Summarize and Explore the Data

Exploratory analysis on any input data describing the structure and the relationships present in the data. The package automatically select the variable and does related descriptive statistics. Analyzing information value, weight of evidence, custom tables, summary statistics, graphical techniques will be performed for both numeric and categorical predictors.

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.

Installation

The package can be installed directly from CRAN.

install.packages("SmartEDA")

You can install SmartEDA from github with:

# install.packages("devtools")
devtools::install_github("cran/SmartEDA")

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.

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

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 dataset, variable names, overall missing summary and data types of each variables

ExpData(data=Carseats,type=1)
ExpData(data=Carseats,type=2)

Summary of numerical variables

To summarise the numeric variables, you can use following r codes from this pacakge

ExpNumStat(Carseats,by="A",gp=NULL,Qnt=seq(0,1,0.1),MesofShape=2,Outlier=TRUE,round=2)
ExpNumStat(Carseats,by="A",gp="Price",Qnt=seq(0,1,0.1),MesofShape=1,Outlier=TRUE,round=2)
ExpNumStat(Carseats,by="GA",gp="Urban",Qnt=seq(0,1,0.1),MesofShape=2,Outlier=TRUE,round=2)

Graphical representation of all numeric features

ExpNumViz(Carseats,gp=NULL,nlim=10,Page=c(2,2),sample=8)
ExpNumViz(Carseats,gp="Urban",type=1,nlim=NULL,fname=NULL,col=c("pink","yellow","orange"),Page=c(2,2),sample=8)	

Summary of Categorical variables

ExpCTable(Carseats,Target=NULL,margin=1,clim=10,nlim=5,round=2,bin=NULL,per=T)
ExpCatViz(Carseats,target=NULL,fname=NULL,clim=10,margin=2,Page = c(2,1),sample=4)
ExpCTable(Carseats,Target="Price",margin=1,clim=10,nlim=NULL,round=2,bin=4,per=F)
ExpCatStat(Carseats,Target="Urban",result = "Stat",clim=10,nlim=5,Pclass="Yes")
ExpCatStat(Carseats,Target="Urban",result = "IV",clim=10,nlim=5,Pclass="Yes")
ExpCTable(Carseats,Target="Urban",margin=1,clim=10,nlim=NULL,round=2,bin=NULL,per=F)

Graphical representation of all categorical variables

ExpCatViz(Carseats,target="Urban",fname=NULL,clim=10,col=NULL,margin=2,Page = c(2,1),sample=2)

Create HTML EDA report

Create a exploratory data analysis report in HTML format

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

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.

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"))