Exploratory Data Analysis
Kaustubh Saraf
2020-05-02
EDA Using R
Packages Required
- tidyverse : This package consists of 6 core packages out of which the below 3 are most important for this project:
- ggplot2: Used for creating powerful visualizations
- dplyr: Used for data manipulation
- tidyr: Used for data modifications
rpart, rpart.plot and ROCR : These packages are used for building classification and regression models using decision trees. Further, we can visualize the tree structure and evaluate the performance of the models
forecast, tseries and sarima : These packages are used to model the time-series data including the seasonal component in the series if any
library(tidyverse) #used for data manipulation
library(rmarkdown) #used for formatting the markdown file
library(sqldf) #using SQL commands in RData Preparation
CustomerData<-read.csv("data/CustomerData.csv",header=TRUE,sep=",")1.1 Take a look into the data
head(CustomerData)## CustomerID Region TownSize Gender Age EducationYears JobCategory
## 1 3964-QJWTRG-NPN 1 2 Female 20 15 Professional
## 2 0648-AIPJSP-UVM 5 5 Male 22 17 Sales
## 3 5195-TLUDJE-HVO 3 4 Female 67 14 Sales
## 4 4459-VLPQUH-3OL 4 3 Male 23 16 Sales
## 5 8158-SMTQFB-CNO 2 2 Male 26 16 Sales
## 6 9662-FUSYIM-1IV 4 4 Male 64 17 Service
## UnionMember EmploymentLength Retired HHIncome DebtToIncomeRatio CreditDebt
## 1 Yes 0 No 31000 11.1 1.200909
## 2 No 0 No 15000 18.6 1.222020
## 3 No 16 No 35000 9.9 0.928620
## 4 No 0 No 20000 5.7 0.022800
## 5 No 1 No 23000 1.7 0.214659
## 6 No 22 No 107000 5.6 1.060584
## OtherDebt LoanDefault MaritalStatus HouseholdSize NumberPets NumberCats
## 1 2.240091 Yes Unmarried 3 0 0
## 2 1.567980 Yes Unmarried 2 6 0
## 3 2.536380 No Married 3 3 2
## 4 1.117200 Yes Married 5 0 0
## 5 0.176341 No Married 4 0 0
## 6 4.931416 No Unmarried 1 11 1
## NumberDogs NumberBirds HomeOwner CarsOwned CarOwnership CarBrand CarValue
## 1 0 0 0 2 Own Domestic 14300
## 2 0 0 1 2 Own Foreign 6800
## 3 1 0 1 3 Own Foreign 18800
## 4 0 0 1 3 Own Foreign 8700
## 5 0 0 0 1 Lease Foreign 10600
## 6 1 0 1 0 -1 -1 -1000
## CommuteTime PoliticalPartyMem Votes CreditCard CardTenure CardItemsMonthly
## 1 22 Yes Yes Mast 2 5
## 2 29 Yes No Visa 4 5
## 3 24 Yes No Visa 35 9
## 4 38 No No Visa 5 17
## 5 32 No No Disc 8 8
## 6 23 No No Visa 18 11
## CardSpendMonth ActiveLifestyle PhoneCoTenure VoiceLastMonth VoiceOverTenure
## 1 816.6 No 5 19.50 34.40
## 2 426.0 Yes 39 26.70 330.60
## 3 1842.2 No 65 85.20 1858.35
## 4 3409.9 Yes 36 18.00 199.45
## 5 2551.0 Yes 21 9.15 74.10
## 6 2282.7 No 28 24.30 264.90
## EquipmentRental EquipmentLastMonth EquipmentOverTenure CallingCard
## 1 Yes 29.50 126.10 Yes
## 2 Yes 54.85 1975.00 Yes
## 3 No 0.00 0.00 Yes
## 4 No 0.00 0.00 Yes
## 5 No 0.00 0.00 Yes
## 6 Yes 35.50 970.95 Yes
## WirelessData DataLastMonth DataOverTenure Multiline VM Pager Internet
## 1 No 0.00 0.00 Yes Yes Yes No
## 2 Yes 45.65 1683.55 Yes Yes Yes 4
## 3 No 0.00 0.00 Yes No No No
## 4 No 0.00 0.00 Yes No No 2
## 5 Yes 19.05 410.80 No Yes No 3
## 6 No 0.00 0.00 No No Yes No
## CallerID CallWait CallForward ThreeWayCalling EBilling TVWatchingHours OwnsPC
## 1 No Yes Yes Yes No 13 No
## 2 Yes No Yes No Yes 18 Yes
## 3 No No No No No 21 No
## 4 No No No No Yes 26 Yes
## 5 Yes Yes Yes Yes No 27 Yes
## 6 Yes Yes Yes Yes No 21 No
## OwnsMobileDevice OwnsGameSystem OwnsFax NewsSubscriber
## 1 Yes Yes No No
## 2 Yes Yes Yes Yes
## 3 No No No Yes
## 4 Yes Yes No Yes
## 5 No Yes No No
## 6 No No No No1.2 Size of the dataset 1.2.1 How larger is my dataset? How many rows? How many columns?
dim(CustomerData)## [1] 5000 591.2.2 How many columns?
ncol(CustomerData)## [1] 591.2.3 How many rows?
nrow(CustomerData)## [1] 50001.3 Glimpse at the variables
1.3.1 What is the name of my columns?
names(CustomerData)## [1] "CustomerID" "Region" "TownSize"
## [4] "Gender" "Age" "EducationYears"
## [7] "JobCategory" "UnionMember" "EmploymentLength"
## [10] "Retired" "HHIncome" "DebtToIncomeRatio"
## [13] "CreditDebt" "OtherDebt" "LoanDefault"
## [16] "MaritalStatus" "HouseholdSize" "NumberPets"
## [19] "NumberCats" "NumberDogs" "NumberBirds"
## [22] "HomeOwner" "CarsOwned" "CarOwnership"
## [25] "CarBrand" "CarValue" "CommuteTime"
## [28] "PoliticalPartyMem" "Votes" "CreditCard"
## [31] "CardTenure" "CardItemsMonthly" "CardSpendMonth"
## [34] "ActiveLifestyle" "PhoneCoTenure" "VoiceLastMonth"
## [37] "VoiceOverTenure" "EquipmentRental" "EquipmentLastMonth"
## [40] "EquipmentOverTenure" "CallingCard" "WirelessData"
## [43] "DataLastMonth" "DataOverTenure" "Multiline"
## [46] "VM" "Pager" "Internet"
## [49] "CallerID" "CallWait" "CallForward"
## [52] "ThreeWayCalling" "EBilling" "TVWatchingHours"
## [55] "OwnsPC" "OwnsMobileDevice" "OwnsGameSystem"
## [58] "OwnsFax" "NewsSubscriber"#OR
colnames(CustomerData)## [1] "CustomerID" "Region" "TownSize"
## [4] "Gender" "Age" "EducationYears"
## [7] "JobCategory" "UnionMember" "EmploymentLength"
## [10] "Retired" "HHIncome" "DebtToIncomeRatio"
## [13] "CreditDebt" "OtherDebt" "LoanDefault"
## [16] "MaritalStatus" "HouseholdSize" "NumberPets"
## [19] "NumberCats" "NumberDogs" "NumberBirds"
## [22] "HomeOwner" "CarsOwned" "CarOwnership"
## [25] "CarBrand" "CarValue" "CommuteTime"
## [28] "PoliticalPartyMem" "Votes" "CreditCard"
## [31] "CardTenure" "CardItemsMonthly" "CardSpendMonth"
## [34] "ActiveLifestyle" "PhoneCoTenure" "VoiceLastMonth"
## [37] "VoiceOverTenure" "EquipmentRental" "EquipmentLastMonth"
## [40] "EquipmentOverTenure" "CallingCard" "WirelessData"
## [43] "DataLastMonth" "DataOverTenure" "Multiline"
## [46] "VM" "Pager" "Internet"
## [49] "CallerID" "CallWait" "CallForward"
## [52] "ThreeWayCalling" "EBilling" "TVWatchingHours"
## [55] "OwnsPC" "OwnsMobileDevice" "OwnsGameSystem"
## [58] "OwnsFax" "NewsSubscriber"1.3.2 What is the structure of the data
str(CustomerData)## 'data.frame': 5000 obs. of 59 variables:
