Programming with Data

]

# Week 11: .fancy[Programming with Data]

### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M243.4 2.6l-224 96c-14 6-21.8 21-18.7 35.8S16.8 160 32 160v8c0 13.3 10.7 24 24 24H456c13.3 0 24-10.7 24-24v-8c15.2 0 28.3-10.7 31.3-25.6s-4.8-29.9-18.7-35.8l-224-96c-8.1-3.4-17.2-3.4-25.2 0zM128 224H64V420.3c-.6 .3-1.2 .7-1.8 1.1l-48 32c-11.7 7.8-17 22.4-12.9 35.9S17.9 512 32 512H480c14.1 0 26.5-9.2 30.6-22.7s-1.1-28.1-12.9-35.9l-48-32c-.6-.4-1.2-.7-1.8-1.1V224H384V416H344V224H280V416H232V224H168V416H128V224zm128-96c-17.7 0-32-14.3-32-32s14.3-32 32-32s32 14.3 32 32s-14.3 32-32 32z"/></svg> EMSE 4571: Intro to Programming for Analytics
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M272 304h-96C78.8 304 0 382.8 0 480c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32C448 382.8 369.2 304 272 304zM48.99 464C56.89 400.9 110.8 352 176 352h96c65.16 0 119.1 48.95 127 112H48.99zM224 256c70.69 0 128-57.31 128-128c0-70.69-57.31-128-128-128S96 57.31 96 128C96 198.7 153.3 256 224 256zM224 48c44.11 0 80 35.89 80 80c0 44.11-35.89 80-80 80S144 172.1 144 128C144 83.89 179.9 48 224 48z"/></svg> John Paul Helveston
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M152 64H296V24C296 10.75 306.7 0 320 0C333.3 0 344 10.75 344 24V64H384C419.3 64 448 92.65 448 128V448C448 483.3 419.3 512 384 512H64C28.65 512 0 483.3 0 448V128C0 92.65 28.65 64 64 64H104V24C104 10.75 114.7 0 128 0C141.3 0 152 10.75 152 24V64zM48 448C48 456.8 55.16 464 64 464H384C392.8 464 400 456.8 400 448V192H48V448z"/></svg> April 13, 2023

]

---

# Quiz 6

## Write your name on the quiz!

## Rules:

- Work alone; no outside help of any kind is allowed.
- No calculators, no notes, no books, no computers, no phones.

]

]

---

# Week 11: .fancy[Programming with Data]

### 1. Writing functions for data frames
### 2. Writing custom plot functions

### BREAK

### 3. Iteration with purrr

---

# Week 11: .fancy[Programming with Data]

### 1. .orange[Writing functions for data frames]
### 2. Writing custom plot functions

### BREAK

### 3. Iteration with purrr

---

# Motivation

I want a summary of a variable in a data frame:

```r
head(diamonds)
```

```
#> # A tibble: 6 × 10
#>   carat cut       color clarity depth table price     x     y     z
#>   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
#> 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
#> 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
#> 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
#> 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
#> 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
```

---

# Motivation

I want a summary of a variable in a data frame:

```r
length(diamonds$price)
```

```
#> [1] 53940
```

```r
mean(diamonds$price)
```

```
#> [1] 3932.8
```

```r
sd(diamonds$price)
```

```
#> [1] 3989.44
```

---

# Motivation

I want a summary of a variable in a data frame:

```r
diamonds %>%
  summarise(
    n = n(),
    mean = mean(price),
    sd = sd(price)
  )
```

```
#> # A tibble: 1 × 3
#>       n  mean    sd
#>   <int> <dbl> <dbl>
#> 1 53940 3933. 3989.
```

---

# Motivation

I can get **grouped** summaries really fast now:

```r
diamonds %>%
* group_by(cut) %>%
  summarise(
    n = n(),
    mean = mean(price),
    sd = sd(price)
  )
```

```
#> # A tibble: 5 × 4
#>   cut           n  mean    sd
#>   <ord>     <int> <dbl> <dbl>
#> 1 Fair       1610 4359. 3560.
#> 2 Good       4906 3929. 3682.
#> 3 Very Good 12082 3982. 3936.
#> 4 Premium   13791 4584. 4349.
#> 5 Ideal     21551 3458. 3808.
```

]

```r
diamonds %>%
* group_by(color) %>%
  summarise(
    n = n(),
    mean = mean(price),
    sd = sd(price)
  )
```

```
#> # A tibble: 7 × 4
#>   color     n  mean    sd
#>   <ord> <int> <dbl> <dbl>
#> 1 D      6775 3170. 3357.
#> 2 E      9797 3077. 3344.
#> 3 F      9542 3725. 3785.
#> 4 G     11292 3999. 4051.
#> 5 H      8304 4487. 4216.
#> 6 I      5422 5092. 4722.
#> 7 J      2808 5324. 4438.
```

]

---

# Convert this to a function

```r
diamonds %>%
  group_by(color) %>%
  summarise(
    n = n(),
    mean = mean(price),
    sd = sd(price)
  )
```

]

```r
my_summary <- function(df, var) {
  df %>%
*   group_by(var) %>%
    summarise(
      n = n(),
      mean = mean(price),
      sd = sd(price)
    )
}
```

]

---

# ...but this doesn't work

```r
my_summary <- function(df, var) {
  df %>%
*   group_by(var) %>%
    summarise(
      n = n(),
      mean = mean(price),
      sd = sd(price)
    )
}

my_summary(diamonds, color)
```

```
#> Error in `group_by()`:
#> ! Must group by variables found in `.data`.
#> ✖ Column `var` is not found.
```

---

# **Solution**: "embrace" your variables 🤗

```r
my_summary <- function(df, var) {
  df %>%
*   group_by({{ var }}) %>%
    summarise(
      n = n(),
      mean = mean(price),
      sd = sd(price)
    )
}
```

]

```r
my_summary(diamonds, cut)
```

```r
my_summary(diamonds, color)
```

]

---

### Make it even more general!

```r
my_summary <- function(df, group, var) {
  df %>%
    group_by({{ group }}) %>%
    summarise(
      n = n(),
      mean = mean({{ var }}),
      sd = sd({{ var }})
    )
}
```

```r
my_summary(diamonds, cut, price)
```

---

### Make it even more general!

```r
my_summary <- function(df, group, var) {
  df %>%
    group_by({{ group }}) %>%
    summarise(
      n = n(),
      mean = mean({{ var }}),
      sd = sd({{ var }})
    )
}
```

```r
my_summary(diamonds, color, carat)
```

```
#> # A tibble: 7 × 4
#>   color     n  mean    sd
#>   <ord> <int> <dbl> <dbl>
#> 1 D      6775 0.658 0.360
#> 2 E      9797 0.658 0.369
#> 3 F      9542 0.737 0.398
#> 4 G     11292 0.771 0.441
#> 5 H      8304 0.912 0.521
#> 6 I      5422 1.03  0.579
#> 7 J      2808 1.16  0.596
```

---

### Use it on a different data frame!

```r
library(palmerpenguins)

glimpse(penguins)
```

```
#> Rows: 344
#> Columns: 8
#> $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
#> $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
#> $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
#> $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
#> $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
#> $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
#> $ sex               <fct> male, female, female, NA, female, male, female, male…
#> $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…
```

]

```r
my_summary(penguins, sex, body_mass_g)
```

```
#> # A tibble: 3 × 4
#>   sex        n  mean    sd
#>   <fct>  <int> <dbl> <dbl>
#> 1 female   165 3862.  666.
#> 2 male     168 4546.  788.
#> 3 <NA>      11   NA    NA
```

```r
my_summary(penguins, species, bill_length_mm)
```

```
#> # A tibble: 3 × 4
#>   species       n  mean    sd
#>   <fct>     <int> <dbl> <dbl>
#> 1 Adelie      152  NA   NA   
#> 2 Chinstrap    68  48.8  3.34
#> 3 Gentoo      124  NA   NA
```

]

---

### Defining a filter condition

```r
filter_summary <- function(df, condition, var) {
  df %>%
*   filter({{ condition }}) %>%
    summarise(
      n = n(),
      mean = mean({{ var }}, na.rm = TRUE),
      sd = sd({{ var }}, na.rm = TRUE)
    )
}
```

```r
filter_summary(penguins, species == 'Adelie', bill_length_mm)
```

```
#> # A tibble: 1 × 3
#>       n  mean    sd
#>   <int> <dbl> <dbl>
#> 1   152  38.8  2.66
```

---

## Your turn - write the following functions

```r
my_subset <- function(df, condition, cols)
```

Returns a subset of `df` by filtering the rows based on `condition` and only includes the select `cols`. Example:

```r
nycflights13::flights %>% 
  my_subset(month == 12, c("carrier", "flight"))
```

```
#> # A tibble: 5 × 2
#>   carrier flight
#>   <chr>    <int>
#> 1 B6         745
#> 2 B6         839
#> 3 US        1895
#> 4 UA        1487
#> 5 AA        2243
```

]

```r
count_p <- function(df, group)
```

Returns a summary data frame of the count of rows in `df` by `group` as well as the percentage of those counts.

```r
nycflights13::flights %>%
  count_p(carrier)
```

```
#> # A tibble: 6 × 3
#>   carrier     n      p
#>   <chr>   <int>  <dbl>
#> 1 UA      58665 0.174 
#> 2 B6      54635 0.162 
#> 3 EV      54173 0.161 
#> 4 DL      48110 0.143 
#> 5 AA      32729 0.0972
#> 6 MQ      26397 0.0784
```

]

---

### Testing data frame functions

Function:

```r
my_summary <- function(df, group, var) {
  df %>%
    group_by({{ group }}) %>%
    summarise(
      n = n(),
      mean = mean({{ var }}),
      sd = sd({{ var }})
    )
}
```

]

