2 How to use this book


This chapter describes different ways the reader can use thise book to learn about using R and data science tools in their education job. Job descriptions, lifestyles, and programming experience differ for everyone. Learning how to program in R on the job or at home will also look different to each reader. Applying R and data science tools in an education job requires learning these skills in a practical and meaningful context. The chapter describes three suggested ways to learn from the book, taking the reader’s experience into account. It also introduces the reader to ways they can support and contribute to the book’s content. This reinforces the theme of building content based on stories from the data science in education community.

We’ve heard it from fellow data scientists and experienced it ourselves—learning a programming language is hard. Like learning a foreign language, it is not just about mastering vocabulary. It’s also about learning the language’s norms, its underlying structure, and the metaphors that hold the whole thing together.

The beginning of the learning journey is particularly challenging because it feels slow. If you have experience as an educator or consultant, you already have efficient solutions you use in your day-to-day work. Introducing code to your workflow slows you down at first because you won’t be as fast as you are with your favorite spreadsheet software. However, you’re probably reading this book because you realize that learning how to analyze data using R is like investing in your own personal infrastructure—it takes time while you’re building the initial skills, but the investment pays off when you start solving complex problems faster and at scale. One person we spoke with shared this story about their learning journey:

The first six months were hard. I knew how quickly I could do a pivot table in Excel. It took longer in R because I had to go through the syntax and take the book out. I forced myself to do it, though. In the long term, I’d be a better data scientist. I’m so glad I thought that way, but it was hard the first few months.

Our message is this: learning R for your education job is doable, challenging, and rewarding all at once. We wrote this book for you because we do this work every day. We’re not writing as education data science masters. We’re writing as people who learned R and data science after we chose education. And like you, improving the lives of students is our daily practice. Learning to use R and data science helped us do that. Join us in enjoying all that comes with R and data science—both the challenge of learning and the joy of solving problems in creative and efficient ways.

2.1 Different strokes for different data scientists in education

As we learned in the introduction, it’s tough to define data science in education because people are educated in all kinds of settings and in all kinds of age groups. Education organizations require different roles to make it work, which creates different kinds of data science uses. A teacher’s approach to data analysis is different from an administrator’s or an operations manager’s.

We also know that learning data science and R is not in the typical job description. Most readers of this book are educators working with data and looking to expand their tools. You might even be an educator who doesn’t work with data, but you’ve discovered a love for learning about the lives of students through data. Either way, learning data science and R is probably not in your job description.

Like most professionals in education, you’ve got a full work schedule and challenging demands in the name of improving the student experience. Your busy workday doesn’t include regular professional development time or self-driven learning. You also have a life outside of work, including family, hobbies, and relaxation. We struggle with this ourselves, so we’ve designed this book to be used in lots of different ways. The important part in learning this material is to establish a routine that allows you to engage and practice the content every day, even if for just a few minutes at a time. That will make the content ever-present in your mind and will help you shift your mindset so you start seeing even more opportunities for practice.

We want all readers to have a rewarding experience, and so we believe there should be different ways to use this book. Here are some of those ways:

2.1.1 Read the book cover to cover (and how to keep going)

We wrote this book assuming you’re at the start of your journey learning R and using data science in your education job. The book takes you from installing R to practicing more advanced data science skills like text analysis.

If you’ve never written a line of R code, we welcome you to the community! We wrote this book for you. Consider reading the book cover to cover and doing all the analysis walkthroughs. Remember that you’ll get more from a few minutes of practice every day than you will from long hours of practice every once in a while. Typing code every day, even if it doesn’t always run, is a daily practice that invites learning and “a-ha” moments. We know how easy it is to avoid coding when it doesn’t feel successful (we’ve been there), so we’ve designed this book to deliver frequent small wins to keep the momentum going. But even then, we all eventually hit a wall in our learning. When that happens, take a break and then come back and keep coding. When daily coding becomes a habit, so does the learning.

