Subsetting data with dplyr

Last updated on 2024-06-04 | Edit this page

Overview

Questions

  • How can I subset the number of columns in my data set?
  • How can I reduce the number of rows in my data set?

Objectives

  • Use select() to reduce columns
  • Use tidyselectors like starts_with() within select() to reduce columns
  • Use filter() to reduce rows
  • Understand common logical operations using filter()

Key Points

  • Subsetting rows and columns
  • Using tidyselectors
  • Understanding logical operations

Motivation


In many cases, we are working with data sets that contain more data than we need, or we want to inspect certain parts of the data set before we continue. Subsetting data sets can be challenging in base R, because there is a fair bit of repetition. This can make code difficult to readn and understand.

The {dplyr} package


The {dplyr} package provides a number of very useful functions for manipulating data sets in a way that will reduce the probability of making errors, and even save you some typing time. As an added bonus, you might even find the {dplyr} grammar easier to read.

We’re going to cover 6 of the most commonly used functions as well as using pipes (|>) to combine them.

  1. select() (covered in this session)
  2. filter() (covered in this session)
  3. arrange() (covered in this session)
  4. mutate() (covered in next session)
  5. group_by() (covered in Day 2 session)
  6. summarize() (covered in Day 2 session)

Selecting columns


Let us first talk about selecting columns. In {dplyr}, the function name for selecting columns is select()! Most {tidyverse} function names for functions are inspired by English grammar, which will help us when we are writing our code.

We first need to make sure we have the tidyverse loaded and the penguins data set at hand.

R

library(tidyverse)
penguins <- read_csv("data/penguins.csv")

To select data, we must first tell select which data set we are selecting from, and then give it our selection. Here, we are asking R to select() from the penguins data set the island, species and sex columns

R

select(penguins, island, species, sex)

OUTPUT

# A tibble: 344 × 3
   island    species sex   
   <fct>     <fct>   <fct> 
 1 Torgersen Adelie  male  
 2 Torgersen Adelie  female
 3 Torgersen Adelie  female
 4 Torgersen Adelie  <NA>  
 5 Torgersen Adelie  female
 6 Torgersen Adelie  male  
 7 Torgersen Adelie  female
 8 Torgersen Adelie  male  
 9 Torgersen Adelie  <NA>  
10 Torgersen Adelie  <NA>  
# ℹ 334 more rows

When we use select() we don’t need to use quotations, we write in the names directly. We can also use the numeric indexes for the column, if we are 100% certain of the order of the columns:

R

select(penguins, 1:3, 6)

OUTPUT

# A tibble: 344 × 4
   species island    bill_length_mm body_mass_g
   <fct>   <fct>              <dbl>       <int>
 1 Adelie  Torgersen           39.1        3750
 2 Adelie  Torgersen           39.5        3800
 3 Adelie  Torgersen           40.3        3250
 4 Adelie  Torgersen           NA            NA
 5 Adelie  Torgersen           36.7        3450
 6 Adelie  Torgersen           39.3        3650
 7 Adelie  Torgersen           38.9        3625
 8 Adelie  Torgersen           39.2        4675
 9 Adelie  Torgersen           34.1        3475
10 Adelie  Torgersen           42          4250
# ℹ 334 more rows

In some cases, we want to remove columns, and not necessarily state all columns we want to keep. Select also allows for this by adding a minus (-) sign in front of the column name you don’t want.

R

select(penguins, -bill_length_mm, -bill_depth_mm)

OUTPUT

# A tibble: 344 × 6
   species island    flipper_length_mm body_mass_g sex     year
   <fct>   <fct>                 <int>       <int> <fct>  <int>
 1 Adelie  Torgersen               181        3750 male    2007
 2 Adelie  Torgersen               186        3800 female  2007
 3 Adelie  Torgersen               195        3250 female  2007
 4 Adelie  Torgersen                NA          NA <NA>    2007
 5 Adelie  Torgersen               193        3450 female  2007
 6 Adelie  Torgersen               190        3650 male    2007
 7 Adelie  Torgersen               181        3625 female  2007
 8 Adelie  Torgersen               195        4675 male    2007
 9 Adelie  Torgersen               193        3475 <NA>    2007
10 Adelie  Torgersen               190        4250 <NA>    2007
# ℹ 334 more rows

Challenge 1

Select the columns sex, year, and species from the penguins dataset.

