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  • Goals

The goal of this module is to give a brief introduction to the world of quick data analysis using dplyr, to serve as reference when working with your own datasets.

By the end of this lesson, you should be able to:

  1. use dplyr examples to understand the principles behind this method of data analysis
  2. Streamline and increase legibility of code by using pipes
  • Summarizing and Operating: the dPlyr world

For this section, let’s load the file mammal_stats.csv again. This is a subset of a “species-level database of extant and recently extinct mammals.

You may already have this loaded in your current R Studio Session, but if not, you know the drill:

    mammals <- read.csv('mammals.csv')

You’ll notice that as we work on larger datasets, viewing and visualizing the entire dataset can become more and more difficult. Similarly, analyzing the datasets becomes more complex. While SQL gives us useful tools for managing big datasets, There are other tools in R that I find helpful for dealing with medium-size datasets such as this - both for stats and for visualization.

The answer lies in a handy library called dplyr. dplyr will allow us to perform more complex operations on single dataframes in intuitive ways.

First off, though, let’s explore some very handy sorting and viewing functions in dplyr. glimpse() is a quick and pretty alternative to head():


If i want to shrink the dataset, we can select() columns. We can do that either manually (by naming the columns we want), or by using an operation. where the column name contains() a certain string, or starts_with() or ends_with() one.

    head(select(mammals, order, species)) #narrows down to these two columns
    head(select(mammals, species, starts_with("adult"))) #the column species, and any column that starts with "adult"
    head(select(mammals, -order)) #every row, except `Order`.

We can also select certain rows using the function filter(). As rows aren’t named the same way columns are, we will instead use the logical operators >, < , ==, etc. to select the rows we want.

    filter(mammals, order == "Carnivora") # only carnivores
    filter(mammals, order == "Carnivora" & adult_body_mass_g < 5000) # only carnivores smaller than 5kg
    filter(mammals, order == "Carnivora" | adult_body_mass_g <= 5000) #Any carnivore or animal less than or equal to 5kg

We can also arrange the rows in a dataset based on whichever column you want, using arrange().

    head(arrange(mammals, adult_body_mass_g)) #row 1 is the smallest mammals, the bumblebee bat.
    head(arrange(mammals, desc(adult_body_mass_g))) #sorts by descending. row 1 is the blue whale.
    head(arrange(mammals, order, adult_body_mass_g)) #sorts first alphabetically by order, then by mass within order.

You can see how these can be immediately helpful for certain tasks. A lot of these functions are doable in different ways as well (i.e. logical indexing), but using these function can improve the legibility of your code.

EXERCISE 1 - animals

What is the heaviest carnivore? Plot the masses and lengths of JUST the marine mammals. Use ggplot!

The bumblebee bat. Wikipedia Commmons

With these large datasets, dplyr also lets you quickly summarize the data. It operates on a principle called split - apply - recombine which is a key component of the tidyverse: we will split up the data, apply some sort of operation, and combine the results to display them.

Suppose we want to find the average body masss of each order. We first want to split up the data by order using the function group_by(), apply the mean() function to the column adult_body_mass_g, and report all of the results using the function summarise().

    a <- group_by(mammals, order)
    summarize(a, mean_mass = mean(adult_body_mass_g, na.rm = TRUE))

To we can add other functions here, such as max(), min(), and sd().

    summarize(a, mean_mass = mean(adult_body_mass_g, na.rm = TRUE), sd_mass = sd(adult_body_mass_g, na.rm = TRUE))

summarize makes a new dataset, but mutate will add these columns instead to the original dataframe.

    a <- group_by(mammals, order)
    mutate(a, mean_mass = mean(adult_body_mass_g, na.rm = TRUE))

This outputs the same numbers as the equivalent summarize function, but puts them in a new column on the same dataset.

What if we want to figure out how the mass of each animal relates to other animals of its order? To do this, we will divide each species’ body mass by its order’s mean body mass.

    a <- group_by(mammals, order)
    mutate(a, mean_mass = mean(adult_body_mass_g, na.rm = TRUE), normalized_mass= a dult_body_mass_g / mean_mass)

Using Pipes for code streamlining

You might be noticing that in each of these examples, we are feeding the result of the first line into the second line, using a as an intermediate variable. While this is functional, there is a more legible solution called Pipes. Pipes uses the operation %>% to push the results of one line to the next. for example, instead of writing

   a = group_by(mammals, order)
   mutate(a, mean_mass <- mean(adult_body_mass_g, na.rm = TRUE))

we would write

    a = mammals %>%  #take the mammals data
        group_by(order) %>% #split it up by "order"
        mutate(mean_mass = mean(adult_body_mass_g, na.rm = TRUE))

This can make it easy to follow the logical workflow, which makes more and more sense as your operations become more complex. Suppose we want to find the organisms with the biggest mass relative to the rest of its order. We want to split the data by order, apply the mutate functions from above, sort by normalized_mass, and only display the species, adult_body_mass_g, and normalized_mass columns. In longhand it would look like this:

    a = group_by(mammals, order)
    b = mutate(a, mean_mass = mean(adult_body_mass_g, na.rm = TRUE), normalized_mass = adult_body_mass_g / mean_mass)
    c = arrange(b, desc(normalized_mass))
    d = select(c, species, normalized_mass)

pipes makes it less messy by reducing the number of variables:

    e = mammals %>%
        group_by(order) %>%
        mutate(mean_mass = mean(adult_body_mass_g, na.rm = TRUE),
        normalized_mass = adult_body_mass_g / mean_mass) %>%
        arrange(desc(normalized_mass)) %>%
        select(species, normalized_mass, adult_body_mass_g)

This lets us see that many of the animals relatively large for their size are rodents. It seems to make sense that the smaller your order’s average mass, the easier it would be to be 116x larger than the average!

EXERCISE 2 - Data exploration. Try to use pipes!

Which order has the most species? Which order has the widest range of body mass (max-min)? Which species of carnivore has the largest body length to body mass ratio? (Hint: that’s adult_head_body_len_mm / adult_body_mass_g')

Hint For “the most species”, try length(species).

Sources and Umi’s additional tips/tricks:

  • A great set of slides that expounds on this: Date Wrangling in R
  • This is where I learned dplyr: Sean Anderson. He actually helped me over twitter in suggesting dPlyr - so a shout-out to him for being awesome and accessible!
  • This is another dplyr tutorial that may help in addition to that first one: Kevin Markham
  • Sometimes dPlyr might not do exactly what you want. In reality, dPlyr is a streamlined sequel of an older, slightly more powerful (but slower) library called plyr. Sean Anderson’s plyr tutorial. While dplyr always takes in a dataframe and outputs a dataframe (summarize and mutate), plyr can take in a dataframe, list, or array and output a dataframe, list, or array. There are also individual R functions that go from array to array (apply) or data frame to data frame (aggregate) but plyr brings them all under one roof for easier syntax.