Remove numbers from section intros

communicate.Rmd

@@ -1,6 +1,6 @@
 # (PART) Communicate {.unnumbered}
 
-# Introduction {#communicate-intro}
+# Introduction {#communicate-intro .unnumbered}
 
 So far, you've learned the tools to get your data into R, tidy it into a form convenient for analysis, and then understand your data through transformation, and visualisation.
 However, it doesn't matter how great your analysis is unless you can explain it to others: you need to **communicate** your results.
@@ -11,15 +11,15 @@ knitr::include_graphics("diagrams/data-science-communicate.png")
 
 Communication is the theme of the following four chapters:
 
--   In [R Markdown], you will learn about R Markdown, a tool for integrating prose, code, and results.
+-   In \[R Markdown\], you will learn about R Markdown, a tool for integrating prose, code, and results.
     You can use R Markdown in notebook mode for analyst-to-analyst communication, and in report mode for analyst-to-decision-maker communication.
     Thanks to the power of R Markdown formats, you can even use the same document for both purposes.
 
--   In [Graphics for communication], you will learn how to take your exploratory graphics and turn them into expository graphics, graphics that help the newcomer to your analysis understand what's going on as quickly and easily as possible.
+-   In \[Graphics for communication\], you will learn how to take your exploratory graphics and turn them into expository graphics, graphics that help the newcomer to your analysis understand what's going on as quickly and easily as possible.
 
--   In [R Markdown formats], you'll learn a little about the many other varieties of outputs you can produce using R Markdown, including dashboards, websites, and books.
+-   In \[R Markdown formats\], you'll learn a little about the many other varieties of outputs you can produce using R Markdown, including dashboards, websites, and books.
 
--   We'll finish up with [R Markdown workflow], where you'll learn about the "analysis notebook" and how to systematically record your successes and failures so that you can learn from them.
+-   We'll finish up with \[R Markdown workflow\], where you'll learn about the "analysis notebook" and how to systematically record your successes and failures so that you can learn from them.
 
 Unfortunately, these chapters focus mostly on the technical mechanics of communication, not the really hard problems of communicating your thoughts to other humans.
 However, there are lot of other great books about communication, which we'll point you to at the end of each chapter.

import.Rmd

@@ -1,6 +1,6 @@
 # (PART) Import {.unnumbered}
 
-# Introduction {#import-intro}
+# Introduction {#import-intro .unnumbered}
 
 In this part of the book, you'll learn how to get your into R.
 We'll focus on plain-text rectangular formats, spreadsheets, databases, and web data.

program.Rmd

@@ -1,6 +1,6 @@
 # (PART) Program {.unnumbered}
 
-# Introduction {#program-intro}
+# Introduction {#program-intro .unnumbered}
 
 In this part of the book, you'll improve your programming skills.
 Programming is a cross-cutting skill needed for all data science work: you must use a computer to do data science; you cannot do it in your head, or with pencil and paper.

tidy.Rmd

@@ -1,6 +1,6 @@
 # (PART) Tidy {.unnumbered}
 
-# Introduction {#wrangle-intro}
+# Introduction {#wrangle-intro .unnumbered}
 
 In this part of the book, you'll learn about data tidying, the art of getting your data into R in a useful form for visualisation and modelling.
 Data wrangling is very important: without it you can't work with your own data!

transform.Rmd

@@ -1,6 +1,6 @@
 # (PART) Transform {.unnumbered}
 
-# Introduction {#data-types-intro}
+# Introduction {#data-types-intro .unnumbered}
 
 In this part of the book, you'll learn about various types of data the columns of a data frame can contain and how to transform them.
 The transformations you might want to apply to a column vary depending on the type of data you're working with, for example if you have text strings you might want to extract or remove certain pieces while if you have numerical data, you might want to rescale them.

whole-game.Rmd

@@ -1,6 +1,6 @@
 # (PART) Whole game {.unnumbered}
 
-# Introduction {#explore-intro}
+# Introduction {#explore-intro .unnumbered}
 
 The goal of the first part of this book is to introduce you the data science workflow including data **importing**, **tidying**, and data **exploration** as quickly as possible.
 Data exploration is the art of looking at your data, rapidly generating hypotheses, quickly testing them, then repeating again and again and again.