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diff --git a/data-structures.Rmd b/data-structures.Rmd
diff --git a/diagrams/data-structures-overview.png b/diagrams/data-structures-overview.png
diff --git a/diagrams/data-structures.graffle b/diagrams/data-structures.graffle
diff --git a/functions.Rmd b/functions.Rmd
@@ -1,4  1,4 @@
-```{r, include = FALSE}
 ```{r setup, include = FALSE}
 library(stringr)
 ```
 

diff --git a/import.Rmd b/import.Rmd
@@ -7,12  7,11 @@ library(readr)
 
 ## Overview
 
-You can't apply any of the tools you've applied so far to your own work, unless you can get your own data into R. In this chapter, you'll learn how to import:
 You can't apply any of the tools you've applied so far to your own work, unless you can get your own data into R. In this chapter, you'll learn how to:
 
-* Flat files (like csv) with readr.
-* Database queries with DBI.
-* Data from web APIs with httr.
-* Binary file formats (like excel or sas), with haven and readxl.
 * Import flat files (like csv) with readr.
 * 
 * Cache intermediate results in a fast file format like feather or RDS.
 
 The common link between all these packages is they all aim to take your data and turn it into a data frame in R, so you can tidy it and then analyse it.
 
@@ -245,10  244,28 @@ The settings you are most like to need to change are:
 * Parse these example files.
 * Parse this fixed width file.
 
-## Databases
 ## Other file formats
 
-## Web APIs
 * Excel: readxl
 * SPSS: haven
 * Stata: haven
 * SAS: haven
 
-## Binary files
 Databases. All powered by the DBI package which provides a common interface.
 
-Needs to discuss how data types in different languages are converted to R. Similarly for missing values.
 * RPostgres
 * RMySQL
 * RSQLite
 * Avoid JDBC un
 
 Hierarchical:
 
 * XML: xml2
 * JSON: jsonlite 
 
 
 
 ## Binary file formats
 
 Feather.
 RDS.
diff --git a/index.rmd b/index.rmd
@@ -1,8  1,12 @@
 ---
 knit: "bookdown::render_book"
 title: "R for Data Science"
-output:
-  - bookdown::gitbook
 author: ["Garrett Grolemund", "Hadley Wickham"]
 description: "This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots---and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualizing, and exploring data."
 url: 'http\://r4ds.had.co.nz/'
 github-repo: hadley/r4ds
 twitter-handle: hadley
 cover-image: cover.png
 ---
 
 # Welcome

diff --git a/variation.Rmd b/variation.Rmd
@@ -26,7  26,7 @@ Rectangular data provides a clear record of variation, but that doesn't mean it
 
 mat <- as.data.frame(matrix(morley$Speed   299500, ncol = 10))
 
-knitr::kable(mat, caption = "*The speed of light is* the *universal constant, but variation obscures its value, here demonstrated by Albert Michelson in 1879. Michelson measured the speed of light 100 times and observed 30 different values (in km/sec).*", col.names = c("\\s", "\\s", "\\s", "\\s", "\\s", "\\s", "\\s", "\\s", "\\s", "\\s"))
 knitr::kable(mat, caption = "*The speed of light is* the *universal constant, but variation obscures its value, here demonstrated by Albert Michelson in 1879. Michelson measured the speed of light 100 times and observed 30 different values (in km/sec).*", col.names = rep("", ncol(mat)))
 ```
 
 

diff --git a/work.Rmd b/work.Rmd
@@ -8,7  8,7 @@ Throughout this book we work with "tibbles" instead of the traditional data fram
 library(tibble)
 ```
 
-## Creating tibbles
 ## Creating tibbles {#tibbles}
 
 The majority of the functions that you'll use in this book already produce tibbles. But if you're working with functions from other packages, you might need to coerce a regular data frame a tibble. You can do that with `as_data_frame()`:
-Original file line number
+Diff line change
@@ Expand Up @@
     mat <- as.data.frame(matrix(morley$Speed   299500, ncol = 10))
-    knitr::kable(mat, caption = "*The speed of light is* the *universal constant, but variation obscures its value, here demonstrated by Albert Michelson in 1879. Michelson measured the speed of light 100 times and observed 30 different values (in km/sec).*", col.names = c("\\s", "\\s", "\\s", "\\s", "\\s", "\\s", "\\s", "\\s", "\\s", "\\s"))
+    knitr::kable(mat, caption = "*The speed of light is* the *universal constant, but variation obscures its value, here demonstrated by Albert Michelson in 1879. Michelson measured the speed of light 100 times and observed 30 different values (in km/sec).*", col.names = rep("", ncol(mat)))
     ```
@@ Expand Down @@