8 · Types (exercises) — source
A. Stamm 
This page shows the source file of the exercise below. Use the button to download it as a plain file and run it in your own workspace.
---
title: "Data Types"
---
```cdf
#| label: setup
#| include: false
library(tidyverse)
library(babynames)
library(nycflights13)
library(lubridate)
library(hms)
```
# Logicals
## Your Turn 1
Use `flights` to create `delayed`, a variable that displays whether a flight was delayed (`arr_delay > 0`).
Then, remove all rows that contain an NA in `delayed`.
Finally, create a summary table that shows:
1. How many flights were delayed
2. What proportion of flights were delayed
```cdf
```
# Strings
## Your Turn 2
Fill in the blanks to:
1. Isolate the last letter of every name
2. Create a logical variable that displays whether the last letter is one of "a", "e", "i", "o", "u", or "y".
3. Use a weighted mean to calculate the proportion of children whose name ends in a vowel (by `year` and `sex`)
4. and then display the results as a line plot.
*(Hint: Be sure to remove each `_` before turning eval to true)*
```cdf
#| eval: false
babynames |>
_______(last = _________,
vowel = __________) |>
group_by(__________) |>
_________(p_vowel = weighted.mean(vowel, n)) |>
_________ +
__________
```
# Factors
## Your Turn 3
Repeat the demonstration, some of whose code is below, to make a sensible graph of average TV consumption by marital status.
*(Hint: Be sure to remove each `_` before turning eval to true)*
```cdf
#| eval: false
gss_cat |>
filter(_is.na(________)) |>
group_by(________) |>
summarise(_________________) |>
ggplot() +
geom_point(mapping = aes(x = _______, y = _________________________))
```
## Your Turn 4
Do you think liberals or conservatives watch more TV?
Compute average tv hours by party ID an then plot the results.
```cdf
```
# Dates and Times
## Your Turn 5
What is the best time of day to fly?
Use the `hour` and `minute` variables in `flights` to make a new variable that shows the time of each flight as an hms.
Then use a smooth line to plot the relationship between time of day and `arr_delay`.
```cdf
```
## Your Turn 6
What is the best day of the week to fly?
Look at the code skeleton for Your Turn 7. Discuss with your neighbor:
* What does each line do?
* What will the missing parts need to do?
## Your Turn 7
Fill in the blank to:
Extract the day of the week of each flight (as a full name) from `time_hour`.
Plot the average arrival delay by day as a column chart (bar chart).
*(Hint: Be sure to remove each `_` before turning eval to true)*
```cdf
#| eval: false
flights |>
mutate(weekday = _______________________________) |>
group_by(weekday) |>
filter(!is.na(arr_delay)) |>
summarise(avg_delay = mean(arr_delay)) |>
ggplot() +
geom_col(mapping = aes(x = weekday, y = avg_delay))
```
***
# Take Aways
Dplyr gives you three _general_ functions for manipulating data: `mutate()`, `summarise()`, and `group_by()`. Augment these with functions from the packages below, which focus on specific types of data.
Package | Data Type
--------- | --------
stringr | strings
forcats | factors
hms | times
lubridate | dates and times