# SmartEDA [![CRAN status](https://www.r-pkg.org/badges/version/SmartEDA)](https://cran.r-project.org/package=SmartEDA) <img src="man/figures/smarteda_logo.png" align="right" width="130" height="150"/>

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---

# Background
In a quality statistical data analysis the initial step has to be exploratory. Exploratory data analysis begins with the univariate exploratory analyis - examining the variable one at a time. Next comes bivariate analysis followed by multivariate analyis. SmartEDA package helps in getting the complete exploratory data analysis just by running the function instead of writing lengthy r code.

# Functionalities of SmartEDA

The SmartEDA R package has four unique functionalities as

* Descriptive statistics
* Data visualization
* Custom table
* HTML EDA report

![SmartEDA](https://github.com/daya6489/SmartEDA/blob/master/man/figures/smarteda_funtions.PNG)

# Comparison with other packages

SmartEDA package with other similar packages available in CRAN for exploratory data analysis viz. dlookr, DataExplorer, Hmisc, exploreR, RtutoR and summarytools. The metric for evaluation is the availability of various desired features for performing an Exploratory data analysis

![SmartEDA](https://github.com/daya6489/SmartEDA/blob/master/man/figures/SmartEDA_comp.PNG)

# Journal of Open Source Software Article
An article describing SmartEDA package for exploratory data analysis approach has been published in [arxiv](https://arxiv.org/pdf/1903.04754.pdf) and Journal of Open Source Software [JOSS](https://joss.theoj.org/papers/10.21105/joss.01509). Please cite the paper if you use SmartEDA in your work!

# Installation

The package can be installed directly from CRAN.

```R
install.packages("SmartEDA")
```

You can install the latest development verion of the [SmartEDA](https://github.com/daya6489/SmartEDA) from github with:
	
```R
install.packages("devtools")
devtools::install_github("daya6489/SmartEDA",ref = "develop")
```

# Example

## Data
In this vignette, we will be using a simulated data set containing sales of child car seats at 400 different stores. 

Data Source [ISLR package](https://www.rdocumentation.org/packages/ISLR/versions/1.2/topics/Carseats).

Install the package "ISLR" to get the example data set.

```R
	install.packages("ISLR")
	library("ISLR")
	install.packages("SmartEDA")
	library("SmartEDA")
	## Load sample dataset from ISLR pacakge
	Carseats= ISLR::Carseats
```

## Overview of the data
Understanding the dimensions of the data set, variable names, overall missing summary and data types of each variables

```R
## overview of the data; 
	ExpData(data=Carseats,type=1)
## structure of the data	
	ExpData(data=Carseats,type=2)
```

## Summary of numerical variables
To summarise the numeric variables, you can use following r codes from this pacakge

```R
## Summary statistics by – overall
	ExpNumStat(Carseats,by="A",gp=NULL,Qnt=seq(0,1,0.1),MesofShape=2,Outlier=TRUE,round=2)
## Summary statistics by – overall with correlation	
	ExpNumStat(Carseats,by="A",gp="Price",Qnt=seq(0,1,0.1),MesofShape=1,Outlier=TRUE,round=2)
## Summary statistics by – category
	ExpNumStat(Carseats,by="GA",gp="Urban",Qnt=seq(0,1,0.1),MesofShape=2,Outlier=TRUE,round=2)
```

## weighted summary for numerical variables
```R
ExpNumStat(mtcars,by="A",round=2, weight = "wt")
```

## Graphical representation of all numeric features

```R
## Generate Boxplot by category
ExpNumViz(mtcars,target="gear",type=2,nlim=25,fname = file.path(tempdir(),"Mtcars2"),Page = c(2,2))
## Generate Density plot
ExpNumViz(mtcars,target=NULL,type=3,nlim=25,fname = file.path(tempdir(),"Mtcars3"),Page = c(2,2))
## Generate Scatter plot
ExpNumViz(mtcars,target="carb",type=3,nlim=25,fname = file.path(tempdir(),"Mtcars4"),Page = c(2,2))