## $ CustomerID : Factor w/ 5000 levels "0002-GTOKLU-YVY",..: 1980 347 2606 2221 4037 4826 3670 4454 4531 1758 ...
## $ Region : int 1 5 3 4 2 4 2 3 2 2 ...
## $ TownSize : Factor w/ 6 levels "#NULL!","1","2",..: 3 6 5 4 3 5 6 5 4 3 ...
## $ Gender : Factor w/ 3 levels "","Female","Male": 2 3 2 3 3 3 2 2 2 3 ...
## $ Age : int 20 22 67 23 26 64 52 44 66 47 ...
## $ EducationYears : int 15 17 14 16 16 17 14 16 12 11 ...
## $ JobCategory : Factor w/ 7 levels "","Agriculture",..: 5 6 6 6 6 7 5 5 5 4 ...
## $ UnionMember : Factor w/ 2 levels "No","Yes": 2 1 1 1 1 1 1 1 1 1 ...
## $ EmploymentLength : int 0 0 16 0 1 22 10 11 15 19 ...
## $ Retired : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 2 1 ...
## $ HHIncome : num 31000 15000 35000 20000 23000 107000 77000 97000 16000 84000 ...
## $ DebtToIncomeRatio : num 11.1 18.6 9.9 5.7 1.7 5.6 1.9 14.4 2.6 4.1 ...
## $ CreditDebt : num 1.2009 1.222 0.9286 0.0228 0.2147 ...
## $ OtherDebt : num 2.24 1.568 2.536 1.117 0.176 ...
## $ LoanDefault : Factor w/ 2 levels "No","Yes": 2 2 1 2 1 1 1 1 1 1 ...
## $ MaritalStatus : Factor w/ 2 levels "Married","Unmarried": 2 2 1 1 1 2 2 1 2 2 ...
## $ HouseholdSize : int 3 2 3 5 4 1 1 2 1 2 ...
## $ NumberPets : int 0 6 3 0 0 11 2 10 1 1 ...
## $ NumberCats : int 0 0 2 0 0 1 0 0 1 1 ...
## $ NumberDogs : int 0 0 1 0 0 1 2 2 0 0 ...
## $ NumberBirds : int 0 0 0 0 0 0 0 0 0 0 ...
## $ HomeOwner : int 0 1 1 1 0 1 0 1 1 1 ...
## $ CarsOwned : int 2 2 3 3 1 0 2 1 1 4 ...
## $ CarOwnership : Factor w/ 3 levels "-1","Lease","Own": 3 3 3 3 2 1 3 3 3 3 ...
## $ CarBrand : Factor w/ 3 levels "-1","Domestic",..: 2 3 3 3 3 1 2 2 3 2 ...
## $ CarValue : num 14300 6800 18800 8700 10600 -1000 25600 55500 8600 41000 ...
## $ CommuteTime : Factor w/ 42 levels "#NULL!","10",..: 14 21 16 30 24 15 24 23 17 21 ...
## $ PoliticalPartyMem : Factor w/ 2 levels "No","Yes": 2 2 2 1 1 1 1 1 2 2 ...
## $ Votes : Factor w/ 2 levels "No","Yes": 2 1 1 1 1 1 1 2 2 2 ...
## $ CreditCard : Factor w/ 5 levels "AMEX","Disc",..: 3 5 5 5 2 5 4 1 3 4 ...
## $ CardTenure : int 2 4 35 5 8 18 3 25 26 2 ...
## $ CardItemsMonthly : int 5 5 9 17 8 11 20 6 12 11 ...
## $ CardSpendMonth : num 817 426 1842 3410 2551 ...
## $ ActiveLifestyle : Factor w/ 2 levels "No","Yes": 1 2 1 2 2 1 1 1 2 2 ...
## $ PhoneCoTenure : int 5 39 65 36 21 28 15 46 53 3 ...
## $ VoiceLastMonth : num 19.5 26.7 85.2 18 9.15 ...
## $ VoiceOverTenure : Factor w/ 4438 levels "#NULL!","0.90",..: 2414 2366 1232 1367 3889 1923 2862 3524 139 1389 ...
## $ EquipmentRental : Factor w/ 2 levels "No","Yes": 2 2 1 1 1 2 1 1 1 1 ...
## $ EquipmentLastMonth : num 29.5 54.9 0 0 0 ...
## $ EquipmentOverTenure: num 126 1975 0 0 0 ...
## $ CallingCard : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 1 2 2 1 ...
## $ WirelessData : Factor w/ 2 levels "No","Yes": 1 2 1 1 2 1 1 1 1 1 ...
## $ DataLastMonth : num 0 45.6 0 0 19.1 ...
## $ DataOverTenure : num 0 1684 0 0 411 ...
## $ Multiline : Factor w/ 2 levels "No","Yes": 2 2 2 2 1 1 1 2 2 1 ...
## $ VM : Factor w/ 2 levels "No","Yes": 2 2 1 1 2 1 1 1 1 1 ...
## $ Pager : Factor w/ 2 levels "No","Yes": 2 2 1 1 1 2 1 1 1 1 ...
## $ Internet : Factor w/ 5 levels "2","3","4","No",..: 4 3 4 1 2 4 5 4 4 4 ...
## $ CallerID : Factor w/ 2 levels "No","Yes": 1 2 1 1 2 2 1 2 1 1 ...
## $ CallWait : Factor w/ 2 levels "No","Yes": 2 1 1 1 2 2 1 2 1 1 ...
## $ CallForward : Factor w/ 2 levels "No","Yes": 2 2 1 1 2 2 2 2 1 1 ...
## $ ThreeWayCalling : Factor w/ 2 levels "No","Yes": 2 1 1 1 2 2 1 2 1 1 ...
## $ EBilling : Factor w/ 2 levels "No","Yes": 1 2 1 2 1 1 1 1 1 1 ...
## $ TVWatchingHours : int 13 18 21 26 27 21 19 13 25 21 ...
## $ OwnsPC : Factor w/ 2 levels "No","Yes": 1 2 1 2 2 1 2 1 1 1 ...
## $ OwnsMobileDevice : Factor w/ 2 levels "No","Yes": 2 2 1 2 1 1 2 1 1 1 ...
## $ OwnsGameSystem : Factor w/ 2 levels "No","Yes": 2 2 1 2 2 1 1 1 1 1 ...
## $ OwnsFax : Factor w/ 2 levels "No","Yes": 1 2 1 1 1 1 1 1 1 1 ...
## $ NewsSubscriber : Factor w/ 2 levels "No","Yes": 1 2 2 2 1 1 1 2 1 1 ...1.3.3 How to get the format a column
class(CustomerData[,1])## [1] "factor"1.4 Summary statistics of the dataset 1.4.1 Getting the summary of the dataset
summary(CustomerData)## CustomerID Region TownSize Gender
## 0002-GTOKLU-YVY: 1 Min. :1.000 #NULL!: 2 : 33
## 0003-RLTRGE-IW2: 1 1st Qu.:2.000 1 :1436 Female:2494
## 0003-UTGKPR-PRU: 1 Median :3.000 2 :1048 Male :2473
## 0008-ZIQQOT-SGB: 1 Mean :3.001 3 : 907
## 0012-CIVYLF-839: 1 3rd Qu.:4.000 4 : 857
## 0014-DOIOFX-LXB: 1 Max. :5.000 5 : 750
## (Other) :4994
## Age EducationYears JobCategory UnionMember
## Min. :18.00 Min. : 6.00 : 15 No :4244
## 1st Qu.:31.00 1st Qu.:12.00 Agriculture : 212 Yes: 756
## Median :47.00 Median :14.00 Crafts : 452
## Mean :47.03 Mean :14.54 Labor : 686
## 3rd Qu.:62.00 3rd Qu.:17.00 Professional:1380
## Max. :79.00 Max. :23.00 Sales :1635
## Service : 620
## EmploymentLength Retired HHIncome DebtToIncomeRatio
## Min. : 0.00 No :4262 Min. : 9000 Min. : 0.000
## 1st Qu.: 2.00 Yes: 738 1st Qu.: 24000 1st Qu.: 5.100
## Median : 7.00 Median : 38000 Median : 8.800
## Mean : 9.73 Mean : 54760 Mean : 9.954
## 3rd Qu.:15.00 3rd Qu.: 67000 3rd Qu.:13.600
## Max. :52.00 Max. :1073000 Max. :43.100
##
## CreditDebt OtherDebt LoanDefault MaritalStatus
## Min. : 0.0000 Min. : 0.0000 No :3829 Married :2401
## 1st Qu.: 0.3855 1st Qu.: 0.9803 Yes:1171 Unmarried:2599
## Median : 0.9264 Median : 2.0985
## Mean : 1.8573 Mean : 3.6545
## 3rd Qu.: 2.0638 3rd Qu.: 4.3148
## Max. :109.0726 Max. :141.4591
##
## HouseholdSize NumberPets NumberCats NumberDogs
## Min. :1.000 Min. : 0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :2.000 Median : 2.000 Median :0.0000 Median :0.0000
## Mean :2.202 Mean : 3.067 Mean :0.5003 Mean :0.3928
## 3rd Qu.:3.000 3rd Qu.: 5.000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :9.000 Max. :21.000 Max. :6.0000 Max. :7.0000
## NA's :8 NA's :6 NA's :7 NA's :8
## NumberBirds HomeOwner CarsOwned CarOwnership CarBrand
## Min. :0.0000 Min. :0.0000 Min. :0.000 -1 : 497 -1 : 497
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.000 Lease: 799 Domestic:2287
## Median :0.0000 Median :1.0000 Median :2.000 Own :3704 Foreign :2216
## Mean :0.1112 Mean :0.6296 Mean :2.131
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :5.0000 Max. :1.0000 Max. :8.000
## NA's :34 NA's :13
## CarValue CommuteTime PoliticalPartyMem Votes CreditCard
## Min. :-1000 24 : 336 No :3093 No :2410 AMEX: 986
## 1st Qu.: 9200 23 : 335 Yes:1907 Yes:2590 Disc:1344
## Median :17000 27 : 331 Mast:1200
## Mean :23233 25 : 330 Othe: 223
## 3rd Qu.:31100 22 : 325 Visa:1247
## Max. :99600 26 : 311
## (Other):3032
## CardTenure CardItemsMonthly CardSpendMonth ActiveLifestyle
## Min. : 0.00 Min. : 0.00 Min. : 0 No :2670
## 1st Qu.: 6.00 1st Qu.: 8.00 1st Qu.: 1834 Yes:2330
## Median :14.00 Median :10.00 Median : 2764
## Mean :16.66 Mean :10.18 Mean : 3372
## 3rd Qu.:26.00 3rd Qu.:12.00 3rd Qu.: 4185
## Max. :40.00 Max. :23.00 Max. :39264
##
## PhoneCoTenure VoiceLastMonth VoiceOverTenure EquipmentRental
## Min. : 0.0 Min. : 2.70 2.05 : 8 No :3296
## 1st Qu.:18.0 1st Qu.: 17.10 2.60 : 6 Yes:1704
## Median :38.0 Median : 28.65 14.30 : 5
## Mean :38.2 Mean : 40.41 16.45 : 5
## 3rd Qu.:59.0 3rd Qu.: 49.65 1.40 : 4
## Max. :72.0 Max. :539.55 1.85 : 4
## (Other):4968
## EquipmentLastMonth EquipmentOverTenure CallingCard WirelessData
## Min. : 0.00 Min. : 0.0 No :1419 No :3656
## 1st Qu.: 0.00 1st Qu.: 0.0 Yes:3581 Yes:1344
## Median : 0.00 Median : 0.0
## Mean : 12.99 Mean : 470.2
## 3rd Qu.: 30.80 3rd Qu.: 510.2
## Max. :106.30 Max. :6525.3
##
## DataLastMonth DataOverTenure Multiline VM Pager
## Min. : 0.00 Min. : 0.00 No :2558 No :3485 No :3782
## 1st Qu.: 0.00 1st Qu.: 0.00 Yes:2442 Yes:1515 Yes:1218
## Median : 0.00 Median : 0.00
## Mean : 10.70 Mean : 421.99
## 3rd Qu.: 20.96 3rd Qu.: 89.96
## Max. :186.25 Max. :12858.65
##
## Internet CallerID CallWait CallForward ThreeWayCalling EBilling
## 2 : 545 No :2624 No :2605 No :2597 No :2610 No :3257
## 3 : 598 Yes:2376 Yes:2395 Yes:2403 Yes:2390 Yes:1743
## 4 : 585
## No :2498
## Yes: 774
##
##
## TVWatchingHours OwnsPC OwnsMobileDevice OwnsGameSystem OwnsFax
## Min. : 0.00 No :1836 No :2604 No :2626 No :4106
## 1st Qu.:17.00 Yes:3164 Yes:2396 Yes:2374 Yes: 894
## Median :20.00
## Mean :19.64
## 3rd Qu.:23.00
## Max. :36.00
##
## NewsSubscriber
## No :2637
## Yes:2363
##
##
##
##
## 1.4.2 Getting some other statistics
#Standard Deviation
sd(CustomerData$Age)## [1] 17.77034#Quantiles
quantile(CustomerData$Age)## 0% 25% 50% 75% 100%
## 18 31 47 62 791.4.3 Getting statistics for multiple columns
apply(CustomerData[,c(5,6,11)], 2, sd) ## Age EducationYears HHIncome
## 17.770338 3.281083 55377.5111541.4.4 Summarizing the dataset to aggregate data and calculate various statistical values
#Mean
