Data cleaning
#install.packages("mice") mice - Multivariate Imputation by chained Equation
#install.packages("VIM")
library(mice)package 㤼㸱mice㤼㸲 was built under R version 3.6.1Loading required package: lattice
Attaching package: 㤼㸱mice㤼㸲
The following objects are masked from 㤼㸱package:base㤼㸲:
cbind, rbindlibrary(VIM) package 㤼㸱VIM㤼㸲 was built under R version 3.6.1Loading required package: colorspace
package 㤼㸱colorspace㤼㸲 was built under R version 3.6.1Loading required package: grid
Loading required package: data.table
data.table 1.12.2 using 2 threads (see ?getDTthreads). Latest news: r-datatable.com
Registered S3 methods overwritten by 'car':
method from
influence.merMod lme4
cooks.distance.influence.merMod lme4
dfbeta.influence.merMod lme4
dfbetas.influence.merMod lme4
VIM is ready to use.
Since version 4.0.0 the GUI is in its own package VIMGUI.
Please use the package to use the new (and old) GUI.
Suggestions and bug-reports can be submitted at: https://github.com/alexkowa/VIM/issues
Attaching package: 㤼㸱VIM㤼㸲
The following object is masked from 㤼㸱package:datasets㤼㸲:
sleepdata <- read.csv("file:///C:/Users/badal/Desktop/datset_/vehicleMiss.csv" , header = T)
head(data)any(is.na(data))[1] TRUEsummary(data) vehicle fm Mileage lh lc mc State
Min. : 1.0 Min. :-1.000 Min. : 1 Min. : 0.000 Min. : 0.0 Min. : 0.0 TX :290
1st Qu.: 406.8 1st Qu.: 4.000 1st Qu.: 5778 1st Qu.: 1.500 1st Qu.: 106.5 1st Qu.: 119.7 CA :199
Median : 812.5 Median :10.000 Median :17000 Median : 2.600 Median : 195.4 Median : 119.7 FL :167
Mean : 812.5 Mean : 9.414 Mean :20559 Mean : 3.294 Mean : 242.8 Mean : 179.4 GA : 75
3rd Qu.:1218.2 3rd Qu.:14.000 3rd Qu.:30061 3rd Qu.: 4.300 3rd Qu.: 317.8 3rd Qu.: 175.5 AZ : 61
Max. :1624.0 Max. :23.000 Max. :99983 Max. :35.200 Max. :3234.4 Max. :3891.1 (Other):817
NA's :13 NA's :6 NA's :8 NA's : 15 missing data
#percentage_missing_data
p <- function(x) {sum(is.na(x))/length(x)*100}
apply(data, 2,p) vehicle fm Mileage lh lc mc State
0.0000000 0.0000000 0.8004926 0.3694581 0.4926108 0.0000000 0.9236453 md.pattern(data) vehicle fm mc lh lc Mileage State
1586 1 1 1 1 1 1 1 0
11 1 1 1 1 1 1 0 1
13 1 1 1 1 1 0 1 1
6 1 1 1 1 0 1 1 1
2 1 1 1 1 0 1 0 2
4 1 1 1 0 1 1 1 1
2 1 1 1 0 1 1 0 2
0 0 0 6 8 13 15 42
md.pairs(data)$rr
vehicle fm Mileage lh lc mc State
vehicle 1624 1624 1611 1618 1616 1624 1609
fm 1624 1624 1611 1618 1616 1624 1609
Mileage 1611 1611 1611 1605 1603 1611 1596
lh 1618 1618 1605 1618 1610 1618 1605
lc 1616 1616 1603 1610 1616 1616 1603
mc 1624 1624 1611 1618 1616 1624 1609
State 1609 1609 1596 1605 1603 1609 1609
$rm
vehicle fm Mileage lh lc mc State
vehicle 0 0 13 6 8 0 15
fm 0 0 13 6 8 0 15
Mileage 0 0 0 6 8 0 15
lh 0 0 13 0 8 0 13
lc 0 0 13 6 0 0 13
mc 0 0 13 6 8 0 15
State 0 0 13 4 6 0 0
$mr
vehicle fm Mileage lh lc mc State
vehicle 0 0 0 0 0 0 0
fm 0 0 0 0 0 0 0
Mileage 13 13 0 13 13 13 13
lh 6 6 6 0 6 6 4
lc 8 8 8 8 0 8 6
mc 0 0 0 0 0 0 0
State 15 15 15 13 13 15 0
$mm
vehicle fm Mileage lh lc mc State
vehicle 0 0 0 0 0 0 0
fm 0 0 0 0 0 0 0
Mileage 0 0 13 0 0 0 0
lh 0 0 0 6 0 0 2
lc 0 0 0 0 8 0 2
mc 0 0 0 0 0 0 0
State 0 0 0 2 2 0 15marginplot(data[,c("Mileage", "lc")])
impute……………….polyreg: multinominal logstic regression
impute <- mice(data[,-1], m=4, seed = 123)
iter imp variable
1 1 Mileage lh lc State
1 2 Mileage lh lc State
1 3 Mileage lh lc State
1 4 Mileage lh lc State
2 1 Mileage lh lc State
2 2 Mileage lh lc State
2 3 Mileage lh lc State
2 4 Mileage lh lc State
3 1 Mileage lh lc State
3 2 Mileage lh lc State
3 3 Mileage lh lc State
3 4 Mileage lh lc State
4 1 Mileage lh lc State
4 2 Mileage lh lc State
4 3 Mileage lh lc State
4 4 Mileage lh lc State
5 1 Mileage lh lc State
5 2 Mileage lh lc State
5 3 Mileage lh lc State
5 4 Mileage lh lc Stateprint(impute)Class: mids
Number of multiple imputations: 4
Imputation methods:
fm Mileage lh lc mc State
"" "pmm" "pmm" "pmm" "" "polyreg"
PredictorMatrix:
fm Mileage lh lc mc State
fm 0 1 1 1 1 1
Mileage 1 0 1 1 1 1
lh 1 1 0 1 1 1
lc 1 1 1 0 1 1
mc 1 1 1 1 0 1
State 1 1 1 1 1 0impute$imp$Mileagedata[253,]summary(data$Mileage) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1 5778 17000 20559 30061 99983 13 complete data set
data<- complete(impute,2)summary(data) fm Mileage lh lc mc State
Min. :-1.000 Min. : 1 Min. : 0.000 Min. : 0.0 Min. : 0.0 TX :292
1st Qu.: 4.000 1st Qu.: 5691 1st Qu.: 1.500 1st Qu.: 106.4 1st Qu.: 119.7 CA :200
Median :10.000 Median :16994 Median : 2.600 Median : 195.6 Median : 119.7 FL :167
Mean : 9.414 Mean :20531 Mean : 3.301 Mean : 242.8 Mean : 179.4 GA : 75
3rd Qu.:14.000 3rd Qu.:30057 3rd Qu.: 4.300 3rd Qu.: 317.8 3rd Qu.: 175.5 AZ : 61
Max. :23.000 Max. :99983 Max. :35.200 Max. :3234.4 Max. :3891.1 LA : 48
(Other):781 datastripplot(impute,pch = 20 ,cex =1.2)
xyplot(impute, lc ~ lh | .imp, pch =20, cex =1.2)
kdata <- read.csv("file:///C:/Users/badal/Desktop/datset_/vehicleMiss.csv" , header = T)
head(kdata)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