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.

selected_col <- select(surveys, plot_id, species_id, weight)
head(selected_col)

To choose rows, use filter():

surveys1995 <- filter(surveys, year == 1995)
head(surveys1995)

Pipes

The pipe operator (%>%) from the magrittr package makes it easy to chain these actions together: the output of one function becomes the input of the next.

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

Another cumbersome bit of typing. In RStudio, type Ctrl + Shift + M and the %>% operator will be inserted.

In the above we use the pipe to send the surveys data set first through filter, to keep rows where wgt 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)

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 the square-root of hindfoot_length values (e.g. a new column hindfoot_sqrt). In this hindfoot_sqrt column, there are no NA values and all values are < 3.

Hint: think about how the commands should be ordered

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. group_by() splits the data into groups upon which some operations can be run. 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 to group_by() and counts the total number of records for each category. group_by() is often used together with summarize() which collapses each group into a single-row summary of that group. So to view mean 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))

It looks like most of these species were never weighed. We could then discard rows where mean_weight is NA with filter():

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

Another thing we might do here is sort rows by mean_weight, using arrange().

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

If you want them sorted from highest to lowest, use desc().

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

Also note that you can include multiple summaries.

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

Challenge

How many times was each plot_type surveyed?

Challenge

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

Challenge

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

Hint: Use filter() rather than summarize().

A bit of data cleaning

In preparations for the plotting, let’s do a bit of data cleaning: remove rows with missing species_id, weight, hindfoot_length, or sex.

surveys_complete <- surveys %>%
    filter(species_id != "", !is.na(weight)) %>%
    filter(!is.na(hindfoot_length), sex != "")

There are a lot of species with low counts. Let’s remove the species with less than 10 counts.

# count records per species
species_counts <- surveys_complete %>%
    group_by(species_id) %>%
    tally

head(species_counts)

# get names of the species with counts >= 10
frequent_species <-  species_counts %>%
    filter(n >= 10) %>%
    select(species_id)

# filter out the less-frequent species
surveys_complete <- surveys_complete %>%
    filter(species_id %in% frequent_species$species_id)

Handy dplyr cheatsheet