aggregate(.~Gender, CustomerData, mean) ## Gender CustomerID Region TownSize Age EducationYears JobCategory
## 1 2626.625 3.375000 4.218750 45.09375 13.62500 5.281250
## 2 Female 2516.975 2.980551 3.690438 46.92788 14.55916 5.120340
## 3 Male 2487.859 3.017213 3.670902 47.26066 14.53975 5.110246
## UnionMember EmploymentLength Retired HHIncome DebtToIncomeRatio CreditDebt
## 1 1.062500 9.531250 1.125000 43562.50 13.734375 1.684107
## 2 1.151135 9.643031 1.140600 54381.28 9.967990 1.818785
## 3 1.152869 9.900000 1.155738 55605.33 9.892664 1.910504
## OtherDebt LoanDefault MaritalStatus HouseholdSize NumberPets NumberCats
## 1 4.623205 1.281250 1.500000 2.375000 1.906250 0.4375000
## 2 3.546985 1.232172 1.518639 2.243922 3.175446 0.5129660
## 3 3.766094 1.233607 1.520492 2.155738 2.965574 0.4905738
## NumberDogs NumberBirds HomeOwner CarsOwned CarOwnership CarBrand CarValue
## 1 0.2500000 0.00000000 0.6250000 2.562500 2.750000 2.375000 20012.50
## 2 0.3829011 0.12520259 0.6333063 2.139789 2.645867 2.352107 23115.60
## 3 0.4040984 0.09959016 0.6290984 2.108607 2.632377 2.333197 23461.84
## CommuteTime PoliticalPartyMem Votes CreditCard CardTenure CardItemsMonthly
## 1 17.68750 1.312500 1.500000 3.125000 14.65625 9.25000
## 2 17.42828 1.384117 1.500000 2.985008 16.46272 10.12763
## 3 17.34836 1.379918 1.536066 2.768852 16.93975 10.23975
## CardSpendMonth ActiveLifestyle PhoneCoTenure VoiceLastMonth VoiceOverTenure
## 1 2393.588 1.593750 37.18750 41.13281 1927.469
## 2 3224.937 1.475284 38.03566 39.37202 2212.532
## 3 3537.625 1.453279 38.43402 41.54717 2214.435
## EquipmentRental EquipmentLastMonth EquipmentOverTenure CallingCard
## 1 1.343750 12.19219 390.2406 1.625000
## 2 1.346840 13.09801 467.7458 1.711102
## 3 1.333607 12.88115 473.7890 1.720902
## WirelessData DataLastMonth DataOverTenure Multiline VM Pager
## 1 1.187500 6.97500 240.4828 1.343750 1.250000 1.125000
## 2 1.267018 10.38349 409.6659 1.481361 1.301053 1.239465
## 3 1.271721 11.11086 439.5887 1.498361 1.305328 1.249590
## Internet CallerID CallWait CallForward ThreeWayCalling EBilling
## 1 3.687500 1.406250 1.531250 1.406250 1.468750 1.281250
## 2 3.465559 1.456240 1.465559 1.472853 1.468801 1.359400
## 3 3.466393 1.496311 1.490984 1.489754 1.485246 1.338115
## TVWatchingHours OwnsPC OwnsMobileDevice OwnsGameSystem OwnsFax
## 1 20.78125 1.468750 1.500000 1.437500 1.156250
## 2 19.61062 1.631280 1.469206 1.476094 1.182739
## 3 19.65574 1.636885 1.488934 1.474180 1.175000
## NewsSubscriber
## 1 1.437500
## 2 1.461507
## 3 1.484426#Std. Deviation
aggregate(.~Gender, CustomerData, sd)## Gender CustomerID Region TownSize Age EducationYears JobCategory
## 1 1313.017 1.263635 1.601096 17.70431 3.616717 1.250403
## 2 Female 1434.558 1.425500 1.429819 17.74132 3.294513 1.304984
## 3 Male 1452.206 1.416137 1.420972 17.80884 3.269634 1.336611
## UnionMember EmploymentLength Retired HHIncome DebtToIncomeRatio CreditDebt
## 1 0.2459347 10.658888 0.3360108 33944.87 8.249051 2.790234
## 2 0.3582525 9.653597 0.3476786 50498.87 6.491034 2.906208
## 3 0.3599348 9.752400 0.3626808 60531.37 6.292995 3.899551
## OtherDebt LoanDefault MaritalStatus HouseholdSize NumberPets NumberCats
## 1 6.106568 0.4568034 0.5080005 1.601411 2.998488 0.8007053
## 2 4.553509 0.4223036 0.4997537 1.438252 3.430769 0.8791106
## 3 6.155443 0.4232115 0.4996823 1.336837 3.400981 0.8440961
## NumberDogs NumberBirds HomeOwner CarsOwned CarOwnership CarBrand CarValue
## 1 0.5679618 0.0000000 0.4918694 1.543718 0.5679618 0.6090712 16495.66
## 2 0.7944779 0.5266749 0.4819995 1.303665 0.6513022 0.6508449 21028.28
## 3 0.8006657 0.4682628 0.4831452 1.313852 0.6633504 0.6555378 21631.71
## CommuteTime PoliticalPartyMem Votes CreditCard CardTenure
## 1 5.844421 0.4709291 0.5080005 1.385408 10.50840
## 2 6.001564 0.4864843 0.5001013 1.431937 11.90170
## 3 5.857047 0.4854657 0.4987998 1.450231 12.16583
## CardItemsMonthly CardSpendMonth ActiveLifestyle PhoneCoTenure VoiceLastMonth
## 1 3.398292 1310.899 0.4989909 21.67865 37.04573
## 2 3.419750 2321.157 0.4994899 22.65831 36.61488
## 3 3.371901 2589.466 0.4979144 22.69309 40.21021
## VoiceOverTenure EquipmentRental EquipmentLastMonth EquipmentOverTenure
## 1 1195.770 0.4825587 17.91682 917.6471
## 2 1288.189 0.4760606 19.05465 898.3427
## 3 1271.001 0.4715977 19.42142 928.8496
## CallingCard WirelessData DataLastMonth DataOverTenure Multiline VM
## 1 0.4918694 0.3965578 16.38938 885.4337 0.4825587 0.4399413
## 2 0.4533422 0.4424914 18.98478 950.5515 0.4997537 0.4588089
## 3 0.4486479 0.4449382 20.70989 1056.9149 0.5000998 0.4606406
## Pager Internet CallerID CallWait CallForward ThreeWayCalling EBilling
## 1 0.3360108 0.9651174 0.4989909 0.5070073 0.4989909 0.5070073 0.4568034
## 2 0.4268436 1.2036414 0.4981823 0.4989135 0.4993636 0.4991268 0.4799219
## 3 0.4328645 1.2130972 0.5000889 0.5000212 0.4999975 0.4998847 0.4731648
## TVWatchingHours OwnsPC OwnsMobileDevice OwnsGameSystem OwnsFax
## 1 5.374578 0.5070073 0.5080005 0.5040161 0.3689020
## 2 5.152667 0.4825555 0.4991520 0.4995294 0.3865308
## 3 5.158370 0.4809961 0.4999800 0.4994353 0.3800450
## NewsSubscriber
## 1 0.5040161
## 2 0.4986171
## 3 0.49985981.4.5 One-way counting
table(CustomerData$Gender)##
## Female Male
## 33 2494 24731.4.6 Two way counting table
table(CustomerData$Gender,CustomerData$UnionMember)##
## No Yes
## 31 2
## Female 2115 379
## Male 2098 375EDA using Visualizations
2.1.1 Single Histogram
hist(CustomerData$Age, col="yellow", breaks=20)
2.1.2 Histogram combined with density curve
hist(CustomerData$Age, prob=T, col="yellow", breaks=20, main="Histogram and Density of Age", xlim=c(min(CustomerData$Age)-1,max(CustomerData$Age)+1), xlab="Age")
lines(density(CustomerData$Age), col="red", lwd=2)
# Add a vertical line that indicates the average of Sepal Length
abline(v=mean(CustomerData$Age), col="blue", lty=2, lwd=1.5)
- You can change the color of the histogram, the density curve and the vertical line
- In addition to that, you can set the range of your x-axis by varying values in xlim
- Further, you can use median or other aggregations to plot the vertical line
2.1.3 Plotting multiple bar charts
avg<- apply(CustomerData[,c(5,6)], 2, mean)
barplot(avg, ylab = "Average")
2.1.4 Plotting multiple bar charts for same variable split by another variable
counts <- table(CustomerData$UnionMember,CustomerData$Gender)
barplot(counts, main="Union Members by Gender",
xlab="Gender", col=c("darkblue","yellow"),
legend = rownames(counts), beside=TRUE)
2.2 Finding the outliers
2.2.1 Single box plot
boxplot(CustomerData$Age)
- The center line represents the median and the two ends of the box represent the 25th and 75th percentiles or 1st and 3rd Quantile
- The outer lines represent 1.5 x IQR (Inter Quantile Range is the width of the box)
2.2.2 Plotting multiple box plots in same graph
boxplot(CustomerData[,c(5,6,9)], notch=T, col=c("blue", "yellow","red"))
2.2.3 Box plot of single variable with groups
boxplot(CustomerData[,'HouseholdSize']~CustomerData[,'Gender'], notch=T, ylab="Household Size", col="blue")
2.3.1 Scatter plot to observe the relation between two variables
plot(CustomerData$Age, CustomerData$HouseholdSize, xlab = "Age", ylab = "HH Size", main = "Household Size vs Age")
2.3.2 Scatter plot of multiple variables
pairs(CustomerData[,c(5,6,11)])
2.4 Plotting multiple graphs in same window
# set arrangement of multiple plots. 2,2 will allow us to plot 4 graphs in a window
par(mfrow=c(2,2))
# set margins
par(mar=c(4.5, 4.2, 3, 1.5))
#Graph1
hist(CustomerData$Age, xlab = "Age", cex.lab=1.5, col = "yellow")
#Graph2
hist(CustomerData$HHIncome, xlab = "Household Income", col = "red")
#Graph3 (pch allows us to change shapes)
plot(CustomerData$Age, CustomerData$HouseholdSize, xlab = "Age", ylab = "Household Size", main= "Household Size vs Age", pch=17)
#Graph4
boxplot(CustomerData[,c(5,6)], notch=T, col=c("red", "blue"))
Tidyverse for EDA
Row Manipulations
3.1 Filtering data
3.1.1 Method 1 : Using subset() function
subset(x = CustomerData, subset = Age > 25 & HHIncome > 400000)## CustomerID Region TownSize Gender Age EducationYears JobCategory
