Project 1
mathew
2025-01-29
introduction: this is Project 1
Set up working directory
setwd("/cloud/project")Load Libraries
library(readxl)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(tidyr)load data files
#open data file "car_survey_1"
Car1<-read.csv("Car_Survey_1.csv")
str(Car1)## 'data.frame': 1049 obs. of 22 variables:
## $ Resp : chr "Res1" "Res2" "Res3" "Res4" ...
## $ Att_1 : int 6 7 7 4 6 6 1 6 3 6 ...
## $ Att_2 : int 6 5 7 1 6 6 1 5 2 6 ...
## $ Enj_1 : int 6 5 7 1 6 6 1 5 3 4 ...
## $ Enj_2 : int 6 2 5 1 5 5 1 3 2 4 ...
## $ Perform_1 : int 5 2 5 1 5 5 2 5 2 4 ...
## $ Perform_2 : int 6 6 5 1 2 5 2 5 3 4 ...
## $ Perform_3 : int 3 7 3 1 1 7 2 2 1 1 ...
## $ WOM_1 : int 3 5 6 7 7 5 2 4 6 5 ...
## $ WOM_2 : int 3 5 6 7 7 5 3 6 6 6 ...
## $ Futu_Pur_1 : int 3 6 7 3 7 7 5 4 7 6 ...
## $ Futu_Pur_2 : int 3 6 7 3 6 7 2 4 7 6 ...
## $ Valu_Percp_1: int 5 6 5 6 6 7 2 4 6 6 ...
## $ Valu_Percp_2: int 2 7 7 5 5 7 2 4 6 6 ...
## $ Pur_Proces_1: int 6 7 7 5 6 7 2 4 6 6 ...
## $ Pur_Proces_2: int 4 6 7 4 7 7 6 4 6 6 ...
## $ Residence : int 2 2 1 2 1 2 2 1 2 1 ...
## $ Pay_Meth : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Insur_Type : chr "Collision" "Collision" "Collision" "Collision" ...
## $ Gender : chr "Male" "Male" "Male" "Male" ...
## $ Age : int 18 18 19 19 19 19 19 21 21 21 ...
## $ Education : int 2 2 2 2 2 2 2 2 2 2 ...head(Car1, n=5)## Resp Att_1 Att_2 Enj_1 Enj_2 Perform_1 Perform_2 Perform_3 WOM_1 WOM_2
## 1 Res1 6 6 6 6 5 6 3 3 3
## 2 Res2 7 5 5 2 2 6 7 5 5
## 3 Res3 7 7 7 5 5 5 3 6 6
## 4 Res4 4 1 1 1 1 1 1 7 7
## 5 Res5 6 6 6 5 5 2 1 7 7
## Futu_Pur_1 Futu_Pur_2 Valu_Percp_1 Valu_Percp_2 Pur_Proces_1 Pur_Proces_2
## 1 3 3 5 2 6 4
## 2 6 6 6 7 7 6
## 3 7 7 5 7 7 7
## 4 3 3 6 5 5 4
## 5 7 6 6 5 6 7
## Residence Pay_Meth Insur_Type Gender Age Education
## 1 2 2 Collision Male 18 2
## 2 2 2 Collision Male 18 2
## 3 1 2 Collision Male 19 2
## 4 2 2 Collision Male 19 2
## 5 1 2 Collision Female 19 2#open data file "car_survey_2"
Car2<-read.csv("Car_Survey_2.csv")
str(Car2)## 'data.frame': 1049 obs. of 9 variables:
## $ Respondents: chr "Res1" "Res2" "Res3" "Res4" ...
## $ Region : chr "European" "European" "European" "European" ...
## $ Model : chr "Ford Expedition" "Ford Expedition" "Ford Expedition" "Ford Expedition" ...
## $ MPG : int 15 15 15 15 15 15 15 15 15 15 ...
## $ Cyl : int 8 8 8 8 8 8 8 8 8 8 ...
## $ acc1 : num 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 ...
## $ C_cost. : num 16 16 16 16 16 16 16 16 16 16 ...
## $ H_Cost : num 14 14 14 14 14 14 14 14 14 14 ...
## $ Post.Satis : int 4 3 5 5 5 3 3 6 3 5 ...head(Car2, n=5)## Respondents Region Model MPG Cyl acc1 C_cost. H_Cost Post.Satis
## 1 Res1 European Ford Expedition 15 8 5.5 16 14 4
## 2 Res2 European Ford Expedition 15 8 5.5 16 14 3
## 3 Res3 European Ford Expedition 15 8 5.5 16 14 5
## 4 Res4 European Ford Expedition 15 8 5.5 16 14 5
## 5 Res5 European Ford Expedition 15 8 5.5 16 14 5names(Car2)[1]<-c("Resp")
head(Car2, n=1)## Resp Region Model MPG Cyl acc1 C_cost. H_Cost Post.Satis
## 1 Res1 European Ford Expedition 15 8 5.5 16 14 4#Merge
Car_Total<-merge(Car1, Car2, by="Resp")
str(Car_Total)## 'data.frame': 1049 obs. of 30 variables:
## $ Resp : chr "Res1" "Res10" "Res100" "Res1000" ...
## $ Att_1 : int 6 6 6 6 6 3 2 7 2 6 ...
## $ Att_2 : int 6 6 7 6 6 1 2 7 1 6 ...
## $ Enj_1 : int 6 4 7 7 7 4 1 7 2 6 ...
## $ Enj_2 : int 6 4 3 6 6 3 2 6 1 5 ...
## $ Perform_1 : int 5 4 5 6 6 5 2 5 2 5 ...
## $ Perform_2 : int 6 4 6 6 6 6 2 6 2 5 ...
## $ Perform_3 : int 3 1 6 6 6 6 1 5 2 5 ...
## $ WOM_1 : int 3 5 3 6 4 2 6 6 7 3 ...
## $ WOM_2 : int 3 6 5 6 4 6 7 6 7 3 ...
## $ Futu_Pur_1 : int 3 6 6 6 4 6 6 6 7 6 ...
## $ Futu_Pur_2 : int 3 6 6 6 6 6 5 7 7 6 ...
## $ Valu_Percp_1: int 5 6 7 4 5 5 4 6 4 5 ...
## $ Valu_Percp_2: int 2 6 6 6 6 4 4 5 6 6 ...
## $ Pur_Proces_1: int 6 6 5 6 6 5 4 5 6 6 ...
## $ Pur_Proces_2: int 4 6 5 3 7 5 5 5 7 5 ...
## $ Residence : int 2 1 2 2 1 1 1 2 1 2 ...
## $ Pay_Meth : int 2 2 1 3 3 3 3 3 3 3 ...
## $ Insur_Type : chr "Collision" "Collision" "Collision" "Liability" ...
## $ Gender : chr "Male" "Male" "Female" "Female" ...
## $ Age : int 18 21 32 24 24 25 26 26 27 27 ...
## $ Education : int 2 2 1 2 2 2 2 2 2 2 ...
## $ Region : chr "European" "European" "American" "Asian" ...
## $ Model : chr "Ford Expedition" "Ford Expedition" "Toyota Rav4" "Toyota Corolla" ...
## $ MPG : int 15 15 24 26 26 26 26 26 26 26 ...
## $ Cyl : int 8 8 4 4 4 4 4 4 4 4 ...
## $ acc1 : num 5.5 5.5 8.2 8 8 8 8 8 8 8 ...
## $ C_cost. : num 16 16 10 7 7 7 7 7 7 7 ...
## $ H_Cost : num 14 14 8 6 6 6 6 6 6 6 ...
## $ Post.Satis : int 4 5 4 6 5 6 5 6 7 6 ...write.csv(Car_Total, "Car_Total", row.names=FALSE) # avoid row numbersGraph of number of Cars per Region
# Create Graph
ggplot(Car_Total,aes(x=Region, fill = Region))+
theme_bw()+
geom_bar()+
geom_text(stat="count", aes(label=..count..), vjust=0) +
labs(y="number of Cars",
x = "Region",
title = "Number of Cars by Region")## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Graph of Types of Cars per each Region
#Create Graph
ggplot(Car_Total,aes(x=Region,fill=Model))+
theme_bw()+
geom_bar()+
labs(y="Number of Cars",
title = "Number of Cars by model and Region")
Simplifying the Graph into just Brands
cars_sep <-Car_Total %>% separate(Model,into = c("Brand", "Model"),
sep = " ", extra = "merge")Graph of the Brands
head(cars_sep)## Resp Att_1 Att_2 Enj_1 Enj_2 Perform_1 Perform_2 Perform_3 WOM_1 WOM_2
## 1 Res1 6 6 6 6 5 6 3 3 3
## 2 Res10 6 6 4 4 4 4 1 5 6
## 3 Res100 6 7 7 3 5 6 6 3 5
## 4 Res1000 6 6 7 6 6 6 6 6 6
## 5 Res1001 6 6 7 6 6 6 6 4 4
## 6 Res1002 3 1 4 3 5 6 6 2 6
## Futu_Pur_1 Futu_Pur_2 Valu_Percp_1 Valu_Percp_2 Pur_Proces_1 Pur_Proces_2
## 1 3 3 5 2 6 4
## 2 6 6 6 6 6 6
## 3 6 6 7 6 5 5
## 4 6 6 4 6 6 3
## 5 4 6 5 6 6 7
## 6 6 6 5 4 5 5
## Residence Pay_Meth Insur_Type Gender Age Education Region Brand Model
## 1 2 2 Collision Male 18 2 European Ford Expedition
## 2 1 2 Collision Male 21 2 European Ford Expedition
## 3 2 1 Collision Female 32 1 American Toyota Rav4
## 4 2 3 Liability Female 24 2 Asian Toyota Corolla
## 5 1 3 Liability Female 24 2 Asian Toyota Corolla
## 6 1 3 Liability Female 25 2 Asian Toyota Corolla
## MPG Cyl acc1 C_cost. H_Cost Post.Satis
## 1 15 8 5.5 16 14 4
## 2 15 8 5.5 16 14 5
## 3 24 4 8.2 10 8 4
## 4 26 4 8.0 7 6 6
## 5 26 4 8.0 7 6 5
## 6 26 4 8.0 7 6 6#Graph
ggplot(cars_sep,aes(x=Region,fill=Brand))+
theme_bw()+
geom_bar()+
labs(y="Number of Cars",
title = "Number of Cars by Brands across Region")
Amount of each Brand purchased total
#count numbers of cars by brand
count(cars_sep, cars_sep$Make, cars_sep$Brand, name = "Freq")## cars_sep$Brand Freq
## 1 Buick 31
## 2 Chevrolet 64
## 3 Chrysler 169
## 4 Dodge 41
## 5 Fiat 18
## 6 Ford 202
## 7 Honda 159
## 8 Kia 34
## 9 Lincoln 39
## 10 Toyota 292Amount of each Brand Purchased per Region
#create a crosstabulation table for Brand by Region
xtabs(~Region + Brand, cars_sep)## Brand
## Region Buick Chevrolet Chrysler Dodge Fiat Ford Honda Kia Lincoln
## American 17 22 54 22 9 81 38 15 0
## Asian 0 0 24 0 0 24 58 0 0
## European 10 21 13 0 9 29 29 19 0
## Middle Eastern 4 21 78 19 0 68 34 0 39
## Brand
## Region Toyota
## American 102
## Asian 102
## European 80
## Middle Eastern 8Table of the Count per Region
#count the total number of cars by Brand across Region
brand_region_counts <-table(cars_sep$Brand, cars_sep$Region)
print(brand_region_counts)##
## American Asian European Middle Eastern
## Buick 17 0 10 4
## Chevrolet 22 0 21 21
## Chrysler 54 24 13 78
## Dodge 22 0 0 19
## Fiat 9 0 9 0
## Ford 81 24 29 68
## Honda 38 58 29 34
## Kia 15 0 19 0
## Lincoln 0 0 0 39
## Toyota 102 102 80 8Mean Attitude of each Brand per Region
#attitude toward car brand by region
brand_region_table <- aggregate(Att_1~Brand+Region, cars_sep, mean)
print(brand_region_table)## Brand Region Att_1
## 1 Buick American 5.529412
## 2 Chevrolet American 5.227273
## 3 Chrysler American 5.019608
## 4 Dodge American 4.818182
## 5 Fiat American 4.666667
## 6 Ford American 4.567901
## 7 Honda American 5.657895
## 8 Kia American 3.266667
## 9 Toyota American 5.254902
## 10 Chrysler Asian 5.291667
## 11 Ford Asian 4.166667
## 12 Honda Asian 5.568966
## 13 Toyota Asian 4.881188
## 14 Buick European 5.000000
## 15 Chevrolet European 5.000000
## 16 Chrysler European 5.230769
## 17 Fiat European 3.777778
## 18 Ford European 5.241379
## 19 Honda European 4.758621
## 20 Kia European 3.789474
## 21 Toyota European 4.800000
## 22 Buick Middle Eastern 6.000000
## 23 Chevrolet Middle Eastern 5.904762
## 24 Chrysler Middle Eastern 4.038462
## 25 Dodge Middle Eastern 4.473684
## 26 Ford Middle Eastern 4.014706
## 27 Honda Middle Eastern 5.500000
## 28 Lincoln Middle Eastern 5.692308
## 29 Toyota Middle Eastern 5.375000Graph of the “Mean Attitude of each Brand per Region”
#graph
ggplot(brand_region_table,aes(x=Region, y=Att_1, group=Brand))+
geom_line(aes(color=Brand))+
geom_point(aes(color=Brand))+
labs(y="Att_1 Mean",
title = "Attitudw Mean by Brand and Region")
Isolating on the Attitudes of Toyota’s in each Region
#filter by a brand (toyota) from the brand_region_table
Toyota_Att1_mean <- brand_region_table %>%
filter(Brand == "Toyota")
#view the results
print(Toyota_Att1_mean)## Brand Region Att_1
## 1 Toyota American 5.254902
## 2 Toyota Asian 4.881188
## 3 Toyota European 4.800000
## 4 Toyota Middle Eastern 5.375000Graph of the Isolation of the Attitude of Toyota in each Region
#Create a graph (data visualization of the results)
ggplot(Toyota_Att1_mean,aes(x=Region,y=Att_1, group=Brand))+
geom_line(aes(color=Brand))+
geom_point(aes(color=Brand))+
scale_y_continuous(limits = c(3,6))+
labs(y="Att_1 Mean",
title = "Attitude Mean for Toyota by Region")
Comparing Toyota & Honda per Region
#Filter for competing brands (Toyota vs Handa) and comparing attitudes
Multiple_Att1_Mean <- brand_region_table %>%
filter(Brand == "Toyota" | Brand =="Honda")
#View the results
print(Multiple_Att1_Mean)## Brand Region Att_1
## 1 Honda American 5.657895
## 2 Toyota American 5.254902
## 3 Honda Asian 5.568966
## 4 Toyota Asian 4.881188
## 5 Honda European 4.758621
## 6 Toyota European 4.800000
## 7 Honda Middle Eastern 5.500000
## 8 Toyota Middle Eastern 5.375000Filtering Data of Toyota into three Age Groups
#demographic groups by age variable (convert age into 3 groups)
cars_sep$AgeGrp <- cut(cars_sep$Age, breaks = c(0, 30, 50, Inf),
lables = c("Young Adults", "Adults", "Mature Adults"),
right=FALSE)
names(cars_sep)## [1] "Resp" "Att_1" "Att_2" "Enj_1" "Enj_2"
