863-SEM1-Descriptive Analyse
Phương Anh
27/02/2022
1. Read and set up Data Loyalty for SEM1
# Read Data directly
t = "F:/NGHIEN CUU SINH/NCS - PHUONG ANH/Part 2-Satisfaction and Loyalty/So lieu/So lieu - PA xu ly-for SEM/So lieu - PA xu ly-for SEM-27.2.2022/863-Loyaltyofbuspassenger_NonBus_Mising-outliersSEM1.csv"
SEM1 = read.csv(t, header = T)
head(SEM1)## ID MCCar MCMotor MCBus AGE CITY FRE TripPurpose Departure TimeUseonBus
## 1 3 0 0 1 1 2 1 2 0 4
## 2 4 0 0 1 1 2 2 4 0 4
## 3 5 0 0 1 1 2 1 2 1 4
## 4 6 0 0 1 1 2 1 2 1 1
## 5 7 0 0 1 1 2 1 2 1 4
## 6 8 0 1 1 1 2 1 2 1 6
## TravelTime PSW1 PSW2 PSW3 PSW4 PSW5 PSW6 PSW7 PSS1 PSS2 PSS3 PSS4 PSS5 PSS6
## 1 3.00 4 5 6 4 6 4 5 6 6 6 6 4 6
## 2 2.00 2 5 3 2 4 5 3 2 3 4 2 2 2
## 3 0.17 4 6 6 4 1 4 2 2 6 6 4 2 2
## 4 4.00 5 5 5 2 6 4 2 6 6 4 1 4 4
## 5 2.00 3 2 4 4 2 6 5 3 3 5 2 1 2
## 6 2.00 2 5 6 5 4 4 6 3 4 6 2 1 1
## PSS7 PSB1 PSB2 PSB3 PSB4 PSB5 PSB6 PSB7 PSB8 PSQ1 PSQ2 PSQ3 PSQ4 PSQ5 PSQ6
## 1 4 6 6 4 6 5 6 6 6 6 6 4 4 6 4
## 2 2 3 3 4 4 6 6 6 6 4 6 5 4 4 5
## 3 2 2 6 6 4 6 6 4 2 2 2 2 2 2 4
## 4 6 5 6 6 5 5 6 7 6 6 5 4 5 5 5
## 5 2 2 2 5 5 5 6 4 3 6 3 6 3 6 6
## 6 2 2 5 4 4 4 6 4 2 4 4 5 4 5 5
## PSQ7 PSQ8 PSQ9 PSQ10 PSQ11 PSQ12 PSQ13 PSQ14 PSQ15 PSQ16 PSQ17 PSQ18 SAT1
## 1 4 4 6 6 6 5 6 4 4 6 4 2 6
## 2 6 4 6 6 4 4 5 4 3 4 3 6 5
## 3 1 2 1 1 1 6 6 2 6 2 5 2 2
## 4 5 4 4 5 5 5 5 4 5 5 5 5 5
## 5 4 3 6 4 4 4 5 5 3 5 3 5 4
## 6 4 3 3 4 4 4 5 3 3 3 3 4 4
## SAT2 SAT3 LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7 IMA1 IMA2 IMA3 IMA4 IMA5 PHB1
## 1 6 6 4 6 5 7 6 6 6 6 6 6 4 6 6
## 2 5 4 4 5 4 4 4 4 5 4 5 6 5 4 3
## 3 2 2 2 6 6 4 4 2 2 4 2 5 2 2 5
## 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 5 4 4 4 5 5 4 4 4 6 5 4 5 4 5 5
## 6 5 5 4 3 2 5 5 6 6 4 3 2 2 4 4
## PHB2 PHB3 PHB4 PHB5 PEV1 PEV2 PEV3 PEV4 ATM1 ATM2 ATM3 ATM4 ATM5 ATM6 ATM7
## 1 7 6 7 7 4 4 4 4 3 5 6 4 2 4 6
## 2 4 