Data sorting and pipes dplyr

Last updated on 2022-11-29 | Edit this page

Overview

Questions

  • How can I sort the rows in my data?
  • How can I avoid storing intermediate data objects?

Objectives

  • Use arrange() to sort rows
  • Use the pipe |> to chain commands together

Motivation


Getting an overview of our data can be challenging. Breaking it up in smaller pieces can help us get a better understanding of its content. Being able to subset data is one part of that, another is to be able to re-arrange rows to get a clearer idea of their content.

Creating subsetted objects


So far, we have kept working on the penguins data set, without actually altering it. So far, all our actions have been executed, then forgotten by R. Like it never happened. This is actually quite smart, since it makes it harder to do mistakes you can have difficulties changing.

To store the changes, we have to “assign” the data to a new object in the R environment. Like the penguins data set, which already is an object in our environment we have called “penguins”.

We will now store a filtered version including only the chinstrap penguins, in an object we call chinstraps.

R

chinstraps <- filter(penguins, species == "Chinstrap")

You will likely notice that when we execute this command, nothing is output to the console. That is expected. When we assign the output of a function somewhere, and everything works (i.e., no errors or warnings), nothing happens in the console.

But you should be able to see the new chinstraps object in your environment, and when we type chinstraps in the R console, it prints our chinstraps data.

R

chinstraps

OUTPUT

# A tibble: 68 × 8
   species   island bill_length_mm bill_depth_mm flipper_l…¹ body_…² sex    year
   <fct>     <fct>           <dbl>         <dbl>       <int>   <int> <fct> <int>
 1 Chinstrap Dream            46.5          17.9         192    3500 fema…  2007
 2 Chinstrap Dream            50            19.5         196    3900 male   2007
 3 Chinstrap Dream            51.3          19.2         193    3650 male   2007
 4 Chinstrap Dream            45.4          18.7         188    3525 fema…  2007
 5 Chinstrap Dream            52.7          19.8         197    3725 male   2007
 6 Chinstrap Dream            45.2          17.8         198    3950 fema…  2007
 7 Chinstrap Dream            46.1          18.2         178    3250 fema…  2007
 8 Chinstrap Dream            51.3          18.2         197    3750 male   2007
 9 Chinstrap Dream            46            18.9         195    4150 fema…  2007
10 Chinstrap Dream            51.3          19.9         198    3700 male   2007
# … with 58 more rows, and abbreviated variable names ¹​flipper_length_mm,
#   ²​body_mass_g

Maybe in this chinstrap data we are also not interested in the bill measurements, so we want to remove them.

R

chinstraps <- select(chinstraps, -starts_with("bill"))
chinstraps

OUTPUT

# A tibble: 68 × 6
   species   island flipper_length_mm body_mass_g sex     year
   <fct>     <fct>              <int>       <int> <fct>  <int>
 1 Chinstrap Dream                192        3500 female  2007
 2 Chinstrap Dream                196        3900 male    2007
 3 Chinstrap Dream                193        3650 male    2007
 4 Chinstrap Dream                188        3525 female  2007
 5 Chinstrap Dream                197        3725 male    2007
 6 Chinstrap Dream                198        3950 female  2007
 7 Chinstrap Dream                178        3250 female  2007
 8 Chinstrap Dream                197        3750 male    2007
 9 Chinstrap Dream                195        4150 female  2007
10 Chinstrap Dream                198        3700 male    2007
# … with 58 more rows

Now our data has two less columns, and many fewer rows. A simpler data set for us to work with. But assigning the chinstrap data twice like this is a lot of typing, and there is a simpler way, using something we call the “pipe”.

Challenge 1

Create a new data set called “biscoe”, where you only have data from “Biscoe” island, and where you only have the first 4 columns of data.

R

 biscoe <- filter(penguins, island == "Biscoe") 
 biscoe <- select(biscoe, 1:4)

The pipe |>


We often want to string together series of functions. This is achieved using pipe operator |>. This takes the value on the left, and passes it as the first argument to the function call on the right.

|> is not limited to {dplyr} functions. It’s an alternative way of writing any R code.

You can enable the pipe in RStudio by going to Tools -> Global options -> Code -> Use native pipe operator.