If you get stuck in an advanced chapter and you need a break, try reviewing an earlier chapter. You’ll be surprised at how much you learn from reviewing old material with the benefit of new experiences. Sometimes that kind of back-to-basics attitude is what we need to get a fresh perspective on new challenges.

2.1.2 Pick a chapter of interest and start there

We interviewed R users in education as research for this book. We chose people with different levels of experience in R, in the education field, and in statistics. We asked each interviewee to rate their level of experience on a scale from 1 to 5, with 1 being “no experience” and 5 being “very experienced”. You can try this now—take a moment to rate your level of experience in:

  • Using R
  • Education as a field
  • Statistics

If you rated yourself as a 1 in Using R, we recommend reading the book from beginning to end as part of a daily practice. If you rated yourself higher than a 1, consider reviewing the table of contents and skimming all the chapters first. If a particular chapter calls to you, feel free to start your daily practice there. Eventually, we do hope you choose to experience the whole book, even if you start somewhere in the middle.

For example, you might be working through a specific use case in your education job—perhaps you are analyzing student quiz scores, evaluating a school program, introducing a data science technique to your teammates, or designing data dashboards. If this describes your situation, feel free to find a section in the book that inspires you or shows you techniques that apply to your project.

This book is primarily about learning to use R as a tool for data science in education. Your experience level with R should be the main factor when you decide how to enjoy the book. But do consider how you rated your level of experience with education and statistics. If these are areas you want to focus on, take your time understanding the education scenarios and statistics techniques we describe. All three disciplines are important parts of being a data scientist in education.

2.1.3 Read through the walkthroughs and run the code

If you’re experienced in data analysis using R, you may be interested in starting with the walkthroughs. Each walkthrough is designed to demonstrate basic analytic routines using datasets that look familiar to people working in the education field.

In this approach, we suggest readers be intentional about what they want to learn from the walkthroughs. For example, readers may seek out examples of aggregated datasets, exploratory data analysis, the {ggplot2} package, or the pivot_longer() function. Read the walkthrough and run the code in your R console as you go. After you successfully run the code, experiment with the functions and techniques you learned by changing the code and seeing new results (or new error messages!). After running the code in the walkthroughs, reflect on how what you learned can be applied to the datasets, problems, and analytic routines in your education work.

One last note on this approach to the book: we believe that doing data science in education using R is, at its heart, an endeavor aimed at improving the student experience. The skills taught in the walkthroughs are only one part of doing data science in education using R. As an experienced R user, you know that this endeavor involves complex problems and collaboration. Since part of your task may be to convince others around you of the merits of your analytic tools and approaches, we’ve written this book with that context in mind. Chapter 15 in particular explores ways to introduce these skills to your education job and invite others into analytic activities. We believe you’ll glean useful perspectives from chapters on concepts you’re already familiar with, too.

2.2 A note on statistics

Data science is the intersection between content expertise, programming, and statistics. You’ll want to grow all three of these as you learn more about using data science in your education job. Your education knowledge will lead you to the right problems, your statistics skills will bring rigor to your analysis, and your programming skills will scale your analysis to reach more people.

What happens when we remove one of these pieces? Consider a data scientist working in education who is an expert programmer and statistician but has not learned about the real-life conditions that generate education data. She might make analysis decisions that overlook the nuances in the data. As another example, consider a data scientist who is an expert statistician and an education veteran, but who has not learned to code. He will find it difficult to scale his analysis up, thereby foregoing the chance to make the largest possible improvement to the student experience. Finally, consider a data scientist who is an expert programmer and an education veteran. She can only scale surface-level analysis and might miss chances to understand causal relationships or predict student outcomes.

In this book, we will spend a lot of time learning R by way of recognizable education data examples. But doing a deep dive into statistics and how to use statistical techniques responsibly is better covered by books dedicated solely to the topic. It’s hard to overstate how important this part of the learning is on the lives of students and educators. One education data scientist we spoke to said this about the difference between building a model for an online retailer and building a model in education:

It’s not a big deal if an online shopper gets mistakenly shown 1000 brooms but if I got my model wrong and we close a school, that will change a child’s entire life.

