1.1.1 Loading the gapminder and dplyr packages

Before you can work with the gapminder dataset, you’ll need to load two R packages that contain the tools for working with it, then display the gapminder dataset so that you can see what it contains.

# Load the gapminder package
library(gapminder)

# Load the dplyr package
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

# Look at the gapminder dataset
gapminder

## # A tibble: 1,704 × 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.
## # … with 1,694 more rows

Notice that you can see the gapminder dataset in the output. This is called ‘printing’ a dataset. ### Understanding a daraframe Type gapminder in the console, to display the object.

How many observations (rows) are in the dataset?

10 + 1,694 more rows = 1704

1.2.1 Filtering for one year

The filter verb extracts particular observations based on a condition.

Example:

library(gapminder)
library(dplyr)

# Filter the gapminder dataset for the year 1957
gapminder %>%
    filter(year == 1957) 

## # A tibble: 142 × 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1957    30.3  9240934      821.
##  2 Albania     Europe     1957    59.3  1476505     1942.
##  3 Algeria     Africa     1957    45.7 10270856     3014.
##  4 Angola      Africa     1957    32.0  4561361     3828.
##  5 Argentina   Americas   1957    64.4 19610538     6857.
##  6 Australia   Oceania    1957    70.3  9712569    10950.
##  7 Austria     Europe     1957    67.5  6965860     8843.
##  8 Bahrain     Asia       1957    53.8   138655    11636.
##  9 Bangladesh  Asia       1957    39.3 51365468      662.
## 10 Belgium     Europe     1957    69.2  8989111     9715.
## # … with 132 more rows

Notice that all the observations in the output have the year 1957.

1.2.2 Filtering for one country and one year

You can also use the filter() verb to set two conditions, which could retrieve a single observation.

Just like in the last exercise, you can do this in two lines of code, starting with gapminder %>% and having the filter() on the second line. Keeping one verb on each line helps keep the code readable. Note that each time, you’ll put the pipe %>% at the end of the first line (like gapminder %>%); putting the pipe at the beginning of the second line will throw an error.

library(gapminder)
library(dplyr)

# Filter for Vietnam in 2002
gapminder %>%
    filter(year == 2002) %>%
    filter(country == "Vietnam")

## # A tibble: 1 × 6
##   country continent  year lifeExp      pop gdpPercap
##   <fct>   <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Vietnam Asia       2002    73.0 80908147     1764.

1.3.1 Arranging observations by life expectancy

You use arrange() to sort observations in ascending or descending order of a particular variable. In this case, you’ll sort the dataset based on the lifeExp variable.

library(gapminder)
library(dplyr)

# Sort in ascending order of lifeExp
gapminder %>%
    arrange(lifeExp)

## # A tibble: 1,704 × 6
##    country      continent  year lifeExp     pop gdpPercap
##    <fct>        <fct>     <int>   <dbl>   <int>     <dbl>
##  1 Rwanda       Africa     1992    23.6 7290203      737.
##  2 Afghanistan  Asia       1952    28.8 8425333      779.
##  3 Gambia       Africa     1952    30    284320      485.
##  4 Angola       Africa     1952    30.0 4232095     3521.
##  5 Sierra Leone Africa     1952    30.3 2143249      880.
##  6 Afghanistan  Asia       1957    30.3 9240934      821.
##  7 Cambodia     Asia       1977    31.2 6978607      525.
##  8 Mozambique   Africa     1952    31.3 6446316      469.
##  9 Sierra Leone Africa     1957    31.6 2295678     1004.
## 10 Burkina Faso Africa     1952    32.0 4469979      543.
## # … with 1,694 more rows

# Sort in descending order of lifeExp
gapminder %>%
    arrange(desc(lifeExp))

## # A tibble: 1,704 × 6
##    country          continent  year lifeExp       pop gdpPercap
##    <fct>            <fct>     <int>   <dbl>     <int>     <dbl>
##  1 Japan            Asia       2007    82.6 127467972    31656.
##  2 Hong Kong, China Asia       2007    82.2   6980412    39725.
##  3 Japan            Asia       2002    82   127065841    28605.
##  4 Iceland          Europe     2007    81.8    301931    36181.
##  5 Switzerland      Europe     2007    81.7   7554661    37506.
##  6 Hong Kong, China Asia       2002    81.5   6762476    30209.
##  7 Australia        Oceania    2007    81.2  20434176    34435.
##  8 Spain            Europe     2007    80.9  40448191    28821.
##  9 Sweden           Europe     2007    80.9   9031088    33860.
## 10 Israel           Asia       2007    80.7   6426679    25523.
## # … with 1,694 more rows