Make two data frames and compare them

```r
test_my_summary <- function() {
  
  cat("Testing my_summary()...")

df1 <- diamonds %>% 
    my_summary(cut, price)  
  
  df2 <- diamonds %>% 
    group_by(cut) %>% 
    summarise(
      n = n(),
      mean = mean(price),
      sd = sd(price)
    )
  
* stopifnot(identical(df1, df2))
  
  cat("passed!")
}

test_my_summary()
```

```
#> Testing my_summary()...passed!
```

]

---

# Week 11: .fancy[Programming with Data]

### 1. Writing functions for data frames
### 2. .orange[Writing custom plot functions]

### BREAK

### 3. Iteration with purrr

---

# Motivation

I want to see a histogram of multiple variables

```r
diamonds %>%
  ggplot() +
  geom_histogram((aes(x = price)))
```

]

```r
diamonds %>%
  ggplot() +
  geom_histogram((aes(x = carat)))
```

]

---

# Convert this to a function

```r
my_hist <- function(df, var) {
  df %>%
    ggplot() +
    geom_histogram((aes(x = {{ var }}))) # <<
}
```

```r
my_hist(diamonds, price)
```

]

```r
my_hist(diamonds, carat)
```

]

---

## Combine with other functions

```r
filtered_hist <- function(df, condition, var) {
  df %>%
*   filter({{ condition }}) %>%
    ggplot() +
    geom_histogram((aes(x = {{ var }}))) 
}
```

```r
filtered_hist(diamonds, color == "E", price)
```

]

```r
filtered_hist(diamonds, color == "J", price)
```

]

---

## Your turn

Write the function `filtered_scatter` which plots a scatterplot based on a condition, then use it for the two examples below.

```r
filtered_scatter <- function(df, condition, x, y)
```

```r
filtered_scatter(
  penguins, sex == "male", 
  x = body_mass_g, y = bill_length_mm)
```

]

```r
filtered_scatter(
  penguins, species == "Gentoo", 
  x = body_mass_g, y = flipper_length_mm)
```

]

---

## You can add layers to your custom plot functions

```r
filtered_scatter(
  penguins, species == "Gentoo", 
  x = body_mass_g, y = flipper_length_mm) + 
* theme_bw()
```

---

# .fancy[Break]

---

# Week 11: .fancy[Programming with Data]

### 1. Writing functions for data frames
### 2. Writing custom plot functions

### BREAK

### 3. .orange[Iteration with purrr]

#### .center[Much of this content is adapted from Shannon Pileggi's workshop at https://github.com/shannonpileggi/iterating-well-with-purrr]

---

# Iterating _without_ purrr

---

## Gapminder example

```r
library(gapminder)
library(tidyverse)

head(gapminder)
```

```
#> # A tibble: 6 × 6
#>   country     continent  year lifeExp      pop gdpPercap
#>   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
#> 1 Afghanistan Asia       1952    28.8  8425333      779.
#> 2 Afghanistan Asia       1957    30.3  9240934      821.
#> 3 Afghanistan Asia       1962    32.0 10267083      853.
#> 4 Afghanistan Asia       1967    34.0 11537966      836.
#> 5 Afghanistan Asia       1972    36.1 13079460      740.
#> 6 Afghanistan Asia       1977    38.4 14880372      786.
```

Hans Rosling discusses Gapminder data https://youtu.be/hVimVzgtD6w

---

## Gapminder life expectancy

---

#### .center[What am I doing here? Are there mistakes?]

```r
africa <- gapminder[gapminder$continent == "Africa", ]
africa_mm <- max(africa$lifeExp) - min(africa$lifeExp)

americas <- gapminder[gapminder$continent == "Americas", ]
americas_mm <- max(americas$lifeExp) - min(americas$lifeExp)

asia <- gapminder[gapminder$continent == "Asia", ]
asia_mm <- max(asia$lifeExp) - min(africa$lifeExp)

europe <- gapminder[gapminder$continent == "Europe", ]
europe_mm <- max(europe$lifeExp) - min(europe$lifeExp)

oceania <- gapminder[gapminder$continent == "Oceania", ]
oceania_mm <- max(europe$lifeExp) - min(oceania$lifeExp)

cbind(
  continent = c("Africa", "Asias", "Europe", "Oceania"),
  max_minus_min = c(africa_mm, americas_mm, asia_mm, europe_mm, oceania_mm)
)
```

---

### 1.  What are the drawbacks of this code?

### 2.  How would you do it instead?

```r
africa <- gapminder[gapminder$continent == "Africa", ]
africa_mm <- max(africa$lifeExp) - min(africa$lifeExp)

americas <- gapminder[gapminder$continent == "Americas", ]
americas_mm <- max(americas$lifeExp) - min(americas$lifeExp)

asia <- gapminder[gapminder$continent == "Asia", ]
asia_mm <- max(asia$lifeExp) - min(africa$lifeExp)

europe <- gapminder[gapminder$continent == "Europe", ]
europe_mm <- max(europe$lifeExp) - min(europe$lifeExp)

oceania <- gapminder[gapminder$continent == "Oceania", ]
oceania_mm <- max(europe$lifeExp) - min(oceania$lifeExp)

cbind(
  continent = c("Africa", "Asias", "Europe", "Oceania"),
  max_minus_min = c(africa_mm, americas_mm, asia_mm, europe_mm, oceania_mm)
)
```

---

## An alternative solution

```r
gapminder %>% 
 group_by(continent) %>% 
 summarize(max_minus_min = max(lifeExp) - min(lifeExp))
```

`group_by` approach

```
#> # A tibble: 5 × 2
#>   continent max_minus_min
#>   <fct>             <dbl>
#> 1 Africa             52.8
#> 2 Americas           43.1
#> 3 Asia               53.8
#> 4 Europe             38.2
#> 5 Oceania            12.1
```

]

previous approach

```
#>      continent max_minus_min
#> [1,] "Africa"  "52.843"     
#> [2,] "Asias"   "43.074"     
#> [3,] "Europe"  "59.004"     
#> [4,] "Oceania" "38.172"     
#> [5,] "Africa"  "12.637"
```

]

---

## More iteration

```r
year <- 2017:2021
location <- c("Orlando", "San Diego", "Austin", "San Francisco", "remote")

conf <- rep("", length(year))
for (i in 1:length(conf)) {
 conf[i] <- paste0("The ", year[i], " RStudio Conference was in ", location[i], ".")
}
conf
```

```
#> [1] "The 2017 RStudio Conference was in Orlando."      
#> [2] "The 2018 RStudio Conference was in San Diego."    
#> [3] "The 2019 RStudio Conference was in Austin."       
#> [4] "The 2020 RStudio Conference was in San Francisco."
#> [5] "The 2021 RStudio Conference was in remote."
```

#### Can you think of other ways to do this?

---

## More iteration, cont.

```r
year <- 2017:2021
location <- c("Orlando", "San Diego", "Austin", "San Francisco", "remote")
```

```r
paste0("The ", year, " RStudio Conference was in ", location, ".")
```

```r
glue::glue("The {year} RStudio Conference was in {location}.")
```

```
#> The 2017 RStudio Conference was in Orlando.
#> The 2018 RStudio Conference was in San Diego.
#> The 2019 RStudio Conference was in Austin.
#> The 2020 RStudio Conference was in San Francisco.
#> The 2021 RStudio Conference was in remote.
```

---

# Introducing `purrr`

Loaded automatically with `library(tidyverse)`

---

## `purrr::map(x, f, ...)`

<br>

## for every element of `x` do `f`

---

background-image: url("images/lego1.jpeg")
background-size: contain
class: center

<br>

## `x = minis`

## `f = add_antenna`

---

background-image: url("images/lego2.jpeg")
background-size: contain
class: center

<br>

## `map(minis, add_antenna)`

---

## for every element of `x` do `f`

---

## `map()` returns a list

Vector example

```r
addTen <- function(x) {
  return(x + 10)
}
```

```r
map(c(1, 7, 13), addTen)
```

```
#> [[1]]
#> [1] 11
#> 
#> [[2]]
#> [1] 17
#> 
#> [[3]]
#> [1] 23
```

---

## .center[Working with lists feels like...]

---

## .center[Subsetting lists]

---

```r
x <- list(c(1, 2, 3), "a", c(4, 5, 6))
```

]

```r
x[1]
```

```
#> [[1]]
#> [1] 1 2 3
```

```r
x[[1]]
```

```
#> [1] 1 2 3
```

]

