Manipulating and analyzing data with tidyverse

1. Data manipulation using dplyr and tidyr

Bracket subsetting is handy, but it can be cumbersome and difficult to read, especially for complicated operations. Enter dplyr. dplyr is a package for making tabular data manipulation easier. It pairs nicely with tidyr which enables you to swiftly convert between different data formats for plotting and analysis.

Packages in R are basically sets of additional functions that let you do more stuff. The functions we’ve been using so far, like str() or data.frame(), come built into R; packages give you access to more of them. 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 RStudio session we are running already has the tidyverse package installed. This is an “umbrella-package” that installs several packages useful for data analysis which work together well such as tidyr, dplyr, ggplot2, tibble, etc.

The tidyverse package tries to address 3 common issues that arise when doing data analysis with some of the functions that come with R:

1. The results from a base R function sometimes depend on the type of data.
2. Using R expressions in a non-standard way, which can be confusing for new learners.
3. Hidden arguments, having default operations that new learners are not aware of.

One example of the advantages of tidyverse is that when working with large CSV files, importing data with read_csv of tidyverse is faster than with read.csv that we used earlier.

Note: To install tidyverse, one could type install.packages("tidyverse") straight into the console. In fact, it would be 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.

To load the package type:

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

2. What are dplyr and tidyr?

The package dplyr provides easy tools for the most common data manipulation tasks, and is built to work directly with data frames.

Note: While we won’t be covering this topic further here, an additional feature is the ability to work directly with data stored in an external database. This addresses a common problem with R in that all operations are conducted in-memory and thus the amount of data you can work with is limited by available memory. The database connections essentially remove that limitation in that you can connect to a database of many hundreds of GB, conduct queries on it directly, and pull back into R only what you need for analysis.

The package tidyr addresses the common problem of wanting to reshape your data for plotting and use by different R functions. Sometimes we want data sets where we have one row per measurement. Sometimes we want a data frame where each measurement type has its own column, and rows are instead more aggregated groups - like plots or aquaria. Moving back and forth between these formats is nontrivial, and tidyr gives you tools for this and more sophisticated data manipulation.

To learn more about dplyr and tidyr after the workshop, you may want to check out this handy data transformation with dplyr cheatsheet and this one about tidyr.

We’ll read in our data using the read_csv() function, from the tidyverse package readr, instead of read.csv().

surveys <- read_csv("/home/rstudio/shared/portal_data_joined.csv")

#> 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.  

## inspect the data
str(surveys)

## preview the data
View(surveys)

The data are now in a format referred to as a “tibble”. Tibbles tweak some of the behaviors of the data frame objects we introduced previously. The data structure is very similar to a data frame. For our purposes the only differences are that:

1. In addition to displaying the data type of each column under its name, it only prints the first few rows of data and only as many columns as fit on one screen.
2. Columns of class character are never converted into factors

Note: in R versions <4.0, converting strings to factors was the default behaviour and had to be prevented when importing data with read.csv by adding the argument stringsAsFactors = FALSE.

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
• summarize(): create summary statistics on (grouped) data
• arrange(): sort results
• count(): count discrete values

3. 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.

select(surveys, plot_id, species_id, weight)

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

select(surveys, -record_id, -species_id)

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

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

filter(surveys, year == 1995)

4. 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:

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:

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, lets you take the output of one function and send it directly to the next. This is useful when you need to do many things to the same dataset. Pipes in R look like |> (often called the native pipe operator or base R pipe) or %>% (the magrittr pipe because it comes from the magrittr package, installed automatically with dplyr). For most purposes both pipes work the same way, but it is better to pick one and use it consistently. In the course materials, we will use the native pipe |>.

If you use RStudio, you can type the pipe with Ctrl + Shift + M if you have a PC or Cmd + Shift + M if you have a Mac. If this gives you the magrittr pipe %>%, you can change the shortcut output to the native pipe |> in the RStudio menu Tools -> Global Options -> Code -> Use native pipe operator.

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 above example, we take the data frame surveys, then we filter for rows with weight < 5, then we select the 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:

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

surveys_sml

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

Challenge

Go through the exercises in Data Manipulation Exercise Block 1 (~5 mins).

5. The mutate function

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:

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():

surveys |>
  mutate(weight_kg = weight / 1000,
         weight_kg2 = weight_kg * 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).

surveys |>
  mutate(weight_kg = weight / 1000) |>
  head()

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:

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

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

Go through Data Manipulation Exercise Block 2 (~5-10 mins).

6. Split-apply-combine and summarize

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. dplyr makes this very easy through the function summarize and the argument .by.

When groups are defined using the argument .by, the functionsummarize() collapses each group into a single-row summary of that 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:

surveys |>
  summarize(mean_weight = mean(weight, na.rm = TRUE), .by = sex)

You may have noticed that the output from these calls doesn’t run off the screen anymore. It’s one of the advantages of the tibble format.