1.3.2 Filtering and arranging

You’ll often need to use the pipe operator (%>%) to combine multiple dplyr verbs in a row. In this case, you’ll combine a filter() with an arrange() to find the highest population countries in a particular year.

library(gapminder)
library(dplyr)

# Filter for the year 1957, then arrange in descending order of population
gapminder %>%
    filter(year == 1957) %>%
    arrange(desc(pop))

## # A tibble: 142 × 6
##    country        continent  year lifeExp       pop gdpPercap
##    <fct>          <fct>     <int>   <dbl>     <int>     <dbl>
##  1 China          Asia       1957    50.5 637408000      576.
##  2 India          Asia       1957    40.2 409000000      590.
##  3 United States  Americas   1957    69.5 171984000    14847.
##  4 Japan          Asia       1957    65.5  91563009     4318.
##  5 Indonesia      Asia       1957    39.9  90124000      859.
##  6 Germany        Europe     1957    69.1  71019069    10188.
##  7 Brazil         Americas   1957    53.3  65551171     2487.
##  8 United Kingdom Europe     1957    70.4  51430000    11283.
##  9 Bangladesh     Asia       1957    39.3  51365468      662.
## 10 Italy          Europe     1957    67.8  49182000     6249.
## # … with 132 more rows

1.4.1 Using mutate to change or create a column

Suppose we want life expectancy to be measured in months instead of years: you’d have to multiply the existing value by 12. You can use the mutate() verb to change this column, or to create a new column that’s calculated this way.

library(gapminder)
library(dplyr)

# Use mutate to change lifeExp to be in months
gapminder %>%
    mutate(lifeExp = lifeExp*12)

## # A tibble: 1,704 × 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    346.  8425333      779.
##  2 Afghanistan Asia       1957    364.  9240934      821.
##  3 Afghanistan Asia       1962    384. 10267083      853.
##  4 Afghanistan Asia       1967    408. 11537966      836.
##  5 Afghanistan Asia       1972    433. 13079460      740.
##  6 Afghanistan Asia       1977    461. 14880372      786.
##  7 Afghanistan Asia       1982    478. 12881816      978.
##  8 Afghanistan Asia       1987    490. 13867957      852.
##  9 Afghanistan Asia       1992    500. 16317921      649.
## 10 Afghanistan Asia       1997    501. 22227415      635.
## # … with 1,694 more rows

# Use mutate to create a new column called lifeExpMonths
gapminder %>%
    mutate(lifeExpMonths= lifeExp*12)

## # A tibble: 1,704 × 7
##    country     continent  year lifeExp      pop gdpPercap lifeExpMonths
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>         <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.          346.
##  2 Afghanistan Asia       1957    30.3  9240934      821.          364.
##  3 Afghanistan Asia       1962    32.0 10267083      853.          384.
##  4 Afghanistan Asia       1967    34.0 11537966      836.          408.
##  5 Afghanistan Asia       1972    36.1 13079460      740.          433.
##  6 Afghanistan Asia       1977    38.4 14880372      786.          461.
##  7 Afghanistan Asia       1982    39.9 12881816      978.          478.
##  8 Afghanistan Asia       1987    40.8 13867957      852.          490.
##  9 Afghanistan Asia       1992    41.7 16317921      649.          500.
## 10 Afghanistan Asia       1997    41.8 22227415      635.          501.
## # … with 1,694 more rows

1.4.2 Combining filter, mutate, and arrange

Combine all three of the verbs to find the countries with the highest life expectancy, in months, in the year 2007.

library(gapminder)
library(dplyr)

# Filter, mutate, and arrange the gapminder dataset
gapminder %>%
    filter(year == 2007) %>%
    mutate(lifeExpMonths = lifeExp*12) %>%
    arrange(desc(lifeExpMonths))

## # A tibble: 142 × 7
##    country          continent  year lifeExp       pop gdpPercap lifeExpMonths
##    <fct>            <fct>     <int>   <dbl>     <int>     <dbl>         <dbl>
##  1 Japan            Asia       2007    82.6 127467972    31656.          991.
##  2 Hong Kong, China Asia       2007    82.2   6980412    39725.          986.
##  3 Iceland          Europe     2007    81.8    301931    36181.          981.
##  4 Switzerland      Europe     2007    81.7   7554661    37506.          980.
##  5 Australia        Oceania    2007    81.2  20434176    34435.          975.
##  6 Spain            Europe     2007    80.9  40448191    28821.          971.
##  7 Sweden           Europe     2007    80.9   9031088    33860.          971.
##  8 Israel           Asia       2007    80.7   6426679    25523.          969.
##  9 France           Europe     2007    80.7  61083916    30470.          968.
## 10 Canada           Americas   2007    80.7  33390141    36319.          968.
## # … with 132 more rows

2.1.1 Variable assignment

Throughout the exercises in this chapter, you’ll be visualizing a subset of the gapminder data from the year 1952. First, you’ll have to load the ggplot2 package, and create a gapminder_1952 dataset to visualize.