---

# Example data: `sw_people`

```r
library(repurrrsive)
```

```r
sw_people
```

```
#> [[1]]
#> [[1]]$name
#> [1] "Luke Skywalker"
#> 
#> [[1]]$height
#> [1] "172"
#> 
#> [[1]]$mass
#> [1] "77"
#> 
#> [[1]]$hair_color
#> [1] "blond"
#> 
#> [[1]]$skin_color
#> [1] "fair"
#> 
#> [[1]]$eye_color
#> [1] "blue"
#> 
#> [[1]]$birth_year
#> [1] "19BBY"
#> 
#> [[1]]$gender
#> [1] "male"
#> 
#> [[1]]$homeworld
#> [1] "http://swapi.co/api/planets/1/"
#> 
#> [[1]]$films
#> [1] "http://swapi.co/api/films/6/" "http://swapi.co/api/films/3/"
#> [3] "http://swapi.co/api/films/2/" "http://swapi.co/api/films/1/"
#> [5] "http://swapi.co/api/films/7/"
#> 
#> [[1]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[1]]$vehicles
#> [1] "http://swapi.co/api/vehicles/14/" "http://swapi.co/api/vehicles/30/"
#> 
#> [[1]]$starships
#> [1] "http://swapi.co/api/starships/12/" "http://swapi.co/api/starships/22/"
#> 
#> [[1]]$created
#> [1] "2014-12-09T13:50:51.644000Z"
#> 
#> [[1]]$edited
#> [1] "2014-12-20T21:17:56.891000Z"
#> 
#> [[1]]$url
#> [1] "http://swapi.co/api/people/1/"
#> 
#> 
#> [[2]]
#> [[2]]$name
#> [1] "C-3PO"
#> 
#> [[2]]$height
#> [1] "167"
#> 
#> [[2]]$mass
#> [1] "75"
#> 
#> [[2]]$hair_color
#> [1] "n/a"
#> 
#> [[2]]$skin_color
#> [1] "gold"
#> 
#> [[2]]$eye_color
#> [1] "yellow"
#> 
#> [[2]]$birth_year
#> [1] "112BBY"
#> 
#> [[2]]$gender
#> [1] "n/a"
#> 
#> [[2]]$homeworld
#> [1] "http://swapi.co/api/planets/1/"
#> 
#> [[2]]$films
#> [1] "http://swapi.co/api/films/5/" "http://swapi.co/api/films/4/"
#> [3] "http://swapi.co/api/films/6/" "http://swapi.co/api/films/3/"
#> [5] "http://swapi.co/api/films/2/" "http://swapi.co/api/films/1/"
#> 
#> [[2]]$species
#> [1] "http://swapi.co/api/species/2/"
#> 
#> [[2]]$created
#> [1] "2014-12-10T15:10:51.357000Z"
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#> [[2]]$edited
#> [1] "2014-12-20T21:17:50.309000Z"
#> 
#> [[2]]$url
#> [1] "http://swapi.co/api/people/2/"
#> 
#> 
#> [[3]]
#> [[3]]$name
#> [1] "R2-D2"
#> 
#> [[3]]$height
#> [1] "96"
#> 
#> [[3]]$mass
#> [1] "32"
#> 
#> [[3]]$hair_color
#> [1] "n/a"
#> 
#> [[3]]$skin_color
#> [1] "white, blue"
#> 
#> [[3]]$eye_color
#> [1] "red"
#> 
#> [[3]]$birth_year
#> [1] "33BBY"
#> 
#> [[3]]$gender
#> [1] "n/a"
#> 
#> [[3]]$homeworld
#> [1] "http://swapi.co/api/planets/8/"
#> 
#> [[3]]$films
#> [1] "http://swapi.co/api/films/5/" "http://swapi.co/api/films/4/"
#> [3] "http://swapi.co/api/films/6/" "http://swapi.co/api/films/3/"
#> [5] "http://swapi.co/api/films/2/" "http://swapi.co/api/films/1/"
#> [7] "http://swapi.co/api/films/7/"
#> 
#> [[3]]$species
#> [1] "http://swapi.co/api/species/2/"
#> 
#> [[3]]$created
#> [1] "2014-12-10T15:11:50.376000Z"
#> 
#> [[3]]$edited
#> [1] "2014-12-20T21:17:50.311000Z"
#> 
#> [[3]]$url
#> [1] "http://swapi.co/api/people/3/"
#> 
#> 
#> [[4]]
#> [[4]]$name
#> [1] "Darth Vader"
#> 
#> [[4]]$height
#> [1] "202"
#> 
#> [[4]]$mass
#> [1] "136"
#> 
#> [[4]]$hair_color
#> [1] "none"
#> 
#> [[4]]$skin_color
#> [1] "white"
#> 
#> [[4]]$eye_color
#> [1] "yellow"
#> 
#> [[4]]$birth_year
#> [1] "41.9BBY"
#> 
#> [[4]]$gender
#> [1] "male"
#> 
#> [[4]]$homeworld
#> [1] "http://swapi.co/api/planets/1/"
#> 
#> [[4]]$films
#> [1] "http://swapi.co/api/films/6/" "http://swapi.co/api/films/3/"
#> [3] "http://swapi.co/api/films/2/" "http://swapi.co/api/films/1/"
#> 
#> [[4]]$species
#> [1] "http://swapi.co/api/species/1/"
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#> [[4]]$starships
#> [1] "http://swapi.co/api/starships/13/"
#> 
#> [[4]]$created
#> [1] "2014-12-10T15:18:20.704000Z"
#> 
#> [[4]]$edited
#> [1] "2014-12-20T21:17:50.313000Z"
#> 
#> [[4]]$url
#> [1] "http://swapi.co/api/people/4/"
#> 
#> 
#> [[5]]
#> [[5]]$name
#> [1] "Leia Organa"
#> 
#> [[5]]$height
#> [1] "150"
#> 
#> [[5]]$mass
#> [1] "49"
#> 
#> [[5]]$hair_color
#> [1] "brown"
#> 
#> [[5]]$skin_color
#> [1] "light"
#> 
#> [[5]]$eye_color
#> [1] "brown"
#> 
#> [[5]]$birth_year
#> [1] "19BBY"
#> 
#> [[5]]$gender
#> [1] "female"
#> 
#> [[5]]$homeworld
#> [1] "http://swapi.co/api/planets/2/"
#> 
#> [[5]]$films
#> [1] "http://swapi.co/api/films/6/" "http://swapi.co/api/films/3/"
#> [3] "http://swapi.co/api/films/2/" "http://swapi.co/api/films/1/"
#> [5] "http://swapi.co/api/films/7/"
#> 
#> [[5]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[5]]$vehicles
#> [1] "http://swapi.co/api/vehicles/30/"
#> 
#> [[5]]$created
#> [1] "2014-12-10T15:20:09.791000Z"
#> 
#> [[5]]$edited
#> [1] "2014-12-20T21:17:50.315000Z"
#> 
#> [[5]]$url
#> [1] "http://swapi.co/api/people/5/"
#> 
#> 
#> [[6]]
#> [[6]]$name
#> [1] "Owen Lars"
#> 
#> [[6]]$height
#> [1] "178"
#> 
#> [[6]]$mass
#> [1] "120"
#> 
#> [[6]]$hair_color
#> [1] "brown, grey"
#> 
#> [[6]]$skin_color
#> [1] "light"
#> 
#> [[6]]$eye_color
#> [1] "blue"
#> 
#> [[6]]$birth_year
#> [1] "52BBY"
#> 
#> [[6]]$gender
#> [1] "male"
#> 
#> [[6]]$homeworld
#> [1] "http://swapi.co/api/planets/1/"
#> 
#> [[6]]$films
#> [1] "http://swapi.co/api/films/5/" "http://swapi.co/api/films/6/"
#> [3] "http://swapi.co/api/films/1/"
#> 
#> [[6]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[6]]$created
#> [1] "2014-12-10T15:52:14.024000Z"
#> 
#> [[6]]$edited
#> [1] "2014-12-20T21:17:50.317000Z"
#> 
#> [[6]]$url
#> [1] "http://swapi.co/api/people/6/"
#> 
#> 
#> [[7]]
#> [[7]]$name
#> [1] "Beru Whitesun lars"
#> 
#> [[7]]$height
#> [1] "165"
#> 
#> [[7]]$mass
#> [1] "75"
#> 
#> [[7]]$hair_color
#> [1] "brown"
#> 
#> [[7]]$skin_color
#> [1] "light"
#> 
#> [[7]]$eye_color
#> [1] "blue"
#> 
#> [[7]]$birth_year
#> [1] "47BBY"
#> 
#> [[7]]$gender
#> [1] "female"
#> 
#> [[7]]$homeworld
#> [1] "http://swapi.co/api/planets/1/"
#> 
#> [[7]]$films
#> [1] "http://swapi.co/api/films/5/" "http://swapi.co/api/films/6/"
#> [3] "http://swapi.co/api/films/1/"
#> 
#> [[7]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[7]]$created
#> [1] "2014-12-10T15:53:41.121000Z"
#> 
#> [[7]]$edited
#> [1] "2014-12-20T21:17:50.319000Z"
#> 
#> [[7]]$url
#> [1] "http://swapi.co/api/people/7/"
#> 
#> 
#> [[8]]
#> [[8]]$name
#> [1] "R5-D4"
#> 
#> [[8]]$height
#> [1] "97"
#> 
#> [[8]]$mass
#> [1] "32"
#> 
#> [[8]]$hair_color
#> [1] "n/a"
#> 
#> [[8]]$skin_color
#> [1] "white, red"
#> 
#> [[8]]$eye_color
#> [1] "red"
#> 
#> [[8]]$birth_year
#> [1] "unknown"
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#> [[8]]$gender
#> [1] "n/a"
#> 
#> [[8]]$homeworld
#> [1] "http://swapi.co/api/planets/1/"
#> 
#> [[8]]$films
#> [1] "http://swapi.co/api/films/1/"
#> 
#> [[8]]$species
#> [1] "http://swapi.co/api/species/2/"
#> 
#> [[8]]$created
#> [1] "2014-12-10T15:57:50.959000Z"
#> 
#> [[8]]$edited
#> [1] "2014-12-20T21:17:50.321000Z"
#> 
#> [[8]]$url
#> [1] "http://swapi.co/api/people/8/"
#> 
#> 
#> [[9]]
#> [[9]]$name
#> [1] "Biggs Darklighter"
#> 
#> [[9]]$height
#> [1] "183"
#> 
#> [[9]]$mass
#> [1] "84"
#> 
#> [[9]]$hair_color
#> [1] "black"
#> 
#> [[9]]$skin_color
#> [1] "light"
#> 
#> [[9]]$eye_color
#> [1] "brown"
#> 
#> [[9]]$birth_year
#> [1] "24BBY"
#> 
#> [[9]]$gender
#> [1] "male"
#> 
#> [[9]]$homeworld
#> [1] "http://swapi.co/api/planets/1/"
#> 
#> [[9]]$films
#> [1] "http://swapi.co/api/films/1/"
#> 
#> [[9]]$species
#> [1] "http://swapi.co/api/species/1/"
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#> [[9]]$starships
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#> 
#> [[9]]$created
#> [1] "2014-12-10T15:59:50.509000Z"
#> 
#> [[9]]$edited
#> [1] "2014-12-20T21:17:50.323000Z"
#> 
#> [[9]]$url
#> [1] "http://swapi.co/api/people/9/"
#> 
#> 
#> [[10]]
#> [[10]]$name
#> [1] "Obi-Wan Kenobi"
#> 
#> [[10]]$height
#> [1] "182"
#> 
#> [[10]]$mass
#> [1] "77"
#> 
#> [[10]]$hair_color
#> [1] "auburn, white"
#> 
#> [[10]]$skin_color
#> [1] "fair"
#> 
#> [[10]]$eye_color
#> [1] "blue-gray"
#> 
#> [[10]]$birth_year
#> [1] "57BBY"
#> 
#> [[10]]$gender
#> [1] "male"
#> 
#> [[10]]$homeworld
#> [1] "http://swapi.co/api/planets/20/"
#> 
#> [[10]]$films
#> [1] "http://swapi.co/api/films/5/" "http://swapi.co/api/films/4/"
#> [3] "http://swapi.co/api/films/6/" "http://swapi.co/api/films/3/"
#> [5] "http://swapi.co/api/films/2/" "http://swapi.co/api/films/1/"
#> 
#> [[10]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[10]]$vehicles
#> [1] "http://swapi.co/api/vehicles/38/"
#> 
#> [[10]]$starships
#> [1] "http://swapi.co/api/starships/48/" "http://swapi.co/api/starships/59/"
#> [3] "http://swapi.co/api/starships/64/" "http://swapi.co/api/starships/65/"
