Data Frame Manipulation with dplyr
Overview
Teaching: 40 min
Exercises: 15 minQuestions
How can I manipulate data frames without repeating myself?
Objectives
To be able to use the six main data frame manipulation ‘verbs’ with pipes in
dplyr.To understand how
group_by()andsummarize()can be combined to summarize datasets.Be able to analyze a subset of data using logical filtering.
Manipulation of data frames means many things to many researchers, we often select certain observations (rows) or variables (columns), we often group the data by a certain variable(s), or we even calculate summary statistics. We can do these operations using the normal base R operations:
mean(gapminder[gapminder$continent == "Africa", "gdpPercap"])
[1] 2193.755
mean(gapminder[gapminder$continent == "Americas", "gdpPercap"])
[1] 7136.11
mean(gapminder[gapminder$continent == "Asia", "gdpPercap"])
[1] 7902.15
But this isn’t very nice because there is a fair bit of repetition. Repeating yourself will cost you time, both now and later, and potentially introduce some nasty bugs.
The dplyr package
Luckily, the dplyr
package provides a number of very useful functions for manipulating data frames
in a way that will reduce the above repetition, reduce the probability of making
errors, and probably even save you some typing. As an added bonus, you might
even find the dplyr grammar easier to read.
Tip: Tidyverse
dplyrpackage belongs to a broader family of opinionated R packages specifically designed for data science called “Tidyverse”. Some of these packages will be covered along this course, but you can find more complete information here: https://www.tidyverse.org/.
Here we’re going to cover 5 of the most commonly used functions as well as using
pipes (%>%) to combine them.
select()filter()group_by()summarize()mutate()
If you have have not installed this package earlier, please do so:
install.packages('dplyr')
Now let’s load the package:
library("dplyr")
Using select()
If, for example, we wanted to move forward with only a few of the variables in
our data frame we could use the select() function. This will keep only the
variables you select.
year_country_gdp <- select(gapminder, year, country, gdpPercap)
If we want to remove one column only from the gapminder data, for example,
removing the continent column.
smaller_gapminder_data <- select(gapminder, -continent)
If we open up year_country_gdp we’ll see that it only contains the year,
country and gdpPercap. Above we used ‘normal’ grammar, but the strengths of
dplyr lie in combining several functions using pipes. Since the pipes grammar
is unlike anything we’ve seen in R before, let’s repeat what we’ve done above
using pipes.
year_country_gdp <- gapminder %>% select(year, country, gdpPercap)
To help you understand why we wrote that in that way, let’s walk through it step
by step. First we summon the gapminder data frame and pass it on, using the pipe
symbol %>%, to the next step, which is the select() function. In this case
we don’t specify which data object we use in the select() function since in
gets that from the previous pipe. Fun Fact: There is a good chance you have
encountered pipes before in the shell. In R, a pipe symbol is %>% while in the
shell it is | but the concept is the same!
Tip: Renaming data frame columns in dplyr
In Chapter 4 we covered how you can rename columns with base R by assigning a value to the output of the
names()function. Just like select, this is a bit cumbersome, but thankfully dplyr has arename()function.Within a pipeline, the syntax is
rename(new_name = old_name). For example, we may want to rename the gdpPercap column name from ourselect()statement above.tidy_gdp <- year_country_gdp %>% rename(gdp_per_capita = gdpPercap) head(tidy_gdp)year country gdp_per_capita 1 1952 Afghanistan 779.4453 2 1957 Afghanistan 820.8530 3 1962 Afghanistan 853.1007 4 1967 Afghanistan 836.1971 5 1972 Afghanistan 739.9811 6 1977 Afghanistan 786.1134
Using filter()
If we now want to move forward with the above, but only with European
countries, we can combine select and filter
year_country_gdp_euro <- gapminder %>%
filter(continent == "Europe") %>%
select(year, country, gdpPercap)
If we now want to show life expectancy of European countries but only for a specific year (e.g., 2007), we can do as below.
europe_lifeExp_2007 <- gapminder %>%
filter(continent == "Europe", year == 2007) %>%
select(country, lifeExp)
Challenge 1
Write a single command (which can span multiple lines and includes pipes) that will produce a data frame that has the African values for
lifeExp,countryandyear, but not for other Continents. How many rows does your data frame have and why?Solution to Challenge 1
year_country_lifeExp_Africa <- gapminder %>% filter(continent == "Africa") %>% select(year, country, lifeExp)
As with last time, first we pass the gapminder data frame to the filter()
function, then we pass the filtered version of the gapminder data frame to the
select() function. Note: The order of operations is very important in this
case. If we used ‘select’ first, filter would not be able to find the variable
continent since we would have removed it in the previous step.
