Creating a slide presentation with beamer and latex

latex_slides.Rnw

\documentclass{beamer}

\begin{document}

\title{Positive and Negative Words in Dracula}
\author{Ron Richardson}


\begin{frame}
  \titlepage
\end{frame}

\begin{frame}
  \frametitle{Outline}
    \tableofcontents
\end{frame}


\section{Install and Load Libraries}
\begin{frame}[fragile]
  \frametitle{Install and Load Libraries}
    \begin{itemize}
      \item<1->
      <<warning=FALSE, message=FALSE>>=
      library(dplyr)
      @
      \item<2->
      <<warning=FALSE, message=FALSE>>=
      library(gutenbergr)
      @
      \item<3->
      <<warning=FALSE, message=FALSE>>=
      library(tidytext)
      @
      \item<4->
      <<warning=FALSE, message=FALSE>>=
      library(stringr)
      @
      \item<5->
      <<warning=FALSE, message=FALSE>>=
      library(ggplot2)
      @
    \end{itemize}
\end{frame}

\section{Searching for a Novel}
\begin{frame}[fragile]
  \frametitle{Searching for a Novel}
  <<warning=FALSE, message=FALSE>>=
  df<-gutenberg_works(str_detect(title, 'Dracula'))
  df$gutenberg_id
  df$title
  @
\end{frame}

\section{Download Dracula from Project Gutenberg}
\begin{frame}[fragile]
  \frametitle{Download Dracula from Project Gutenberg}
  <<warning=FALSE, message=FALSE>>=
  dracula<-gutenberg_download(345)
  colnames(dracula)
  substr(dracula$text[500], 1, 21)
  @
\end{frame}

\section{Unpack the Words}
\begin{frame}[fragile]
  \frametitle{Unpack the Words}
  <<warning=FALSE, message=FALSE>>=
  words<-dracula%>%
    unnest_tokens(word, text)
  colnames(words)
  words[500,]
  @ 
\end{frame}

\section{The Bing Lexicon}
\begin{frame}[fragile]
  \frametitle{The Bing Lexicon}
  <<warning=FALSE, message=FALSE>>=
  bing<-get_sentiments('bing')
  colnames(bing)
  bing[500,]
  @ 
\end{frame}

\section{Joining Dracula with Bing}
\begin{frame}[fragile]
  \frametitle{Joining Dracula with Bing}
  <<warning=FALSE, message=FALSE>>=
  words<-inner_join(words, bing)
  words$gutenberg_id<-NULL
  colnames(words)
  @ 
\end{frame}

\section{Top 10 Positive Words}
\begin{frame}[fragile, allowframebreaks]
  \frametitle{Top 10 Positive Words}
  <<warning=FALSE, message=FALSE>>=
  words_pos<-words%>%
    filter(sentiment=='positive')%>%
    group_by(word)%>%
    summarize(count=n(), sentiment=first(sentiment))%>%
    arrange(count)%>%
    top_n(10, wt=count)
  @
  \framebreak
  <<>>=
  words_pos
  @
\end{frame}

\section{Top 10 Negative Words}
\begin{frame}[fragile]
  \frametitle{Top 10 Negative Words}
  <<echo=FALSE>>=
  words_neg<-words%>%
    filter(sentiment=='negative')%>%
    group_by(word)%>%
    summarize(count=n(), sentiment=first(sentiment))%>%
    arrange(count)%>%
    top_n(10, wt=count)
  
  words_neg
  @
\end{frame}

\section{The Comparison Barplot}
\begin{frame}[fragile, allowframebreaks]
  \frametitle{The Comparison Barplot}
  <<warning=FALSE, message=FALSE>>=
  words_pos$word<-factor(words_pos$word, levels=words_pos$word)
  words_neg$word<-factor(words_neg$word, levels=words_neg$word)
  
  words<-rbind(words_pos, words_neg)
  @
  \framebreak
  <<warning=FALSE, message=FALSE>>=
  plot<-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')+  # which column(s) distinguishes the groups
    scale_fill_manual(values=c('#000000', '#ea6205'))+
    scale_color_manual(values=c('#ea6205', '#000000'))
  @
  \framebreak
  <<echo=FALSE, fig.height=4>>=
  plot
  @
\end{frame}

\end{document}