---
title: 'HW06 Part 1: Complete the sections'
author: "your name"
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
  html_document:
    df_print: paged
editor_options:
  chunk_output_type: inline
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

- Change "your name" in the YAML header above to your name.

- As usual, enter the examples in code chunks and run them, unless told otherwise.

## Chapter 10: Tibbles

Read [R4ds Chapter 10: Tibbles](https://r4ds.had.co.nz/tibbles.html), sections 1-3.

### 10.1: Introduction

Load the tidyverse package. 

```{r echo=FALSE}
library(tidyverse)
```


### 10.2: Creating tibbles

Enter your code chunks for Section 10.2 here.

Describe what each chunk code does. 


### 10.3: Tibbles vs data.frame

Enter your code chunks for Section 10.2 here.

Describe what each chunk code does. 


### 10.4: Not required

#### Section 10.5 Questions

Answer the questions *completely.* Use code chunks, text, or both, as necessary.

**1:** How can you tell if an object is a tibble? (Hint: try printing `mtcars`, which is a regular data frame). Identify at least two ways to tell if an object is a tibble. *Hint:* What does `as_tibble()` do? What does `class()` do? What does `str()` do?



**2:** Compare and contrast the following operations on a data.frame and equivalent tibble. What is different? Why might the default data frame behaviours cause you frustration?

```{r}
df <- data.frame(abc = 1, xyz = "a")
df$x
df[, "xyz"]
df[, c("abc", "xyz")]
```


## Chapter 11: Importing data

Read [R4ds Chapter 11: Data Import](https://r4ds.had.co.nz/data-import.html), sections 1, 2, and 5.

### 11.1 Introduction

Nothing to do here unless you took a break and need to reload `tidyverse`.

### 11.2 Getting started.

Do *not* run the first code chunk of this section, which begins with `heights <- read_csv("data/heights.csv")`. You do not have that data file so the code will not run.

Enter and run the remaining chunks in this section.


#### 11.2 Questions

**1:** What function would you use to read a file where fields were separated with "|"?



**2:** (This question is modified from the text.) Finish the two lines of `read_delim` code so that the first one would read a comma-separated file and the second would read a tab-separated file. You only need to worry about the delimiter. Do not worry about other arguments. Replace the dots in each line with the rest of your code. 

# Comma-separated
`file <- read_delim("file.csv", ...)`

# Tab-separated
`file <- read_delim("file.csv", ...)`


**3:** What are the two most important arguments to `read_fwf()`? Why?


**4:** Skip this question


**5: ** Identify what is wrong with each of the following inline CSV files. What happens when you run the code?

```{r}
read_csv("a,b\n1,2,3\n4,5,6")
read_csv("a,b,c\n1,2\n1,2,3,4")
read_csv("a,b\n\"1")
read_csv("a,b\n1,2\na,b")
read_csv("a;b\n1;3")
```

### 11.3 and 11.4: Not required

### 11.5: Writing to a file

Just read this section. You may find it helpful in the future to save a data file to your hard drive. It is basically the same format as reading a file, except that you must specify the data object to save, in addition to the path and file name.

### 11.6 Not required

## Chapter 18: Pipes

Read [R4ds Chapter 18: Pipes](https://r4ds.had.co.nz/pipes.html), sections 1-3.

Nothing to do otherwise for this chapter. Is this easy or what?

**Note:** Trying using pipes for all of the remaining examples. That will help you understand them.

## Chapter 12: Tidy Data

Read [R4ds Chapter 12: Tidy Data](https://r4ds.had.co.nz/tidy-data.html), sections 1-3, 7. 

### 12.1 Introduction

Nothing to do here unless you took a break and need to reload the `tidyverse.`

### 12.2 Tidy data

Study Figure 12.1 and relate the diagram to the three rules listed just above them. Relate that back to the example I gave you in the notes. Bear this in mind as you make data tidy in the second part of this assignment.

