By: Brad Lindblad

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What

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

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:

# install.packages("schrute") 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) #> Observations: 55,130 #> Variables: 9 #> $ index <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, … #> $ season <chr> "01", "01", "01", "01", "01", "01", "01", "01",… #> $ episode <chr> "01", "01", "01", "01", "01", "01", "01", "01",… #> $ episode_name <chr> "Pilot", "Pilot", "Pilot", "Pilot", "Pilot", "P… #> $ director <chr> "Ken Kwapis", "Ken Kwapis", "Ken Kwapis", "Ken … #> $ writer <chr> "Ricky Gervais;Stephen Merchant;Greg Daniels", … #> $ character <chr> "Michael", "Jim", "Michael", "Jim", "Michael", … #> $ text <chr> "All right Jim. Your quarterlies look very good… #> $ text_w_direction <chr> "All right Jim. Your quarterlies look very good…

mydata %>% dplyr::filter(season == '01') %>% dplyr::filter(episode == '01') %>% dplyr::slice(1:3) %>% knitr::kable()

index season episode episode_name director writer character text text_w_direction 1 01 01 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? 2 01 01 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… 3 01 01 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?

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 570450 records, where each record contains a word from the script.

token.mydata %>% dplyr::filter(season == '01') %>% dplyr::filter(episode == '01') %>% dplyr::slice(1:3) %>% knitr::kable()

index season episode episode_name director writer character text_w_direction word 1 01 01 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 1 01 01 Pilot Ken Kwapis Ricky Gervais;Stephen Merchant;Greg Daniels Michael All right Jim. Your quarterlies look very good. How are things at the library? right 1 01 01 Pilot Ken Kwapis Ricky Gervais;Stephen Merchant;Greg Daniels Michael All right Jim. Your quarterlies look very good. How are things at the library? 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: 18,946 x 2 #> word n #> <chr> <int> #> 1 yeah 2930 #> 2 hey 2232 #> 3 michael 1859 #> 4 uh 1459 #> 5 gonna 1399 #> 6 dwight 1340 #> 7 jim 1168 #> 8 time 1147 #> 9 pam 1044 #> 10 guys 945 #> # … with 18,936 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.

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