In a world awash with data, the ability to think and compute with data has become an important skill for students in many fields. For that reason, inclusion of some level of statistical computing in many introductory-level courses has grown more common in recent years. Existing literature has documented multiple success stories of teaching statistics with R, bolstered by the capabilities of R Markdown. In this article, we present an in-class data visualization activity intended to expose students to R and R Markdown during the first week of an introductory statistics class. The activity begins with a brief lecture on exploratory data analysis in R. Students are then placed in small groups tasked with exploring a new dataset to produce three visualizations that describe particular insights that are not immediately obvious from the data. Upon completion, students will have produced a series of univariate and multivariate visualizations on a real dataset and practiced describing them.

Computing makes up a large and growing component of data science and statistics courses. Many of those courses, especially when taught by faculty who are statisticians by training, teach R as the programming language. A number of instructors have opted to build much of their teaching around use of the tidyverse. The tidyverse, in the words of its developers, "is a collection of R packages that share a high-level design philosophy and low-level grammar and data structures, so that learning one package makes it easier to learn the next" (Wickham et al. 2019). The shared principles have led to the widespread adoption of the tidyverse ecosystem. No small part of this usage is because the tidyverse tools have been intentionally designed to ease the learning process and cognitive load for users as they engage with each new piece of the larger ecosystem. Moreover, the functionality offered by the packages within the tidyverse spans the entire data science cycle, which includes data import, visualisation, wrangling, modeling, and communication. We believe the tidyverse provides an effective and efficient pathway to data science mastery for students at a variety of different levels of experience. In this paper, we introduce the tidyverse from an educator's perspective, touching on the what (a brief introduction to the tidyverse), the why (pedagogical benefits, opportunities, and challenges), the how (scoping and implementation options), and the where (details on courses, curricula, and student populations).

Heavy alcohol use and its associated negative consequences continue to be an important health issue among adolescents. Of particular concern are risky drinking practices such as playing drinking games. Although retrospective accounts indicate that drinking game participation is common among high school students, it has yet to be assessed in current high school students. Utilizing data from high school students who reported current drinking game participation (n=178), we used latent class analysis to investigate the negative consequences resulting from gaming and examined underlying demographic and alcohol-related behavioral characteristics of students as a function of the resultant classes. Three classes of "gamers" emerged: (1) a "lower-risk" group who had a lower probability of endorsing negative consequences compared to the other groups, (2) a "higher-risk" group who reported that they experienced hangovers and difficulties limiting their drinking, got physically sick, and became rude, obnoxious, or insulting, and (3) a "sexual regret" group who reported that they experienced poor recall and unplanned sexual activity that they later regretted. Although the frequency of participating in drinking games did not differ between these three groups, results indicated that the "lower-risk" group consumed fewer drinks in a typical gaming session compared to the other two groups. The present findings suggest that drinking games are common among high school students, but that mere participation and frequency of play are not necessarily the best indicators of risk. Instead, examination of other constructs such as game-related alcohol consumption, consequences, or psychosocial variables such as impulsivity may be more useful.