The shortcut to insert the pipe operator is Ctrl+Shift+M for Windows/Linux, and Cmd+Shift+M for Mac.

In the chinstraps example, we had the following code to filter the rows and then select our columns.

R

chinstraps <- filter(penguins, species == "Chinstrap")
chinstraps <- select(chinstraps, -starts_with("bill"))

Here we first create the chinstraps data from the filtered penguins data set. Then use that chinstraps data to reduce the columns and write it again back to the same chinstraps object. It’s a little messy. With the pipe, we can make it more streamlined.

R

chinstraps <- penguins |> 
  filter(species == "Chinstrap") |> 
  select(-starts_with("bill"))

The end result is the same, but there is less typing and we can “read” the pipeline of data subsetting more like language, if we know how. You can read the pipe operator as “and then”.

So if we translate the code above to human language we could read it as:

take the penguins data set, and then keep only rows for the chinstrap penguins, and then remove the columns starting with bill and assign the end result to chinstraps.

Learning to read pipes is a great skill, R is not the only programming language that can do this (though the operator is different between languages, the functionality exists in many).

We can do the entire pipe chain step by step to see what is happening.

R

penguins

OUTPUT

# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_…¹ body_…² sex    year
   <fct>   <fct>              <dbl>         <dbl>      <int>   <int> <fct> <int>
 1 Adelie  Torgersen           39.1          18.7        181    3750 male   2007
 2 Adelie  Torgersen           39.5          17.4        186    3800 fema…  2007
 3 Adelie  Torgersen           40.3          18          195    3250 fema…  2007
 4 Adelie  Torgersen           NA            NA           NA      NA <NA>   2007
 5 Adelie  Torgersen           36.7          19.3        193    3450 fema…  2007
 6 Adelie  Torgersen           39.3          20.6        190    3650 male   2007
 7 Adelie  Torgersen           38.9          17.8        181    3625 fema…  2007
 8 Adelie  Torgersen           39.2          19.6        195    4675 male   2007
 9 Adelie  Torgersen           34.1          18.1        193    3475 <NA>   2007
10 Adelie  Torgersen           42            20.2        190    4250 <NA>   2007
# … with 334 more rows, and abbreviated variable names ¹​flipper_length_mm,
#   ²​body_mass_g

R

penguins |> 
  filter(species == "Chinstrap")

OUTPUT

# A tibble: 68 × 8
   species   island bill_length_mm bill_depth_mm flipper_l…¹ body_…² sex    year
   <fct>     <fct>           <dbl>         <dbl>       <int>   <int> <fct> <int>
 1 Chinstrap Dream            46.5          17.9         192    3500 fema…  2007
 2 Chinstrap Dream            50            19.5         196    3900 male   2007
 3 Chinstrap Dream            51.3          19.2         193    3650 male   2007
 4 Chinstrap Dream            45.4          18.7         188    3525 fema…  2007
 5 Chinstrap Dream            52.7          19.8         197    3725 male   2007
 6 Chinstrap Dream            45.2          17.8         198    3950 fema…  2007
 7 Chinstrap Dream            46.1          18.2         178    3250 fema…  2007
 8 Chinstrap Dream            51.3          18.2         197    3750 male   2007
 9 Chinstrap Dream            46            18.9         195    4150 fema…  2007
10 Chinstrap Dream            51.3          19.9         198    3700 male   2007
# … with 58 more rows, and abbreviated variable names ¹​flipper_length_mm,
#   ²​body_mass_g

R

penguins |> 
  filter(species == "Chinstrap") |> 
  select(-starts_with("bill"))

OUTPUT

# A tibble: 68 × 6
   species   island flipper_length_mm body_mass_g sex     year
   <fct>     <fct>              <int>       <int> <fct>  <int>
 1 Chinstrap Dream                192        3500 female  2007
 2 Chinstrap Dream                196        3900 male    2007
 3 Chinstrap Dream                193        3650 male    2007
 4 Chinstrap Dream                188        3525 female  2007
 5 Chinstrap Dream                197        3725 male    2007
 6 Chinstrap Dream                198        3950 female  2007
 7 Chinstrap Dream                178        3250 female  2007
 8 Chinstrap Dream                197        3750 male    2007
 9 Chinstrap Dream                195        4150 female  2007
10 Chinstrap Dream                198        3700 male    2007
# … with 58 more rows

So, for each chain step, the output of the previous step is fed into the next step, and that way the commands build on each other until a final end result is made.