3 4 6 5 6 6 4 3 5 4 4 4 4 5
## 3 2 6 6 6 6 5 6 6 2 6 4 2 2 4 2
## 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 5 6 6 6 5 6 6 6 6 3 4 2 2 5 4 3
## 6 6 7 6 4 7 7 5 2 1 4 5 2 1 4 2
## PPI1 PPI2 PPI3 SIM1 SIM2 SIM3 SIM4 PPA1 PPA2 PPA3 PPA4 SBE1 SBE2 SBE3 SBE4
## 1 6 6 6 4 4 4 2 4 6 4 4 4 4 4 4
## 2 4 4 4 4 4 4 4 4 4 4 4 5 4 5 5
## 3 4 2 2 4 4 4 2 4 6 6 2 6 6 6 6
## 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 5 2 3 4 6 6 5 5 5 5 5 5 5 5 5 5
## 6 5 2 2 2 2 2 2 2 3 3 2 2 2 2 2
## EXB1 EXB2 EXB3 PVA1 PVA2 PVA3 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 4 4 3 6 6 6 0 1 0 1 0
## 2 4 5 3 5 5 3 0 0 1 0 0
## 3 4 6 2 6 2 4 0 0 0 0 1
## 4 5 5 5 5 5 5 0 1 0 1 1
## 5 5 5 4 6 2 3 0 1 0 1 1
## 6 5 5 4 6 5 5 0 1 0 1 0
## MarriedStatus Occupation Education Income
## 1 1 1 2 1
## 2 1 1 2 1
## 3 1 1 2 1
## 4 1 1 2 1
## 5 1 1 2 1
## 6 1 1 3 1names(SEM1)## [1] "ID" "MCCar" "MCMotor" "MCBus"
## [5] "AGE" "CITY" "FRE" "TripPurpose"
## [9] "Departure" "TimeUseonBus" "TravelTime" "PSW1"
## [13] "PSW2" "PSW3" "PSW4" "PSW5"
## [17] "PSW6" "PSW7" "PSS1" "PSS2"
## [21] "PSS3" "PSS4" "PSS5" "PSS6"
## [25] "PSS7" "PSB1" "PSB2" "PSB3"
## [29] "PSB4" "PSB5" "PSB6" "PSB7"
## [33] "PSB8" "PSQ1" "PSQ2" "PSQ3"
## [37] "PSQ4" "PSQ5" "PSQ6" "PSQ7"
## [41] "PSQ8" "PSQ9" "PSQ10" "PSQ11"
## [45] "PSQ12" "PSQ13" "PSQ14" "PSQ15"
## [49] "PSQ16" "PSQ17" "PSQ18" "SAT1"
## [53] "SAT2" "SAT3" "LOY1" "LOY2"
## [57] "LOY3" "LOY4" "LOY5" "LOY6"
## [61] "LOY7" "IMA1" "IMA2" "IMA3"
## [65] "IMA4" "IMA5" "PHB1" "PHB2"
## [69] "PHB3" "PHB4" "PHB5" "PEV1"
## [73] "PEV2" "PEV3" "PEV4" "ATM1"
## [77] "ATM2" "ATM3" "ATM4" "ATM5"
## [81] "ATM6" "ATM7" "PPI1" "PPI2"
## [85] "PPI3" "SIM1" "SIM2" "SIM3"
## [89] "SIM4" "PPA1" "PPA2" "PPA3"
## [93] "PPA4" "SBE1" "SBE2" "SBE3"
## [97] "SBE4" "EXB1" "EXB2" "EXB3"
## [101] "PVA1" "PVA2" "PVA3" "EC_Stop"
## [105] "WC_Stop" "EC_Bus" "WC_Bus" "Gender"