#> [5] "http://swapi.co/api/starships/74/"
#> 
#> [[10]]$created
#> [1] "2014-12-10T16:16:29.192000Z"
#> 
#> [[10]]$edited
#> [1] "2014-12-20T21:17:50.325000Z"
#> 
#> [[10]]$url
#> [1] "http://swapi.co/api/people/10/"
#> 
#> 
#> [[11]]
#> [[11]]$name
#> [1] "Anakin Skywalker"
#> 
#> [[11]]$height
#> [1] "188"
#> 
#> [[11]]$mass
#> [1] "84"
#> 
#> [[11]]$hair_color
#> [1] "blond"
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#> [[11]]$skin_color
#> [1] "fair"
#> 
#> [[11]]$eye_color
#> [1] "blue"
#> 
#> [[11]]$birth_year
#> [1] "41.9BBY"
#> 
#> [[11]]$gender
#> [1] "male"
#> 
#> [[11]]$homeworld
#> [1] "http://swapi.co/api/planets/1/"
#> 
#> [[11]]$films
#> [1] "http://swapi.co/api/films/5/" "http://swapi.co/api/films/4/"
#> [3] "http://swapi.co/api/films/6/"
#> 
#> [[11]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[11]]$vehicles
#> [1] "http://swapi.co/api/vehicles/44/" "http://swapi.co/api/vehicles/46/"
#> 
#> [[11]]$starships
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#> 
#> [[11]]$created
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#> [[11]]$edited
#> [1] "2014-12-20T21:17:50.327000Z"
#> 
#> [[11]]$url
#> [1] "http://swapi.co/api/people/11/"
#> 
#> 
#> [[12]]
#> [[12]]$name
#> [1] "Wilhuff Tarkin"
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#> [[12]]$height
#> [1] "180"
#> 
#> [[12]]$mass
#> [1] "unknown"
#> 
#> [[12]]$hair_color
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#> 
#> [[12]]$skin_color
#> [1] "fair"
#> 
#> [[12]]$eye_color
#> [1] "blue"
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#> [[12]]$birth_year
#> [1] "64BBY"
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#> [[12]]$gender
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#> [[12]]$homeworld
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#> [[12]]$films
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#> [[12]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[12]]$created
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#> 
#> [[12]]$edited
#> [1] "2014-12-20T21:17:50.330000Z"
#> 
#> [[12]]$url
#> [1] "http://swapi.co/api/people/12/"
#> 
#> 
#> [[13]]
#> [[13]]$name
#> [1] "Chewbacca"
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#> [[13]]$height
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#> 
#> [[13]]$mass
#> [1] "112"
#> 
#> [[13]]$hair_color
#> [1] "brown"
#> 
#> [[13]]$skin_color
#> [1] "unknown"
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#> [[13]]$eye_color
#> [1] "blue"
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#> [[13]]$birth_year
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#> [[13]]$gender
#> [1] "male"
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#> [[13]]$homeworld
#> [1] "http://swapi.co/api/planets/14/"
#> 
#> [[13]]$films
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#> [3] "http://swapi.co/api/films/2/" "http://swapi.co/api/films/1/"
#> [5] "http://swapi.co/api/films/7/"
#> 
#> [[13]]$species
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#> 
#> [[13]]$vehicles
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#> [[13]]$starships
#> [1] "http://swapi.co/api/starships/10/" "http://swapi.co/api/starships/22/"
#> 
#> [[13]]$created
#> [1] "2014-12-10T16:42:45.066000Z"
#> 
#> [[13]]$edited
#> [1] "2014-12-20T21:17:50.332000Z"
#> 
#> [[13]]$url
#> [1] "http://swapi.co/api/people/13/"
#> 
#> 
#> [[14]]
#> [[14]]$name
#> [1] "Han Solo"
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#> 
#> [[14]]$mass
#> [1] "80"
#> 
#> [[14]]$hair_color
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#> 
#> [[14]]$skin_color
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#> 
#> [[14]]$eye_color
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#> 
#> [[14]]$birth_year
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#> [[14]]$gender
#> [1] "male"
#> 
#> [[14]]$homeworld
#> [1] "http://swapi.co/api/planets/22/"
#> 
#> [[14]]$films
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#> 
#> [[14]]$species
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#> 
#> [[14]]$starships
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#> 
#> [[14]]$created
#> [1] "2014-12-10T16:49:14.582000Z"
#> 
#> [[14]]$edited
#> [1] "2014-12-20T21:17:50.334000Z"
#> 
#> [[14]]$url
#> [1] "http://swapi.co/api/people/14/"
#> 
#> 
#> [[15]]
#> [[15]]$name
#> [1] "Greedo"
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#> 
#> [[15]]$mass
#> [1] "74"
#> 
#> [[15]]$hair_color
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#> 
#> [[15]]$skin_color
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#> 
#> [[15]]$eye_color
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#> 
#> [[15]]$birth_year
#> [1] "44BBY"
#> 
#> [[15]]$gender
#> [1] "male"
#> 
#> [[15]]$homeworld
#> [1] "http://swapi.co/api/planets/23/"
#> 
#> [[15]]$films
#> [1] "http://swapi.co/api/films/1/"
#> 
#> [[15]]$species
#> [1] "http://swapi.co/api/species/4/"
#> 
#> [[15]]$created
#> [1] "2014-12-10T17:03:30.334000Z"
#> 
#> [[15]]$edited
#> [1] "2014-12-20T21:17:50.336000Z"
#> 
#> [[15]]$url
#> [1] "http://swapi.co/api/people/15/"
#> 
#> 
#> [[16]]
#> [[16]]$name
#> [1] "Jabba Desilijic Tiure"
#> 
#> [[16]]$height
#> [1] "175"
#> 
#> [[16]]$mass
#> [1] "1,358"
#> 
#> [[16]]$hair_color
#> [1] "n/a"
#> 
#> [[16]]$skin_color
#> [1] "green-tan, brown"
#> 
#> [[16]]$eye_color
#> [1] "orange"
#> 
#> [[16]]$birth_year
#> [1] "600BBY"
#> 
#> [[16]]$gender
#> [1] "hermaphrodite"
#> 
#> [[16]]$homeworld
#> [1] "http://swapi.co/api/planets/24/"
#> 
#> [[16]]$films
#> [1] "http://swapi.co/api/films/4/" "http://swapi.co/api/films/3/"
#> [3] "http://swapi.co/api/films/1/"
#> 
#> [[16]]$species
#> [1] "http://swapi.co/api/species/5/"
#> 
#> [[16]]$created
#> [1] "2014-12-10T17:11:31.638000Z"
#> 
#> [[16]]$edited
#> [1] "2014-12-20T21:17:50.338000Z"
#> 
#> [[16]]$url
#> [1] "http://swapi.co/api/people/16/"
#> 
#> 
#> [[17]]
#> [[17]]$name
#> [1] "Wedge Antilles"
#> 
#> [[17]]$height
#> [1] "170"
#> 
#> [[17]]$mass
#> [1] "77"
#> 
#> [[17]]$hair_color
#> [1] "brown"
#> 
#> [[17]]$skin_color
#> [1] "fair"
#> 
#> [[17]]$eye_color
#> [1] "hazel"
#> 
#> [[17]]$birth_year
#> [1] "21BBY"
#> 
#> [[17]]$gender
#> [1] "male"
#> 
#> [[17]]$homeworld
#> [1] "http://swapi.co/api/planets/22/"
#> 
#> [[17]]$films
#> [1] "http://swapi.co/api/films/3/" "http://swapi.co/api/films/2/"
#> [3] "http://swapi.co/api/films/1/"
#> 
#> [[17]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[17]]$vehicles
#> [1] "http://swapi.co/api/vehicles/14/"
#> 
#> [[17]]$starships
#> [1] "http://swapi.co/api/starships/12/"
#> 
#> [[17]]$created
#> [1] "2014-12-12T11:08:06.469000Z"
#> 
#> [[17]]$edited
#> [1] "2014-12-20T21:17:50.341000Z"
#> 
#> [[17]]$url
#> [1] "http://swapi.co/api/people/18/"
#> 
#> 
#> [[18]]
#> [[18]]$name
#> [1] "Jek Tono Porkins"
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#> [[18]]$height
#> [1] "180"
#> 
#> [[18]]$mass
#> [1] "110"
#> 
#> [[18]]$hair_color
#> [1] "brown"
#> 
#> [[18]]$skin_color
#> [1] "fair"
#> 
#> [[18]]$eye_color
#> [1] "blue"
#> 
#> [[18]]$birth_year
#> [1] "unknown"
#> 
#> [[18]]$gender
#> [1] "male"
#> 
#> [[18]]$homeworld
#> [1] "http://swapi.co/api/planets/26/"
#> 
#> [[18]]$films
#> [1] "http://swapi.co/api/films/1/"
#> 
#> [[18]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[18]]$starships
#> [1] "http://swapi.co/api/starships/12/"
#> 
#> [[18]]$created
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#> 
#> [[18]]$edited
#> [1] "2014-12-20T21:17:50.343000Z"
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#> [1] "191"
#> 
#> [[75]]$mass
#> [1] "unknown"
#> 
#> [[75]]$hair_color
#> [1] "none"
#> 
#> [[75]]$skin_color
#> [1] "grey"
#> 
#> [[75]]$eye_color
#> [1] "gold"
#> 
#> [[75]]$birth_year
#> [1] "unknown"
#> 
#> [[75]]$gender
#> [1] "male"
#> 
#> [[75]]$homeworld
#> [1] "http://swapi.co/api/planets/57/"
#> 
#> [[75]]$films
#> [1] "http://swapi.co/api/films/5/"
#> 
#> [[75]]$species
#> [1] "http://swapi.co/api/species/34/"
#> 
#> [[75]]$created
#> [1] "2014-12-20T17:58:17.049000Z"
#> 
#> [[75]]$edited
#> [1] "2014-12-20T21:17:50.484000Z"
#> 
#> [[75]]$url
#> [1] "http://swapi.co/api/people/77/"
#> 
#> 
#> [[76]]
#> [[76]]$name
#> [1] "Shaak Ti"
#> 
#> [[76]]$height
#> [1] "178"
#> 
#> [[76]]$mass
#> [1] "57"
#> 
#> [[76]]$hair_color
#> [1] "none"
#> 
