Make updates for second edition

README.md

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 # Data Science in Education Using R <img src='man/figures/logo.png' align="right" height="120" />
 
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-* [Note from Our Publisher](#Note-from-Our-Publisher)
-* [How to Contribute](#Contributing)
-* [Contact Us](#Contact-Us)
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+
+This repository is for the second edition of _Data Science in Education_ Using R, which is a work in progress. 
+
+* [Note from Our Publisher](#Note-from-Our-Publisher) 
+
+* [How to Contribute](#Contributing) 
+
+* [Contact Us](#Contact-Us) 
 
 ## Note from Our Publisher
 
 The authors of this text and the publisher Taylor and Francis are pleased to make Data Science in Education Using R available via bookdown at [datascienceineducation.com](https://datascienceineducation.com). They request that readers access the book via the website or in print form only and do not download or reproduce copies in any other form. Any attempt to do so will be considered a contravention of the publisher’s terms of availability.
 
 ## Reading the Book
 
-We wrote this book for you and are excited to share it! You can read the current version at [datascienceineducation.com](https://datascienceineducation.com). The print version is available now through [Routledge](https://www.routledge.com/Data-Science-in-Education-Using-R/Estrellado-Freer-Mostipak-Rosenberg-Velasquez/p/book/9780367422257). 
+We're excited to share this book with you! You can read the current version at [datascienceineducation.com](https://datascienceineducation.com). The print version is available now through [Routledge](https://www.routledge.com/Data-Science-in-Education-Using-R/Estrellado-Freer-Mostipak-Rosenberg-Velasquez/p/book/9780367422257). 
+
+## About the Book
 
-## The Aims of This Book
+School districts, government agencies, and education businesses generate data at a dizzying pace. They serve it to teachers, administrators, and education consultants in a mind-boggling variety of formats. Educators and educational data practitioners want to improve the lives of students with this data. But the data is often not in a “ready-to-analyze” format. Sometimes, educators need to use high-cost proprietary systems to access and prepare data before using it.
 
-School districts, government agencies, and education businesses are generating data at a dizzying pace. They're serving it to teachers, administrators, and education consultants in a mind-boggling variety of formats. Educators and educational data practitioners wanting to use data to improve the lives of students know the questions they want to ask, but the available data is often not ready to be analyzed.  Sometimes educators need to use high-cost proprietary systems to access and prepare data before using it to answer their questions. 
+As a result, it's hard for enthusiastic practitioners to feel a connection between research questions and the data they need to answer them. To get value from the data-deluge, some practitioners are adopting data science tools, like R. 
 
-Educational data rarely comes in a “ready-to-analyze” format. As a result, it's hard for enthusiastic practitioners to feel a connection between their questions and the data needed to answer them. To get value from the data-deluge, some educational data practitioners are adopting data science tools, like R. R is an Open Source programming language for data analysis. When data science meets education, the numbers confined to websites and PDF reports are set free. Teachers, administrators, and consultants apply programming and statistics to prepare data, transform it, visualize it, and analyze it to answer questions that make a difference for their students.
+R is an Open Source programming language for data analysis. When data science meets education, practitioners can use the numbers previously confined to websites and PDF reports. Teachers, administrators, and consultants can apply programming and statistics to prepare data, transform it, visualize it, and analyze it. These practices empower practitioners to answer questions that make a difference for their students.
 
-Our book focuses on *data science in education*, which we define as using data science techniques like preparing, exploring, visualizing, and modeling data, in order to support schooling at all levels. We want to make a case for learning about data science through field-specific examples. Understanding the unique challenges and starting to use a common field-specific language is important for mastering data science in education. We feel that discussing data science using education-specific scenarios more effectively speaks to the needs of educators.
+Our book focuses on data science in education, which we define as using data science techniques to support schooling at all levels. These techniques include preparing, exploring, visualizing, and modeling data. 
 
-Technology is transforming both the administrative and student-facing sides of education. It's becoming increasingly important for educators - not just people hired to analyze data - to understand what stories this new data tells them them about their students. Our book empowers educators from elementary school to higher education to transform educational data into actionable insights so it helps them serve their students and institutions. We wrote our book to be used as a main textbook in a graduate data science in education course. We also wrote it as a practical reference for data scientists working with education data.
+These techniques shouldn't be learned separately from education use cases. We propose learning about data science through field-specific examples. Using common field-specific language is important for learning techniques that are practical for the job. We feel that discussing data science using education-specific scenarios will make learning more fun and meaningful.
+
+Technology is transforming education for administrators, staff, and students. It is increasingly important for educators -- not just data analysts -- to use data to reveal the stories of their students. Our book empowers educators from elementary school to higher education to transform educational data into actionable insights. We wrote our book as a main textbook in graduate data science in education courses. We also wrote it as a practical reference for data scientists working with education data.
 
