Summarize and Explore the Data

R package that automates most of exploratory analyses tasks in modeling

Dayananda Ubrangala, Kiran R, Ravi Prasad Kondapalli, Sayan Putatunda

2022-12-03

1. Introduction

The document introduces the SmartEDA package and how it can help you to build exploratory data analysis.

SmartEDA includes multiple custom functions to perform initial exploratory analysis on any input data describing the structure and the relationships present in the data. The generated output can be obtained in both summary and graphical form. The graphical form or charts can also be exported as reports.

सर्वस्य लोचनं शास्त्रं
Science is the only eye

अनेकसंशयोच्छेदि, परोक्षार्थस्य दर्शक|
सर्वस्य लोचनं शास्त्रं, यस्य नास्त्यन्ध एव सः ||

It blasts many doubts, foresees what is not obvious |
Science is the eye of everyone, one who hasnt got it, is like a blind ||

SmartEDA package helps you to construct a good base of data understanding. The capabilities and functionalities are listed below

  1. SmartEDA package will make you capable of applying different types of EDA without having to

    • remember the different R package names
    • write lengthy R scripts
    • manual effort to prepare the EDA report

  2. No need to categorize the variables into Character, Numeric, Factor etc. SmartEDA functions automatically categorize all the features into the right data type (Character, Numeric, Factor etc.) based on the input data.

  3. ggplot2 functions are used for graphical presentation of data

  4. Rmarkdown and knitr functions were used for build HTML reports

To summarize, SmartEDA package helps in getting the complete exploratory data analysis just by running the function instead of writing lengthy r code.

Journal of Open Source Software Article

An article describing SmartEDA pacakge for exploratory data analysis approach has been published in arxiv and Journal of Open Source Software JOSS. Please cite the paper if you use SmartEDA in your work!

2. 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

2.1 Overview of the data

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

# Overview of the data - Type = 1
ExpData(data=Carseats,type=1)

# Structure of the data - Type = 2
ExpData(data=Carseats,type=2)
Index Variable_Name Variable_Type Sample_n Missing_Count Per_of_Missing No_of_distinct_values
1 Sales numeric 400 0 0 336
2 CompPrice numeric 400 0 0 73
3 Income numeric 400 0 0 98
4 Advertising numeric 400 0 0 28
5 Population numeric 400 0 0 275
6 Price numeric 400 0 0 101
7 ShelveLoc factor 400 0 0 3
8 Age numeric 400 0 0 56
9 Education numeric 400 0 0 9
10 Urban factor 400 0 0 2
11 US factor 400 0 0 2

2.2 Add summary statistics into Metadata ouput

# Metadata Information with additional statistics like mean, median and variance
ExpData(data=Carseats,type=2, fun = c("mean", "median", "var"))
Index Variable_Name Variable_Type Sample_n Missing_Count Per_of_Missing No_of_distinct_values mean median var
1 Sales numeric 400 0 0 336 7.50 7.49 7.98
2 CompPrice numeric 400 0 0 73 124.97 125.00 235.15
3 Income numeric 400 0 0 98 68.66 69.00 783.22
4 Advertising numeric 400 0 0 28 6.64 5.00 44.23
5 Population numeric 400 0 0 275 264.84 272.00 21719.81
6 Price numeric 400 0 0 101 115.80 117.00 560.58
7 ShelveLoc factor 400 0 0 3 NA NA NA
8 Age numeric 400 0 0 56 53.32 54.50 262.45
9 Education numeric 400 0 0 9 13.90 14.00 6.87
10 Urban factor 400 0 0 2 NA NA NA
11 US factor 400 0 0 2 NA NA NA 
# Derive Quantile 
quantile_10 = function(x){
  quantile_10 = quantile(x, na.rm = TRUE, 0.1)
}

quantile_90 = function(x){
  quantile_90 = quantile(x, na.rm = TRUE, 0.9)
}

output_e1 <- ExpData(data=Carseats, type=2, fun=c("quantile_10", "quantile_90"))
Index Variable_Name Variable_Type Sample_n Missing_Count Per_of_Missing No_of_distinct_values quantile_10 quantile_90
1 Sales numeric 400 0 0 336 4.12 11.3
2 CompPrice numeric 400 0 0 73 106.00 145.0
3 Income numeric 400 0 0 98 30.00 107.0
4 Advertising numeric 400 0 0 28 0.00 16.0
5 Population numeric 400 0 0 275 58.90 467.0
6 Price numeric 400 0 0 101 87.00 146.0
7 ShelveLoc factor 400 0 0 3 NA NA
8 Age numeric 400 0 0 56 30.00 76.0
9 Education numeric 400 0 0 9 10.00 17.1
10 Urban factor 400 0 0 2 NA NA
11 US factor 400 0 0 2 NA NA