## [6] "Perform_1" "Perform_2" "Perform_3" "WOM_1" "WOM_2"
## [11] "Futu_Pur_1" "Futu_Pur_2" "Valu_Percp_1" "Valu_Percp_2" "Pur_Proces_1"
## [16] "Pur_Proces_2" "Residence" "Pay_Meth" "Insur_Type" "Gender"
## [21] "Age" "Education" "Region" "Brand" "Model"
## [26] "MPG" "Cyl" "acc1" "C_cost." "H_Cost"
## [31] "Post.Satis" "AgeGrp"head(cars_sep, n=5)## Resp Att_1 Att_2 Enj_1 Enj_2 Perform_1 Perform_2 Perform_3 WOM_1 WOM_2
## 1 Res1 6 6 6 6 5 6 3 3 3
## 2 Res10 6 6 4 4 4 4 1 5 6
## 3 Res100 6 7 7 3 5 6 6 3 5
## 4 Res1000 6 6 7 6 6 6 6 6 6
## 5 Res1001 6 6 7 6 6 6 6 4 4
## Futu_Pur_1 Futu_Pur_2 Valu_Percp_1 Valu_Percp_2 Pur_Proces_1 Pur_Proces_2
## 1 3 3 5 2 6 4
## 2 6 6 6 6 6 6
## 3 6 6 7 6 5 5
## 4 6 6 4 6 6 3
## 5 4 6 5 6 6 7
## Residence Pay_Meth Insur_Type Gender Age Education Region Brand Model
## 1 2 2 Collision Male 18 2 European Ford Expedition
## 2 1 2 Collision Male 21 2 European Ford Expedition
## 3 2 1 Collision Female 32 1 American Toyota Rav4
## 4 2 3 Liability Female 24 2 Asian Toyota Corolla
## 5 1 3 Liability Female 24 2 Asian Toyota Corolla
## MPG Cyl acc1 C_cost. H_Cost Post.Satis AgeGrp
## 1 15 8 5.5 16 14 4 [0,30)
## 2 15 8 5.5 16 14 5 [0,30)
## 3 24 4 8.2 10 8 4 [30,50)
## 4 26 4 8.0 7 6 6 [0,30)
## 5 26 4 8.0 7 6 5 [0,30)#Age group analysis for Toyota across regions
filtered_data_Toyota <- cars_sep %>%
filter(Brand == "Toyota")
print(filtered_data_Toyota)## Resp Att_1 Att_2 Enj_1 Enj_2 Perform_1 Perform_2 Perform_3 WOM_1 WOM_2
## 1 Res100 6 7 7 3 5 6 6 3 5
## 2 Res1000 6 6 7 6 6 6 6 6 6
## 3 Res1001 6 6 7 6 6 6 6 4 4
## 4 Res1002 3 1 4 3 5 6 6 2 6
## 5 Res1003 2 2 1 2 2 2 1 6 7
## 6 Res1004 7 7 7 6 5 6 5 6 6
## 7 Res1005 2 1 2 1 2 2 2 7 7
## 8 Res1006 6 6 6 5 5 5 5 3 3
## 9 Res1007 4 4 4 2 3 5 3 7 7
## 10 Res101 6 6 7 6 5 6 3 5 6
## 11 Res1019 6 6 6 5 5 4 4 4 4
## 12 Res102 7 7 7 6 6 6 1 7 7
## 13 Res1020 6 7 6 6 6 5 5 7 7
## 14 Res1021 6 6 6 3 5 6 5 6 6
## 15 Res1022 6 6 6 4 4 7 7 5 5
## 16 Res1023 7 7 6 6 6 5 5 6 6
## 17 Res1024 6 5 4 5 4 6 5 6 7
## 18 Res1025 7 7 7 6 6 6 6 7 7
## 19 Res1026 6 7 7 7 7 6 6 7 7
## 20 Res1027 7 7 7 6 6 7 7 7 7
## 21 Res1028 4 7 7 6 7 6 7 7 6
## 22 Res1029 7 7 7 7 7 7 7 7 7
## 23 Res103 7 7 7 6 6 7 2 6 6
## 24 Res1030 7 7 7 7 7 7 7 7 7
## 25 Res1031 6 6 6 5 5 4 4 6 6
## 26 Res1032 7 6 6 5 6 6 6 7 7
## 27 Res1033 7 6 5 6 6 6 6 7 7
## 28 Res1034 7 7 7 7 7 6 5 7 6
## 29 Res1035 7 7 7 7 7 7 7 7 7
## 30 Res1036 6 5 6 5 5 6 6 7 7
## 31 Res1037 6 7 6 6 7 5 5 5 5
## 32 Res1038 6 5 5 5 5 5 5 5 5
## 33 Res1039 6 6 7 5 5 4 5 5 5
## 34 Res104 7 7 7 7 7 6 6 3 4
## 35 Res1040 5 6 5 6 6 4 4 7 7
## 36 Res105 7 6 6 5 5 5 1 6 7
## 37 Res106 6 7 6 7 7 5 6 7 7
## 38 Res107 7 7 7 7 7 7 2 5 5
## 39 Res108 6 7 7 6 6 7 5 5 4
## 40 Res109 6 6 6 5 5 6 NA NA 4
## 41 Res110 7 7 7 7 7 6 2 6 6
## 42 Res111 6 7 7 7 7 6 3 5 5
## 43 Res112 6 6 6 5 5 6 6 7 7
## 44 Res113 5 4 4 6 7 5 6 3 4
## 45 Res114 6 5 5 5 5 5 5 5 5
## 46 Res115 5 5 5 5 5 5 5 6 6
## 47 Res116 6 7 7 6 6 4 3 6 6
## 48 Res117 6 6 7 5 6 3 5 6 6
## 49 Res118 6 6 5 6 6 6 6 6 7
## 50 Res119 6 6 NA 5 4 4 4 4 4
## 51 Res120 6 6 6 5 5 5 3 6 6
## 52 Res167 7 5 5 4 6 5 3 5 5
## 53 Res168 NA 3 3 3 6 5 3 5 6
## 54 Res169 2 4 6 6 6 6 4 7 7
## 55 Res170 1 6 6 5 5 5 6 6 6
## 56 Res171 1 5 6 4 4 4 2 6 7
## 57 Res172 2 6 6 5 7 6 5 7 7
## 58 Res173 7 5 5 6 6 5 5 7 7
## 59 Res174 1 7 7 7 7 5 5 7 7
## 60 Res175 3 6 6 5 5 6 2 6 6
## 61 Res176 3 6 6 5 5 6 7 6 6
## 62 Res177 2 3 5 5 5 5 7 5 5
## 63 Res204 5 5 3 4 5 6 5 3 3
## 64 Res205 2 6 6 5 5 3 1 7 7
## 65 Res206 4 3 4 4 5 5 2 6 6
## 66 Res207 3 5 5 5 5 6 1 7 7
## 67 Res208 7 5 5 6 6 6 3 6 6
## 68 Res209 4 3 3 4 6 5 1 7 7
## 69 Res210 6 3 4 3 6 6 4 4 4
## 70 Res211 4 4 5 4 6 5 5 3 3
## 71 Res212 3 6 6 7 4 3 4 6 6
## 72 Res213 6 2 3 3 5 6 4 7 7
## 73 Res214 6 6 6 6 7 6 6 6 6
## 74 Res215 3 2 3 3 4 4 4 6 6
## 75 Res216 5 6 6 7 7 4 4 5 5
## 76 Res217 4 4 4 4 6 6 4 6 6
## 77 Res218 6 6 6 6 6 5 4 6 6
## 78 Res219 4 3 3 4 6 5 4 6 6
## 79 Res220 4 7 7 7 5 5 5 4 5
## 80 Res221 6 6 6 6 6 6 4 5 5
## 81 Res222 6 3 4 NA 6 6 4 6 7
## 82 Res223 6 3 4 3 6 6 4 6 5
## 83 Res224 6 3 4 3 6 6 4 7 7
## 84 Res225 4 7 7 5 7 3 2 7 7
## 85 Res226 7 6 6 6 7 7 5 4 5
## 86 Res227 4 4 5 4 6 5 1 7 7
## 87 Res228 5 7 7 7 7 4 3 6 6
## 88 Res229 6 6 6 6 7 4 3 5 5
## 89 Res230 3 7 6 1 7 6 3 6 6
## 90 Res231 7 7 7 7 5 6 2 6 6
## 91 Res232 2 6 6 6 7 5 2 6 7
## 92 Res233 4 6 7 5 6 2 1 6 6
## 93 Res234 5 4 4 3 7 6 2 6 6
## 94 Res235 2 5 5 7 5 4 1 4 5
## 95 Res236 5 4 4 4 6 6 1 7 7
## 96 Res237 5 4 4 5 6 7 2 5 5
## 97 Res238 6 3 4 4 5 6 3 5 5
## 98 Res239 5 3 4 4 6 6 5 3 3
## 99 Res240 3 5 3 3 6 2 4 6 7
## 100 Res241 5 5 5 5 7 5 7 7 7
## 101 Res367 2 6 6 6 7 3 6 3 3
## 102 Res368 1 7 7 7 7 7 5 4 6
## 103 Res369 3 6 6 3 6 6 5 2 3
## 104 Res370 1 7 7 7 7 7 5 3 4
## 105 Res371 4 4 4 4 4 4 5 3 4
## 106 Res372 5 3 3 3 4 5 6 4 5
## 107 Res373 2 6 6 6 6 6 3 6 6
## 108 Res374 2 7 7 7 6 7 1 6 6
## 109 Res375 3 6 6 3 6 6 2 6 6
## 110 Res376 1 6 6 4 6 7 1 7 7
## 111 Res377 2 5 5 5 6 3 2 5 6
## 112 Res378 3 6 6 4 7 6 3 6 6
## 113 Res379 2 6 6 5 6 6 5 3 3
## 114 Res380 2 6 6 6 6 6 1 7 7
## 115 Res381 5 4 5 6 4 3 2 6 6
## 116 Res382 3 5 5 5 6 6 1 7 7
## 117 Res383 2 6 7 6 7 7 3 6 6
## 118 Res384 2 6 5 5 6 3 1 7 7
## 119 Res385 1 7 7 7 7 6 4 4 4
## 120 Res386 2 7 7 4 7 2 5 3 3
## 121 Res387 5 4 5 6 4 3 2 6 6
## 122 Res388 1 7 7 7 7 6 2 7 7
## 123 Res389 3 6 6 5 5 6 3 6 6
## 124 Res390 3 5 5 5 6 6 1 6 6
## 125 Res464 4 3 4 3 5 2 3 4 6
## 126 Res465 1 6 6 6 6 3 6 6 6
## 127 Res466 3 6 6 5 6 5 6 6 6
## 128 Res467 1 7 7 6 7 7 3 6 5
## 129 Res468 1 7 7 6 7 5 2 6 6
## 130 Res469 5 4 5 5 3 4 1 7 7
## 131 Res470 3 5 7 5 6 3 4 5 6
## 132 Res471 5 5 4 5 5 6 1 6 6
## 133 Res472 4 6 6 6 5 2 1 7 7
## 134 Res473 2 7 7 6 7 6 3 2 2
## 135 Res474 1 5 5 4 6 5 2 6 6
## 136 Res475 3 6 5 3 6 6 3 6 6
## 137 Res476 2 6 6 6 6 6 5 5 5
## 138 Res477 1 7 7 6 6 6 3 5 6
## 139 Res478 4 5 6 6 6 6 2 7 7
## 140 Res479 1 6 6 6 6 3 1 7 7
## 141 Res480 1 7 7 6 7 6 2 5 5
## 142 Res481 3 2 2 3 7 6 1 7 7
## 143 Res482 2 6 6 6 4 6 4 7 7
## 144 Res483 3 6 6 5 6 5 2 6 6
## 145 Res484 5 5 5 3 6 5 6 7 6
## 146 Res485 3 5 6 6 6 5 3 7 6
## 147 Res486 2 7 7 7 7 6 6 4 6
## 148 Res487 1 7 7 6 7 7 5 6 5
## 149 Res488 2 5 5 6 7 5 3 6 5
## 150 Res489 1 7 7 7 6 6 6 6 5
## 151 Res490 4 7 7 7 6 7 7 7 6
## 152 Res491 2 6 6 5 6 6 6 7 7
## 153 Res492 6 7 6 7 7 6 6 7 6
## 154 Res493 7 7 7 7 7 7 7 7 7
## 155 Res594 5 4 6 2 2 4 2 3 4
## 156 Res595 7 7 7 6 6 6 6 6 6
## 157 Res596 7 7 5 2 4 3 5 6 5
## 158 Res597 5 6 4 3 4 5 5 6 6
## 159 Res598 6 4 4 4 4 4 2 3 4
## 160 Res599 7 6 7 6 7 7 7 7 6
## 161 Res600 6 7 6 2 2 7 7 7 7
## 162 Res601 7 7 7 7 7 7 7 7 7
## 163 Res602 4 4 4 4 4 3 3 5 5
## 164 Res603 7 6 7 2 1 5 5 7 7
## 165 Res604 6 6 5 5 5 5 5 7 6
## 166 Res605 6 5 5 5 5 4 5 5 6
## 167 Res606 7 6 6 5 4 5 5 7 7
## 168 Res607 4 2 5 2 4 2 5 3 6
## 169 Res608 6 6 6 2 2 1 1 6 4
## 170 Res609 2 5 3 3 2 7 5 4 6
## 171 Res634 6 4 5 2 2 3 4 3 3
## 172 Res635 5 5 6 5 4 5 5 6 6
## 173 Res636 7 7 7 6 6 5 5 5 6
## 174 Res637 6 6 6 5 5 2 2 6 6
## 175 Res638 6 7 7 5 6 7 6 6 7
## 176 Res639 7 6 7 6 6 6 7 7 7
## 177 Res640 5 5 6 2 4 4 5 6 5
## 178 Res641 6 6 6 2 4 3 4 5 3
## 179 Res642 5 4 4 4 5 5 5 6 7
## 180 Res643 3 2 2 2 3 2 2 6 6
## 181 Res644 5 5 4 6 5 5 4 4 5
## 182 Res645 6 5 5 3 3 5 4 4 5
## 183 Res646 1 1 2 2 1 2 1 6 6
## 184 Res647 4 4 4 1 2 4 4 4 4
## 185 Res648 6 6 6 5 5 3 4 3 5
## 186 Res649 5 5 5 3 3 3 2 2 2
## 187 Res650 6 6 6 5 5 5 5 4 5
## 188 Res724 7 6 7 5 5 6 5 7 7
## 189 Res725 7 7 7 7 7 7 7 7 7
## 190 Res726 7 7 7 6 5 7 7 7 7
## 191 Res727 7 7 7 7 7 7 7 7 7
## 192 Res728 7 7 7 7 7 7 7 7 7
## 193 Res729 5 5 5 5 5 5 5 6 6
## 194 Res730 6 