 By the end of this book the reader will understand:
 
 * The diversity of data analysis skills and applications in the education field 
-* Special considerations that come with analyzing education data
-* That good data analysis has a basic workflow 
-* The wonderful opportunity we have to shape the usefulness of data science in our education jobs
 
-And, the reader will be able to:
+* Unique considerations for analyzing education data 
+
+* Using effective analysis workflows 
+
+* An increased belief in shaping data science in our education 
+
+… and the reader will be able to:
+
+* Better define their role as a data analyst and educator 
+
+* Identify and apply solutions to education data’s unique challenges, including cleaning data and using aggregated student data 
 
-* Reflect on and define their role as a data analyst and educator
-* Identify and apply solutions to education data’s unique challenges, such as cleaning datasets and working with aggregate student data 
-* Apply a basic analytic workflow through practice with education datasets
-* Be thoughtful, empathetic, and effective when introducing data science techniques in their education jobs
+* Apply a basic analytic workflow through practice with education datasets 
+
+* Introduce data science to their workplace in a thoughtful, empathetic, and effective manner 
 
 ## Chapters 
 
-1. [Introduction: Data Science in Education - You’re Invited to the Party!](https://datascienceineducation.com/c01.html)
+1. [Introduction: Data Science in Education - You’re Invited to the Party!](https://datascienceineducation.com/c01.html) 
 
-2. [How to Use This Book](https://datascienceineducation.com/c02.html)
+2. [How to Use This Book](https://datascienceineducation.com/c02.html) 
 
-3. [What Does Data Science in Education Look Like?](https://datascienceineducation.com/c03.html)
+3. [What Does Data Science in Education Look Like?](https://datascienceineducation.com/c03.html) 
 
-4. [Special Considerations](https://datascienceineducation.com/c04.html)
+4. [Special Considerations](https://datascienceineducation.com/c04.html) 
 
-5. [Getting Started with R and R Studio](https://datascienceineducation.com/c05.html)
+5. [Getting Started with R and R Studio](https://datascienceineducation.com/c05.html) 
 
-6. [Foundational Skills](https://datascienceineducation.com/c06.html)
+6. [Foundational Skills](https://datascienceineducation.com/c06.html) 
 
-7. [Walkthrough 1: The Education Dataset Science Pipeline With Online Science Class Data](https://datascienceineducation.com/c07.html)
+7. [Walkthrough 1: The Education Dataset Science Pipeline With Online Science Class Data](https://datascienceineducation.com/c07.html) 
 
-8. [Walkthrough 2: Approaching Gradebook Data From a Data Science Perspective](https://datascienceineducation.com/c08.html)
+8. [Walkthrough 2: Approaching Gradebook Data From a Data Science Perspective](https://datascienceineducation.com/c08.html) 
 
-9. [Walkthrough 3: Using School-Level Aggregate Data to Illuminate Educational Inequities](https://datascienceineducation.com/c09.html)
+9. [Walkthrough 3: Using School-Level Aggregate Data to Illuminate Educational Inequities](https://datascienceineducation.com/c09.html) 
 
-10. [Walkthrough 4: Longitudinal Analysis With Federal Students With Disabilities Data](https://datascienceineducation.com/c10.html)
+10. [Walkthrough 4: Longitudinal Analysis With Federal Students With Disabilities Data](https://datascienceineducation.com/c10.html) 
 
-11. [Walkthrough 5: Text Analysis With Social Media Data](https://datascienceineducation.com/c11.html)
+11. [Walkthrough 5: Text Analysis With Social Media Data](https://datascienceineducation.com/c11.html) 
 
-12. [Walkthrough 6: Exploring Relationships Using Social Network Analysis With Social Media Data](https://datascienceineducation.com/c12.html)
+12. [Walkthrough 6: Exploring Relationships Using Social Network Analysis With Social Media Data](https://datascienceineducation.com/c12.html) 
 
-13. [Walkthrough 7: The Role (and Usefulness) of Multi-Level Models](https://datascienceineducation.com/c13.html)
+13. [Walkthrough 7: The Role (and Usefulness) of Multi-Level Models](https://datascienceineducation.com/c13.html) 
 
-14. [Walkthrough 8: Predicting Students’ Final Grades Using Machine Learning Methods with Online Course Data](https://datascienceineducation.com/c14.html)
+14. [Walkthrough 8: Predicting Students’ Final Grades Using Machine Learning Methods with Online Course Data](https://datascienceineducation.com/c14.html) 
 
-15. [Introducing Data Science Tools To Your Education Job](https://datascienceineducation.com/c15.html)
+15. [Introducing Data Science Tools To Your Education Job](https://datascienceineducation.com/c15.html) 
 
-16. [Teaching Data Science](https://datascienceineducation.com/c16.html)
+16. [Teaching Data Science](https://datascienceineducation.com/c16.html) 
 
-17. [Learning More](https://datascienceineducation.com/c17.html)
+17. [Learning More](https://datascienceineducation.com/c17.html) 
 
-18. [Additional Resources](https://datascienceineducation.com/c18.html)
+18. [Additional Resources](https://datascienceineducation.com/c18.html) 
 
 19. [Conclusion: Where to Next?](https://datascienceineducation.com/c19.html) 
 
@@ -85,44 +99,44 @@ And, the reader will be able to:
 
 This project started in the #dataedu [Slack channel](https://dataedu.slack.com/). You can join the workspace [here](https://join.slack.com/t/dataedu/shared_invite/enQtNzQ3ODcwNzM0NDgwLTQzMTE1YjdiMTg0NWExYTljNTg5YzU1NjY4NGE3MjA0ODRiNGM5NGYyNzRmNDk5Yjk0OTYyYWU4Zjc0ZTgyYTg). 
 