3. Exploratory data analysis (EDA)

This function shows the EDA output for 3 different cases

  1. Target variable is not defined
  2. Target variable is continuous
  3. Target variable is categorical

3.1 Example for case 1: Target variable is not defined

3.1.1 Summary of numerical variables

Summary of all numerical variables

ExpNumStat(Carseats,by="A",gp=NULL,Qnt=seq(0,1,0.1),MesofShape=2,Outlier=TRUE,round=2,Nlim=10)
Compute Weighted Summary Statistics for numerical variable
carseat = ISLR::Carseats
## Compute random weight
carseat$wt = stats::runif( nrow(carseat), 0.5, 1.5 )
wt_summary = ExpNumStat(carseat,by="A",gp=NULL,round=2,Nlim=10, weight = "wt")
wt_summary[,c("Vname","TN","W_count","mean", "W_Mean", "SD","W_Sd")]
##         Vname  TN W_count   mean W_Mean     SD   W_Sd
## 4 Advertising 400  400.37   6.64   6.64   6.65   6.65
## 7         Age 400  400.37  53.32  53.24  16.20  16.18
## 2   CompPrice 400  400.37 124.97 124.86  15.33  15.12
## 3      Income 400  400.37  68.66  68.80  27.99  27.90
## 5  Population 400  400.37 264.84 264.64 147.38 148.29
## 6       Price 400  400.37 115.80 115.70  23.68  23.26
## 1       Sales 400  400.37   7.50   7.45   2.82   2.78
## With group by statement
wt_summary = ExpNumStat(carseat,by="GA",gp="ShelveLoc",round=2,Nlim=10, weight = "wt")
wt_summary[,c("Vname","Group","TN","W_count","mean", "W_Mean", "SD","W_Sd")]
##          Vname            Group  TN W_count   mean W_Mean     SD   W_Sd
## 4  Advertising    ShelveLoc:All 400  400.37   6.64   6.64   6.65   6.65
## 11 Advertising    ShelveLoc:Bad  96   99.77   6.22   6.05   6.46   6.33
## 18 Advertising   ShelveLoc:Good  85   81.56   7.35   7.00   6.80   6.65
## 25 Advertising ShelveLoc:Medium 219  219.05   6.54   6.78   6.68   6.81
## 7          Age    ShelveLoc:All 400  400.37  53.32  53.24  16.20  16.18
## 14         Age    ShelveLoc:Bad  96   99.77  52.05  52.03  17.41  17.30
## 21         Age   ShelveLoc:Good  85   81.56  52.61  52.37  15.43  15.51
## 28         Age ShelveLoc:Medium 219  219.05  54.16  54.13  15.97  15.92
## 2    CompPrice    ShelveLoc:All 400  400.37 124.97 124.86  15.33  15.12
## 9    CompPrice    ShelveLoc:Bad  96   99.77 124.01 124.32  15.18  14.86
## 16   CompPrice   ShelveLoc:Good  85   81.56 125.75 125.66  14.98  14.93
## 23   CompPrice ShelveLoc:Medium 219  219.05 125.10 124.80  15.58  15.37
## 3       Income    ShelveLoc:All 400  400.37  68.66  68.80  27.99  27.90
## 10      Income    ShelveLoc:Bad  96   99.77  72.24  72.06  26.91  27.46
## 17      Income   ShelveLoc:Good  85   81.56  67.98  68.29  28.31  27.92
## 24      Income ShelveLoc:Medium 219  219.05  67.35  67.51  28.31  28.10
## 5   Population    ShelveLoc:All 400  400.37 264.84 264.64 147.38 148.29
## 12  Population    ShelveLoc:Bad  96   99.77 275.29 268.35 147.23 148.56
## 19  Population   ShelveLoc:Good  85   81.56 267.05 269.45 127.25 130.63
## 26  Population ShelveLoc:Medium 219  219.05 259.40 261.16 154.88 154.75
## 6        Price    ShelveLoc:All 400  400.37 115.80 115.70  23.68  23.26
## 13       Price    ShelveLoc:Bad  96   99.77 114.27 113.95  23.78  23.74
## 20       Price   ShelveLoc:Good  85   81.56 117.88 117.02  25.13  24.30
## 27       Price ShelveLoc:Medium 219  219.05 115.65 116.01  23.10  22.70
## 1        Sales    ShelveLoc:All 400  400.37   7.50   7.45   2.82   2.78
## 8        Sales    ShelveLoc:Bad  96   99.77   5.52   5.57   2.36   2.41
## 15       Sales   ShelveLoc:Good  85   81.56  10.21  10.23   2.50   2.42
## 22       Sales ShelveLoc:Medium 219  219.05   7.31   7.28   2.27   2.21