## 18 0649-TBFJFL-QU4 5 2 Male 63 14 Labor
## 755 5071-YMPEFZ-4BK 3 5 Male 63 17 Agriculture
## 990 8162-PHLLNH-12V 5 1 Female 68 15 Labor
## 1103 8402-SILWTV-4YR 4 4 Male 58 19 Service
## 2062 8607-AMZELA-S5B 5 1 Female 57 18 Agriculture
## 2080 6879-SZBXXQ-ERC 4 4 Female 58 16 Service
## 2193 2329-EIXEIO-VD3 5 5 Male 52 18 Labor
## 2347 2435-DERRXC-V3A 5 1 Female 70 19 Labor
## 3069 9069-XTCDOO-RZV 4 3 Male 57 21 Sales
## 3213 6308-REILUL-K6N 3 1 Male 54 22 Professional
## 3624 9204-WXRZIL-7QG 5 3 Male 70 16 Labor
## 4917 4490-YKVPRY-KYA 5 4 Male 58 21 Professional
## 4950 2885-KFFQPU-BNO 4 4 Male 56 17 Crafts
## UnionMember EmploymentLength Retired HHIncome DebtToIncomeRatio CreditDebt
## 18 Yes 29 No 424000 10.7 13.111352
## 755 No 29 No 515000 3.9 2.289690
## 990 Yes 31 No 411000 8.3 10.506804
## 1103 No 20 No 1073000 19.7 109.072596
## 2062 No 23 No 472000 1.2 1.376352
## 2080 No 20 No 409000 6.7 6.987765
## 2193 No 24 No 995000 21.0 67.490850
## 2347 No 37 No 418000 13.9 14.351194
## 3069 No 11 No 780000 13.1 35.252100
## 3213 No 11 No 642000 4.6 11.251692
## 3624 No 35 No 526000 10.2 31.279116
## 4917 No 9 No 437000 14.9 48.704524
## 4950 No 19 No 575000 5.2 4.215900
## OtherDebt LoanDefault MaritalStatus HouseholdSize NumberPets NumberCats
## 18 32.256648 No Married 2 0 0
## 755 17.795310 No Married 2 9 0
## 990 23.606196 No Married 2 2 2
## 1103 102.308404 Yes Married 2 8 0
## 2062 4.287648 No Unmarried 1 0 0
## 2080 20.415235 No Unmarried 4 4 1
## 2193 141.459150 Yes Unmarried 1 1 0
## 2347 43.750806 Yes Unmarried 1 0 0
## 3069 66.927900 Yes Married 2 0 0
## 3213 18.280308 No Married 2 2 0
## 3624 22.372884 Yes Unmarried 1 1 1
## 4917 16.408476 Yes Married 3 4 0
## 4950 25.684100 Yes Unmarried 1 7 1
## NumberDogs NumberBirds HomeOwner CarsOwned CarOwnership CarBrand CarValue
## 18 0 0 1 3 Own Domestic 88600
## 755 0 0 1 2 Own Foreign 88500
## 990 0 0 1 2 Own Foreign 91300
## 1103 2 0 1 4 Own Domestic 77600
## 2062 0 0 1 2 Own Domestic 88000
## 2080 2 1 1 1 Own Foreign 93200
## 2193 1 0 1 3 Own Domestic 99600
## 2347 0 0 1 2 Own Foreign 97300
## 3069 0 0 1 2 Own Foreign 93100
## 3213 2 0 1 1 Own Foreign 86300
## 3624 0 0 1 2 Own Foreign 92200
## 4917 0 0 1 2 Own Foreign 92300
## 4950 0 0 1 1 Own Foreign 93600
## CommuteTime PoliticalPartyMem Votes CreditCard CardTenure CardItemsMonthly
## 18 29 No Yes Mast 36 10
## 755 20 No No Disc 36 8
## 990 13 Yes No Visa 33 6
## 1103 32 Yes Yes Disc 37 13
## 2062 19 Yes No Disc 17 10
## 2080 24 Yes Yes Mast 17 14
## 2193 31 No Yes AMEX 19 12
## 2347 25 Yes Yes Disc 14 13
## 3069 35 No Yes Disc 33 9
## 3213 25 No Yes Disc 21 10
## 3624 29 No Yes AMEX 40 7
## 4917 19 No Yes Mast 22 8
## 4950 33 No No AMEX 23 8
## CardSpendMonth ActiveLifestyle PhoneCoTenure VoiceLastMonth
## 18 4957.5 No 70 104.40
## 755 3230.4 Yes 67 72.30
## 990 1541.8 No 66 120.30
## 1103 29693.9 Yes 72 140.70
## 2062 4239.4 No 22 53.10
## 2080 12952.9 No 48 20.40
## 2193 16039.1 Yes 57 39.60
## 2347 8788.6 No 31 17.70
## 3069 3739.2 No 68 114.00
## 3213 5136.8 No 46 27.45
## 3624 10907.9 Yes 72 104.55
## 4917 2082.2 No 53 51.15
## 4950 5140.4 No 50 40.20
## VoiceOverTenure EquipmentRental EquipmentLastMonth EquipmentOverTenure
## 18 2540.15 No 0.0 0.00
## 755 1590.70 No 0.0 0.00
## 990 2613.15 Yes 42.9 2845.75
## 1103 3393.10 No 0.0 0.00
## 2062 328.70 No 0.0 0.00
## 2080 331.30 No 0.0 0.00
## 2193 761.45 No 0.0 0.00
## 2347 196.35 No 0.0 0.00
## 3069 2582.40 No 0.0 0.00
## 3213 438.80 Yes 83.9 3886.10
## 3624 2405.90 Yes 46.7 3244.45
## 4917 913.40 Yes 64.7 3332.05
## 4950 643.85 Yes 48.1 2455.90
## CallingCard WirelessData DataLastMonth DataOverTenure Multiline VM Pager
## 18 Yes No 0.00 0.00 Yes Yes No
## 755 Yes No 0.00 0.00 Yes Yes No
## 990 Yes No 0.00 0.00 Yes No No
## 1103 Yes Yes 44.90 3174.75 Yes No Yes
## 2062 Yes No 0.00 0.00 No Yes No
## 2080 No No 0.00 0.00 No No No
## 2193 Yes Yes 40.75 2113.45 Yes Yes Yes
## 2347 Yes No 0.00 0.00 Yes No No
## 3069 Yes Yes 93.80 6273.95 Yes Yes Yes
## 3213 Yes Yes 104.25 4603.15 Yes Yes Yes
## 3624 Yes Yes 53.05 3634.70 Yes Yes Yes
## 4917 Yes Yes 51.60 2701.50 Yes Yes Yes
## 4950 No Yes 33.15 1612.40 No Yes Yes
## Internet CallerID CallWait CallForward ThreeWayCalling EBilling
## 18 No Yes No Yes Yes Yes
## 755 Yes No Yes Yes Yes No
## 990 3 No No Yes No No
## 1103 4 No Yes No Yes No
## 2062 Yes Yes Yes Yes Yes No
## 2080 No Yes Yes Yes Yes No
## 2193 3 Yes Yes Yes Yes Yes
## 2347 4 No Yes No Yes No
## 3069 No Yes Yes Yes Yes No
## 3213 4 Yes Yes Yes Yes Yes
## 3624 Yes Yes Yes Yes Yes No
## 4917 Yes Yes Yes Yes Yes Yes
## 4950 4 Yes Yes No Yes Yes
## TVWatchingHours OwnsPC OwnsMobileDevice OwnsGameSystem OwnsFax
## 18 24 No No No No
## 755 25 Yes Yes Yes No
## 990 11 Yes No Yes Yes
## 1103 28 Yes Yes No No
## 2062 24 Yes Yes Yes Yes
## 2080 28 No Yes Yes No
## 2193 22 Yes Yes No Yes
## 2347 27 Yes No No No
## 3069 18 No Yes No Yes
## 3213 18 Yes Yes Yes Yes
## 3624 17 Yes No No No
## 4917 23 Yes Yes Yes Yes
## 4950 11 Yes Yes Yes Yes
## NewsSubscriber
## 18 Yes
## 755 Yes
## 990 Yes
## 1103 Yes
## 2062 No
## 2080 No
## 2193 No
## 2347 No
## 3069 Yes
## 3213 No
## 3624 Yes
## 4917 Yes
## 4950 No# OR
subset(CustomerData, Age > 25 & HHIncome > 400000)## CustomerID Region TownSize Gender Age EducationYears JobCategory
## 18 0649-TBFJFL-QU4 5 2 Male 63 14 Labor
## 755 5071-YMPEFZ-4BK 3 5 Male 63 17 Agriculture
## 990 8162-PHLLNH-12V 5 1 Female 68 15 Labor
## 1103 8402-SILWTV-4YR 4 4 Male 58 19 Service
## 2062 8607-AMZELA-S5B 5 1 Female 57 18 Agriculture
## 2080 6879-SZBXXQ-ERC 4 4 Female 58 16 Service
## 2193 2329-EIXEIO-VD3 5 5 Male 52 18 Labor
## 2347 2435-DERRXC-V3A 5 1 Female 70 19 Labor
## 3069 9069-XTCDOO-RZV 4 3 Male 57 21 Sales
## 3213 6308-REILUL-K6N 3 1 Male 54 22 Professional
## 3624 9204-WXRZIL-7QG 5 3 Male 70 16 Labor
## 4917 4490-YKVPRY-KYA 5 4 Male 58 21 Professional
## 4950 2885-KFFQPU-BNO 4 4 Male 56 17 Crafts
## UnionMember EmploymentLength Retired HHIncome DebtToIncomeRatio CreditDebt
## 18 Yes 29 No 424000 10.7 13.111352
## 755 No 29 No 515000 3.9 2.289690
## 990 Yes 31 No 411000 8.3 10.506804
## 1103 No 20 No 1073000 19.7 109.072596
## 2062 No 23 No 472000 1.2 1.376352
## 2080 No 20 No 409000 6.7 6.987765
## 2193 No 24 No 995000 21.0 67.490850
## 2347 No 37 No 418000 13.9 14.351194
## 3069 No 11 No 780000 13.1 35.252100
## 3213 No 11 No 642000 4.6 11.251692
## 3624 No 35 No 526000 10.2 31.279116
## 4917 No 9 No 437000 14.9 48.704524
## 4950 No 19 No 575000 5.2 4.215900
## OtherDebt LoanDefault MaritalStatus HouseholdSize NumberPets NumberCats
## 18 32.256648 No Married 2 0 0
## 755 17.795310 No Married 2 9 0
## 990 23.606196 No Married 2 2 2
## 1103 102.308404 Yes Married 2 8 0
## 2062 4.287648 No Unmarried 1 0 0
## 2080 20.415235 No Unmarried 4 4 1
## 2193 141.459150 Yes Unmarried 1 1 0
## 2347 43.750806 Yes Unmarried 1 0 0
## 3069 66.927900 Yes Married 2 0 0
## 3213 18.280308 No Married 2 2 0
## 3624 22.372884 Yes Unmarried 1 1 1
## 4917 16.408476 Yes Married 3 4 0
## 4950 25.684100 Yes Unmarried 1 7 1
## NumberDogs NumberBirds HomeOwner CarsOwned CarOwnership CarBrand CarValue
## 18 0 0 1 3 Own Domestic 88600
## 755 0 0 1 2 Own Foreign 88500
## 990 0 0 1 2 Own Foreign 91300
## 1103 2 0 1 4 Own Domestic 77600
## 2062 0 0 1 2 Own Domestic 88000
## 2080 2 1 1 1 Own Foreign 93200
## 2193 1 0 1 3 Own Domestic 99600
## 2347 0 0 1 2 Own Foreign 97300
## 3069 0 0 1 2 Own Foreign 93100
## 3213 2 0 1 1 Own Foreign 86300
## 3624 0 0 1 2 Own Foreign 92200
## 4917 0 0 1 2 Own Foreign 92300
## 4950 0 0 1 1 Own Foreign 93600
## CommuteTime PoliticalPartyMem Votes CreditCard CardTenure CardItemsMonthly
## 18 29 No Yes Mast 36 10
## 755 20 No No Disc 36 8
## 990 13 Yes No Visa 33 6
## 1103 32 Yes Yes Disc 37 13
## 2062 19 Yes No Disc 17 10
## 2080 24 Yes Yes Mast 17 14
## 2193 31 No Yes AMEX 19 12
## 2347 25 Yes Yes Disc 14 13
## 3069 35 No Yes Disc 33 9
## 3213 25 No Yes Disc 21 10
## 3624 29 No Yes AMEX 40 7
## 4917 19 No Yes Mast 22 8
## 4950 33 No No AMEX 23 8
## CardSpendMonth ActiveLifestyle PhoneCoTenure VoiceLastMonth
## 18 4957.5 No 70 104.40
## 755 3230.4 Yes 67 72.30
## 990 1541.8 No 66 120.30
## 1103 29693.9 Yes 72 140.70
## 2062 4239.4 No 22 53.10
## 2080 12952.9 No 48 20.40
## 2193 16039.1 Yes 57 39.60
## 2347 8788.6 No 31 17.70
## 3069 3739.2 No 68 114.00
## 3213 5136.8 No 46 27.45
## 3624 10907.9 Yes 72 104.55
## 4917 2082.2 No 53 51.15
## 4950 5140.4 No 50 40.20
## VoiceOverTenure EquipmentRental EquipmentLastMonth EquipmentOverTenure
## 18 2540.15 No 0.0 0.00
## 755 1590.70 No 0.0 0.00
## 990 2613.15 Yes 42.9 2845.75
## 1103 3393.10 No 0.0 0.00
## 2062 328.70 No 0.0 0.00
## 2080 331.30 No 0.0 0.00
## 2193 761.45 No 0.0 0.00
## 2347 196.35 No 0.0 0.00
## 3069 2582.40 No 0.0 0.00
## 3213 438.80 Yes 83.9 3886.10
## 3624 2405.90 Yes 46.7 3244.45
## 4917 913.40 Yes 64.7 3332.05
## 4950 643.85 Yes 48.1 2455.90
## CallingCard WirelessData DataLastMonth DataOverTenure Multiline VM Pager
## 18 Yes No 0.00 0.00 Yes Yes No
## 755 Yes No 0.00 0.00 Yes Yes No
## 990 Yes No 0.00 0.00 Yes No No
## 1103 Yes Yes 44.90 3174.75 Yes No Yes
## 2062 Yes No 0.00 0.00 No Yes No
## 2080 No No 0.00 0.00 No No No
## 2193 Yes Yes 40.75 2113.45 Yes Yes Yes
## 2347 Yes No 0.00 0.00 Yes No No
## 3069 Yes Yes 93.80 6273.95 Yes Yes Yes
## 3213 Yes Yes 104.25 4603.15 Yes Yes Yes
## 3624 Yes Yes 53.05 3634.70 Yes Yes Yes
## 4917 Yes Yes 51.60 2701.50 Yes Yes Yes
## 4950 No Yes 33.15 1612.40 No Yes Yes
## Internet CallerID CallWait CallForward ThreeWayCalling EBilling
## 18 No Yes No Yes Yes Yes
## 755 Yes No Yes Yes Yes No
## 990 3 No No Yes No No
## 1103 4 No Yes No Yes No
## 2062 Yes Yes Yes Yes Yes No
## 2080 No Yes Yes Yes Yes No
## 2193 3 Yes Yes Yes Yes Yes
## 2347 4 No Yes No Yes No
## 3069 No Yes Yes Yes Yes No
## 3213 4 Yes Yes Yes Yes Yes
## 3624 Yes Yes Yes Yes Yes No
## 4917 Yes Yes Yes Yes Yes Yes
## 4950 4 Yes Yes No Yes Yes
## TVWatchingHours OwnsPC OwnsMobileDevice OwnsGameSystem OwnsFax
## 18 24 No No No No
## 755 25 Yes Yes Yes No
## 990 11 Yes No Yes Yes
## 1103 28 Yes Yes No No
## 2062 24 Yes Yes Yes Yes
## 2080 28 No Yes Yes No
## 2193 22 Yes Yes No Yes
## 2347 27 Yes No No No
## 3069 18 No Yes No Yes
## 3213 18 Yes Yes Yes Yes
## 3624 17 Yes No No No
## 4917 23 Yes Yes Yes Yes
## 4950 11 Yes Yes Yes Yes
## NewsSubscriber
## 18 Yes
## 755 Yes
## 990 Yes
## 1103 Yes
## 2062 No
## 2080 No
## 2193 No
## 2347 No
## 3069 Yes
## 3213 No
## 3624 Yes
## 4917 Yes
## 4950 No# OR
CustomerData[(CustomerData$Age > 25 & CustomerData$HHIncome > 400000), ]## CustomerID Region TownSize Gender Age EducationYears JobCategory