R

select(penguins, sex, year, species)

OUTPUT

# A tibble: 344 × 3
   sex     year species
   <fct>  <int> <fct>  
 1 male    2007 Adelie 
 2 female  2007 Adelie 
 3 female  2007 Adelie 
 4 <NA>    2007 Adelie 
 5 female  2007 Adelie 
 6 male    2007 Adelie 
 7 female  2007 Adelie 
 8 male    2007 Adelie 
 9 <NA>    2007 Adelie 
10 <NA>    2007 Adelie 
# ℹ 334 more rows

Challenge 2

Change your selection so that species comes before sex. What is the difference in the output?

R

select(penguins, species, sex, year)

OUTPUT

# A tibble: 344 × 3
   species sex     year
   <fct>   <fct>  <int>
 1 Adelie  male    2007
 2 Adelie  female  2007
 3 Adelie  female  2007
 4 Adelie  <NA>    2007
 5 Adelie  female  2007
 6 Adelie  male    2007
 7 Adelie  female  2007
 8 Adelie  male    2007
 9 Adelie  <NA>    2007
10 Adelie  <NA>    2007
# ℹ 334 more rows

select does not only subset columns, but it can also re-arrange them. The columns appear in the order your selection is specified.

Tidy selections

These selections are quite convenient and fast! But they can be even better.

For instance, what if we want to choose all the columns with millimeter measurements? That could be quite convenient, making sure the variables we are working with have the same measurement scale.

We could of course type them all out, but the penguins data set has names that make it even easier for us, using something called tidy-selectors.

Here, we use a tidy-selector ends_with(), can you guess what it does? yes, it looks for columns that end with the string you provide it, here "mm".

R

select(penguins, ends_with("mm"))

OUTPUT

# A tibble: 344 × 3
   bill_length_mm bill_depth_mm flipper_length_mm
            <dbl>         <dbl>             <int>
 1           39.1          18.7               181
 2           39.5          17.4               186
 3           40.3          18                 195
 4           NA            NA                  NA
 5           36.7          19.3               193
 6           39.3          20.6               190
 7           38.9          17.8               181
 8           39.2          19.6               195
 9           34.1          18.1               193
10           42            20.2               190
# ℹ 334 more rows

So convenient! There are several other tidy-selectors you can choose, which you can find here, but often people resort to three specific ones:

  • ends_with() - column names ending with a character string
  • starts_with() - column names starting with a character string
  • contains() - column names containing a character string

If you are working with a well named data set, these functions should make your data selecting much simpler. And if you are making your own data, you can think of such convenient naming for your data, so your work can be easier for you and others.

Lets only pick the measurements of the bill, we are not so interested in the flipper. Then we might want to change to starts_with() in stead.

R

select(penguins, starts_with("bill"))

OUTPUT

# A tibble: 344 × 2
   bill_length_mm bill_depth_mm
            <dbl>         <dbl>
 1           39.1          18.7
 2           39.5          17.4
 3           40.3          18  
 4           NA            NA  
 5           36.7          19.3
 6           39.3          20.6
 7           38.9          17.8
 8           39.2          19.6
 9           34.1          18.1
10           42            20.2
# ℹ 334 more rows

The tidy selector can be combined with each other and other selectors. So you can build exactly the data you want!

R

select(penguins, island, species, year, starts_with("bill"))

OUTPUT

# A tibble: 344 × 5
   island    species  year bill_length_mm bill_depth_mm
   <fct>     <fct>   <int>          <dbl>         <dbl>
 1 Torgersen Adelie   2007           39.1          18.7
 2 Torgersen Adelie   2007           39.5          17.4
 3 Torgersen Adelie   2007           40.3          18  
 4 Torgersen Adelie   2007           NA            NA  
 5 Torgersen Adelie   2007           36.7          19.3
 6 Torgersen Adelie   2007           39.3          20.6
 7 Torgersen Adelie   2007           38.9          17.8
 8 Torgersen Adelie   2007           39.2          19.6
 9 Torgersen Adelie   2007           34.1          18.1
10 Torgersen Adelie   2007           42            20.2
# ℹ 334 more rows

Challenge 3

Select all columns containing an underscore (“_“).

