Working with data in the tidyverse

Last updated on 2025-10-02 | Edit this page

Estimated time: 30 minutes

Overview

Questions

  • How do you work with tabular data in R
  • Why use the tidyverse to wrangle data?

Objectives

  • Load external data from a .csv file into a data frame.
  • Install and load packages.
  • Describe what a data frame is.
  • Summarize the contents of a data frame.
  • Wrangle data with dplyr.
  • Export a data frame to a .csv file.

Loading the survey data

We are investigating the animal species diversity and weights found within plots at our study site. The dataset is stored as a comma separated value (CSV) file. Each row holds information for a single animal, and the columns represent:

Column Description
record_id Unique id for the observation
month month of observation
day day of observation
year year of observation
plot_id ID of a particular experimental plot of land
species_id 2-letter code
sex sex of animal (“M”, “F”)
hindfoot_length length of the hindfoot in mm
weight weight of the animal in grams
genus genus of animal
species species of animal
taxon e.g. Rodent, Reptile, Bird, Rabbit
plot_type type of plot

Downloading the data

We created the folder that will store the downloaded data (data_raw) in the chapter “Before we start”. If you skipped that part, it may be a good idea to have a look now, to make sure your working directory is set up properly.

We are going to use the R function download.file() to download the CSV file that contains the survey data from Figshare, and we will use read_csv() to load the content of the CSV file into R.

Inside the download.file command, the first entry is a character string with the source URL (“https://ndownloader.figshare.com/files/2292169”). This source URL downloads a CSV file from figshare. The text after the comma (“data_raw/portal_data_joined.csv”) is the destination of the file on your local machine. You’ll need to have a folder on your machine called “data_raw” where you’ll download the file. So this command downloads a file from Figshare, names it “portal_data_joined.csv” and adds it to a preexisting folder named “data_raw”.

R 

download.file(url = "https://ndownloader.figshare.com/files/2292169",
              destfile = "data_raw/portal_data_joined.csv")

Reading the data into R

The file has now been downloaded to the destination you specified, but R has not yet loaded the data from the file into memory. To do this, we can use the read_csv() function from the `readr`` package.

Packages in R are basically sets of additional functions that let you do more stuff. The functions we’ve been using so far, like round(), sqrt(), or c() come built into R. Packages give you access to additional functions beyond base R. A similar function to read_csv() from the tidyverse package is read.csv() from base R. We don’t have time to cover their differences but notice that the exact spelling determines which function is used. Before you use a package for the first time you need to install it on your machine, and then you should import it in every subsequent R session when you need it.

The tidyverse package contains readr and other packages we’ll use. To install it, we can type install.packages("tidyverse") straight into the console. In fact, it’s better to write this in the console than in our script for any package, as there’s no need to re-install packages every time we run the script. Then, to load the package type:

R 

## load the tidyverse packages, incl. dplyr
library(tidyverse)

Now we can use the functions from any tidyverse package. Let’s use read_csv() to read the data into a data frame (we will learn more about data frames later):

R 

surveys <- read_csv("data_raw/portal_data_joined.csv")

OUTPUT 

#> Rows: 34786 Columns: 13
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (6): species_id, sex, genus, species, taxa, plot_type
#> dbl (7): record_id, month, day, year, plot_id, hindfoot_length, weight
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

When you execute read_csv on a data file, it looks through the first 1000 rows of each column and guesses its data type. For example, in this dataset, read_csv() reads weight as col_double (a numeric data type), and species as col_character. You have the option to specify the data type for a column manually by using the col_types argument in read_csv.

Callout

Note

read_csv() is actually a special case of read_delim() that assumes fields are delineated by commas. Check out the help by typing ?read_csv to learn more.

You may sometimes see a similar function called read.csv() with a dot instead of an underscore. This is an older function that doesn’t require tidyverse with different arguments and slightly different output.