## 18 0649-TBFJFL-QU4 5 2 Male 63 14 Labor
## 755 5071-YMPEFZ-4BK 3 5 Male 63 17 Agriculture
## 990 8162-PHLLNH-12V 5 1 Female 68 15 Labor
## 1103 8402-SILWTV-4YR 4 4 Male 58 19 Service
## 2062 8607-AMZELA-S5B 5 1 Female 57 18 Agriculture
## 2080 6879-SZBXXQ-ERC 4 4 Female 58 16 Service
## 2193 2329-EIXEIO-VD3 5 5 Male 52 18 Labor
## 2347 2435-DERRXC-V3A 5 1 Female 70 19 Labor
## 3069 9069-XTCDOO-RZV 4 3 Male 57 21 Sales
## 3213 6308-REILUL-K6N 3 1 Male 54 22 Professional
## 3624 9204-WXRZIL-7QG 5 3 Male 70 16 Labor
## 4917 4490-YKVPRY-KYA 5 4 Male 58 21 Professional
## 4950 2885-KFFQPU-BNO 4 4 Male 56 17 Crafts
## UnionMember EmploymentLength Retired HHIncome DebtToIncomeRatio CreditDebt
## 18 Yes 29 No 424000 10.7 13.111352
## 755 No 29 No 515000 3.9 2.289690
## 990 Yes 31 No 411000 8.3 10.506804
## 1103 No 20 No 1073000 19.7 109.072596
## 2062 No 23 No 472000 1.2 1.376352
## 2080 No 20 No 409000 6.7 6.987765
## 2193 No 24 No 995000 21.0 67.490850
## 2347 No 37 No 418000 13.9 14.351194
## 3069 No 11 No 780000 13.1 35.252100
## 3213 No 11 No 642000 4.6 11.251692
## 3624 No 35 No 526000 10.2 31.279116
## 4917 No 9 No 437000 14.9 48.704524
## 4950 No 19 No 575000 5.2 4.215900
## OtherDebt LoanDefault MaritalStatus HouseholdSize NumberPets NumberCats
## 18 32.256648 No Married 2 0 0
## 755 17.795310 No Married 2 9 0
## 990 23.606196 No Married 2 2 2
## 1103 102.308404 Yes Married 2 8 0
## 2062 4.287648 No Unmarried 1 0 0
## 2080 20.415235 No Unmarried 4 4 1
## 2193 141.459150 Yes Unmarried 1 1 0
## 2347 43.750806 Yes Unmarried 1 0 0
## 3069 66.927900 Yes Married 2 0 0
## 3213 18.280308 No Married 2 2 0
## 3624 22.372884 Yes Unmarried 1 1 1
## 4917 16.408476 Yes Married 3 4 0
## 4950 25.684100 Yes Unmarried 1 7 1
## NumberDogs NumberBirds HomeOwner CarsOwned CarOwnership CarBrand CarValue
## 18 0 0 1 3 Own Domestic 88600
## 755 0 0 1 2 Own Foreign 88500
## 990 0 0 1 2 Own Foreign 91300
## 1103 2 0 1 4 Own Domestic 77600
## 2062 0 0 1 2 Own Domestic 88000
## 2080 2 1 1 1 Own Foreign 93200
## 2193 1 0 1 3 Own Domestic 99600
## 2347 0 0 1 2 Own Foreign 97300
## 3069 0 0 1 2 Own Foreign 93100
## 3213 2 0 1 1 Own Foreign 86300
## 3624 0 0 1 2 Own Foreign 92200
## 4917 0 0 1 2 Own Foreign 92300
## 4950 0 0 1 1 Own Foreign 93600
## CommuteTime PoliticalPartyMem Votes CreditCard CardTenure CardItemsMonthly
## 18 29 No Yes Mast 36 10
## 755 20 No No Disc 36 8
## 990 13 Yes No Visa 33 6
## 1103 32 Yes Yes Disc 37 13
## 2062 19 Yes No Disc 17 10
## 2080 24 Yes Yes Mast 17 14
## 2193 31 No Yes AMEX 19 12
## 2347 25 Yes Yes Disc 14 13
## 3069 35 No Yes Disc 33 9
## 3213 25 No Yes Disc 21 10
## 3624 29 No Yes AMEX 40 7
## 4917 19 No Yes Mast 22 8
## 4950 33 No No AMEX 23 8
## CardSpendMonth ActiveLifestyle PhoneCoTenure VoiceLastMonth
## 18 4957.5 No 70 104.40
## 755 3230.4 Yes 67 72.30
## 990 1541.8 No 66 120.30
## 1103 29693.9 Yes 72 140.70
## 2062 4239.4 No 22 53.10
## 2080 12952.9 No 48 20.40
## 2193 16039.1 Yes 57 39.60
## 2347 8788.6 No 31 17.70
## 3069 3739.2 No 68 114.00
## 3213 5136.8 No 46 27.45
## 3624 10907.9 Yes 72 104.55
## 4917 2082.2 No 53 51.15
## 4950 5140.4 No 50 40.20
## VoiceOverTenure EquipmentRental EquipmentLastMonth EquipmentOverTenure
## 18 2540.15 No 0.0 0.00
## 755 1590.70 No 0.0 0.00
## 990 2613.15 Yes 42.9 2845.75
## 1103 3393.10 No 0.0 0.00
## 2062 328.70 No 0.0 0.00
## 2080 331.30 No 0.0 0.00
## 2193 761.45 No 0.0 0.00
## 2347 196.35 No 0.0 0.00
## 3069 2582.40 No 0.0 0.00
## 3213 438.80 Yes 83.9 3886.10
## 3624 2405.90 Yes 46.7 3244.45
## 4917 913.40 Yes 64.7 3332.05
## 4950 643.85 Yes 48.1 2455.90
## CallingCard WirelessData DataLastMonth DataOverTenure Multiline VM Pager
## 18 Yes No 0.00 0.00 Yes Yes No
## 755 Yes No 0.00 0.00 Yes Yes No
## 990 Yes No 0.00 0.00 Yes No No
## 1103 Yes Yes 44.90 3174.75 Yes No Yes
## 2062 Yes No 0.00 0.00 No Yes No
## 2080 No No 0.00 0.00 No No No
## 2193 Yes Yes 40.75 2113.45 Yes Yes Yes
## 2347 Yes No 0.00 0.00 Yes No No
## 3069 Yes Yes 93.80 6273.95 Yes Yes Yes
## 3213 Yes Yes 104.25 4603.15 Yes Yes Yes
## 3624 Yes Yes 53.05 3634.70 Yes Yes Yes
## 4917 Yes Yes 51.60 2701.50 Yes Yes Yes
## 4950 No Yes 33.15 1612.40 No Yes Yes
## Internet CallerID CallWait CallForward ThreeWayCalling EBilling
## 18 No Yes No Yes Yes Yes
## 755 Yes No Yes Yes Yes No
## 990 3 No No Yes No No
## 1103 4 No Yes No Yes No
## 2062 Yes Yes Yes Yes Yes No
## 2080 No Yes Yes Yes Yes No
## 2193 3 Yes Yes Yes Yes Yes
## 2347 4 No Yes No Yes No
## 3069 No Yes Yes Yes Yes No
## 3213 4 Yes Yes Yes Yes Yes
## 3624 Yes Yes Yes Yes Yes No
## 4917 Yes Yes Yes Yes Yes Yes
## 4950 4 Yes Yes No Yes Yes
## TVWatchingHours OwnsPC OwnsMobileDevice OwnsGameSystem OwnsFax
## 18 24 No No No No
## 755 25 Yes Yes Yes No
## 990 11 Yes No Yes Yes
## 1103 28 Yes Yes No No
## 2062 24 Yes Yes Yes Yes
## 2080 28 No Yes Yes No
## 2193 22 Yes Yes No Yes
## 2347 27 Yes No No No
## 3069 18 No Yes No Yes
## 3213 18 Yes Yes Yes Yes
## 3624 17 Yes No No No
## 4917 23 Yes Yes Yes Yes
## 4950 11 Yes Yes Yes Yes
## NewsSubscriber
## 18 Yes
## 755 Yes
## 990 Yes
## 1103 Yes
## 2062 No
## 2080 No
## 2193 No
## 2347 No
## 3069 Yes
## 3213 No
## 3624 Yes
## 4917 Yes
## 4950 No3.1.2 Method 2 : Using SQL to filter data
sqldf('select * from CustomerData where `Age` > 25 and `HHIncome` > 400000')## CustomerID Region TownSize Gender Age EducationYears JobCategory
## 1 0649-TBFJFL-QU4 5 2 Male 63 14 Labor
## 2 5071-YMPEFZ-4BK 3 5 Male 63 17 Agriculture
## 3 8162-PHLLNH-12V 5 1 Female 68 15 Labor
## 4 8402-SILWTV-4YR 4 4 Male 58 19 Service
## 5 8607-AMZELA-S5B 5 1 Female 57 18 Agriculture
## 6 6879-SZBXXQ-ERC 4 4 Female 58 16 Service
## 7 2329-EIXEIO-VD3 5 5 Male 52 18 Labor
## 8 2435-DERRXC-V3A 5 1 Female 70 19 Labor
## 9 9069-XTCDOO-RZV 4 3 Male 57 21 Sales
## 10 6308-REILUL-K6N 3 1 Male 54 22 Professional
## 11 9204-WXRZIL-7QG 5 3 Male 70 16 Labor
## 12 4490-YKVPRY-KYA 5 4 Male 58 21 Professional
## 13 2885-KFFQPU-BNO 4 4 Male 56 17 Crafts
## UnionMember EmploymentLength Retired HHIncome DebtToIncomeRatio CreditDebt
## 1 Yes 29 No 424000 10.7 13.111352
## 2 No 29 No 515000 3.9 2.289690
## 3 Yes 31 No 411000 8.3 10.506804
## 4 No 20 No 1073000 19.7 109.072596
## 5 No 23 No 472000 1.2 1.376352
## 6 No 20 No 409000 6.7 6.987765
## 7 No 24 No 995000 21.0 67.490850
## 8 No 37 No 418000 13.9 14.351194
## 9 No 11 No 780000 13.1 35.252100
## 10 No 11 No 642000 4.6 11.251692
## 11 No 35 No 526000 10.2 31.279116
## 12 No 9 No 437000 14.9 48.704524
## 13 No 19 No 575000 5.2 4.215900
## OtherDebt LoanDefault MaritalStatus HouseholdSize NumberPets NumberCats
## 1 32.256648 No Married 2 0 0
## 2 17.795310 No Married 2 9 0
## 3 23.606196 No Married 2 2 2
## 4 102.308404 Yes Married 2 8 0
## 5 4.287648 No Unmarried 1 0 0
## 6 20.415235 No Unmarried 4 4 1
## 7 141.459150 Yes Unmarried 1 1 0
## 8 43.750806 Yes Unmarried 1 0 0
## 9 66.927900 Yes Married 2 0 0
## 10 18.280308 No Married 2 2 0
## 11 22.372884 Yes Unmarried 1 1 1
## 12 16.408476 Yes Married 3 4 0
## 13 25.684100 Yes Unmarried 1 7 1
## NumberDogs NumberBirds HomeOwner CarsOwned CarOwnership CarBrand CarValue
## 1 0 0 1 3 Own Domestic 88600
## 2 0 0 1 2 Own Foreign 88500
## 3 0 0 1 2 Own Foreign 91300
## 4 2 0 1 4 Own Domestic 77600
## 5 0 0 1 2 Own Domestic 88000
## 6 2 1 1 1 Own Foreign 93200
## 7 1 0 1 3 Own Domestic 99600
## 8 0 0 1 2 Own Foreign 97300
## 9 0 0 1 2 Own Foreign 93100
## 10 2 0 1 1 Own Foreign 86300
## 11 0 0 1 2 Own Foreign 92200
## 12 0 0 1 2 Own Foreign 92300
## 13 0 0 1 1 Own Foreign 93600
## CommuteTime PoliticalPartyMem Votes CreditCard CardTenure CardItemsMonthly
## 1 29 No Yes Mast 36 10
## 2 20 No No Disc 36 8
## 3 13 Yes No Visa 33 