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Introduction
In a previous post, we showed how to manipulate data in R. In particular, we illustrated how to create and manipulate vectors, factors, lists and data frames. This served as an introduction to R and was aimed at beginners. Moreover, as long as it was possible, all manipulations were made in base R, that is, without having to load any package.
In this post, we would like to show again how to manipulate data in R, but this time using the {dplyr} package.
The {dplyr} package, developed by Hadley Wickham and colleagues at posit, provides a complete set of functions that help you solve the most common data manipulation challenges such as:
- filtering observations based on their values
- extracting observations based on their values or positions
- sampling observations based on a specific number or fraction of rows
- sorting observations based on one or several variables
- selecting variables based on their names or positions
- renaming variables
- adding new variables based on existing ones
- summarizing observations or variables to a single descriptive measure
- performing any operation by group
- categorizing observations into two or more groups
- etc.
More information about the package can be found at dplyr.tidyverse.org.
In this post, we will present the most common functions for data manipulation and data management using the {dplyr} package (illustrated on a data frame). This is however not an exhaustive list! It is likely that you will need other functions that the ones presented here. For the interested readers, see the end of this post for further resources.
A question I am often asked is whether it is best to first learn data manipulation with base R and then with {dplyr}, or directly learn {dplyr}.
Everyone may not agrees, but here is what I tend to answer. The interest in terms of efficiency and clarity/readability of the code that {dplyr} brings is obvious. So even when I give a training targeted to statisticians, I present the {dplyr} package. However, I still do believe that learning data manipulation with base R is important for two reasons:
- Everyone will, at some point, be confronted with code written in base R (either from collaboration with other R users, or from code found in textbooks or online).
- Through the feedback I receive from my students, I notice that
{dplyr}is relatively easy to learn when you are familiar with base R (which is quite rewarding for those who struggled at the beginning).
For these reasons, I tend to teach data manipulation with base R first and then slowly switch to {dplyr} (up to the point that for some advanced training courses, I hardly use base R at all by the end of the course). As a side note, this is the approach I follow for data visualization in R as well: I teach first how to plot data with base R, then I gradually teach them how to use {ggplot2}.
With this approach, some students may have the impression that they wasted their time learning base R. At least, some may have this impression during the training. However, as soon as the training is done and they have to learn R by themselves or work on real projects, they are grateful of having learned both.
I am curious to hear from other teachers regarding their approach, so feel free to share your opinion.
Data
To present the different functions, we will use the data frame penguins, available within the {palmerpenguins} package:
# install.packages("palmerpenguins")
library(palmerpenguins)
Before going further, we rename the data frame as dat:
dat <- penguins
I like to call data frames I am working on with a generic name such as dat for two reasons:
- Every time I need to write the name of the data frame, it is usually shorter to write
datthan to write the name of the data frame (which is in this casepenguins). - If I need to do similar analyses or plots on different data frames, the code I wrote in the past can be reused with only a few modifications. With this very simple trick, most of the time I only have to edit the names of the variables, but the name of the data frame does not need to be changed (which saves me a lot of time).
The data frame contains data for 344 penguins and 8 variables describing the species, the island, some measurements of the size of the bill, flipper and body mass, the sex and the study year. More information about the data frame can be found by running ?penguins (after loading the {palmerpenguins} package).
For this post, we will focus only on the variables species, body_mass_g, sex and year.
Before proceeding with the different data manipulation techniques, let’s first inspect the data by displaying its structure, the first 6 rows and a summary of it:
str(dat) # structure of the data ## tibble [344 × 4] (S3: tbl_df/tbl/data.frame) ## $ species : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ... ## $ body_mass_g: int [1:344] 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ... ## $ sex : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ... ## $ year : int [1:344] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ... head(dat) # display first 6 rows ## # A tibble: 6 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Adelie 3750 male 2007 ## 2 Adelie 3800 female 2007 ## 3 Adelie 3250 female 2007 ## 4 Adelie NA <NA> 2007 ## 5 Adelie 3450 female 2007 ## 6 Adelie 3650 male 2007 summary(dat) # summary ## species body_mass_g sex year ## Adelie :152 Min. :2700 female:165 Min. :2007 ## Chinstrap: 68 1st Qu.:3550 male :168 1st Qu.:2007 ## Gentoo :124 Median :4050 NA's : 11 Median :2008 ## Mean :4202 Mean :2008 ## 3rd Qu.:4750 3rd Qu.:2009 ## Max. :6300 Max. :2009 ## NA's :2
{dplyr} package
Without further ado, let’s illustrate the different functions for data manipulation available in the {dplyr} package in the following sections.