## [109] "MarriedStatus" "Occupation" "Education" "Income"dim(SEM1)## [1] 863 1122. Desscriptive statistic
# 2.1. Subset Data SEM1 (6 constructs and 28 items)
SEM1 <- SEM1[, c(2, 3, 5, 6, 7, 8, 9, 10, 11, 26, 27, 28, 29, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112)]
head(SEM1)## MCCar MCMotor AGE CITY FRE TripPurpose Departure TimeUseonBus TravelTime PSB1
## 1 0 0 1 2 1 2 0 4 3.00 6
## 2 0 0 1 2 2 4 0 4 2.00 3
## 3 0 0 1 2 1 2 1 4 0.17 2
## 4 0 0 1 2 1 2 1 1 4.00 5
## 5 0 0 1 2 1 2 1 4 2.00 2
## 6 0 1 1 2 1 2 1 6 2.00 2
## PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7 IMA1 IMA2 IMA3 IMA4 IMA5
## 1 6 4 6 4 6 5 7 6 6 6 6 6 6 4 6
## 2 3 4 4 4 5 4 4 4 4 5 4 5 6 5 4
## 3 6 6 4 2 6 6 4 4 2 2 4 2 5 2 2
## 4 6 6 5 5 5 5 5 5 5 5 5 5 5 5 5
## 5 2 5 5 4 5 5 4 4 4 6 5 4 5 4 5
## 6 5 4 4 4 3 2 5 5 6 6 4 3 2 2 4
## PHB1 PHB2 PHB3 PHB4 PHB5 PEV1 PEV2 PEV3 PEV4 PVA1 PVA2 PVA3 EC_Stop WC_Stop
## 1 6 7 6 7 7 4 4 4 4 6 6 6 0 1
## 2 3 4 3 4 6 5 6 6 4 5 5 3 0 0
## 3 5 2 6 6 6 6 5 6 6 6 2 4 0 0
## 4 5 5 5 5 5 5 5 5 5 5 5 5 0 1
## 5 5 6 6 6 5 6 6 6 6 6 2 3 0 1
## 6 4 6 7 6 4 7 7 5 2 6 5 5 0 1
## EC_Bus WC_Bus Gender MarriedStatus Occupation Education Income
## 1 0 1 0 1 1 2 1
## 2 1 0 0 1 1 2 1
## 3 0 0 1 1 1 2 1
## 4 0 1 1 1 1 2 1
## 5 0 1 1 1 1 2 1
## 6 0 1 0 1 1 3 1names(SEM1)## [1] "MCCar" "MCMotor" "AGE" "CITY"
## [5] "FRE" "TripPurpose" "Departure" "TimeUseonBus"
## [9] "TravelTime" "PSB1" "PSB2" "PSB3"
## [13] "PSB4" "LOY1" "LOY2" "LOY3"
## [17] "LOY4" "LOY5" "LOY6" "LOY7"
## [21] "IMA1" "IMA2" "IMA3" "IMA4"
## [25] "IMA5" "PHB1" "PHB2" "PHB3"
## [29] "PHB4" "PHB5" "PEV1" "PEV2"
## [33] "PEV3" "PEV4" "PVA1" "PVA2"
## [37] "PVA3" "EC_Stop" "WC_Stop" "EC_Bus"
## [41] "WC_Bus" "Gender" "MarriedStatus" "Occupation"
## [45] "Education" "Income"dim(SEM1)## [1] 863 46# Data coding
str(SEM1)## 'data.frame': 863 obs. of 46 variables:
