Using Mathlink Cubes to Introduce Data Wrangling with Examples in R

TL;DR

The paper addresses the challenge of teaching data wrangling to undergraduates by introducing a tangible, manipulative-based approach that precedes coding. It introduces mathlink cubes as a physical representation of a data frame and maps common wrangling operations to cube manipulations before translating them into R/dplyr code. Through a 75-minute classroom activity with groups of three, the authors demonstrate how filtering, selecting, mutating, arranging, grouping, and summarizing can be practiced hands-on and then implemented in R, with positive student feedback. The work suggests that concrete manipulatives can facilitate collaboration, reduce coding anxiety, and provide a scalable blueprint for undergraduate data science pedagogy, with resources and slides available for replication.

Abstract

This paper explores an innovative approach to teaching data wrangling skills to students through hands-on activities before transitioning to coding. Data wrangling, a critical aspect of data analysis, involves cleaning, transforming, and restructuring data. We introduce the use of a physical tool, mathlink cubes, to facilitate a tangible understanding of data sets. This approach helps students grasp the concepts of data wrangling before implementing them in coding languages such as R. We detail a classroom activity that includes hands-on tasks paralleling common data wrangling processes such as filtering, selecting, and mutating, followed by their coding equivalents using R's `dplyr` package.

Figures (16)

• Figure 1: Mathlink cubes arranged to represent a data set, where each cube corresponds to a unique observation and the distinct shapes on their faces, 'triange', 'square', 'pentagon', and 'hexagon', denote variable values, 3, 4, 5, and 6, respectively. This 'data set' contains 6 variables, 'red', 'orange', 'yellow', 'green', 'blue', and 'purple'.
• Figure 2: Linear arrangement of mathlink cubes (appearing in the red square), illustrating a single observation in the data set represented in Figure \ref{['fig-1']}, with variables differentiated by color and values indicated by the shapes visible on the cube sides
• Figure 3: Original arrangement
• Figure 4: Filtered arrangement
• Figure 6: Original arrangement

Theorems & Definitions (12)

• Example 4.1
• Example 4.2
• Example 4.3
• Example 4.4
• Example 4.5
• Example 4.6
• Example 4.7
• Example 4.8
• Example 4.9
• Example 4.10