R/data-ipo.R

In openintro: Data Sets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

#' Facebook, Google, and LinkedIn IPO filings
#'
#' On Feb 1st, 2011, Facebook Inc. filed an S-1 form with the Securities and
#' Exchange Commission as part of their initial public offering (IPO).  This
#' dataset includes the text of that document as well as text from the IPOs of
#' two competing companies: Google and LinkedIn.
#'
#' Each of the three prospectuses is encoded in UTF-8 format and contains some
#' non-word characters related to the layout of the original documents.  For
#' analysis on the words, it is recommended that the data be processed with
#' packages such as [tidytext](https://juliasilge.github.io/tidytext/).
#' See examples below.
#'
#' @name ipo
#' @docType data
#' @format The format is a list of three character vectors.  Each vector
#' contains the line-by-line text of the IPO Prospectus of Facebook, Google,
#' and LinkedIn, respectively.
#' @references Zweig, J., 2020. Mark Zuckerberg: CEO For Life?. WSJ.
#' @source All IPO prospectuses are available from the U.S. Securities and Exchange Commission:
#' [Facebook](https://www.sec.gov/Archives/edgar/data/1326801/000119312512034517/d287954ds1.htm),
#' [Google](https://www.sec.gov/Archives/edgar/data/1288776/000119312504073639/ds1.htm),
#' [LinkedIn](https://www.sec.gov/Archives/edgar/data/1271024/000119312511016022/ds1.htm).
#' @keywords datasets ipo corpus text mining
#' @examples
#'
#' library(tidytext)
#' library(tibble)
#' library(dplyr)
#' library(ggplot2)
#' library(forcats)
#'
#' # Analyzing Facebook IPO text
#'
#' facebook <- tibble(text = ipo$facebook, company = "Facebook")
#'
#' facebook %>%
#'   unnest_tokens(word, text) %>%
#'   anti_join(stop_words) %>%
#'   count(word, sort = TRUE) %>%
#'   slice_head(n = 20) %>%
#'   ggplot(aes(y = fct_reorder(word, n), x = n, fill = n)) +
#'   geom_col() +
#'   labs(
#'     title = "Top 20 most common words in Facebook IPO",
#'     x = "Frequency",
#'     y = "Word"
#'   )
#'
#' # Comparisons to Google and LinkedIn IPO texts
#'
#' google <- tibble(text = ipo$google, company = "Google")
#' linkedin <- tibble(text = ipo$linkedin, company = "LinkedIn")
#'
#' ipo_texts <- bind_rows(facebook, google, linkedin)
#'
#' ipo_texts %>%
#'   unnest_tokens(word, text) %>%
#'   count(company, word, sort = TRUE) %>%
#'   bind_tf_idf(word, company, n) %>%
#'   arrange(desc(tf_idf)) %>%
#'   group_by(company) %>%
#'   slice_max(tf_idf, n = 15) %>%
#'   ungroup() %>%
#'   ggplot(aes(tf_idf, fct_reorder(word, tf_idf), fill = company)) +
#'   geom_col(show.legend = FALSE) +
#'   facet_wrap(~company, ncol = 3, scales = "free") +
#'   labs(x = "tf-idf", y = NULL)
"ipo"