3.1.2 Distributions of numerical variables

Graphical representation of all numeric features

  • Density plot (Univariate)
# Note: Variable excluded (if unique value of variable which is less than or eaual to 10 [nlim=10])
plot1 <- ExpNumViz(Carseats,target=NULL,nlim=10,Page=c(2,2),sample=4)
plot1[[1]]

3.1.3. Summary of categorical variables

  • frequency for all categorical independent variables
ExpCTable(Carseats,Target=NULL,margin=1,clim=10,nlim=3,round=2,bin=NULL,per=T)
Variable Valid Frequency Percent CumPercent
ShelveLoc Bad 96 24.00 24.00
ShelveLoc Good 85 21.25 45.25
ShelveLoc Medium 219 54.75 100.00
ShelveLoc TOTAL 400 NA NA
Urban No 118 29.50 29.50
Urban Yes 282 70.50 100.00
Urban TOTAL 400 NA NA
US No 142 35.50 35.50
US Yes 258 64.50 100.00
US TOTAL 400 NA NA

NA is Not Applicable

3.1.4. Distributions of categorical variables

  • Bar plots for all categorical variables
plot2 <- ExpCatViz(Carseats,target=NULL,col ="slateblue4",clim=10,margin=2,Page = c(2,2),sample=4)
plot2[[1]]

3.2 Example for case 2: Target variable is continuous

3.2.1. Target variable

Summary of continuous dependent variable

  1. Variable name - Price
  2. Variable description - Price company charges for car seats at each site
summary(Carseats[,"Price"])
##      Price      
##  Min.   : 24.0  
##  1st Qu.:100.0  
##  Median :117.0  
##  Mean   :115.8  
##  3rd Qu.:131.0  
##  Max.   :191.0

3.2.2 Summary of numerical variables

Summary statistics when dependent variable is continuous Price.

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

If Target variable is continuous, summary statistics will add the correlation column (Correlation between Target variable vs all independent variables)

3.2.3 Distributions of numerical variables

Graphical representation of all numeric variables

  • Scatter plot (Bivariate) - with continuous dependent variable

Scatter plot between all numeric variables and target variable Price. This plot help to examine how well a target variable is correlated with dependent variables.

Dependent variable is Price (continuous).

#Note: sample=8 means randomly selected 8 scatter plots
#Note: nlim=4 means included numeric variable with unique value is more than 4
plot3 <- ExpNumViz(Carseats,target="Price",nlim=4,scatter=FALSE,fname=NULL,col="green",Page=c(2,2),sample=8)
plot3[[1]]

  • Scatter plot (Bivariate) - between all the numerical variables
#Note: sample=8 means randomly selected 8 scatter plots
#Note: nlim=4 means included numeric variable with unique value is more than 4
plot31 <- ExpNumViz(Carseats,target="US",nlim=4,scatter=TRUE,fname=NULL,Page=c(2,1),sample=4)
plot31[[1]]