As for any package, we first need to install and load it before using it:
# install.packages("dplyr")
library(dplyr)
Note that all functions presented below requires tidy data, which means that:
- each variable is in its own column,
- each observation, or case, is in its own row, and
- each value is in its own cell.
Filter observations
Filtering observations based on their values can be done with the filter() function. This function works on both quantitative and qualitative variables:
# filter observations based on a quantitative variable filter(dat, body_mass_g > 4000) ## # A tibble: 172 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Adelie 4675 male 2007 ## 2 Adelie 4250 <NA> 2007 ## 3 Adelie 4400 male 2007 ## 4 Adelie 4500 male 2007 ## 5 Adelie 4200 male 2007 ## 6 Adelie 4150 male 2007 ## 7 Adelie 4650 male 2007 ## 8 Adelie 4400 male 2007 ## 9 Adelie 4600 male 2007 ## 10 Adelie 4150 male 2007 ## # ℹ 162 more rows # filter observations based on a qualitative variable filter(dat, sex == "female") ## # A tibble: 165 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Adelie 3800 female 2007 ## 2 Adelie 3250 female 2007 ## 3 Adelie 3450 female 2007 ## 4 Adelie 3625 female 2007 ## 5 Adelie 3200 female 2007 ## 6 Adelie 3700 female 2007 ## 7 Adelie 3450 female 2007 ## 8 Adelie 3325 female 2007 ## 9 Adelie 3400 female 2007 ## 10 Adelie 3800 female 2007 ## # ℹ 155 more rows
You can combine several conditions with & (if the conditions must be cumulative) or | (if the conditions are alternatives), for instance:
# filter observations based on 2 cumulative conditions filter(dat, body_mass_g > 4000 & sex == "female") ## # A tibble: 58 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Gentoo 4500 female 2007 ## 2 Gentoo 4450 female 2007 ## 3 Gentoo 4550 female 2007 ## 4 Gentoo 4800 female 2007 ## 5 Gentoo 4400 female 2007 ## 6 Gentoo 4650 female 2007 ## 7 Gentoo 4650 female 2007 ## 8 Gentoo 4200 female 2007 ## 9 Gentoo 4150 female 2007 ## 10 Gentoo 4800 female 2007 ## # ℹ 48 more rows
Notice that variable names do not have to be written inside single nor double quotation marks ('' or ""). This is the case for all functions presented below.
The pipe operator
As you can see from the code above, the filter() functions requires the name of the data frame as first argument, then the condition (with the usual logical operators >, <, >=, <=, ==, !=, %in%, etc.) as second argument.
Specifying the name of the data frame as first argument is required for all functions presented in this list. However, there is a workaround to specifying the data frame’s name inside the functions: the pipe operator (|> or %>%).1
The pipe operator allows to perform a sequence of several operations, that is, chain a sequence of calculations together. It is particularly useful when you are performing several operations on a data frame, and you do not want to save the output at each intermediate step. We will see below how to use the pipe operator with several operations, but for now I would like to introduce it with only one operation at a time.
As you can see with the filter() function, the pipe operator is not compulsory. However, I recommend it so much (even to beginners) for its easy of use, convenience, code readability and popularity that from now on functions available in {dplyr} will be presented together with the pipe operator.
So with the pipe operator, the code above becomes:
# filter observations based on a quantitative variable dat |> filter(body_mass_g > 4000) ## # A tibble: 172 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Adelie 4675 male 2007 ## 2 Adelie 4250 <NA> 2007 ## 3 Adelie 4400 male 2007 ## 4 Adelie 4500 male 2007 ## 5 Adelie 4200 male 2007 ## 6 Adelie 4150 male 2007 ## 7 Adelie 4650 male 2007 ## 8 Adelie 4400 male 2007 ## 9 Adelie 4600 male 2007 ## 10 Adelie 4150 male 2007 ## # ℹ 162 more rows # filter observations based on a qualitative variable dat |> filter(sex == "female") ## # A tibble: 165 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Adelie 3800 female 2007 ## 2 Adelie 3250 female 2007 ## 3 Adelie 3450 female 2007 ## 4 Adelie 3625 female 2007 ## 5 Adelie 3200 female 2007 ## 6 Adelie 3700 female 2007 ## 7 Adelie 3450 female 2007 ## 8 Adelie 3325 female 2007 ## 9 Adelie 3400 female 2007 ## 10 Adelie 3800 female 2007 ## # ℹ 155 more rows # filter observations based on 2 cumulative conditions dat |> filter(body_mass_g > 4000 & sex == "female") ## # A tibble: 58 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Gentoo 4500 female 2007 ## 2 Gentoo 4450 female 2007 ## 3 Gentoo 4550 female 2007 ## 4 Gentoo 4800 female 2007 ## 5 Gentoo 4400 female 2007 ## 6 Gentoo 4650 female 2007 ## 7 Gentoo 4650 female 2007 ## 8 Gentoo 4200 female 2007 ## 9 Gentoo 4150 female 2007 ## 10 Gentoo 4800 female 2007 ## # ℹ 48 more rows
The pipe operator simply takes the results of one operation into the next operation below it, making the code extremely easy to write and read.