5 5 1 1 6 6 7 6
## 195 Res731 7 7 7 6 7 6 6 7 7
## 196 Res732 5 4 5 4 5 4 5 4 5
## 197 Res733 7 7 7 4 5 7 7 4 7
## 198 Res734 6 7 5 7 4 6 6 6 6
## 199 Res735 7 6 5 6 7 6 5 2 7
## 200 Res736 6 7 6 6 7 7 6 4 2
## 201 Res737 7 7 7 7 7 7 7 7 7
## 202 Res738 7 7 7 6 5 5 4 4 5
## 203 Res739 6 6 5 6 6 7 6 7 5
## 204 Res740 6 6 6 5 5 7 6 7 7
## 205 Res741 6 7 6 7 6 6 6 5 7
## 206 Res742 5 5 4 5 5 4 2 5 5
## 207 Res743 7 7 7 6 7 7 7 7 7
## 208 Res744 6 6 6 5 5 5 5 6 6
## 209 Res745 5 5 5 5 6 6 6 7 7
## 210 Res746 6 6 6 6 5 5 5 5 6
## 211 Res747 5 5 6 5 5 3 4 6 5
## 212 Res748 7 7 7 7 7 6 5 7 7
## 213 Res749 7 7 7 7 7 6 7 7 7
## 214 Res750 7 7 7 6 6 6 6 7 7
## 215 Res751 5 3 1 1 1 7 7 7 7
## 216 Res752 6 5 4 5 3 5 5 4 4
## 217 Res753 7 7 7 7 7 5 6 7 7
## 218 Res754 6 6 5 3 3 5 5 7 6
## 219 Res755 4 2 2 1 1 5 4 4 4
## 220 Res756 6 6 6 6 5 6 5 5 6
## 221 Res757 7 6 7 6 6 7 6 7 7
## 222 Res758 4 1 2 1 1 4 4 7 1
## 223 Res759 6 5 3 1 2 2 2 5 4
## 224 Res760 7 7 6 1 1 5 5 7 7
## 225 Res761 7 6 7 5 5 3 5 6 7
## 226 Res762 7 6 6 6 4 5 5 5 5
## 227 Res854 7 6 5 5 7 5 5 3 4
## 228 Res855 6 7 6 5 5 4 4 4 5
## 229 Res856 7 7 7 5 5 7 7 7 4
## 230 Res857 4 4 6 4 4 4 4 4 4
## 231 Res858 7 7 7 5 5 7 6 4 4
## 232 Res859 5 4 5 4 5 5 4 4 5
## 233 Res860 7 7 7 6 6 7 7 7 4
## 234 Res861 7 7 7 6 6 4 6 7 7
## 235 Res862 7 6 7 6 5 7 6 7 4
## 236 Res863 7 7 7 7 7 7 7 7 3
## 237 Res864 6 6 6 6 6 4 5 5 6
## 238 Res865 7 7 6 5 5 5 5 6 6
## 239 Res866 5 4 6 4 3 6 7 4 4
## 240 Res867 6 6 7 5 5 4 4 6 6
## 241 Res868 6 7 6 7 7 6 6 4 3
## 242 Res869 6 4 5 5 5 6 7 4 5
## 243 Res870 6 6 5 4 4 6 6 6 6
## 244 Res871 6 6 7 5 5 4 4 4 5
## 245 Res872 7 1 7 5 7 7 7 7 4
## 246 Res873 7 6 7 6 6 6 6 7 7
## 247 Res874 7 7 7 7 7 7 7 7 7
## 248 Res875 6 6 6 2 5 5 5 5 4
## 249 Res876 5 5 5 3 3 3 3 7 7
## 250 Res877 6 7 6 5 5 4 3 5 3
## 251 Res878 4 4 4 4 4 4 4 4 4
## 252 Res879 7 6 7 4 5 6 6 7 7
## 253 Res880 7 7 7 7 7 6 7 7 7
## 254 Res881 6 6 5 6 6 6 6 6 6
## 255 Res882 6 6 7 6 6 6 6 7 7
## 256 Res883 6 6 6 6 6 5 5 7 6
## 257 Res884 4 4 4 5 4 3 3 2 3
## 258 Res885 4 4 3 2 3 2 2 6 6
## 259 Res886 6 6 7 6 6 5 5 6 6
## 260 Res887 5 5 4 4 4 3 3 7 7
## 261 Res888 4 4 4 3 3 5 5 5 6
## 262 Res889 6 6 6 4 3 3 2 7 6
## 263 Res890 5 6 5 2 3 5 5 7 6
## 264 Res891 7 7 7 7 7 7 7 7 7
## 265 Res892 6 5 6 5 5 6 6 7 6
## 266 Res893 6 6 6 5 5 5 5 6 6
## 267 Res894 6 6 6 5 5 5 6 7 6
## 268 Res951 7 7 7 6 6 6 6 4 4
## 269 Res952 6 4 4 3 3 4 4 4 5
## 270 Res953 7 6 7 5 5 5 5 6 6
## 271 Res954 7 7 7 3 5 5 5 5 6
## 272 Res955 4 4 3 4 4 5 7 4 4
## 273 Res956 7 6 7 6 7 6 6 6 7
## 274 Res957 7 7 7 6 6 7 7 3 3
## 275 Res958 6 6 5 1 2 6 6 4 4
## 276 Res959 5 4 4 2 4 2 2 4 3
## 277 Res96 7 7 6 NA 6 6 6 7 6
## 278 Res960 6 7 6 3 3 7 6 6 7
## 279 Res961 2 1 2 1 2 3 3 6 6
## 280 Res962 6 2 1 1 1 5 6 4 4
## 281 Res97 6 5 6 5 5 6 3 5 6
## 282 Res98 6 5 5 4 3 6 3 5 5
## 283 Res99 7 7 7 5 5 6 1 7 7
## 284 Res991 4 1 1 1 2 3 1 5 5
## 285 Res992 3 2 2 3 2 2 2 5 5
## 286 Res993 3 3 3 1 1 2 2 3 3
## 287 Res994 4 3 4 4 4 3 4 7 6
## 288 Res995 6 6 7 6 6 6 6 6 6
## 289 Res996 6 6 4 4 5 4 4 3 3
## 290 Res997 2 2 2 3 3 2 2 7 7
## 291 Res998 5 5 5 5 5 4 4 6 5
## 292 Res999 4 5 6 5 5 4 4 4 4
## Futu_Pur_1 Futu_Pur_2 Valu_Percp_1 Valu_Percp_2 Pur_Proces_1 Pur_Proces_2
## 1 6 6 7 6 5 5
## 2 6 6 4 6 6 3
## 3 4 6 5 6 6 7
## 4 6 6 5 4 5 5
## 5 6 5 4 4 4 5
## 6 6 7 6 5 5 5
## 7 7 7 4 6 6 7
## 8 6 6 5 6 6 5
## 9 5 6 6 2 2 5
## 10 4 3 3 2 2 2
## 11 6 6 6 4 5 5
## 12 6 6 6 7 7 7
## 13 7 7 7 3 3 7
## 14 5 6 6 5 5 6
## 15 6 6 6 5 5 6
## 16 5 5 5 5 5 5
## 17 9 5 4 3 3 6
## 18 3 4 6 6 6 6
## 19 6 6 5 4 6 6
## 20 3 2 6 4 3 6
## 21 7 6 6 4 5 4
## 22 6 5 6 6 6 6
## 23 6 7 6 5 6 5
## 24 6 6 6 5 5 6
## 25 7 6 4 6 6 5
## 26 5 5 5 5 5 4
## 27 6 6 4 6 4 5
## 28 4 5 4 5 5 6
## 29 6 3 5 5 5 6
## 30 6 5 5 6 6 6
## 31 5 6 3 7 6 6
## 32 5 6 3 5 6 4
## 33 6 6 6 6 6 6
## 34 4 4 4 2 3 2
## 35 7 7 7 5 6 6
## 36 6 6 6 5 5 5
## 37 6 7 7 5 7 4
## 38 6 5 6 3 6 2
## 39 6 6 5 5 7 1
## 40 NA NA NA NA NA NA
## 41 4 4 5 5 5 1
## 42 6 7 6 6 5 1
## 43 6 6 5 3 3 3
## 44 6 6 6 6 6 1
## 45 6 7 6 6 5 1
## 46 6 6 7 5 7 2
## 47 5 6 4 5 5 2
## 48 6 6 6 5 5 2
## 49 6 6 6 7 6 2
## 50 4 5 5 4 3 2
## 51 7 7 5 5 7 4
## 52 5 6 6 5 6 5
## 53 6 7 5 3 5 4
## 54 7 7 6 7 2 3
## 55 5 5 5 4 2 2
## 56 5 6 4 5 5 5
## 57 7 7 7 5 2 2
## 58 6 6 6 7 2 2
## 59 4 4 4 3 2 2
## 60 5 4 3 2 2 2
## 61 4 4 4 1 NA NA
## 62 7 7 7 7 2 2
## 63 5 5 5 5 5 6
## 64 4 4 4 3 3 3
## 65 3 2 4 4 5 4
## 66 3 3 5 4 7 2
## 67 6 6 6 6 6 2
## 68 5 6 6 5 3 2
## 69 7 6 6 6 7 2
## 70 6 6 6 4 3 2
## 71 7 6 7 6 6 1
## 72 7 7 NA 6 7 2
## 73 6 6 6 5 6 3
## 74 6 6 6 6 6 4
## 75 NA 6 5 5 5 5
## 76 6 6 6 6 6 6
## 77 6 6 6 6 6 4
## 78 6 5 5 6 6 5
## 79 5 5 6 5 5 2
## 80 7 7 6 7 7 2
## 81 7 7 6 6 7 2
## 82 6 6 5 6 5 3
## 83 6 7 7 7 7 4
## 84 6 6 6 6 6 6
## 85 7 7 7 7 7 3
## 86 4 5 2 3 5 2
## 87 3 3 7 6 6 6
## 88 6 6 5 6 6 4
## 89 4 5 4 5 5 4
## 90 7 7 7 6 6 1
## 91 6 6 6 4 4 4
## 92 3 3 6 6 6 1
## 93 7 7 7 6 7 1
## 94 4 4 6 7 7 1
## 95 6 6 6 4 5 2
## 96 6 6 6 5 5 1
## 97 4 4 4 4 5 3
## 98 6 7 4 5 4 3
## 99 6 6 5 5 5 2
## 100 6 6 4 4 4 2
## 101 6 6 5 3 3 2
## 102 6 7 6 5 7 5
## 103 6 5 5 6 4 2
## 104 6 6 6 5 5 5
## 105 6 6 6 6 6 6
## 106 5 5 5 6 6 2
## 107 6 6 6 6 6 2
## 108 7 6 6 5 6 1
## 109 7 7 7 5 6 4
## 110 6 7 6 6 5 4
## 111 5 6 5 6 6 2
## 112 7 6 6 5 5 2
## 113 7 7 7 4 5 4
## 114 3 5 6 5 6 5
## 115 6 6 6 4 5 4
## 116 6 6 6 6 6 2
## 117 6 6 6 5 6 6
## 118 6 5 5 5 7 5
## 119 7 5 6 5 5 5
## 120 5 5 5 4 4 4
## 121 6 6 6 5 5 6
## 122 6 5 6 4 4 5
## 123 7 7 7 2 2 2
## 124 6 6 4 5 5 4
## 125 1 4 6 5 4 6
## 126 6 6 6 6 6 5
## 127 4 4 5 2 6 1
## 128 3 3 6 7 6 7
## 129 2 2 7 6 6 7
## 130 2 2 3 7 6 1
## 131 4 4 5 4 4 4
## 132 3 5 5 3 4 4
## 133 3 6 4 4 3 5
## 134 6 6 6 6 6 5
## 135 7 7 7 6 7 6
## 136 6 6 6 7 7 7
## 137 5 5 5 3 4 4
## 138 3 5 2 2 4 5
## 139 6 5 6 5 5 5
## 140 7 6 6 7 6 6
## 141 4 2 5 4 2 4
## 142 6 6 7 6 6 6
## 143 6 5 3 4 4 5
## 144 7 7 7 6 6 5
## 145 5 6 6 6 5 6
## 146 7 6 7 4 6 6
## 147 7 7 7 5 4 7
## 148 6 6 6 6 6 6
## 149 6 6 6 5 5 5
## 150 7 6 7 7 7 7
## 151 7 7 7 7 6 7
## 152 3 3 7 6 5 7
## 153 6 4 4 4 3 4
## 154 6 6 6 4 6 7
## 155 6 6 6 6 6 6
## 156 7 6 6 6 6 7
## 157 5 5 4 5 5 5
## 158 4 3 7 7 7 7
## 159 4 4 7 7 7 7
## 160 3 3 6 7 7 7
## 161 3 3 3 5 4 5
## 162 4 4 4 4 4 5
## 163 3 3 4 4 4 4
## 164 4 4 4 4 4 4
## 165 4 4 4 1 2 7
## 166 4 4 4 4 4 4
## 167 5 5 6 5 4 5
## 168 3 3 4 4 5 4
## 169 6 4 5 4 5 6
## 170 5 4 5 5 5 5
## 171 5 6 7 6 6 7
## 172 7 7 7 7 7 7
## 173 6 3 4 5 4 7
## 174 4 6 4 6 6 5
## 175 6 4 6 4 4 4
## 176 6 6 6 6 6 6
## 177 3 4 4 1 1 2
## 178 5 5 6 7 6 1
## 179 5 6 6 6 5 6
## 180 4 4 6 4 5 3
## 181 6 6 6 5 6 6
## 182 6 6 6 6 6 6
## 183 3 3 7 2 4 6
## 184 2 3 4 1 1 2
## 185 4 4 5 5 6 5
## 186 4 3 3 2 3 3
## 187 4 4 4 3 2 5
## 188 3 2 6 6 6 6
## 189 5 5 5 5 6 6
## 190 4 2 3 7 6 3
## 191 5 5 5 5 6 6
## 192 5 5 5 5 5 6
## 193 3 2 2 3 2 2
## 194 4 5 5 4 5 4
## 195 5 5 5 5 4 4
## 196 4 5 5 6 5 4
## 197 6 4 6 4 4 4
## 198 5 6 5 5 6 1
## 199 3 3 5 7 6 1
## 200 4 4 4 5 4 4
## 201 4 4 4 2 3 4
## 202 2 2 3 2 3 2
## 203 3 3 3 5 4 3
## 204 4 5 6 5 5 5
## 205 3 6 7 6 5 6
## 206 4 4 4 6 6 6
## 207 4 4 4 4 4 4
## 208 7 7 7 6 6 6
## 209 5 6 6 5 5 5
## 210 6 6 6 7 6 5
## 211 6 6 4 6 6 6
## 212 3 3 7 6 7 6
## 213 4 4 4 6 6 6
## 214 7 6 6 6 6 6
## 215 4 5 5 3 4 4
## 216 6 7 6 7 6 6
## 217 4 4 6 6 5 6
## 218 4 4 5 4 4 4
## 219 4 3 4 5 4 5
## 220 6 6 6 5 6 5
## 221 3 3 6 6 6 6
## 222 7 6 7 6 6 7
## 223 4 3 6 5 5 6
## 224 3 3 6 5 6 6
## 225 5 6 5 6 5 4
## 226 3 3 6 6 6 6
## 227 6 7 6 5 5 6
## 228 3 5 6 2 2 4
## 229 5 6 