R⁶ — Using pandoc from R + A Neat Package For Reading Subtitles

April 30, 2017
By

(This article was first published on R – rud.is, and kindly contributed to R-bloggers)

Once I realized that my planned, larger post would not come to fruition today I took the R⁶ post (i.e. “minimal expository, keen focus) route, prompted by a Twitter discussion with some R mates who needed to convert “lightly formatted” Microsoft Word (docx) documents to markdown. Something like this:

to:

Does pandoc work?
=================

Simple document with **bold** and *italics*.

This is definitely a job that pandoc can handle.

pandoc is a Haskell (yes, Haskell) program created by John MacFarlane and is an amazing tool for transcoding documents. And, if you’re a “modern” R/RStudio user, you likely use it every day because it’s ultimately what powers rmarkdown / knitr.

Yes, you read that correctly. You’re beautiful PDF, Word and HTML R reports are powered by — and, would not be possible without — Haskell.

Doing the aforementioned conversion from docx to markdown is super-simple from R:

rmarkdown::pandoc_convert("simple.docx", "markdown", output="simple.md")

Give the help on rmarkdown::pandoc_convert() a read as well as the very thorough and helpful documentation over at [pandoc.org])(http://pandoc.org) to see the power available at your command.

Just One More Thing

This section — technically — violates the R⁶ principle so you can stop reading if you’re a purist 🙂

There’s a neat, non-on-CRAN package by François Keck called subtoolshttps://github.com/fkeck/subtools which can slice, dice and reformat digital content subtitles. There are multiple formats for these subtitle files and it seems to be able to handle them all.

There was a post (earlier in April) about Ranking the Negativity of Black Mirror Episodes. That post is python and I’ve never had time to fully replicate it in R.

Here’s a snippet (sans expository) that can get you started pulling in subtitles into R and tidytext. I would have written scraper code but the various subtitle aggregation sites make that a task suited for something like my splashr package and I just had no cycles to write the code. So, I grabbed the first season of “The Flash” and use the Bing sentiment lexicon from tidytext to see how the season looked.

The overall scoring for a given episode is naive and can definitely be improved upon.

Definitely drop a link to anything you create in the comments!

# devtools::install_github("fkeck/subtools")

library(subtools)
library(tidytext)
library(hrbrthemes)
library(tidyverse)

data(stop_words)

bing <- get_sentiments("bing")
afinn <- get_sentiments("afinn")

fils <- list.files("flash/01", pattern = "srt$", full.names = TRUE)

pb <- progress_estimated(length(fils))

map_df(1:length(fils), ~{

  pb$tick()$print()

  read.subtitles(fils[.x]) %>%
    sentencify() %>%
    .$subtitles %>%
    unnest_tokens(word, Text) %>%
    anti_join(stop_words, by="word") %>%
    inner_join(bing, by="word") %>%
    inner_join(afinn, by="word") %>%
    mutate(season = 1, ep = .x)

}) %>% as_tibble() -> season_sentiments


count(season_sentiments, ep, sentiment) %>%
  mutate(pct = n/sum(n),
         pct = ifelse(sentiment == "negative", -pct, pct)) -> bing_sent

ggplot() +
  geom_ribbon(data = filter(bing_sent, sentiment=="positive"),
              aes(ep, ymin=0, ymax=pct, fill=sentiment), alpha=3/4) +
  geom_ribbon(data = filter(bing_sent, sentiment=="negative"),
              aes(ep, ymin=0, ymax=pct, fill=sentiment), alpha=3/4) +
  scale_x_continuous(expand=c(0,0.5), breaks=seq(1, 23, 2)) +
  scale_y_continuous(expand=c(0,0), limits=c(-1,1),
                     labels=c("100%\nnegative", "50%", "0", "50%", "positive\n100%")) +
  labs(x="Season 1 Episode", y=NULL, title="The Flash — Season 1",
       subtitle="Sentiment balance per episode") +
  scale_fill_ipsum(name="Sentiment") +
  guides(fill = guide_legend(reverse=TRUE)) +
  theme_ipsum_rc(grid="Y") +
  theme(axis.text.y=element_text(vjust=c(0, 0.5, 0.5, 0.5, 1)))

To leave a comment for the author, please follow the link and comment on their blog: R – rud.is.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)