R

select(penguins, contains("_"))

OUTPUT

# A tibble: 344 × 4
   bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
            <dbl>         <dbl>             <int>       <int>
 1           39.1          18.7               181        3750
 2           39.5          17.4               186        3800
 3           40.3          18                 195        3250
 4           NA            NA                  NA          NA
 5           36.7          19.3               193        3450
 6           39.3          20.6               190        3650
 7           38.9          17.8               181        3625
 8           39.2          19.6               195        4675
 9           34.1          18.1               193        3475
10           42            20.2               190        4250
# ℹ 334 more rows

Challenge 4

Select the species and sex columns, in addition to all columns ending with “mm”

R

select(penguins, species, sex, ends_with("mm"))

OUTPUT

# A tibble: 344 × 5
   species sex    bill_length_mm bill_depth_mm flipper_length_mm
   <fct>   <fct>           <dbl>         <dbl>             <int>
 1 Adelie  male             39.1          18.7               181
 2 Adelie  female           39.5          17.4               186
 3 Adelie  female           40.3          18                 195
 4 Adelie  <NA>             NA            NA                  NA
 5 Adelie  female           36.7          19.3               193
 6 Adelie  male             39.3          20.6               190
 7 Adelie  female           38.9          17.8               181
 8 Adelie  male             39.2          19.6               195
 9 Adelie  <NA>             34.1          18.1               193
10 Adelie  <NA>             42            20.2               190
# ℹ 334 more rows

Challenge 5

De-select all the columns with bill measurements

R

select(penguins, -starts_with("bill"))

OUTPUT

# A tibble: 344 × 6
   species island    flipper_length_mm body_mass_g sex     year
   <fct>   <fct>                 <int>       <int> <fct>  <int>
 1 Adelie  Torgersen               181        3750 male    2007
 2 Adelie  Torgersen               186        3800 female  2007
 3 Adelie  Torgersen               195        3250 female  2007
 4 Adelie  Torgersen                NA          NA <NA>    2007
 5 Adelie  Torgersen               193        3450 female  2007
 6 Adelie  Torgersen               190        3650 male    2007
 7 Adelie  Torgersen               181        3625 female  2007
 8 Adelie  Torgersen               195        4675 male    2007
 9 Adelie  Torgersen               193        3475 <NA>    2007
10 Adelie  Torgersen               190        4250 <NA>    2007
# ℹ 334 more rows

Tidy selections with where

The last tidy-selector we’ll mention is where(). where() is a very special tidy selector, that uses logical evaluations to select the data. Let’s have a look at it in action, and see if we can explain it better that way.

Say you are running a correlation analysis. For correlations, you need all the columns in your data to be numeric, as you cannot correlate strings or categories. Going through each individual column and seeing if it is numeric is a bit of a chore. That is where where() comes in!

R

select(penguins, where(is.numeric))

OUTPUT

# A tibble: 344 × 5
   bill_length_mm bill_depth_mm flipper_length_mm body_mass_g  year
            <dbl>         <dbl>             <int>       <int> <int>
 1           39.1          18.7               181        3750  2007
 2           39.5          17.4               186        3800  2007
 3           40.3          18                 195        3250  2007
 4           NA            NA                  NA          NA  2007
 5           36.7          19.3               193        3450  2007
 6           39.3          20.6               190        3650  2007
 7           38.9          17.8               181        3625  2007
 8           39.2          19.6               195        4675  2007
 9           34.1          18.1               193        3475  2007
10           42            20.2               190        4250  2007
# ℹ 334 more rows

Magic! Let’s break that down. is.numeric() is a function in R that checks if a vector is numeric. If the vector is numeric, it returns TRUE if not it returns FALSE.

R

is.numeric(5)

OUTPUT

[1] TRUE

R

is.numeric("something")

OUTPUT

[1] FALSE

Let us look at the penguins data set again

R

penguins

OUTPUT

# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>

The penguins data is stored as a tibble, which is a special kind of data set in R that gives a nice print out of the data. Notice, right below the column name, there is some information in <> marks. This tells us the class of the columns. Species and island are factors, while bill columns are “double” which is a decimal numeric class.

where() goes through all the columns and checks if they are numeric, and returns the ones that are.

