05-Data-Types.Rmd

---
title: "Data Types"
output: html_notebook
---

```{r setup}
library(tidyverse)
library(babynames)
library(nycflights13)
library(stringr)
library(forcats)
library(lubridate)
library(hms)
```

## Your Turn 1

Use `flights` to create `delayed`, the 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}

```


## Your Turn 2

In your group, fill in the blanks to:

1. Isolate the last letter of every name and create a logical variable that displays whether the last letter is one of "a", "e", "i", "o", "u", or "y".  
2. Use a weighted mean to calculate the proportion of children whose name ends in a vowel (by `year` and `sex`)   
3. and then display the results as a line plot.

```{r}
babynames %>%
  _______(last = _________, 
          vowel = __________) %>%
  group_by(__________) %>%
  _________(p_vowel = weighted.mean(vowel, n)) %>%
  _________ +
  __________
```

## Your Turn 3

Repeat the previous exercise, some of whose code is below, to make a sensible graph of average TV consumption by marital status.

```{r}
gss_cat %>%
  drop_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}

```

## Your Turn 5

What is the best time of day to fly?

Use the `hour` and `minute` variables in `flights` to compute the time of day for 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

Fill in the blanks to:

Extract the day of the week of each flight (as a full name) from `time_hour`. 

Calculate the average `arr_delay` by day of the week.

Plot the results as a column chart (bar chart) with `geom_col()`.

```{r}
flights %>% 
  mutate(weekday = _______________________________) %>% 
  __________________ %>% 
  drop_na(arr_delay) %>% 
  summarise(avg_delay = _______________) %>% 
  ggplot() +
    ___________(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