## $ MCCar : int 0 0 0 0 0 0 0 0 0 0 ...
## $ MCMotor : int 0 0 0 0 0 1 1 1 1 1 ...
## $ AGE : int 1 1 1 1 1 1 1 4 1 1 ...
## $ CITY : int 2 2 2 2 2 2 2 2 2 2 ...
## $ FRE : int 1 2 1 1 1 1 4 1 1 1 ...
## $ TripPurpose : int 2 4 2 2 2 2 4 1 2 2 ...
## $ Departure : int 0 0 1 1 1 1 1 1 1 1 ...
## $ TimeUseonBus : int 4 4 4 1 4 6 1 4 4 3 ...
## $ TravelTime : num 3 2 0.17 4 2 2 1 2 2.5 1.5 ...
## $ PSB1 : int 6 3 2 5 2 2 1 6 2 4 ...
## $ PSB2 : int 6 3 6 6 2 5 1 6 4 4 ...
## $ PSB3 : int 4 4 6 6 5 4 5 6 4 7 ...
## $ PSB4 : int 6 4 4 5 5 4 1 6 4 4 ...
## $ LOY1 : int 4 4 2 5 4 4 2 6 6 6 ...
## $ LOY2 : int 6 5 6 5 5 3 1 6 5 6 ...
## $ LOY3 : int 5 4 6 5 5 2 1 6 6 6 ...
## $ LOY4 : int 7 4 4 5 4 5 2 7 5 6 ...
## $ LOY5 : int 6 4 4 5 4 5 2 7 5 4 ...
## $ LOY6 : int 6 4 2 5 4 6 1 7 5 6 ...
## $ LOY7 : int 6 5 2 5 6 6 2 7 5 7 ...
## $ IMA1 : int 6 4 4 5 5 4 2 6 5 6 ...
## $ IMA2 : int 6 5 2 5 4 3 3 6 6 7 ...
## $ IMA3 : int 6 6 5 5 5 2 2 5 6 7 ...
## $ IMA4 : int 4 5 2 5 4 2 2 6 6 7 ...
## $ IMA5 : int 6 4 2 5 5 4 2 6 6 7 ...
## $ PHB1 : int 6 3 5 5 5 4 3 6 6 7 ...
## $ PHB2 : int 7 4 2 5 6 6 3 7 5 7 ...
## $ PHB3 : int 6 3 6 5 6 7 1 4 3 7 ...
## $ PHB4 : int 7 4 6 5 6 6 4 6 7 7 ...
## $ PHB5 : int 7 6 6 5 5 4 4 6 7 7 ...
## $ PEV1 : int 4 5 6 5 6 7 4 6 7 7 ...
## $ PEV2 : int 4 6 5 5 6 7 2 7 7 7 ...
## $ PEV3 : int 4 6 6 5 6 5 2 6 6 7 ...
## $ PEV4 : int 4 4 6 5 6 2 2 6 6 7 ...
## $ PVA1 : int 6 5 6 5 6 6 6 6 6 6 ...
## $ PVA2 : int 6 5 2 5 2 5 4 5 4 6 ...
## $ PVA3 : int 6 3 4 5 3 5 5 6 5 6 ...
## $ EC_Stop : int 0 0 0 0 0 0 0 0 0 0 ...
## $ WC_Stop : int 1 0 0 1 1 1 0 0 1 0 ...
## $ EC_Bus : int 0 1 0 0 0 0 0 0 0 0 ...
## $ WC_Bus : int 1 0 0 1 1 1 0 0 1 0 ...
## $ Gender : int 0 0 1 1 1 0 0 0 0 0 ...
## $ MarriedStatus: int 1 1 1 1 1 1 1 0 1 1 ...
## $ Occupation : int 1 1 1 1 1 1 1 2 1 1 ...
## $ Education : int 2 2 2 2 2 3 5 1 2 3 ...
## $ Income : int 1 1 1 1 1 1 1 1 1 1 ...attach(SEM1)
SEM1 = within(SEM1, {
MCCar = factor(MCCar,labels = c("No", "Yes"))
MCMotor = factor(MCMotor,labels = c("No", "Yes"))
AGE = factor(AGE, labels = c("16-25", "26-35", "36-45", "46-55", ">55"))
CITY = factor(CITY,labels = c("DaNang", "HoChiMinh"))
FRE = factor(FRE, labels = c(">=3 days/week", "2days/month-2days/week", "2days/year-1day/month", "<2 days/year"))
TripPurpose = factor(TripPurpose, labels = c("Working", "Studying", "Shopping", "Entertaining", "Others"))
Departure = factor(Departure, labels = c("Normal", "Peak-Hour"))
TimeUseonBus = factor(TimeUseonBus, labels = c("Using.telephone", "Reading", "Listening", "Nothing", "Talking", "Others"))
EC_Stop = factor(EC_Stop, labels = c("Never", "Ever"))
WC_Stop = factor(WC_Stop, labels = c("Never", "Ever"))
EC_Bus = factor(EC_Bus, labels = c("Never", "Ever"))
WC_Bus = factor(WC_Bus, labels = c("Never", "Ever"))
Gender = factor(Gender, labels = c("Female", "Male"))
MarriedStatus = factor(MarriedStatus, labels = c("Married", "Single"))
Occupation = factor(Occupation, labels = c("Students/Pupils", "Full.time.job", "Part.time.job", "Retirement", "No.job", "Housewife", "Others"))
Education = factor(Education, labels = c("Secondary.school", "Undergraduate", "High.school", "Postgraduate", "Others"))
Income = factor(Income, labels = c("<5millions", "5-10millions", "10-15millions", ">15millions"))
} )
str(SEM1)## 'data.frame': 863 obs. of 46 variables:
## $ MCCar : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ MCMotor : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 2 2 2 2 2 ...
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 1 1 1 1 1 1 1 3 1 1 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 2 2 2 2 2 2 2 2 2 2 ...