# Load the ggplot2 package as well
library(gapminder)
library(dplyr)
library(ggplot2)

# Create gapminder_1952
gapminder_1952 <- gapminder %>%
    filter(year == 1952)
gapminder_1952

## # A tibble: 142 × 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.
##  2 Albania     Europe     1952    55.2  1282697     1601.
##  3 Algeria     Africa     1952    43.1  9279525     2449.
##  4 Angola      Africa     1952    30.0  4232095     3521.
##  5 Argentina   Americas   1952    62.5 17876956     5911.
##  6 Australia   Oceania    1952    69.1  8691212    10040.
##  7 Austria     Europe     1952    66.8  6927772     6137.
##  8 Bahrain     Asia       1952    50.9   120447     9867.
##  9 Bangladesh  Asia       1952    37.5 46886859      684.
## 10 Belgium     Europe     1952    68    8730405     8343.
## # … with 132 more rows

2.1.2 Comparing population and GDP per capita

When you’re exploring data visually, you’ll often need to try different combinations of variables and aesthetics.

library(gapminder)
library(dplyr)
library(ggplot2)

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Change to put pop on the x-axis and gdpPercap on the y-axis
ggplot(gapminder_1952, aes(x = pop, y = gdpPercap)) +
  geom_point()

2.1.3 Comparing population and life expectancy

In this exercise, you’ll use ggplot2 to create a scatter plot from scratch, to compare each country’s population with its life expectancy in the year 1952.

library(gapminder)
library(dplyr)
library(ggplot2)

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Create a scatter plot with pop on the x-axis and lifeExp on the y-axis
ggplot(gapminder_1952, aes(x = pop, y = lifeExp))+ geom_point()

2.2.1 Putting the x-axis on a log scale

You previously created a scatter plot with population on the x-axis and life expectancy on the y-axis. Since population is spread over several orders of magnitude, with some countries having a much higher population than others, it’s a good idea to put the x-axis on a log scale.

library(gapminder)
library(dplyr)
library(ggplot2)

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Change this plot to put the x-axis on a log scale
ggplot(gapminder_1952, aes(x = pop, y = lifeExp)) +
  geom_point()

  scale_x_log10()

## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1

2.2.2 Putting the x- and y- axes on a log scale

Suppose you want to create a scatter plot with population on the x-axis and GDP per capita on the y-axis. Both population and GDP per-capita are better represented with log scales, since they vary over many orders of magnitude.

library(gapminder)
library(dplyr)
library(ggplot2)

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Scatter plot comparing pop and gdpPercap, with both axes on a log scale
ggplot(gapminder_1952, aes(x = pop, y = gdpPercap))+ geom_point()

scale_x_log10()

## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1

scale_y_log10()

## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1

2.3.1 Adding color to a scatter plot

In this lesson you learned how to use the color aesthetic, which can be used to show which continent each point in a scatter plot represents.

library(gapminder)
library(dplyr)
library(ggplot2)

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Scatter plot comparing pop and lifeExp, with color representing continent
ggplot(gapminder_1952, aes(x = pop, y = lifeExp, color = continent))+ geom_point()

scale_x_log10()

## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1

2.3.2 Adding size and color to a plot

In the last exercise, you created a scatter plot communicating information about each country’s population, life expectancy, and continent. Now you’ll use the size of the points to communicate even more.

library(gapminder)
library(dplyr)
library(ggplot2)

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Add the size aesthetic to represent a country's gdpPercap
ggplot(gapminder_1952, aes(x = pop, y = lifeExp, color = continent, size = gdpPercap)) +
  geom_point() +
  scale_x_log10()

2.4.1 Creating a subgraph for each continent

You’ve learned to use faceting to divide a graph into subplots based on one of its variables, such as the continent.

library(gapminder)
library(dplyr)
library(ggplot2)

