Getting the web links of the speeches

To scrape the data (or the texts), we first need to get the web links of the texts. Luckily, the web links of the 2018 SoSAs can be found here. The code below scrapes the table of the web links.

# required packages
library(pdftools) # needed to download and extract text from .pdf files
library(rvest) # needed to download and extract text from html files
library(stringr) # needed for string manipulation
library(dplyr) # needed for dataframe manipulation
library(tm) # needed for text mining


# get the table of governors and the links of the speeches
# sp stands for speeches
sp_url <- "https://www.multistate.us/2018-state-of-the-state-addresses-0"
sp_webpage <- read_html(sp_url)
sp_tabl <- sp_webpage %>%
  html_nodes("table") %>%
  .[[1]] %>%
  html_table(header = 1)

Party <- c("R", "I", "R", "R", "D", "D", "D", "D", "R", "R",
           "D", "R", "R", "R", "R", "R", "R", "D", "R", "R", 
           "R", "R", "D", "R", "R", "D", "R", "R", "R", "R", 
           "R", "D", "D", "R", "R", "R", "D", "D", "D", "R", 
           "R", "R", "R", "R", "R", "D", "D", "R", "R", "R")

sp_tabl$Party <- Party

sp_tabl <- sp_tabl %>%
  filter(Date != 'None')
links <- sp_webpage %>%
  html_nodes(xpath = '//table/..//a') %>%
  html_attr('href')
sp_tabl$Links <- links
sp_tabl <- sp_tabl %>%
  filter(State != 'Texas') # Texas’s file is not a speech. So remove it.

Below is the table of the web links and some metadata of the speeches

library(knitr)
library(kableExtra)
kable(head(sp_tabl[, -4]), "html") %>%
  kable_styling(bootstrap_options = "striped", full_width = F)

Downloading and extracting the texts

rvest is a popular R package for scraping html files. However, some of the files we are scraping are in pdf format. Therefore, we will have to supplement rvest with the pdftools package to scrape both the .pdf and .html files. To do so, we write a loop that checks whether the file to download is a .pdf or not. If the file is a pdf, then the pdftools’ functions are used to download the file and extract its text. If the file is not a pdf, then we use rvest’s functions to download the file and extract its text. Unfortunately, some of the links are dead, others do not link to .pdf nor .html files. So, we use tryCatch to prevent the loop from crashing, just because the code cannot download a file. The following code does the trick.

# Download the files, then extract the text data

sp_number = 0
missing <- NULL
for (url in sp_tabl$Links) {
  sp_number = sp_number + 1
  has_a_pdf <- str_detect(string = url, pattern = '.pdf')
  if(has_a_pdf){ # scrape text from .pdf files
    text <- tryCatch(pdf_text(url), 
                     error = function(e) e)
    if(inherits(text, "error")){
      missing <- c(missing, sp_number) 
      next
    }
    text <- paste(unlist(strsplit(text, "\n")), collapse = "")
    path <- paste0('speeches/', sp_tabl$State[sp_number], ".txt")
    fileConn<-file(path)
    writeLines(text, fileConn)
    close(fileConn)
  } else{  # scrape text from html files
    text <- tryCatch(read_html(url), # to prevent errors from crashing the loop
                     error = function(e) e)
    if(inherits(text, "error")){
      missing <- c(missing, sp_number) 
      next
    }
    text <- text %>% 
      html_nodes(xpath = '//p') %>%
      html_text()
    path <- paste0('speeches/', sp_tabl$State[sp_number], ".txt")
    fileConn<-file(path)
    writeLines(text, fileConn)
    close(fileConn)
  }
  Sys.sleep(5) # slows down the files request. 
}

The code above downloads the files, extracts the text, and saves the files in the directory provided. I saved the texts in a folder named speeches. A quick check of the .txt files in the speeches directory shows that a couple of files are empty. That may be due to several possible reasons. (1) some files are not .pdf, nor .html (Utah). (2) A file may be a .pdf but the link does not contain a pdf so it fails to be treated as pdf (Wyoming). (3) Others files have unusual html tags for the text (West Virginia). In sum, inconsistency is a problem when scraping data from several sources. And, in practice, we have to iterate the process to detect the possible inconsistencies and adjust the code accordingly. For further notes on web scraping with R, see this, this, or this.

