Set up working directory
getwd()
## [1] "D:/34884/Documents"
setwd("D:/34884/Documents")
Load packages
library(ggplot2)
library(readxl)
library(stringr)
library(dplyr)
##
## 载入程序包：'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(car)
## 载入需要的程序包：carData
##
## 载入程序包：'car'
## The following object is masked from 'package:dplyr':
##
## recode
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ readr 2.1.5
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ car::recode() masks dplyr::recode()
## ✖ purrr::some() masks car::some()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(visreg)
Read data file Car_Total
upload data file
Car_Total<-read.csv("Car_Total.csv")
see the variable names in the data file
names(Car_Total)
## [1] "Resp" "Att_1"
## [3] "Att_2" "Enj_1"
## [5] "Enj_2" "Perform_1"
## [7] "Perform_2" "Perform_3"
## [9] "WOM_1" "WOM_2"
## [11] "Futu_Pur_1" "Futu_Pur_2"
## [13] "Valu_Percp_1" "Valu_Percp_2"
## [15] "Pur_Proces_1" "Pur_Proces_2"
## [17] "Residence" "Pay_Meth"
## [19] "Insur_Type" "Gender"
## [21] "Age" "Education"
## [23] "Region" "Model"
## [25] "MPG" "Cyl"
## [27] "acc1" "C_cost."
## [29] "H_Cost" "Post.Satis"
## [31] "Make" "Model_v1"
## [33] "Attitude" "Parent"
## [35] "Enjoyment" "Perform"
## [37] "Word_of_Mouth" "Future_Purchase_Intention"
## [39] "Value_Perception" "Purchase_Process"
assign the data file into a new dataframe
ct <- Car_Total
see the new dataframe
head(ct, 50)
## 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
## 7 Res1003 2 2 1 2 2 2 1 6 7
## 8 Res1004 7 7 7 6 5 6 5 6 6
## 9 Res1005 2 1 2 1 2 2 2 7 7
## 10 Res1006 6 6 6 5 5 5 5 3 3
## 11 Res1007 4 4 4 2 3 5 3 7 7
## 12 Res1008 5 5 5 3 3 5 5 5 5
## 13 Res1009 4 4 7 7 7 7 7 7 7
## 14 Res101 6 6 7 6 5 6 3 5 6
## 15 Res1010 7 7 7 4 7 4 4 5 5
## 16 Res1011 7 7 7 2 6 2 5 6 6
## 17 Res1012 6 6 6 6 5 3 4 5 5
## 18 Res1013 4 4 5 1 1 3 4 7 6
## 19 Res1014 5 5 5 4 3 5 5 5 5
## 20 Res1015 4 5 5 5 4 7 5 7 7
## 21 Res1016 6 6 7 7 6 7 6 7 7
## 22 Res1017 6 6 7 5 5 6 5 7 7
## 23 Res1018 4 3 4 2 3 5 4 7 6
## 24 Res1019 6 6 6 5 5 4 4 4 4
## 25 Res102 7 7 7 6 6 6 1 7 7
## 26 Res1020 6 7 6 6 6 5 5 7 7
## 27 Res1021 6 6 6 3 5 6 5 6 6
## 28 Res1022 6 6 6 4 4 7 7 5 5
## 29 Res1023 7 7 6 6 6 5 5 6 6
## 30 Res1024 6 5 4 5 4 6 5 6 7
## 31 Res1025 7 7 7 6 6 6 6 7 7
## 32 Res1026 6 7 7 7 7 6 6 7 7
## 33 Res1027 7 7 7 6 6 7 7 7 7
## 34 Res1028 4 7 7 6 7 6 7 7 6
## 35 Res1029 7 7 7 7 7 7 7 7 7
## 36 Res103 7 7 7 6 6 7 2 6 6
## 37 Res1030 7 7 7 7 7 7 7 7 7
## 38 Res1031 6 6 6 5 5 4 4 6 6
## 39 Res1032 7 6 6 5 6 6 6 7 7
## 40 Res1033 7 6 5 6 6 6 6 7 7
## 41 Res1034 7 7 7 7 7 6 5 7 6
## 42 Res1035 7 7 7 7 7 7 7 7 7
## 43 Res1036 6 5 6 5 5 6 6 7 7
## 44 Res1037 6 7 6 6 7 5 5 5 5
## 45 Res1038 6 5 5 5 5 5 5 5 5
## 46 Res1039 6 6 7 5 5 4 5 5 5
## 47 Res104 7 7 7 7 7 6 6 3 4
## 48 Res1040 5 6 5 6 6 4 4 7 7
## 49 Res1041 4 4 4 3 5 6 6 2 6
## 50 Res1042 5 5 1 2 2 2 1 6 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 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
## 7 6 5 4 4 4 5
## 8 6 7 6 5 5 5
## 9 7 7 4 6 6 7
## 10 6 6 5 6 6 5
## 11 5 6 6 2 2 5
## 12 6 6 6 5 6 6
## 13 5 5 5 4 4 3
## 14 4 3 3 2 2 2
## 15 6 5 6 6 6 6
## 16 6 5 5 6 7 7
## 17 4 4 4 4 4 4
## 18 6 6 6 5 4 4
## 19 5 5 4 6 6 6
## 20 5 5 5 3 3 6
## 21 6 5 5 4 5 6
## 22 5 5 5 4 4 4
## 23 6 7 6 6 6 6
## 24 6 6 6 4 5 5
## 25 6 6 6 7 7 7
## 26 7 7 7 3 3 7
## 27 5 6 6 5 5 6
## 28 6 6 6 5 5 6
## 29 5 5 5 5 5 5
## 30 9 5 4 3 3 6
## 31 3 4 6 6 6 6
## 32 6 6 5 4 6 6
## 33 3 2 6 4 3 6
## 34 7 6 6 4 5 4
## 35 6 5 6 6 6 6
## 36 6 7 6 5 6 5
## 37 6 6 6 5 5 6
## 38 7 6 4 6 6 5
## 39 5 5 5 5 5 4
## 40 6 6 4 6 4 5
## 41 4 5 4 5 5 6
## 42 6 3 5 5 5 6
## 43 6 5 5 6 6 6
## 44 5 6 3 7 6 6
## 45 5 6 3 5 6 4
## 46 6 6 6 6 6 6
## 47 4 4 4 2 3 2
## 48 7 7 7 5 6 6
## 49 6 7 4 6 6 3
## 50 6 6 4 4 4 4
## Residence Pay_Meth Insur_Type Gender Age Education Region
## 1 2 2 Collision Male 18 2 European
## 2 1 2 Collision Male 21 2 European
## 3 2 1 Collision Female 32 1 American
## 4 2 3 Liability Female 24 2 Asian
## 5 1 3 Liability Female 24 2 Asian
## 6 1 3 Liability Female 25 2 Asian
## 7 1 3 Liability Female 26 2 Asian
## 8 2 3 Liability Female 26 2 Asian
## 9 1 3 Liability Female 27 2 Asian
## 10 2 3 Liability Female 27 2 Asian
## 11 2 3 Liability Male 27 1 Asian
## 12 2 3 Liability Female 29 2 Asian
## 13 1 3 Collision Female 32 2 Asian
## 14 2 2 Collision Female 32 2 American
## 15 1 1 Collision Female 32 2 American
## 16 2 1 Collision Female 32 2 American
## 17 2 1 Collision Female 32 2 American
## 18 2 1 Collision Female 34 2 American
## 19 1 1 Collision Female 34 2 American
## 20 2 3 Collision Female 34 2 American
## 21 2 1 Collision Female 34 3 American
## 22 2 1 Collision Female 35 3 American
## 23 2 1 Collision Female 35 1 American
## 24 1 1 Collision Male 36 3 American
## 25 2 2 Liability Female 32 3 American
## 26 1 3 Collision Female 36 2 American
## 27 2 1 Collision Female 36 1 American
## 28 1 1 Collision Female 36 2 American
## 29 1 2 Collision Female 36 3 American
## 30 2 3 Collision Female 37 2 American
## 31 1 3 Collision Female 38 3 American
## 32 2 1 Collision Female 39 2 American
## 33 1 3 Collision Female 42 1 American
## 34 2 3 Collision Female 45 2 American
## 35 2 1 Collision Female 45 2 American
## 36 2 2 Liability Female 34 1 American
## 37 1 1 Collision Female 46 2 American
## 38 2 3 Collision Male 48 3 American
## 39 2 3 Collision Male 49 1 American
## 40 2 2 Collision Female 49 2 European
## 41 2 2 Collision Male 50 2 European
## 42 2 2 Comprehensive Female 52 2 European
## 43 2 2 Comprehensive Male 53 2 European
## 44 2 2 Comprehensive Female 53 2 European
## 45 1 2 Comprehensive Male 53 3 European
## 46 1 2 Comprehensive Female 54 2 European
## 47 2 2 Liability Female 34 2 American
## 48 2 2 