## $ FRE : Factor w/ 4 levels ">=3 days/week",..: 1 2 1 1 1 1 4 1 1 1 ...
## $ TripPurpose : Factor w/ 5 levels "Working","Studying",..: 2 4 2 2 2 2 4 1 2 2 ...
## $ Departure : Factor w/ 2 levels "Normal","Peak-Hour": 1 1 2 2 2 2 2 2 2 2 ...
## $ TimeUseonBus : Factor w/ 6 levels "Using.telephone",..: 4 4 4 1 4 6 1 4 4 3 ...
## $ TravelTime : num 3 2 0.17 4 2 2 1 2 2.5 1.5 ...
## $ PSB1 : int 6 3 2 5 2 2 1 6 2 4 ...
## $ PSB2 : int 6 3 6 6 2 5 1 6 4 4 ...
## $ PSB3 : int 4 4 6 6 5 4 5 6 4 7 ...
## $ PSB4 : int 6 4 4 5 5 4 1 6 4 4 ...
## $ LOY1 : int 4 4 2 5 4 4 2 6 6 6 ...
## $ LOY2 : int 6 5 6 5 5 3 1 6 5 6 ...
## $ LOY3 : int 5 4 6 5 5 2 1 6 6 6 ...
## $ LOY4 : int 7 4 4 5 4 5 2 7 5 6 ...
## $ LOY5 : int 6 4 4 5 4 5 2 7 5 4 ...
## $ LOY6 : int 6 4 2 5 4 6 1 7 5 6 ...
## $ LOY7 : int 6 5 2 5 6 6 2 7 5 7 ...
## $ IMA1 : int 6 4 4 5 5 4 2 6 5 6 ...
## $ IMA2 : int 6 5 2 5 4 3 3 6 6 7 ...
## $ IMA3 : int 6 6 5 5 5 2 2 5 6 7 ...
## $ IMA4 : int 4 5 2 5 4 2 2 6 6 7 ...
## $ IMA5 : int 6 4 2 5 5 4 2 6 6 7 ...
## $ PHB1 : int 6 3 5 5 5 4 3 6 6 7 ...
## $ PHB2 : int 7 4 2 5 6 6 3 7 5 7 ...
## $ PHB3 : int 6 3 6 5 6 7 1 4 3 7 ...
## $ PHB4 : int 7 4 6 5 6 6 4 6 7 7 ...
## $ PHB5 : int 7 6 6 5 5 4 4 6 7 7 ...
## $ PEV1 : int 4 5 6 5 6 7 4 6 7 7 ...
## $ PEV2 : int 4 6 5 5 6 7 2 7 7 7 ...
## $ PEV3 : int 4 6 6 5 6 5 2 6 6 7 ...
## $ PEV4 : int 4 4 6 5 6 2 2 6 6 7 ...
## $ PVA1 : int 6 5 6 5 6 6 6 6 6 6 ...
## $ PVA2 : int 6 5 2 5 2 5 4 5 4 6 ...
## $ PVA3 : int 6 3 4 5 3 5 5 6 5 6 ...
## $ EC_Stop : Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ WC_Stop : Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ EC_Bus : Factor w/ 2 levels "Never","Ever": 1 2 1 1 1 1 1 1 1 1 ...
## $ WC_Bus : Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 1 1 1 1 1 ...
## $ MarriedStatus: Factor w/ 2 levels "Married","Single": 2 2 2 2 2 2 2 1 2 2 ...
## $ Occupation : Factor w/ 7 levels "Students/Pupils",..: 1 1 1 1 1 1 1 2 1 1 ...
## $ Education : Factor w/ 5 levels "Secondary.school",..: 2 2 2 2 2 3 5 1 2 3 ...
## $ Income : Factor w/ 4 levels "<5millions","5-10millions",..: 1 1 1 1 1 1 1 1 1 1 ...dim(SEM1)## [1] 863 46# 2.2. Descritive Table
library(tableone)
require(tableone)
library(magrittr)
summary(SEM1)## MCCar MCMotor AGE CITY FRE
## No :843 No :701 16-25:421 DaNang :415 >=3 days/week :500
## Yes: 20 Yes:162 26-35:169 HoChiMinh:448 2days/month-2days/week:166
## 36-45:102 2days/year-1day/month : 98
## 46-55: 77 <2 days/year : 99
## >55 : 94
##
##
## TripPurpose Departure TimeUseonBus TravelTime
## Working :305 Normal :219 Using.telephone:201 Min. : 0.000
## Studying :297 Peak-Hour:644 Reading : 53 1st Qu.: 0.500
## Shopping : 59 Listening :137 Median : 1.000
## Entertaining: 99 Nothing :419 Mean : 1.272
## Others :103 Talking : 33 3rd Qu.: 2.000
## Others : 20 Max. :20.000
##
## PSB1 PSB2 PSB3 PSB4 LOY1
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:3.00 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:4.00 1st Qu.:5.000
## Median :5.00 Median :6.000 Median :6.000 Median :5.00 Median :6.000
## Mean :4.64 Mean :5.067 Mean :5.484 Mean :4.81 Mean :5.488
## 3rd Qu.:6.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.00 3rd Qu.:6.000
## Max. :7.00 Max. :7.000 Max. :7.000 Max. :7.00 Max. :7.000
##
## LOY2 LOY3 LOY4 LOY5 LOY6
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.00 1st Qu.:5.000 1st Qu.:4.000
## Median :6.000 Median :6.000 Median :6.00 Median :6.000 Median :5.000
## Mean :5.513 Mean :5.596 Mean :5.32 Mean :5.373 Mean :5.075
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.00 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.00 Max. :7.000 Max. :7.000
