Introduction to the Tidyverse
Bowen
5/16/2020
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.
library(gapminder)
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, unionhead(gapminder)## # A tibble: 6 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.# Filter the gapminder dataset for the year 1957
gapminder %>%
filter(year == 1957)## # A tibble: 142 x 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# Filter for China in 2002
gapminder %>%
filter(year == 2002, country == 'China')## # A tibble: 1 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 China Asia 2002 72.0 1280400000 3119.# Sort in ascending order of lifeExp
gapminder %>%
arrange(lifeExp)## # A tibble: 1,704 x 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 x 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 x 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# Use mutate to change lifeExp to be in months
gapminder %>%
mutate(lifeExp = lifeExp * 12)## # A tibble: 1,704 x 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 x 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 %>%
mutate(lifeExpMonths = lifeExp * 12) %>%
filter(year == 2007) %>%
arrange(desc(lifeExpMonths))## # A tibble: 142 x 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 rowsData visualization
Often a better way to understand and present data as a graph. 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.
# Load the ggplot2 package as well
library(ggplot2)
# Create gapminder_1952
gapminder_1952 = gapminder %>%
filter(year == 1952)
# Put pop on the x-axis and gdpPercap on the y-axis
ggplot(gapminder_1952, aes(x = pop, y = gdpPercap)) +
geom_point()
# 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()
# 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()
# 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()
# 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()
# 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)
Grouping and summarizing
So far you’ve been answering questions about individual country-year pairs, but you may be interested in aggregations of the data, such as the average life expectancy of all countries within each year. Here you’ll learn to use the group by and summarize verbs, which collapse large datasets into manageable summaries.
# Summarize to find the median life expectancy
gapminder %>%
summarize(medianLifeExp = median(lifeExp))## # A tibble: 1 x 1
## medianLifeExp
## <dbl>
## 1 60.7# Filter for 1957 then summarize the median life expectancy
gapminder %>%
filter(year == 1957) %>%
summarize(medianLifeExp = median(lifeExp))## # A tibble: 1 x 1
## medianLifeExp
## <dbl>
## 1 48.4# 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 x 2
## medianLifeExp maxGdpPercap
## <dbl> <dbl>
## 1 48.4 113523.# 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 x 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 capita in each continent in 1957
gapminder %>%
filter(year == 1957) %>%
group_by(continent) %>%
summarize(medianLifeExp = median(lifeExp), maxGdpPercap = max(gdpPercap))## # A tibble: 5 x 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))## # A tibble: 60 x 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# 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)
# Plot the change in medianGdpPercap in each continent over time
by_year_continent = gapminder %>%
group_by(continent, year) %>%
summarize(medianGdpPercap = median(gdpPercap))
ggplot(by_year_continent, aes(x = year, y = medianGdpPercap, color = continent)) +
geom_point() +
expand_limits(y = 0)
# 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()
Types of visualizations
In this chapter, you’ll 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.
# Create a line plot showing the change in medianGdpPercap over time
by_year = gapminder %>%
group_by(year) %>%
summarize(medianGdpPercap = median(gdpPercap))
ggplot(by_year, aes(x = year, y = medianGdpPercap)) +
geom_line() +
expand_limits(y = 0)
# Create a line plot showing the change in medianGdpPercap by continent over time
by_year_continent = gapminder %>%
group_by(continent, year) %>%
summarize(medianGdpPercap = median(gdpPercap))
ggplot(by_year_continent, aes(x = year, y = medianGdpPercap, color = continent)) +
geom_line() +
expand_limits(y = 0)
# 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()
# Create a bar plot of gdpPercap by country
oceania_1952 = gapminder %>%
filter(continent == 'Oceania', year == 1952)
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()
# 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 per capita across continents")