can't have underscores in figure chunk names

Dave Robinson · Dave Robinson · commit 310148cd5094 · 2017-03-31T15:45:40.000-04:00
diff --git a/05-document-term-matrices.Rmd b/05-document-term-matrices.Rmd
@@ -16,11 +16,11 @@ However, most of the existing R tools for natural language processing, besides t
 
 Computer scientist Hal Abelson has observed that "No matter how complex and polished the individual operations are, it is often the quality of the glue that most directly determines the power of the system" [@Friedman:2008:EPL:1378240]. In that spirit, this chapter will discuss the "glue" that connects the tidy text format with other important packages and data structures, allowing you to rely on both existing text mining packages and the suite of tidy tools to perform your analysis.
 
-```{r tidy_flowchart_ch5, echo = FALSE, out.width = '100%', fig.cap = "A flowchart of a typical text analysis that combines tidytext with other tools and data formats, particularly the tm or quanteda packages. This chapter shows how to convert back and forth between document-term matrices and tidy data frames, as well as converting from a Corpus object to a text data frame."}
+```{r tidyflowchartch5, echo = FALSE, out.width = '100%', fig.cap = "A flowchart of a typical text analysis that combines tidytext with other tools and data formats, particularly the tm or quanteda packages. This chapter shows how to convert back and forth between document-term matrices and tidy data frames, as well as converting from a Corpus object to a text data frame."}
 knitr::include_graphics("images/tidyflow-ch-5.png")
 ```
 
-Figure \@ref(fig:tidy_flowchart_ch5) illustrates how an analysis might switch between tidy and non-tidy data structures and tools. This chapter will focus on the process of tidying document-term matrices, as well as casting a tidy data frame into a sparse matrix. We'll also expore how to tidy Corpus objects, which combine raw text with document metadata, into text data frames, leading to a case study of ingesting and analyzing financial articles.
+Figure \@ref(fig:tidyflowchartch5) illustrates how an analysis might switch between tidy and non-tidy data structures and tools. This chapter will focus on the process of tidying document-term matrices, as well as casting a tidy data frame into a sparse matrix. We'll also expore how to tidy Corpus objects, which combine raw text with document metadata, into text data frames, leading to a case study of ingesting and analyzing financial articles.
 
 ## Tidying a document-term matrix {#tidy-dtm}
 
diff --git a/06-topic-models.Rmd b/06-topic-models.Rmd
@@ -14,11 +14,11 @@ In text mining, we often have collections of documents, such as blog posts or ne
 
 Latent Dirichlet allocation (LDA) is a particularly popular method for fitting a topic model. It treats each document as a mixture of topics, and each topic as a mixture of words. This allows documents to "overlap" each other in terms of content, rather than being separated into discrete groups, in a way that mirrors typical use of natural language.
 
-```{r tidy_flowchart_ch6, echo = FALSE, out.width = '100%', fig.cap = "A flowchart of a text analysis that incorporates topic modeling. The topicmodels package takes a Document-Term Matrix as input and produces a model that can be tided by tidytext, such that it can be manipulated and visualized with dplyr and ggplot2."}
+```{r tidyflowchartch6, echo = FALSE, out.width = '100%', fig.cap = "A flowchart of a text analysis that incorporates topic modeling. The topicmodels package takes a Document-Term Matrix as input and produces a model that can be tided by tidytext, such that it can be manipulated and visualized with dplyr and ggplot2."}
 knitr::include_graphics("images/tidyflow-ch-6.png")
 ```
 
-As Figure \@ref(fig:tidy_flowchart_ch6) shows, we can use tidy text principles to approach topic modeling with the same set of tidy tools we've used throughout this book. In this chapter, we'll learn to work with `LDA` objects from the [topicmodels package](https://cran.r-project.org/package=topicmodels), particularly tidying such models so that they can be manipulated with ggplot2 and dplyr. We'll also explore an example of clustering chapters from several books, where we can see that a topic model "learns" to tell the difference between the four books based on the text content.
+As Figure \@ref(fig:tidyflowchartch6) shows, we can use tidy text principles to approach topic modeling with the same set of tidy tools we've used throughout this book. In this chapter, we'll learn to work with `LDA` objects from the [topicmodels package](https://cran.r-project.org/package=topicmodels), particularly tidying such models so that they can be manipulated with ggplot2 and dplyr. We'll also explore an example of clustering chapters from several books, where we can see that a topic model "learns" to tell the difference between the four books based on the text content.
 
 ## Latent Dirichlet allocation
 
