Exercise 05

Exercise due on 2024-10-23

ℹ️ See week 5 for related slides and readings

1 Overview

This week’s exercises are excerpted from Ch. 25 Functions in R for Data Science. This exercise is practice for writing three types of functions:

• Mutate (or vector) functions
• Data frame functions
• Plotting functions

2 Mutate (or vector) functions

Practice turning the following code snippets into functions. Think about what each function does. What would you call it? How many arguments does it need?

mean(is.na(x))
mean(is.na(y))
mean(is.na(z))

x / sum(x, na.rm = TRUE)
y / sum(y, na.rm = TRUE)
z / sum(z, na.rm = TRUE)

round(x / sum(x, na.rm = TRUE) * 100, 1)
round(y / sum(y, na.rm = TRUE) * 100, 1)
round(z / sum(z, na.rm = TRUE) * 100, 1)

Given a vector of birthdates, write a function to compute the age in years.

The {lubridate} package makes working with dates in R much easier. Take a look at the package Getting started guide or Ch. 17 Dates and Times from R4DS for more information.

3 Data frame functions

Using the datasets from {nycflights13}, write a function that:

1. Finds all flights that were cancelled (i.e. is.na(arr_time)) or delayed by more than an hour.

flights |> filter_severe()

2. Counts the number of cancelled flights and the number of flights delayed by more than an hour.

flights |> group_by(dest) |> summarize_severe()

3. Finds all flights that were cancelled or delayed by more than a user supplied number of hours:

flights |> filter_severe(hours = 2)

4. Summarizes the weather to compute the minimum, mean, and maximum, of a user supplied variable:

weather |> summarize_weather(temp)

5. Converts the user supplied variable that uses clock time (e.g., dep_time, arr_time, etc.) into a decimal time (i.e. hours + (minutes / 60)).

flights |> standardize_time(sched_dep_time)

Question 4 and 5 may require an understanding of tidy evaluation using the “embracing” syntax. Go back and review our reading if you are not sure how this works!

4 Plotting functions

Build up a rich plotting function by incrementally implementing each of the steps below:

1. Draw a map using an sf object.

2. Map an variable from the data to a single aesthetic attribute, such as fill, color, or size.

3. Add annotation to the map using geom_sf_text() or geom_sf_label(). You could add labels to the highest or lowest values or add labels based on a second variable.

4. Optional: Apply a spatial or geometric transformation function, e.g. sf::st_centroid(), sf::st_filter(), or sf::st_buffer(), to part or all of the input data before plotting. Consider how spatial transformation can be part of the process of visualizing spatial data.

5. Add a title and labels. Your function should allow a use to customize the labels as needed.

Please give your function an appropriate name and provide one or more examples showing how your function works with different input data sources.

plot_function <- function(
  # Function arguments
  ...
  ) {
  # Body of the function
}