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Scatter plot comparing pop and lifeExp, faceted by continent
ggplot(gapminder_1952, aes(x = pop, y=lifeExp, color = continent, ))+ geom_point() + 
scale_x_log10() +
facet_wrap(~ continent)

2.4.2 Faceting by year

All of the graphs in this chapter have been visualizing statistics within one year. Now that you’re able to use faceting, however, you can create a graph showing all the country-level data from 1952 to 2007, to understand how global statistics have changed over time.

library(gapminder)
library(dplyr)
library(ggplot2)

# Scatter plot comparing gdpPercap and lifeExp, with color representing continent
# and size representing population, faceted by year
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, color = continent, size= pop))+ 
geom_point()+
scale_x_log10()+
facet_wrap(~ year)

3.1.1 Summarizing the median life expectancy

You’ve seen how to find the mean life expectancy and the total population across a set of observations, but mean() and sum() are only two of the functions R provides for summarizing a collection of numbers. Here, you’ll learn to use the median() function in combination with summarize().

By the way, dplyr displays some messages when it’s loaded that we’ve been hiding so far. They’ll show up in red and start with:

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

This will occur in future exercises each time you load dplyr: it’s mentioning some built-in functions that are overwritten by dplyr. You won’t need to worry about this message within this course.

library(gapminder)
library(dplyr)

# Summarize to find the median life expectancy
gapminder %>%
    summarize(medianLifeExp = median(lifeExp))

## # A tibble: 1 × 1
##   medianLifeExp
##           <dbl>
## 1          60.7

3.1.2 Summarizing the median life expectancy in 1957

Rather than summarizing the entire dataset, you may want to find the median life expectancy for only one particular year. In this case, you’ll find the median in the year 1957.

library(gapminder)
library(dplyr)

# Filter for 1957 then summarize the median life expectancy
gapminder %>%
    filter(year == 1957) %>%
    summarize (medianLifeExp = median(lifeExp))

## # A tibble: 1 × 1
##   medianLifeExp
##           <dbl>
## 1          48.4

3.1.3 Summarizing multiple variables in 1957

The summarize() verb allows you to summarize multiple variables at once. In this case, you’ll use the median() function to find the median life expectancy and the max() function to find the maximum GDP per capita.

library(gapminder)
library(dplyr)

# Filter for 1957 then summarize the median life expectancy and the maximum GDP per capita
gapminder %>%
    filter(year == 1957) %>%
    summarize (medianLifeExp = median(lifeExp), maxGdpPercap = max(gdpPercap))

## # A tibble: 1 × 2
##   medianLifeExp maxGdpPercap
##           <dbl>        <dbl>
## 1          48.4      113523.

3.2.1 Summarizing by year

In a previous exercise, you found the median life expectancy and the maximum GDP per capita in the year 1957. Now, you’ll perform those two summaries within each year in the dataset, using the group_by verb.

library(gapminder)
library(dplyr)

# Find median life expectancy and maximum GDP per capita in each year
gapminder %>%
    group_by(year) %>%
    summarize(medianLifeExp = median(lifeExp), maxGdpPercap = max(gdpPercap))

## # A tibble: 12 × 3
##     year medianLifeExp maxGdpPercap
##    <int>         <dbl>        <dbl>
##  1  1952          45.1      108382.
##  2  1957          48.4      113523.
##  3  1962          50.9       95458.
##  4  1967          53.8       80895.
##  5  1972          56.5      109348.
##  6  1977          59.7       59265.
##  7  1982          62.4       33693.
##  8  1987          65.8       31541.
##  9  1992          67.7       34933.
## 10  1997          69.4       41283.
## 11  2002          70.8       44684.
## 12  2007          71.9       49357.

3.2.2 Summarizing by continent

You can group by any variable in your dataset to create a summary. Rather than comparing across time, you might be interested in comparing among continents. You’ll want to do that within one year of the dataset: let’s use 1957.

library(gapminder)
library(dplyr)

# Find median life expectancy and maximum GDP per capita in each continent in 1957
gapminder %>%
    filter ( year == 1957) %>%
    group_by (continent) %>%
    summarize(medianLifeExp = median(lifeExp), maxGdpPercap = max(gdpPercap))

## # A tibble: 5 × 3
##   continent medianLifeExp maxGdpPercap
##   <fct>             <dbl>        <dbl>
## 1 Africa             40.6        5487.
## 2 Americas           56.1       14847.
## 3 Asia               48.3      113523.
## 4 Europe             67.6       17909.
## 5 Oceania            70.3       12247.

