Introduction to the Tidyverse
Arif Y.
27 March 2022
Libraries
library(gapminder)
library(tidyverse)0. Course Description
This is an introduction to the programming language R, focused on a powerful set of tools known as the Tidyverse. You’ll learn the intertwined processes of data manipulation and visualization using the tools dplyr and ggplot2. You’ll learn to manipulate data by filtering, sorting, and summarizing a real dataset of historical country data in order to answer exploratory questions. You’ll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. You’ll get a taste of the value of exploratory data analysis and the power of Tidyverse tools.
1. Data Wrangling
In this chapter, you’ll learn to do three things with a table: filter for particular observations, arrange the observations in a desired order, and mutate to add or change a column. You’ll see how each of these steps allows you to answer questions about your data.
1.1 filter()
The filter verb extracts particular observations based on a condition. In this exercise you’ll filter for observations from a particular year.
gapminder %>% filter(year==2007)## # A tibble: 142 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 2007 43.8 31889923 975.
## 2 Albania Europe 2007 76.4 3600523 5937.
## 3 Algeria Africa 2007 72.3 33333216 6223.
## 4 Angola Africa 2007 42.7 12420476 4797.
## 5 Argentina Americas 2007 75.3 40301927 12779.
## 6 Australia Oceania 2007 81.2 20434176 34435.
## 7 Austria Europe 2007 79.8 8199783 36126.
## 8 Bahrain Asia 2007 75.6 708573 29796.
## 9 Bangladesh Asia 2007 64.1 150448339 1391.
## 10 Belgium Europe 2007 79.4 10392226 33693.
## # … with 132 more rowsgapminder %>% filter(country=="UnitedStates")## # A tibble: 0 × 6
## # … with 6 variables: country <fct>, continent <fct>, year <int>,
## # lifeExp <dbl>, pop <int>, gdpPercap <dbl>### Filter for China in 2002
gapminder %>% filter(year== 2002, country=="China")## # A tibble: 1 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 China Asia 2002 72.0 1280400000 3119.1.2 arrange()
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.
# 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### 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 rows1.3 mutate()
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.
### Use mutate to create a new column called lifeExpMonths.
gapminder %>% mutate(lifeExpMonths=12*lifeExp)## # 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### Filter, mutate, and arrange the gapminder dataset.
gapminder %>% filter(year==2007) %>% mutate(lifeExpMonths=12*lifeExp) %>% 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 rows2. Data Visualizations
In this chapter, you’ll learn the essential skills of data visualization using the ggplot2 package, and you’ll see how the dplyr and ggplot2 packages work closely together to create informative graphs.
### Create gapminder_1952.
gapminder_1952 <- gapminder %>% filter(year == 1952)
ggplot(gapminder_1952, aes(x = pop, y = gdpPercap)) + geom_point()
2.1 log() scales
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.
### 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() + scale_y_log10()
2.2 Additional aesthetics
### 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()
### 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()
2.3 Faceting
### Scatter plot comparing pop and lifeExp, faceted by continent.
ggplot(gapminder_1952, aes(x=pop, y=lifeExp)) +
geom_point() + scale_x_log10() + facet_wrap(~continent)
### # 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. Grouping and summarizing
You’ll learn to use the group by and summarize verbs, which collapse large datasets into manageable summaries.
### Find median life expectancy and maximum GDP per capital 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.### Find median life expectancy and maximum GDP per capital 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.### 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 rows3.1 Visualizing summarized data
### Create a scatter plot showing the change in medianLifeExp over time
by_year <- gapminder %>%
group_by(year) %>%
summarize(medianLifeExp = median(lifeExp),
maxGdpPercap = max(gdpPercap))
ggplot(by_year, aes(x = year, y = medianLifeExp)) +
geom_point() + expand_limits(y = 0)
### Summarize medianGdpPercap within each continent within each year: by_year_continent, and plot the change in medianGdpPercap in each continent over time
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.ggplot(by_year_continent, aes(x = year, y = medianGdpPercap, color = continent)) +
geom_point() +
expand_limits(y = 0)
### Summarize the median GDP and median life expectancy per continent in 2007, and use a scatter plot to compare the median GDP and median life expectancy.
by_continent_2007 <- gapminder %>%
filter(year == 2007) %>%
group_by(continent) %>%
summarize(medianGdpPercap = median(gdpPercap),
medianLifeExp = median(lifeExp))
ggplot(by_continent_2007, aes(x = medianGdpPercap, y = medianLifeExp, color = continent)) +
geom_point()
4. Types of visualizations
You will learn how to create line plots, bar plots, histograms, and boxplots. You’ll see how each plot requires different methods of data manipulation and preparation, and you’ll understand how each of these plot types plays a different role in data analysis.
### Summarize the median gdpPercap by year, then save it as by_year, and create a line plot showing the change in medianGdpPercap over time
by_year <- gapminder %>% group_by(year) %>% summarize(medianGdpPercap = median(gdpPercap))
by_year## # A tibble: 12 × 2
## year medianGdpPercap
## <int> <dbl>
## 1 1952 1969.
## 2 1957 2173.
## 3 1962 2335.
## 4 1967 2678.
## 5 1972 3339.
## 6 1977 3799.
## 7 1982 4216.
## 8 1987 4280.
## 9 1992 4386.
## 10 1997 4782.
## 11 2002 5320.
## 12 2007 6124.ggplot(by_year, aes(x=year, y=medianGdpPercap)) + geom_line() + expand_limits(y = 0)
### Summarize the median gdpPercap by year & continent, save as by_year_continent, and create a line plot showing the change in medianGdpPercap by continent over time.
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.ggplot(by_year_continent, aes(x=year, y=medianGdpPercap, color = continent)) + geom_line() + expand_limits(y = 0)
### Summarize the median gdpPercap by continent in 1952, and create a bar plot showing medianGdp by continent
by_continent <- gapminder %>%
filter(year == 1952) %>%
group_by(continent) %>%
summarize(medianGdpPercap = median(gdpPercap))
ggplot(by_continent, aes(x = continent, y = medianGdpPercap)) + geom_col()
### Filter for observations in the Oceania continent in 1952, and create a bar plot of gdpPercap by country
oceania_1952 <- gapminder %>% filter(year == 1952, continent == "Oceania")
ggplot(oceania_1952, aes(x = country, y = gdpPercap)) + geom_col()
### Create a histogram of population (pop_by_mil)
gapminder_1952 <- gapminder %>%
filter(year == 1952) %>%
mutate(pop_by_mil = pop / 1000000)
ggplot(gapminder_1952, aes(x=pop_by_mil)) + geom_histogram(bins=50)
### Create a histogram of population (pop), with x on a log scale
gapminder_1952 <- gapminder %>%
filter(year == 1952)
ggplot(gapminder_1952, aes(x=pop)) + geom_histogram() + scale_x_log10()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
### Create a boxplot comparing gdpPercap among continents
gapminder_1952 <- gapminder %>%
filter(year == 1952)
ggplot(gapminder_1952, aes(x = continent, y= gdpPercap)) + geom_boxplot() + scale_y_log10()
The End.
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