Document Type
Article
Publication Date
4-23-2022
Publication Title
Technology Innovations in Statistics Education
Volume
14
Issue
1
Abstract
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). These shared principles have led to the widespread adoption of the tidyverse ecosystem. A large part of this usage is because the tidyverse tools have been intentionally designed to ease the learning process and make it easier for users to learn new functions as they engage with additional pieces 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 for undergraduate students at all levels and majors to gain computational skills and thinking needed throughout the data science cycle. In this paper, we introduce the tidyverse from an educator’s perspective. We provide a brief introduction to the tidyverse, demonstrate how foundational statistics and data science tasks are accomplished with the tidyverse, and discuss the strengths of the tidyverse, particularly in the context of teaching and learning.
Recommended Citation
Çetinkaya-Rundel, Mine; Hardin, Johanna; Baumer, Benjamin; McNamara, Amelia; Horton, Nicholas J.; and Rundel, Colin W., "An Educator’s Perspective of the Tidyverse" (2022). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/sds_facpubs/47
Digital Object Identifier (DOI)
dx.doi.org/10.5070/T514154352
Rights
© 2022 by the author(s) Licensed to Smith College and distributed CC-BY under the Smith College Faculty Open Access Policy.
Included in
Data Science Commons, Other Computer Sciences Commons, Statistics and Probability Commons
Comments
Peer reviewed accepted manuscript.