##
## LOY7 IMA1 IMA2 IMA3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:4.500 1st Qu.:5.000
## Median :6.000 Median :5.000 Median :6.000 Median :6.000
## Mean :5.626 Mean :5.209 Mean :5.217 Mean :5.364
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## IMA4 IMA5 PHB1 PHB2 PHB3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.00
## Median :6.000 Median :6.000 Median :6.000 Median :6.000 Median :6.00
## Mean :5.366 Mean :5.409 Mean :5.292 Mean :5.601 Mean :5.44
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.00
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.00
##
## PHB4 PHB5 PEV1 PEV2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:5.500 1st Qu.:5.000 1st Qu.:5.000
## Median :6.000 Median :6.000 Median :6.000 Median :6.000
## Mean :5.868 Mean :5.798 Mean :5.757 Mean :5.578
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## PEV3 PEV4 PVA1 PVA2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.000
## Median :6.000 Median :6.000 Median :6.000 Median :6.000
## Mean :5.524 Mean :5.479 Mean :5.855 Mean :5.436
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## PVA3 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## Min. :1.000 Never:827 Never:742 Never:823 Never:748 Female:503
## 1st Qu.:5.000 Ever : 36 Ever :121 Ever : 40 Ever :115 Male :360
## Median :6.000
## Mean :5.716
## 3rd Qu.:6.000
## Max. :7.000
##
## MarriedStatus Occupation Education Income
## Married:334 Students/Pupils:362 Secondary.school: 59 <5millions :460
## Single :529 Full.time.job :304 Undergraduate :279 5-10millions :248
## Part.time.job : 68 High.school :363 10-15millions:118
## Retirement : 46 Postgraduate :105 >15millions : 37
## No.job : 4 Others : 57
## Housewife : 52
## Others : 27library(table1)##
## Attaching package: 'table1'## The following objects are masked from 'package:base':
##
## units, units<-Tab1_SEM1 <- table1(~ PSB1 + PSB2 + PSB3 + PSB4 + LOY1 + LOY2 + LOY3 + LOY4 + LOY5 + LOY6 + LOY7 + IMA1 + IMA2 + IMA3 + IMA4 + IMA5 + PHB1 + PHB2 + PHB3 + PHB4 + PHB5 + PEV1 + PEV2 + PEV3 + PEV4 + PVA1 + PVA2 + PVA3 + EC_Stop + WC_Stop + EC_Bus + WC_Bus + Gender + MarriedStatus + Occupation + Education + Income + MCCar + MCMotor + AGE + CITY + FRE + TripPurpose + Departure + TimeUseonBus + TravelTime| CITY , data = SEM1)
Tab1_SEM1| DaNang (N=415) |
HoChiMinh (N=448) |
Overall (N=863) |
|
|---|---|---|---|
| PSB1 | |||
| Mean (SD) | 5.56 (1.27) | 3.79 (1.90) | 4.64 (1.85) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 4.00 [1.00, 7.00] | 5.00 [1.00, 7.00] |
| PSB2 | |||
| Mean (SD) | 5.59 (1.23) | 4.58 (1.77) | 5.07 (1.61) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSB3 | |||
| Mean (SD) | 5.74 (1.05) | 5.25 (1.39) | 5.48 (1.26) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSB4 | |||
| Mean (SD) | 5.38 (1.24) | 4.28 (1.70) | 4.81 (1.59) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 4.00 [1.00, 7.00] | 5.00 [1.00, 7.00] |
| LOY1 | |||
| Mean (SD) | 5.74 (0.896) | 5.26 (1.24) | 5.49 (1.11) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| LOY2 | |||
| Mean (SD) | 5.78 (0.872) | 5.26 (1.31) | 5.51 (1.15) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| LOY3 | |||
| Mean (SD) | 5.82 (0.887) | 5.39 (1.21) | 5.60 (1.09) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| LOY4 | |||
| Mean (SD) | 5.53 (1.06) | 5.13 (1.38) | 5.32 (1.25) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| LOY5 | |||