We can see the contents of the first few lines of the data by typing its name: surveys. By default, this will show you as many rows and columns of the data as fit on your screen. If you wanted the first 50 rows, you could type print(surveys, n = 50)

We can also extract the first few lines of this data using the function head():

R 

head(surveys)

OUTPUT 

#> # A tibble: 6 × 13
#>   record_id month   day  year plot_id species_id sex   hindfoot_length weight
#>       <dbl> <dbl> <dbl> <dbl>   <dbl> <chr>      <chr>           <dbl>  <dbl>
#> 1         1     7    16  1977       2 NL         M                  32     NA
#> 2        72     8    19  1977       2 NL         M                  31     NA
#> 3       224     9    13  1977       2 NL         <NA>               NA     NA
#> 4       266    10    16  1977       2 NL         <NA>               NA     NA
#> 5       349    11    12  1977       2 NL         <NA>               NA     NA
#> 6       363    11    12  1977       2 NL         <NA>               NA     NA
#> # ℹ 4 more variables: genus <chr>, species <chr>, taxa <chr>, plot_type <chr>

Unlike the print() function, head() returns the extracted data. You could use it to assign the first 100 rows of surveys to an object using surveys_sample <- head(surveys, 100). This can be useful if you want to try out complex computations on a subset of your data before you apply them to the whole data set. There is a similar function that lets you extract the last few lines of the data set. It is called (you might have guessed it) tail().

To open the dataset in RStudio’s Data Viewer, use the view() function:

R 

view(surveys)
Callout

Note

There are two functions for viewing which are case-sensitive. Using view() with a lowercase ‘v’ is part of tidyverse, whereas using View() with an uppercase ‘V’ is loaded through base R in the utils package.

What are data frames?

When we loaded the data into R, it got stored as an object of class tibble, which is a special kind of data frame (the difference is not important for our purposes, but you can learn more about tibbles here). Data frames are the de facto data structure for most tabular data, and what we use for statistics and plotting. Data frames can be created by hand, but most commonly they are generated by functions like read_csv(); in other words, when importing spreadsheets from your hard drive or the web.

A data frame is the representation of data in the format of a table where the columns are vectors that all have the same length. Because columns are vectors, each column must contain a single type of data (e.g., characters, integers, factors). For example, here is a figure depicting a data frame comprising a numeric, a character, and a logical vector.

We can see this also when inspecting the structure of a data frame with the function str():

R 

str(surveys)

Inspecting data frames

We already saw how the functions head() and str() can be useful to check the content and the structure of a data frame. Here is a non-exhaustive list of functions to get a sense of the content/structure of the data. Let’s try them out!

  • Size:

    • dim(surveys) - returns a vector with the number of rows in the first element, and the number of columns as the second element (the dimensions of the object)
    • nrow(surveys) - returns the number of rows
    • ncol(surveys) - returns the number of columns

  • Content:

    • head(surveys) - shows the first 6 rows
    • tail(surveys) - shows the last 6 rows
    • glimpse(surveys) - turns a data frame on its side to focus on the columns. Similar to str() but with nicer printing.

  • Names:

    • names(surveys) - returns the column names (synonym of colnames() for data.frame objects)
    • rownames(surveys) - returns the row names

  • Summary:

    • str(surveys) - structure of the object and information about the class, length and content of each column
    • summary(surveys) - summary statistics for each column

Note: most of these functions are “generic”; they can be used on other types of objects besides data.frame.

Callout

tidyverse vs. base R The tidyverse package is an “umbrella-package” that installs dplyr and several other useful packages for data analysis, such as ggplot2, tibble, etc.

As we begin to delve more deeply into the tidyverse, we should briefly pause to mention some of the reasons for focusing on the tidyverse set of tools. In R, there are often many ways to get a job done, and there are other approaches that can accomplish tasks similar to the tidyverse.

The phrase base R is used to refer to approaches that utilize functions contained in R’s default packages. We have already used some base R functions, such as str(), head(), and mean(), and we will be using more scattered throughout this lesson. However, there are some key base R approaches we will not be teaching. These include square bracket subsetting and base plotting. You may come across code written by other people that looks like surveys[1:10, 2] or plot(surveys$weight, surveys$hindfoot_length), which are base R commands. If you’re interested in learning more about these approaches, you can check out other Carpentries lessons like the Software Carpentry Programming with R lesson.

We choose to teach the tidyverse set of packages because they share a similar syntax and philosophy, making them consistent and producing highly readable code. They are also very flexible and powerful, with a growing number of packages designed according to similar principles and to work well with the rest of the packages. The tidyverse packages tend to have very clear documentation and wide array of learning materials that tend to be written with novice users in mind. Finally, the tidyverse has only continued to grow, and has strong support from RStudio, which implies that these approaches will be relevant into the future.