3.2.3 Summarizing by continent and year

Instead of grouping just by year, or just by continent, you’ll now group by both continent and year to summarize within each.

library(gapminder)
library(dplyr)

# Find median life expectancy and maximum GDP per capita in each continent/year combination
gapminder %>%
    group_by(continent, year) %>%
    summarize(medianLifeExp=median(lifeExp), maxGdpPercap = max(gdpPercap))

## `summarise()` has grouped output by 'continent'. You can override using the
## `.groups` argument.

## # A tibble: 60 × 4
## # Groups:   continent [5]
##    continent  year medianLifeExp maxGdpPercap
##    <fct>     <int>         <dbl>        <dbl>
##  1 Africa     1952          38.8        4725.
##  2 Africa     1957          40.6        5487.
##  3 Africa     1962          42.6        6757.
##  4 Africa     1967          44.7       18773.
##  5 Africa     1972          47.0       21011.
##  6 Africa     1977          49.3       21951.
##  7 Africa     1982          50.8       17364.
##  8 Africa     1987          51.6       11864.
##  9 Africa     1992          52.4       13522.
## 10 Africa     1997          52.8       14723.
## # … with 50 more rows

3.3.1 Visualizing median life expectancy over time

In the last chapter, you summarized the gapminder data to calculate the median life expectancy within each year. This code is provided for you, and is saved (with <-) as the by_year dataset.

Now you can use the ggplot2 package to turn this into a visualization of changing life expectancy over time.

library(gapminder)
library(dplyr)
library(ggplot2)

by_year <- gapminder %>%
  group_by(year) %>%
  summarize(medianLifeExp = median(lifeExp),
            maxGdpPercap = max(gdpPercap))

# Create a scatter plot showing the change in medianLifeExp over time
ggplot(by_year, aes(x = year, y = medianLifeExp))+ 
geom_point()+
expand_limits(y=0)

3.3.2 Visualizing median GDP per capita per continent over time

In the last exercise you were able to see how the median life expectancy of countries changed over time. Now you’ll examine the median GDP per capita instead, and see how the trend differs among continents.

library(gapminder)
library(dplyr)
library(ggplot2)

# Summarize medianGdpPercap within each continent within each year: by_year_continent
by_year_continent <- gapminder %>%
    group_by(continent, year) %>%
    summarize (medianGdpPercap = median(gdpPercap))

## `summarise()` has grouped output by 'continent'. You can override using the
## `.groups` argument.

by_year_continent

## # A tibble: 60 × 3
## # Groups:   continent [5]
##    continent  year medianGdpPercap
##    <fct>     <int>           <dbl>
##  1 Africa     1952            987.
##  2 Africa     1957           1024.
##  3 Africa     1962           1134.
##  4 Africa     1967           1210.
##  5 Africa     1972           1443.
##  6 Africa     1977           1400.
##  7 Africa     1982           1324.
##  8 Africa     1987           1220.
##  9 Africa     1992           1162.
## 10 Africa     1997           1180.
## # … with 50 more rows

# Plot the change in medianGdpPercap in each continent over time
ggplot(by_year_continent, aes(x = year, y = medianGdpPercap, color = continent))+ 
geom_point()+
expand_limits(y=0)

3.3.3 Comparing median life expectancy and median GDP per continent in 2007

In these exercises you’ve generally created plots that show change over time. But as another way of exploring your data visually, you can also use ggplot2 to plot summarized data to compare continents within a single year.

library(gapminder)
library(dplyr)
library(ggplot2)

# Summarize the median GDP and median life expectancy per continent in 2007
by_continent_2007 <- gapminder %>%
  filter(year == 2007) %>%
  group_by(continent) %>%
  summarize(medianGdpPercap = median(gdpPercap),medianLifeExp = median(lifeExp))

# Use a scatter plot to compare the median GDP and median life expectancy
ggplot(by_continent_2007, aes(x = medianGdpPercap, y = medianLifeExp, color = continent)) +
  geom_point()+ 
  expand_limits(y=0)

4.1.1 Visualizing median GDP per capita over time

A line plot is useful for visualizing trends over time. In this exercise, you’ll examine how the median GDP per capita has changed over time.

library(gapminder)
library(dplyr)
library(ggplot2)

# Summarize the median gdpPercap by year, then save it as by_year
by_year <- gapminder %>%
    group_by(year) %>%
    summarize(medianGdpPercap = median(gdpPercap))

# Create a line plot showing the change in medianGdpPercap over time
ggplot(by_year, aes(x = year, y = medianGdpPercap)) + geom_line() + expand_limits(y=0)

4.1.2 Visualizing median GDP per capita by continent over time

In the last exercise you used a line plot to visualize the increase in median GDP per capita over time. Now you’ll examine the change within each continent.

library(gapminder)
library(dplyr)
library(ggplot2)

# Summarize the median gdpPercap by year & continent, save as by_year_continent
by_year_continent <- gapminder %>%
    group_by(year, continent) %>%
    summarize(medianGdpPercap = median(gdpPercap))

## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.