| Mean (SD) | 5.62 (1.05) | 5.14 (1.39) | 5.37 (1.26) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| LOY6 | |||
| Mean (SD) | 5.33 (1.34) | 4.83 (1.59) | 5.08 (1.50) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 5.00 [1.00, 7.00] |
| LOY7 | |||
| Mean (SD) | 5.90 (0.915) | 5.37 (1.38) | 5.63 (1.21) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| IMA1 | |||
| Mean (SD) | 5.36 (0.977) | 5.07 (1.27) | 5.21 (1.15) |
| Median [Min, Max] | 5.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 5.00 [1.00, 7.00] |
| IMA2 | |||
| Mean (SD) | 5.51 (0.892) | 4.95 (1.34) | 5.22 (1.18) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| IMA3 | |||
| Mean (SD) | 5.61 (0.858) | 5.14 (1.28) | 5.36 (1.12) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| IMA4 | |||
| Mean (SD) | 5.68 (0.874) | 5.07 (1.34) | 5.37 (1.18) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| IMA5 | |||
| Mean (SD) | 5.72 (0.832) | 5.13 (1.27) | 5.41 (1.12) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PHB1 | |||
| Mean (SD) | 5.40 (1.09) | 5.19 (1.53) | 5.29 (1.34) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PHB2 | |||
| Mean (SD) | 5.82 (1.01) | 5.40 (1.45) | 5.60 (1.27) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PHB3 | |||
| Mean (SD) | 5.46 (1.19) | 5.42 (1.38) | 5.44 (1.29) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PHB4 | |||
| Mean (SD) | 6.06 (0.853) | 5.69 (1.33) | 5.87 (1.14) |
| Median [Min, Max] | 6.00 [3.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PHB5 | |||
| Mean (SD) | 6.03 (0.861) | 5.59 (1.46) | 5.80 (1.23) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PEV1 | |||
| Mean (SD) | 5.95 (0.890) | 5.58 (1.55) | 5.76 (1.29) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PEV2 | |||
| Mean (SD) | 5.77 (1.04) | 5.40 (1.60) | 5.58 (1.37) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PEV3 | |||
| Mean (SD) | 5.70 (1.10) | 5.36 (1.59) | 5.52 (1.39) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PEV4 | |||
| Mean (SD) | 5.68 (1.07) | 5.29 (1.57) | 5.48 (1.37) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PVA1 | |||
| Mean (SD) | 5.95 (0.909) | 5.76 (1.19) | 5.86 (1.07) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PVA2 | |||
| Mean (SD) | 5.53 (1.19) | 5.35 (1.29) | 5.44 (1.25) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PVA3 | |||
| Mean (SD) | 5.90 (0.815) | 5.54 (1.25) | 5.72 (1.08) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| EC_Stop | |||
| Never | 411 (99.0%) | 416 (92.9%) | 827 (95.8%) |
| Ever | 4 (1.0%) | 32 (7.1%) | 36 (4.2%) |
| WC_Stop | |||
| Never | 393 (94.7%) | 349 (77.9%) | 742 (86.0%) |
| Ever | 22 (5.3%) | 99 (22.1%) | 121 (14.0%) |
| EC_Bus | |||
| Never | 412 (99.3%) | 411 (91.7%) | 823 (95.4%) |
| Ever | 3 (0.7%) | 37 (8.3%) | 40 (4.6%) |
| WC_Bus | |||
| Never | 398 (95.9%) | 350 (78.1%) | 748 (86.7%) |
| Ever | 17 (4.1%) | 98 (21.9%) | 115 (13.3%) |
| Gender | |||
| Female | 231 (55.7%) | 272 (60.7%) | 503 (58.3%) |
| Male | 184 (44.3%) | 176 (39.3%) | 360 (41.7%) |
| MarriedStatus | |||
| Married | 150 (36.1%) | 184 (41.1%) | 334 (38.7%) |
| Single | 265 (63.9%) | 264 (58.9%) | 529 (61.3%) |
| Occupation | |||
| Students/Pupils | 202 (48.7%) | 160 (35.7%) | 362 (41.9%) |
| Full.time.job | 115 (27.7%) | 189 (42.2%) | 304 (35.2%) |
| Part.time.job | 28 (6.7%) | 40 (8.9%) | 68 (7.9%) |
| Retirement | 33 (8.0%) | 13 (2.9%) | 46 (5.3%) |
| No.job | 3 (0.7%) | 1 (0.2%) | 4 (0.5%) |
| Housewife | 27 (6.5%) | 25 (5.6%) | 52 (6.0%) |