Comprehensive Female 55 2 European
## 49 2 3 Collision Male 18 2 European
## 50 2 3 Collision Female 18 1 European
## Model MPG Cyl acc1 C_cost. H_Cost Post.Satis Make Model_v1
## 1 Ford Expedition 15 8 5.5 16 14 4 Ford Expedition
## 2 Ford Expedition 15 8 5.5 16 14 5 Ford Expedition
## 3 Toyota Rav4 24 4 8.2 10 8 4 Toyota Rav4
## 4 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 5 Toyota Corolla 26 4 8.0 7 6 5 Toyota Corolla
## 6 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 7 Toyota Corolla 26 4 8.0 7 6 5 Toyota Corolla
## 8 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 9 Toyota Corolla 26 4 8.0 7 6 7 Toyota Corolla
## 10 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 11 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 12 Honda Pilot 20 6 6.5 12 10 5 Honda Pilot
## 13 Honda Pilot 20 6 6.5 12 10 4 Honda Pilot
## 14 Toyota Rav4 24 4 8.2 10 8 5 Toyota Rav4
## 15 Honda Pilot 20 6 6.5 12 10 4 Honda Pilot
## 16 Honda Pilot 20 6 6.5 12 10 6 Honda Pilot
## 17 Honda Pilot 20 6 6.5 12 10 4 Honda Pilot
## 18 Honda Pilot 20 6 6.5 12 10 5 Honda Pilot
## 19 Honda Pilot 20 6 6.5 12 10 5 Honda Pilot
## 20 Honda Pilot 20 6 6.5 12 10 5 Honda Pilot
## 21 Honda Pilot 20 6 6.5 12 10 4 Honda Pilot
## 22 Honda Pilot 20 6 6.5 12 10 6 Honda Pilot
## 23 Honda Pilot 20 6 6.5 12 10 6 Honda Pilot
## 24 Toyota Corolla 26 4 8.0 7 6 5 Toyota Corolla
## 25 Toyota Rav4 24 4 8.2 10 8 7 Toyota Rav4
## 26 Toyota Corolla 26 4 8.0 7 6 7 Toyota Corolla
## 27 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 28 Toyota Corolla 26 4 8.0 7 6 5 Toyota Corolla
## 29 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 30 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 31 Toyota Corolla 26 4 8.0 7 6 5 Toyota Corolla
## 32 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 33 Toyota Corolla 26 4 8.0 7 6 5 Toyota Corolla
## 34 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 35 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 36 Toyota Rav4 24 4 8.2 10 8 6 Toyota Rav4
## 37 Toyota Corolla 26 4 8.0 7 6 7 Toyota Corolla
## 38 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 39 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 40 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 41 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 42 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 43 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 44 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 45 Toyota Corolla 26 4 8.0 7 6 6 Toyota Corolla
## 46 Toyota Corolla 26 4 8.0 7 6 7 Toyota Corolla
## 47 Toyota Rav4 24 4 8.2 10 8 6 Toyota Rav4
## 48 Toyota Corolla 26 4 8.0 7 6 7 Toyota Corolla
## 49 Fiat 500x 22 4 8.0 8 6 7 Fiat 500x
## 50 Fiat 500x 22 4 8.0 8 6 6 Fiat 500x
## Attitude Parent Enjoyment Perform Word_of_Mouth Future_Purchase_Intention
## 1 6.0 Ford 6.0 4.666667 3.0 3.0
## 2 6.0 Ford 4.0 3.000000 5.5 6.0
## 3 6.5 Toyota 5.0 5.666667 4.0 6.0
## 4 6.0 Toyota 6.5 6.000000 6.0 6.0
## 5 6.0 Toyota 6.5 6.000000 4.0 5.0
## 6 2.0 Toyota 3.5 5.666667 4.0 6.0
## 7 2.0 Toyota 1.5 1.666667 6.5 5.5
## 8 7.0 Toyota 6.5 5.333333 6.0 6.5
## 9 1.5 Toyota 1.5 2.000000 7.0 7.0
## 10 6.0 Toyota 5.5 5.000000 3.0 6.0
## 11 4.0 Toyota 3.0 3.666667 7.0 5.5
## 12 5.0 Honda 4.0 4.333333 5.0 6.0
## 13 4.0 Honda 7.0 7.000000 7.0 5.0
## 14 6.0 Toyota 6.5 4.666667 5.5 3.5
## 15 7.0 Honda 5.5 5.000000 5.0 5.5
## 16 7.0 Honda 4.5 4.333333 6.0 5.5
## 17 6.0 Honda 6.0 4.000000 5.0 4.0
## 18 4.0 Honda 3.0 2.666667 6.5 6.0
## 19 5.0 Honda 4.5 4.333333 5.0 5.0
## 20 4.5 Honda 5.0 5.333333 7.0 5.0
## 21 6.0 Honda 7.0 6.333333 7.0 5.5
## 22 6.0 Honda 6.0 5.333333 7.0 5.0
## 23 3.5 Honda 3.0 4.000000 6.5 6.5
## 24 6.0 Toyota 5.5 4.333333 4.0 6.0
## 25 7.0 Toyota 6.5 4.333333 7.0 6.0
## 26 6.5 Toyota 6.0 5.333333 7.0 7.0
## 27 6.0 Toyota 4.5 5.333333 6.0 5.5
## 28 6.0 Toyota 5.0 6.000000 5.0 6.0
## 29 7.0 Toyota 6.0 5.333333 6.0 5.0
## 30 5.5 Toyota 4.5 5.000000 6.5 7.0
## 31 7.0 Toyota 6.5 6.000000 7.0 3.5
## 32 6.5 Toyota 7.0 6.333333 7.0 6.0
## 33 7.0 Toyota 6.5 6.666667 7.0 2.5
## 34 5.5 Toyota 6.5 6.666667 6.5 6.5
## 35 7.0 Toyota 7.0 7.000000 7.0 5.5
## 36 7.0 Toyota 6.5 5.000000 6.0 6.5
## 37 7.0 Toyota 7.0 7.000000 7.0 6.0
## 38 6.0 Toyota 5.5 4.333333 6.0 6.5
## 39 6.5 Toyota 5.5 6.000000 7.0 5.0
## 40 6.5 Toyota 5.5 6.000000 7.0 6.0
## 41 7.0 Toyota 7.0 6.000000 6.5 4.5
## 42 7.0 Toyota 7.0 7.000000 7.0 4.5
## 43 5.5 Toyota 5.5 5.666667 7.0 5.5
## 44 6.5 Toyota 6.0 5.666667 5.0 5.5
## 45 5.5 Toyota 5.0 5.000000 5.0 5.5
## 46 6.0 Toyota 6.0 4.666667 5.0 6.0
## 47 7.0 Toyota 7.0 6.333333 3.5 4.0
## 48 5.5 Toyota 5.5 4.666667 7.0 7.0
## 49 4.0 Chrysler 3.5 5.666667 4.0 6.5
## 50 5.0 Chrysler 1.5 1.666667 6.5 6.0
## Value_Perception Purchase_Process
## 1 3.5 5.0
## 2 6.0 6.0
## 3 6.5 5.0
## 4 5.0 4.5
## 5 5.5 6.5
## 6 4.5 5.0
## 7 4.0 4.5
## 8 5.5 5.0
## 9 5.0 6.5
## 10 5.5 5.5
## 11 4.0 3.5
## 12 5.5 6.0
## 13 4.5 3.5
## 14 2.5 2.0
## 15 6.0 6.0
## 16 5.5 7.0
## 17 4.0 4.0
## 18 5.5 4.0
## 19 5.0 6.0
## 20 4.0 4.5
## 21 4.5 5.5
## 22 4.5 4.0
## 23 6.0 6.0
## 24 5.0 5.0
## 25 6.5 7.0
## 26 5.0 5.0
## 27 5.5 5.5
## 28 5.5 5.5
## 29 5.0 5.0
## 30 3.5 4.5
## 31 6.0 6.0
## 32 4.5 6.0
## 33 5.0 4.5
## 34 5.0 4.5
## 35 6.0 6.0
## 36 5.5 5.5
## 37 5.5 5.5
## 38 5.0 5.5
## 39 5.0 4.5
## 40 5.0 4.5
## 41 4.5 5.5
## 42 5.0 5.5
## 43 5.5 6.0
## 44 5.0 6.0
## 45 4.0 5.0
## 46 6.0 6.0
## 47 3.0 2.5
## 48 6.0 6.0
## 49 5.0 4.5
## 50 4.0 4.0
The number of cars sold per brand in different Region
ggplot(ct,aes(x=Make,fill=Make))+
geom_bar(position = "dodge")+
geom_text(stat="count", aes(label=..count..), vjust=0, size=5, color="blue")+
facet_wrap(~ Region)+
labs(title = "Sales volume of various types of brand", x = "Brand", y = "Number of car sale",)+
theme_minimal()