# Create a line plot showing the change in medianGdpPercap by continent over time
ggplot(by_year_continent, aes(x= year, y = medianGdpPercap, color = continent)) + geom_line()+ expand_limits(y=0)

4.2.1 Visualizing median GDP per capita by continent

A bar plot is useful for visualizing summary statistics, such as the median GDP in each continent.

library(gapminder)
library(dplyr)
library(ggplot2)

# Summarize the median gdpPercap by continent in 1952
by_continent <- gapminder %>%
    filter (year == 1952) %>%
    group_by (continent) %>%
    summarize (medianGdpPercap = median(gdpPercap))

# Create a bar plot showing medianGdp by continent
ggplot(by_continent, aes(x = continent, y = medianGdpPercap, color = continent))+ geom_col()

4.2.2 Visualizing GDP per capita by country in Oceania

You’ve created a plot where each bar represents one continent, showing the median GDP per capita for each. But the x-axis of the bar plot doesn’t have to be the continent: you can instead create a bar plot where each bar represents a country.

In this exercise, you’ll create a bar plot comparing the GDP per capita between the two countries in the Oceania continent (Australia and New Zealand).

library(gapminder)
library(dplyr)
library(ggplot2)

# Filter for observations in the Oceania continent in 1952
oceania_1952 <- gapminder %>%
    filter(year == 1952, continent == "Oceania")
# Create a bar plot of gdpPercap by country
ggplot(oceania_1952, aes(x = country, y = gdpPercap))+ geom_col()

4.3.1 Visualizing population

A histogram is useful for examining the distribution of a numeric variable. In this exercise, you’ll create a histogram showing the distribution of country populations (by millions) in the year 1952.

Code for generating this dataset, gapminder_1952, is provided.

library(gapminder)
library(dplyr)
library(ggplot2)

gapminder_1952 <- gapminder %>%
  filter(year == 1952) %>%
  mutate(pop_by_mil = pop / 1000000)

# Create a histogram of population (pop_by_mil)
ggplot(gapminder_1952, aes(x= pop_by_mil))+ geom_histogram(bins = 50)

Notice that most of the distribution is in the smallest (leftmost) bins. In the next exercise you’ll put the x-axis on a log scale.

4.3.2 Visualizing population with x-axis on a log scale

In the last exercise you created a histogram of populations across countries. You might have noticed that there were several countries with a much higher population than others, which causes the distribution to be very skewed, with most of the distribution crammed into a small part of the graph. (Consider that it’s hard to tell the median or the minimum population from that histogram).

To make the histogram more informative, you can try putting the x-axis on a log scale.

library(gapminder)
library(dplyr)
library(ggplot2)

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Create a histogram of population (pop), with x on a log scale
ggplot(gapminder_1952, aes(x= pop))+ geom_histogram()+ scale_x_log10()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Notice that on a log scale, the distribution of country populations is approximately symmetrical.

4.4.1 Comparing GDP per capita across continents

A boxplot is useful for comparing a distribution of values across several groups. In this exercise, you’ll examine the distribution of GDP per capita by continent. Since GDP per capita varies across several orders of magnitude, you’ll need to put the y-axis on a log scale.

library(gapminder)
library(dplyr)
library(ggplot2)

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Create a boxplot comparing gdpPercap among continents
ggplot(gapminder_1952, aes(x = continent, y = gdpPercap))+ geom_boxplot() + scale_y_log10()

4.4.2 Adding a title to your graph

There are many other options for customizing a ggplot2 graph, which you can learn about in other DataCamp courses. You can also learn about them from online resources, which is an important skill to develop.

As the final exercise in this course, you’ll practice looking up ggplot2 instructions by completing a task we haven’t shown you how to do.

library(gapminder)
library(dplyr)
library(ggplot2)

gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Add a title to this graph: "Comparing GDP per capita across continents"
ggplot(gapminder_1952, aes(x = continent, y = gdpPercap)) +
  geom_boxplot() +
  scale_y_log10() +
  ggtitle("Comparing GDP percapita across continents")