Data wrangling with dplyr

Next, we’re going to learn some of the most common dplyr functions:

  • select(): subset columns
  • filter(): subset rows on conditions
  • mutate(): create new columns by using information from other columns
  • group_by() and summarize(): create summary statistics on grouped data
  • arrange(): sort results
  • count(): count discrete values

Selecting columns and filtering rows

To select columns of a data frame, use select(). The first argument to this function is the data frame (surveys), and the subsequent arguments are the columns to keep.

R 

select(surveys, plot_id, species_id, weight)

To select all columns except certain ones, put a “-” in front of the variable to exclude it.

R 

select(surveys, -record_id, -species_id)

This will select all the variables in surveys except record_id and species_id.

To choose rows based on a specific criterion, use filter():

R 

filter(surveys, year == 1995)

Pipes

What if you want to select and filter at the same time? There are three ways to do this: use intermediate steps, nested functions, or pipes.

With intermediate steps, you create a temporary data frame and use that as input to the next function, like this:

R 

surveys2 <- filter(surveys, weight < 5)
surveys_sml <- select(surveys2, species_id, sex, weight)

This is readable, but can clutter up your workspace with lots of objects that you have to name individually. With multiple steps, that can be hard to keep track of.

You can also nest functions (i.e. one function inside of another), like this:

R 

surveys_sml <- select(filter(surveys, weight < 5), species_id, sex, weight)

This is handy, but can be difficult to read if too many functions are nested, as R evaluates the expression from the inside out (in this case, filtering, then selecting).

The last option, pipes, are a recent addition to R. Pipes let you take the output of one function and send it directly to the next, which is useful when you need to do many things to the same dataset. There are two Pipes in R: 1) %>% (called magrittr pipe; made available via the magrittr package, installed automatically with dplyr) or 2) |> (called native R pipe and it comes preinstalled with R v4.1.0 onwards). Both the pipes are, by and large, function similarly with a few differences (For more information, check: https://www.tidyverse.org/blog/2023/04/base-vs-magrittr-pipe/). The choice of which pipe to be used can be changed in the Global settings in R studio and once that is done, you can type the pipe with:

  • Ctrl + Shift + M if you have a PC or Cmd + Shift + M if you have a Mac.

R 

surveys %>%
  filter(weight < 5) %>%
  select(species_id, sex, weight)

In the above code, we use the pipe to send the surveys dataset first through filter() to keep rows where weight is less than 5, then through select() to keep only the species_id, sex, and weight columns. Since %>% takes the object on its left and passes it as the first argument to the function on its right, we don’t need to explicitly include the data frame as an argument to the filter() and select() functions any more.

Some may find it helpful to read the pipe like the word “then.” For instance, in the example above, we took the data frame surveys, then we filtered for rows with weight < 5, then we selected columns species_id, sex, and weight. The dplyr functions by themselves are somewhat simple, but by combining them into linear workflows with the pipe we can accomplish more complex manipulations of data frames.

If we want to create a new object with this smaller version of the data, we can assign it a new name:

R 

surveys_sml <- surveys %>%
  filter(weight < 5) %>%
  select(species_id, sex, weight)

surveys_sml

Note that the final data frame is the leftmost part of this expression.

Challenge

Challenge

Using pipes, subset the surveys data to include animals collected before 1995 and retain only the columns year, sex, and weight.

R 

surveys %>%
    filter(year < 1995) %>%
    select(year, sex, weight)

Mutate

Frequently you’ll want to create new columns based on the values in existing columns, for example to do unit conversions, or to find the ratio of values in two columns. For this we’ll use mutate().

To create a new column of weight in kg:

R 

surveys %>%
  mutate(weight_kg = weight / 1000)

You can also create a second new column based on the first new column within the same call of mutate():

R 

surveys %>%
  mutate(weight_kg = weight / 1000,
         weight_lb = weight_kg * 2.2)

If this runs off your screen and you just want to see the first few rows, you can use a pipe to view the head() of the data. (Pipes work with non-dplyr functions, too, as long as the dplyr or magrittr package is loaded).