6 2 3 6
## 230 7 6 6 7 7 5
## 231 7 6 7 3 3 7
## 232 6 7 5 5 7 7
## 233 4 5 7 3 3 7
## 234 5 3 4 6 6 7
## 235 6 6 6 6 6 6
## 236 4 4 3 3 6 6
## 237 7 6 7 6 7 6
## 238 6 5 6 6 6 6
## 239 6 6 6 6 7 6
## 240 5 6 6 4 5 6
## 241 6 6 6 5 6 6
## 242 6 6 7 4 7 7
## 243 6 6 6 6 6 7
## 244 6 7 5 3 4 2
## 245 6 6 6 3 5 6
## 246 7 7 7 6 6 7
## 247 6 6 6 6 6 6
## 248 6 4 6 4 5 6
## 249 6 6 6 6 6 6
## 250 6 6 6 2 6 5
## 251 6 6 6 4 6 5
## 252 6 6 6 3 6 6
## 253 6 7 6 2 3 7
## 254 6 6 5 6 6 6
## 255 4 4 7 4 3 6
## 256 5 6 4 5 5 6
## 257 4 4 4 4 4 4
## 258 7 7 7 2 6 6
## 259 6 6 6 6 6 6
## 260 6 6 6 6 6 6
## 261 6 6 6 4 5 6
## 262 6 6 6 2 6 6
## 263 6 6 6 6 6 6
## 264 7 7 7 6 6 7
## 265 6 6 4 2 3 4
## 266 4 5 4 5 6 5
## 267 6 6 6 5 5 4
## 268 6 6 6 4 4 6
## 269 4 6 6 2 5 6
## 270 5 5 5 6 6 2
## 271 6 5 6 6 6 6
## 272 7 7 7 7 7 7
## 273 3 3 2 2 5 2
## 274 7 6 7 6 6 6
## 275 4 7 6 5 6 3
## 276 6 6 4 5 6 5
## 277 6 7 NA 5 7 3
## 278 6 7 7 5 4 6
## 279 4 5 5 4 4 6
## 280 6 6 5 6 6 6
## 281 6 6 6 4 4 5
## 282 6 6 5 6 6 5
## 283 4 4 4 7 6 6
## 284 6 6 5 5 5 6
## 285 6 6 6 6 6 6
## 286 3 3 6 4 6 6
## 287 6 6 6 6 5 6
## 288 6 7 5 6 6 6
## 289 7 6 6 5 5 7
## 290 5 6 5 1 5 1
## 291 7 7 7 5 4 6
## 292 6 5 4 5 5 4
## Residence Pay_Meth Insur_Type Gender Age Education Region Brand
## 1 2 1 Collision Female 32 1 American Toyota
## 2 2 3 Liability Female 24 2 Asian Toyota
## 3 1 3 Liability Female 24 2 Asian Toyota
## 4 1 3 Liability Female 25 2 Asian Toyota
## 5 1 3 Liability Female 26 2 Asian Toyota
## 6 2 3 Liability Female 26 2 Asian Toyota
## 7 1 3 Liability Female 27 2 Asian Toyota
## 8 2 3 Liability Female 27 2 Asian Toyota
## 9 2 3 Liability Male 27 1 Asian Toyota
## 10 2 2 Collision Female 32 2 American Toyota
## 11 1 1 Collision Male 36 3 American Toyota
## 12 2 2 Liability Female 32 3 American Toyota
## 13 1 3 Collision Female 36 2 American Toyota
## 14 2 1 Collision Female 36 1 American Toyota
## 15 1 1 Collision Female 36 2 American Toyota
## 16 1 2 Collision Female 36 3 American Toyota
## 17 2 3 Collision Female 37 2 American Toyota
## 18 1 3 Collision Female 38 3 American Toyota
## 19 2 1 Collision Female 39 2 American Toyota
## 20 1 3 Collision Female 42 1 American Toyota
## 21 2 3 Collision Female 45 2 American Toyota
## 22 2 1 Collision Female 45 2 American Toyota
## 23 2 2 Liability Female 34 1 American Toyota
## 24 1 1 Collision Female 46 2 American Toyota
## 25 2 3 Collision Male 48 3 American Toyota
## 26 2 3 Collision Male 49 1 American Toyota
## 27 2 2 Collision Female 49 2 European Toyota
## 28 2 2 Collision Male 50 2 European Toyota
## 29 2 2 Comprehensive Female 52 2 European Toyota
## 30 2 2 Comprehensive Male 53 2 European Toyota
## 31 2 2 Comprehensive Female 53 2 European Toyota
## 32 1 2 Comprehensive Male 53 3 European Toyota
## 33 1 2 Comprehensive Female 54 2 European Toyota
## 34 2 2 Liability Female 34 2 American Toyota
## 35 2 2 Comprehensive Female 55 2 European Toyota
## 36 2 3 Liability Female 34 1 American Toyota
## 37 1 3 Liability Female 34 2 American Toyota
## 38 2 1 Liability Female 35 2 American Toyota
## 39 1 1 Liability Female 35 1 American Toyota
## 40 1 3 Liability Male 36 2 American Toyota
## 41 2 1 Liability Female 36 1 American Toyota
## 42 2 3 Liability Female 36 1 American Toyota
## 43 1 3 Liability Female 36 1 American Toyota
## 44 2 1 Liability Female 36 2 American Toyota
## 45 1 3 Liability Female 37 2 American Toyota
## 46 NA 3 Liability Female 38 2 American Toyota
## 47 2 3 Liability Female 39 3 American Toyota
## 48 NA 3 Liability Female 42 2 American Toyota
## 49 1 3 Liability Female 45 2 American Toyota
## 50 2 3 Liability Female 45 2 American Toyota
## 51 1 3 Liability Female 46 2 American Toyota
## 52 1 2 Collision Male 27 1 Asian Toyota
## 53 1 2 Collision Male 29 2 Asian Toyota
## 54 2 2 Collision Male 32 2 Asian Toyota
## 55 1 2 Collision Male 32 2 Asian Toyota
## 56 1 2 Collision Male 32 3 Asian Toyota
## 57 2 2 Collision Female 32 1 Asian Toyota
## 58 NA 2 Collision Female 34 1 Asian Toyota
## 59 NA 1 Collision Male 34 1 Asian Toyota
## 60 2 1 Collision Female 34 1 Asian Toyota
## 61 1 1 Collision Female 34 1 Asian Toyota
## 62 1 1 Collision Female 35 2 Asian Toyota
## 63 1 2 Liability Male 57 1 American Toyota
## 64 1 2 Liability Female 57 1 American Toyota
## 65 2 1 Liability Female 57 2 American Toyota
## 66 2 2 Liability Male 57 2 American Toyota
## 67 1 1 Liability Male 60 2 American Toyota
## 68 1 2 Liability Female 60 2 American Toyota
## 69 1 1 Liability 18 2 American Toyota
## 70 2 1 Liability Male 18 1 American Toyota
## 71 1 1 Liability Female 18 2 American Toyota
## 72 1 1 Liability Male 19 1 American Toyota
## 73 1 1 Liability Male 19 3 American Toyota
## 74 2 1 Liability Female 19 2 American Toyota
## 75 2 1 Liability Male 19 3 American Toyota
## 76 1 3 Liability Female 19 2 American Toyota
## 77 2 3 Liability Female 21 2 European Toyota
## 78 2 3 Liability Female 21 1 European Toyota
## 79 2 3 Liability 21 2 European Toyota
## 80 1 3 Liability Female 21 2 European Toyota
## 81 2 3 Liability Female 21 2 European Toyota
## 82 2 3 22 1 European Toyota
## 83 2 3 Liability Female 23 1 European Toyota
## 84 2 3 Liability Female 23 2 European Toyota
## 85 2 3 Liability Female 23 2 European Toyota
## 86 2 3 Liability Male 23 2 European Toyota
## 87 2 3 Collision Female 24 1 European Toyota
## 88 2 3 Collision Female 24 2 European Toyota
## 89 2 3 Collision Female 24 1 European Toyota
## 90 2 3 Collision Female 24 1 European Toyota
## 91 2 3 Collision Female 25 2 European Toyota
## 92 2 3 Collision Female 26 3 European Toyota
## 93 2 3 Collision Female 26 2 European Toyota
## 94 2 2 Collision Female 27 3 European Toyota
## 95 2 2 Collision Female 27 2 European Toyota
## 96 1 2 Collision Female 27 1 European Toyota
## 97 1 2 Collision Female 29 2 European Toyota
## 98 2 2 Comprehensive Male 32 2 European Toyota
## 99 1 2 Comprehensive Female 32 2 European Toyota
## 100 1 2 Comprehensive Female 32 1 European Toyota
## 101 2 2 Collision Female 23 1 European Toyota
## 102 1 2 Collision Female 24 2 European Toyota
## 103 2 2 Collision Male 24 2 European Toyota
## 104 1 2 Collision Female 24 2 European Toyota
## 105 2 2 Collision Female 24 2 European Toyota
## 106 1 2 Collision Female 25 2 European Toyota
## 107 1 2 Collision Female 26 2 European Toyota
## 108 2 2 Collision Female 26 2 European Toyota
## 109 1 2 Collision Female 27 2 European Toyota
## 110 1 2 Collision Female 27 2 European Toyota
## 111 1 2 Liability Female 27 1 European Toyota
## 112 2 2 Collision Female 29 2 European Toyota
## 113 1 2 Collision Female 32 2 European Toyota
## 114 1 2 Collision Female 32 2 European Toyota
## 115 1 1 Collision Male 32 3 European Toyota
## 116 2 1 Comprehensive Male 32 2 European Toyota
## 117 2 1 Comprehensive Female 34 3 European Toyota
## 118 2 1 Comprehensive Male 34 1 European Toyota
## 119 2 1 Comprehensive Female 34 1 European Toyota
## 120 1 1 Comprehensive Male 34 2 European Toyota
## 121 2 1 Comprehensive Female 35 2 European Toyota
## 122 1 1 Comprehensive Male 35 2 European Toyota
## 123 2 3 Comprehensive Female 36 1 Middle Eastern Toyota
## 124 1 3 Comprehensive Female 36 1 Middle Eastern Toyota
## 125 2 3 Liability Male 37 2 American Toyota
## 126 1 2 Liability Female 38 2 American Toyota
## 127 1 3 Liability Female 39 2 American Toyota
## 128 2 3 Liability Male 42 2 American Toyota
## 129 1 3 Liability Male 45 2 American Toyota
## 130 1 3 Liability Female 45 2 American Toyota