## 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.
New dataset with selected variables
honda_vs_competitors <- ct %>%
select(Region, Parent, Model, Make, MPG, Pay_Meth, Insur_Type, Attitude, Enjoyment, Perform, Future_Purchase_Intention, Post.Satis, Word_of_Mouth, Value_Perception, Purchase_Process) %>%
na.omit()
assign the data file into a new dataframe with the Average value of
each variable
honda_vs_competitors_summary <- honda_vs_competitors %>% group_by(Region, Parent, Model, Make, MPG, Pay_Meth, Insur_Type) %>% summarise(Avg_Attitude = mean(Attitude, na.rm = TRUE),
Avg_Enjoyment = mean(Enjoyment, na.rm = TRUE),
Avg_Perform = mean(Perform, na.rm = TRUE),
Avg_WOM = mean(Word_of_Mouth, na.rm = TRUE),
Avg_Future_Purchase = mean(Future_Purchase_Intention, na.rm = TRUE),
Avg_Value_Perception = mean(Value_Perception, na.rm = TRUE),
Avg_Post_Satis = mean(Post.Satis, na.rm = TRUE))
## `summarise()` has grouped output by 'Region', 'Parent', 'Model', 'Make', 'MPG',
## 'Pay_Meth'. You can override using the `.groups` argument.
Enjoyment by Region and Brand
ggplot(honda_vs_competitors_summary, aes(x = Parent, y = Avg_Enjoyment, fill = Make)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Enjoyment by Region and Brand", x = "Make", y = "Average Enjoyment") +
facet_wrap(~ Region) +
theme_minimal()
Summary of Average Enjoyment by Make and Parent
summary_avg_enjoyment <- honda_vs_competitors_summary %>%
group_by(Region, Make, Parent) %>%
summarize(Avg_Enjoyment = round(mean(Avg_Enjoyment, na.rm = TRUE), 2), .groups = "drop")
Display the summarized data
print(summary_avg_enjoyment, n = Inf)
## # A tibble: 29 × 4
## Region Make Parent Avg_Enjoyment
## <chr> <chr> <chr> <dbl>
## 1 American Buick General Motors 4.7
## 2 American Chevrolet General Motors 4.82
## 3 American Chrysler Chrysler 5.04
## 4 American Dodge Chrysler 4.26
## 5 American Fiat Chrysler 4.64
## 6 American Ford Ford 5.19
## 7 American Honda Honda 5.69
## 8 American Kia Kia 5.85
## 9 American Toyota Toyota 5.44
## 10 Asian Chrysler Chrysler 5.02
## 11 Asian Ford Ford 5.32
## 12 Asian Honda Honda 4.89
## 13 Asian Toyota Toyota 5.03
## 14 European Buick General Motors 4.85
## 15 European Chevrolet General Motors 4.22
## 16 European Chrysler Chrysler 4.56
## 17 European Fiat Chrysler 3.71
## 18 European Ford Ford 4.88
## 19 European Honda Honda 5.25
## 20 European Kia Kia 5.45
## 21 European Toyota Toyota 5.2
## 22 Middle Eastern Buick General Motors 5.12
## 23 Middle Eastern Chevrolet General Motors 5.54
## 24 Middle Eastern Chrysler Chrysler 4.86
## 25 Middle Eastern Dodge Chrysler 4.07
## 26 Middle Eastern Ford Ford 5.27
## 27 Middle Eastern Honda Honda 4.82
## 28 Middle Eastern Lincoln Ford 5
## 29 Middle Eastern Toyota Toyota 4.96
Post Purchase satisfaction by Payment Method
ggplot(honda_vs_competitors_summary, aes(x = Pay_Meth, y = Avg_Post_Satis, fill = Make)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Enjoyment by Region and Brand", x = "Make", y = "Average Enjoyment") +
facet_wrap(~ Region) +
theme_minimal()
Summary of Average Post Purchase Satisfaction by Payment Method
summary_avg_Post_Sat_by_Pay_Meth <- honda_vs_competitors_summary %>%
group_by(Pay_Meth, Make) %>%
summarize(Avg_Post_Satis_by_Pay_Meth = round(mean(Avg_Post_Satis, na.rm = TRUE), 2), .groups = "drop")
Display the summarized data
print(summary_avg_Post_Sat_by_Pay_Meth, n = Inf)
## # A tibble: 27 × 3
## Pay_Meth Make Avg_Post_Satis_by_Pay_Meth
## <int> <chr> <dbl>
## 1 1 Buick 5.39
## 2 1 Chevrolet 5.83
## 3 1 Chrysler 5.46
## 4 1 Dodge 4.56
## 5 1 Fiat 4
## 6 1 Ford 4.91
## 7 1 Honda 5.42
## 8 1 Kia 5.61
## 9 1 Toyota 5.68
## 10 2 Buick 6.04
## 11 2 Chevrolet 4.15
## 12 2 Chrysler 4.92
## 13 2 Dodge 4.68
## 14 2 Ford 5.19
## 15 2 Honda 5.46
## 16 2 Kia 5.94
## 17 2 Lincoln 5.67
## 18 2 Toyota 5.75
## 19 3 Buick 4.75
## 20 3 Chevrolet 5.03
## 21 3 Chrysler 5.13
## 22 3 Dodge 3.9
## 23 3 Fiat 4.93
## 24 3 Ford 5.38
## 25 3 Honda 5.46
## 26 3 Lincoln 5.76
## 27 3 Toyota 5.31
Post Purchase satisfaction by Insurance Type
ggplot(honda_vs_competitors_summary, aes(x = Insur_Type, y = Avg_Post_Satis, fill = Make)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Enjoyment by Region and Brand", x = "Make", y = "Average Enjoyment") +
facet_wrap(~ Region) +
theme_minimal()
Summary of Average Post Purchase Satisfaction by Insurance Type
summary_avg_Post_Sat_by_Insur_Type <- honda_vs_competitors_summary %>%
group_by(Insur_Type, Make) %>%
summarize(Avg_Post_Satis = round(mean(Avg_Post_Satis, na.rm = TRUE), 2), .groups = "drop")
Display the summarized data
print(summary_avg_Post_Sat_by_Insur_Type, n = Inf)
## # A tibble: 29 × 3
## Insur_Type Make Avg_Post_Satis
## <chr> <chr> <dbl>
## 1 "" Chrysler 3
## 2 "" Dodge 5
## 3 "" Ford 4
## 4 "" Honda 7
## 5 "" Toyota 5
## 6 "Collision" Buick 5.53
## 7 "Collision" Chevrolet 5.6
## 8 "Collision" Chrysler 5.39
## 9 "Collision" Dodge 3.87
## 10 "Collision" Fiat 4.46
## 11 "Collision" Ford 5.15
## 12 "Collision" Honda 5.24
## 13 "Collision" Kia 5.75
## 14 "Collision" Lincoln 5.71
## 15 "Collision" Toyota 5.61
## 16 "Comprehensive" Buick 5.68
## 17 "Comprehensive" Chevrolet 4.68
## 18 "Comprehensive" Chrysler 5.21
## 19 "Comprehensive" Dodge 4.86
## 20 "Comprehensive" Ford 5.07
## 21 "Comprehensive" Honda 5.67
## 22 "Comprehensive" Kia 5.5
## 23 "Comprehensive" Toyota 5.94
## 24 "Liability" Chevrolet 4.34
## 25 "Liability" Chrysler 5.24
## 26 "Liability" Ford 5.6
## 27 "Liability" Honda 5.58
## 28 "Liability" Kia 5.99
## 29 "Liability" Toyota 5.35
Post Purchase satisfaction by mile per gallon
ggplot(honda_vs_competitors_summary, aes(x = MPG, y = Avg_Post_Satis, fill = Make)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Post Purchase Satisfaction by Miles Per Gallon", x = "Miles Per Gallon", y = "Post Purchase Satisfaction") +
facet_wrap(~ Make) +
theme_minimal()
Summary of Average Post Purchase Satisfaction by Make and Mlies per