R

select(penguins, where(is.numeric))

OUTPUT

# A tibble: 344 × 5
   bill_length_mm bill_depth_mm flipper_length_mm body_mass_g  year
            <dbl>         <dbl>             <int>       <int> <int>
 1           39.1          18.7               181        3750  2007
 2           39.5          17.4               186        3800  2007
 3           40.3          18                 195        3250  2007
 4           NA            NA                  NA          NA  2007
 5           36.7          19.3               193        3450  2007
 6           39.3          20.6               190        3650  2007
 7           38.9          17.8               181        3625  2007
 8           39.2          19.6               195        4675  2007
 9           34.1          18.1               193        3475  2007
10           42            20.2               190        4250  2007
# ℹ 334 more rows

Challenge 6

Select only the columns that are factors from the penguins data set.

R

select(penguins, where(is.factor))

OUTPUT

# A tibble: 344 × 3
   species island    sex   
   <fct>   <fct>     <fct> 
 1 Adelie  Torgersen male  
 2 Adelie  Torgersen female
 3 Adelie  Torgersen female
 4 Adelie  Torgersen <NA>  
 5 Adelie  Torgersen female
 6 Adelie  Torgersen male  
 7 Adelie  Torgersen female
 8 Adelie  Torgersen male  
 9 Adelie  Torgersen <NA>  
10 Adelie  Torgersen <NA>  
# ℹ 334 more rows

Challenge 7

Select the columns island, species, as well as all numeric columns from the penguins data set.

R

select(penguins, island, species, where(is.numeric))

OUTPUT

# A tibble: 344 × 7
   island    species bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>     <fct>            <dbl>         <dbl>             <int>       <int>
 1 Torgersen Adelie            39.1          18.7               181        3750
 2 Torgersen Adelie            39.5          17.4               186        3800
 3 Torgersen Adelie            40.3          18                 195        3250
 4 Torgersen Adelie            NA            NA                  NA          NA
 5 Torgersen Adelie            36.7          19.3               193        3450
 6 Torgersen Adelie            39.3          20.6               190        3650
 7 Torgersen Adelie            38.9          17.8               181        3625
 8 Torgersen Adelie            39.2          19.6               195        4675
 9 Torgersen Adelie            34.1          18.1               193        3475
10 Torgersen Adelie            42            20.2               190        4250
# ℹ 334 more rows
# ℹ 1 more variable: year <int>

Filtering rows


Now that we know how to select the columns we want, we should take a look at how we filter the rows. Row filtering is done with the function filter(), which takes statements that can be evaluated to TRUE or FALSE.

What do we mean with statements that can be evaluated to TRUE or FALSE? In the example with where() we used the is.numeric function to evaluate if the columns where numeric or not. We will be doing the same for rows!

Now, using is.numeric on a row won’t help, because every row-value in a column will be of the same type, that is how the data set works. All values in a column must be of the same type.

So what can we do? Well, we can check if the values meet certain criteria or not. Like values being above 20, or factors being a specific factor.

R

filter(penguins, body_mass_g < 3000)

OUTPUT

# A tibble: 9 × 8
  species   island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>     <fct>              <dbl>         <dbl>             <int>       <int>
1 Adelie    Dream               37.5          18.9               179        2975
2 Adelie    Biscoe              34.5          18.1               187        2900
3 Adelie    Biscoe              36.5          16.6               181        2850
4 Adelie    Biscoe              36.4          17.1               184        2850
5 Adelie    Dream               33.1          16.1               178        2900
6 Adelie    Biscoe              37.9          18.6               193        2925
7 Adelie    Torgersen           38.6          17                 188        2900
8 Chinstrap Dream               43.2          16.6               187        2900
9 Chinstrap Dream               46.9          16.6               192        2700
# ℹ 2 more variables: sex <fct>, year <int>

Here, we’ve filtered so that we only have observations where the body mass was less than 3 kilos. We can also filter for specific values, but beware! you must use double equals (==) for comparisons, as single equals (=) are for argument names in functions.

