R Notebook

Part 2 Solutions

### Load the libraries will we need
library(readr)
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

library(ggplot2)

Read the data and check

gapminder <- read_csv("raw_data/gapminder.csv")

Rows: 1704 Columns: 6── Column specification ──────────────────────────────────────────────────
Delimiter: ","
chr (2): country, continent
dbl (4): year, lifeExp, pop, gdpPercap
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(gapminder)

In-class exercises

Create a subset of the data where the population less than a million in the year 2002

filter(gapminder, pop < 1e6, year == 2002)

Create a subset of the data where the life expectancy is greater than 75 in the years prior to 1987

filter(gapminder, lifeExp > 75, year < 1987)

Create a subset of the European data where the life expectancy is between 75 and 80 in the years 2002 or 2007.

filter(gapminder, continent == "Europe", lifeExp > 75, lifeExp < 80 , year == 2002 | year == 2007)

Can also use the between function from dplyr and the %in% function

filter(gapminder, continent == "Europe", 
       between(lifeExp, 75,80), 
       year %in% c(2002,2007))

Write a workflow to do the following:-

• Filter the data to include just observations from the year 2002
• Re-arrange the table so that the countries from each continent are ordered according to decreasing wealth. i.e. the wealthiest countries first
• Select all the columns apart from year
• Write the data frame out to a file in out_data/ folder

# Less-efficient solution before pipes are introduced

# create out_data folder before we start (no warning given if it already exists)
dir.create("out_data", showWarnings = FALSE)

gapminder2 <- filter(gapminder, year == 2002)
gapminder3 <- arrange(gapminder2, continent, desc(gdpPercap))
gapminder4 <- select(gapminder3, -year)
write_csv(gapminder4, "out_data/gapminder_2002.csv")

Re-written using pipes

filter(gapminder, year == 2002) %>% 
  arrange(continent, desc(gdpPercap)) %>% 
  select(-year) %>% 
write_csv("out_data/gapminder_piped_2002.csv")

The violin plot is a popular alternative to the boxplot. Create a violin plot with geom_violin to visualise the differences in GDP between different continents.

ggplot(gapminder, aes(x = continent, y = gdpPercap)) + geom_violin()

Create a subset of the gapminder data frame containing just the rows for your country of birth

# don't forget that R is case-sensitive!

uk_data <- filter(gapminder, country == "United Kingdom")

Has there been an increase in life expectancy over time? - visualise the trend using a scatter plot (geom_point), line graph (geom_line) or smoothed line (geom_smooth).

ggplot(uk_data, aes(x = year, y = lifeExp)) + geom_point()

ggplot(uk_data, aes(x = year, y = lifeExp)) + geom_line()

ggplot(uk_data, aes(x = year, y = lifeExp)) + geom_smooth()

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

## can combine all plots
ggplot(uk_data, aes(x = year, y = lifeExp)) + geom_point() + geom_smooth()

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Note: this exercise could also make use of the piping technique

filter(gapminder, country == "United Kingdom") %>% 
  ggplot(aes(x = year, y = lifeExp)) + geom_point() + geom_smooth()

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

What happens when you modify the geom_boxplot example to compare the gdp distributions for different years? - Look at the message ggplot2 prints above the plot and try to modify the code to give a separate boxplot for each year

# this is how we might expect the code to look like
ggplot(gapminder, aes(x = year, y = gdpPercap)) + geom_boxplot()

Warning: Continuous x aesthetic -- did you forget aes(group=...)?

The previous output hints that you might want to group by year - otherwise it thinks that year is a numerical variable

ggplot(gapminder, aes(x = year, y = gdpPercap, group=year)) + geom_boxplot()

You will often see this alternative of using the as.factor function to make year into a categorical variable.

ggplot(gapminder, aes(x = as.factor(year), y = gdpPercap)) + geom_boxplot()

Homework

Task 1

Add an extra column; the first letter of each country name. Assigning a new variable on each line

gapminder2 <- mutate(gapminder, FirstLetter = substr(country, 1,1))
gapminder3 <- filter(gapminder2, FirstLetter == "Z")
gapminder3

A more efficient solution

gapminder %>% 
  mutate(FirstLetter = substr(country,1,1)) %>% 
  filter(FirstLetter == "Z")

Task 2 - Heatmap of life expectancy

## Get the European countries
filter(gapminder, continent == "Europe") %>% 
## make heatmap. See the fill aesthetic to be life expectancy
ggplot(aes(x=year,y=country,fill=lifeExp)) + geom_tile()