You do not have to run any of the examples in this section.

### 12.3

Read from the start of the chapter through 12.3.1. Run the examples in section 12.3.1 (Longer), including the example with `left_join()`. We'll cover joins later. Skip section 12.3.2.

#### 12.3.3 Questions

**2:** Why does this code fail? Fix it so it works.

```{r error=TRUE}
table4a %>% 
  pivot_longer(c(1999,2000), names_to = "year", values_to = "cases")
#> Error in inds_combine(.vars, ind_list): Position must be between 0 and n
```

**4:** Tidy the simple tibble below using `pivot_longer()`. Use suitable names for `names_to` and `values_to` arguments. Enter your code to tidy the data in this chunk, after the code to create the `preg` tibble.

```{r}
preg <- tribble(
  ~pregnant, ~male, ~female,
  "yes",     NA,    10,
  "no",      20,    12
)
```

Skip the rest of Chapter 12. On to the last chapter.


## Chapter 5: Data transformation

Read [R4ds Chapter 5: Data Transformation](https://r4ds.had.co.nz/transform.html), sections 1-4.

Time to [get small.](https://www.youtube.com/watch?v=GOrdzCHnpw4) 

### 5.1: Introduction

Load the necessary libraries. As usual, type the examples into and run the code chunks.

### 5.2: Filter rows with `filter()`

Study Figure 5.1 carefully. Once you learn the `&`, `|`, and `!` logic, you will find them to be very powerful tools.


#### 5.2 Questions

**1.1:** Find all flights with a delay of 2 hours or more.


**1.2:** Flew to Houston (IAH or HOU)


**1.3:** Were operated by United (UA), American (AA), or Delta (DL).


**1.4:** Departed in summer (July, August, and September).


**1.5:** Arrived more than two hours late, but didn’t leave late.

**1.6:** Were delayed by at least an hour, but made up over 30 minutes in flight. This is a tricky one. Do your best.


**1.7:** Departed between midnight and 6am (inclusive)


**2:** Another useful dplyr filtering helper is `between()`. What does it do? Can you use it to simplify the code needed to answer the previous challenges?


**3:** How many flights have a missing dep_time? What other variables are missing? What might these rows represent?


**4:** Why is `NA ^ 0` not missing? Why is `NA | TRUE` not missing? Why is `FALSE & NA` not missing? Can you figure out the general rule? (`NA * 0` is a tricky counterexample!)

**Note:** For some context, see [this thread](https://blog.revolutionanalytics.com/2016/07/understanding-na-in-r.html)


### 5.3 Arrange with `arrange()`


#### 5.3 Questions

**1:** How could you use `arrange()` to sort all missing values to the start? (Hint: use is.na()). **Note:** This one should still have the earliest departure dates after the `NA`s. *Hint:* What does `desc()` do?


**2:** Sort flights to find the most delayed flights. Find the flights that left earliest. 

This question is asking for the flights that were most delayed (left latest after scheduled departure time) and least delayed (left ahead of scheduled time).


**3:** Sort flights to find the fastest flights. Interpret fastest to mean shortest time in the air.


*Optional challenge:* fastest flight could refer to fastest air speed. Speed is measured in miles per hour but time is minutes. Arrange the data by fastest air speed.


**4:** Which flights travelled the longest? Which travelled the shortest?


### 5.4 Select columns with `select()`

#### 5.4 Questions

**1:** Brainstorm as many ways as possible to select `dep_time`, `dep_delay`, `arr_time`, and `arr_delay` from flights. Find at least three ways.


**2:** What happens if you include the name of a variable multiple times in a `select()` call?



**3:** What does the `one_of()` function do? Why might it be helpful in conjunction with this vector?

`vars <- c("year", "month", "day", "dep_delay", "arr_delay")`



**4:** Does the result of running the following code surprise you? How do the select helpers deal with case by default? How can you change that default?

`select(flights, contains("TIME"))`