# Using schrute

# What

This is a package that does/has only one thing: the complete transcriptions of all episodes of The Office! (US version).

Use this data set to master NLP or text analysis. Let’s scratch the surface of the subject with a few examples from the excellent Text Mining with R book, by Julia Silge and David Robinson.

First install the package from CRAN:

    library(schrute)

There is only one data set with the schrute package; assign it to a variable

    mydata <- schrute::theoffice

Take a peek at the format:

    dplyr::glimpse(mydata)
    #> Rows: 55,130
    #> Columns: 12
    #> $ index            <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16…
    #> $ season           <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
    #> $ episode          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
    #> $ episode_name     <chr> "Pilot", "Pilot", "Pilot", "Pilot", "Pilot", "Pilot",…
    #> $ director         <chr> "Ken Kwapis", "Ken Kwapis", "Ken Kwapis", "Ken Kwapis…
    #> $ writer           <chr> "Ricky Gervais;Stephen Merchant;Greg Daniels", "Ricky…
    #> $ character        <chr> "Michael", "Jim", "Michael", "Jim", "Michael", "Micha…
    #> $ text             <chr> "All right Jim. Your quarterlies look very good. How …
    #> $ text_w_direction <chr> "All right Jim. Your quarterlies look very good. How …
    #> $ imdb_rating      <dbl> 7.6, 7.6, 7.6, 7.6, 7.6, 7.6, 7.6, 7.6, 7.6, 7.6, 7.6…
    #> $ total_votes      <int> 3706, 3706, 3706, 3706, 3706, 3706, 3706, 3706, 3706,…
    #> $ air_date         <chr> "2005-03-24", "2005-03-24", "2005-03-24", "2005-03-24…

     mydata %>%
      dplyr::filter(season == 1) %>%
      dplyr::filter(episode == 1) %>%
      dplyr::slice(1:3) %>%
      knitr::kable()

index | season | episode | episode_name | director | writer | character | text | text_w_direction | imdb_rating | total_votes | air_date
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
1 | 1 | 1 | Pilot | Ken Kwapis | Ricky Gervais;Stephen Merchant;Greg Daniels | Michael | All right Jim. Your quarterlies look very good. How are things at the library? | All right Jim. Your quarterlies look very good. How are things at the library? | 7.6 | 3706 | 2005-03-24
2 | 1 | 1 | Pilot | Ken Kwapis | Ricky Gervais;Stephen Merchant;Greg Daniels | Jim | Oh, I told you. I couldn’t close it. So… | Oh, I told you. I couldn’t close it. So… | 7.6 | 3706 | 2005-03-24
3 | 1 | 1 | Pilot | Ken Kwapis | Ricky Gervais;Stephen Merchant;Greg Daniels | Michael | So you’ve come to the master for guidance? Is this what you’re saying, grasshopper? | So you’ve come to the master for guidance? Is this what you’re saying, grasshopper? | 7.6 | 3706 | 2005-03-24

So what we have is the season, episode number and name, character, the line spoken and the line spoken with the stage direction (cue).

We can tokenize all of the lines with a few lines from the tidytext package:

    token.mydata <- mydata %>%
      tidytext::unnest_tokens(word, text)

This increases our data set to 571612 records, where each record contains a word from the script.

     token.mydata %>%
      dplyr::filter(season == 1) %>%
      dplyr::filter(episode == 1) %>%
      dplyr::slice(1:3) %>%
      knitr::kable()

index | season | episode | episode_name | director | writer | character | text_w_direction | imdb_rating | total_votes | air_date | word
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
1 | 1 | 1 | Pilot | Ken Kwapis | Ricky Gervais;Stephen Merchant;Greg Daniels | Michael | All right Jim. Your quarterlies look very good. How are things at the library? | 7.6 | 3706 | 2005-03-24 | all
1 | 1 | 1 | Pilot | Ken Kwapis | Ricky Gervais;Stephen Merchant;Greg Daniels | Michael | All right Jim. Your quarterlies look very good. How are things at the library? | 7.6 | 3706 | 2005-03-24 | right
1 | 1 | 1 | Pilot | Ken Kwapis | Ricky Gervais;Stephen Merchant;Greg Daniels | Michael | All right Jim. Your quarterlies look very good. How are things at the library? | 7.6 | 3706 | 2005-03-24 | jim

If we want to analyze the entire data set, we need to remove some stop words first:

    stop_words <- tidytext::stop_words

    tidy.token.mydata <- token.mydata %>%
      dplyr::anti_join(stop_words, by = "word")

And then see what the most common words are:

    tidy.token.mydata %>%
      dplyr::count(word, sort = TRUE)
    #> # A tibble: 18,960 × 2
    #>    word        n
    #>    <chr>   <int>
    #>  1 yeah     2930
    #>  2 hey      2232
    #>  3 michael  1860
    #>  4 uh       1463
    #>  5 gonna    1405
    #>  6 dwight   1345
    #>  7 jim      1162
    #>  8 time     1149
    #>  9 pam      1043
    #> 10 guys      947
    #> # … with 18,950 more rows

    tidy.token.mydata %>%
      dplyr::count(word, sort = TRUE) %>%
      dplyr::filter(n > 400) %>%
      dplyr::mutate(word = stats::reorder(word, n)) %>%
      ggplot2::ggplot(ggplot2::aes(word, n)) +
      ggplot2::geom_col() +
      ggplot2::xlab(NULL) +
      ggplot2::coord_flip() +
      ggplot2::theme_minimal()

Feel free to keep going with this. Now that you have the time line (episode, season) and the character for each line and word in the series, you can perform an unlimited number of analyses. Some ideas: - Sentiment by character - Sentiment by character by season - Narcissism by season (ahem.. Nard Dog season 8-9) - Lines by character - Etc.