Tidyverse for Effective Plotting

In this blog post, we explore the Tidyverse, a collection of R packages designed for data science, with a focus on its powerful capabilities for creating effective plots. We’ll discuss how to get students quickly immersed in data manipulation and visualization, the benefits of teaching table operations over procedural logic, and why the popular pipe operator (%>%) isn’t as daunting as it may seem. Moreover, we’ll look into why Tidyverse promotes a unified way of performing tasks, assess how much of base R should still be part of learning, and feature insights from notable data scientist David Robinson. Finally, we’ll wrap up the discussion by summarizing key points in a tabulated format to guide your next steps in mastering Tidyverse for your data visualization needs.

Get students doing powerful things quickly

The Tidyverse philosophy aligns perfectly with the pedagogical goal of getting students up and running with data quickly. For beginners, there is nothing more motivating than seeing practical results with minimal code. The Tidyverse packages, especially ggplot2 for plotting, provide students with the tools to visualize trends, patterns, and anomalies in their datasets, fostering a sense of accomplishment and encouraging further exploration.

By prioritizing the immediate application of skills, educators can hook students with easily graspable functions that yield polished and professional results. Students can focus on higher-level analytical tasks without getting bogged down by complex procedural programming. This allows them to dedicate more time to interpreting the data, rather than cleaning it.

Don’t teach procedural logic (loops and conditionals), teach tables (group by and join)

The traditional approach to teaching programming emphasizes loops and conditionals, which, while foundational, can overwhelm beginners. Teaching data manipulation through the lens of tables — using functions like

group_by()

and

join()

— allows students to think in terms of data itself, rather than the mechanics of control flow. This approach aligns more closely with real-world data analysis tasks, where understanding the relationships and structures within data is crucial.

Rethinking this paradigm shift from procedural logic to tabular data structures using the

dplyr

package equips students with a more intuitive grasp of data operations. These functions encapsulate typical data handling scenarios succinctly, making them accessible and empowering students to perform complex analyses more confidently and efficiently.

%>% isn’t too hard for beginners

The pipe operator (

%>%

) stands as one of the hallmark features of the Tidyverse. Although it initially appears intimidating to new users, its consistency and simplicity quickly become apparent. This operator allows for the seamless chaining of commands, turning what would be intricate nested functions into easy-to-read, linear sequences.

Using

%>%

encourages logical thinking in steps, resembling the natural progression of how tasks are conceptualized and executed. For beginners, becoming familiar with this feature can eliminate a lot of syntactical friction, as it implicitly passes the left-hand side result directly into the right-hand side function — a powerful tool in promoting readable and scalable code.

There’s only one way to do it

Unlike the more flexible but sometimes overwhelming alternatives offered by base R, the Tidyverse often prescribes a ‘best practice’ method for various tasks. This can streamline the learning curve for beginners and reduce the cognitive load associated with choosing from too many alternatives.

Adopting these standardized methods can unify a learning cohort, as everyone follows the same logical pathways to solve problems. This consistency not only builds confidence among students but also eases the collaborative aspect of data science, where rapidly understanding peer-written code is beneficial.

What from base R should be taught, and when?

While the Tidyverse provides an accessible entry point for data science, understanding base R is still vital for a comprehensive skillset. Key base R concepts, such as data types and basic functions, should be introduced after students are comfortable with Tidyverse operations, ensuring a foundational comprehension without overwhelming them early on.

Some base R techniques are irreplaceable in niche use cases, and providing a timeline for introducing these skills can prepare students for real-world challenges. The aim is to support a balanced learning approach, wherein students appreciate the succinctness of Tidyverse while recognizing the robustness of base R when required.

Conclusion: You have permission not to be boring

Teaching, and by extension learning, does not have to be a monotonous recital of arcane functions and methods. The innovative nature of the Tidyverse epitomizes this, as it supports dynamic and engaging data analysis processes. As educators, embracing this tool can revitalize the curriculum and genuinely pique students’ interest.

By leveraging the creative potential of Tidyverse, students and practitioners alike are authorized to explore data in ways that inspire storytelling and insight discovery, empowering them to tackle data analysis with not just efficiency but also a spark of creativity.

David Robinson

David Robinson, a prominent data scientist, exemplifies the pragmatic and innovative use of the Tidyverse. His contributions to the community, especially via online resources and his own writings, provide an invaluable perspective on how these tools can be harnessed effectively for real-world applications.

Robinson’s insights advocate for an approach to data science that values clarity and simplicity — values that resonate deeply within the Tidyverse’s design philosophy. His work demonstrates that with the right tools, complex data stories can be told easily, underscoring the Tidyverse’s place in modern data analysis.