This way, instead of specifying the data frame’s name as first argument in the filter() function (or any other function within the {dplyr} package), we simply specify the data frame’s name and then the desired function, combined together thanks to the pipe operator.
Extract observations
It is possible to extract observations based on:
- their positions, or
- their values.
Based on their positions
Extracting observations based on their positions can be done with the slice() function:
# extract rows 2, 5 and 37 dat |> slice(c(2, 5, 37)) ## # A tibble: 3 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Adelie 3800 female 2007 ## 2 Adelie 3450 female 2007 ## 3 Adelie 3950 male 2007
Moreover, extracting the first or last rows can be done with slice_head() and slice_tail():
# extract first 3 rows dat |> slice_head(n = 3) ## # A tibble: 3 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Adelie 3750 male 2007 ## 2 Adelie 3800 female 2007 ## 3 Adelie 3250 female 2007 # extract last 3 rows dat |> slice_tail(n = 3) ## # A tibble: 3 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Chinstrap 3775 male 2009 ## 2 Chinstrap 4100 male 2009 ## 3 Chinstrap 3775 female 2009
Based on their values
To extract observations based on values of a variable, use:
slice_min()to select rows with the lowest values (with a defined proportion), andslice_max()to select rows with the highest values (with a defined proportion).
# extract observations with 25% lowest body mass dat |> slice_min(body_mass_g, prop = 0.25) ## # A tibble: 89 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Chinstrap 2700 female 2008 ## 2 Adelie 2850 female 2008 ## 3 Adelie 2850 female 2008 ## 4 Adelie 2900 female 2008 ## 5 Adelie 2900 female 2008 ## 6 Adelie 2900 female 2009 ## 7 Chinstrap 2900 female 2007 ## 8 Adelie 2925 female 2009 ## 9 Adelie 2975 <NA> 2007 ## 10 Adelie 3000 female 2007 ## # ℹ 79 more rows # extract observations with 25% highest body mass dat |> slice_max(body_mass_g, prop = 0.25) ## # A tibble: 90 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Gentoo 6300 male 2007 ## 2 Gentoo 6050 male 2007 ## 3 Gentoo 6000 male 2008 ## 4 Gentoo 6000 male 2009 ## 5 Gentoo 5950 male 2008 ## 6 Gentoo 5950 male 2009 ## 7 Gentoo 5850 male 2007 ## 8 Gentoo 5850 male 2007 ## 9 Gentoo 5850 male 2009 ## 10 Gentoo 5800 male 2008 ## # ℹ 80 more rows
Sample observations
Sampling observations can be done in two ways:
- Random sample of a number of rows with
sample_n() - Random sample of a fraction of rows with
sample_frac()
# random sample of 3 rows dat |> sample_n(size = 3) ## # A tibble: 3 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Adelie 3450 female 2007 ## 2 Chinstrap 3675 female 2009 ## 3 Gentoo 4500 female 2007 # random sample of half of the rows dat |> sample_frac(size = 1 / 2) ## # A tibble: 172 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Adelie 4150 male 2008 ## 2 Gentoo 5800 male 2008 ## 3 Adelie 3650 male 2009 ## 4 Adelie 3500 male 2009 ## 5 Adelie 3450 female 2007 ## 6 Adelie 4300 male 2009 ## 7 Chinstrap 3400 female 2008 ## 8 Adelie 3950 male 2007 ## 9 Chinstrap 3325 female 2009 ## 10 Adelie 3950 male 2008 ## # ℹ 162 more rows
Note that, as with the sample() function within base R, size can be greater than the size of the data frame. In this case, some rows will be duplicated, and you will need to specify the argument replace = TRUE.
Alternatively, it is possible to obtain a random sample of a number of rows or fraction or rows with slice_sample(). For this, use:
- the argument
nto select a number of rows, or - the argument
propto select a fraction of rows.