Using group_by()
Now, we were supposed to be reducing the error prone repetitiveness of what can
be done with base R, but up to now we haven’t done that since we would have to
repeat the above for each continent. Instead of filter(), which will only pass
observations that meet your criteria (in the above: continent=="Europe"), we
can use group_by(), which will essentially use every unique criteria that you
could have used in filter.
str(gapminder)
'data.frame': 1704 obs. of 6 variables:
$ country : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
$ pop : num 8425333 9240934 10267083 11537966 13079460 ...
$ continent: chr "Asia" "Asia" "Asia" "Asia" ...
$ lifeExp : num 28.8 30.3 32 34 36.1 ...
$ gdpPercap: num 779 821 853 836 740 ...
str(gapminder %>% group_by(continent))
gropd_df [1,704 × 6] (S3: grouped_df/tbl_df/tbl/data.frame)
$ country : chr [1:1704] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
$ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
$ pop : num [1:1704] 8425333 9240934 10267083 11537966 13079460 ...
$ continent: chr [1:1704] "Asia" "Asia" "Asia" "Asia" ...
$ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
$ gdpPercap: num [1:1704] 779 821 853 836 740 ...
- attr(*, "groups")= tibble [5 × 2] (S3: tbl_df/tbl/data.frame)
..$ continent: chr [1:5] "Africa" "Americas" "Asia" "Europe" ...
..$ .rows : list<int> [1:5]
.. ..$ : int [1:624] 25 26 27 28 29 30 31 32 33 34 ...
.. ..$ : int [1:300] 49 50 51 52 53 54 55 56 57 58 ...
.. ..$ : int [1:396] 1 2 3 4 5 6 7 8 9 10 ...
.. ..$ : int [1:360] 13 14 15 16 17 18 19 20 21 22 ...
.. ..$ : int [1:24] 61 62 63 64 65 66 67 68 69 70 ...
.. ..@ ptype: int(0)
..- attr(*, ".drop")= logi TRUE
You will notice that the structure of the data frame where we used group_by()
(grouped_df) is not the same as the original gapminder (data.frame). A
grouped_df can be thought of as a list where each item in the listis a
data.frame which contains only the rows that correspond to the a particular
value continent (at least in the example above).
Using summarize()
The above was a bit on the uneventful side but group_by() is much more
exciting in conjunction with summarize(). This will allow us to create new
variable(s) by using functions that repeat for each of the continent-specific
data frames. That is to say, using the group_by() function, we split our
original data frame into multiple pieces, then we can run functions
(e.g. mean() or sd()) within summarize().
gdp_bycontinents <- gapminder %>%
group_by(continent) %>%
summarize(mean_gdpPercap = mean(gdpPercap))
continent mean_gdpPercap
<fctr> <dbl>
1 Africa 2193.755
2 Americas 7136.110
3 Asia 7902.150
4 Europe 14469.476
5 Oceania 18621.609
That allowed us to calculate the mean gdpPercap for each continent, but it gets even better.
Challenge 2
Calculate the average life expectancy per country. Which has the longest average life expectancy and which has the shortest average life expectancy?
Solution to Challenge 2
lifeExp_bycountry <- gapminder %>% group_by(country) %>% summarize(mean_lifeExp = mean(lifeExp)) lifeExp_bycountry %>% filter(mean_lifeExp == min(mean_lifeExp) | mean_lifeExp == max(mean_lifeExp))# A tibble: 2 × 2 country mean_lifeExp <chr> <dbl> 1 Iceland 76.5 2 Sierra Leone 36.8Another way to do this is to use the
dplyrfunctionarrange(), which arranges the rows in a data frame according to the order of one or more variables from the data frame. It has similar syntax to other functions from thedplyrpackage. You can usedesc()insidearrange()to sort in descending order.lifeExp_bycountry %>% arrange(mean_lifeExp) %>% head(1)# A tibble: 1 × 2 country mean_lifeExp <chr> <dbl> 1 Sierra Leone 36.8lifeExp_bycountry %>% arrange(desc(mean_lifeExp)) %>% head(1)# A tibble: 1 × 2 country mean_lifeExp <chr> <dbl> 1 Iceland 76.5Alphabetical order works too
lifeExp_bycountry %>% arrange(desc(country)) %>% head(1)# A tibble: 1 × 2 country mean_lifeExp <chr> <dbl> 1 Zimbabwe 52.7
The function group_by() allows us to group by multiple variables. Let’s group by year and continent.
gdp_bycontinents_byyear <- gapminder %>%
group_by(continent, year) %>%
summarize(mean_gdpPercap = mean(gdpPercap))
`summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.
