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Tidy Data
Open accessAbstract
A huge amount of effort is spent cleaning data to get it ready for analysis, but there has been little research on how to make data cleaning as easy and effective as possible. This paper tackles a small, but important, component of data cleaning: data tidying. Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. This framework makes it easy to tidy messy datasets because only a small set of tools are needed to deal with a wide range of un-tidy datasets. This structure also makes it easier to develop tidy tools for data analysis, tools that both input and output tidy datasets. The advantages of a consistent data structure and matching tools are demonstrated with a case study free from mundane data manipulation chores.
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Cited by
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., et al. (2019) · Journal of Open Source Software · doi:10.21105/joss.01686 · Cited by 22,138
Self-citationseaborn: statistical data visualization
Waskom, M. (2021) · Journal of Open Source Software · doi:10.21105/joss.03021 · Cited by 7,310
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Pasichnyi, O., Levihn, F., Shahrokni, H., Wallin, J., & Kordas, O. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.05.373 · Cited by 92
Assessing taxonomic metagenome profilers with OPAL
Meyer, F., Bremges, A., Belmann, P., Janssen, S., McHardy, A. C., & Koslicki, D. (2019) · Genome Biology · doi:10.1186/s13059-019-1646-y · Cited by 80
The ISB Cancer Genomics Cloud: A Flexible Cloud-Based Platform for Cancer Genomics Research
Reynolds, S. M., Miller, M., Lee, P., Leinonen, K., Paquette, S. M., Rodebaugh, Z., Hahn, A., Gibbs, D. L., Slagel, J., Longabaugh, W. J., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-0617 · Cited by 63
Metadata
- Authors
- Wickham, H.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2014
- Volume
- 59
- Issue
- 10
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W4235243453
- Cited by
- 901
- References
- 0
- FWCI
- 30.70
- Type
- journal article
- Language
- en
- Open access
- diamond
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
