Learning Objectives

  • Understand the purpose of the dplyr and tidyr packages.
  • Select certain columns in a data frame with the dplyr function select.
  • Select certain rows in a data frame according to filtering conditions with the dplyr function filter .
  • Link the output of one dplyr function to the input of another function with the ‘pipe’ operator %>%.
  • Add new columns to a data frame that are functions of existing columns with mutate.
  • Understand the split-apply-combine concept for data analysis.
  • Use summarize, group_by, and tally to split a data frame into groups of observations, apply a summary statistics for each group, and then combine the results.
  • Understand the concept of a wide and a long table format and for which purpose those formats are useful.
  • Understand what key-value pairs are.
  • Reshape a data frame from long to wide format and back with the spread and gather commands from the tidyr package.
  • Export a data frame to a .csv file.

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. You should already have installed the tidyverse package. This is an “umbrella-package” that installs several packages useful for data analysis which work together well such as tidyr, dplyr, ggplot2, etc. To load the package type:

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

What are dplyr and tidyr?

The package dplyr provides easy tools for the most common data manipulation tasks. It is built to work directly with data frames, with many common tasks optimized by being written in a compiled language (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 are 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 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.

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 based on a specific criteria, 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 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 things are evaluated from the 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 dataset. Pipes in R look like %>% and are made available via the magrittr package, installed automatically with dplyr. 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.

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

In the above, 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 it as an argument to the filter() and select() 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 survey data to include individuals collected before 1995 and retain only 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)

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, as long as the dplyr or magrittr package is loaded).

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

Note that we don’t include parentheses at the end of our call to head() above. When piping into a function with no additional arguments, you can call the function with or without parentheses (e.g. head or 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 everything that is not an NA.

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_half containing values that are half the hindfoot_length values. In this hindfoot_half column, there are no NAs and all values are less than 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 arguments the column names that contain the categorical variables for which you want to calculate the summary statistics. So to view the mean weight by sex:

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. That’s because dplyr has changed our data.frame object to an object of class tbl_df, also known as a “tibble”. Tibble’s 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.

You can also group by multiple columns:

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))

Here, again, the output from these calls doesn’t run off the screen anymore. Recall that dplyr has changed our object fromdata.frame to tbl_df. 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)) %>%
  group_by(sex, species_id) %>%
  summarize(mean_weight = mean(weight)) %>%
  print(n = 15)

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:

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

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

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

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

  4. You saw above how to count the number of individuals of each sex using a combination of group_by() and tally(). How could you get the same result using group_by() and summarize()? Hint: see ?n.

Reshaping with gather and spread

dplyr is one part of a larger tidyverse that enables you to work with data in tidy data formats. tidyr enables a wide range of manipulations of the structure data itself. For example, the survey data presented here is in almost in what we call a long format - every observation of every individual is its own row. This is an ideal format for data with a rich set of information per observation. It makes it difficult, however, to look at the relationships between measurements across plots. For example, what is the relationship between mean weights of different genera across the entire data set?

To answer that question, we’d want each plot to have a single row, with all of the measurements in a single plot having their own column. This is called a wide data format. For the surveys data as we have it right now, this is going to be one heck of a wide data frame! However, if we were to summarize data within plots and species, we might begin to have some relationships we’d want to examine.

Let’s see this in action. First, using dplyr, let’s create a data frame with the mean body weight of each genera by plot.

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

head(surveys_gw)

Long to Wide with spread

Now, to make this long data wide, we use spread from tidyr to spread out the different taxa into columns. spread takes three arguments - the data, the key column, or column with identifying information, the values column - the one with the numbers. We’ll use a pipe so we can ignore the data argument.

surveys_gw_wide <- surveys_gw %>%
  spread(genus, mean_weight)

head(surveys_gw_wide)

Notice that some genera have NA values. That’s because some of those genera don’t have any record in that plot. Sometimes it is fine to leave those as NA. Sometimes we want to fill them as zeros, in which case we would add the argument fill=0.

surveys_gw %>%
  spread(genus, mean_weight, fill = 0) %>%
  head

We can now do things like plot the weight of Baiomys against Chaetodipus or examine their correlation.

surveys_gw %>%
  spread(genus, mean_weight, fill = 0) %>%
  cor(use = "pairwise.complete")

Wide to long with gather

What if we had the opposite problem, and wanted to go from a wide to long format? For that, we use gather to sweep up a set of columns into one key-value pair. We give it the arguments of a new key and value column name, and then we specify which columns we either want or do not want gathered up. So, to go backwards from surveys_gw_wide, and exclude plot_id from the gathering, we would do the following:

surveys_gw_long <- surveys_gw_wide %>%
  gather(genus, mean_weight, -plot_id)

head(surveys_gw_long)

Note that now the NA genera are included in the long format. Going from wide to long to wide can be a useful way to balance out a dataset so every replicate has the same composition.

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 what to gather than what to leave alone. And if the columns are in a row, we don’t even need to list them all out - just use the : operator!

surveys_gw_wide %>%
  gather(genus, mean_weight, Baiomys:Spermophilus) %>%
  head

Challenge

  1. Make a wide data frame with year as columns, plot_id as rows, and the values are the number of genera per plot. You will need to summarize before reshaping, and use the function n_distinct to get the number of unique types of a genera. It’s a powerful function! See ?n_distinct for more.

  2. Now take that data frame, and make it long again, so each row is a unique plot_id year combination.

  3. The surveys data set is not truly wide or long because there are two columns of measurement - hindfoot_length and weight. This makes it difficult to do things like look at the relationship between mean values of each measurement per year in different plot types. Let’s walk through a common solution for this type of problem. First, use gather to create a truly long dataset where we have a key column called measurement and a value column that takes on the value of either hindfoot_length or weight. Hint: You’ll need to specify which columns are being gathered.

  4. With this new truly long data set, calculate the average of each measurement in each year for each different plot_type. Then spread them into a wide data set with a column for hindfoot_length and weight. Hint: Remember, you only need to specify the key and value columns for spread.

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 datasets 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_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. In contrast, our script will generate the contents of the data_output 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 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 should 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)

## 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)

Page build on: 2017-08-10 13:02:22

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