xplrrr is an R package to make exploratory data analysis (EDA) simple and seamless. EDA is a crucial phase of the data science workflow as it allows us get a fist glimpse of the data. It is important to identify statistical characteristics of the data so that researchers can properly set up the rest of the analysis. This package will provide the tools required to conduct a thorough EDA.
Once the package is approved and released to CRAN, you will be able to install it like this: CRAN with:
install.packages("xplrrr")For now, install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("UBC-MDS/xplrrr")explore_summarywill display a table with basic summary statistics and wholistic information about the data including column names for both categorical and numerical columns.explore_outlierswill provide a thorough method of identifying outliers in the data based on standard deviation.explore_missingwill show exactly where there is missing data and how much data is missing.explore_feature_mapwill generate a faceted plot on pairwise feature relationships and correlations as well as individual feature distributions.
This is a basic example which shows you how to solve a common problem:
library(xplrrr)
explore_summary(airquality)
#> min. 1st Qu. median 3rd Qu. max. mean var
#> Ozone 1.0 18.00 31.5 63.25 168.0 42.129310 1088.200525
#> Solar.R 7.0 115.75 205.0 258.75 334.0 185.931507 8110.519414
#> Wind 1.7 7.40 9.7 11.50 20.7 9.957516 12.411539
#> Temp 56.0 72.00 79.0 85.00 97.0 77.882353 89.591331
#> Month 5.0 6.00 7.0 8.00 9.0 6.993464 2.006536
#> Day 1.0 8.00 16.0 23.00 31.0 15.803922 78.579721
explore_outliers(airquality)
#> outlier_count
#> Ozone 6
#> Solar.R 0
#> Wind 5
#> Temp 3
#> Month 0
#> Day 0
missing <- explore_missing(airquality, type = "location")
head(missing)
#> Ozone Solar.R Wind Temp Month Day Index
#> 5 NA NA 14.3 56 5 5 5
#> 6 28 NA 14.9 66 5 6 6
#> 10 NA 194 8.6 69 5 10 10
#> 11 7 NA 6.9 74 5 11 11
#> 25 NA 66 16.6 57 5 25 25
#> 26 NA 266 14.9 58 5 26 26
explore_missing(airquality, type = "count")
#> Number.of.missing.values Proportion.of.missing.data
#> Ozone 37 0.24183007
#> Solar.R 7 0.04575163
#> Wind 0 0.00000000
#> Temp 0 0.00000000
#> Month 0 0.00000000
#> Day 0 0.00000000
explore_feature_map(iris)
This R package is built using the tidyverse ecosystem that will help first time data science users more easily get started with their data projects. A similar package, DataExplorer is another EDA tool available. There are not many EDA packages that exist because the tidyverse ecosystem allows full control of data wrangling and visualization, however users who are not experts with these packages will find xplrrr very useful.
Please find up-to-date official documentation at https://ubc-mds.github.io/xplrrr
Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given. See CONTRIBUTING.md for further details.
| Name | Github ID |
|---|---|
| Braden Tam | bradentam |
| Furqan Khan | fkhan72 |
| Serhiy Pokrovskyy | pokrovskyy |
| Yu Fang | lori94 |
For the complete list of project contributors, see CONTRIBUTORS.md