## 131 1 3 Liability Female 46 2 American Toyota
## 132 1 2 Liability Male 48 1 American Toyota
## 133 2 2 Liability Female 49 2 American Toyota
## 134 2 2 Liability Male 49 2 American Toyota
## 135 1 1 Liability Male 50 2 American Toyota
## 136 1 2 Liability Female 52 2 American Toyota
## 137 2 2 Comprehensive Male 53 2 American Toyota
## 138 1 3 Comprehensive Female 53 2 Asian Toyota
## 139 2 3 Comprehensive Female 53 2 Asian Toyota
## 140 1 3 Comprehensive Female 54 2 Asian Toyota
## 141 1 3 Comprehensive Female 55 2 Asian Toyota
## 142 1 3 Comprehensive Female 55 2 Asian Toyota
## 143 1 3 Comprehensive Female 55 3 Asian Toyota
## 144 2 3 Comprehensive Female 56 2 Asian Toyota
## 145 1 3 Comprehensive Female 57 2 Asian Toyota
## 146 1 3 Collision Female 57 2 Asian Toyota
## 147 1 3 Collision Female 57 2 Asian Toyota
## 148 1 3 Collision Male 57 2 Asian Toyota
## 149 1 2 Collision Female 60 2 Asian Toyota
## 150 2 2 Collision Female 60 2 Asian Toyota
## 151 1 2 Collision Female 18 2 Asian Toyota
## 152 1 2 Collision Female 18 3 Asian Toyota
## 153 1 2 Collision Female 18 2 Asian Toyota
## 154 2 2 Collision Female 19 2 Asian Toyota
## 155 1 3 Comprehensive Male 34 2 Middle Eastern Toyota
## 156 1 3 Comprehensive Female 34 3 Middle Eastern Toyota
## 157 2 3 Comprehensive Female 34 2 Middle Eastern Toyota
## 158 1 3 Comprehensive Male 35 2 Middle Eastern Toyota
## 159 2 3 Liability Male 35 2 Middle Eastern Toyota
## 160 2 3 Liability Female 36 1 Middle Eastern Toyota
## 161 2 1 Liability Female 36 2 Asian Toyota
## 162 1 1 Liability Male 36 1 Asian Toyota
## 163 2 1 Liability Female 36 1 Asian Toyota
## 164 1 1 Liability Male 36 3 Asian Toyota
## 165 1 1 Liability Male 37 2 Asian Toyota
## 166 1 1 Liability Female 38 1 Asian Toyota
## 167 2 1 Liability Male 39 2 Asian Toyota
## 168 2 1 Liability Female 42 2 Asian Toyota
## 169 1 1 Liability Female 45 3 Asian Toyota
## 170 1 3 Liability Female 45 3 Asian Toyota
## 171 1 1 Collision Female 19 3 American Toyota
## 172 1 1 Collision Female 19 2 American Toyota
## 173 1 1 Collision Female 19 3 American Toyota
## 174 1 1 Collision Female 19 3 American Toyota
## 175 2 2 Collision Female 21 2 American Toyota
## 176 1 1 Collision Female 21 3 American Toyota
## 177 1 2 Collision Female 21 3 American Toyota
## 178 1 1 Collision Male 21 3 American Toyota
## 179 1 1 Collision Male 21 2 American Toyota
## 180 1 1 Collision Female 22 3 American Toyota
## 181 2 3 Liability Male 23 3 American Toyota
## 182 1 1 Liability Female 23 3 American Toyota
## 183 1 3 Liability Male 23 2 American Toyota
## 184 1 1 Liability Female 23 2 American Toyota
## 185 1 1 Liability Male 24 2 American Toyota
## 186 1 3 Liability Female 24 2 American Toyota
## 187 1 3 Collision Female 24 2 American Toyota
## 188 1 2 Collision Male 26 3 European Toyota
## 189 1 2 Collision Female 27 3 European Toyota
## 190 1 2 Collision Female 27 2 European Toyota
## 191 1 2 Collision Male 27 2 European Toyota
## 192 1 2 Collision Male 29 3 European Toyota
## 193 1 2 Collision Female 32 3 European Toyota
## 194 1 2 Collision Female 32 3 European Toyota
## 195 2 2 Collision Male 32 2 European Toyota
## 196 1 2 Liability Female 32 2 European Toyota
## 197 1 2 Liability Male 34 3 European Toyota
## 198 1 2 Liability Male 34 2 European Toyota
## 199 1 2 Liability Female 34 2 European Toyota
## 200 1 2 Liability Male 34 2 European Toyota
## 201 1 2 Liability Female 35 3 European Toyota
## 202 1 2 Liability Female 35 3 European Toyota
## 203 1 2 Liability Female 36 1 European Toyota
## 204 1 2 Liability Female 36 2 European Toyota
## 205 1 2 Liability Female 36 3 European Toyota
## 206 2 2 Liability Female 36 3 European Toyota
## 207 1 2 Liability Female 36 3 European Toyota
## 208 2 2 Liability Female 37 3 European Toyota
## 209 1 2 Liability Female 38 3 European Toyota
## 210 1 2 Liability Female 39 2 European Toyota
## 211 1 2 Collision Male 42 3 European Toyota
## 212 2 2 Collision Female 45 3 European Toyota
## 213 2 2 Collision Female 45 2 European Toyota
## 214 2 3 Collision Female 46 2 Asian Toyota
## 215 2 3 Collision Female 48 3 Asian Toyota
## 216 1 3 Collision Female 49 3 Asian Toyota
## 217 1 3 Collision Female 49 3 Asian Toyota
## 218 1 3 Collision Female 50 3 Asian Toyota
## 219 1 3 Collision Female 52 3 Asian Toyota
## 220 1 3 Collision Female 53 3 Asian Toyota
## 221 1 3 Collision Female 53 3 Asian Toyota
## 222 1 3 Collision Female 53 3 Asian Toyota
## 223 1 3 Collision Male 54 3 Asian Toyota
## 224 2 3 Collision Female 55 3 Asian Toyota
## 225 2 3 Collision Female 55 3 Asian Toyota
## 226 2 2 Collision Female 55 3 Asian Toyota
## 227 1 2 Comprehensive Male 23 3 Asian Toyota
## 228 1 3 Comprehensive Female 23 2 Asian Toyota
## 229 1 3 Comprehensive Female 23 2 Asian Toyota
## 230 1 3 Comprehensive Male 23 3 Asian Toyota
## 231 1 3 Comprehensive Male 24 2 Asian Toyota
## 232 1 3 Comprehensive Female 24 2 Asian Toyota
## 233 1 3 Collision Female 24 2 Asian Toyota
## 234 1 2 Collision Male 24 2 Asian Toyota
## 235 1 2 Collision Female 25 2 Asian Toyota
## 236 2 2 Collision Male 26 3 Asian Toyota
## 237 2 1 Collision Male 26 2 Asian Toyota
## 238 1 1 Collision Female 27 2 Asian Toyota
## 239 2 1 Collision Male 27 3 Asian Toyota
## 240 2 3 Collision Female 27 2 Asian Toyota
## 241 1 3 Collision Female 29 3 Asian Toyota
## 242 1 3 Collision Female 32 2 Asian Toyota
## 243 1 3 Collision Female 32 2 Asian Toyota
## 244 1 3 Collision Female 32 3 Asian Toyota
## 245 1 1 Collision Female 32 2 Asian Toyota
## 246 2 1 Collision Female 34 3 Asian Toyota
## 247 1 1 Collision Female 34 2 Asian Toyota
## 248 1 1 Collision Female 34 3 Asian Toyota
## 249 1 3 Collision Female 34 3 American Toyota
## 250 1 1 Collision Male 35 2 American Toyota
## 251 1 3 Collision Female 35 3 American Toyota
## 252 1 3 Collision Female 36 1 American Toyota
## 253 1 3 Collision Female 36 3 American Toyota
## 254 2 1 Collision Female 36 2 American Toyota
## 255 1 2 Collision Female 36 1 American Toyota
## 256 2 1 Collision Female 36 2 American Toyota
## 257 2 1 Liability Female 37 1 American Toyota
## 258 1 1 Liability Female 38 1 American Toyota
## 259 2 2 Liability Female 39 1 American Toyota
## 260 2 1 Liability Female 42 1 American Toyota
## 261 1 1 Liability Female 45 1 American Toyota
## 262 1 1 Liability Male 45 1 American Toyota
## 263 1 1 Liability Female 46 1 American Toyota
## 264 1 1 Liability Female 48 1 American Toyota
## 265 1 1 Liability Female 49 2 American Toyota
## 266 2 1 Liability Female 49 1 American Toyota
## 267 2 1 Liability Female 50 1 American Toyota
## 268 1 3 Comprehensive Male 36 1 Asian Toyota
## 269 1 3 Comprehensive Female 36 1 Asian Toyota
## 270 2 3 Comprehensive Female 36 1 Asian Toyota
## 271 2 3 Comprehensive Male 37 1 Asian Toyota
## 272 1 3 Collision Female 38 1 Asian Toyota
## 273 2 3 Collision Female 39 1 Asian Toyota
## 274 1 3 Collision Female 42 3 Asian Toyota
## 275 2 3 Collision Female 45 2 Asian Toyota
## 276 2 3 Collision Female 45 2 Asian Toyota
## 277 1 1 Collision Female 27 1 American Toyota
## 278 2 3 Collision Female 46 2 Asian Toyota
## 279 1 3 Collision Male 48 2 Asian Toyota
## 280 2 3 Collision Male 49 2 Asian Toyota
## 281 2 1 Collision Male 27 3 American Toyota
## 282 1 1 Collision Female 29 1 American Toyota
## 283 2 1 Collision Female 32 1 American Toyota
## 284 2 3 Liability Male 21 1 Asian Toyota
## 285 2 3 Liability Female 21 2 Asian Toyota
## 286 2 3 Liability Male 22 2 Asian Toyota
## 287 2 3 