That is already quite powerful, but it gets even better! You’re not limited to defining 1 new variable in summarize().
gdp_pop_bycontinents_byyear <- gapminder %>%
group_by(continent, year) %>%
summarize(mean_gdpPercap = mean(gdpPercap),
sd_gdpPercap = sd(gdpPercap),
mean_pop = mean(pop),
sd_pop = sd(pop))
`summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.
count() and n()
A very common operation is to count the number of observations for each
group. The dplyr package comes with two related functions that help with this.
For instance, if we wanted to check the number of countries included in the
dataset for the year 2002, we can use the count() function. It takes the name
of one or more columns that contain the groups we are interested in, and we can
optionally sort the results in descending order by adding sort=TRUE:
gapminder %>%
filter(year == 2002) %>%
count(continent, sort = TRUE)
continent n
1 Africa 52
2 Asia 33
3 Europe 30
4 Americas 25
5 Oceania 2
If we need to use the number of observations in calculations, the n() function
is useful. It will return the total number of observations in the current group rather than counting the number of observations in each group within a specific column. For instance, if we wanted to get the standard error of the life expectency per continent:
gapminder %>%
group_by(continent) %>%
summarize(se_le = sd(lifeExp)/sqrt(n()))
# A tibble: 5 × 2
continent se_le
<chr> <dbl>
1 Africa 0.366
2 Americas 0.540
3 Asia 0.596
4 Europe 0.286
5 Oceania 0.775
You can also chain together several summary operations; in this case calculating the minimum, maximum, mean and se of each continent’s per-country life-expectancy:
gapminder %>%
group_by(continent) %>%
summarize(
mean_le = mean(lifeExp),
min_le = min(lifeExp),
max_le = max(lifeExp),
se_le = sd(lifeExp)/sqrt(n()))
# A tibble: 5 × 5
continent mean_le min_le max_le se_le
<chr> <dbl> <dbl> <dbl> <dbl>
1 Africa 48.9 23.6 76.4 0.366
2 Americas 64.7 37.6 80.7 0.540
3 Asia 60.1 28.8 82.6 0.596
4 Europe 71.9 43.6 81.8 0.286
5 Oceania 74.3 69.1 81.2 0.775
Using mutate()
We can also create new variables prior to (or even after) summarizing information using mutate().
gdp_pop_bycontinents_byyear <- gapminder %>%
mutate(gdp_billion = gdpPercap*pop/10^9) %>%
group_by(continent,year) %>%
summarize(mean_gdpPercap = mean(gdpPercap),
sd_gdpPercap = sd(gdpPercap),
mean_pop = mean(pop),
sd_pop = sd(pop),
mean_gdp_billion = mean(gdp_billion),
sd_gdp_billion = sd(gdp_billion))
`summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.
Connect mutate with logical filtering: ifelse
When creating new variables, we can hook this with a logical condition. A simple combination of
mutate() and ifelse() facilitates filtering right where it is needed: in the moment of creating something new.
This easy-to-read statement is a fast and powerful way of discarding certain data (even though the overall dimension
of the data frame will not change) or for updating values depending on this given condition.
## keeping all data but "filtering" after a certain condition
# calculate GDP only for people with a life expectation above 25
gdp_pop_bycontinents_byyear_above25 <- gapminder %>%
mutate(gdp_billion = ifelse(lifeExp > 25, gdpPercap * pop / 10^9, NA)) %>%
group_by(continent, year) %>%
summarize(mean_gdpPercap = mean(gdpPercap),
sd_gdpPercap = sd(gdpPercap),
mean_pop = mean(pop),
sd_pop = sd(pop),
mean_gdp_billion = mean(gdp_billion),
sd_gdp_billion = sd(gdp_billion))
`summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.
## updating only if certain condition is fullfilled
# for life expectations above 40 years, the gpd to be expected in the future is scaled
gdp_future_bycontinents_byyear_high_lifeExp <- gapminder %>%
mutate(gdp_futureExpectation = ifelse(lifeExp > 40, gdpPercap * 1.5, gdpPercap)) %>%
group_by(continent, year) %>%
summarize(mean_gdpPercap = mean(gdpPercap),
mean_gdpPercap_expected = mean(gdp_futureExpectation))
`summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.
Key Points
Use the
dplyrpackage to manipulate data frames.Use
select()to choose variables from a data frame.Use
filter()to choose data based on values.Use
group_by()andsummarize()to work with subsets of data.Use
mutate()to create new variables.