And as before, we still are seeing the output of the command chain in the console, meaning we are not storing it. Let us do that, again using the assignment.

R

chinstraps <- penguins |> 
  filter(species == "Chinstrap") |> 
  select(-starts_with("bill"))

chinstraps

OUTPUT

# A tibble: 68 × 6
   species   island flipper_length_mm body_mass_g sex     year
   <fct>     <fct>              <int>       <int> <fct>  <int>
 1 Chinstrap Dream                192        3500 female  2007
 2 Chinstrap Dream                196        3900 male    2007
 3 Chinstrap Dream                193        3650 male    2007
 4 Chinstrap Dream                188        3525 female  2007
 5 Chinstrap Dream                197        3725 male    2007
 6 Chinstrap Dream                198        3950 female  2007
 7 Chinstrap Dream                178        3250 female  2007
 8 Chinstrap Dream                197        3750 male    2007
 9 Chinstrap Dream                195        4150 female  2007
10 Chinstrap Dream                198        3700 male    2007
# … with 58 more rows

Challenge 2

Create a new data set called “biscoe”, where you only have data from “Biscoe” island, and where you only have the first 4 columns of data. This time use the pipe.

R

penguins |> 
  filter(island == "Biscoe") |> 
  select(1:4)

OUTPUT

# A tibble: 168 × 4
   species island bill_length_mm bill_depth_mm
   <fct>   <fct>           <dbl>         <dbl>
 1 Adelie  Biscoe           37.8          18.3
 2 Adelie  Biscoe           37.7          18.7
 3 Adelie  Biscoe           35.9          19.2
 4 Adelie  Biscoe           38.2          18.1
 5 Adelie  Biscoe           38.8          17.2
 6 Adelie  Biscoe           35.3          18.9
 7 Adelie  Biscoe           40.6          18.6
 8 Adelie  Biscoe           40.5          17.9
 9 Adelie  Biscoe           37.9          18.6
10 Adelie  Biscoe           40.5          18.9
# … with 158 more rows

Sorting rows


So far, we have looked at subsetting the data. But some times, we want to reorganize the data without altering it. In tables, we are used to be able to sort columns in ascending or descending order.

This can also be done with {dplyr}’s arrange() function. arrange does not alter the data per se, just the order in which the rows are stored.

R

penguins |> 
  arrange(island)

OUTPUT

# A tibble: 344 × 8
   species island bill_length_mm bill_depth_mm flipper_len…¹ body_…² sex    year
   <fct>   <fct>           <dbl>         <dbl>         <int>   <int> <fct> <int>
 1 Adelie  Biscoe           37.8          18.3           174    3400 fema…  2007
 2 Adelie  Biscoe           37.7          18.7           180    3600 male   2007
 3 Adelie  Biscoe           35.9          19.2           189    3800 fema…  2007
 4 Adelie  Biscoe           38.2          18.1           185    3950 male   2007
 5 Adelie  Biscoe           38.8          17.2           180    3800 male   2007
 6 Adelie  Biscoe           35.3          18.9           187    3800 fema…  2007
 7 Adelie  Biscoe           40.6          18.6           183    3550 male   2007
 8 Adelie  Biscoe           40.5          17.9           187    3200 fema…  2007
 9 Adelie  Biscoe           37.9          18.6           172    3150 fema…  2007
10 Adelie  Biscoe           40.5          18.9           180    3950 male   2007
# … with 334 more rows, and abbreviated variable names ¹​flipper_length_mm,
#   ²​body_mass_g

Here we have sorted the data by the island column. Since island is a factor, it will order by the facor levels, which in this case has Biscoe island as the first category. If we sort a numeric column, it will sort by numeric value.