R

filter(penguins, body_mass_g == 2900)

OUTPUT

# A tibble: 4 × 8
  species   island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>     <fct>              <dbl>         <dbl>             <int>       <int>
1 Adelie    Biscoe              34.5          18.1               187        2900
2 Adelie    Dream               33.1          16.1               178        2900
3 Adelie    Torgersen           38.6          17                 188        2900
4 Chinstrap Dream               43.2          16.6               187        2900
# ℹ 2 more variables: sex <fct>, year <int>

What is happening, is that R will check if the values in body_mass_g are the same as 2900 (TRUE) or not (FALSE), and will do this for every row in the data set. Then at the end, it will discard all those that are FALSE, and keep those that are TRUE.

Challenge 8

Filter the data so you only have observations from the “Dream” island.

R

filter(penguins, island == "Dream")

OUTPUT

# A tibble: 124 × 8
   species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>           <dbl>         <dbl>             <int>       <int>
 1 Adelie  Dream            39.5          16.7               178        3250
 2 Adelie  Dream            37.2          18.1               178        3900
 3 Adelie  Dream            39.5          17.8               188        3300
 4 Adelie  Dream            40.9          18.9               184        3900
 5 Adelie  Dream            36.4          17                 195        3325
 6 Adelie  Dream            39.2          21.1               196        4150
 7 Adelie  Dream            38.8          20                 190        3950
 8 Adelie  Dream            42.2          18.5               180        3550
 9 Adelie  Dream            37.6          19.3               181        3300
10 Adelie  Dream            39.8          19.1               184        4650
# ℹ 114 more rows
# ℹ 2 more variables: sex <fct>, year <int>

Challenge 9

Filter the data so you only have observations after 2008

R

filter(penguins, year >= 2008)

OUTPUT

# A tibble: 234 × 8
   species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>           <dbl>         <dbl>             <int>       <int>
 1 Adelie  Biscoe           39.6          17.7               186        3500
 2 Adelie  Biscoe           40.1          18.9               188        4300
 3 Adelie  Biscoe           35            17.9               190        3450
 4 Adelie  Biscoe           42            19.5               200        4050
 5 Adelie  Biscoe           34.5          18.1               187        2900
 6 Adelie  Biscoe           41.4          18.6               191        3700
 7 Adelie  Biscoe           39            17.5               186        3550
 8 Adelie  Biscoe           40.6          18.8               193        3800
 9 Adelie  Biscoe           36.5          16.6               181        2850
10 Adelie  Biscoe           37.6          19.1               194        3750
# ℹ 224 more rows
# ℹ 2 more variables: sex <fct>, year <int>

Multiple filters

Many times, we will want to have several filters applied at once. What if you only want Adelie penguins that are below 3 kilos? filter() can take as many statements as you want! Combine them by adding commas (,) between each statement, and that will work as ‘and’.

R

filter(penguins, 
       species == "Chinstrap",
       body_mass_g < 3000)

OUTPUT

# A tibble: 2 × 8
  species   island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>     <fct>           <dbl>         <dbl>             <int>       <int>
1 Chinstrap Dream            43.2          16.6               187        2900
2 Chinstrap Dream            46.9          16.6               192        2700
# ℹ 2 more variables: sex <fct>, year <int>

You can also use the & sign, which in R is the comparison character for ‘and’, like == is for ‘equals’.

R

filter(penguins, 
       species == "Chinstrap" &
         body_mass_g < 3000)

OUTPUT

# A tibble: 2 × 8
  species   island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>     <fct>           <dbl>         <dbl>             <int>       <int>
1 Chinstrap Dream            43.2          16.6               187        2900
2 Chinstrap Dream            46.9          16.6               192        2700
# ℹ 2 more variables: sex <fct>, year <int>

Here we are filtering the penguins data set keeping only the species “Chinstrap” and those below 3.5 kilos. And we can keep going!