# random sample of 3 rows dat |> slice_sample(n = 3) ## # A tibble: 3 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Adelie 3200 female 2007 ## 2 Adelie 3800 female 2007 ## 3 Gentoo 4800 female 2007 # random sample of half of the rows dat |> slice_sample(prop = 1 / 2) ## # A tibble: 172 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Adelie 3900 male 2009 ## 2 Adelie 3275 female 2009 ## 3 Gentoo 5050 male 2008 ## 4 Gentoo 4700 female 2009 ## 5 Gentoo 4600 female 2008 ## 6 Gentoo 4875 <NA> 2009 ## 7 Adelie 3700 <NA> 2007 ## 8 Gentoo 3950 female 2008 ## 9 Gentoo 4550 female 2007 ## 10 Adelie 3500 female 2008 ## # ℹ 162 more rows
Sort observations
Sorting observations can be done with the arrange() function:
# sort observations based on body mass (ascending order) dat |> arrange(body_mass_g) ## # A tibble: 344 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Chinstrap 2700 female 2008 ## 2 Adelie 2850 female 2008 ## 3 Adelie 2850 female 2008 ## 4 Adelie 2900 female 2008 ## 5 Adelie 2900 female 2008 ## 6 Adelie 2900 female 2009 ## 7 Chinstrap 2900 female 2007 ## 8 Adelie 2925 female 2009 ## 9 Adelie 2975 <NA> 2007 ## 10 Adelie 3000 female 2007 ## # ℹ 334 more rows
By default, arrange() uses the ascending order. To sort in descending order, use desc() inside arrange():
# sort observations based on body mass (descending order) dat |> arrange(desc(body_mass_g)) ## # A tibble: 344 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Gentoo 6300 male 2007 ## 2 Gentoo 6050 male 2007 ## 3 Gentoo 6000 male 2008 ## 4 Gentoo 6000 male 2009 ## 5 Gentoo 5950 male 2008 ## 6 Gentoo 5950 male 2009 ## 7 Gentoo 5850 male 2007 ## 8 Gentoo 5850 male 2007 ## 9 Gentoo 5850 male 2009 ## 10 Gentoo 5800 male 2008 ## # ℹ 334 more rows
As with filter(), arrange() can be used for several variables and works both on quantitative and qualitative variables:
# sort observations based on two variables dat |> arrange(sex, body_mass_g) ## # A tibble: 344 × 4 ## species body_mass_g sex year ## <fct> <int> <fct> <int> ## 1 Chinstrap 2700 female 2008 ## 2 Adelie 2850 female 2008 ## 3 Adelie 2850 female 2008 ## 4 Adelie 2900 female 2008 ## 5 Adelie 2900 female 2008 ## 6 Adelie 2900 female 2009 ## 7 Chinstrap 2900 female 2007 ## 8 Adelie 2925 female 2009 ## 9 Adelie 3000 female 2007 ## 10 Adelie 3000 female 2009 ## # ℹ 334 more rows
The code above sorts the observations first based on the sex (in alphabetical order) and then based on the body mass (in ascending order, so from lowest to highest).
Note that if the qualitative variable is defined as an ordered factor, the sorting is based on level order, not alphabetical order!
Select variables
Selecting variables can be done with the select() function, based on:
- the position of the variable(s), or
- the name(s) of the variable(s).
# select variables by their positions dat |> select(c(2, 4)) ## # A tibble: 344 × 2 ## body_mass_g year ## <int> <int> ## 1 3750 2007 ## 2 3800 2007 ## 3 3250 2007 ## 4 NA 2007 ## 5 3450 2007 ## 6 3650 2007 ## 7 3625 2007 ## 8 4675 2007 ## 9 3475 2007 ## 10 4250 2007 ## # ℹ 334 more rows # select variables by their names dat |> select(body_mass_g, year) ## # A tibble: 344 × 2 ## body_mass_g year ## <int> <int> ## 1 3750 2007 ## 2 3800 2007 ## 3 3250 2007 ## 4 NA 2007 ## 5 3450 2007 ## 6 3650 2007 ## 7 3625 2007 ## 8 4675 2007 ## 9 3475 2007 ## 10 4250 2007 ## # ℹ 334 more rows
Note that it is also possible to remove variables. For this, use the - sign in front of their positions or names:
# remove variables by their positions dat |> select(-c(2, 4)) ## # A tibble: 344 × 2 ## species sex ## <fct> <fct> ## 1 Adelie male ## 2 Adelie female ## 3 Adelie female ## 4 Adelie <NA> ## 5 Adelie female ## 6 Adelie male ## 7 Adelie female ## 8 Adelie male ## 9 Adelie <NA> ## 10 Adelie <NA> ## # ℹ 334 more rows # remove variables by their names dat |> select(-c(body_mass_g, year)) ## # A tibble: 344 × 2 ## species sex ## <fct> <fct> ## 1 Adelie male ## 2 Adelie female ## 3 Adelie female ## 4 Adelie <NA> ## 5 Adelie female ## 6 Adelie male ## 7 Adelie female ## 8 Adelie male ## 9 Adelie <NA> ## 10 Adelie <NA> ## # ℹ 334 more rows
It is also possible to select variables with a sequence of names:
