The fivethirtyeight R Package: "Tame Data" Principles for Introductory Statistics and Data Science Courses

Kim, Albert Y.; Ismay, Chester; Chunn, Jennifer

doi:10.5070/t5111035892

Cited by 10 publications

(9 citation statements)

References 8 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The need to bring real secondary data into the classroom generates a need to prepare that data so that it is suitable for addressing learning objectives. Kim et al (2018) discuss the continuum from "raw" to "clean," and note that, for some objectives (such as learning to clean data), perfectly clean is too much and raw is not enough. The happy medium is "tame" data (their terminology) that is just clean enough to fill its intended educational role.…”

Section: Discussionmentioning

confidence: 99%

Secondary Data in the Secondary Data Science and Statistics Classroom

Bargagliotti¹,

Gould²

2022

Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference

View full text Add to dashboard Cite

The complexity of modern data raises the stakes of what it means to be data literate, challenging statistics educators who wish to engage students with real data. Two authors of the revised Guidelines for Assessment in Instruction and Statistics Education Pre-K–12 report discuss the use of secondary data, illustrating these challenges with an exploration originating from a What's Going On in This Graph exercise. In particular, the authors discuss two important issues when using secondary data for Pre-K–12 students (ages 5–18): the importance of using interrogative questions to determine the provenance of data in order to assess its suitability to the task at hand as well as to consider ethical concerns, and the need to "tame" the data if the data collection scheme is advanced.

show abstract

Section: Discussionmentioning

confidence: 99%

Secondary Data in the Secondary Data Science and Statistics Classroom

Bargagliotti¹,

Gould²

2022

Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference

View full text Add to dashboard Cite

show abstract

“…Note that the R programming environment has the appropriate package "The fivethirtyeight R", which facilitates the use of Twitter resources FiveThirtyEight in data courses [4]. We added some tweeters to the recommendation list, including Ukrainian resources as well as resources related to population censuses.…”

Section: Conversational Stylementioning

confidence: 99%

Using Twitter in Ukrainian sociology majors training

2020

View full text Add to dashboard Cite

The article deals with the problem of using cloud technologies in the training of sociology students in Ukraine. The popularity of Twitter in Ukraine is analyzed. The possibilities of using Twitter as a learning tool in classroom are discussed. List of recommended tweeters, including Ukrainian resources as well as resources related to population censuses is proposed. The article offers examples of student activities for Social Statistics and Demographics courses. The article demonstrates that new forms of student’s activity related to data analysis introduced by academics and practitioners (building art objects and storytelling based on data; shared data collection by citizens through mobile devices, “play with data” modern data visualization services) can be realized with Twitter resources and can help overcome the barriers that arise while studying quantitative methods.

show abstract

“…The data is first organized into "tidy data" (Wickham 2014) and then further wrangled using step-wise piping with a split-apply-combine strategy for mutating new variables (Wickham 2011). Tidy data shouldn't be confused with "tame data, " which Kim, Ismay, and Chunn (2018) coined to refer to textbook datasets suitable for teaching, particularly teaching statistics. The resulting (tame) data is provided in a new R package called yowie, which includes the code so the process is reproducible and could be used to further refresh the data as new records are made available in the NLSY79 database.…”

Section: Introductionmentioning

confidence: 99%

A Journey from Wild to Textbook Data to Reproducibly Refresh the Wages Data from the National Longitudinal Survey of Youth Database

Amaliah

Cook

Tanaka

et al. 2022

Journal of Statistics and Data Science Education

View full text Add to dashboard Cite

Textbook data is essential for teaching statistics and data science methods because it is clean, allowing the instructor to focus on methodology. Ideally textbook datasets are refreshed regularly, especially when they are subsets taken from an ongoing data collection. It is also important to use contemporary data for teaching, to imbue the sense that the methodology is relevant today. This article describes the trials and tribulations of refreshing a textbook dataset on wages, extracted from the National Longitudinal Survey of Youth (NLSY79) in the early 1990s. The data is useful for teaching modeling and exploratory analysis of longitudinal data. Subsets of NLSY79, including the wages data, can be found in supplementary materials from numerous textbooks and research articles. The NLSY79 database has been continually updated through to 2018, so new records are available. Here we describe our journey to refresh the wages data, and document the process so that the data can be regularly updated into the future. Our journey was difficult because the steps and decisions taken to get from the raw data to the wages textbook subset have not been clearly articulated. We have been diligent to provide a reproducible workflow for others to follow, which also hopefully inspires more attempts at refreshing data for teaching. Three new datasets and the code to produce them are provided in the open source R package called yowie. Supplementary materials for this article are available online.

show abstract

The fivethirtyeight R Package: "Tame Data" Principles for Introductory Statistics and Data Science Courses

Cited by 10 publications

References 8 publications

Secondary Data in the Secondary Data Science and Statistics Classroom

Secondary Data in the Secondary Data Science and Statistics Classroom

Using Twitter in Ukrainian sociology majors training

A Journey from Wild to Textbook Data to Reproducibly Refresh the Wages Data from the National Longitudinal Survey of Youth Database

Contact Info

Product

Resources

About