R 

surveys %>%
  mutate(weight_kg = weight / 1000)

The first few rows of the output are full of NAs, so if we wanted to remove those we could insert a filter() in the chain:

R 

surveys %>%
  filter(!is.na(weight)) %>%
  mutate(weight_kg = weight / 1000)

is.na() is a function that determines whether something is an NA. The ! symbol negates the result, so we’re asking for every row where weight is not an NA.

Challenge

Challenge

Create a new data frame from the surveys data that meets the following criteria: contains only the species_id column and a new column called hindfoot_cm containing the hindfoot_length values (currently in mm) converted to centimeters. In this hindfoot_cm column, there are no NAs and all values are less than 3.

Hint: think about how the commands should be ordered to produce this data frame!

R 

surveys_hindfoot_cm <- surveys %>%
    filter(!is.na(hindfoot_length)) %>%
    mutate(hindfoot_cm = hindfoot_length / 10) %>%
    filter(hindfoot_cm < 3) %>%
    select(species_id, hindfoot_cm)

Split-apply-combine data analysis and the summarize() function

Many data analysis tasks can be approached using the split-apply-combine paradigm: split the data into groups, apply some analysis to each group, and then combine the results. Key functions of dplyr for this workflow are group_by() and summarize().

The group_by() and summarize() functions

group_by() is often used together with summarize(), which collapses each group into a single-row summary of that group. group_by() takes as arguments the column names that contain the categorical variables for which you want to calculate the summary statistics. So to compute the mean weight by sex:

R 

surveys %>%
  group_by(sex) %>%
  summarize(mean_weight = mean(weight, na.rm = TRUE))

You may also have noticed that the output from these calls doesn’t run off the screen anymore. It’s one of the advantages of tbl_df over data frame.

You can also group by multiple columns:

R 

surveys %>%
  group_by(sex, species_id) %>%
  summarize(mean_weight = mean(weight, na.rm = TRUE))

OUTPUT 

#> `summarise()` has grouped output by 'sex'. You can override using the `.groups`
#> argument.

Here, we used tail() to look at the last six rows of our summary. Before, we had used head() to look at the first six rows. We can see that the sex column contains NA values because some animals had escaped before their sex and body weights could be determined. The resulting mean_weight column does not contain NA but NaN (which refers to “Not a Number”) because mean() was called on a vector of NA values while at the same time setting na.rm = TRUE. To avoid this, we can remove the missing values for weight before we attempt to calculate the summary statistics on weight. Because the missing values are removed first, we can omit na.rm = TRUE when computing the mean:

R 

surveys %>%
  filter(!is.na(weight)) %>%
  group_by(sex, species_id) %>%
  summarize(mean_weight = mean(weight))

OUTPUT 

#> `summarise()` has grouped output by 'sex'. You can override using the `.groups`
#> argument.

Here, again, the output from these calls doesn’t run off the screen anymore. If you want to display more data, you can use the print() function at the end of your chain with the argument n specifying the number of rows to display:

R 

surveys %>%
  filter(!is.na(weight)) %>%
  group_by(sex, species_id) %>%
  summarize(mean_weight = mean(weight))

OUTPUT 

#> `summarise()` has grouped output by 'sex'. You can override using the `.groups`
#> argument.

Once the data are grouped, you can also summarize multiple variables at the same time (and not necessarily on the same variable). For instance, we could add a column indicating the minimum weight for each species for each sex:

R 

surveys %>%
  filter(!is.na(weight)) %>%
  group_by(sex, species_id) %>%
  summarize(mean_weight = mean(weight),
            min_weight = min(weight))

OUTPUT 

#> `summarise()` has grouped output by 'sex'. You can override using the `.groups`
#> argument.

It is sometimes useful to rearrange the result of a query to inspect the values. For instance, we can sort on min_weight to put the lighter species first:

R 

surveys %>%
  filter(!is.na(weight)) %>%
  group_by(sex, species_id) %>%
  summarize(mean_weight = mean(weight),
            min_weight = min(weight)) %>%
  arrange(min_weight)

OUTPUT 

#> `summarise()` has grouped output by 'sex'. You can override using the `.groups`
#> argument.

To sort in descending order, we need to add the desc() function. If we want to sort the results by decreasing order of mean weight:

R 

surveys %>%
  filter(!is.na(weight)) %>%
  group_by(sex, species_id) %>%
  summarize(mean_weight = mean(weight),
            min_weight = min(weight)) %>%
  arrange(desc(mean_weight))

OUTPUT 

#> `summarise()` has grouped output by 'sex'. You can override using the `.groups`
#> argument.