gallon
summary_avg_Post_Sat <- honda_vs_competitors_summary %>%
group_by(Model, MPG) %>%
summarize(Avg_Post_Satis = round(mean(Avg_Post_Satis, na.rm = TRUE), 2), .groups = "drop")
Display the summarized data
print(summary_avg_Post_Sat, n = Inf)
## # A tibble: 15 × 3
## Model MPG Avg_Post_Satis
## <chr> <int> <dbl>
## 1 "Buick Encore" 17 5.58
## 2 "Chevrolet Camaro" 14 4.89
## 3 "Chrysler Jeep" 18 5.16
## 4 "Dodge Journey" 16 4.47
## 5 "Fiat 500x" 22 4.46
## 6 "Ford Expedition" 15 4.61
## 7 "Ford Explorer" 19 5.53
## 8 "Honda CRV" 26 5.54
## 9 "Honda Pilot" 20 5.33
## 10 "Kia Sorento" 22 5.83
## 11 "Lincoln Navigator " 15 5.71
## 12 "Toyota Corolla" 26 6.15
## 13 "Toyota Highlander" 20 5.3
## 14 "Toyota Rav4" 20 5
## 15 "Toyota Rav4" 24 5.44
Step 1
select the text values with “Honda”
stringr::str_detect(ct$Model, "Honda")
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [13] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [85] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [337] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
## [493] TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [505] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [517] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [613] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [625] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [637] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
## [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [757] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [769] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [781] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [793] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [805] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [817] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [829] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [889] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [901] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [913] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [925] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
## [937] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [949] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [961] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [973] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1009] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1033] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
## [1045] FALSE FALSE FALSE FALSE FALSE
just see the dataframe
head(ct[str_detect(ct$Model,"Honda"),], 50)
## Resp Att_1 Att_2 Enj_1 Enj_2 Perform_1 Perform_2 Perform_3 WOM_1 WOM_2
## 12 Res1008 5 5 5 3 3 5 5 5 5
## 13 Res1009 4 4 7 7 7 7 7 7 7
## 15 Res1010 7 7 7 4 7 4 4 5 5
## 16 Res1011 7 7 7 2 6 2 5 6 6
## 17 Res1012 6 6 6 6 5 3 4 5 5
## 18 Res1013 4 4 5 1 1 3 4 7 6
## 19 Res1014 5 5 5 4 3 5 5 5 5
## 20 Res1015 4 5 5 5 4 7 5 7 7
## 21 Res1016 6 6 7 7 6 7 6 7 7
## 22 Res1017 6 6 7 5 5 6 5 7 7
## 23 Res1018 4 3 4 2 3 5 4 7 6
## 76 Res121 7 7 7 6 6 6 1 7 7
## 77 Res122 6 7 6 3 5 5 3 5 6
## 78 Res123 7 7 7 7 7 7 5 4 4
## 79 Res124 7 7 7 7 7 7 3 6 5
## 80 Res125 6 6 5 4 4 5 6 6 6
## 81 Res126 6 6 6 5 5 4 3 7 7
## 82 Res127 6 6 6 5 5 5 3 6 5
## 83 Res128 7 5 6 2 2 7 6 6 6
## 84 Res129 7 7 7 7 7 5 4 6 6
## 86 Res130 5 5 5 3 2 5 5 5 5
## 328 Res349 6 7 7 6 6 5 5 4 3
## 330 Res350 2 6 6 6 6 7 3 5 5
## 331 Res351 3 5 5 4 6 5 4 5 5
## 332 Res352 6 4 5 4 6 6 5 6 5
## 333 Res353 5 6 7 6 7 6 5 2 2
## 334 Res354 6 7 7 7 7 5 3 6 6
## 335 Res355 4 3 4 4 7 5 2 6 6
## 336 Res356 2 6 6 5 4 4 4 6 6
## 337 Res357 2 6 6 6 6 6 3 5 5
## 338 Res358 1 7 7 7 7 7 7 5 5
## 339 Res359 3 6 6 5 6 5 3 6 6
## 341 Res360 1 6 6 5 4 6 1 7 7
## 342 Res361 2 6 6 4 7 5 1 7 7
## 343 Res362 1 7 7 7 7 3 4 5 5
## 344 Res363 2 5 6 6 5 4 5 4 6
## 345 Res364 3 6 6 5 6 5 5 3 4
## 346 Res365 5 3 3 4 4 3 3 3 3
## 347 Res366 1 7 7 7 6 5 5 7 7
## 437 Res447 6 4 4 3 7 6 4 4 6
## 438 Res448 5 6 6 5 6 3 5 5 5
## 439 Res449 6 5 5 5 6 5 5 7 5
## 441 Res450 5 3 3 4 4 4 6 6 5
## 442 Res451 4 5 5 5 7 4 5 5 5
## 443 Res452 1 7 7 7 6 7 6 5 4
## 489 Res494 6 6 7 5 5 5 5 4 4
## 490 Res495 6 6 6 6 6 6 5 4 4
## 491 Res496 5 5 4 2 2 4 4 5 5
## 492 Res497 7 6 7 6 6 5 5 6 6
## 493 Res498 7 7 7 7 7 6 6 6 7
## Futu_Pur_1 Futu_Pur_2 Valu_Percp_1 Valu_Percp_2 Pur_Proces_1 Pur_Proces_2
## 12 6 6 6 5 6 6
## 13 5 5 5 4 4 3
## 15 6 5 6 6 6 6
## 16 6 5 5 6 7 7
## 17 4 4 4 4 4 4
## 18 6 6 6 5 4 4
## 19 5 5 4 6 6 6
## 20 5 5 5 3 3 6
## 21 6 5 5 4 5 6
## 22 5 5 5 4 4 4
## 23 6 7 6 6 6 6
## 76 2 2 1 5 4 1
## 77 7 7 6 5 6 5
## 78 5 6 5 5 5 4
## 79 5 5 5 4 5 3
## 80 4 5 4 4 4 2
## 81 6 6 6 5 5 2
## 82 4 4 4 4 4 4
## 83 4 5 5 6 7 2
## 84 7 7 6 6 6 5
## 86 6 6 5 6 6 6
## 328 7 7 7 6 6 6
## 330 7 7 7 7 7 2
## 331 5 3 2 5 6 3
## 332 6 6 6 7 7 2
## 333 6 7 7 7 7 2
## 334 3 3 3 3 4 2
## 335 7 7 7 6 6 5
## 336 2 2 1 1 3 1
## 337 5 6 4 6 6 4
## 338 7 6 6 6 2 6
## 339 5 6 5 5 6 2
## 341 6 5 6 6 6 2
## 342 7 7 6 6 7 2
## 343 4 6 4 4 4 2
## 344 7 6 6 5 4 2
## 345 6 6 6 4 5 5
## 346 7 6 5 7 7 2
## 347 6 7 6 7 5 6
## 437 1 5 5 5 4 5
## 438 3 3 7 5 6 6
## 439 4 4 4 3 5 5
## 441 6 7 6 6 6 5
## 442 7 6 6 7 7 7
## 443 7 7 7 7 5 7
## 489 7 6 6 7 6 7
## 490 7 7 5 6 6 7
## 491 4 4 4 6 5 5
## 492 4 4 4 6 6 6
## 493 2 2 7 7 7 7
## Residence Pay_Meth Insur_Type Gender Age Education Region Model
## 12 2 3 Liability Female 29 2 Asian Honda Pilot
## 13 1 3 Collision Female 32 2 Asian Honda Pilot
## 15 1 1 Collision Female 32 2 American Honda Pilot
## 16 2 1 Collision Female 32 2 American Honda Pilot
## 17 2 1 Collision Female 32 2 American Honda Pilot
## 18 2 1 Collision Female 34 2 American Honda Pilot
## 19 1 1 Collision Female 34 2 American Honda Pilot
## 20 2 3 Collision Female 34 2 American Honda Pilot
## 21 2 1 Collision Female 34 3 American Honda Pilot
## 22 2 1 Collision Female 35 3 American Honda Pilot
## 23 2 1 Collision Female 35 1 