#> [[76]]$skin_color
#> [1] "red, blue, white"
#> 
#> [[76]]$eye_color
#> [1] "black"
#> 
#> [[76]]$birth_year
#> [1] "unknown"
#> 
#> [[76]]$gender
#> [1] "female"
#> 
#> [[76]]$homeworld
#> [1] "http://swapi.co/api/planets/58/"
#> 
#> [[76]]$films
#> [1] "http://swapi.co/api/films/5/" "http://swapi.co/api/films/6/"
#> 
#> [[76]]$species
#> [1] "http://swapi.co/api/species/35/"
#> 
#> [[76]]$created
#> [1] "2014-12-20T18:44:01.103000Z"
#> 
#> [[76]]$edited
#> [1] "2014-12-20T21:17:50.486000Z"
#> 
#> [[76]]$url
#> [1] "http://swapi.co/api/people/78/"
#> 
#> 
#> [[77]]
#> [[77]]$name
#> [1] "Grievous"
#> 
#> [[77]]$height
#> [1] "216"
#> 
#> [[77]]$mass
#> [1] "159"
#> 
#> [[77]]$hair_color
#> [1] "none"
#> 
#> [[77]]$skin_color
#> [1] "brown, white"
#> 
#> [[77]]$eye_color
#> [1] "green, yellow"
#> 
#> [[77]]$birth_year
#> [1] "unknown"
#> 
#> [[77]]$gender
#> [1] "male"
#> 
#> [[77]]$homeworld
#> [1] "http://swapi.co/api/planets/59/"
#> 
#> [[77]]$films
#> [1] "http://swapi.co/api/films/6/"
#> 
#> [[77]]$species
#> [1] "http://swapi.co/api/species/36/"
#> 
#> [[77]]$vehicles
#> [1] "http://swapi.co/api/vehicles/60/"
#> 
#> [[77]]$starships
#> [1] "http://swapi.co/api/starships/74/"
#> 
#> [[77]]$created
#> [1] "2014-12-20T19:43:53.348000Z"
#> 
#> [[77]]$edited
#> [1] "2014-12-20T21:17:50.488000Z"
#> 
#> [[77]]$url
#> [1] "http://swapi.co/api/people/79/"
#> 
#> 
#> [[78]]
#> [[78]]$name
#> [1] "Tarfful"
#> 
#> [[78]]$height
#> [1] "234"
#> 
#> [[78]]$mass
#> [1] "136"
#> 
#> [[78]]$hair_color
#> [1] "brown"
#> 
#> [[78]]$skin_color
#> [1] "brown"
#> 
#> [[78]]$eye_color
#> [1] "blue"
#> 
#> [[78]]$birth_year
#> [1] "unknown"
#> 
#> [[78]]$gender
#> [1] "male"
#> 
#> [[78]]$homeworld
#> [1] "http://swapi.co/api/planets/14/"
#> 
#> [[78]]$films
#> [1] "http://swapi.co/api/films/6/"
#> 
#> [[78]]$species
#> [1] "http://swapi.co/api/species/3/"
#> 
#> [[78]]$created
#> [1] "2014-12-20T19:46:34.209000Z"
#> 
#> [[78]]$edited
#> [1] "2014-12-20T21:17:50.491000Z"
#> 
#> [[78]]$url
#> [1] "http://swapi.co/api/people/80/"
#> 
#> 
#> [[79]]
#> [[79]]$name
#> [1] "Raymus Antilles"
#> 
#> [[79]]$height
#> [1] "188"
#> 
#> [[79]]$mass
#> [1] "79"
#> 
#> [[79]]$hair_color
#> [1] "brown"
#> 
#> [[79]]$skin_color
#> [1] "light"
#> 
#> [[79]]$eye_color
#> [1] "brown"
#> 
#> [[79]]$birth_year
#> [1] "unknown"
#> 
#> [[79]]$gender
#> [1] "male"
#> 
#> [[79]]$homeworld
#> [1] "http://swapi.co/api/planets/2/"
#> 
#> [[79]]$films
#> [1] "http://swapi.co/api/films/6/" "http://swapi.co/api/films/1/"
#> 
#> [[79]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[79]]$created
#> [1] "2014-12-20T19:49:35.583000Z"
#> 
#> [[79]]$edited
#> [1] "2014-12-20T21:17:50.493000Z"
#> 
#> [[79]]$url
#> [1] "http://swapi.co/api/people/81/"
#> 
#> 
#> [[80]]
#> [[80]]$name
#> [1] "Sly Moore"
#> 
#> [[80]]$height
#> [1] "178"
#> 
#> [[80]]$mass
#> [1] "48"
#> 
#> [[80]]$hair_color
#> [1] "none"
#> 
#> [[80]]$skin_color
#> [1] "pale"
#> 
#> [[80]]$eye_color
#> [1] "white"
#> 
#> [[80]]$birth_year
#> [1] "unknown"
#> 
#> [[80]]$gender
#> [1] "female"
#> 
#> [[80]]$homeworld
#> [1] "http://swapi.co/api/planets/60/"
#> 
#> [[80]]$films
#> [1] "http://swapi.co/api/films/5/" "http://swapi.co/api/films/6/"
#> 
#> [[80]]$created
#> [1] "2014-12-20T20:18:37.619000Z"
#> 
#> [[80]]$edited
#> [1] "2014-12-20T21:17:50.496000Z"
#> 
#> [[80]]$url
#> [1] "http://swapi.co/api/people/82/"
#> 
#> 
#> [[81]]
#> [[81]]$name
#> [1] "Tion Medon"
#> 
#> [[81]]$height
#> [1] "206"
#> 
#> [[81]]$mass
#> [1] "80"
#> 
#> [[81]]$hair_color
#> [1] "none"
#> 
#> [[81]]$skin_color
#> [1] "grey"
#> 
#> [[81]]$eye_color
#> [1] "black"
#> 
#> [[81]]$birth_year
#> [1] "unknown"
#> 
#> [[81]]$gender
#> [1] "male"
#> 
#> [[81]]$homeworld
#> [1] "http://swapi.co/api/planets/12/"
#> 
#> [[81]]$films
#> [1] "http://swapi.co/api/films/6/"
#> 
#> [[81]]$species
#> [1] "http://swapi.co/api/species/37/"
#> 
#> [[81]]$created
#> [1] "2014-12-20T20:35:04.260000Z"
#> 
#> [[81]]$edited
#> [1] "2014-12-20T21:17:50.498000Z"
#> 
#> [[81]]$url
#> [1] "http://swapi.co/api/people/83/"
#> 
#> 
#> [[82]]
#> [[82]]$name
#> [1] "Finn"
#> 
#> [[82]]$height
#> [1] "unknown"
#> 
#> [[82]]$mass
#> [1] "unknown"
#> 
#> [[82]]$hair_color
#> [1] "black"
#> 
#> [[82]]$skin_color
#> [1] "dark"
#> 
#> [[82]]$eye_color
#> [1] "dark"
#> 
#> [[82]]$birth_year
#> [1] "unknown"
#> 
#> [[82]]$gender
#> [1] "male"
#> 
#> [[82]]$homeworld
#> [1] "http://swapi.co/api/planets/28/"
#> 
#> [[82]]$films
#> [1] "http://swapi.co/api/films/7/"
#> 
#> [[82]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[82]]$created
#> [1] "2015-04-17T06:52:40.793621Z"
#> 
#> [[82]]$edited
#> [1] "2015-04-17T06:52:40.793674Z"
#> 
#> [[82]]$url
#> [1] "http://swapi.co/api/people/84/"
#> 
#> 
#> [[83]]
#> [[83]]$name
#> [1] "Rey"
#> 
#> [[83]]$height
#> [1] "unknown"
#> 
#> [[83]]$mass
#> [1] "unknown"
#> 
#> [[83]]$hair_color
#> [1] "brown"
#> 
#> [[83]]$skin_color
#> [1] "light"
#> 
#> [[83]]$eye_color
#> [1] "hazel"
#> 
#> [[83]]$birth_year
#> [1] "unknown"
#> 
#> [[83]]$gender
#> [1] "female"
#> 
#> [[83]]$homeworld
#> [1] "http://swapi.co/api/planets/28/"
#> 
#> [[83]]$films
#> [1] "http://swapi.co/api/films/7/"
#> 
#> [[83]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[83]]$created
#> [1] "2015-04-17T06:54:01.495077Z"
#> 
#> [[83]]$edited
#> [1] "2015-04-17T06:54:01.495128Z"
#> 
#> [[83]]$url
#> [1] "http://swapi.co/api/people/85/"
#> 
#> 
#> [[84]]
#> [[84]]$name
#> [1] "Poe Dameron"
#> 
#> [[84]]$height
#> [1] "unknown"
#> 
#> [[84]]$mass
#> [1] "unknown"
#> 
#> [[84]]$hair_color
#> [1] "brown"
#> 
#> [[84]]$skin_color
#> [1] "light"
#> 
#> [[84]]$eye_color
#> [1] "brown"
#> 
#> [[84]]$birth_year
#> [1] "unknown"
#> 
#> [[84]]$gender
#> [1] "male"
#> 
#> [[84]]$homeworld
#> [1] "http://swapi.co/api/planets/28/"
#> 
#> [[84]]$films
#> [1] "http://swapi.co/api/films/7/"
#> 
#> [[84]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[84]]$starships
#> [1] "http://swapi.co/api/starships/77/"
#> 
#> [[84]]$created
#> [1] "2015-04-17T06:55:21.622786Z"
#> 
#> [[84]]$edited
#> [1] "2015-04-17T06:55:21.622835Z"
#> 
#> [[84]]$url
#> [1] "http://swapi.co/api/people/86/"
#> 
#> 
#> [[85]]
#> [[85]]$name
#> [1] "BB8"
#> 
#> [[85]]$height
#> [1] "unknown"
#> 
#> [[85]]$mass
#> [1] "unknown"
#> 
#> [[85]]$hair_color
#> [1] "none"
#> 
#> [[85]]$skin_color
#> [1] "none"
#> 
#> [[85]]$eye_color
#> [1] "black"
#> 
#> [[85]]$birth_year
#> [1] "unknown"
#> 
#> [[85]]$gender
#> [1] "none"
#> 
#> [[85]]$homeworld
#> [1] "http://swapi.co/api/planets/28/"
#> 
#> [[85]]$films
#> [1] "http://swapi.co/api/films/7/"
#> 
#> [[85]]$species
#> [1] "http://swapi.co/api/species/2/"
#> 
#> [[85]]$created
#> [1] "2015-04-17T06:57:38.061346Z"
#> 
#> [[85]]$edited
#> [1] "2015-04-17T06:57:38.061453Z"
#> 
#> [[85]]$url
#> [1] "http://swapi.co/api/people/87/"
#> 
#> 
#> [[86]]
#> [[86]]$name
#> [1] "Captain Phasma"
#> 
#> [[86]]$height
#> [1] "unknown"
#> 
#> [[86]]$mass
#> [1] "unknown"
#> 
#> [[86]]$hair_color
#> [1] "unknown"
#> 
#> [[86]]$skin_color
#> [1] "unknown"
#> 
#> [[86]]$eye_color
#> [1] "unknown"
#> 
#> [[86]]$birth_year
#> [1] "unknown"
#> 
#> [[86]]$gender
#> [1] "female"
#> 
#> [[86]]$homeworld
#> [1] "http://swapi.co/api/planets/28/"
#> 
#> [[86]]$films
#> [1] "http://swapi.co/api/films/7/"
#> 
#> [[86]]$created
#> [1] "2015-10-13T10:35:39.229823Z"
#> 
#> [[86]]$edited
#> [1] "2015-10-13T10:35:39.229894Z"
#> 
#> [[86]]$url
#> [1] "http://swapi.co/api/people/88/"
#> 
#> 
#> [[87]]
#> [[87]]$name
#> [1] "Padmé Amidala"
#> 
#> [[87]]$height
#> [1] "165"
#> 
#> [[87]]$mass
#> [1] "45"
#> 
#> [[87]]$hair_color
#> [1] "brown"
#> 
#> [[87]]$skin_color
#> [1] "light"
#> 
#> [[87]]$eye_color
#> [1] "brown"
#> 
#> [[87]]$birth_year
#> [1] "46BBY"
#> 
#> [[87]]$gender
#> [1] "female"
#> 
#> [[87]]$homeworld
#> [1] "http://swapi.co/api/planets/8/"
#> 
#> [[87]]$films
#> [1] "http://swapi.co/api/films/5/" "http://swapi.co/api/films/4/"
#> [3] "http://swapi.co/api/films/6/"
#> 
#> [[87]]$species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> [[87]]$starships
#> [1] "http://swapi.co/api/starships/49/" "http://swapi.co/api/starships/64/"
#> [3] "http://swapi.co/api/starships/39/"
#> 
#> [[87]]$created
#> [1] "2014-12-19T17:28:26.926000Z"
#> 
#> [[87]]$edited
#> [1] "2016-04-20T17:06:31.502555Z"
#> 
#> [[87]]$url
#> [1] "http://swapi.co/api/people/35/"
```

