title: “Week 8: Apply it to your data 7” author: “Luke Davies” date: “2022-10-05” output: html_document editor_options: chunk_output_type: console —

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, original_language, release_date, popularity, revenue, budget) 

Pivoting

long to wide form

data_long <- data %>%
    
    pivot_longer(cols = c(revenue, budget),
                 names_to = "metric", 
                 values_to = "value") 

wide to long form

small_long <- small_data %>%
  pivot_longer(cols = c(revenue, budget), 
               names_to = "financial_type", 
               values_to = "amount")

Separating and Uniting

data_sep <- data %>%
  separate(col = release_date, 
           into = c("year", "month", "day"), 
           sep = "-") 

Separate a column

small_sep <- small_data %>%
  separate(col = release_date, 
           into = c("year", "month", "day"), 
           sep = "-")

Unite two columns

small_united <- small_data %>%
  unite(col = "movie_info", 
        original_title, original_language, 
        sep = " - Lang: ")

Missing Values

small_cleaned <- small_data %>% 
  drop_na(revenue)