6
## 4 32 Yes Yes Disc 37 13
## 5 19 Yes No Disc 17 10
## 6 24 Yes Yes Mast 17 14
## 7 31 No Yes AMEX 19 12
## 8 25 Yes Yes Disc 14 13
## 9 35 No Yes Disc 33 9
## 10 25 No Yes Disc 21 10
## 11 29 No Yes AMEX 40 7
## 12 19 No Yes Mast 22 8
## 13 33 No No AMEX 23 8
## CardSpendMonth ActiveLifestyle PhoneCoTenure VoiceLastMonth VoiceOverTenure
## 1 4957.5 No 70 104.40 2540.15
## 2 3230.4 Yes 67 72.30 1590.70
## 3 1541.8 No 66 120.30 2613.15
## 4 29693.9 Yes 72 140.70 3393.10
## 5 4239.4 No 22 53.10 328.70
## 6 12952.9 No 48 20.40 331.30
## 7 16039.1 Yes 57 39.60 761.45
## 8 8788.6 No 31 17.70 196.35
## 9 3739.2 No 68 114.00 2582.40
## 10 5136.8 No 46 27.45 438.80
## 11 10907.9 Yes 72 104.55 2405.90
## 12 2082.2 No 53 51.15 913.40
## 13 5140.4 No 50 40.20 643.85
## EquipmentRental EquipmentLastMonth EquipmentOverTenure CallingCard
## 1 No 0.0 0.00 Yes
## 2 No 0.0 0.00 Yes
## 3 Yes 42.9 2845.75 Yes
## 4 No 0.0 0.00 Yes
## 5 No 0.0 0.00 Yes
## 6 No 0.0 0.00 No
## 7 No 0.0 0.00 Yes
## 8 No 0.0 0.00 Yes
## 9 No 0.0 0.00 Yes
## 10 Yes 83.9 3886.10 Yes
## 11 Yes 46.7 3244.45 Yes
## 12 Yes 64.7 3332.05 Yes
## 13 Yes 48.1 2455.90 No
## WirelessData DataLastMonth DataOverTenure Multiline VM Pager Internet
## 1 No 0.00 0.00 Yes Yes No No
## 2 No 0.00 0.00 Yes Yes No Yes
## 3 No 0.00 0.00 Yes No No 3
## 4 Yes 44.90 3174.75 Yes No Yes 4
## 5 No 0.00 0.00 No Yes No Yes
## 6 No 0.00 0.00 No No No No
## 7 Yes 40.75 2113.45 Yes Yes Yes 3
## 8 No 0.00 0.00 Yes No No 4
## 9 Yes 93.80 6273.95 Yes Yes Yes No
## 10 Yes 104.25 4603.15 Yes Yes Yes 4
## 11 Yes 53.05 3634.70 Yes Yes Yes Yes
## 12 Yes 51.60 2701.50 Yes Yes Yes Yes
## 13 Yes 33.15 1612.40 No Yes Yes 4
## CallerID CallWait CallForward ThreeWayCalling EBilling TVWatchingHours
## 1 Yes No Yes Yes Yes 24
## 2 No Yes Yes Yes No 25
## 3 No No Yes No No 11
## 4 No Yes No Yes No 28
## 5 Yes Yes Yes Yes No 24
## 6 Yes Yes Yes Yes No 28
## 7 Yes Yes Yes Yes Yes 22
## 8 No Yes No Yes No 27
## 9 Yes Yes Yes Yes No 18
## 10 Yes Yes Yes Yes Yes 18
## 11 Yes Yes Yes Yes No 17
## 12 Yes Yes Yes Yes Yes 23
## 13 Yes Yes No Yes Yes 11
## OwnsPC OwnsMobileDevice OwnsGameSystem OwnsFax NewsSubscriber
## 1 No No No No Yes
## 2 Yes Yes Yes No Yes
## 3 Yes No Yes Yes Yes
## 4 Yes Yes No No Yes
## 5 Yes Yes Yes Yes No
## 6 No Yes Yes No No
## 7 Yes Yes No Yes No
## 8 Yes No No No No
## 9 No Yes No Yes Yes
## 10 Yes Yes Yes Yes No
## 11 Yes No No No Yes
## 12 Yes Yes Yes Yes Yes
## 13 Yes Yes Yes Yes No3.1.3 Method 3 : Using filter() function
CustomerData1 <- filter(CustomerData, Age>25 & HHIncome>400000)
CustomerData2 <- filter(CustomerData1, Gender=="Female", EducationYears<=20 | UnionMember=="Yes")3.2.1 Random subsetting of data
# Randomly sampling 90% data
CustomerDataSample1 <- CustomerData[sample(x = nrow(CustomerData), size = nrow(CustomerData)*0.90),]
# OR
CustomerDataSample2 <- sample_frac(CustomerData, 0.9)
# To sample fixed number of rows
CustomerDataSample3 <- sample_n(CustomerData, 1000)3.2.2 Randomly splitting data into two groups
index <- sample(nrow(CustomerData),nrow(CustomerData)*0.90)
CustomerData.train = CustomerData[index,]
CustomerData.test = CustomerData[-index,]3.3 Sorting data Here we are extracting the first 5 rows. This can be modified based on the requirements
CustomerData[order(CustomerData$Age, decreasing = TRUE)[1:5], ] ## CustomerID Region TownSize Gender Age EducationYears JobCategory
## 172 8214-NVCQAC-3HH 2 2 Male 79 12 Crafts
## 176 8931-XMXDFT-RXL 1 1 Female 79 7 Agriculture
## 185 1734-EAYBEZ-PZ0 2 1 Male 79 16 Professional
## 227 4092-XAPMSL-F1S 1 1 Female 79 12 Professional
## 312 2175-VGCTPF-370 4 4 Male 79 12 Sales
## UnionMember EmploymentLength Retired HHIncome DebtToIncomeRatio CreditDebt
## 172 No 39 Yes 47000 7.9 1.095335
## 176 No 45 Yes 20000 19.2 1.409280
## 185 No 15 Yes 26000 18.1 2.301234
## 227 No 13 Yes 10000 6.8 0.060520
## 312 No 6 Yes 9000 3.8 0.166896
## OtherDebt LoanDefault MaritalStatus HouseholdSize NumberPets NumberCats
## 172 2.617665 No Married 2 0 0
## 176 2.430720 No Unmarried 1 2 0
## 185 2.404766 No Unmarried 1 0 0
## 227 0.619480 No Married 2 4 3
## 312 0.175104 No Married 2 1 0
## NumberDogs NumberBirds HomeOwner CarsOwned CarOwnership CarBrand CarValue
## 172 0 0 1 1 Lease Foreign 19300
## 176 0 0 1 1 Lease Foreign 10000
## 185 0 0 0 4 Lease Domestic 11500
## 227 1 0 0 1 Own Foreign 4200
## 312 1 0 0 3 Lease Domestic 3800
## CommuteTime PoliticalPartyMem Votes CreditCard CardTenure CardItemsMonthly
## 172 26 No No Disc 39 10
## 176 22 No Yes Visa 39 9
## 185 21 Yes No AMEX 29 14
## 227 27 Yes No Mast 19 14
## 312 27 Yes Yes Mast 15 5
## CardSpendMonth ActiveLifestyle PhoneCoTenure VoiceLastMonth VoiceOverTenure
## 172 4017.7 Yes 71 57.45 1313.45
## 176 1228.1 No 72 54.75 1307.20
## 185 2316.3 No 67 54.15 1144.85
## 227 2363.9 Yes 45 44.25 687.25
## 312 653.6 No 34 16.50 225.70
## EquipmentRental EquipmentLastMonth EquipmentOverTenure CallingCard
## 172 No 0 0 Yes
## 176 No 0 0 Yes
## 185 No 0 0 Yes
## 227 No 0 0 No
## 312 No 0 0 Yes
## WirelessData DataLastMonth DataOverTenure Multiline VM Pager Internet
## 172 Yes 35.65 2467.8 No Yes No No
## 176 No 0.00 0.0 Yes No No No
## 185 No 0.00 0.0 Yes No No No
## 227 No 0.00 0.0 No No No No
## 312 No 0.00 0.0 No No No No
## CallerID CallWait CallForward ThreeWayCalling EBilling TVWatchingHours
## 172 Yes Yes Yes Yes No 20
## 176 Yes No Yes Yes No 10
## 185 No No No No No 25
## 227 No No No No No 15
## 312 No Yes Yes Yes No 0
## OwnsPC OwnsMobileDevice OwnsGameSystem OwnsFax NewsSubscriber
## 172 No No No Yes Yes
## 176 No No No No Yes
## 185 Yes No No No Yes
## 227 No No No No No
## 312 No No No No No# OR
arrange(CustomerData, desc(Age))[1:5, ] #Descending## CustomerID Region TownSize Gender Age EducationYears JobCategory
## 1 8214-NVCQAC-3HH 2 2 Male 79 12 Crafts
## 2 8931-XMXDFT-RXL 1 1 Female 79 7 Agriculture
## 3 1734-EAYBEZ-PZ0 2 1 Male 79 16 Professional
## 4 4092-XAPMSL-F1S 1 1 Female 79 12 Professional
## 5 2175-VGCTPF-370 4 4 Male 79 12 Sales
## UnionMember EmploymentLength Retired HHIncome DebtToIncomeRatio CreditDebt
## 1 No 39 Yes 47000 7.9 1.095335
## 2 No 45 Yes 20000 19.2 1.409280
## 3 No 15 Yes 26000 18.1 2.301234
## 4 No 13 Yes 10000 6.8 0.060520
## 5 No 6 Yes 9000 3.8 0.166896
## OtherDebt LoanDefault MaritalStatus HouseholdSize NumberPets NumberCats
## 1 2.617665 No Married 2 0 0
## 2 2.430720 No Unmarried 1 2 0
## 3 2.404766 No Unmarried 1 0 0
## 4 0.619480 No Married 2 4 3
## 5 0.175104 No Married 2 1 0
## NumberDogs NumberBirds HomeOwner CarsOwned CarOwnership CarBrand CarValue
## 1 0 0 1 1 Lease Foreign 19300
## 2 0 0 1 1 Lease Foreign 10000
## 3 0 0 0 4 Lease Domestic 11500
## 4 1 0 0 1 Own Foreign 4200
## 5 1 0 0 3 Lease Domestic 3800
## CommuteTime PoliticalPartyMem Votes CreditCard CardTenure CardItemsMonthly
## 1 26 No No Disc 39 10
## 2 22 No Yes Visa 39 9
## 3 21 Yes No AMEX 29 14
## 4 27 Yes No Mast 19 14
## 5 27 Yes Yes Mast 15 5
## CardSpendMonth ActiveLifestyle PhoneCoTenure VoiceLastMonth VoiceOverTenure
## 1 4017.7 Yes 71 57.45 1313.45
## 2 1228.1 No 72 54.75 1307.20
## 3 2316.3 No 67 54.15 1144.85
## 4 2363.9 Yes 45 44.25 687.25
## 5 653.6 No 34 16.50 225.70
## EquipmentRental EquipmentLastMonth EquipmentOverTenure CallingCard
## 1 No 0 0 Yes
## 2 No 0 0 Yes
## 3 No 0 0 Yes
## 4 No 0 0 No
## 5 No 0 0 Yes
## WirelessData DataLastMonth DataOverTenure Multiline VM Pager Internet
## 1 Yes 35.65 2467.8 No Yes No No
## 2 No 0.00 0.0 Yes No No No
## 3 No 0.00 0.0 Yes No No No
## 4 No 0.00 0.0 No No No No
## 5 No 0.00 0.0 No No No No
## CallerID CallWait CallForward ThreeWayCalling EBilling TVWatchingHours OwnsPC
## 1 Yes Yes Yes Yes No 20 No
## 2 Yes No Yes Yes No 10 No
## 3 No No No No No 25 Yes
## 4 No No No No No 15 No
## 5 No Yes Yes Yes No 0 No
## OwnsMobileDevice OwnsGameSystem OwnsFax NewsSubscriber
## 1 No No Yes Yes
## 2 No No No Yes
## 3 No No No Yes
## 4 No No No No
## 5 No No No No# OR
arrange(CustomerData, Age)[1:5, ] #Ascending## CustomerID Region TownSize Gender Age EducationYears JobCategory