# select all variables from species to sex dat |> select(species:sex) ## # A tibble: 344 × 3 ## species body_mass_g sex ## <fct> <int> <fct> ## 1 Adelie 3750 male ## 2 Adelie 3800 female ## 3 Adelie 3250 female ## 4 Adelie NA <NA> ## 5 Adelie 3450 female ## 6 Adelie 3650 male ## 7 Adelie 3625 female ## 8 Adelie 4675 male ## 9 Adelie 3475 <NA> ## 10 Adelie 4250 <NA> ## # ℹ 334 more rows
Last but not least, select() can also be used as an easy way to rearrange columns in the desired order:
# put sex as first column, then all the others dat |> select(sex, species:year) ## # A tibble: 344 × 4 ## sex species body_mass_g year ## <fct> <fct> <int> <int> ## 1 male Adelie 3750 2007 ## 2 female Adelie 3800 2007 ## 3 female Adelie 3250 2007 ## 4 <NA> Adelie NA 2007 ## 5 female Adelie 3450 2007 ## 6 male Adelie 3650 2007 ## 7 female Adelie 3625 2007 ## 8 male Adelie 4675 2007 ## 9 <NA> Adelie 3475 2007 ## 10 <NA> Adelie 4250 2007 ## # ℹ 334 more rows
Rename variables
To rename variables, use the rename() function:
# rename variables
dat |>
rename(
body_mass = body_mass_g, # rename body_mass_g into body_mass
study_year = year # rename year into study_year
)
## # A tibble: 344 × 4
## species body_mass sex study_year
## <fct> <int> <fct> <int>
## 1 Adelie 3750 male 2007
## 2 Adelie 3800 female 2007
## 3 Adelie 3250 female 2007
## 4 Adelie NA <NA> 2007
## 5 Adelie 3450 female 2007
## 6 Adelie 3650 male 2007
## 7 Adelie 3625 female 2007
## 8 Adelie 4675 male 2007
## 9 Adelie 3475 <NA> 2007
## 10 Adelie 4250 <NA> 2007
## # ℹ 334 more rows
This might not be intuitive (at least it was not for me at the time of learning this package), so bear in mind that you always need to write first the new name and then the old name (separated with the = sign).
Create or modify variables
You can create or modify certain variables of the data frame with mutate(), based on:
- another variable, or
- a vector of your choice.
# create a new variable based on an existing one
dat |>
mutate(
body_mass_kg = body_mass_g / 1000
)
## # A tibble: 344 × 5
## species body_mass_g sex year body_mass_kg
## <fct> <int> <fct> <int> <dbl>
## 1 Adelie 3750 male 2007 3.75
## 2 Adelie 3800 female 2007 3.8
## 3 Adelie 3250 female 2007 3.25
## 4 Adelie NA <NA> 2007 NA
## 5 Adelie 3450 female 2007 3.45
## 6 Adelie 3650 male 2007 3.65
## 7 Adelie 3625 female 2007 3.62
## 8 Adelie 4675 male 2007 4.68
## 9 Adelie 3475 <NA> 2007 3.48
## 10 Adelie 4250 <NA> 2007 4.25
## # ℹ 334 more rows
# create a new variable from a vector of your choice
dat |>
mutate(
ID = 1:nrow(dat)
)
## # A tibble: 344 × 5
## species body_mass_g sex year ID
## <fct> <int> <fct> <int> <int>
## 1 Adelie 3750 male 2007 1
## 2 Adelie 3800 female 2007 2
## 3 Adelie 3250 female 2007 3
## 4 Adelie NA <NA> 2007 4
## 5 Adelie 3450 female 2007 5
## 6 Adelie 3650 male 2007 6
## 7 Adelie 3625 female 2007 7
## 8 Adelie 4675 male 2007 8
## 9 Adelie 3475 <NA> 2007 9
## 10 Adelie 4250 <NA> 2007 10
## # ℹ 334 more rows
Note that if you create a variable with a name which already exists in the data frame, the old variable will be erased and replaced by the new one.
Like rename(), mutate() requires the argument to be written as name = expression, where name is name of the column created or modified and expression is the formula for calculating the values.
Summarize observations
Often, you will want to summarize the data with some descriptive statistics. This can be done with the summarize() function, in addition to most functions used for descriptive statistics (mean(), median(), min(), max(), sd(), var(), etc.):
# compute mean and sd of body mass
dat |>
summarize(
body_mass_mean = mean(body_mass_g, na.rm = TRUE),
body_mass_sd = sd(body_mass_g, na.rm = TRUE)
)
## # A tibble: 1 × 2
## body_mass_mean body_mass_sd
## <dbl> <dbl>
## 1 4202. 802.
Note that:
- The
na.rm = TRUEargument is used to omit missing values in the computation of the summary statistics. summarize()andsummarise()give the exact same results.