R

filter(penguins, 
       species == "Chinstrap",
       body_mass_g < 3000,
       sex == "male")

OUTPUT

# A tibble: 0 × 8
# ℹ 8 variables: species <fct>, island <fct>, bill_length_mm <dbl>,
#   bill_depth_mm <dbl>, flipper_length_mm <int>, body_mass_g <int>, sex <fct>,
#   year <int>

Challenge 10

Filter the data so you only have observations after 2008, and from “Biscoe” island

R

filter(penguins, 
       year >= 2008,
       island == "Biscoe")

OUTPUT

# A tibble: 124 × 8
   species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>           <dbl>         <dbl>             <int>       <int>
 1 Adelie  Biscoe           39.6          17.7               186        3500
 2 Adelie  Biscoe           40.1          18.9               188        4300
 3 Adelie  Biscoe           35            17.9               190        3450
 4 Adelie  Biscoe           42            19.5               200        4050
 5 Adelie  Biscoe           34.5          18.1               187        2900
 6 Adelie  Biscoe           41.4          18.6               191        3700
 7 Adelie  Biscoe           39            17.5               186        3550
 8 Adelie  Biscoe           40.6          18.8               193        3800
 9 Adelie  Biscoe           36.5          16.6               181        2850
10 Adelie  Biscoe           37.6          19.1               194        3750
# ℹ 114 more rows
# ℹ 2 more variables: sex <fct>, year <int>

Challenge 11

Filter the data so you only have observations of male penguins of the Chinstrap species

R

filter(penguins, 
       sex == "male",
       species == "Chinstrap")

OUTPUT

# A tibble: 34 × 8
   species   island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>     <fct>           <dbl>         <dbl>             <int>       <int>
 1 Chinstrap Dream            50            19.5               196        3900
 2 Chinstrap Dream            51.3          19.2               193        3650
 3 Chinstrap Dream            52.7          19.8               197        3725
 4 Chinstrap Dream            51.3          18.2               197        3750
 5 Chinstrap Dream            51.3          19.9               198        3700
 6 Chinstrap Dream            51.7          20.3               194        3775
 7 Chinstrap Dream            52            18.1               201        4050
 8 Chinstrap Dream            50.5          19.6               201        4050
 9 Chinstrap Dream            50.3          20                 197        3300
10 Chinstrap Dream            49.2          18.2               195        4400
# ℹ 24 more rows
# ℹ 2 more variables: sex <fct>, year <int>

The difference between & (and) and |(or)

But what if we want all the Chinstrap penguins or if body mass is below 3 kilos? When we use the comma (or the &), we make sure that all statements are TRUE. But what if we want it so that either statement is true? Then we can use the or character | .

R

filter(penguins, 
       species == "Chinstrap" | 
         body_mass_g < 3000)

OUTPUT

# A tibble: 75 × 8
   species   island   bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>     <fct>             <dbl>         <dbl>             <int>       <int>
 1 Adelie    Dream              37.5          18.9               179        2975
 2 Adelie    Biscoe             34.5          18.1               187        2900
 3 Adelie    Biscoe             36.5          16.6               181        2850
 4 Adelie    Biscoe             36.4          17.1               184        2850
 5 Adelie    Dream              33.1          16.1               178        2900
 6 Adelie    Biscoe             37.9          18.6               193        2925
 7 Adelie    Torgers…           38.6          17                 188        2900
 8 Chinstrap Dream              46.5          17.9               192        3500
 9 Chinstrap Dream              50            19.5               196        3900
10 Chinstrap Dream              51.3          19.2               193        3650
# ℹ 65 more rows
# ℹ 2 more variables: sex <fct>, year <int>

This now gives us both all chinstrap penguins, and the smallest Adelie penguins! By combining AND and OR statements this way, we can slowly create the filtering we are after.

Challenge 12

Filter the data so you only have observations of either male penguins or the Chinstrap species

R

filter(penguins, 
       sex == "male" |
       species == "Chinstrap")

OUTPUT

# A tibble: 202 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.3          20.6               190        3650
 3 Adelie  Torgersen           39.2          19.6               195        4675
 4 Adelie  Torgersen           38.6          21.2               191        3800
 5 Adelie  Torgersen           34.6          21.1               198        4400
 6 Adelie  Torgersen           42.5          20.7               197        4500
 7 Adelie  Torgersen           46            21.5               194        4200
 8 Adelie  Biscoe              37.7          18.7               180        3600
 9 Adelie  Biscoe              38.2          18.1               185        3950
10 Adelie  Biscoe              38.8          17.2               180        3800
# ℹ 192 more rows
# ℹ 2 more variables: sex <fct>, year <int>

Wrap-up

Now we’ve learned about subsetting our data, so we can create data sets that are suited to our needs.