]

]

---

## How many films was each Star Wars character in?

## `map(sw_people, f = 🤷)`

#### Workflow:

### 1. Do it for one element.
### 2. Find the general recipe.
### 3. Drop into `map()` to do for all.

---

# 1. Do it for one element

```r
x <- sw_people[[1]]
x
```

```
#> $name
#> [1] "Luke Skywalker"
#> 
#> $height
#> [1] "172"
#> 
#> $mass
#> [1] "77"
#> 
#> $hair_color
#> [1] "blond"
#> 
#> $skin_color
#> [1] "fair"
#> 
#> $eye_color
#> [1] "blue"
#> 
#> $birth_year
#> [1] "19BBY"
#> 
#> $gender
#> [1] "male"
#> 
#> $homeworld
#> [1] "http://swapi.co/api/planets/1/"
#> 
#> $films
#> [1] "http://swapi.co/api/films/6/" "http://swapi.co/api/films/3/"
#> [3] "http://swapi.co/api/films/2/" "http://swapi.co/api/films/1/"
#> [5] "http://swapi.co/api/films/7/"
#> 
#> $species
#> [1] "http://swapi.co/api/species/1/"
#> 
#> $vehicles
#> [1] "http://swapi.co/api/vehicles/14/" "http://swapi.co/api/vehicles/30/"
#> 
#> $starships
#> [1] "http://swapi.co/api/starships/12/" "http://swapi.co/api/starships/22/"
#> 
#> $created
#> [1] "2014-12-09T13:50:51.644000Z"
#> 
#> $edited
#> [1] "2014-12-20T21:17:56.891000Z"
#> 
#> $url
#> [1] "http://swapi.co/api/people/1/"
```

]

View the variables we have to work with:

```r
names(x)
```

```
#>  [1] "name"       "height"     "mass"       "hair_color" "skin_color"
#>  [6] "eye_color"  "birth_year" "gender"     "homeworld"  "films"     
#> [11] "species"    "vehicles"   "starships"  "created"    "edited"    
#> [16] "url"
```

Extract the films

```r
x$films
```

```
#> [1] "http://swapi.co/api/films/6/" "http://swapi.co/api/films/3/"
#> [3] "http://swapi.co/api/films/2/" "http://swapi.co/api/films/1/"
#> [5] "http://swapi.co/api/films/7/"
```

]

---

# 1. Do it for one element

### How many films was each Star Wars character in?

Character 1:

```r
x <- sw_people[[1]]
length(x$films)
```

```
#> [1] 5
```

]

Character 2:

```r
x <- sw_people[[2]]
length(x$films)
```

```
#> [1] 6
```

]

---

# 2. Find the general recipe

### How many films was each Star Wars character in?

```r
x <- sw_people[[1]]
length(x$films)
```

```
#> [1] 5
```

#### Recipe:

### `x <- sw_people[[index]]`

### `length(x$films)`

---

# 3. Drop into `map()` to do for all items in list.

#### Recipe:

### `x <- sw_people[[index]]`

### `length(x$films)`

]

#### Do for all items in list:

```r
get_film_length <- function(x) {
    return(length(x$films))
}

map(sw_people, get_film_length)
```

```
#> [[1]]
#> [1] 5
#> 
#> [[2]]
#> [1] 6
#> 
#> [[3]]
#> [1] 7
#> 
#> [[4]]
#> [1] 4
#> 
#> [[5]]
#> [1] 5
#> 
#> [[6]]
#> [1] 3
#> 
#> [[7]]
#> [1] 3
#> 
#> [[8]]
#> [1] 1
#> 
#> [[9]]
#> [1] 1
#> 
#> [[10]]
#> [1] 6
#> 
#> [[11]]
#> [1] 3
#> 
#> [[12]]
#> [1] 2
#> 
#> [[13]]
#> [1] 5
#> 
#> [[14]]
#> [1] 4
#> 
#> [[15]]
#> [1] 1
#> 
#> [[16]]
#> [1] 3
#> 
#> [[17]]
#> [1] 3
#> 
#> [[18]]
#> [1] 1
#> 
#> [[19]]
#> [1] 5
#> 
#> [[20]]
#> [1] 5
#> 
#> [[21]]
#> [1] 3
#> 
#> [[22]]
#> [1] 1
#> 
#> [[23]]
#> [1] 1
#> 
#> [[24]]
#> [1] 2
#> 
#> [[25]]
#> [1] 1
#> 
#> [[26]]
#> [1] 2
#> 
#> [[27]]
#> [1] 1
#> 
#> [[28]]
#> [1] 1
#> 
#> [[29]]
#> [1] 1
#> 
#> [[30]]
#> [1] 1
#> 
#> [[31]]
#> [1] 1
#> 
#> [[32]]
#> [1] 3
#> 
#> [[33]]
#> [1] 1
#> 
#> [[34]]
#> [1] 2
#> 
#> [[35]]
#> [1] 1
#> 
#> [[36]]
#> [1] 1
#> 
#> [[37]]
#> [1] 1
#> 
#> [[38]]
#> [1] 2
#> 
#> [[39]]
#> [1] 1
#> 
#> [[40]]
#> [1] 1
#> 
#> [[41]]
#> [1] 2
#> 
#> [[42]]
#> [1] 1
#> 
#> [[43]]
#> [1] 1
#> 
#> [[44]]
#> [1] 3
#> 
#> [[45]]
#> [1] 1
#> 
#> [[46]]
#> [1] 1
#> 
#> [[47]]
#> [1] 1
#> 
#> [[48]]
#> [1] 3
#> 
#> [[49]]
#> [1] 3
#> 
#> [[50]]
#> [1] 3
#> 
#> [[51]]
#> [1] 2
#> 
#> [[52]]
#> [1] 2
#> 
#> [[53]]
#> [1] 2
#> 
#> [[54]]
#> [1] 1
#> 
#> [[55]]
#> [1] 3
#> 
#> [[56]]
#> [1] 2
#> 
#> [[57]]
#> [1] 1
#> 
#> [[58]]
#> [1] 1
#> 
#> [[59]]
#> [1] 1
#> 
#> [[60]]
#> [1] 2
#> 
#> [[61]]
#> [1] 2
#> 
#> [[62]]
#> [1] 1
#> 
#> [[63]]
#> [1] 1
#> 
#> [[64]]
#> [1] 2
#> 
#> [[65]]
#> [1] 2
#> 
#> [[66]]
#> [1] 1
#> 
#> [[67]]
#> [1] 1
#> 
#> [[68]]
#> [1] 1
#> 
#> [[69]]
#> [1] 1
#> 
#> [[70]]
#> [1] 1
#> 
#> [[71]]
#> [1] 1
#> 
#> [[72]]
#> [1] 1
#> 
#> [[73]]
#> [1] 2
#> 
#> [[74]]
#> [1] 1
#> 
#> [[75]]
#> [1] 1
#> 
#> [[76]]
#> [1] 2
#> 
#> [[77]]
#> [1] 1
#> 
#> [[78]]
#> [1] 1
#> 
#> [[79]]
#> [1] 2
#> 
#> [[80]]
#> [1] 2
#> 
#> [[81]]
#> [1] 1
#> 
#> [[82]]
#> [1] 1
#> 
#> [[83]]
#> [1] 1
#> 
#> [[84]]
#> [1] 1
#> 
#> [[85]]
#> [1] 1
#> 
#> [[86]]
#> [1] 1
#> 
#> [[87]]
#> [1] 3
```

