Intro Hello! It’s my distinct pleasure to be guest-blogging for you today! My name is Kelly Bodwin and I’m an Assistant Professor of Statistics at Cal Poly, San Luis Obispo. In my two years at Cal Poly I have taught Stat 218 - An introductory course for non-majors Stat 419 - An upper-level course in multivariate analysis Stat 331 - Our department’s introductory R class In all of these courses, I make extensive use of R.

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Image credit: xkcd.com Statistics for science Figuring out truth is really hard to do. Teaching students how to attempt it may be even harder. As statisticians we know that statistical significance isn’t truth, but we still hope that the process by which we analyze data will lead us on a path toward scientific discovery. How do we teach students the best way to stay on the path of using statistics to move science forward?

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What is the learnr package? The learnr package makes it easy to turn any R Markdown document into an interactive tutorial. With this vehicle instructors can offer interactive tools to their students to allow them to explore datasets in use from the class, a textbook, or even collected themselves. Straight from Garrett Grolemund’s fantastic introduction to the package, tutorials can include any or all of the following: Narrative, figures, illustrations, and equations.

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The Importance of Good Coding Style To begin with another quote from Hadley Wickham: “Good coding style is like using correct punctuation. You can manage without it, but it sure makes things easier to read.” In this way, coding style is very much an example of a negative virtue. You are much more likely to be told if you have a bad coding style than you are if you have a good coding style.

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Throughout the summer 2019 blog series, we have given teaching tips, best data science practices, links to compilations of papers in data science and in teaching data science, and ways to participate in the larger data science community. That is, for anyone already working in a data science community (e.g., in a job or an academic institution) or for anyone doing data science for fun, we have provided myriad rabbit holes in which to get lost for your entire summer.

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Shiny Recap Yesterday we introduced R Shiny and discussed how it allows you to build interactive web applications straight from R. We saw a few examples highlighting the wondrous interactivity of exploratory data analysis, data visualization, and data models that it enables. If you didn’t catch yesterday’s post, check it out at https://teachdatascience.com/shiny1/ and be sure to go play around with some Shiny apps at RStudio’s gallery at https://shiny.

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What is R Shiny? shiny is a powerful and flexible R package that makes it easy to build interactive web applications and dynamic dashboards straight from R. These apps can be hosted on a standalone webpage or embedded in R Markdown documents. Not only does shiny allow you to build these web apps from R, but it enables their construction using only R code. Knowledge of HTML and web development is not required at all, though it can be used to enhance your apps in numerous ways.

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As educators, it is exciting that our course enrollments are up and students are excited about data science topics, models, software, and careers. It is also validating that when they graduate, our students are able to support themselves doing interesting and engaging work. However, it can be sometimes disheartening to realize how many of our data science students use their skills to maximize the number of times viewers click on ads.

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As statistics educators, it is often easier to focus our teaching on methods instead of communication. And while many of us understand the value of good communication, actually teaching it is difficult and outside of our comfort zone. There has been quite a bit of work done on the science of visualization (e.g., the Grammar of Graphics by Wilkinson). There is general consensus that teaching students to communicate using visualizations is of paramount importance (see recent blog entries: National Academies Report on Data Science and GAISE).

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When people ask about how to get their students engaged with R in their introductory statistics and data science courses we offer three pieces of advice: keep it simple (discussed in the “Less Volume, More Creativity” blog entry) engage students to provide peer-tutoring and drop-in office hours to assist with questions and coding to complement class and office hours (at Amherst College this is coordinated by the Statistics and Data Science Fellows) have students use a dedicated server to access R Slide credit: Mine Çetinkaya-Rundel

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