3.2.4. Summary of categorical variables

Summary of categorical variables

  • frequency for all categorical independent variables by descretized Price
##bin=4, descretized 4 categories based on quantiles
ExpCTable(Carseats,Target="Price",margin=1,clim=10,round=2,bin=4,per=F)
Compute Weighted Summary Statistics for categorical variable
carseat = ISLR::Carseats
## Compute random weight
carseat$wt = stats::runif( nrow(carseat), 0.5, 1.5 )
wt_summary = ExpCTable(carseat,margin=1,clim=10,round=2,bin=4,per=F, weight = "wt")
wt_summary
##     Variable  Valid Frequency Percent CumPercent
## 1  ShelveLoc    Bad        96   24.20      24.20
## 2  ShelveLoc   Good        84   21.25      45.45
## 3  ShelveLoc Medium       216   54.55     100.00
## 4  ShelveLoc  TOTAL       396      NA         NA
## 5      Urban     No       118   29.81      29.81
## 6      Urban    Yes       277   70.19     100.00
## 7      Urban  TOTAL       395      NA         NA
## 8         US     No       140   35.39      35.39
## 9         US    Yes       255   64.61     100.00
## 10        US  TOTAL       395      NA         NA
## 11 Education     10        47   11.93      11.93
## 12 Education     11        46   11.56      23.49
## 13 Education     12        51   13.02      36.51
## 14 Education     13        44   11.20      47.71
## 15 Education     14        37    9.44      57.15
## 16 Education     15        37    9.27      66.42
## 17 Education     16        46   11.57      77.99
## 18 Education     17        49   12.37      90.36
## 19 Education     18        38    9.64     100.00
## 20 Education  TOTAL       395      NA         NA

3.3 Example for case 3: Target variable is categorical

3.3.1. Summary of categorical dependent variable

  1. Variable name - Urban
  2. Variable description - Whether the store is in an urban or rural location
Urban Frequency Descriptions
No 118 Store location
Yes 282 Store location

3.3.2 Summary of numerical variables

Summary of all numeric variables

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

3.3.3 Distributions of Numerical variables

  • Box plots for all numerical variables vs categorical dependent variable - Bivariate comparision only with categories

Boxplot for all the numeric attributes by each category of Urban

plot4 <- ExpNumViz(Carseats,target="Urban",type=1,nlim=3,fname=NULL,col=c("darkgreen","springgreen3","springgreen1"),Page=c(2,2),sample=8)
plot4[[1]]

3.3.4 Summary of categorical variables

Cross tabulation with target variable

  • Custom tables between all categorical independent variables and target variable Urban
ExpCTable(Carseats,Target="Urban",margin=1,clim=10,nlim=3,round=2,bin=NULL,per=F)
VARIABLE CATEGORY Urban:No Urban:Yes TOTAL
ShelveLoc Bad 22 74 96
ShelveLoc Good 28 57 85
ShelveLoc Medium 68 151 219
ShelveLoc TOTAL 118 282 400
US No 46 96 142
US Yes 72 186 258
US TOTAL 118 282 400

Information Value

ExpCatStat(Carseats,Target="Urban",result = "IV",clim=10,nlim=5,bins=10,Pclass="Yes",plot=FALSE,top=20,Round=2)
Variable Class Out_1 Out_0 TOTAL Per_1 Per_0 Odds WOE IV Ref_1 Ref_0 Target
ShelveLoc.1 Bad 74 22 96 0.26 0.19 1.55 0.31 0.02 Yes No Urban
ShelveLoc.2 Good 57 28 85 0.20 0.24 0.81 -0.19 0.01 Yes No Urban
ShelveLoc.3 Medium 151 68 219 0.54 0.58 0.85 -0.07 0.00 Yes No Urban
US.1 No 96 46 142 0.34 0.39 0.81 -0.14 0.01 Yes No Urban
US.2 Yes 186 72 258 0.66 0.61 1.24 0.08 0.00 Yes No Urban
Sales.1 [0,4.11] 29 11 40 0.10 0.09 1.11 0.10 0.00 Yes No Urban
Sales.2 (4.11,5.05] 29 11 40 0.10 0.09 1.11 0.10 0.00 Yes No Urban
Sales.3 (5.05,5.86] 26 14 40 0.09 0.12 0.75 -0.29 0.01 Yes No Urban
Sales.4 (5.86,6.59] 30 10 40 0.11 0.08 1.29 0.32 0.01 Yes No Urban
Sales.5 (6.59,7.49] 32 9 41 0.11 0.08 1.55 0.32 0.01 Yes No Urban
Sales.6 (7.49,8.07] 30 9 39 0.11 0.08 1.44 0.32 0.01 Yes No Urban
Sales.7 (8.07,8.8] 24 16 40 0.09 0.14 0.59 -0.45 0.02 Yes No Urban
Sales.8 (8.8,9.71] 26 14 40 0.09 0.12 0.75 -0.29 0.01 Yes No Urban
Sales.9 (9.71,11.28] 26 14 40 0.09 0.12 0.75 -0.29 0.01 Yes No Urban
Sales.10 (11.28,16.27] 30 10 40 0.11 0.08 1.29 0.32 0.01 Yes No Urban