]

---

## for every element of `x` do `f`

]

```r
get_film_length <- function(x) {
    return(length(x$films))
}

map(sw_people, get_film_length)
```

]

---

# Simplify it with "anonymous" functions

Version 1: **Custom function**

```r
get_film_length <- function(x) {
    return(length(x$films))
}

map(sw_people, get_film_length)
```

]

Version 2: **Anonymous function**

```r
map(sw_people, function(x) length(x$films))
```

]

---

## Anonymous functions

Three ways of specifying anonymous functions:

```r
map(sw_people, function(x) length( x$films)) # supported in base R
map(sw_people,        \(x) length( x$films)) # supported R > 4.1.0
map(sw_people,           ~ length(.x$films)) # supported in purrr
```

---

# Quick practice

- How many `vehicles` does each Star Wars character have?

(use `sw_people`)

- How many `titles` does each character in Game of Thrones have?

(use `got_chars`)

---

## Type specific map variants

```r
map_int(sw_people, \(x) length(x$films))
```

```
#>  [1] 5 6 7 4 5 3 3 1 1 6 3 2 5 4 1 3 3 1 5 5 3 1 1 2 1 2 1 1 1 1 1 3 1 2 1 1 1 2
#> [39] 1 1 2 1 1 3 1 1 1 3 3 3 2 2 2 1 3 2 1 1 1 2 2 1 1 2 2 1 1 1 1 1 1 1 2 1 1 2
#> [77] 1 1 2 2 1 1 1 1 1 1 3
```

`map_lgl()`: Returns a _logical_ vector

`map_int()`: Returns a _integer_ vector

`map_dbl()`: Returns a _double_ vector

`map_chr()`: Returns a _character_ vector

---

# Quick practice

#### Replace `map()` with type-specific `map()`.

```r
# What's each character's name?
map(got_chars, \(x) x$name)
map(sw_people, \(x) x$name)

# What color is each SW character's hair?
map(sw_people, \(x) x$hair_color)

# Is the GoT character alive?
map(got_chars, \(x) x$alive)

# Is the SW character female?
map(sw_people, \(x) x$gender == "female")

# How heavy is each SW character?
map(sw_people, \(x) x$mass)
```

---

## How many films was each Star Wars character in?

```r
map(sw_people, \(x) length(x$films))
```

]

### .center[.red[Wait, which character?]]

]

