https://github.com/Yacine87/EDA_R_Packages
EDA is a must to do step in the data science workflow. Working on data, wrangling & transforming them is time consuming, and it determine the success degree of the next steps (pre preocessing, modelling, communicating outputs & decision making). This repo will show you how to perform EDA in R using the tidyverse ecosystem, and will introduce a comparative approach between the main packages in R whcich could let you perform automated EDA & generating automated EDA html or pdf reports, ready to be communicated.
https://github.com/Yacine87/EDA_R_Packages
dataexplorer dlookr eda exploratory-data-analysis exploratory-data-visualizations explorer hmisc missing-data outliers r rmarkdown rtutor smarteda statistical-tests summarytools tidyverse
Last synced: 4 months ago
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EDA is a must to do step in the data science workflow. Working on data, wrangling & transforming them is time consuming, and it determine the success degree of the next steps (pre preocessing, modelling, communicating outputs & decision making). This repo will show you how to perform EDA in R using the tidyverse ecosystem, and will introduce a comparative approach between the main packages in R whcich could let you perform automated EDA & generating automated EDA html or pdf reports, ready to be communicated.
- Host: GitHub
- URL: https://github.com/Yacine87/EDA_R_Packages
- Owner: Yacine87
- License: mit
- Created: 2020-05-31T12:36:57.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-31T20:36:49.000Z (almost 5 years ago)
- Last Synced: 2024-08-13T07:13:44.079Z (8 months ago)
- Topics: dataexplorer, dlookr, eda, exploratory-data-analysis, exploratory-data-visualizations, explorer, hmisc, missing-data, outliers, r, rmarkdown, rtutor, smarteda, statistical-tests, summarytools, tidyverse
- Language: R
- Homepage:
- Size: 4.88 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# EDA_R_Packages
EDA is a must to step in the data science workflow. Working on data, wrangling & transforming them is time consuming, and it determine the success degree of the next steps (pre preocessing, modelling, communicating outputs & decision making). This repo will show you how to perform EDA in R using the tidyverse ecosystem, and will introduce a comparative approach between the main packages in R whcich could let you perform automated EDA & generating automated EDA html or pdf reports, ready to be communicated.
# Scope of work
We are going to compare here the most known (in my awareness) R packages dedicated to EDA & automated EDA.
Here is a non exhaustive list:
The tidyverse: the most known & revolutionary packages' ecosystem (collection of packages) in R. Covering all the DS workflow. ## See https://www.tidyverse.org/
Note that the above packages have dependencies with the tidyverse's package as dplyr, ggplot2, etc.
SmartEDA # see https://github.com/daya6489/SmartEDA
dlookr # see https://github.com/choonghyunryu/dlookr
DataExplorer # https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html
Hmisc # see https://hbiostat.org/R/Hmisc/
exploreR # see https://cran.r-project.org/web/packages/exploreR/index.html
RtutoR # see https://cran.r-project.org/web/packages/RtutoR/index.html
summarytools # see https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html
# Packages installation
To install successfuly SmartEDA, dlookr, etc, you must install Rtools version 4.0 from https://cran.r-project.org/bin/windows/Rtools/