## 1 9723-VUGZBJ-ZQA 3 3 Male 18 13 Service
## 2 3754-JJTSIX-HW4 5 5 Female 18 13 Professional
## 3 1103-KTXUJS-ZPH 3 1 Female 18 13 Service
## 4 2736-QDRAAN-VAI 5 5 Female 18 13 Sales
## 5 1163-JCDRWZ-Q3K 4 2 Male 18 13 Professional
## UnionMember EmploymentLength Retired HHIncome DebtToIncomeRatio CreditDebt
## 1 No 0 No 15000 10.9 0.516660
## 2 No 0 No 19000 4.1 0.101270
## 3 No 0 No 15000 4.9 0.210210
## 4 No 0 No 21000 4.5 0.179550
## 5 No 0 No 19000 22.2 3.766674
## OtherDebt LoanDefault MaritalStatus HouseholdSize NumberPets NumberCats
## 1 1.118340 Yes Unmarried 1 0 0
## 2 0.677730 No Married 6 2 0
## 3 0.524790 No Married 4 1 1
## 4 0.765450 Yes Married 4 2 2
## 5 0.451326 Yes Married 5 0 0
## NumberDogs NumberBirds HomeOwner CarsOwned CarOwnership CarBrand CarValue
## 1 0 0 0 3 Lease Domestic 6100
## 2 1 0 0 5 Own Foreign 8700
## 3 0 0 1 2 Lease Domestic 9100
## 4 0 0 1 3 Own Domestic 13400
## 5 0 0 1 4 Own Domestic 10600
## CommuteTime PoliticalPartyMem Votes CreditCard CardTenure CardItemsMonthly
## 1 30 No Yes Visa 1 7
## 2 24 Yes No Othe 0 14
## 3 17 Yes Yes Mast 0 14
## 4 44 Yes Yes Disc 0 12
## 5 25 Yes No Mast 0 7
## CardSpendMonth ActiveLifestyle PhoneCoTenure VoiceLastMonth VoiceOverTenure
## 1 563.2 Yes 2 12.15 7.20
## 2 2242.5 No 6 40.05 75.25
## 3 2318.1 No 2 42.30 28.65
## 4 3884.1 No 7 12.60 28.20
## 5 1240.2 No 7 13.95 31.90
## EquipmentRental EquipmentLastMonth EquipmentOverTenure CallingCard
## 1 No 0.00 0.0 No
## 2 No 0.00 0.0 No
## 3 Yes 30.55 26.1 Yes
## 4 No 0.00 0.0 Yes
## 5 Yes 29.75 186.3 No
## WirelessData DataLastMonth DataOverTenure Multiline VM Pager Internet
## 1 No 0 0 No No No No
## 2 No 0 0 No No No No
## 3 No 0 0 Yes No No 3
## 4 No 0 0 No No Yes No
## 5 No 0 0 Yes Yes No Yes
## CallerID CallWait CallForward ThreeWayCalling EBilling TVWatchingHours OwnsPC
## 1 No No No No No 24 No
## 2 No No No No Yes 35 No
## 3 No No No No Yes 14 Yes
## 4 Yes Yes Yes Yes No 20 No
## 5 No No No No No 21 Yes
## OwnsMobileDevice OwnsGameSystem OwnsFax NewsSubscriber
## 1 Yes Yes No No
## 2 No Yes No No
## 3 Yes Yes No Yes
## 4 No No No No
## 5 Yes Yes No No# Sorting using multiple columns
CustomerData_sort<- arrange(CustomerData, Age, desc(HHIncome))[1:5,]Column Manipulations
3.4 Variable details 3.4.1 Extracting column names
names(CustomerData)## [1] "CustomerID" "Region" "TownSize"
## [4] "Gender" "Age" "EducationYears"
## [7] "JobCategory" "UnionMember" "EmploymentLength"
## [10] "Retired" "HHIncome" "DebtToIncomeRatio"
## [13] "CreditDebt" "OtherDebt" "LoanDefault"
## [16] "MaritalStatus" "HouseholdSize" "NumberPets"
## [19] "NumberCats" "NumberDogs" "NumberBirds"
## [22] "HomeOwner" "CarsOwned" "CarOwnership"
## [25] "CarBrand" "CarValue" "CommuteTime"
## [28] "PoliticalPartyMem" "Votes" "CreditCard"
## [31] "CardTenure" "CardItemsMonthly" "CardSpendMonth"
## [34] "ActiveLifestyle" "PhoneCoTenure" "VoiceLastMonth"
## [37] "VoiceOverTenure" "EquipmentRental" "EquipmentLastMonth"
## [40] "EquipmentOverTenure" "CallingCard" "WirelessData"
## [43] "DataLastMonth" "DataOverTenure" "Multiline"
## [46] "VM" "Pager" "Internet"
## [49] "CallerID" "CallWait" "CallForward"
## [52] "ThreeWayCalling" "EBilling" "TVWatchingHours"
## [55] "OwnsPC" "OwnsMobileDevice" "OwnsGameSystem"
## [58] "OwnsFax" "NewsSubscriber"3.4.2 Selecting columns
CustomerData[, c("Age", "HHIncome")]## Age HHIncome
## 1 20 31000
## 2 22 15000
## 3 67 35000
## 4 23 20000
## 5 26 23000
## 6 64 107000
## 7 52 77000
## 8 44 97000
## 9 66 16000
## 10 47 84000
## 11 59 47000
## 12 33 19000
## 13 44 73000
## 14 58 63000
## 15 72 17000
## 16 66 23000
## 17 57 171000
## 18 63 424000
## 19 28 23000
## 20 78 22000
## 21 61 35000
## 22 70 28000
## 23 61 12000
## 24 37 29000
## 25 39 130000
## 26 73 69000
## 27 26 24000
## 28 24 29000
## 29 77 11000
## 30 36 30000
## 31 55 80000
## 32 60 51000
## 33 59 30000
## 34 28 17000
## 35 53 141000
## 36 36 45000
## 37 47 137000
## 38 75 10000
## 39 49 73000
## 40 59 63000
## 41 55 50000
## 42 25 50000
## 43 78 28000
## 44 48 23000
## 45 47 75000
## 46 75 28000
## 47 43 33000
## 48 45 44000
## 49 21 31000
## 50 53 284000
## 51 30 49000
## 52 58 15000
## 53 35 17000
## 54 48 83000
## 55 57 82000
## 56 61 62000
## 57 56 138000
## 58 29 35000
## 59 68 35000
## 60 22 20000
## 61 46 25000
## 62 24 33000
## 63 78 32000
## 64 73 16000
## 65 26 64000
## 66 44 31000
## 67 30 18000
## 68 44 41000
## 69 21 22000
## 70 62 14000
## 71 33 82000
## 72 65 91000
## 73 38 74000
## 74 34 46000
## 75 78 14000
## 76 47 68000
## 77 60 101000
## 78 61 121000
## 79 77 19000
## 80 21 30000
## 81 31 43000
## 82 74 18000
## 83 27 42000
## 84 64 327000
## 85 72 30000
## 86 66 96000
## 87 55 143000
## 88 20 25000
## 89 59 68000
## 90 69 78000
## 91 43 38000
## 92 62 56000
## 93 32 65000
## 94 78 17000
## 95 64 62000
## 96 75 11000
## 97 74 108000
## 98 20 19000
## 99 65 26000
## 100 66 21000
## 101 45 82000
## 102 44 60000
## 103 59 100000
## 104 38 81000
## 105 65 34000
## 106 26 23000
## 107 70 107000
## 108 65 66000
## 109 61 107000
## 110 63 69000
## 111 70 224000
## 112 64 28000
## 113 56 31000
## 114 43 32000
## 115 53 82000
## 116 36 54000
## 117 52 42000
## 118 44 58000
## 119 48 73000
## 120 60 36000
## 121 67 51000
## 122 77 27000
## 123 72 36000
## 124 44 59000
## 125 75 41000
## 126 78 59000
## 127 55 128000
## 128 29 54000
## 129 43 44000
## 130 18 15000
## 131 28 17000
## 132 70 59000
## 133 43 57000
## 134 56 29000
## 135 66 12000
## 136 73 9000
## 137 26 85000
## 138 54 25000
## 139 39 48000
## 140 24 29000
## 141 26 64000
## 142 19 13000
## 143 60 131000
## 144 24 10000
## 145 31 27000
## 146 61 11000
## 147 24 51000
## 148 66 57000
## 149 50 54000
## 150 64 98000
## 151 46 28000
## 152 29 69000
## 153 69 149000
## 154 60 155000
## 155 50 49000
## 156 73 17000
## 157 68 31000
## 158 57 39000
## 159 29 156000
## 160 63 17000
## 161 49 60000
## 162 51 122000
## 163 22 32000
## 164 51 43000
## 165 53 142000
## 166 75 123000
## 167 30 32000
## 168 65 14000
## 169 35 17000
## 170 58 58000
## 171 30 39000
## 172 79 47000
## 173 62 49000
## 174 27 29000
## 175 34 51000
## 176 79 20000
## 177 68 74000
## 178 37 193000
## 179 52 94000
## 180 23 21000
## 181 57 121000
## 182 40 24000
## 183 66 53000
## 184 61 56000
## 185 79 26000
## 186 75 19000
## 187 35 47000
## 188 22 19000
## 189 73 164000
## 190 23 16000
## 191 19 23000
## 192 77 13000
## 193 51 74000
## 194 56 129000
## 195 46 56000
## 196 69 59000
## 197 38 36000
## 198 52 96000
## 199 67 22000
## 200 28 43000
## 201 36 29000
## 202 39 28000
## 203 55 29000
## 204 65 18000
## 205 73 14000
## 206 34 36000
## 207 67 152000
## 208 54 84000
## 209 31 32000
## 210 66 22000
## 211 51 143000
## 212 35 22000
## 213 47 50000
## 214 65 42000
## 215 23 13000
## 216 34 55000
## 217 78 23000
## 218 68 15000
## 219 47 45000
## 220 33 20000
## 221 36 48000
## 222 32 42000
## 223 64 35000
## 224 54 38000
## 225 62 66000
## 226 18 19000
## 227 79 10000
## 228 70 21000
## 229 42 114000
## 230 69 41000
## 231 24 23000
## 232 26 21000
## 233 61 110000
## 234 36 29000
## 235 68 102000
## 236 35 22000
## 237 55 42000
## 238 49 36000
## 239 20 19000
## 240 25 31000
## 241 47 66000
## 242 66 23000
## 243 18 15000
## 244 25 21000
## 245 68 237000
## 246 61 32000
## 247 49 208000
## 248 26 82000
## 249 62 182000
## 250 20 17000
## 251 74 62000
## 252 30 30000
## 253 64 18000
## 254 22 20000
## 255 30 109000
## 256 66 88000
## 257 42 46000
## 258 27 22000
## 259 45 58000
## 260 75 28000
## 261 43 127000
## 262 35 51000
## 263 31 29000
## 264 70 141000
## 265 22 20000
## 266 69 64000
## 267 42 30000
## 268 57 19000
## 269 43 22000
## 270 56 79000
## 271 65 55000
## 272 66 350000
## 273 42 81000
## 274 78 17000
## 275 24 30000
## 276 68 10000
## 277 23 26000
## 278 22 23000
## 279 52 50000
## 280 26 21000
## 281 32 22000
## 282 49 60000
## 283 55 101000
## 284 42 65000
## 285 65 32000
## 286 19 25000
## 287 39 34000
## 288 25 37000
## 289 23 31000
## 290 30 19000
## 291 50 59000
## 292 49 37000
## 293 32 30000
## 294 45 83000
## 295 34 31000
## 296 19 43000
## 297 22 19000
## 298 30 64000
## 299 46 24000
## 300 51 24000
## 301 52 33000
## 302 27 17000
## 303 38 37000
## 304 18 21000
## 305 21 29000
## 306 42 34000
## 307 68 15000
## 308 56 43000
## 309 31 78000
## 310 18 19000
## 311 32 29000
## 312 79 9000
## 313 64 66000
## 314 53 32000
## 315 40 42000
## 316 39 50000
## 317 49 50000
## 318 75 74000
## 319 29 20000
## 320 32 36000
## 321 23 64000
## 322 79 9000
## 323 74 16000
## 324 38 76000
## 325 44 66000
## 326 68 11000
## 327 19 13000
## 328 28 55000
## 329 65 28000
## 330 60 191000
## 331 29 71000
## 332 72 17000
## 333 48 27000
## 334 33 53000
## 335 26 37000
## 336 75 21000
## 337 29 47000
## 338 30 30000
## 339 74 9000
## 340 24 18000
## 341 64 69000
## 342 74 51000
## 343 61 35000
## 344 20 28000
## 345 64 143000
## 346 66 80000
## 347 59 18000
## 348 54 41000
## 349 55 32000
## 350 71 310000
## 351 55 182000
## 352 44 154000
## 353 63 49000
## 354 61 143000
## 355 32 25000
## 356 19 19000
## 357 41 31000
## 358 68 10000
## 359 18 19000
## 360 54 83000
## 361 52 165000
## 362 70 117000
## 363 43 43000
## 364 77 58000
## 365 52 27000
## 366 30 40000
## 367 55 143000
## 368 54 118000
## 369 31 18000
## 370 60 20000
## 371 49 31000
## 372 39 36000
## 373 25 13000
## 374 74 11000
## 375 46 43000
## 376 33 25000
## 377 26 36000
## 378 62 11000
## 379 22 36000
## 380 43 42000
## 381 62 48000
## 382 51 31000
## 383 37 16000
## 384 57 47000
## 385 75 14000
## 386 68 47000
## 387 55 181000
## 388 39 92000
## 389 31 33000
## 390 