---

# Use a tibble to get the character name as well!

Returns a list of data frames:

```r
map(sw_people, \(x) tibble(
    name = x$name, 
    n_vehicles = length(x$films)
  )
)
```

```
#> [[1]]
#> # A tibble: 1 × 2
#>   name           n_vehicles
#>   <chr>               <int>
#> 1 Luke Skywalker          5
#> 
#> [[2]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 C-3PO          6
#> 
#> [[3]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 R2-D2          7
#> 
#> [[4]]
#> # A tibble: 1 × 2
#>   name        n_vehicles
#>   <chr>            <int>
#> 1 Darth Vader          4
#> 
#> [[5]]
#> # A tibble: 1 × 2
#>   name        n_vehicles
#>   <chr>            <int>
#> 1 Leia Organa          5
#> 
#> [[6]]
#> # A tibble: 1 × 2
#>   name      n_vehicles
#>   <chr>          <int>
#> 1 Owen Lars          3
#> 
#> [[7]]
#> # A tibble: 1 × 2
#>   name               n_vehicles
#>   <chr>                   <int>
#> 1 Beru Whitesun lars          3
#> 
#> [[8]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 R5-D4          1
#> 
#> [[9]]
#> # A tibble: 1 × 2
#>   name              n_vehicles
#>   <chr>                  <int>
#> 1 Biggs Darklighter          1
#> 
#> [[10]]
#> # A tibble: 1 × 2
#>   name           n_vehicles
#>   <chr>               <int>
#> 1 Obi-Wan Kenobi          6
#> 
#> [[11]]
#> # A tibble: 1 × 2
#>   name             n_vehicles
#>   <chr>                 <int>
#> 1 Anakin Skywalker          3
#> 
#> [[12]]
#> # A tibble: 1 × 2
#>   name           n_vehicles
#>   <chr>               <int>
#> 1 Wilhuff Tarkin          2
#> 
#> [[13]]
#> # A tibble: 1 × 2
#>   name      n_vehicles
#>   <chr>          <int>
#> 1 Chewbacca          5
#> 
#> [[14]]
#> # A tibble: 1 × 2
#>   name     n_vehicles
#>   <chr>         <int>
#> 1 Han Solo          4
#> 
#> [[15]]
#> # A tibble: 1 × 2
#>   name   n_vehicles
#>   <chr>       <int>
#> 1 Greedo          1
#> 
#> [[16]]
#> # A tibble: 1 × 2
#>   name                  n_vehicles
#>   <chr>                      <int>
#> 1 Jabba Desilijic Tiure          3
#> 
#> [[17]]
#> # A tibble: 1 × 2
#>   name           n_vehicles
#>   <chr>               <int>
#> 1 Wedge Antilles          3
#> 
#> [[18]]
#> # A tibble: 1 × 2
#>   name             n_vehicles
#>   <chr>                 <int>
#> 1 Jek Tono Porkins          1
#> 
#> [[19]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 Yoda           5
#> 
#> [[20]]
#> # A tibble: 1 × 2
#>   name      n_vehicles
#>   <chr>          <int>
#> 1 Palpatine          5
#> 
#> [[21]]
#> # A tibble: 1 × 2
#>   name      n_vehicles
#>   <chr>          <int>
#> 1 Boba Fett          3
#> 
#> [[22]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 IG-88          1
#> 
#> [[23]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 Bossk          1
#> 
#> [[24]]
#> # A tibble: 1 × 2
#>   name             n_vehicles
#>   <chr>                 <int>
#> 1 Lando Calrissian          2
#> 
#> [[25]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 Lobot          1
#> 
#> [[26]]
#> # A tibble: 1 × 2
#>   name   n_vehicles
#>   <chr>       <int>
#> 1 Ackbar          2
#> 
#> [[27]]
#> # A tibble: 1 × 2
#>   name       n_vehicles
#>   <chr>           <int>
#> 1 Mon Mothma          1
#> 
#> [[28]]
#> # A tibble: 1 × 2
#>   name         n_vehicles
#>   <chr>             <int>
#> 1 Arvel Crynyd          1
#> 
#> [[29]]
#> # A tibble: 1 × 2
#>   name                  n_vehicles
#>   <chr>                      <int>
#> 1 Wicket Systri Warrick          1
#> 
#> [[30]]
#> # A tibble: 1 × 2
#>   name      n_vehicles
#>   <chr>          <int>
#> 1 Nien Nunb          1
#> 
#> [[31]]
#> # A tibble: 1 × 2
#>   name         n_vehicles
#>   <chr>             <int>
#> 1 Qui-Gon Jinn          1
#> 
#> [[32]]
#> # A tibble: 1 × 2
#>   name        n_vehicles
#>   <chr>            <int>
#> 1 Nute Gunray          3
#> 
#> [[33]]
#> # A tibble: 1 × 2
#>   name          n_vehicles
#>   <chr>              <int>
#> 1 Finis Valorum          1
#> 
#> [[34]]
#> # A tibble: 1 × 2
#>   name          n_vehicles
#>   <chr>              <int>
#> 1 Jar Jar Binks          2
#> 
#> [[35]]
#> # A tibble: 1 × 2
#>   name         n_vehicles
#>   <chr>             <int>
#> 1 Roos Tarpals          1
#> 
#> [[36]]
#> # A tibble: 1 × 2
#>   name       n_vehicles
#>   <chr>           <int>
#> 1 Rugor Nass          1
#> 
#> [[37]]
#> # A tibble: 1 × 2
#>   name     n_vehicles
#>   <chr>         <int>
#> 1 Ric Olié          1
#> 
#> [[38]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 Watto          2
#> 
#> [[39]]
#> # A tibble: 1 × 2
#>   name    n_vehicles
#>   <chr>        <int>
#> 1 Sebulba          1
#> 
#> [[40]]
#> # A tibble: 1 × 2
#>   name          n_vehicles
#>   <chr>              <int>
#> 1 Quarsh Panaka          1
#> 
#> [[41]]
#> # A tibble: 1 × 2
#>   name           n_vehicles
#>   <chr>               <int>
#> 1 Shmi Skywalker          2
#> 
#> [[42]]
#> # A tibble: 1 × 2
#>   name       n_vehicles
#>   <chr>           <int>
#> 1 Darth Maul          1
#> 
#> [[43]]
#> # A tibble: 1 × 2
#>   name        n_vehicles
#>   <chr>            <int>
#> 1 Bib Fortuna          1
#> 
#> [[44]]
#> # A tibble: 1 × 2
#>   name        n_vehicles
#>   <chr>            <int>
#> 1 Ayla Secura          3
#> 
#> [[45]]
#> # A tibble: 1 × 2
#>   name     n_vehicles
#>   <chr>         <int>
#> 1 Dud Bolt          1
#> 
#> [[46]]
#> # A tibble: 1 × 2
#>   name    n_vehicles
#>   <chr>        <int>
#> 1 Gasgano          1
#> 
#> [[47]]
#> # A tibble: 1 × 2
#>   name           n_vehicles
#>   <chr>               <int>
#> 1 Ben Quadinaros          1
#> 
#> [[48]]
#> # A tibble: 1 × 2
#>   name       n_vehicles
#>   <chr>           <int>
#> 1 Mace Windu          3
#> 
#> [[49]]
#> # A tibble: 1 × 2
#>   name         n_vehicles
#>   <chr>             <int>
#> 1 Ki-Adi-Mundi          3
#> 
#> [[50]]
#> # A tibble: 1 × 2
#>   name      n_vehicles
#>   <chr>          <int>
#> 1 Kit Fisto          3
#> 
#> [[51]]
#> # A tibble: 1 × 2
#>   name      n_vehicles
#>   <chr>          <int>
#> 1 Eeth Koth          2
#> 
#> [[52]]
#> # A tibble: 1 × 2
#>   name       n_vehicles
#>   <chr>           <int>
#> 1 Adi Gallia          2
#> 
#> [[53]]
#> # A tibble: 1 × 2
#>   name        n_vehicles
#>   <chr>            <int>
#> 1 Saesee Tiin          2
#> 
#> [[54]]
#> # A tibble: 1 × 2
#>   name        n_vehicles
#>   <chr>            <int>
#> 1 Yarael Poof          1
#> 
#> [[55]]
#> # A tibble: 1 × 2
#>   name     n_vehicles
#>   <chr>         <int>
#> 1 Plo Koon          3
#> 
#> [[56]]
#> # A tibble: 1 × 2
#>   name       n_vehicles
#>   <chr>           <int>
#> 1 Mas Amedda          2
#> 
#> [[57]]
#> # A tibble: 1 × 2
#>   name         n_vehicles
#>   <chr>             <int>
#> 1 Gregar Typho          1
#> 
#> [[58]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 Cordé          1
#> 
#> [[59]]
#> # A tibble: 1 × 2
#>   name        n_vehicles
#>   <chr>            <int>
#> 1 Cliegg Lars          1
#> 
#> [[60]]
#> # A tibble: 1 × 2
#>   name              n_vehicles
#>   <chr>                  <int>
#> 1 Poggle the Lesser          2
#> 
#> [[61]]
#> # A tibble: 1 × 2
#>   name            n_vehicles
#>   <chr>                <int>
#> 1 Luminara Unduli          2
#> 
#> [[62]]
#> # A tibble: 1 × 2
#>   name          n_vehicles
#>   <chr>              <int>
#> 1 Barriss Offee          1
#> 
#> [[63]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 Dormé          1
#> 
#> [[64]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 Dooku          2
#> 
#> [[65]]
#> # A tibble: 1 × 2
#>   name                n_vehicles
#>   <chr>                    <int>
#> 1 Bail Prestor Organa          2
#> 
#> [[66]]
#> # A tibble: 1 × 2
#>   name       n_vehicles
#>   <chr>           <int>
#> 1 Jango Fett          1
#> 
#> [[67]]
#> # A tibble: 1 × 2
#>   name       n_vehicles
#>   <chr>           <int>
#> 1 Zam Wesell          1
#> 
#> [[68]]
#> # A tibble: 1 × 2
#>   name            n_vehicles
#>   <chr>                <int>
#> 1 Dexter Jettster          1
#> 
#> [[69]]
#> # A tibble: 1 × 2
#>   name    n_vehicles
#>   <chr>        <int>
#> 1 Lama Su          1
#> 
#> [[70]]
#> # A tibble: 1 × 2
#>   name    n_vehicles
#>   <chr>        <int>
#> 1 Taun We          1
#> 
#> [[71]]
#> # A tibble: 1 × 2
#>   name       n_vehicles
#>   <chr>           <int>
#> 1 Jocasta Nu          1
#> 
#> [[72]]
#> # A tibble: 1 × 2
#>   name          n_vehicles
#>   <chr>              <int>
#> 1 Ratts Tyerell          1
#> 
#> [[73]]
#> # A tibble: 1 × 2
#>   name   n_vehicles
#>   <chr>       <int>
#> 1 R4-P17          2
#> 
#> [[74]]
#> # A tibble: 1 × 2
#>   name       n_vehicles
#>   <chr>           <int>
#> 1 Wat Tambor          1
#> 
#> [[75]]
#> # A tibble: 1 × 2
#>   name     n_vehicles
#>   <chr>         <int>
#> 1 San Hill          1
#> 
#> [[76]]
#> # A tibble: 1 × 2
#>   name     n_vehicles
#>   <chr>         <int>
#> 1 Shaak Ti          2
#> 
#> [[77]]
#> # A tibble: 1 × 2
#>   name     n_vehicles
#>   <chr>         <int>
#> 1 Grievous          1
#> 
#> [[78]]
#> # A tibble: 1 × 2
#>   name    n_vehicles
#>   <chr>        <int>
#> 1 Tarfful          1
#> 
#> [[79]]
#> # A tibble: 1 × 2
#>   name            n_vehicles
#>   <chr>                <int>
#> 1 Raymus Antilles          2
#> 
#> [[80]]
#> # A tibble: 1 × 2
#>   name      n_vehicles
#>   <chr>          <int>
#> 1 Sly Moore          2
#> 
#> [[81]]
#> # A tibble: 1 × 2
#>   name       n_vehicles
#>   <chr>           <int>
#> 1 Tion Medon          1
#> 
#> [[82]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 Finn           1
#> 
#> [[83]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 Rey            1
#> 
#> [[84]]
#> # A tibble: 1 × 2
#>   name        n_vehicles
#>   <chr>            <int>
#> 1 Poe Dameron          1
#> 
#> [[85]]
#> # A tibble: 1 × 2
#>   name  n_vehicles
#>   <chr>      <int>
#> 1 BB8            1
#> 
#> [[86]]
#> # A tibble: 1 × 2
#>   name           n_vehicles
#>   <chr>               <int>
#> 1 Captain Phasma          1
#> 
#> [[87]]
#> # A tibble: 1 × 2
#>   name          n_vehicles
#>   <chr>              <int>
#> 1 Padmé Amidala          3
```

]

Use `map_df()` to merge the data frames

```r
*map_df(sw_people, \(x) tibble(
    name = x$name, 
    n_vehicles = length(x$films)
  )
)
```

```
#> # A tibble: 87 × 2
#>    name               n_vehicles
#>    <chr>                   <int>
#>  1 Luke Skywalker              5
#>  2 C-3PO                       6
#>  3 R2-D2                       7
#>  4 Darth Vader                 4
#>  5 Leia Organa                 5
#>  6 Owen Lars                   3
#>  7 Beru Whitesun lars          3
#>  8 R5-D4                       1
#>  9 Biggs Darklighter           1
#> 10 Obi-Wan Kenobi              6
#> # … with 77 more rows
```

]

---

## Your turn

Try to answer these questions:

1. Which SW film has the most characters? (use `sw_films`)
2. Which SW species has the highest average lifespan? (use `sw_species`)
3. Which GoT character(s) have been played by multiple actors? (use `got_chars`)

---

## Sometimes you really need do something on each row

Use a `for` loop to iterate across each row in a data frame:

```r
for (i in 1:nrow(df)) {
  row <- df[i,]
  # Do stuff with row
}
```

---

## .center[Example: tagging a new daily covid case record]

```r
covid_dc <- read_csv(here::here('data', 'us_covid.csv')) %>% 
  filter(state == 'District of Columbia') %>% 
  select(-state)

head(covid_dc)
```

```
#> # A tibble: 6 × 6
#>   date         day cases_daily deaths_daily cases_total deaths_total
#>   <date>     <dbl>       <dbl>        <dbl>       <dbl>        <dbl>
#> 1 2020-01-23     1           0            0           0            0
#> 2 2020-01-24     2           0            0           0            0
#> 3 2020-01-25     3           0            0           0            0
#> 4 2020-01-26     4           0            0           0            0
#> 5 2020-01-27     5           0            0           0            0
#> 6 2020-01-28     6           0            0           0            0
```

---

## .center[Example: tagging a new daily covid case record]

Initialize new column

```r
covid_dc$new_record <- FALSE

glimpse(covid_dc)
```

```
#> Rows: 403
#> Columns: 7
#> $ date         <date> 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26, 2020-01-…
#> $ day          <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17…
#> $ cases_daily  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ deaths_daily <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ cases_total  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ deaths_total <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ new_record   <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F…
```

]

Now loop through each row and check if a new record is met

```r
record <- 0
for (i in 1:nrow(covid_dc)) {
  
  # Get the nubmer of cases on row i
  num_cases <- covid_dc[i,]$cases_daily
  
  # Check if new record is met
  if (num_cases > record) {    
    
    # Update new record in covid_dc
    covid_dc[i, ]$new_record <- TRUE 
    
    # Update new record
    record <- num_cases
  }
}
```

]

---

---

## Preview [HW 11](https://p4a.seas.gwu.edu/2023-Spring/hw/11-programming-with-data.html)