By default, arrange sorts in ascending order. If you want it sorted by descending order, wrap the column name in desc()

R

penguins |> 
  arrange(desc(island))

OUTPUT

# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_…¹ body_…² sex    year
   <fct>   <fct>              <dbl>         <dbl>      <int>   <int> <fct> <int>
 1 Adelie  Torgersen           39.1          18.7        181    3750 male   2007
 2 Adelie  Torgersen           39.5          17.4        186    3800 fema…  2007
 3 Adelie  Torgersen           40.3          18          195    3250 fema…  2007
 4 Adelie  Torgersen           NA            NA           NA      NA <NA>   2007
 5 Adelie  Torgersen           36.7          19.3        193    3450 fema…  2007
 6 Adelie  Torgersen           39.3          20.6        190    3650 male   2007
 7 Adelie  Torgersen           38.9          17.8        181    3625 fema…  2007
 8 Adelie  Torgersen           39.2          19.6        195    4675 male   2007
 9 Adelie  Torgersen           34.1          18.1        193    3475 <NA>   2007
10 Adelie  Torgersen           42            20.2        190    4250 <NA>   2007
# … with 334 more rows, and abbreviated variable names ¹​flipper_length_mm,
#   ²​body_mass_g

Challenge 3

Arrange the penguins data set by body_mass_g.

R

penguins |> 
  arrange(body_mass_g)

OUTPUT

# A tibble: 344 × 8
   species   island    bill_length_mm bill_depth_mm flippe…¹ body_…² sex    year
   <fct>     <fct>              <dbl>         <dbl>    <int>   <int> <fct> <int>
 1 Chinstrap Dream               46.9          16.6      192    2700 fema…  2008
 2 Adelie    Biscoe              36.5          16.6      181    2850 fema…  2008
 3 Adelie    Biscoe              36.4          17.1      184    2850 fema…  2008
 4 Adelie    Biscoe              34.5          18.1      187    2900 fema…  2008
 5 Adelie    Dream               33.1          16.1      178    2900 fema…  2008
 6 Adelie    Torgersen           38.6          17        188    2900 fema…  2009
 7 Chinstrap Dream               43.2          16.6      187    2900 fema…  2007
 8 Adelie    Biscoe              37.9          18.6      193    2925 fema…  2009
 9 Adelie    Dream               37.5          18.9      179    2975 <NA>   2007
10 Adelie    Dream               37            16.9      185    3000 fema…  2007
# … with 334 more rows, and abbreviated variable names ¹​flipper_length_mm,
#   ²​body_mass_g

Challenge 4

Arrange the penguins data set by descending order of flipper_length_mm.

R

penguins |> 
  arrange(desc(flipper_length_mm))

OUTPUT

# A tibble: 344 × 8
   species island bill_length_mm bill_depth_mm flipper_len…¹ body_…² sex    year
   <fct>   <fct>           <dbl>         <dbl>         <int>   <int> <fct> <int>
 1 Gentoo  Biscoe           54.3          15.7           231    5650 male   2008
 2 Gentoo  Biscoe           50            16.3           230    5700 male   2007
 3 Gentoo  Biscoe           59.6          17             230    6050 male   2007
 4 Gentoo  Biscoe           49.8          16.8           230    5700 male   2008
 5 Gentoo  Biscoe           48.6          16             230    5800 male   2008
 6 Gentoo  Biscoe           52.1          17             230    5550 male   2009
 7 Gentoo  Biscoe           51.5          16.3           230    5500 male   2009
 8 Gentoo  Biscoe           55.1          16             230    5850 male   2009
 9 Gentoo  Biscoe           49.5          16.2           229    5800 male   2008
10 Gentoo  Biscoe           49.8          15.9           229    5950 male   2009
# … with 334 more rows, and abbreviated variable names ¹​flipper_length_mm,
#   ²​body_mass_g

Challenge 5

You can arrange on multiple columns! Try arranging the penguins data set by ascending island and descending flipper_length_mm, using a comma between the two arguments.

R

penguins |> 
  arrange(island, desc(flipper_length_mm))

OUTPUT

# A tibble: 344 × 8
   species island bill_length_mm bill_depth_mm flipper_len…¹ body_…² sex    year
   <fct>   <fct>           <dbl>         <dbl>         <int>   <int> <fct> <int>
 1 Gentoo  Biscoe           54.3          15.7           231    5650 male   2008
 2 Gentoo  Biscoe           50            16.3           230    5700 male   2007
 3 Gentoo  Biscoe           59.6          17             230    6050 male   2007
 4 Gentoo  Biscoe           49.8          16.8           230    5700 male   2008
 5 Gentoo  Biscoe           48.6          16             230    5800 male   2008
 6 Gentoo  Biscoe           52.1          17             230    5550 male   2009
 7 Gentoo  Biscoe           51.5          16.3           230    5500 male   2009
 8 Gentoo  Biscoe           55.1          16             230    5850 male   2009
 9 Gentoo  Biscoe           49.5          16.2           229    5800 male   2008
10 Gentoo  Biscoe           49.8          15.9           229    5950 male   2009
# … with 334 more rows, and abbreviated variable names ¹​flipper_length_mm,
#   ²​body_mass_g

