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Title
Abstract
Introduction
Starting with a (Tidy) Data Frame
Data Wrangling
Data Visualization
User-Centered Design
References
All Topics
Mathematics

An educator's perspective of the tidyverse

Profile image of Benjamin BaumerBenjamin Baumer

2021, eScholarship (California Digital Library)

https://doi.org/10.48550/ARXIV.2108.03510
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53 pages

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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

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Randall Pruim

The R Journal

The mosaic package provides a simplified and systematic introduction to the core functionality related to descriptive statistics, visualization, modeling, and simulation-based inference required in first and second courses in statistics. This introduction to the package describes some of the guiding principles behind the design of the package and provides illustrative examples of several of the most important functions it implements. These can be combined to help students "think with data" using R in their early course work, starting with simple, yet powerful, declarative commands. Motivation In order to make sense of rich data that is increasingly available, students need computational tools and facility for data management, exploratory analysis, visualization, and modeling (e.g., Nolan and Lang (2010); Horton et al. (2015); Horton and Hardin (2015); Ridgway (2016)). To extract useful information from the complex systems that generate this rich data, students should learn to "think with data" (in the phrase coined by Diane Lambert of Google): using previous results from data to drive statistical investigations and to inform the choice of analysis and presentation possibilities. Yet many students enter statistics courses with little or no computational experience. Software such as R that encompass the tools for thinking with data are sometimes regarded as off-putting and inaccessible to students. With the mosaic package, we have sought to remove unnecessary difficulty in the use of R by students. Our students have demonstrated that it is feasible to integrate computing into our curricula early and often, in a way that provides students with success, confidence, and room to grow. A guiding principle: Less volume, more creativity The mosaic (Pruim et al., 2016b) package reflects attempts by each of the authors to make the power of R accessible and rewarding to students, especially in the context of undergraduate statistics courses, and, in one case, also in calculus. One of the guiding principles behind the development of the mosaic package has been "Less volume, more creativity." By "less volume," we mean reducing the cognitive load involved in using R. By "more creativity" we mean two things. First, we want to provide access to R tools in a way that fosters choices and decision making by students. Such decisions might be about modes of analysis or visualization or about the variables and covariates to consider when exploring relationships with data. Second, we want to encourage students to creatively engage with statistical inference methods via simulation techniques such as randomization and resampling with data analysis and presentation. Our route to "less volume" is to provide a set of three command templates-formulas, functions, and extractors-that standardize usage across many tasks and that highlight the connections between graphical summaries, numerical summaries, models, and inference. The templates themselves are designed to be consistent and concise. Consistency enables a student to generalize from a specific task to a wide set of possibilities. Conciseness means avoiding unnecessary elements so that the essential inputs to a computation are made clear and organized according a syntax that makes the role of each input understandable and predictable. The importance of multivariate thinking The importance of giving students experience with multivariable thinking is a point of emphasis in the newly revised ASA Guidelines for assessment and instruction in statistics education (GAISE) report (ASA GAISE College working group et al., 2016): When students leave an introductory course, they will likely encounter situations within their own fields of study in which multiple variables relate to one another in intricate ways. We should prepare our students for challenging questions that require investigating and exploring relationships among more than two variables. (page 16) Perhaps the best place to start is to consider how a third variable can change our understanding of the relationship between two variables.. .. Simple approaches (such as

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R Programming for Data Science Roger D. Peng
CHENG XU
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WHY DO WE NEED TO APPLY AND EXTEND THE R LANGUAGE TO THE TEACHING OF MATHEMATICS AND STATISTICS IN UNIVERSITIES?
IJARW Research Publication, Hongtruong Pham

IJARW, 2022

This paper introduces the application advantages of R software in mathematical statistics teaching and the extended functions of big data processing and analysis. Combining the course characteristics and teaching content of mathematical statistics, it gives examples of the various applications of R software in mathematical statistics teaching, as well as the extension and applicability of R under massive data, and discusses the positive effects of R software on mathematical statistics teaching.

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R – a Global Sensation in Data Science
Ana Maria Dobre

The main objective of this paper is to expose the evolution of R, as the most used data analysis tool among statisticians and the academic researchers. Its flexibility and complexity simply gained the statisticians and data scientists. The paper examines some of the reasons behind the popularity of R, using tools like SWOT analysis. R software environment offers integrated tools for a very large area of data analysis, from computations and data mining to high-effects visualization. As an example, we performed in this paper an illustration of 3D plotting.

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