```

## Summary of Categorical variables	

```R
## Frequency or custom tables for categorical variables
	ExpCTable(Carseats,Target=NULL,margin=1,clim=10,nlim=5,round=2,bin=NULL,per=T)
	ExpCTable(Carseats,Target="Price",margin=1,clim=10,nlim=NULL,round=2,bin=4,per=F)
	ExpCTable(Carseats,Target="Urban",margin=1,clim=10,nlim=NULL,round=2,bin=NULL,per=F)	

## Summary statistics of categorical variables
	ExpCatStat(Carseats,Target="Urban",result = "Stat",clim=10,nlim=5,Pclass="Yes")
## Inforamtion value and Odds value
	ExpCatStat(Carseats,Target="Urban",result = "IV",clim=10,nlim=5,Pclass="Yes")
```
## weighted count for categorical variables
```R
ExpCTable(mtcars,  margin = 1, clim = 10, nlim = 3, bin = NULL, per = FALSE, weight = "wt"")
```

## Graphical representation of all categorical variables

```R
## column chart
	ExpCatViz(Carseats,target="Urban",fname=NULL,clim=10,col=NULL,margin=2,Page = c(2,1),sample=2)
## Stacked bar graph
	ExpCatViz(Carseats,target="Urban",fname=NULL,clim=10,col=NULL,margin=2,Page = c(2,1),sample=2)
## Variable importance graph using information values
  ExpCatStat(Carseats,Target="Urban",result="Stat",Pclass="Yes",plot=TURE,top=20,Round=2)
```
## Variable importance based on Information value

```R
  ExpCatStat(Carseats,Target="Urban",result = "Stat",clim=10,nlim=5,bins=10,Pclass="Yes",plot=TRUE,top=10,Round=2)
```

## Create HTML EDA report
Create a exploratory data analysis report in HTML format

```R
	ExpReport(Carseats,Target="Urban",label=NULL,op_file="test.html",op_dir=getwd(),sc=2,sn=2,Rc="Yes")
```

## Quantile-quantile plot for numeric variables

```R
	ExpOutQQ(CData,nlim=10,fname=NULL,Page=c(2,2),sample=4)
```
![](man/figures/qqplot-1-1.png)<!-- -->

## Parallel Co-ordinate plots

```R
## Defualt ExpParcoord funciton
	ExpParcoord(CData,Group=NULL,Stsize=NULL,Nvar=c("Price","Income","Advertising","Population","Age","Education"))
## With Stratified rows and selected columns only
  ExpParcoord(CData,Group="ShelveLoc",Stsize=c(10,15,20),Nvar=c("Price","Income"),Cvar=c("Urban","US"))
## Without stratification
  ExpParcoord(CData,Group="ShelveLoc",Nvar=c("Price","Income"),Cvar=c("Urban","US"),scale=NULL)
## Scale change  
  ExpParcoord(CData,Group="US",Nvar=c("Price","Income"),Cvar=c("ShelveLoc"),scale="std")
## Selected numeric variables
  ExpParcoord(CData,Group="ShelveLoc",Stsize=c(10,15,20),Nvar=c("Price","Income","Advertising","Population","Age","Education"))
## Selected categorical variables
  ExpParcoord(CData,Group="US",Stsize=c(15,50),Cvar=c("ShelveLoc","Urban"))
```
![](man/figures/ccp6-1.png)<!-- -->

## Two independent plots side by side for the same variable

To plot graph from same variable when Target=NULL vs. when Target = categorical variable (binary or multi-class variable)

```R
target = "gear"
categorical_features <- c("vs", "am", "carb")
numeircal_features <- c("mpg", "cyl", "disp", "hp", "drat", "wt", "qsec")

num_1 <- ExpTwoPlots(mtcars, 
                     plot_type = "numeric",
                     iv_variables = numeircal_features,
                     target = "gear",
                     lp_arg_list = list(alpha=0.5, color = "red", fill= "white", binwidth=1),
                     lp_geom_type = 'histogram',
                     rp_arg_list = list(alpha=0.5, fill = c("red", "orange", "pink"),  binwidth=1),
                     rp_geom_type = 'histogram',
                     fname = "dub2.pdf",
                     page = c(2,1),
                     theme = "Default")

```
![](man/figures/c24_1.png)<!-- -->

## Univariate Outlier analysis

In statistics, an outlier is a data point that differs significantly from other observations. An outlier may be due to variability in the measurement or it may indicate experimental error; the latter are sometimes excluded from the data set.An outlier can cause serious problems in statistical analyses.

