Import your data

data <- read_excel("myData.xlsx")
data
## # A tibble: 32,754 × 20
##         id original_title original_language overview tagline release_date       
##      <dbl> <chr>          <chr>             <chr>    <chr>   <dttm>             
##  1  760161 Orphan: First… en                After e… "There… 2022-07-27 00:00:00
##  2  760741 Beast          en                A recen… "Fight… 2022-08-11 00:00:00
##  3  882598 Smile          en                After w… "Once … 2022-09-23 00:00:00
##  4  717728 Jeepers Creep… en                Forced … "Evil … 2022-09-15 00:00:00
##  5  772450 Presencias     es                A man w…  <NA>   2022-09-07 00:00:00
##  6 1014226 Sonríe         es                <NA>      <NA>   2022-08-18 00:00:00
##  7  913290 Barbarian      en                In town… "Some … 2022-09-08 00:00:00
##  8  830788 The Invitation en                After t… "You a… 2022-08-24 00:00:00
##  9  927341 Hunting Ava B… en                Billion… "\"If … 2022-04-01 00:00:00
## 10  762504 Nope           en                Residen… "What’… 2022-07-20 00:00:00
## # ℹ 32,744 more rows
## # ℹ 14 more variables: title <chr>, popularity <dbl>, revenue <dbl>,
## #   budget <dbl>, poster_path <chr>, vote_count <dbl>, vote_average <dbl>,
## #   runtime <dbl>, status <chr>, adult <lgl>, backdrop_path <chr>,
## #   genre_names <chr>, collection <chr>, collection_name <chr>
small_data <- data %>%
  slice(1:5) %>%
  select(id, original_title, release_date, popularity, revenue,)

Chapter 15

Create a factor

small_data <- small_data %>%
  mutate(original_title = factor(original_title))

Modify factor order

Make two bar charts here - one before ordering another after

ggplot(small_data, aes(y = original_title, x = popularity)) +
  geom_col() +
  labs(title = "Alphabetical Order (Default)")

ggplot(small_data, aes(y = fct_reorder(original_title, popularity), x = popularity)) +
  geom_col() +
  labs(y = "Movie Title", title = "Reordered by Popularity")

Modify factor levels

Show examples of three functions: * fct_recode

small_data %>%
  mutate(original_title = fct_recode(original_title, 
                                     "JC Reborn" = "Jeepers Creepers: Reborn"))
## # A tibble: 5 × 5
##       id original_title     release_date        popularity  revenue
##    <dbl> <fct>              <dttm>                   <dbl>    <dbl>
## 1 760161 Orphan: First Kill 2022-07-27 00:00:00      5089.  9572765
## 2 760741 Beast              2022-08-11 00:00:00      2172. 56000000
## 3 882598 Smile              2022-09-23 00:00:00      1864. 45000000
## 4 717728 JC Reborn          2022-09-15 00:00:00       822.  2892594
## 5 772450 Presencias         2022-09-07 00:00:00      1021.        0
  • fct_collapse
small_data %>%
  mutate(original_title = fct_collapse(original_title,
                                       "2022 Hits" = c("Beast", "Smile")))
## # A tibble: 5 × 5
##       id original_title           release_date        popularity  revenue
##    <dbl> <fct>                    <dttm>                   <dbl>    <dbl>
## 1 760161 Orphan: First Kill       2022-07-27 00:00:00      5089.  9572765
## 2 760741 2022 Hits                2022-08-11 00:00:00      2172. 56000000
## 3 882598 2022 Hits                2022-09-23 00:00:00      1864. 45000000
## 4 717728 Jeepers Creepers: Reborn 2022-09-15 00:00:00       822.  2892594
## 5 772450 Presencias               2022-09-07 00:00:00      1021.        0
  • fct_lump
small_data %>%
  mutate(original_title = fct_lump(original_title, n = 3))
## # A tibble: 5 × 5
##       id original_title           release_date        popularity  revenue
##    <dbl> <fct>                    <dttm>                   <dbl>    <dbl>
## 1 760161 Orphan: First Kill       2022-07-27 00:00:00      5089.  9572765
## 2 760741 Beast                    2022-08-11 00:00:00      2172. 56000000
## 3 882598 Smile                    2022-09-23 00:00:00      1864. 45000000
## 4 717728 Jeepers Creepers: Reborn 2022-09-15 00:00:00       822.  2892594
## 5 772450 Presencias               2022-09-07 00:00:00      1021.        0

Chapter 16

No need to do anything here.