Identify distinct values
Identifying distinct values of a variable can be done with distinct():
# find the distinct species dat |> distinct(species) ## # A tibble: 3 × 1 ## species ## <fct> ## 1 Adelie ## 2 Gentoo ## 3 Chinstrap
Identifying distinct values is mostly done on qualitative or quantitative discrete variables, but it can be done on any type of variable and with several variables at the same time. If more than one variables is specified, it returns all the combinations of values of the variables.
For instance, with species and study year:
# combination of distinct species and year dat |> distinct(species, year) ## # A tibble: 9 × 2 ## species year ## <fct> <int> ## 1 Adelie 2007 ## 2 Adelie 2008 ## 3 Adelie 2009 ## 4 Gentoo 2007 ## 5 Gentoo 2008 ## 6 Gentoo 2009 ## 7 Chinstrap 2007 ## 8 Chinstrap 2008 ## 9 Chinstrap 2009
Connected operations
Another advantage of using the {dplyr} package is that several operations can be connected all at once, with great readability of the code. This can easily be done with the pipe operator (|> or %>%) introduced earlier.
Until now, we have always seen the same structure: we call a data frame, and then we apply an operation on that data frame. From now on, we will see how to combine more operations into one single chain of operations.
Group by
group_by() allows to modify the way the basic functions are performed. Instead of covering all the rows of the data frame, the operations will cover each of the groups of rows defined by the grouping command. In this way, aggregation operations, using summarize(), will produce statistics for each group rather than for all observations.
For example, we might be interested in computing some descriptive statistics of a quantitative variable, for each level of a qualitative variable (so by group).
In our case, suppose we would like to compute the mean and standard deviation of the body mass, but this time separately for each species:
# compute mean and sd of body mass by species
dat |>
group_by(species) |> # group by species
summarize(
mean = mean(body_mass_g, na.rm = TRUE), # compute mean
sd = sd(body_mass_g, na.rm = TRUE) # compute sd
)
## # A tibble: 3 × 3
## species mean sd
## <fct> <dbl> <dbl>
## 1 Adelie 3701. 459.
## 2 Chinstrap 3733. 384.
## 3 Gentoo 5076. 504.
Applied to the example above, here is how the pipe operator works:
- The first operation (
group_by()) groups observations by species. - Then the output of the first operation is used as the input for the second operation (
summarize()): mean and standard deviation are computed on body mass.
As a result, we have the mean and standard deviation of body mass by group. As you can imagine, all previous operations can be connected to match your needs.
Also note that group_by() can be used for several grouping variables at the same time:
# compute mean and sd of body mass by species and sex
dat |>
group_by(species, sex) |> # group by species and sex
summarize(
mean = mean(body_mass_g, na.rm = TRUE), # compute mean
sd = sd(body_mass_g, na.rm = TRUE) # compute sd
)
## # A tibble: 8 × 4
## # Groups: species [3]
## species sex mean sd
## <fct> <fct> <dbl> <dbl>
## 1 Adelie female 3369. 269.
## 2 Adelie male 4043. 347.
## 3 Adelie <NA> 3540 477.
## 4 Chinstrap female 3527. 285.
## 5 Chinstrap male 3939. 362.
## 6 Gentoo female 4680. 282.
## 7 Gentoo male 5485. 313.
## 8 Gentoo <NA> 4588. 338.
(Note that, as for all connected operations, the name of the data frame needs to be specified only in the first operation.)
Number of observations
Some operations can only be performed inside other operations.
This is the case with the number of observations n(), which can only be used inside summarize():
# number of observations dat |> summarize(n_obs = n()) ## # A tibble: 1 × 1 ## n_obs ## <int> ## 1 344
To refer with the previous connected operation group_by(), we can compute the number of observations by group using the two operations n() and group_by() separated by the pipe operator:
# number of observations by species dat |> group_by(species) |> summarize(n_obs = n()) ## # A tibble: 3 × 2 ## species n_obs ## <fct> <int> ## 1 Adelie 152 ## 2 Chinstrap 68 ## 3 Gentoo 124
Note that n() accepts no parameters and is therefore always written with empty parentheses.