74 13000
## 391 72 43000
## 392 31 34000
## 393 71 193000
## 394 27 33000
## 395 58 54000
## 396 42 117000
## 397 60 73000
## 398 44 33000
## 399 70 102000
## 400 67 103000
## 401 49 36000
## 402 44 64000
## 403 27 30000
## 404 54 74000
## 405 52 81000
## 406 26 37000
## 407 45 63000
## 408 20 23000
## 409 62 20000
## 410 25 24000
## 411 34 37000
## 412 60 33000
## 413 62 45000
## 414 56 54000
## 415 39 71000
## 416 35 51000
## 417 30 26000
## 418 27 70000
## 419 60 30000
## 420 61 105000
## 421 55 259000
## 422 26 40000
## 423 71 25000
## 424 66 49000
## 425 36 55000
## 426 67 65000
## 427 54 24000
## 428 66 9000
## 429 27 22000
## 430 33 98000
## 431 26 30000
## 432 37 57000
## 433 61 28000
## 434 75 63000
## 435 46 53000
## 436 36 29000
## 437 51 56000
## 438 55 208000
## 439 58 15000
## 440 38 55000
## 441 40 101000
## 442 26 19000
## 443 21 19000
## 444 19 32000
## 445 32 19000
## 446 42 78000
## 447 55 91000
## 448 59 78000
## 449 25 45000
## 450 57 293000
## 451 47 29000
## 452 28 25000
## 453 61 61000
## 454 48 94000
## 455 46 37000
## 456 35 38000
## 457 52 27000
## 458 67 57000
## 459 29 24000
## 460 49 44000
## 461 41 95000
## 462 66 32000
## 463 19 17000
## 464 44 32000
## 465 52 41000
## 466 50 207000
## 467 61 339000
## 468 71 59000
## 469 79 11000
## 470 41 24000
## 471 39 145000
## 472 50 60000
## 473 57 25000
## 474 63 91000
## 475 55 54000
## 476 72 50000
## 477 57 96000
## 478 74 66000
## 479 69 105000
## 480 37 147000
## 481 28 39000
## 482 35 53000
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## 5000 41 77000# OR
varname <- c("Age", "HHIncome")
CustomerData_select <- select(CustomerData, varname)
# OR
CustomerData_select <- CustomerData[,varname]3.4.3 Dropping columns
CustomerData_select2 <- select(CustomerData, -Age, -HHIncome)
# OR
varname <- c("Age", "HHIncome")
CustomerData_select2 <- CustomerData[,!names(CustomerData) %in% varname]
names(CustomerData_select2)## [1] "CustomerID" "Region" "TownSize"
## [4] "Gender" "EducationYears" "JobCategory"
## [7] "UnionMember" "EmploymentLength" "Retired"
## [10] "DebtToIncomeRatio" "CreditDebt" "OtherDebt"
## [13] "LoanDefault" "MaritalStatus" "HouseholdSize"
## [16] "NumberPets" "NumberCats" "NumberDogs"
## [19] "NumberBirds" "HomeOwner" "CarsOwned"
## [22] "CarOwnership" "CarBrand" "CarValue"
## [25] "CommuteTime" "PoliticalPartyMem" "Votes"
## [28] "CreditCard" "CardTenure" "CardItemsMonthly"
## [31] "CardSpendMonth" "ActiveLifestyle" "PhoneCoTenure"
## [34] "VoiceLastMonth" "VoiceOverTenure" "EquipmentRental"
## [37] "EquipmentLastMonth" "EquipmentOverTenure" "CallingCard"
## [40] "WirelessData" "DataLastMonth" "DataOverTenure"
## [43] "Multiline" "VM" "Pager"
## [46] "Internet" "CallerID" "CallWait"
## [49] "CallForward" "ThreeWayCalling" "EBilling"
## [52] "TVWatchingHours" "OwnsPC" "OwnsMobileDevice"
## [55] "OwnsGameSystem" "OwnsFax" "NewsSubscriber"3.4.4 Reordering columns
CustomerData_order2 <- select(CustomerData, Age, HHIncome, everything())
names(CustomerData_order2)## [1] "Age" "HHIncome" "CustomerID"
## [4] "Region" "TownSize" "Gender"
## [7] "EducationYears" "JobCategory" "UnionMember"
## [10] "EmploymentLength" "Retired" "DebtToIncomeRatio"
## [13] "CreditDebt" "OtherDebt" "LoanDefault"
## [16] "MaritalStatus" "HouseholdSize" "NumberPets"
## [19] "NumberCats" "NumberDogs" "NumberBirds"
## [22] "HomeOwner" "CarsOwned" "CarOwnership"
## [25] "CarBrand" "CarValue" "CommuteTime"
## [28] "PoliticalPartyMem" "Votes" "CreditCard"
## [31] "CardTenure" "CardItemsMonthly" "CardSpendMonth"
## [34] "ActiveLifestyle" "PhoneCoTenure" "VoiceLastMonth"
## [37] "VoiceOverTenure" "EquipmentRental" "EquipmentLastMonth"
## [40] "EquipmentOverTenure" "CallingCard" "WirelessData"
## [43] "DataLastMonth" "DataOverTenure" "Multiline"
## [46] "VM" "Pager" "Internet"
## [49] "CallerID" "CallWait" "CallForward"
## [52] "ThreeWayCalling" "EBilling" "TVWatchingHours"
## [55] "OwnsPC" "OwnsMobileDevice" "OwnsGameSystem"
## [58] "OwnsFax" "NewsSubscriber"3.4.5 Renaming variables
CustomerData_rename<- rename(CustomerData, Education=EducationYears, Income=HHIncome)
CustomerData_rename1<-select(CustomerData_rename, Education, Income, everything())
names(CustomerData_rename1)## [1] "Education" "Income" "CustomerID"
## [4] "Region" "TownSize" "Gender"
## [7] "Age" "JobCategory" "UnionMember"
## [10] "EmploymentLength" "Retired" "DebtToIncomeRatio"
## [13] "CreditDebt" "OtherDebt" "LoanDefault"
## [16] "MaritalStatus" "HouseholdSize" "NumberPets"
## [19] "NumberCats" "NumberDogs" "NumberBirds"
## [22] "HomeOwner" "CarsOwned" "CarOwnership"
## [25] "CarBrand" "CarValue" "CommuteTime"
## [28] "PoliticalPartyMem" "Votes" "CreditCard"
## [31] "CardTenure" "CardItemsMonthly" "CardSpendMonth"
## [34] "ActiveLifestyle" "PhoneCoTenure" "VoiceLastMonth"
## [37] "VoiceOverTenure" "EquipmentRental" "EquipmentLastMonth"
## [40] "EquipmentOverTenure" "CallingCard" "WirelessData"
## [43] "DataLastMonth" "DataOverTenure" "Multiline"
## [46] "VM" "Pager" "Internet"
## [49] "CallerID" "CallWait" "CallForward"
## [52] "ThreeWayCalling" "EBilling" "TVWatchingHours"
## [55] "OwnsPC" "OwnsMobileDevice" "OwnsGameSystem"
## [58] "OwnsFax" "NewsSubscriber"3.4.6 Creating new variable
There are multiple ways to create new columns. Using mutate is the simplest while other methods might require multiple steps
CustomerData_newvar<- mutate(CustomerData, Age_Income_Ratio=HHIncome/Age)
names(CustomerData_newvar)## [1] "CustomerID" "Region" "TownSize"
## [4] "Gender" "Age" "EducationYears"
## [7] "JobCategory" "UnionMember" "EmploymentLength"
## [10] "Retired" "HHIncome" "DebtToIncomeRatio"
## [13] "CreditDebt" "OtherDebt" "LoanDefault"
## [16] "MaritalStatus" "HouseholdSize" "NumberPets"
## [19] "NumberCats" "NumberDogs" "NumberBirds"
## [22] "HomeOwner" "CarsOwned" "CarOwnership"
## [25] "CarBrand" "CarValue" "CommuteTime"
## [28] "PoliticalPartyMem" "Votes" "CreditCard"
## [31] "CardTenure" "CardItemsMonthly" "CardSpendMonth"
## [34] "ActiveLifestyle" "PhoneCoTenure" "VoiceLastMonth"
## [37] "VoiceOverTenure" "EquipmentRental" "EquipmentLastMonth"
## [40] "EquipmentOverTenure" "CallingCard" "WirelessData"
## [43] "DataLastMonth" "DataOverTenure" "Multiline"
## [46] "VM" "Pager" "Internet"
## [49] "CallerID" "CallWait" "CallForward"
## [52] "ThreeWayCalling" "EBilling" "TVWatchingHours"
## [55] "OwnsPC" "OwnsMobileDevice" "OwnsGameSystem"
## [58] "OwnsFax" "NewsSubscriber" "Age_Income_Ratio"Missing Data Treatment
3.5 Missing data analysis 3.5.1 Getting the number of missing values in the dataset
sum(is.na(CustomerData))## [1] 763.5.2 Getting the missing values for each variable
colSums(is.na(CustomerData))## CustomerID Region TownSize Gender
## 0 0 0 0
## Age EducationYears JobCategory UnionMember
## 0 0 0 0
## EmploymentLength Retired HHIncome DebtToIncomeRatio
## 0 0 0 0
## CreditDebt OtherDebt LoanDefault MaritalStatus
## 0 0 0 0
## HouseholdSize NumberPets NumberCats NumberDogs
## 8 6 7 8
## NumberBirds HomeOwner CarsOwned CarOwnership
## 34 13 0 0
## CarBrand CarValue CommuteTime PoliticalPartyMem
## 0 0 0 0
## Votes CreditCard CardTenure CardItemsMonthly
## 0 0 0 0
## CardSpendMonth ActiveLifestyle PhoneCoTenure VoiceLastMonth
## 0 0 0 0
## VoiceOverTenure EquipmentRental EquipmentLastMonth EquipmentOverTenure
## 0 0 0 0
## CallingCard WirelessData DataLastMonth DataOverTenure
## 0 0 0 0
## Multiline VM Pager Internet
## 0 0 0 0
## CallerID CallWait CallForward ThreeWayCalling
## 0 0 0 0
## EBilling TVWatchingHours OwnsPC OwnsMobileDevice
## 0 0 0 0
## OwnsGameSystem OwnsFax NewsSubscriber
## 0 0 03.5.3 Removing entries with missing values
CustomerData.Clean<- na.omit(CustomerData)
sum(is.na(CustomerData.Clean))## [1] 03.5.4 Removing entries with missing values for a specific variable
CustomerData.Clean1<-CustomerData[!is.na(CustomerData$NumberPets),]
colSums(is.na(CustomerData.Clean1))## CustomerID Region TownSize Gender
## 0 0 0 0
## Age EducationYears JobCategory UnionMember
## 0 0 0 0
## EmploymentLength Retired HHIncome DebtToIncomeRatio
## 0 0 0 0
## CreditDebt OtherDebt LoanDefault MaritalStatus
## 0 0 0 0
## HouseholdSize NumberPets NumberCats NumberDogs
## 8 0 7 8
## NumberBirds HomeOwner CarsOwned CarOwnership
## 33 12 0 0
## CarBrand CarValue CommuteTime PoliticalPartyMem
## 0 0 0 0
## Votes CreditCard CardTenure CardItemsMonthly
## 0 0 0 0
## CardSpendMonth ActiveLifestyle PhoneCoTenure VoiceLastMonth
## 0 0 0 0
## VoiceOverTenure EquipmentRental EquipmentLastMonth EquipmentOverTenure
## 0 0 0 0
## CallingCard WirelessData DataLastMonth DataOverTenure
## 0 0 0 0
## Multiline VM Pager Internet
## 0 0 0 0
## CallerID CallWait CallForward ThreeWayCalling
## 0 0 0 0
## EBilling TVWatchingHours OwnsPC OwnsMobileDevice
## 0 0 0 0
## OwnsGameSystem OwnsFax NewsSubscriber
## 0 0 0