Identifying outliers: There are several methods we can use to identify outliers. In ExpOutliers used two methods (1) Boxplot and (2) Standard Deviation

![SmartEDA](https://github.com/daya6489/SmartEDA/blob/master/man/figures/outlierPlot_image.png)


```R
##Identifying outliers mehtod - Boxplot
ExpOutliers(Carseats, varlist = c("Sales","CompPrice","Income"), method = "boxplot",  capping = c(0.1, 0.9))

##Identifying outliers mehtod - 3 Standard Deviation
ExpOutliers(Carseats, varlist = c("Sales","CompPrice","Income"), method = "3xStDev",  capping = c(0.1, 0.9))

##Identifying outliers mehtod - 2 Standard Deviation
ExpOutliers(Carseats, varlist = c("Sales","CompPrice","Income"), method = "2xStDev",  capping = c(0.1, 0.9))


##Create outlier flag (1,0) if there are any outliers 
ExpOutliers(Carseats, varlist = c("Sales","CompPrice","Income"), method = "3xStDev",  capping = c(0.1, 0.9), outflag = TRUE)

##Impute outlier value by mean or median valie
ExpOutliers(Carseats, varlist = c("Sales","CompPrice","Income"), method = "3xStDev", treatment = "mean", capping = c(0.1, 0.9), outflag = TRUE)

```

## Exploratory analysis - Custom tables, summary statistics
Descriptive summary on all input variables for each level/combination of group variable. Also while running the analysis we can filter row/cases of the data. 

```R
	ExpCustomStat(Carseats,Cvar=c("US","Urban","ShelveLoc"),gpby=FALSE)
	ExpCustomStat(Carseats,Cvar=c("US","Urban"),gpby=TRUE,filt=NULL)
	ExpCustomStat(Carseats,Cvar=c("US","Urban","ShelveLoc"),gpby=TRUE,filt=NULL)
	ExpCustomStat(Carseats,Cvar=c("US","Urban"),gpby=TRUE,filt="Population>150")
	ExpCustomStat(Carseats,Cvar=c("US","ShelveLoc"),gpby=TRUE,filt="Urban=='Yes' & Population>150")
	ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('Count','mean','sum','var','min','max'))
	ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('min','p0.25','median','p0.75','max'))
	ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('Count','mean','sum','var'),filt="Urban=='Yes'")
	ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('Count','mean','sum'),filt="Urban=='Yes' & Population>150")
	ExpCustomStat(data_sam,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('Count','mean','sum','min'),filt="All %ni% c(999,-9)")
	ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Education","Income"),stat = c('Count','mean','sum','var','sd','IQR','median'),filt=c("ShelveLoc=='Good'^Urban=='Yes'^Price>=150^ ^US=='Yes'"))
	ExpCustomStat(Carseats,Cvar = c("Urban","ShelveLoc"), Nvar=c("Population","Sales"), stat = c('Count','Prop','mean','min','P0.25','median','p0.75','max'),gpby=FALSE)
	ExpCustomStat(Carseats,Cvar = c("Urban","US","ShelveLoc"), Nvar=c("CompPrice","Income"), stat = c('Count','Prop','mean','sum','PS','min','max','IQR','sd'), gpby = TRUE)
	ExpCustomStat(Carseats,Cvar = c("Urban","US","ShelveLoc"), Nvar=c("CompPrice","Income"), stat = c('Count','Prop','mean','sum','PS','P0.25','median','p0.75'), gpby = TRUE,filt="Urban=='Yes'")
	ExpCustomStat(data_sam,Cvar = c("Urban","US","ShelveLoc"), Nvar=c("Sales","CompPrice","Income"), stat = c('Count','Prop','mean','sum','PS'), gpby = TRUE,filt="All %ni% c(888,999)")
	ExpCustomStat(Carseats,Cvar = c("Urban","US"), Nvar=c("Population","Sales","CompPrice"), stat = c('Count','Prop','mean','sum','var','min','max'), filt=c("ShelveLoc=='Good'^Urban=='Yes'^Price>=150"))
```

## Articles

See [article wiki page](https://github.com/daya6489/SmartEDA/wiki/Articles).