Barplots of a selected list of words

Barplots are useful for exploring, graphically, count data. Below, we explore the top 20 most used words in all the speeches.

library(ggplot2) # needed for graphs
words_freq <- colSums(Sp_dtm_df[ ,-length(Sp_dtm_df)])
words_freq <- data.frame(words = names(words_freq),
                         freq = unname(words_freq))
words_freq <- words_freq[order(words_freq$freq, decreasing = TRUE),]


p <- ggplot(data = words_freq[1:20, ], aes(x=words, y=freq)) +
  geom_bar(stat="identity", fill="steelblue")
p + coord_flip()

From the barplot, the most used words in the speeches are state, year, and will. Among the top twenty words are: School, Education, Business, and work.

Let’s select a few words, and compare the words relative frequencies by party affiliation. The words selected are: Education, Health, Budget, Economy, and Business. The stemming function did not do a good job. It was meant to convert words such as economy, economical into their root words. But, that did not happen. We will do it manually.

Sp_dtm_df$econom <- Sp_dtm_df$econom + Sp_dtm_df$economi
Sp_dtm_df$economi <- NULL
Sp_dtm_df$health <- Sp_dtm_df$health + Sp_dtm_df$healthi + Sp_dtm_df$healthcar
Sp_dtm_df$healthi <- NULL
Sp_dtm_df$healthcar <- NULL

Now, let’s select the words of interest, and explore them using a barplot.

selected_words <- Sp_dtm_df[, c("budget", "busi", "econom", "educ", "health", "Party")]

selected_words_D <- colSums(selected_words[selected_words$Party == "D" ,-length(selected_words)])
selected_words_D <- data.frame(words = names(selected_words_D),
                         freq = unname(selected_words_D)/sum(unname(selected_words_D)))
selected_words_D$party <- rep("D", 5)
  
selected_words_R <- colSums(selected_words[selected_words$Party == "R" ,-length(selected_words)])
selected_words_R <- data.frame(words = names(selected_words_R),
                         freq = unname(selected_words_R)/sum(unname(selected_words_R)))
selected_words_R$party <- rep("R", 5)

sel_word_D_R <- rbind(selected_words_D, selected_words_R)

p_DR <- ggplot(data = sel_word_D_R, aes(x=words, y=freq, fill = party)) +
  geom_bar(stat="identity", position=position_dodge())
p_DR + scale_fill_manual(values = c('blue','red')) + 
  coord_flip()

The barplot shows that, relatively, Democrats have used the words health, economy, and business more often than Republicans. The Republicans have used the words education and budget more often than the Democrats.

An alternative way to look at the words frequencies is to use a wordcloud. We will do so first, for all governors, then by party affiliation. Before that, we remove the words state, will, and year from the data. I am assuming that they are not important since they are so common in all speeches.

words_freq <- words_freq[words_freq$freq <= 900, ]
# or
Sp_dtm_df = subset(Sp_dtm_df, select = - c(state, will, year))

library("wordcloud")
library("RColorBrewer")
set.seed(4444) # needed to reproduce the exact same wordcloud
wordcloud(words = words_freq$words, freq = words_freq$freq, min.freq = 1,
          max.words=350, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"))

The wordcloud above indicates that people, education, health, business, family, budget are all prominent words in the speeches. Let’s look at the wordcloud by party affiliation (Democrats vs. Republicans).

dem_freq <- colSums(Sp_dtm_df[Sp_dtm_df$Party == "D",-length(Sp_dtm_df)])
rep_freq <- colSums(Sp_dtm_df[Sp_dtm_df$Party == "R",-length(Sp_dtm_df)])
comp_data <- data.frame(Democrats = unname(dem_freq)/sum(unname(dem_freq)),
                        Republicans = unname(rep_freq)/sum(unname(rep_freq)))
row.names(comp_data) <- names(dem_freq)
#comp_data <- round(comp_data*1000, 0)
comparison.cloud(comp_data,max.words=250,random.order=FALSE, colors = c("blue", "red"))

Clearly, Democrats and Republicans governors focused on different words during their 2018 SoSAs. Paradoxically, while Democrates used words related to the economy (fair, build, business, work, train) more often, Republicans used more words related to the state (govern, people, citizen, service, reform). There are much more differences that we can highlight, based on this wordcloud. What other differences can you highlight? I leave that to you.