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DataExplorer

Background

Exploratory Data Analysis (EDA) is the initial and an important phase of data analysis/predictive modeling. During this process, analysts/modelers will have a first look of the data, and thus generate relevant hypotheses and decide next steps. However, the EDA process could be a hassle at times. This R package aims to automate most of data handling and visualization, so that users could focus on studying the data and extracting insights.

Installation

The package can be installed directly from CRAN.

install.packages("DataExplorer")

However, the latest stable version (if any) could be found on GitHub, and installed using devtools package.

if (!require(devtools)) install.packages("devtools")
devtools::install_github("boxuancui/DataExplorer")

If you would like to install the latest development version, you may install the develop branch.

if (!require(devtools)) install.packages("devtools")
devtools::install_github("boxuancui/DataExplorer", ref = "develop")

Examples

The package is extremely easy to use. Almost everything could be done in one line of code. Please refer to the package manuals for more information. You may also find the package vignettes here.

Report

To get a report for the airquality dataset:

library(DataExplorer)
create_report(airquality)

To get a report for the diamonds dataset with response variable price:

library(ggplot2)
create_report(diamonds, y = "price")

Visualization

Instead of running create_report, you may also run each function individually for your analysis, e.g.,

## View basic description for airquality data
introduce(airquality)
rows153
columns6
discrete_columns0
continuous_columns6
all_missing_columns0
total_missing_values44
complete_rows111
total_observations918
memory_usage6,376
## Plot basic description for airquality data
plot_intro(airquality)

## View missing value distribution for airquality data
plot_missing(airquality)

## Left: frequency distribution of all discrete variables
plot_bar(diamonds)
## Right: `price` distribution of all discrete variables
plot_bar(diamonds, with = "price")

## View frequency distribution by a discrete variable
plot_bar(diamonds, by = "cut")

## View histogram of all continuous variables
plot_histogram(diamonds)

## View estimated density distribution of all continuous variables
plot_density(diamonds)

## View quantile-quantile plot of all continuous variables
plot_qq(diamonds)

## View quantile-quantile plot of all continuous variables by feature `cut`
plot_qq(diamonds, by = "cut")

## View overall correlation heatmap
plot_correlation(diamonds)

## View bivariate continuous distribution based on `cut`
plot_boxplot(diamonds, by = "cut")

## Scatterplot `price` with all other continuous features
plot_scatterplot(split_columns(diamonds)$continuous, by = "price", sampled_rows = 1000L)

## Visualize principal component analysis
plot_prcomp(diamonds, maxcat = 5L)
#> 2 features with more than 5 categories ignored!
#> color: 7 categories
#> clarity: 8 categories

Feature Engineering

To make quick updates to your data:

## Group bottom 20% `clarity` by frequency
group_category(diamonds, feature = "clarity", threshold = 0.2, update = TRUE)

## Group bottom 20% `clarity` by `price`
group_category(diamonds, feature = "clarity", threshold = 0.2, measure = "price", update = TRUE)

## Dummify diamonds dataset
dummify(diamonds)
dummify(diamonds, select = "cut")

## Set values for missing observations
df <- data.frame("a" = rnorm(260), "b" = rep(letters, 10))
df[sample.int(260, 50), ] <- NA
set_missing(df, list(0L, "unknown"))

## Update columns
update_columns(airquality, c("Month", "Day"), as.factor)
update_columns(airquality, 1L, function(x) x^2)

## Drop columns
drop_columns(diamonds, 8:10)
drop_columns(diamonds, "clarity")

Articles

See article wiki page.

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Version

Install

install.packages('DataExplorer')

Monthly Downloads

10,724

Version

0.8.2

License

MIT + file LICENSE

Maintainer

Last Published

December 15th, 2020

Functions in DataExplorer (0.8.2)

.getPageLayout

Calculate page layout index
dummify

Dummify discrete features to binary columns
drop_columns

Drop selected variables
group_category

Group categories for discrete features
create_report

Create report
DataExplorer-package

Data Explorer
.ignoreCat

Truncate category
configure_report

Configure report template
plotDataExplorer.grid

Plot objects with gridExtra
.lapply

Parallelization
plot_intro

Plot introduction
plot_boxplot

Create boxplot for continuous features
plotDataExplorer.multiple

Plot multiple objects
plot_scatterplot

Create scatterplot for all features
plot_prcomp

Visualize principal component analysis
plot_qq

Plot QQ plot
plot_str

Visualize data structure
plot_correlation

Create correlation heatmap for discrete features
plotDataExplorer

Default DataExplorer plotting function
plot_density

Plot density estimates
introduce

Describe basic information
profile_missing

Profile missing values
plot_histogram

Plot histogram
split_columns

Split data into discrete and continuous parts
.getAllMissing

Get all missing columns
plot_missing

Plot missing value profile
update_columns

Update variable types or values
set_missing

Set all missing values to indicated value
plotDataExplorer.single

Plot single object
plot_bar

Plot bar chart