Statistical test

et4 <- ExpCatStat(Carseats,Target="Urban",result = "Stat",clim=10,nlim=5,bins=10,Pclass="Yes",plot=FALSE,top=20,Round=2)
Variable Target Unique Chi-squared p-value df IV Value Cramers V Degree of Association Predictive Power
ShelveLoc Urban 3 2.738 0.251 NA 0.03 0.08 Very Weak Not Predictive
US Urban 2 0.887 0.368 NA 0.01 0.05 Very Weak Not Predictive
Sales Urban 10 6.696 0.673 NA 0.09 0.13 Weak Somewhat Predictive
CompPrice Urban 10 4.543 0.890 NA 0.03 0.11 Weak Not Predictive
Income Urban 10 8.428 0.490 NA 0.08 0.15 Weak Not Predictive
Advertising Urban 7 5.565 0.466 NA 0.06 0.12 Weak Not Predictive
Population Urban 10 10.560 0.304 NA 0.14 0.16 Weak Somewhat Predictive
Price Urban 10 11.143 0.245 NA 0.14 0.17 Weak Somewhat Predictive
Age Urban 10 8.414 0.488 NA 0.08 0.15 Weak Not Predictive
Education Urban 8 5.122 0.647 NA 0.05 0.11 Weak Not Predictive

Variable importance based on Information value

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

3.3.5. Distributions of categorical variables

Stacked bar plot with vertical or horizontal bars for all categorical variables

plot5 <- ExpCatViz(Carseats,target="Urban",fname=NULL,clim=5,col=c("slateblue4","slateblue1"),margin=2,Page = c(2,1),sample=2)
plot5[[1]]

4. Quantile-quantile plot for numeric variables

Function definition:

ExpOutQQ (data,nlim=3,fname=NULL,Page=NULL,sample=NULL)
data    : Input dataframe or data.table
nlim    : numeric variable limit
fname   : output file name (Output will be in PDF format)
Page    : output pattern. if Page=c(3,2), It will generate 6 plots with 3 rows and 2 columns
sample  : random number of plots

Carseats data from ISLR package:

options(width = 150)
CData = ISLR::Carseats
qqp <- ExpOutQQ(CData,nlim=10,fname=NULL,Page=c(2,2),sample=4)
qqp[[1]]

5. Parallel Co-ordinate plots

Function definition:

ExpParcoord (data,Group=NULL,Stsize=NULL,Nvar=NULL,Cvar=NULL,scale=NULL)
data    : Input dataframe or data.table
Group   : stratification variables
Stsize  : vector of startum sample sizes
Nvar    : vector of numerice variables, default it will consider all the numeric variable from data
Cvar    : vector of categorical variables, default it will consider all the categorical variable
scale   : scale the variables in the parallel coordinate plot[Default normailized with minimum of the variable is zero and maximum of the variable is one]

5.1 Defualt ExpParcoord funciton

ExpParcoord(CData,Group=NULL,Stsize=NULL,Nvar=c("Price","Income","Advertising","Population","Age","Education"))

5.2 With Stratified rows and selected columns only

ExpParcoord(CData,Group="ShelveLoc",Stsize=c(10,15,20),Nvar=c("Price","Income"),Cvar=c("Urban","US"))

5.3 Without stratification

ExpParcoord(CData,Group="ShelveLoc",Nvar=c("Price","Income"),Cvar=c("Urban","US"),scale=NULL)

5.4 Scale change

std: univariately, subtract mean and divide by standard deviation

ExpParcoord(CData,Group="US",Nvar=c("Price","Income"),Cvar=c("ShelveLoc"),scale="std")

5.5 Selected numeric variables

ExpParcoord(CData,Group="ShelveLoc",Stsize=c(10,15,20),Nvar=c("Price","Income","Advertising","Population","Age","Education"))

5.6 Selected categorical variables

ExpParcoord(CData,Group="US",Stsize=c(15,50),Cvar=c("ShelveLoc","Urban"))

6. Customized Summary Statistics

Used ‘data.table’ package functions

Function definition:

ExpCustomStat(data,Cvar=NULL,Nvar=NULL,stat=NULL,gpby=TRUE,filt=NULL,dcast=FALSE)