Moreover, note also that the count() function is equivalent to summarize(n = n()):
# number of observations dat |> count() ## # A tibble: 1 × 1 ## n ## <int> ## 1 344 # number of observations by species dat |> count(species) ## # A tibble: 3 × 2 ## species n ## <fct> <int> ## 1 Adelie 152 ## 2 Chinstrap 68 ## 3 Gentoo 124
Number of distinct values
n_distinct(), which can also be used only inside summarize(), computes the number of different values/levels of a variable or combination of variables:
# number of distinct species dat |> summarize(n_species = n_distinct(species)) ## # A tibble: 1 × 1 ## n_species ## <int> ## 1 3 # number of distinct species and year of study dat |> summarize(n_species_year = n_distinct(species, year)) ## # A tibble: 1 × 1 ## n_species_year ## <int> ## 1 9
Note that you do not have to specify a name for the output. In that case, the name of the operation will be used. For example:
# number of distinct species dat |> summarize(n_distinct(species)) ## # A tibble: 1 × 1 ## `n_distinct(species)` ## <int> ## 1 3
First, last or nth value
Also only available inside summarize(), the first, last or nth value can be found with the following commands:
# first value of the variable sex dat |> summarize(first(sex)) ## # A tibble: 1 × 1 ## `first(sex)` ## <fct> ## 1 male # last value of the variable sex dat |> summarize(last(sex)) ## # A tibble: 1 × 1 ## `last(sex)` ## <fct> ## 1 female # 37th value of the variable sex dat |> summarize(nth(sex, n = 37)) ## # A tibble: 1 × 1 ## `nth(sex, n = 37)` ## <fct> ## 1 male
Several interesting arguments exist within this function to deal with missing values. For the interested reader, see more information in the documentation of the function (run ?nth()).
If else
A very common data transformation is the well known “if else” technique. This technique is usually used to create, from an existing variable, another variable which can take two levels.
Suppose that we want to create a new variable called body_mass_cat, which takes the value “High” when body_mass_g is equal or greater than a certain threshold, “Low” otherwise. This transformation can be performed with the combination of mutate() and if_else():
# if else
dat |>
mutate(
body_mass_cat = if_else(body_mass_g >= 4000, # condition
"High", # output if condition is true
"Low" # output if condition is false
)
)
## # A tibble: 344 × 5
## species body_mass_g sex year body_mass_cat
## <fct> <int> <fct> <int> <chr>
## 1 Adelie 3750 male 2007 Low
## 2 Adelie 3800 female 2007 Low
## 3 Adelie 3250 female 2007 Low
## 4 Adelie NA <NA> 2007 <NA>
## 5 Adelie 3450 female 2007 Low
## 6 Adelie 3650 male 2007 Low
## 7 Adelie 3625 female 2007 Low
## 8 Adelie 4675 male 2007 High
## 9 Adelie 3475 <NA> 2007 Low
## 10 Adelie 4250 <NA> 2007 High
## # ℹ 334 more rows
The if_else() function works with 3 arguments:
- The condition (in our case:
body_mass_g >= 4000) - The output value when the condition is true (
Highin our case). - The output value when the conditions is false (
Lowin our case).
As you can see from the table above, when body mass is missing, if_else() also returns a missing value, which is often a good thing to prevent observations being classified erroneously.
Case when
If you want to categorize a variable into more than two levels, an if else is not the most appropriate tool. In these cases, a “case when” is more appropriate.
For your information, when I learned R, I used to write nested if else functions, that is, a secondary if else inside a primary if else. Most of the time it worked (with very often a waste of time trying to debug my code), but it is very easy to make a mistake. And even if you managed to make it work, the code is not easy to read at all!
So I highly recommend using this case when technique instead of several if else functions nested within each other.
Suppose we want to classify body mass into 3 categories: low, medium and high. For this illustration, we arbitrarily decide that body mass is low when it is strictly lower than 3500, high when it is strictly higher than 4750 and medium otherwise.
With nested if else functions, here is the code we would need to write:
# nested if else
dat |>
mutate(
body_mass_cat = if_else(body_mass_g < 3500, # first condition
"Low", # output if first condition is true
if_else(body_mass_g > 4750, # second condition when first condition is false
"High", # output when second condition is true
"Medium" # output when second condition is false
)
)
)
## # A tibble: 344 × 5
## species body_mass_g sex year body_mass_cat
## <fct> <int> <fct> <int> <chr>
## 1 Adelie 3750 male 2007 Medium
## 2 Adelie 3800 female 2007 Medium
## 3 Adelie 3250 female 2007 Low
## 4 Adelie NA <NA> 2007 <NA>
## 5 Adelie 3450 female 2007 Low
## 6 Adelie 3650 male 2007 Medium
## 7 Adelie 3625 female 2007 Medium
## 8 Adelie 4675 male 2007 Medium
## 9 Adelie 3475 <NA> 2007 Low
## 10 Adelie 4250 <NA> 2007 Medium
## # ℹ 334 more rows
This code works as follows:
- It evaluates the first condition
body_mass_g < 3500. - If it is true,
body_mass_catisLow. On the contrary, if it is false, it evaluates the second conditionbody_mass_g > 4750. - If this second condition is true,
body_mass_catisHigh, otherwise it isMedium.
