Homework 8 Key

Code

knitr::opts_chunk$set(comment = ">")

library(tidyverse)
library(ggthemes)

load(url("https://github.com/vsass/CSSS508/raw/main/Lectures/Lecture7/data/restaurants.Rdata"))

Answer 1

Given a vector of birthdates¹, write a function to compute the ages in years. Test your function!

age_from_bday <- function(birthdate){
  (today() - mdy(birthdate)) |> time_length(unit = "year") |> round(2)
}

age_from_bday(c("12/05/1980", "November 6, 1983", "Jan 30, 2020", "5-28-23"))

> [1] 42.99 40.07  3.84  0.51

Answer 2

For each of the following functions list all arguments that use tidy evaluation and describe whether they use data-masking or tidy-selection: distinct(), count(), group_by()², rename_with(), slice_min(), slice_sample().

Function	Argument(s)	Tidy-Evaluation Type
`distinct()`	`...`	data-masking
`count()`	`...`	data-masking
	`wt`	data-masking
`group_by()`	`...`	data-masking
`rename_with()`	`.cols`	tidy-select
`slice_min()`	`order_by`	data-masking
	`by`	tidy-select
`slice_sample()`	`by`	tidy-select
	`weight_by`	data-masking

Answer 3:

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

Draw a scatterplot given dataset and x and y variables.
Add a line of best fit (i.e. a linear model with no standard errors).
Add a title that includes the names of the variables used.

Test your function with the built-in datasets cars, swiss, and iris.

scatterplot <- function(dataset, x, y){
  ggplot(data = dataset, aes(x = {{ x }}, y = {{ y }})) + 
    geom_jitter() + 
    geom_smooth(method = lm, se = FALSE) + 
    labs(title = rlang::englue("Relationship between {{ x }} and {{ y }}"), 
         x = str_to_title(rlang::englue("{{ x }}")), 
         y = str_to_title(rlang::englue("{{ y }}"))) + 
    theme_tufte()
}

scatterplot(cars, speed, dist)
scatterplot(swiss, Education, Fertility)
scatterplot(iris, Sepal.Length, Sepal.Width)

Footnotes

Format in mdy().↩︎
The documentation for group_by() doesn’t explicitly tell you which tidy evaluation method it uses so check out this resource to figure it out.↩︎