You can also group by multiple columns:

surveys |>
  summarize(mean_weight = mean(weight, na.rm = TRUE), 
  .by = c(sex, species_id))

When grouping both by sex and species_id, the last few rows are for animals that escaped before their sex and body weights could be determined. We can use tail() to look at the last six rows in the data:

surveys |>
  summarize(mean_weight = mean(weight, na.rm = TRUE), 
  .by = c(sex, species_id)) |>
  tail()

You may notice that the last column does not contain NA but NaN (which refers to “Not a Number”). 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:

surveys |>
  filter(!is.na(weight)) |>
  summarize(mean_weight = mean(weight),
  .by = c(sex, species_id))

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:

surveys |>
  filter(!is.na(weight)) |>
  summarize(mean_weight = mean(weight), .by = c(sex, species_id)) |>
  print(n = 15)

Challenge

There are a few other things we can try out using the split-apply-combine paradigm. Let’s try these out by going through the exercises in Data Manipulation Exercise Block 3 (10-15 mins).

7. 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:

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:

surveys |>
    summarise(count = n(), .by = sex)

For convenience, count() provides the sort argument:

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 a combination of factors, such as sex and species, we would specify the first and the second factor as the arguments of count():

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:

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

Let’s have a look at the counting exercises in Data Manipulation Exercise Block 4 (~10 mins).

8. Reshaping with pivot_wider and pivot_longer

When we previously discussed recommended practices for data organisation, we were actually talking about how to structure our data according to the four rules defining a tidy dataset:

1. Each variable has its own column
2. Each observation has its own row
3. Each value must have its own cell
4. Each type of observational unit forms a table (rather than multiple tables)

In surveys , the rows of surveys contain the values of variables associated with each record (the unit), values such as the weight or sex of each animal associated with each record. What if instead of comparing records, we wanted to compare the different mean weight of each species between plots? (Ignoring plot_type for simplicity).

We’d need to create a new table where each row (the unit) is comprised of values of variables associated with each plot. In practical terms this means the values of the species in genus would become the names of column variables and the cells would contain the values of the mean weight observed on each plot.

Having created a new table, it is straightforward to explore the relationship between the weight of different species within, and between, the plots. The key point here is that we are still following a tidy data structure, but we have reshaped the data according to the observations of interest: average species weight per plot instead of recordings per date.

The opposite transformation would be to transform column names into values of a variable.

We can do both these of transformations with two tidyr functions, pivot_wider() and pivot_longer(). These are relatively new functions that offer improvements over two older functions that perform similar tasks: spread() and gather().

8a. From long to wide format using pivot_wider()

pivot_wider() can be used to reshape a data set from long format to wide format. To do this, we need to provide (at least) the following arguments:

1. The data

2. The names_from variable, whose values will fill the new column variables.

3. The values_from variable, whose values will fill the new column variables.

The following image illustrates the process for a randomly selected data set:

Further arguments include values_fill which, if set, fills in missing values with the value provided. More information on arguments accepted by pivot_wider() is provided on the function website.

Let’s use pivot_wider() to transform surveys to find the mean weight of each species in each plot over the entire survey period. First we use filter(), group_by() and summarise() to filter our observations and variables of interest, and create a new variable for the mean_weight. We use the pipe as before too.

surveys_gw <- surveys |>
  filter(!is.na(weight)) |>
  summarize(mean_weight = mean(weight), .by = c(genus, plot_id))

str(surveys_gw)

This yields surveys_gw where the observations for each plot are spread across multiple rows, 196 observations of 3 variables. Using pivot_wider() with names from the column genus and values from mean_weight, this becomes 24 observations of 11 variables, one row for each plot. The results can be organised by plot_id using arrange() and we again use pipes:

surveys_pivotwider <- surveys_gw |>
  pivot_wider(names_from = genus, 
              values_from = mean_weight) |>
  arrange(plot_id)

surveys_pivotwider

8b. From wide to long format using pivot_longer()

The opposing situation could occur if we had been provided with data in the form of surveys_pivotwider (i.e. in wide format), where the genus names are column names, but we wish to treat them as values of a genus variable instead.

In this situation we want to take the column names and turn them into a pair of new variables. One variable represents the column names as values, and the other variable contains the values previously associated with the column names. Instead of the names_from and values_from arguments used by pivot_wider(), we use the arguments names_to and values_to.

To recreate surveys_gw from surveys_pivotwider, we need to assign names back to the genus column and values to a column called mean_weight, using all columns except plot_id to retrieve the values. Here we drop the plot_id column with an exclamation mark sign. Note that, for the newly (re-)created columns, we also need to provide quotation marks.

surveys_pivotlonger <- surveys_pivotwider |>
  pivot_longer(!plot_id, 
               names_to = "genus",
               values_to = "mean_weight") |>
  arrange(genus)

surveys_pivotlonger

This will reshape a data set from wide to long format. We could also have used a specification for what columns to include. This can be useful if you have a large number of identifying columns, and it’s easier to specify which columns to use than what to leave alone. For that we would use the argument cols:

Challenge

Have a look at Data Manipulation Exercise Block 5 (~15 mins).

9. write_csv for data exporting

We previously used the write.csv function call to export our data from R. The package readr (part of the tidyverse) comes with another function for exporting CSV files: write_csv. You might remember that when using write.csv, it was necessary to add an extra argument to remove row numbers from the resulting CSV file (row.names = FALSE). The write_csv function never writes row names, which can simplify things when saving your data for future use.

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:

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. Second, we will extract only the observations for these more common species:

## 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_output folder.

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

To make sure we’re all on the same page, these steps are replicated in Data Manipulation Exercise Block 6. Take some time (~5-10 mins) to run through them.