02-Transform-Data.Rmd

---
title: "Transform Data"
output: html_notebook
---

```{r setup}
library(tidyverse)
library(babynames)
library(nycflights13)

# Toy datasets to use

pollution <- tribble(
       ~city,   ~size, ~amount, 
  "New York", "large",      23,
  "New York", "small",      14,
    "London", "large",      22,
    "London", "small",      16,
   "Beijing", "large",      121,
   "Beijing", "small",      56
)

band <- tribble(
   ~name,     ~band,
  "Mick",  "Stones",
  "John", "Beatles",
  "Paul", "Beatles"
)

instrument <- tribble(
    ~name,   ~plays,
   "John", "guitar",
   "Paul",   "bass",
  "Keith", "guitar"
)

instrument2 <- tribble(
    ~artist,   ~plays,
   "John", "guitar",
   "Paul",   "bass",
  "Keith", "guitar"
)
```

## babynames

```{r}
babynames
```


## Your Turn 1

Alter the code to select just the `n` column:

```{r}
select(babynames, name, prop)
```

## Quiz

Which of these is NOT a way to select the `name` and `n` columns together?

```{r}
select(babynames, -c(year, sex, prop))
select(babynames, name:n)
select(babynames, starts_with("n"))
select(babynames, ends_with("n"))
```

## Your Turn 2

Show:

* All of the names where prop is greater than or equal to 0.08  
* All of the children named "Sea"  
* All of the names that have a missing value for `n`  

```{r}
filter(babynames, is.na(n))

```

## Your Turn 3

Use Boolean operators to alter the code below to return only the rows that contain:

* Girls named Sea  
* Names that were used by exactly 5 or 6 children in 1880  
* Names that are one of Acura, Lexus, or Yugo

```{r}
filter(babynames, name == "Sea" | name == "Anemone")
```

## Your Turn 4

Arrange babynames by `n`. Add `prop` as a second (tie breaking) variable to arrange on. Can you tell what the smallest value of `n` is?

```{r}

```

## Your Turn 5

Use `desc()` to find the names with the highest prop.
Then, use `desc()` to find the names with the highest n.

```{r}

```

## Your Turn 6

Use `%>%` to write a sequence of functions that: 

1. Filter babynames to just the girls that were born in 2015  
2. Select the `name` and `n` columns  
3. Arrange the results so that the most popular names are near the top.

```{r}

```

## Exam

1. Trim `babynames` to just the rows that contain your `name` and your `sex`  
2. Trim the result to just the columns that will appear in your graph (not strictly necessary, but useful practice)  
3. Plot the results as a line graph with `year` on the x axis and `prop` on the y axis

```{r}

```

## Your Turn 7

Use summarise() to compute three statistics about the data:

1. The first (minimum) year in the dataset  
2. The last (maximum) year in the dataset  
3. The total number of children represented in the data

```{r}

```

## Your Turn 8

Extract the rows where `name == "Khaleesi"`. Then use `summarise()` and a summary functions to find:

1. The total number of children named Khaleesi
2. The first year Khaleesi appeared in the data

```{r}

```

## Your Turn 9

Use `group_by()`, `summarise()`, and `arrange()` to display the ten most popular names. Compute popularity as the total number of children of a single gender given a name.

```{r}

```

## Your Turn 10

Use grouping to calculate and then plot the number of children born each year over time.

```{r}

```

## Your Turn 11

Use `min_rank()` and `mutate()` to rank each row in `babynames` from largest `n` to lowest `n`.

```{r}

```

## Your Turn 12

Compute each name's rank _within its year and sex_. 
Then compute the median rank _for each combination of name and sex_, and arrange the results from highest median rank to lowest.

```{r}

```

## Your Turn 13

Which airlines had the largest arrival delays? Complete the code below.

1. Join `airlines` to `flights`
2. Compute and order the average arrival delays by airline. Display full names, no codes.

```{r}
flights %>%
  drop_na(arr_delay) %>%
                      %>%
  group_by(         ) %>%
                      %>%
  arrange(       ) 
```

## Your Turn 14

How many airports in `airports` are serviced by flights originating in New York (i.e. flights in our dataset?) Notice that the column to join on is named `faa` in the **airports** data set and `dest` in the **flights** data set.


```{r}
__________ %>%
 _________(_________, by = ___________) %>%
  distinct(faa)
```



***

# Take aways

* Extract variables with `select()`  
* Extract cases with `filter()`  
* Arrange cases, with `arrange()`  

* Make tables of summaries with `summarise()`  
* Make new variables, with `mutate()`  
* Do groupwise operations with `group_by()`

* Connect operations with `%>%`  

* Use `left_join()`, `right_join()`, `full_join()`, or `inner_join()` to join datasets
* Use `semi_join()` or `anti_join()` to filter datasets against each other