TO-DO: Add chapter hyperlinks

[wetherc]

Some mentions of other chapters link to them; others don't. Why not make it a party?

Chapter	Location
02-data-fundamentals/ch01-basic-data-types.html	To avoid confusion, we’ll be sticking with the level of measurement terms above throughout the rest of this book, except in our discussion of long-form qualitative data in the <a href="#">survey design chapter</a>. If you come across terms “categorical,” “qualitative data,” or “quantitative data” in other resources or in your work, make sure you know which definition is being used and don’t just assume!
03-collecting-data/ch05-additional-data-collection-methods.html	Other existing documents that are frequently used to compile information include books, newspapers, web traffic logs, and webpages. There are also entire datasets that are available for use. These are covered in more detail in the chapter on <a href="#">Finding External Data</a>.
04-preparing-data-for-use/ch07-data-preparation.html	Keep in mind that a missing value is not inherently the same thing as an intentional non-response! You don’t have the particular information that the question was asking about in either case, but when someone actively chooses not to answer, that in itself is a piece of data you wouldn’t have if the question were unintentionally skipped. Those data aren’t missing: you know exactly where they are, the respondent just doesn’t want you to have them. As discussed in the <a href="#">Survey Design</a> chapter (pg. YYY), it is good to include a “Prefer not to answer” option for questions that may be of a personal nature, such as race or ethnicity, income, political affiliation, etc. That way, you can designate a code for this type of response so when you are going through your dataset later on, you know the difference between the respondents that purposely chose not to provide you a given piece of information and the data that are just missing altogether.
04-preparing-data-for-use/ch07-data-preparation.html	The best solutions are preventive. If you are the one creating the form for user input, do whatever you can to prevent receiving data that will require intensive handling during the data preparation stages. In the <a href="#">Types of Data Checking</a> chapter (pg. YYY), we’ll talk about different strategies for minimizing the number of data preparation tasks that need to be performed.
04-preparing-data-for-use/ch07-data-preparation.html	If you’re not the one collecting the data but can speak with the people who are, try to work with them to identify and resolve any problematic data collection points using the strategies in the <a href="#">Types of Data Checking</a> chapter as a guide.
04-preparing-data-for-use/ch08-data-cleaning.html	Spell Check is another basic check that you can use to find problems in your dataset. We suggest doing this field-by-field rather than trying to do it across the whole dataset at once. The reason for this is that a word that might be considered a misspelling in one variable could be a valid word for another variable. A good example of this is the first name field. If you have a dataset with a first name field, many of those entries could trigger a spell check alert even though they are legitimate names. If instead you focus on a single field at a time, you can more quickly work through the dataset. In the example from the <a href="#">data preparation</a> chapter where students were listing their major on a survey, say one of the students had just pulled an all-nighter and accidentally typed “Mtahmeitcs” instead of “Mathematics.” A spell check on the “Major” field in your dataset would quickly identify the misspelling and you could change it to “Math” or “Mathematics,” depending on which controlled vocabulary term you chose.
05-visualizing-data/ch12-deciding-which-and-how-much-data-to-illustrate.html	Another way to do this would be to publish interactive versions of your visualizations that allow the viewers to dive in and explore the information themselves. If you’re able to share the raw datasets, that’s even better! That way, those who wish to dig deeper and understand the data in new ways will have the option to do so. We’ll talk more about static and interactive graphics later in the <a href="#">Print vs. Web</a> chapter.
06-dont-be-shady/ch17-perception-deception.html	All of this brings us to the question of whether or not it’s a good idea to use icons or pictograms in our visualizations because the simplest icons are defined by their form, not color. Luckily for us, the chapter on <a href="#">The Importance of Color, Font, and Icons</a> offers some wisdom
03-collecting-data/ch06-finding-external-data.html	Good citations give the reader enough information to find the data that you have accessed and used. Wondering what a good citation looks like? Try using an existing citation style manual from <a href="#"> APA</a>, <a href="#">MLA</a>, <a href="#">Chicago</a>, <a href="#">Turabian</a>, or <a href="#">Harvard</a>. Unlike citations for published items (like books), citations for a dataset vary a great deal from style to style.
04-preparing-data-for-use/ch11-data-transformations.html	<p data-type="author">By <a href="#">Kiran PV</a>
05-visualizing-data/ch14-anatomy-of-a-graphic.html	The contributors for this book have also set up a group to give you feedback on your visualizations. If you’re looking for honest feedback from an impartial party, our <a href="#">LinkedIn group</a> would be more than happy to help. We’re a supportive group of data and design nerds who want to help you learn, grow, and design amazing charts. Post your work-in-progress to the group forum and don’t be shy to use the Data. Design. contributors as a resource!

[trinachi]

These should be complete.