Using facet_wrap in ggplot to compare plots side by side.

ggplot_facet_wrap.R

# Import the libraries used
library(gutenbergr)
library(tidytext)
library(dplyr)
library(ggplot2)

# Download text from Project Gutenberg
dracula<-gutenberg_download(345)

# Split the lines of text into words
dracula<-dracula%>%
  unnest_tokens(word, text)

# Join the words with the bing sentiment
bing<-get_sentiments('bing')
dracula<-inner_join(dracula, bing)

# Filter out the top 10 used positive words, keeping the sentiment column
words_pos<-dracula%>%
  filter(sentiment=='positive')%>%
  group_by(word)%>%
  summarize(count=n(), sentiment=first(sentiment))%>%
  arrange(count)%>%
  top_n(10, wt=count)

# Filter out the top 10 used negative words, keeping the sentiment column
words_neg<-dracula%>%
  filter(sentiment=='negative')%>%
  group_by(word)%>%
  summarize(count=n(), sentiment=first(sentiment))%>%
  arrange(count)%>%
  top_n(10, wt=count)

# Convert the word column to a factor for plotting/ordering
words_pos$word<-factor(words_pos$word, levels=words_pos$word)
words_neg$word<-factor(words_neg$word, levels=words_neg$word)

# Row bind the two dataframes into one for plotting
words<-rbind(words_pos, words_neg)

# Plot both, using the sentiment column as the grouping
ggplot()+
  geom_bar(data=words, aes(x=word, y=count), stat="identity")+
  xlab("Word")+
  ylab("Count")+
  coord_flip()+
  ggtitle("Top 10 Positive/Negative Words in Dracula")+
  facet_wrap(~sentiment, scales='free_y')

# Plot both, changing colors for each grouping (e.g. sentiment)
ggplot()+
  geom_bar(data=words, aes(x=word, y=count, fill=sentiment, color=sentiment), stat="identity")+
  xlab("Word")+
  ylab("Count")+
  coord_flip()+
  ggtitle("Top 10 Positive/Negative Words in Dracula")+
  facet_wrap(~sentiment, scales='free_y')+
  scale_fill_manual(values=c('#000000', '#ea6205'))+
  scale_color_manual(values=c('#ea6205', '#000000'))