Liability Male 23 1 Asian Toyota
## 288 2 3 Liability Female 23 2 Asian Toyota
## 289 2 3 Liability Male 23 2 Asian Toyota
## 290 1 3 Liability Female 23 3 Asian Toyota
## 291 2 3 Liability Female 24 2 Asian Toyota
## 292 2 3 Liability Female 24 1 Asian Toyota
## Model MPG Cyl acc1 C_cost. H_Cost Post.Satis AgeGrp
## 1 Rav4 24 4 8.2 10 8.0 4 [30,50)
## 2 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 3 Corolla 26 4 8.0 7 6.0 5 [0,30)
## 4 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 5 Corolla 26 4 8.0 7 6.0 5 [0,30)
## 6 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 7 Corolla 26 4 8.0 7 6.0 7 [0,30)
## 8 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 9 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 10 Rav4 24 4 8.2 10 8.0 5 [30,50)
## 11 Corolla 26 4 8.0 7 6.0 5 [30,50)
## 12 Rav4 24 4 8.2 10 8.0 7 [30,50)
## 13 Corolla 26 4 8.0 7 6.0 7 [30,50)
## 14 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 15 Corolla 26 4 8.0 7 6.0 5 [30,50)
## 16 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 17 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 18 Corolla 26 4 8.0 7 6.0 5 [30,50)
## 19 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 20 Corolla 26 4 8.0 7 6.0 5 [30,50)
## 21 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 22 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 23 Rav4 24 4 8.2 10 8.0 6 [30,50)
## 24 Corolla 26 4 8.0 7 6.0 7 [30,50)
## 25 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 26 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 27 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 28 Corolla 26 4 8.0 7 6.0 6 [50,Inf)
## 29 Corolla 26 4 8.0 7 6.0 6 [50,Inf)
## 30 Corolla 26 4 8.0 7 6.0 6 [50,Inf)
## 31 Corolla 26 4 8.0 7 6.0 6 [50,Inf)
## 32 Corolla 26 4 8.0 7 6.0 6 [50,Inf)
## 33 Corolla 26 4 8.0 7 6.0 7 [50,Inf)
## 34 Rav4 24 4 8.2 10 8.0 6 [30,50)
## 35 Corolla 26 4 8.0 7 6.0 7 [50,Inf)
## 36 Rav4 24 4 8.2 10 8.0 4 [30,50)
## 37 Rav4 24 4 8.2 10 8.0 4 [30,50)
## 38 Rav4 24 4 8.2 10 8.0 6 [30,50)
## 39 Rav4 24 4 8.2 10 8.0 5 [30,50)
## 40 Rav4 24 4 8.2 10 8.0 3 [30,50)
## 41 Rav4 24 4 8.2 10 8.0 5 [30,50)
## 42 Rav4 24 4 8.2 10 8.0 5 [30,50)
## 43 Rav4 24 4 8.2 10 8.0 5 [30,50)
## 44 Rav4 24 4 8.2 10 8.0 6 [30,50)
## 45 Rav4 24 4 8.2 10 8.0 4 [30,50)
## 46 Rav4 24 4 8.2 10 8.0 6 [30,50)
## 47 Rav4 24 4 8.2 10 8.0 5 [30,50)
## 48 Rav4 24 4 8.2 10 8.0 6 [30,50)
## 49 Rav4 24 4 8.2 10 8.0 7 [30,50)
## 50 Rav4 24 4 8.2 10 8.0 5 [30,50)
## 51 Rav4 24 4 8.2 10 8.0 7 [30,50)
## 52 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 53 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 54 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 55 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 56 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 57 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 58 Highlander 20 6 7.2 10 8.5 7 [30,50)
## 59 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 60 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 61 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 62 Highlander 20 6 7.2 10 8.5 7 [30,50)
## 63 Rav4 24 4 8.2 10 8.0 4 [50,Inf)
## 64 Rav4 24 4 8.2 10 8.0 4 [50,Inf)
## 65 Rav4 24 4 8.2 10 8.0 5 [50,Inf)
## 66 Rav4 24 4 8.2 10 8.0 5 [50,Inf)
## 67 Rav4 24 4 8.2 10 8.0 6 [50,Inf)
## 68 Rav4 24 4 8.2 10 8.0 5 [50,Inf)
## 69 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 70 Rav4 24 4 8.2 10 8.0 4 [0,30)
## 71 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 72 Rav4 24 4 8.2 10 8.0 7 [0,30)
## 73 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 74 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 75 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 76 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 77 Rav4 24 4 8.2 10 8.0 7 [0,30)
## 78 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 79 Rav4 24 4 8.2 10 8.0 5 [0,30)
## 80 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 81 Rav4 24 4 8.2 10 8.0 5 [0,30)
## 82 Rav4 24 4 8.2 10 8.0 5 [0,30)
## 83 Rav4 24 4 8.2 10 8.0 7 [0,30)
## 84 Rav4 24 4 8.2 10 8.0 5 [0,30)
## 85 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 86 Rav4 24 4 8.2 10 8.0 4 [0,30)
## 87 Rav4 24 4 8.2 10 8.0 5 [0,30)
## 88 Rav4 24 4 8.2 10 8.0 5 [0,30)
## 89 Rav4 24 4 8.2 10 8.0 5 [0,30)
## 90 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 91 Rav4 24 4 8.2 10 8.0 5 [0,30)
## 92 Rav4 24 4 8.2 10 8.0 5 [0,30)
## 93 Rav4 24 4 8.2 10 8.0 7 [0,30)
## 94 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 95 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 96 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 97 Rav4 24 4 8.2 10 8.0 4 [0,30)
## 98 Rav4 24 4 8.2 10 8.0 6 [30,50)
## 99 Rav4 24 4 8.2 10 8.0 7 [30,50)
## 100 Rav4 20 6 8.2 10 8.0 5 [30,50)
## 101 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 102 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 103 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 104 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 105 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 106 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 107 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 108 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 109 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 110 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 111 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 112 Highlander 20 6 7.2 10 8.5 7 [0,30)
## 113 Highlander 20 6 7.2 10 8.5 7 [30,50)
## 114 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 115 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 116 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 117 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 118 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 119 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 120 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 121 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 122 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 123 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 124 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 125 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 126 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 127 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 128 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 129 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 130 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 131 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 132 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 133 Highlander 20 6 7.2 10 8.5 3 [30,50)
## 134 Highlander 20 6 7.2 10 8.5 7 [30,50)
## 135 Highlander 20 6 7.2 10 8.5 7 [50,Inf)
## 136 Highlander 20 6 7.2 10 8.5 6 [50,Inf)
## 137 Highlander 20 6 7.2 10 8.5 6 [50,Inf)
## 138 Highlander 20 6 7.2 10 8.5 5 [50,Inf)
## 139 Highlander 20 6 7.2 10 8.5 5 [50,Inf)
## 140 Highlander 20 6 7.2 10 8.5 5 [50,Inf)
## 141 Highlander 20 6 7.2 10 8.5 4 [50,Inf)
## 142 Highlander 20 6 7.2 10 8.5 5 [50,Inf)
## 143 Highlander 20 6 7.2 10 8.5 5 [50,Inf)
## 144 Highlander 20 6 7.2 10 8.5 7 [50,Inf)
## 145 Highlander 20 6 7.2 10 8.5 6 [50,Inf)
## 146 Highlander 20 6 7.2 10 8.5 7 [50,Inf)
## 147 Highlander 20 6 7.2 10 8.5 6 [50,Inf)
## 148 Highlander 20 6 7.2 10 8.5 4 [50,Inf)
## 149 Highlander 20 6 7.2 10 8.5 6 [50,Inf)
## 150 Highlander 20 6 7.2 10 8.5 6 [50,Inf)
## 151 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 152 