had an important role as well. There are now hundreds of diseases that are cured with current technology and medicine which were not available in the 20th century. After all, more focus on healthy lifestyle, healthy nutrition, and exercising have had a great impact on this trend as well.

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

brexit_results <- read_csv(here::here("Data","brexit_results.csv"))

glimpse(brexit_results)
## Rows: 632
## Columns: 11
## $ Seat        <chr> "Aldershot", "Aldridge-Brownhills", "Altrincham and Sal...
## $ con_2015    <dbl> 50.592, 52.050, 52.994, 43.979, 60.788, 22.418, 52.454,...
## $ lab_2015    <dbl> 18.333, 22.369, 26.686, 34.781, 11.197, 41.022, 18.441,...
## $ ld_2015     <dbl> 8.824, 3.367, 8.383, 2.975, 7.192, 14.828, 5.984, 2.423...
## $ ukip_2015   <dbl> 17.867, 19.624, 8.011, 15.887, 14.438, 21.409, 18.821, ...
## $ leave_share <dbl> 57.89777, 67.79635, 38.58780, 65.29912, 49.70111, 70.47...
## $ born_in_uk  <dbl> 83.10464, 96.12207, 90.48566, 97.30437, 93.33793, 96.96...
## $ male        <dbl> 49.89896, 48.92951, 48.90621, 49.21657, 48.00189, 49.17...
## $ unemployed  <dbl> 3.637000, 4.553607, 3.039963, 4.261173, 2.468100, 4.742...
## $ degree      <dbl> 13.870661, 9.974114, 28.600135, 9.336294, 18.775591, 6....
## $ age_18to24  <dbl> 9.406093, 7.325850, 6.437453, 7.747801, 5.734730, 8.209...

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)

ggplot(brexit_results, aes(x = leave_share)) +
  geom_density()

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 (or correlation) 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

brexit_results %>% 
  select(leave_share, born_in_uk) %>% 
  cor()
##             leave_share born_in_uk
## leave_share   1.0000000  0.4934295
## born_in_uk    0.4934295  1.0000000

The 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 = "Brexit polling results by consistuency", x = "% of UK native born residents", y = "% of votes for leaving 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, subtitle, 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, and outside, this blockquote.

The visualization of the Brexit polling results of the 2016 indicates positive correlation between the proportion of UK native born residents and proportion of votes for leaving Brexit. This validates the reasoning that the UK born residents might have had more fears regarding the EU’s open immigration policy vs non-UK born residents, i.e. immigrants.

Submit the assignment

Knit the completed R Markdown file as ah HTML or Word document (use the “Knit” button at the top of the script editor window) and upload it to Canvas.

Details

If you want to, please answer the following

  • Who did you collaborate with: Completed just by myself
  • Approximately how much time did you spend on this problem set: 2.5 hours
  • What, if anything, gave you the most trouble: about 1-1.5 hour was spent on trying to setup R, and RStudio and trying to open the .RMD file for the assignment. The rest was pretty straightforward