American Honda Pilot
## 76 2 3 Liability Male 48 2 American Honda CRV
## 77 1 3 Liability Male 49 3 American Honda CRV
## 78 2 3 Liability Female 49 2 American Honda CRV
## 79 1 3 Liability Male 50 2 American Honda CRV
## 80 2 2 Liability Female 52 2 American Honda CRV
## 81 1 2 Liability Male 53 1 European Honda CRV
## 82 1 2 Liability Female 53 2 European Honda CRV
## 83 2 2 Liability Male 53 2 European Honda CRV
## 84 2 1 Liability Female 54 2 European Honda CRV
## 86 1 1 Collision Female 55 2 European Honda CRV
## 328 2 2 Liability Female 60 2 American Honda CRV
## 330 2 2 Liability Female 18 1 American Honda CRV
## 331 1 2 Liability Female 18 2 American Honda CRV
## 332 1 2 Liability Female 18 2 American Honda CRV
## 333 1 2 Liability Female 19 1 American Honda CRV
## 334 1 3 Liability Female 19 2 American Honda CRV
## 335 1 2 Liability Female 19 2 American Honda CRV
## 336 1 2 Liability Female 19 3 American Honda CRV
## 337 2 2 Liability Male 19 1 European Honda CRV
## 338 1 2 Liability Female 21 2 European Honda CRV
## 339 1 2 Liability Female 21 2 European Honda CRV
## 341 1 2 Collision Female 21 1 European Honda CRV
## 342 1 2 Collision Female 21 2 European Honda CRV
## 343 1 2 Collision Female 21 1 European Honda CRV
## 344 1 2 Collision Female 22 2 European Honda CRV
## 345 1 2 Collision Female 23 1 European Honda CRV
## 346 1 2 Collision Female 23 1 European Honda CRV
## 347 2 2 Collision Female 23 2 European Honda CRV
## 437 1 3 Comprehensive Male 27 2 Asian Honda CRV
## 438 1 3 Comprehensive Male 29 2 Asian Honda CRV
## 439 1 3 Comprehensive Female 32 2 Asian Honda CRV
## 441 1 3 Comprehensive Male 32 2 Asian Honda CRV
## 442 2 3 Comprehensive Male 32 2 Asian Honda CRV
## 443 1 3 Comprehensive Female 32 1 Asian Honda CRV
## 489 1 2 Collision Female 19 2 Asian Honda CRV
## 490 1 2 Collision Female 19 2 Asian Honda CRV
## 491 2 2 Collision Female 19 2 Asian Honda CRV
## 492 1 2 Collision Female 19 2 Asian Honda CRV
## 493 2 2 Collision Female 21 2 Asian Honda CRV
## MPG Cyl acc1 C_cost. H_Cost Post.Satis Make Model_v1 Attitude Parent
## 12 20 6 6.5 12 10 5 Honda Pilot 5.0 Honda
## 13 20 6 6.5 12 10 4 Honda Pilot 4.0 Honda
## 15 20 6 6.5 12 10 4 Honda Pilot 7.0 Honda
## 16 20 6 6.5 12 10 6 Honda Pilot 7.0 Honda
## 17 20 6 6.5 12 10 4 Honda Pilot 6.0 Honda
## 18 20 6 6.5 12 10 5 Honda Pilot 4.0 Honda
## 19 20 6 6.5 12 10 5 Honda Pilot 5.0 Honda
## 20 20 6 6.5 12 10 5 Honda Pilot 4.5 Honda
## 21 20 6 6.5 12 10 4 Honda Pilot 6.0 Honda
## 22 20 6 6.5 12 10 6 Honda Pilot 6.0 Honda
## 23 20 6 6.5 12 10 6 Honda Pilot 3.5 Honda
## 76 26 4 8.5 8 7 5 Honda CRV 7.0 Honda
## 77 26 4 8.5 8 7 7 Honda CRV 6.5 Honda
## 78 26 4 8.5 8 7 6 Honda CRV 7.0 Honda
## 79 26 4 8.5 8 7 6 Honda CRV 7.0 Honda
## 80 26 4 8.5 8 7 5 Honda CRV 6.0 Honda
## 81 26 4 8.5 8 7 6 Honda CRV 6.0 Honda
## 82 26 4 8.5 8 7 5 Honda CRV 6.0 Honda
## 83 26 4 8.5 8 7 5 Honda CRV 6.0 Honda
## 84 26 4 8.5 8 7 5 Honda CRV 7.0 Honda
## 86 26 4 8.5 8 7 6 Honda CRV 5.0 Honda
## 328 26 4 8.5 8 7 7 Honda CRV 6.5 Honda
## 330 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 331 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 332 26 4 8.5 8 7 6 Honda CRV 5.0 Honda
## 333 26 4 8.5 8 7 6 Honda CRV 5.5 Honda
## 334 26 4 8.5 8 7 5 Honda CRV 6.5 Honda
## 335 26 4 8.5 8 7 6 Honda CRV 3.5 Honda
## 336 26 4 8.5 8 7 4 Honda CRV 4.0 Honda
## 337 26 4 8.5 8 7 5 Honda CRV 4.0 Honda
## 338 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 339 26 4 8.5 8 7 6 Honda CRV 4.5 Honda
## 341 26 4 8.5 8 7 5 Honda CRV 3.5 Honda
## 342 26 4 8.5 8 7 7 Honda CRV 4.0 Honda
## 343 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 344 26 4 8.5 8 7 7 Honda CRV 3.5 Honda
## 345 26 4 8.5 8 7 6 Honda CRV 4.5 Honda
## 346 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 347 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 437 26 4 8.5 8 7 5 Honda CRV 5.0 Honda
## 438 26 4 8.5 8 7 5 Honda CRV 5.5 Honda
## 439 26 4 8.5 8 7 5 Honda CRV 5.5 Honda
## 441 26 4 8.5 8 7 7 Honda CRV 4.0 Honda
## 442 26 4 8.5 8 7 6 Honda CRV 4.5 Honda
## 443 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 489 26 4 8.5 8 7 5 Honda CRV 6.0 Honda
## 490 26 4 8.5 8 7 6 Honda CRV 6.0 Honda
## 491 26 4 8.5 8 7 5 Honda CRV 5.0 Honda
## 492 26 4 8.5 8 7 3 Honda CRV 6.5 Honda
## 493 26 4 8.5 8 7 4 Honda CRV 7.0 Honda
## Enjoyment Perform Word_of_Mouth Future_Purchase_Intention Value_Perception
## 12 4.0 4.333333 5.0 6.0 5.5
## 13 7.0 7.000000 7.0 5.0 4.5
## 15 5.5 5.000000 5.0 5.5 6.0
## 16 4.5 4.333333 6.0 5.5 5.5
## 17 6.0 4.000000 5.0 4.0 4.0
## 18 3.0 2.666667 6.5 6.0 5.5
## 19 4.5 4.333333 5.0 5.0 5.0
## 20 5.0 5.333333 7.0 5.0 4.0
## 21 7.0 6.333333 7.0 5.5 4.5
## 22 6.0 5.333333 7.0 5.0 4.5
## 23 3.0 4.000000 6.5 6.5 6.0
## 76 6.5 4.333333 7.0 2.0 3.0
## 77 4.5 4.333333 5.5 7.0 5.5
## 78 7.0 6.333333 4.0 5.5 5.0
## 79 7.0 5.666667 5.5 5.0 4.5
## 80 4.5 5.000000 6.0 4.5 4.0
## 81 5.5 4.000000 7.0 6.0 5.5
## 82 5.5 4.333333 5.5 4.0 4.0
## 83 4.0 5.000000 6.0 4.5 5.5
## 84 7.0 5.333333 6.0 7.0 6.0
## 86 4.0 4.000000 5.0 6.0 5.5
## 328 6.5 5.333333 3.5 7.0 6.5
## 330 6.0 5.333333 5.0 7.0 7.0
## 331 4.5 5.000000 5.0 4.0 3.5
## 332 4.5 5.666667 5.5 6.0 6.5
## 333 6.5 6.000000 2.0 6.5 7.0
## 334 7.0 5.000000 6.0 3.0 3.0
## 335 4.0 4.666667 6.0 7.0 6.5
## 336 5.5 4.000000 6.0 2.0 1.0
## 337 6.0 5.000000 5.0 5.5 5.0
## 338 7.0 7.000000 5.0 6.5 6.0
## 339 5.5 4.666667 6.0 5.5 5.0
## 341 5.5 3.666667 7.0 5.5 6.0
## 342 5.0 4.333333 7.0 7.0 6.0
## 343 7.0 4.666667 5.0 5.0 4.0
## 344 6.0 4.666667 5.0 6.5 5.5
## 345 5.5 5.333333 3.5 6.0 5.0
## 346 3.5 3.333333 3.0 6.5 6.0
## 347 7.0 5.333333 7.0 6.5 6.5
## 437 3.5 5.666667 5.0 3.0 5.0
## 438 5.5 4.666667 5.0 3.0 6.0
## 439 5.0 5.333333 6.0 4.0 3.5
## 441 3.5 4.666667 5.5 6.5 6.0
## 442 5.0 5.333333 5.0 6.5 6.5
## 443 7.0 6.333333 4.5 7.0 7.0
## 489 6.0 5.000000 4.0 6.5 6.5
## 490 6.0 5.666667 4.0 7.0 5.5
## 491 3.0 3.333333 5.0 4.0 5.0
## 492 6.5 5.333333 6.0 4.0 5.0
## 493 7.0 6.333333 6.5 2.0 7.0
## Purchase_Process
## 12 6.0
## 13 3.5
## 15 6.0
## 16 7.0
## 17 4.0
## 18 4.0
## 19 6.0
## 20 4.5
## 21 5.5
## 22 4.0
## 23 6.0
## 76 2.5
## 77 5.5
## 78 4.5
## 79 4.0
## 80 3.0
## 81 3.5
## 82 4.0
## 83 4.5
## 84 5.5
## 86 6.0
## 328 6.0
## 330 4.5
## 331 4.5
## 332 4.5
## 333 4.5
## 334 3.0
## 335 5.5
## 336 2.0
## 337 5.0
## 338 4.0
## 339 4.0
## 341 4.0
## 342 4.5
## 343 3.0
## 344 3.0
## 345 5.0
## 346 4.5
## 347 5.5
## 437 4.5
## 438 6.0
## 439 5.0
## 441 5.5
## 442 7.0
## 443 6.0
## 489 6.5
## 490 6.5
## 491 5.0
## 492 6.0
## 493 7.0
assign into a new dataframe
