Recently, Staniak & Biecek (2019) wrote an article in the R Journal exploring several of such packages, so I thought I’d try them out for myself, and take others along with me for that ride.
In R there are many tools available to help you dive in and explore your data. However, in consulting I still see a lot of people using base R’s table and summary functions, followed by a lot of work to get the result into a more presentable format. My own frustrations led to me creating a package (tidyext) for personal use in this area. While that suits me fine, there are tools that can go much further with little effort. Recently, Staniak & Biecek @staniak2019landscape wrote an article in the R Journal exploring several of such packages, so I thought I’d try them out for myself, and take others along with me for that ride.
As this will be a workshop/demo, I’ve created a separate repo and document to make it easier to find, so here is the link: https://m-clark.github.io/exploratory-data-analysis-tools/
The packages demoed are:
If you see mistakes or want to suggest changes, please create an issue on the source repository.
Text and figures are licensed under Creative Commons Attribution CC BY-SA 4.0. Source code is available at https://github.com//m-clark/m-clark.github.io, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Clark (2020, July 10). Michael Clark: Exploratory Data Analysis. Retrieved from https://m-clark.github.io/posts/2020-07-10-eda/
BibTeX citation
@misc{clark2020exploratory,
author = {Clark, Michael},
title = {Michael Clark: Exploratory Data Analysis},
url = {https://m-clark.github.io/posts/2020-07-10-eda/},
year = {2020}
}