---
title: "Data Types"
---

```{r}
#| 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

```{r}

```

# 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)*

```{r}
#| 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)*

```{r}
#| 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.

```{r}

```

# 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`.

```{r}

```

## 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)*

```{r}
#| 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