ct_Honda<-str_detect(ct$Model,"Honda")
assign subset data into a dataframe
subHonda<-ct[ct_Honda,]
see the dataframe
head(subHonda, 50)
## Resp Att_1 Att_2 Enj_1 Enj_2 Perform_1 Perform_2 Perform_3 WOM_1 WOM_2
## 12 Res1008 5 5 5 3 3 5 5 5 5
## 13 Res1009 4 4 7 7 7 7 7 7 7
## 15 Res1010 7 7 7 4 7 4 4 5 5
## 16 Res1011 7 7 7 2 6 2 5 6 6
## 17 Res1012 6 6 6 6 5 3 4 5 5
## 18 Res1013 4 4 5 1 1 3 4 7 6
## 19 Res1014 5 5 5 4 3 5 5 5 5
## 20 Res1015 4 5 5 5 4 7 5 7 7
## 21 Res1016 6 6 7 7 6 7 6 7 7
## 22 Res1017 6 6 7 5 5 6 5 7 7
## 23 Res1018 4 3 4 2 3 5 4 7 6
## 76 Res121 7 7 7 6 6 6 1 7 7
## 77 Res122 6 7 6 3 5 5 3 5 6
## 78 Res123 7 7 7 7 7 7 5 4 4
## 79 Res124 7 7 7 7 7 7 3 6 5
## 80 Res125 6 6 5 4 4 5 6 6 6
## 81 Res126 6 6 6 5 5 4 3 7 7
## 82 Res127 6 6 6 5 5 5 3 6 5
## 83 Res128 7 5 6 2 2 7 6 6 6
## 84 Res129 7 7 7 7 7 5 4 6 6
## 86 Res130 5 5 5 3 2 5 5 5 5
## 328 Res349 6 7 7 6 6 5 5 4 3
## 330 Res350 2 6 6 6 6 7 3 5 5
## 331 Res351 3 5 5 4 6 5 4 5 5
## 332 Res352 6 4 5 4 6 6 5 6 5
## 333 Res353 5 6 7 6 7 6 5 2 2
## 334 Res354 6 7 7 7 7 5 3 6 6
## 335 Res355 4 3 4 4 7 5 2 6 6
## 336 Res356 2 6 6 5 4 4 4 6 6
## 337 Res357 2 6 6 6 6 6 3 5 5
## 338 Res358 1 7 7 7 7 7 7 5 5
## 339 Res359 3 6 6 5 6 5 3 6 6
## 341 Res360 1 6 6 5 4 6 1 7 7
## 342 Res361 2 6 6 4 7 5 1 7 7
## 343 Res362 1 7 7 7 7 3 4 5 5
## 344 Res363 2 5 6 6 5 4 5 4 6
## 345 Res364 3 6 6 5 6 5 5 3 4
## 346 Res365 5 3 3 4 4 3 3 3 3
## 347 Res366 1 7 7 7 6 5 5 7 7
## 437 Res447 6 4 4 3 7 6 4 4 6
## 438 Res448 5 6 6 5 6 3 5 5 5
## 439 Res449 6 5 5 5 6 5 5 7 5
## 441 Res450 5 3 3 4 4 4 6 6 5
## 442 Res451 4 5 5 5 7 4 5 5 5
## 443 Res452 1 7 7 7 6 7 6 5 4
## 489 Res494 6 6 7 5 5 5 5 4 4
## 490 Res495 6 6 6 6 6 6 5 4 4
## 491 Res496 5 5 4 2 2 4 4 5 5
## 492 Res497 7 6 7 6 6 5 5 6 6
## 493 Res498 7 7 7 7 7 6 6 6 7
## Futu_Pur_1 Futu_Pur_2 Valu_Percp_1 Valu_Percp_2 Pur_Proces_1 Pur_Proces_2
## 12 6 6 6 5 6 6
## 13 5 5 5 4 4 3
## 15 6 5 6 6 6 6
## 16 6 5 5 6 7 7
## 17 4 4 4 4 4 4
## 18 6 6 6 5 4 4
## 19 5 5 4 6 6 6
## 20 5 5 5 3 3 6
## 21 6 5 5 4 5 6
## 22 5 5 5 4 4 4
## 23 6 7 6 6 6 6
## 76 2 2 1 5 4 1
## 77 7 7 6 5 6 5
## 78 5 6 5 5 5 4
## 79 5 5 5 4 5 3
## 80 4 5 4 4 4 2
## 81 6 6 6 5 5 2
## 82 4 4 4 4 4 4
## 83 4 5 5 6 7 2
## 84 7 7 6 6 6 5
## 86 6 6 5 6 6 6
## 328 7 7 7 6 6 6
## 330 7 7 7 7 7 2
## 331 5 3 2 5 6 3
## 332 6 6 6 7 7 2
## 333 6 7 7 7 7 2
## 334 3 3 3 3 4 2
## 335 7 7 7 6 6 5
## 336 2 2 1 1 3 1
## 337 5 6 4 6 6 4
## 338 7 6 6 6 2 6
## 339 5 6 5 5 6 2
## 341 6 5 6 6 6 2
## 342 7 7 6 6 7 2
## 343 4 6 4 4 4 2
## 344 7 6 6 5 4 2
## 345 6 6 6 4 5 5
## 346 7 6 5 7 7 2
## 347 6 7 6 7 5 6
## 437 1 5 5 5 4 5
## 438 3 3 7 5 6 6
## 439 4 4 4 3 5 5
## 441 6 7 6 6 6 5
## 442 7 6 6 7 7 7
## 443 7 7 7 7 5 7
## 489 7 6 6 7 6 7
## 490 7 7 5 6 6 7
## 491 4 4 4 6 5 5
## 492 4 4 4 6 6 6
## 493 2 2 7 7 7 7
## Residence Pay_Meth Insur_Type Gender Age Education Region Model
## 12 2 3 Liability Female 29 2 Asian Honda Pilot
## 13 1 3 Collision Female 32 2 Asian Honda Pilot
## 15 1 1 Collision Female 32 2 American Honda Pilot
## 16 2 1 Collision Female 32 2 American Honda Pilot
## 17 2 1 Collision Female 32 2 American Honda Pilot
## 18 2 1 Collision Female 34 2 American Honda Pilot
## 19 1 1 Collision Female 34 2 American Honda Pilot
## 20 2 3 Collision Female 34 2 American Honda Pilot
## 21 2 1 Collision Female 34 3 American Honda Pilot
## 22 2 1 Collision Female 35 3 American Honda Pilot
## 23 2 1 Collision Female 35 1 American Honda Pilot
## 76 2 3 Liability Male 48 2 American Honda CRV
## 77 1 3 Liability Male 49 3 American Honda CRV
## 78 2 3 Liability Female 49 2 American Honda CRV
## 79 1 3 Liability Male 50 2 American Honda CRV
## 80 2 2 Liability Female 52 2 American Honda CRV
## 81 1 2 Liability Male 53 1 European Honda CRV
## 82 1 2 Liability Female 53 2 European Honda CRV
## 83 2 2 Liability Male 53 2 European Honda CRV
## 84 2 1 Liability Female 54 2 European Honda CRV
## 86 1 1 Collision Female 55 2 European Honda CRV
## 328 2 2 Liability Female 60 2 American Honda CRV
## 330 2 2 Liability Female 18 1 American Honda CRV
## 331 1 2 Liability Female 18 2 American Honda CRV
## 332 1 2 Liability Female 18 2 American Honda CRV
## 333 1 2 Liability Female 19 1 American Honda CRV
## 334 1 3 Liability Female 19 2 American Honda CRV
## 335 1 2 Liability Female 19 2 American Honda CRV
## 336 1 2 Liability Female 19 3 American Honda CRV
## 337 2 2 Liability Male 19 1 European Honda CRV
## 338 1 2 Liability Female 21 2 European Honda CRV
## 339 1 2 Liability Female 21 2 European Honda CRV
## 341 1 2 Collision Female 21 1 European Honda CRV
## 342 1 2 Collision Female 21 2 European Honda CRV
## 343 1 2 Collision Female 21 1 European Honda CRV
## 344 1 2 Collision Female 22 2 European Honda CRV
## 345 1 2 Collision Female 23 1 European Honda CRV
## 346 1 2 Collision Female 23 1 European Honda CRV
## 347 2 2 Collision Female 23 2 European Honda CRV
## 437 1 3 Comprehensive Male 27 2 Asian Honda CRV
## 438 1 3 Comprehensive Male 29 2 Asian Honda CRV
## 439 1 3 Comprehensive Female 32 2 Asian Honda CRV
## 441 1 3 Comprehensive Male 32 2 Asian Honda CRV
## 442 2 3 Comprehensive Male 32 2 Asian Honda CRV
## 443 1 3 Comprehensive Female 32 1 Asian Honda CRV
## 489 1 2 Collision Female 19 