As you can see from the results above, it works. However, you will concede that it is easy to make coding mistakes, and that the code is not easy to write nor to read.
To improve this workflow, we now use the case when technique:
# case when, without a default option
dat |>
mutate(
body_mass_cat = case_when(
body_mass_g < 3500 ~ "Low",
body_mass_g >= 3500 & body_mass_g <= 4750 ~ "Medium",
body_mass_g > 4750 ~ "High"
)
)
## # A tibble: 344 × 5
## species body_mass_g sex year body_mass_cat
## <fct> <int> <fct> <int> <chr>
## 1 Adelie 3750 male 2007 Medium
## 2 Adelie 3800 female 2007 Medium
## 3 Adelie 3250 female 2007 Low
## 4 Adelie NA <NA> 2007 <NA>
## 5 Adelie 3450 female 2007 Low
## 6 Adelie 3650 male 2007 Medium
## 7 Adelie 3625 female 2007 Medium
## 8 Adelie 4675 male 2007 Medium
## 9 Adelie 3475 <NA> 2007 Low
## 10 Adelie 4250 <NA> 2007 Medium
## # ℹ 334 more rows
This workflow is much simpler to code and read!
If there are no missing values in the variable(s) used for the condition(s), it can even be simplified to:
# case when, with a default option
dat |>
mutate(
body_mass_cat = case_when(
body_mass_g < 3500 ~ "Low",
body_mass_g > 4750 ~ "High",
.default = "Medium" # default output
)
)
## # A tibble: 344 × 5
## species body_mass_g sex year body_mass_cat
## <fct> <int> <fct> <int> <chr>
## 1 Adelie 3750 male 2007 Medium
## 2 Adelie 3800 female 2007 Medium
## 3 Adelie 3250 female 2007 Low
## 4 Adelie NA <NA> 2007 Medium
## 5 Adelie 3450 female 2007 Low
## 6 Adelie 3650 male 2007 Medium
## 7 Adelie 3625 female 2007 Medium
## 8 Adelie 4675 male 2007 Medium
## 9 Adelie 3475 <NA> 2007 Low
## 10 Adelie 4250 <NA> 2007 Medium
## # ℹ 334 more rows
As you can see, a default output can be specified with .default for observations that do not match any of the conditions.
However, be careful if there are missing values! Indeed, if there is at least one missing value (as in our case), the code above is not correct because observations with missing body_mass_g will be misclassified as Medium.
Therefore, if you really want to specify a default output, I recommend using the code below which keeps missing values as NA:
# case when, with a default option and missing values
dat |>
mutate(
body_mass_cat = case_when(
body_mass_g < 3500 ~ "Low",
body_mass_g > 4750 ~ "High",
is.na(body_mass_g) ~ NA, # keep missing values as NA
.default = "Medium" # default output
)
)
## # A tibble: 344 × 5
## species body_mass_g sex year body_mass_cat
## <fct> <int> <fct> <int> <chr>
## 1 Adelie 3750 male 2007 Medium
## 2 Adelie 3800 female 2007 Medium
## 3 Adelie 3250 female 2007 Low
## 4 Adelie NA <NA> 2007 <NA>
## 5 Adelie 3450 female 2007 Low
## 6 Adelie 3650 male 2007 Medium
## 7 Adelie 3625 female 2007 Medium
## 8 Adelie 4675 male 2007 Medium
## 9 Adelie 3475 <NA> 2007 Low
## 10 Adelie 4250 <NA> 2007 Medium
## # ℹ 334 more rows
I personally prefer to write all categories and not write a default option for improved code readability and robustness of my code, but it is more a personal opinion.
In all cases, no matter if you used an if else or a case when, it is a good practice to check the variable you just created to make sure that you obtain the intended results.
Conclusion and other resources
Thanks for reading.
This article introduced and illustrated the most common data manipulation and data management tools in R, using the {dplyr} package. We also introduced the pipe operator, well known to users of modern R packages.
The {dplyr} package offers many more functions for data manipulation (in particular to merge data frames with the family of join() functions). If you would like to learn more about this package, I recommend starting with the following resources:
- dplyr.tidyverse.org
- Chapter “Data transformation” in the book “R for Data Science”
- Cheatsheet
- Vignette
- For those who are used to base R, a vignette comparing
{dplyr}functions to their base R equivalents
As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.
The keyboard shortcut for the pipe operator is
ctrl + shift + m(Windows) orcmd + shift + m(Mac). It will print%>%, unless you specified to use the native pipe operator|>in the settings of RStudio.↩︎
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