We want this book to be your go-to R reference as you start integrating data science tools into your education job. Our aim is to help you learn R by teaching data science techniques using education datasets. We’ll demonstrate statistics techniques like hypothesis testing and model building and how to run these operations in R. However, the explanations in our chapters will not provide a complete background about the statistical techniques.

We wrote within these boundaries because we believe that the technical and ethical use of statistics techniques deserves its own space. If you already have a foundation in statistics, you will learn how to implement some familiar processes in R. If you have no foundation in statistics, you will be able to take a satisfying leap forward in your learning by successfully using R to run the models and experiencing the model interpretations in our walkthroughs. We provide enough background for you to understand the purpose of the analysis and its results. We encourage you to explore other excellent books like Learning Statistics With R (https://learningstatisticswithr.com/) (Navarro, 2020), as you learn the required nuances of applying statistical techniques to scenarios outside our walkthroughs.

2.3 What this book is not about

While we wrote Data Science in Education Using R to be a wide-ranging introduction to the topic, there is a great deal that this book is not about. Some of these topics are those that we would have liked to have been able to include, but we did not because they did not fit our intention of providing a solid foundation in doing data science in education. We chose to not include other topics because, frankly, excellent resources for those topics already exist. We detail some of what we had to not include in the book here.

  • Git/GitHub: Git and GitHub are version control software programs, which means that they help keep track of different versions of coding files and specific changes that were made for each version. Git and GitHub are parts of many data scientists’ workflows for solo or collaborative work. However, there is a steep learning curve and these tools are not necessary to get started with coding in R. An outstanding introduction to Git and Github is Bryan (2020)’s freely available book Happy Git with R (https://happygitwithr.com/).

  • Building R packages: If you are carrying out the same analyses many times, it may be helpful to create your own package. Packages are collections of code and sometimes data, such as the {roomba} (for tidying complex, nested lists) and {tidyLPA} (for carrying out Latent Profile Analysis) packages that authors of this book created. However, building an R package is not the focus of this book. Hadley Wickham wrote a very helpful—and freely available—book on the topic called R Packages (http://r-pkgs.had.co.nz/) (Wickham, 2015).

  • Advanced statistical methodologies: As noted above, there are other excellent books for learning statistics. While we do discuss basic and advanced statistical methods, this is not a statistical methods book. One advanced statistical book that we think is excellent from a machine learning perspective is James et al. (2013)’s An Introduction to Statistical Learning with Applications in R.

  • Creating a website (or book): As you might already suspect, R is versatile and can be used for more than just performing data analyses. In fact, R can be used to write books (like this one, which we wrote using the {bookdown} package) and create websites (which some of the authors have done using the {blogdown} package). This book does not describe how to create books or websites; there are excellent, freely available books on these topics as well (see Xie et al. (2019)’s blogdown: Creating Websites with R Markdown (https://bookdown.org/yihui/blogdown/) and Xie (2019)’s bookdown: Authoring Books and Technical Documents with R Markdown (https://bookdown.org/yihui/bookdown/).

2.4 Supporting the book

If you find this book useful, please support it by:

2.5 Contributing to the book

We designed this book to be useful and practical for our readers in education. We wrote it as a guide to getting up and running in R, but we know this book does not comprehensively cover every topic related to R. We did this to create a reference that is not intimidating to new users and that creates frequent, small wins while learning to use R.

One question we asked ourselves was: how do we expand this work as data science in education expands as a field? We want readers of this book to be equipped with an agile skillset, and we want this book to continue to provide that even as new R packages are developed and new methods arise. We wrote this book in the open on GitHub so that community members can help us evolve the work, even after it is formally published.

We want this to be the book new data scientists in education have with them as they grow their craft. To achieve that goal, it’s important to us that the stories and examples in the book are based on your stories and examples. Therefore, we’ve built ways for you to share with us.

If you have some experience with Git and want to contribute that way, here’s how you can contribute:

If you are new to data science in education, welcome! We would love to have your feedback by email ().

We hope that as the book evolves, it grows to reflect the changing needs of data scientists in education.