Learning Objectives

By the end of this lesson the learner will:

Data Manipulation using dplyr

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 data manipulation easier.

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.

install.packages("dplyr")

You might get asked to choose a CRAN mirror – this is basically asking you to choose a site to download the package from. The choice doesn’t matter too much; we recommend the RStudio mirror.

library("dplyr")    ## load the package

What is dplyr?

The package dplyr provides easy tools for the most common data manipulation tasks. It is built to work directly with data frames. The thinking behind it was largely inspired by the package plyr which has been in use for some time but suffered from being slow in some cases.dplyr addresses this by porting much of the computation to C++. An additional feature is the ability to work directly with data stored in an external database. The benefits of doing this are that the data can be managed natively in a relational database, queries can be conducted on that database, and only the results of the query returned.

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 have a database of many 100s GB, conduct queries on it directly, and pull back just what you need for analysis in R.

To learn more about dplyr after the workshop, you may want to check out this handy dplyr cheatsheet.

Selecting columns and filtering rows

We’re going to learn some of the most common dplyr functions: select(), filter(), mutate(), group_by(), and summarize(). 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 choose rows, use filter():

filter(surveys, year == 1995)

Pipes

But what if you wanted to select and filter at the same time? There are three ways to do this: use intermediate steps, nested functions, or pipes. With the intermediate steps, you essentially create a temporary data frame and use that as input to the next function. This can clutter up your workspace with lots of objects. You can also nest functions (i.e. one function inside of another). This is handy, but can be difficult to read if too many functions are nested as the process from inside out. The last option, pipes, are a fairly 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 data set. Pipes in R look like %>% and are made available via the magrittr package installed as part of dplyr.

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

In the above we use the pipe to send the surveys data set first through filter, to keep rows where weight was less than 5, and then through select to keep the species and sex columns. When the data frame is being passed to the filter() and select() functions through a pipe, we don’t need to include it as an argument to these functions anymore.

If we wanted to create a new object with this smaller version of the data we could do so by assigning it a new name:

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

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

Mutate

Frequently you’ll want to create new columns based on the values in existing columns, for example to do unit conversions, or 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)

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 packages are loaded).

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

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

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

is.na() is a function that determines whether something is or is not an NA. The ! symbol negates it, so we’re asking for everything that is not an NA.

Challenge

Create a new dataframe from the survey data that meets the following criteria: contains only the species_id column and a column that contains values that are half the hindfoot_length values (e.g. a new column hindfoot_half). In this hindfoot_half column, there are no NA values and all values are < 30.

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

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. dplyr makes this very easy through the use of the group_by() function.

The summarize() function

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

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

You can group by multiple columns too:

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

When grouping both by “sex” and “species_id”, the first rows are for individuals that escaped before their sex could be determined and weighted. 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, we can omit na.rm=TRUE when computing the mean:

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

You may also have noticed, that the output from these calls don’t run off the screen anymore. That’s because dplyr has changed our data.frame to a tbl_df. This is a data structure that’s very similar to a data frame; for our purposes the only difference is that it won’t automatically show tons of data going off the screen, while displaying the data type for each column under its name. If you want to display more data on the screen, you can add the print() function at the end with the argument n specifying the number of rows to display:

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

Once the data is 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:

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

Tallying

When working with data, it is also common to want to know the number of observations found for each factor or combination of factors. For this, dplyr provides tally(). For example, if we wanted to group by sex and find the number of rows of data for each sex, we would do:

surveys %>%
  group_by(sex) %>%
  tally()

Here, tally() is the action applied to the groups created by group_by() and counts the total number of records for each category.

Challenge

How many individuals were caught in each plot_type surveyed?

Challenge

Use group_by() and summarize() to find the mean, min, and max hindfoot length for each species (using species _id).

Challenge

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

Exporting data

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

Similarly to the read.csv() function used to read in CSV into R, there is a write.csv() function that generates CSV files from data frames.

Before using it, we are going to create a new folder, data_output 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 folder should only contain the raw, unaltered data, and should be left alone to make sure we don’t delete or modify it; on the other end the content of data_output directory will be generated by our script, and we know that we can delete the files it contains because we have the script that can re-generate these files.

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

Let’s start by removing observations for which the species_id is missing. In this dataset, the missing species are represented by an empty string and not an NA. Let’s also remove observations for which weight and the hindfoot_length are missing. This dataset will also only contain observations of animals for which the sex has been determined:

surveys_complete <- surveys %>%
  filter(species_id != "",         # remove missing species_id
         !is.na(weight),           # remove missing weight
         !is.na(hindfoot_length),  # remove missing hindfoot_length
         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 dataset 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:

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

## 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 dataset, check that surveys_complete has 30463 rows and 13 columns by typing dim(surveys_complete).

Now that our dataset is ready, we can save it as a CSV file in our data_output folder. By default, write.csv() includes a column with row names (in our case the names are just the row numbers), so we need to add row.names = FALSE so they are not included:

write.csv(surveys_complete, file="data_output/surveys_complete.csv",
          row.names=FALSE)