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.
library() function to load the dplyr package, just like we’ve loaded the gapminder package for you.gapminder, on its own line, to look at the gapminder dataset.# 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 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.
## 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 rowsNow that you’ve loaded the gapminder dataset, you can start examining and understanding it.
We’ve already loaded the gapminder and dplyr packages. Type gapminder in your R terminal, to the right, to display the object.
How many observations (rows) are in the dataset?
The filter verb extracts particular observations based on a condition. In this exercise you’ll filter for observations from a particular year.
Add a filter() line after the pipe (%>%) to extract only the observations from the year 1957. Remember that you use == to compare two values.
library(gapminder)
library(dplyr)
# 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 rowsYou 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.
Filter the gapminder data to retrieve only the observation from China in the year 2002.
library(gapminder)
library(dplyr)
# Filter for China in 2002
gapminder %>%
filter(country == "China", year == 2002)## # 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.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.
gapminder dataset in ascending order of life expectancy (lifeExp).gapminder dataset in descending order of life expectancy.library(gapminder)
library(dplyr)
# 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 rowsThat’s right! Take a look at the countries with the highest and lowest life expectancy- is it similar to what you expected?
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.
Use filter() to extract observations from just the year 1957, then use arrange() to sort in descending order of population (pop).
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 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 rowsSuppose 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.
mutate() to change the existing lifeExp column, by multiplying it by 12: 12 * lifeExp.mutate() to add a new column, called lifeExpMonths, calculated as 12 * lifeExp.library(gapminder)
library(dplyr)
# 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 rowsIn this exercise, you’ll combine all three of the verbs you’ve learned in this chapter, to find the countries with the highest life expectancy, in months, in the year 2007.
In one sequence of pipes on the gapminder dataset:
filter() for observations from the year 2007,mutate() to create a column lifeExpMonths, calculated as 12 * lifeExp, andarrange() in descending order of that new column.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 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 rows