Putting it all together


Now that you have learned about ggplot, filter, select and arrange, we can have a look at how we can combine all these to get a better understanding and control over the data. By piping commands together, we can slowly build a better understanding of the data in our minds.

We can for instance explore the numeric columns arranged by Island

R

penguins |> 
  arrange(island) |>
  select(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           37.8          18.3               174        3400  2007
 2           37.7          18.7               180        3600  2007
 3           35.9          19.2               189        3800  2007
 4           38.2          18.1               185        3950  2007
 5           38.8          17.2               180        3800  2007
 6           35.3          18.9               187        3800  2007
 7           40.6          18.6               183        3550  2007
 8           40.5          17.9               187        3200  2007
 9           37.9          18.6               172        3150  2007
10           40.5          18.9               180        3950  2007
# … with 334 more rows

And we can continue that by looking at the data for only male penguins

R

penguins |> 
  arrange(island) |>
  select(island, where(is.numeric)) |>
  filter(sex == "male")

ERROR

Error in `filter()`:
! Problem while computing `..1 = sex == "male"`.
Caused by error in `mask$eval_all_filter()`:
! object 'sex' not found

Whoops! What happened there? Try looking at the error message and see if you can understand it.

Its telling us that there is no sex column. How can that be? Well, we took it away in our select! Since we’ve only kept numeric data and the island column, the sex column is missing!

The order in which you chain commands together matters. Since the pipe sends the output of the previous command into the next, we have two ways of being able to filter by sex:

  1. by adding sex to our selection
  2. by filtering the data before our selection.

Challenge 6

Fix the previous code bit by applying one of the two solutions suggested.

R

penguins |> 
  arrange(island) |>
  select(sex, island, where(is.numeric)) |>
  filter(sex == "male")

OUTPUT

# A tibble: 168 × 7
   sex   island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g  year
   <fct> <fct>           <dbl>         <dbl>             <int>       <int> <int>
 1 male  Biscoe           37.7          18.7               180        3600  2007
 2 male  Biscoe           38.2          18.1               185        3950  2007
 3 male  Biscoe           38.8          17.2               180        3800  2007
 4 male  Biscoe           40.6          18.6               183        3550  2007
 5 male  Biscoe           40.5          18.9               180        3950  2007
 6 male  Biscoe           40.1          18.9               188        4300  2008
 7 male  Biscoe           42            19.5               200        4050  2008
 8 male  Biscoe           41.4          18.6               191        3700  2008
 9 male  Biscoe           40.6          18.8               193        3800  2008
10 male  Biscoe           37.6          19.1               194        3750  2008
# … with 158 more rows

R

penguins |> 
  filter(sex == "male") |>
  arrange(island) |>
  select(island, where(is.numeric))

OUTPUT

# A tibble: 168 × 6
   island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g  year
   <fct>           <dbl>         <dbl>             <int>       <int> <int>
 1 Biscoe           37.7          18.7               180        3600  2007
 2 Biscoe           38.2          18.1               185        3950  2007
 3 Biscoe           38.8          17.2               180        3800  2007
 4 Biscoe           40.6          18.6               183        3550  2007
 5 Biscoe           40.5          18.9               180        3950  2007
 6 Biscoe           40.1          18.9               188        4300  2008
 7 Biscoe           42            19.5               200        4050  2008
 8 Biscoe           41.4          18.6               191        3700  2008
 9 Biscoe           40.6          18.8               193        3800  2008
10 Biscoe           37.6          19.1               194        3750  2008
# … with 158 more rows

Wrap-up

Now we’ve learned about subsetting and sorting our data, so we can create data sets that are suited to our needs. We also learned about chaining commands, the use of the pipe to create a series of commands that build on each other to create a final wanted output.