-Community members can contribute by making changes through a pull request. We encourage community members to do their pull requests on separate branches. This helps us keep all the changes synced up. 
+Community members can contribute by making changes through a pull request. We encourage community members to do their pull requests on separate branches. This helps us coordinate changes: 
 
- - [How to create an issue](https://help.github.com/en/github/managing-your-work-on-github/creating-an-issue) 
+* [How to create an issue](https://help.github.com/en/github/managing-your-work-on-github/creating-an-issue) 
 
- - [How to do a pull request on a separate branch](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request)
+* [How to do a pull request on a separate branch](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request)
 
 ### Git Issue Labels
 
-To help contributors participate, we're using labels so community members can identify tasks they want to help with. When working on an issue, [assign yourself to the issue](https://help.github.com/en/github/managing-your-work-on-github/assigning-issues-and-pull-requests-to-other-github-users). This helps us keep track of the work and lets us know who to contact for more collaboration. The labels are: 
+To help contributors participate, we use labels to organize tasks. When working on an issue, [assign yourself to the issue](https://help.github.com/en/github/managing-your-work-on-github/assigning-issues-and-pull-requests-to-other-github-users). This helps us track the work and lets us know who to contact for more collaboration. The labels are: 
 
- - `good first issue`: These are requests for changes that we think would be fun and achievable if you're new to git and GitHub. 
+* `good first issue`: These are requests for changes that are fun and achievable if you're new to git and GitHub 
 
- - `discussion`: Sometimes we need help talking through a topic to help us make a good design choice for our readers. These issues won't always result in a change, but they help us clarify what's best for the final product. 
+* `discussion`: Sometimes we need help talking through a topic or design decision. These issues won't always result in a change, but they help us clarify what's best for the final product
 
- - `test code`: These issues are for running code and giving feedback about how it went. If there were problems, you can help us by letting us know what happened. 
+* `test code`: These issues are for running code and giving feedback about the result 
 
- - `bug`: The code isn't running as expected and needs fixing. 
+* `bug`: These issues are for code that isn't running as expected and needs fixing 
 
- - `help wanted`: Need help getting code to run or writing a section. We'll make sure the problem we're working on is clearly described in the issue. 
+* `help wanted`: These issues are for general requests like help with code or writing new content 
 
- - `writing`: New content needed. At least one author will be assigned to `writing` issues, but we welcome collaboration! Feel free to message the author on Slack or in the issue comments to coordinate. 
+* `writing`: These issues are for writing new content. We will assign at least one author to `writing` issues 
 
- - `review draft`: These are requests to read through a draft chapter and provide feedback on the experience, including reability. 
+* `review draft`: These issues are requests to read through a draft chapter and provide feedback 
 
 ## Contact Us 
 
-If you have questions, comments, or ideas you can reach the authors by email at [authors@datascienceineducation.com](mailto:authors@datascienceineducation.com) or on Twitter: 
-
- - Emily [@ebovee09](https://twitter.com/ebovee09)  
-
- - Isabella [@ivelasq3](https://twitter.com/ivelasq3) 
+If you have questions, comments, or ideas contact the authors by email at [authors@datascienceineducation.com](mailto:authors@datascienceineducation.com) or on Twitter: 
 
- - Jesse [@kierisi](https://twitter.com/kierisi) 
-
- - Joshua [@jrosenberg6432](https://twitter.com/jrosenberg6432) 
-
- - Ryan [@ry_estrellado](https://twitter.com/ry_estrellado) 
+ * Emily [@ebovee09](https://twitter.com/ebovee09) 
 
- ## Citation
+ * Isabella [@ivelasq3](https://twitter.com/ivelasq3) 
 
+ * Joshua [@jrosenberg6432](https://twitter.com/jrosenberg6432) 
+
+ * Ryan [@ry_estrellado](https://twitter.com/ry_estrellado) 
+
+ ## Citation
+
+Until we publish the second edition, use the following citation for the first edition of the print version: 
+
 > Bovee, E. A., Estrellado, R. A., Motsipak, J., Rosenberg, J. M., & Velásquez, I. C. (under contract). Data science in education using R. London, England: Routledge. Nb. All authors contributed equally. http://www.datascienceineducation.com/