2 Asian Honda CRV
## 490 1 2 Collision Female 19 2 Asian Honda CRV
## 491 2 2 Collision Female 19 2 Asian Honda CRV
## 492 1 2 Collision Female 19 2 Asian Honda CRV
## 493 2 2 Collision Female 21 2 Asian Honda CRV
## MPG Cyl acc1 C_cost. H_Cost Post.Satis Make Model_v1 Attitude Parent
## 12 20 6 6.5 12 10 5 Honda Pilot 5.0 Honda
## 13 20 6 6.5 12 10 4 Honda Pilot 4.0 Honda
## 15 20 6 6.5 12 10 4 Honda Pilot 7.0 Honda
## 16 20 6 6.5 12 10 6 Honda Pilot 7.0 Honda
## 17 20 6 6.5 12 10 4 Honda Pilot 6.0 Honda
## 18 20 6 6.5 12 10 5 Honda Pilot 4.0 Honda
## 19 20 6 6.5 12 10 5 Honda Pilot 5.0 Honda
## 20 20 6 6.5 12 10 5 Honda Pilot 4.5 Honda
## 21 20 6 6.5 12 10 4 Honda Pilot 6.0 Honda
## 22 20 6 6.5 12 10 6 Honda Pilot 6.0 Honda
## 23 20 6 6.5 12 10 6 Honda Pilot 3.5 Honda
## 76 26 4 8.5 8 7 5 Honda CRV 7.0 Honda
## 77 26 4 8.5 8 7 7 Honda CRV 6.5 Honda
## 78 26 4 8.5 8 7 6 Honda CRV 7.0 Honda
## 79 26 4 8.5 8 7 6 Honda CRV 7.0 Honda
## 80 26 4 8.5 8 7 5 Honda CRV 6.0 Honda
## 81 26 4 8.5 8 7 6 Honda CRV 6.0 Honda
## 82 26 4 8.5 8 7 5 Honda CRV 6.0 Honda
## 83 26 4 8.5 8 7 5 Honda CRV 6.0 Honda
## 84 26 4 8.5 8 7 5 Honda CRV 7.0 Honda
## 86 26 4 8.5 8 7 6 Honda CRV 5.0 Honda
## 328 26 4 8.5 8 7 7 Honda CRV 6.5 Honda
## 330 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 331 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 332 26 4 8.5 8 7 6 Honda CRV 5.0 Honda
## 333 26 4 8.5 8 7 6 Honda CRV 5.5 Honda
## 334 26 4 8.5 8 7 5 Honda CRV 6.5 Honda
## 335 26 4 8.5 8 7 6 Honda CRV 3.5 Honda
## 336 26 4 8.5 8 7 4 Honda CRV 4.0 Honda
## 337 26 4 8.5 8 7 5 Honda CRV 4.0 Honda
## 338 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 339 26 4 8.5 8 7 6 Honda CRV 4.5 Honda
## 341 26 4 8.5 8 7 5 Honda CRV 3.5 Honda
## 342 26 4 8.5 8 7 7 Honda CRV 4.0 Honda
## 343 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 344 26 4 8.5 8 7 7 Honda CRV 3.5 Honda
## 345 26 4 8.5 8 7 6 Honda CRV 4.5 Honda
## 346 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 347 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 437 26 4 8.5 8 7 5 Honda CRV 5.0 Honda
## 438 26 4 8.5 8 7 5 Honda CRV 5.5 Honda
## 439 26 4 8.5 8 7 5 Honda CRV 5.5 Honda
## 441 26 4 8.5 8 7 7 Honda CRV 4.0 Honda
## 442 26 4 8.5 8 7 6 Honda CRV 4.5 Honda
## 443 26 4 8.5 8 7 6 Honda CRV 4.0 Honda
## 489 26 4 8.5 8 7 5 Honda CRV 6.0 Honda
## 490 26 4 8.5 8 7 6 Honda CRV 6.0 Honda
## 491 26 4 8.5 8 7 5 Honda CRV 5.0 Honda
## 492 26 4 8.5 8 7 3 Honda CRV 6.5 Honda
## 493 26 4 8.5 8 7 4 Honda CRV 7.0 Honda
## Enjoyment Perform Word_of_Mouth Future_Purchase_Intention Value_Perception
## 12 4.0 4.333333 5.0 6.0 5.5
## 13 7.0 7.000000 7.0 5.0 4.5
## 15 5.5 5.000000 5.0 5.5 6.0
## 16 4.5 4.333333 6.0 5.5 5.5
## 17 6.0 4.000000 5.0 4.0 4.0
## 18 3.0 2.666667 6.5 6.0 5.5
## 19 4.5 4.333333 5.0 5.0 5.0
## 20 5.0 5.333333 7.0 5.0 4.0
## 21 7.0 6.333333 7.0 5.5 4.5
## 22 6.0 5.333333 7.0 5.0 4.5
## 23 3.0 4.000000 6.5 6.5 6.0
## 76 6.5 4.333333 7.0 2.0 3.0
## 77 4.5 4.333333 5.5 7.0 5.5
## 78 7.0 6.333333 4.0 5.5 5.0
## 79 7.0 5.666667 5.5 5.0 4.5
## 80 4.5 5.000000 6.0 4.5 4.0
## 81 5.5 4.000000 7.0 6.0 5.5
## 82 5.5 4.333333 5.5 4.0 4.0
## 83 4.0 5.000000 6.0 4.5 5.5
## 84 7.0 5.333333 6.0 7.0 6.0
## 86 4.0 4.000000 5.0 6.0 5.5
## 328 6.5 5.333333 3.5 7.0 6.5
## 330 6.0 5.333333 5.0 7.0 7.0
## 331 4.5 5.000000 5.0 4.0 3.5
## 332 4.5 5.666667 5.5 6.0 6.5
## 333 6.5 6.000000 2.0 6.5 7.0
## 334 7.0 5.000000 6.0 3.0 3.0
## 335 4.0 4.666667 6.0 7.0 6.5
## 336 5.5 4.000000 6.0 2.0 1.0
## 337 6.0 5.000000 5.0 5.5 5.0
## 338 7.0 7.000000 5.0 6.5 6.0
## 339 5.5 4.666667 6.0 5.5 5.0
## 341 5.5 3.666667 7.0 5.5 6.0
## 342 5.0 4.333333 7.0 7.0 6.0
## 343 7.0 4.666667 5.0 5.0 4.0
## 344 6.0 4.666667 5.0 6.5 5.5
## 345 5.5 5.333333 3.5 6.0 5.0
## 346 3.5 3.333333 3.0 6.5 6.0
## 347 7.0 5.333333 7.0 6.5 6.5
## 437 3.5 5.666667 5.0 3.0 5.0
## 438 5.5 4.666667 5.0 3.0 6.0
## 439 5.0 5.333333 6.0 4.0 3.5
## 441 3.5 4.666667 5.5 6.5 6.0
## 442 5.0 5.333333 5.0 6.5 6.5
## 443 7.0 6.333333 4.5 7.0 7.0
## 489 6.0 5.000000 4.0 6.5 6.5
## 490 6.0 5.666667 4.0 7.0 5.5
## 491 3.0 3.333333 5.0 4.0 5.0
## 492 6.5 5.333333 6.0 4.0 5.0
## 493 7.0 6.333333 6.5 2.0 7.0
## Purchase_Process
## 12 6.0
## 13 3.5
## 15 6.0
## 16 7.0
## 17 4.0
## 18 4.0
## 19 6.0
## 20 4.5
## 21 5.5
## 22 4.0
## 23 6.0
## 76 2.5
## 77 5.5
## 78 4.5
## 79 4.0
## 80 3.0
## 81 3.5
## 82 4.0
## 83 4.5
## 84 5.5
## 86 6.0
## 328 6.0
## 330 4.5
## 331 4.5
## 332 4.5
## 333 4.5
## 334 3.0
## 335 5.5
## 336 2.0
## 337 5.0
## 338 4.0
## 339 4.0
## 341 4.0
## 342 4.5
## 343 3.0
## 344 3.0
## 345 5.0
## 346 4.5
## 347 5.5
## 437 4.5
## 438 6.0
## 439 5.0
## 441 5.5
## 442 7.0
## 443 6.0
## 489 6.5
## 490 6.5
## 491 5.0
## 492 6.0
## 493 7.0
Filter data for the first t-test on Enjoyment
selected_brands_1 <- ct %>% filter(Make %in% c("Honda", "Buick", "Chevrolet", "Dodge", "Fiat", "Ford", "Kia", "Lincoln", "Toyota", "Chrysler"))
First t-test: Enjoyment between Honda and Toyota
honda_enjoyment <- selected_brands_1 %>% filter(Make == "Honda") %>% pull(Enjoyment)
toyota_enjoyment <- selected_brands_1 %>% filter(Make == "Toyota") %>% pull(Enjoyment)
t_test_1 <- t.test(honda_enjoyment, toyota_enjoyment)
Filter data for the second t-test on Future Purchase Intention
selected_brands_2 <- ct %>% filter(Parent %in% c("Honda", "Chrysler", "Ford", "Kia", "General Motors", "Toyota"))
Third t-test: Future Purchase Intention between General Motors and
Toyota
honda_FPI <- selected_brands_2 %>% filter(Parent == "Honda") %>% pull(Future_Purchase_Intention)
Toyota_FPI <- selected_brands_2 %>% filter(Parent == "Toyota") %>% pull(Future_Purchase_Intention)
t_test_3 <- t.test(as.numeric(honda_FPI), as.numeric(Toyota_FPI))
Print the first test results
print("T-test results for Enjoyment (Honda vs Toyota):")
## [1] "T-test results for Enjoyment (Honda vs Toyota):"
print(t_test_1)