Counting

When working with data, we often want to know the number of observations found for each factor or combination of factors. For this task, dplyr provides count(). For example, if we wanted to count the number of rows of data for each sex, we would do:

R 

surveys %>%
  count(sex)

The count() function is shorthand for something we’ve already seen: grouping by a variable, and summarizing it by counting the number of observations in that group. In other words, surveys %>% count() is equivalent to:

R 

surveys %>%
  group_by(sex) %>%
  summarize(count = n())

For convenience, count() provides the sort argument:

R 

surveys %>%
  count(sex, sort = TRUE)

Previous example shows the use of count() to count the number of rows/observations for one factor (i.e., sex). If we wanted to count combination of factors, such as sex and species, we would specify the first and the second factor as the arguments of count():

R 

surveys %>%
  count(sex, species)

With the above code, we can proceed with arrange() to sort the table according to a number of criteria so that we have a better comparison. For instance, we might want to arrange the table above in (i) an alphabetical order of the levels of the species and (ii) in descending order of the count:

R 

surveys %>%
  count(sex, species) %>%
  arrange(species, desc(n))

From the table above, we may learn that, for instance, there are 75 observations of the albigula species that are not specified for its sex (i.e. NA).

Challenge

Challenge

  1. How many animals were caught in each plot_type surveyed?

R 

surveys %>%
  count(plot_type)
Challenge

Challenge (continued)

  1. Use group_by() and summarize() to find the mean, min, and max hindfoot length for each species (using species_id). Also add the number of observations (hint: see ?n).

R 

surveys %>%
  filter(!is.na(hindfoot_length)) %>%
  group_by(species_id) %>%
  summarize(
      mean_hindfoot_length = mean(hindfoot_length),
      min_hindfoot_length = min(hindfoot_length),
      max_hindfoot_length = max(hindfoot_length),
      n = n()
    )
Challenge

Challenge (continued)

  1. What was the heaviest animal measured in each year? Return the columns year, genus, species_id, and weight.

R 

surveys %>%
  filter(!is.na(weight)) %>%
  group_by(year) %>%
  filter(weight == max(weight)) %>%
  select(year, genus, species, weight) %>%
  arrange(year)

Exporting data

Now that you have learned how to use dplyr to extract information from or summarize your raw data, you may want to export these new data sets to share them with your collaborators or for archival.

Similar to the read_csv() function used for reading CSV files into R, there is a write_csv() function that generates CSV files from data frames.

Before using write_csv(), we are going to create a new folder, data, in our working directory that will store this generated dataset. We don’t want to write generated datasets in the same directory as our raw data. It’s good practice to keep them separate. The data_raw folder should only contain the raw, unaltered data, and should be left alone to make sure we don’t delete or modify it. In contrast, our script will generate the contents of the data directory, so even if the files it contains are deleted, we can always re-generate them.

In preparation for our next lesson on plotting, we are going to prepare a cleaned up version of the data set that doesn’t include any missing data.

Let’s start by removing observations of animals for which weight and hindfoot_length are missing, or the sex has not been determined:

R 

surveys_complete <- surveys %>%
  filter(!is.na(weight),           # remove missing weight
         !is.na(hindfoot_length),  # remove missing hindfoot_length
         !is.na(sex))                # remove missing sex

Because we are interested in plotting how species abundances have changed through time, we are also going to remove observations for rare species (i.e., that have been observed less than 50 times). We will do this in two steps: first we are going to create a data set that counts how often each species has been observed, and filter out the rare species; then, we will extract only the observations for these more common species:

R 

## Extract the most common species_id
species_counts <- surveys_complete %>%
    count(species_id) %>%
    filter(n >= 50)

## Only keep the most common species
surveys_complete <- surveys_complete %>%
  filter(species_id %in% species_counts$species_id)

To make sure that everyone has the same data set, check that surveys_complete has 30463 rows and 13 columns by typing dim(surveys_complete).

Now that our data set is ready, we can save it as a CSV file in our data folder.

R 

write_csv(surveys_complete, file = "data/surveys_complete.csv")