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. # Meet Aboli Mandage

Hi, I’m Aboli Mandage and I pursued mechanical engineering and now studying at London Business School.

What I’ve Been Up To

  • Co-Founded a Startup: I built Sustain & Company, a startup which aims to generate additional income for farmers in India.
  • Internship: At Dr. Reddy’s, I worked as a business development intern for their Korea division.
  • Leadership: I was fortunute to take up lot of leadership opportunities during undergrad such as leading a tech team and guiding students as an internship coordinator.

Connect With Me

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", …
## $ 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, …
head(gapminder, 20) # look at the first 20 rows of the dataframe
## # 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 == "India") # just choosing India, as this is where I come from

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

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 = country_data, mapping = 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_1<- ggplot(data = country_data, mapping = aes(x = year, y = lifeExp))+
   geom_point() +
   geom_smooth(se = FALSE) +
   labs(title = "Life Expectancy in India (1952-2007)")

print(plot_1)
## `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)
## `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= country))+
   geom_point() + 
   geom_smooth(se = FALSE) +
   facet_wrap(~continent) +
   theme(legend.position="none") #remove all legends
## `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. Life expectancy has improved almost everywhere since 1952, but not at the same pace. Africa started lowest and made progress, but growth slowed in the 1980s maybe due to diseases like HIV/AIDS. Asia and the Americas improved rapidly, especially after the 1970s, thanks to better healthcare and living conditions. Europe stayed high with steady gains, while Oceania remained the most stable and consistently high. The gap between continents has narrowed, but it hasn’t disappeared.

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("Data/brexit_results.csv")
glimpse(brexit_results)
## 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 = "Distribution of Brexit Leave Vote Share",
    subtitle = "Percentage of leave votes across UK parliamentary constituencies",
    x = "Number of constituencies",
    y = "Leave vote share(%)"
  )

ggplot(brexit_results, aes(x = leave_share)) +
  geom_density() + 
  labs(
    title = "Density of Brexit Leave Vote Share",
    subtitle = "Smooth curve showing the leave vote share density across UK constituencies",
    x = "Number of constituencies",
    y = "Density of leave vote 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:

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 = "Correlation of UK-born Residents and Brexit Leave Vote Share",
    subtitle = "Scatterplot with linear trendline",
    x = "Residents Born in the UK",
    y = "Leave Vote Share (%)"
  )
## `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.

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