##
## Welch Two Sample t-test
##
## data: honda_enjoyment and toyota_enjoyment
## t = -0.51391, df = 314.11, p-value = 0.6077
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3497420 0.2048794
## sample estimates:
## mean of x mean of y
## 5.147651 5.220083
Print the second test results
print("T-test results for Performance (Honda vs Toyota):")
## [1] "T-test results for Performance (Honda vs Toyota):"
print(t_test_2)
##
## Welch Two Sample t-test
##
## data: honda_performance and toyota_performance
## t = -1.5654, df = 343.72, p-value = 0.1184
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.40184752 0.04567573
## sample estimates:
## mean of x mean of y
## 4.747960 4.926046
Print the third test results
print("T-test results for Future Purchase Intention (Honda vs Toyota):")
## [1] "T-test results for Future Purchase Intention (Honda vs Toyota):"
print(t_test_3)
##
## Welch Two Sample t-test
##
## data: as.numeric(honda_FPI) and as.numeric(Toyota_FPI)
## t = 0.39135, df = 324.68, p-value = 0.6958
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1933068 0.2893142
## sample estimates:
## mean of x mean of y
## 5.369943 5.321939
Visualization
Plot Enjoyment Comparison by Make
ggplot(selected_brands_1, aes(x = Make, y = Enjoyment)) +
geom_boxplot() +
facet_wrap(~ Region) +
ggtitle("Enjoyment Comparison by Brand") +
ylab("Enjoyment Score")
Plot Future Purchase Intention Comparison by Parent
ggplot(selected_brands_2, aes(x = Parent, y = as.numeric(Future_Purchase_Intention))) +
geom_boxplot() +
facet_wrap(~ Region) +
ggtitle("Future Purchase Intention Comparison by Parent") +
ylab("Future Purchase Intention Score")
One-Way ANOVA
Filter data for the second t-test on Future Purchase Intention
selected_brands_3 <- ct %>% filter(Make %in% c("Honda", "Toyota"))
ANOVA: Value Perception across Make
anova_test <- aov(as.numeric(Value_Perception) ~ Make, data = selected_brands_3)
anova_summary <- summary(anova_test)
Print the One-Way ANOVA results
print("ANOVA results for Value Perception (Honda, Toyota):")
## [1] "ANOVA results for Value Perception (Honda, Toyota):"
print(anova_summary)
## Df Sum Sq Mean Sq F value Pr(>F)
## Make 1 2.3 2.301 1.986 0.159
## Residuals 449 520.3 1.159
Convert Value_Perception to numeric
selected_brands_3$Value_Perception <- as.numeric(selected_brands_3$Value_Perception)
Print the Tukey HSD test results
print(tukey_result)
## Tukey multiple comparisons of means
## 99% family-wise confidence level
##
## Fit: aov(formula = Value_Perception ~ Make, data = selected_brands_3)
##
## $Make
## diff lwr upr p adj
## Toyota-Honda -0.1495183 -0.4239788 0.1249422 0.1594601
Plotting Tukey HSD results
plot(tukey_result, las = 1, col = "red")
title(main = "Tukey HSD: Value Perception Differences between Brands")
Step 2
Different in Payment Method within each region
ggplot(subHonda,aes(x=Pay_Meth, fill=Model))+
geom_bar(position = "dodge")+
geom_text(stat="count", aes(label=..count..), vjust=0, size=5, color="blue", position = position_dodge(.9))+
facet_wrap(~ Region)+
labs(title = "Number of payment methods by region", x = "Payment Method", y = "Number of per payment method",)+
theme_minimal()
Different in Insurance Type within each region
ggplot(subHonda,aes(x=Insur_Type, fill=Model))+
geom_bar(position = "dodge")+
geom_text(stat="count", aes(label=..count..), vjust=0, size=5, color="blue", position = position_dodge(.9))+
facet_wrap(~ Region)+
labs(title = "Number of Different Insurance Type by region", x = "Insurance Type", y = "Number of per Insurance Type",)+
theme_minimal()
Setting interval of Age
subHonda$AgeGrp<-cut(subHonda$Age,
breaks = c(18, 28, 40, 65, Inf),
Labels = c("Youth", "Adults", "Middle-aged", "Elderly"),
right=FALSE)
names(Car_Total)
## [1] "Resp" "Att_1"
## [3] "Att_2" "Enj_1"
## [5] "Enj_2" "Perform_1"
## [7] "Perform_2" "Perform_3"
## [9] "WOM_1" "WOM_2"
## [11] "Futu_Pur_1" "Futu_Pur_2"
## [13] "Valu_Percp_1" "Valu_Percp_2"
## [15] "Pur_Proces_1" "Pur_Proces_2"
## [17] "Residence" "Pay_Meth"
## [19] "Insur_Type" "Gender"
## [21] "Age" "Education"
## [23] "Region" "Model"
## [25] "MPG" "Cyl"
## [27] "acc1" "C_cost."
## [29] "H_Cost" "Post.Satis"
## [31] "Make" "Model_v1"
## [33] "Attitude" "Parent"
## [35] "Enjoyment" "Perform"
## [37] "Word_of_Mouth" "Future_Purchase_Intention"
## [39] "Value_Perception" "Purchase_Process"
head(Car_Total$AgeGrp)
## NULL
Different in Age within each region
ggplot(subHonda,aes(x=AgeGrp, fill=Model))+
geom_bar(position = "dodge")+
geom_text(stat="count", aes(label=..count..), vjust=0, size=5, color="blue", position = position_dodge(.9))+
facet_wrap(~ Region)+
labs(title = "Number of different age group by region", x = "Age Group", y = "Number of per Age Group",)+
theme_minimal()
Step 3
Relationship between Miles Per Gallon and Post Purchase Satisfaction
by Gender
ggplot(ct, aes(x = MPG, y = Post.Satis, color = Gender)) +
geom_point(alpha = 0.7) + # Points for individual observations
geom_smooth(method = "lm", se = TRUE) + # Linear trend lines
facet_wrap(~ Make, scales = "free") + # Separate panels for each brand
labs(title = "Miles Per Gallon and Post Purchase Satisfaction by Gender",
x = "Miles Per Gallon",
y = "Post Purchase Satisfaction") +
theme_minimal() +
theme(plot.background = element_rect(fill = "gray"),
panel.background = element_rect(fill = "white"))
## `geom_smooth()` using formula = 'y ~ x'
Relationship between Enjoyment and Cylinder by Gender
ggplot(ct, aes(x = Enjoyment, y = Cyl, color = Gender)) +
geom_point(alpha = 0.7) + # Points for individual observations
geom_smooth(method = "lm", se = TRUE) + # Linear trend lines
facet_wrap(~ Make, scales = "free") + # Separate panels for each brand
labs(title = "Enjoyment and Cylinder by Gender",
x = "Enjoyment",
y = "Cylinder") +
theme_minimal() +
theme(plot.background = element_rect(fill = "gray"),
panel.background = element_rect(fill = "white"))
## `geom_smooth()` using formula = 'y ~ x'