ExpCustomStat examples

ExpCustomStat(Carseats,Cvar="Urban",Nvar=c("Age","Price"),stat=c("mean","count"),gpby=TRUE,dcast=F)
Urban Attribute mean count
Yes Age 53.62057 282
No Age 52.61017 118
Yes Price 116.51418 282
No Price 114.07627 118 
ExpCustomStat(Carseats,Cvar="Urban",Nvar=c("Age","Price"),stat=c("mean","count"),gpby=TRUE,dcast=T)
Attribute mean_No mean_Yes count_No count_Yes
Age 52.61017 53.62057 118 282
Price 114.07627 116.51418 118 282 
ExpCustomStat(Carseats,Cvar=c("Urban","ShelveLoc"),Nvar=c("Age","Price","Advertising","Sales"),stat=c("mean"),gpby=FALSE,dcast=T)
Attribute ShelveLoc_Bad ShelveLoc_Good ShelveLoc_Medium Urban_No Urban_Yes
Advertising 6.218750 7.352941 6.538813 6.203390 6.815603
Age 52.052083 52.611765 54.155251 52.610169 53.620567
Price 114.270833 117.882353 115.652968 114.076271 116.514184
Sales 5.522917 10.214000 7.306575 7.563559 7.468191

7. 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.

Function ExpOutliers can run univariate outlier analysis based on boxplot or SD method. The function returns the summary of oultlier for selected numeric features and adding new features if there are any outlers

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

7.1 Identifying outliers using Boxplot method

ExpOutliers(Carseats, varlist = c("Sales","CompPrice","Income"), method = "boxplot",  treatment = "mean", capping = c(0.1, 0.9))

Summary

Category Sales CompPrice Income
Lower cap : 0.1 4.119 106 30
Upper cap : 0.9 11.3 145 107
Lower bound -0.5 85 -29.62
Upper bound 15.21 165 163.38
Num of outliers 2 2 0
Lower outlier case 43
Upper outlier case 317,377 311
Mean before 7.5 124.97 68.66
Mean after 7.45 124.97 68.66
Median before 7.49 125 69
Median after 7.47 125 69

Output data head view

Sales CompPrice Income Advertising Population Price ShelveLoc Age Education Urban US out_cap_Sales out_cap_CompPrice out_imp_Sales out_imp_CompPrice
9.50 138 73 11 276 120 Bad 42 17 Yes Yes 9.50 138 9.50 138
11.22 111 48 16 260 83 Good 65 10 Yes Yes 11.22 111 11.22 111
10.06 113 35 10 269 80 Medium 59 12 Yes Yes 10.06 113 10.06 113
7.40 117 100 4 466 97 Medium 55 14 Yes Yes 7.40 117 7.40 117
4.15 141 64 3 340 128 Bad 38 13 Yes No 4.15 141 4.15 141
10.81 124 113 13 501 72 Bad 78 16 No Yes 10.81 124 10.81 124

7.2 Identifying outliers using 3 Standard Deviation method

ExpOutliers(Carseats, varlist = c("Sales","CompPrice","Income"), method = "3xStDev",  treatment = "medain", capping = c(0.1, 0.9))

Summary

Category Sales CompPrice Income
Lower cap : 0.1 4.119 106 30
Upper cap : 0.9 11.3 145 107
Lower bound -0.98 78.97 -15.3
Upper bound 15.97 170.98 152.62
Num of outliers 1 2 0
Lower outlier case 43
Upper outlier case 377 311
Mean before 7.5 124.97 68.66
Mean after 7.47 124.97 68.66
Median before 7.49 125 69
Median after 7.49 125 69

Output data head view

Sales CompPrice Income Advertising Population Price ShelveLoc Age Education Urban US out_cap_Sales out_cap_CompPrice out_imp_Sales out_imp_CompPrice
9.50 138 73 11 276 120 Bad 42 17 Yes Yes 9.50 138 9.50 138
11.22 111 48 16 260 83 Good 65 10 Yes Yes 11.22 111 11.22 111
10.06 113 35 10 269 80 Medium 59 12 Yes Yes 10.06 113 10.06 113
7.40 117 100 4 466 97 Medium 55 14 Yes Yes 7.40 117 7.40 117
4.15 141 64 3 340 128 Bad 38 13 Yes No 4.15 141 4.15 141
10.81 124 113 13 501 72 Bad 78 16 No Yes 10.81 124 10.81 124