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 153 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 154 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 155 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 156 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 157 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 158 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 159 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 160 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 161 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 162 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 163 Highlander 20 6 7.2 10 8.5 3 [30,50)
## 164 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 165 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 166 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 167 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 168 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 169 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 170 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 171 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 172 Highlander 20 6 7.2 10 8.5 7 [0,30)
## 173 Highlander 20 6 7.2 10 8.5 4 [0,30)
## 174 Highlander 20 6 7.2 10 8.5 4 [0,30)
## 175 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 176 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 177 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 178 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 179 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 180 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 181 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 182 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 183 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 184 Highlander 20 6 7.2 10 8.5 3 [0,30)
## 185 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 186 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 187 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 188 Highlander 20 6 7.2 10 8.5 4 [0,30)
## 189 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 190 Highlander 20 6 7.2 10 8.5 5 [0,30)
## 191 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 192 Highlander 20 6 7.2 10 8.5 6 [0,30)
## 193 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 194 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 195 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 196 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 197 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 198 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 199 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 200 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 201 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 202 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 203 Highlander 20 6 7.2 10 8.5 3 [30,50)
## 204 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 205 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 206 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 207 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 208 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 209 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 210 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 211 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 212 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 213 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 214 Highlander 20 6 7.2 10 8.5 6 [30,50)
## 215 Highlander 20 6 7.2 10 8.5 4 [30,50)
## 216 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 217 Highlander 20 6 7.2 10 8.5 5 [30,50)
## 218 Highlander 20 6 7.2 10 8.5 5 [50,Inf)
## 219 Highlander 20 6 7.2 10 8.5 3 [50,Inf)
## 220 Highlander 20 6 7.2 10 8.5 6 [50,Inf)
## 221 Highlander 20 6 7.2 10 8.5 5 [50,Inf)
## 222 Highlander 20 6 7.2 10 8.5 6 [50,Inf)
## 223 Highlander 20 6 7.2 10 8.5 5 [50,Inf)
## 224 Highlander 20 6 7.2 10 8.5 5 [50,Inf)
## 225 Highlander 20 6 7.2 10 8.5 6 [50,Inf)
## 226 Highlander 20 6 7.2 10 8.5 5 [50,Inf)
## 227 Corolla 26 4 8.0 7 6.0 7 [0,30)
## 228 Corolla 26 4 8.0 7 6.0 5 [0,30)
## 229 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 230 Corolla 26 4 8.0 7 6.0 7 [0,30)
## 231 Corolla 26 4 8.0 7 6.0 7 [0,30)
## 232 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 233 Corolla 26 4 8.0 7 6.0 5 [0,30)
## 234 Corolla 26 4 8.0 7 6.0 5 [0,30)
## 235 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 236 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 237 Corolla 26 4 8.0 7 6.0 5 [0,30)
## 238 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 239 Corolla 26 4 8.0 7 6.0 7 [0,30)
## 240 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 241 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 242 Corolla 26 4 8.0 7 6.0 5 [30,50)
## 243 Corolla 26 4 8.0 7 6.0 7 [30,50)
## 244 Corolla 26 4 8.0 7 6.0 7 [30,50)
## 245 Corolla 26 4 8.0 7 6.0 7 [30,50)
## 246 Corolla 26 4 8.0 7 6.0 7 [30,50)
## 247 Corolla 26 4 8.0 7 6.0 7 [30,50)
## 248 Corolla 26 4 8.0 7 6.0 5 [30,50)
## 249 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 250 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 251 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 252 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 253 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 254 Corolla 26 4 8.0 7 6.0 7 [30,50)
## 255 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 256 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 257 Corolla 26 4 8.0 7 6.0 5 [30,50)
## 258 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 259 Corolla 26 4 8.0 7 6.0 7 [30,50)
## 260 Corolla 26 4 8.0 7 6.0 7 [30,50)
## 261 Corolla 26 4 8.0 7 6.0 7 [30,50)
## 262 Corolla 26 4 8.0 7 6.0 7 [30,50)
## 263 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 264 Corolla 26 4 8.0 7 6.0 7 [30,50)
## 265 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 266 Corolla 26 4 8.0 7 6.0 5 [30,50)
## 267 Corolla 26 4 8.0 7 6.0 7 [50,Inf)
## 268 Corolla 26 4 8.0 7 6.0 5 [30,50)
## 269 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 270 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 271 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 272 Corolla 26 4 8.0 7 6.0 5 [30,50)
## 273 Corolla 26 4 8.0 7 6.0 5 [30,50)
## 274 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 275 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 276 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 277 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 278 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 279 Corolla 26 4 8.0 7 6.0 5 [30,50)
## 280 Corolla 26 4 8.0 7 6.0 6 [30,50)
## 281 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 282 Rav4 24 4 8.2 10 8.0 6 [0,30)
## 283 Rav4 24 4 8.2 10 8.0 4 [30,50)
## 284 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 285 Corolla 26 4 8.0 7 6.0 5 [0,30)
## 286 Corolla 26 4 8.0 7 6.0 5 [0,30)
## 287 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 288 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 289 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 290 Corolla 26 4 8.0 7 6.0 5 [0,30)
## 291 Corolla 26 4 8.0 7 6.0 6 [0,30)
## 292 Corolla 26 4 8.0 7 6.0 4 [0,30)Graphing Toyota’s Age Group
#create graph showing the number of Toyota cars by age for each region
ggplot(filtered_data_Toyota, aes(x=Region, fill=AgeGrp))+
theme_bw()+
geom_bar()+
labs(y="Number of cars",
title = "Number of Toyota Cars by Age Group and Region")