| Others | 7 (1.7%) | 20 (4.5%) | 27 (3.1%) |
| Education | |||
| Secondary.school | 30 (7.2%) | 29 (6.5%) | 59 (6.8%) |
| Undergraduate | 131 (31.6%) | 148 (33.0%) | 279 (32.3%) |
| High.school | 171 (41.2%) | 192 (42.9%) | 363 (42.1%) |
| Postgraduate | 63 (15.2%) | 42 (9.4%) | 105 (12.2%) |
| Others | 20 (4.8%) | 37 (8.3%) | 57 (6.6%) |
| Income | |||
| <5millions | 272 (65.5%) | 188 (42.0%) | 460 (53.3%) |
| 5-10millions | 83 (20.0%) | 165 (36.8%) | 248 (28.7%) |
| 10-15millions | 51 (12.3%) | 67 (15.0%) | 118 (13.7%) |
| >15millions | 9 (2.2%) | 28 (6.2%) | 37 (4.3%) |
| MCCar | |||
| No | 415 (100%) | 428 (95.5%) | 843 (97.7%) |
| Yes | 0 (0%) | 20 (4.5%) | 20 (2.3%) |
| MCMotor | |||
| No | 414 (99.8%) | 287 (64.1%) | 701 (81.2%) |
| Yes | 1 (0.2%) | 161 (35.9%) | 162 (18.8%) |
| AGE | |||
| 16-25 | 221 (53.3%) | 200 (44.6%) | 421 (48.8%) |
| 26-35 | 76 (18.3%) | 93 (20.8%) | 169 (19.6%) |
| 36-45 | 36 (8.7%) | 66 (14.7%) | 102 (11.8%) |
| 46-55 | 35 (8.4%) | 42 (9.4%) | 77 (8.9%) |
| >55 | 47 (11.3%) | 47 (10.5%) | 94 (10.9%) |
| CITY | |||
| DaNang | 415 (100%) | 0 (0%) | 415 (48.1%) |
| HoChiMinh | 0 (0%) | 448 (100%) | 448 (51.9%) |
| FRE | |||
| >=3 days/week | 263 (63.4%) | 237 (52.9%) | 500 (57.9%) |
| 2days/month-2days/week | 86 (20.7%) | 80 (17.9%) | 166 (19.2%) |
| 2days/year-1day/month | 28 (6.7%) | 70 (15.6%) | 98 (11.4%) |
| <2 days/year | 38 (9.2%) | 61 (13.6%) | 99 (11.5%) |
| TripPurpose | |||
| Working | 116 (28.0%) | 189 (42.2%) | 305 (35.3%) |
| Studying | 163 (39.3%) | 134 (29.9%) | 297 (34.4%) |
| Shopping | 46 (11.1%) | 13 (2.9%) | 59 (6.8%) |
| Entertaining | 52 (12.5%) | 47 (10.5%) | 99 (11.5%) |
| Others | 38 (9.2%) | 65 (14.5%) | 103 (11.9%) |
| Departure | |||
| Normal | 147 (35.4%) | 72 (16.1%) | 219 (25.4%) |
| Peak-Hour | 268 (64.6%) | 376 (83.9%) | 644 (74.6%) |
| TimeUseonBus | |||
| Using.telephone | 107 (25.8%) | 94 (21.0%) | 201 (23.3%) |
| Reading | 22 (5.3%) | 31 (6.9%) | 53 (6.1%) |
| Listening | 54 (13.0%) | 83 (18.5%) | 137 (15.9%) |
| Nothing | 203 (48.9%) | 216 (48.2%) | 419 (48.6%) |
| Talking | 24 (5.8%) | 9 (2.0%) | 33 (3.8%) |
| Others | 5 (1.2%) | 15 (3.3%) | 20 (2.3%) |
| TravelTime | |||
| Mean (SD) | 1.12 (0.831) | 1.41 (1.81) | 1.27 (1.43) |
| Median [Min, Max] | 1.00 [0, 6.00] | 1.00 [0, 20.0] | 1.00 [0, 20.0] |
# Create data for Likert graph by BS (remove ID and TM =7-Bus)
head(SEM1)## MCCar MCMotor AGE CITY FRE TripPurpose Departure
## 1 No No 16-25 HoChiMinh >=3 days/week Studying Normal
## 2 No No 16-25 HoChiMinh 2days/month-2days/week Entertaining Normal
## 3 No No 16-25 HoChiMinh >=3 days/week Studying Peak-Hour
## 4 No No 16-25 HoChiMinh >=3 days/week Studying Peak-Hour
## 5 No No 16-25 HoChiMinh >=3 days/week Studying Peak-Hour
## 6 No Yes 16-25 HoChiMinh >=3 days/week Studying Peak-Hour
## TimeUseonBus TravelTime PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOY5 LOY6
## 1 Nothing 3.00 6 6 4 6 4 6 5 7 6 6
## 2 Nothing 2.00 3 3 4 4 4 5 4 4 4 4
## 3 Nothing 0.17 2 6 6 4 2 6 6 4 4 2
## 4 Using.telephone 4.00 5 6 6 5 5 5 5 5 5 5
## 5 Nothing 2.00 2 2 5 5 4 5 5 4 4 4
## 6 Others 2.00 2 5 4 4 4 3 2 5 5 6
## LOY7 IMA1 IMA2 IMA3 IMA4 IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEV1 PEV2 PEV3 PEV4
## 1 6 6 6 6 4 6 6 7 6 7 7 4 4 4 4
## 2 5 4 5 6 5 4 3 4 3 4 6 5 6 6 4
## 3 2 4 2 5 2 2 5 2 6 6 6 6 5 6 6
## 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 5 6 5 4 5 4 5 5 6 6 6 5 6 6 6 6
## 6 6 4 3 2 2 4 4 6 7 6 4 7 7 5 2
## PVA1 PVA2 PVA3 EC_Stop WC_Stop EC_Bus WC_Bus Gender MarriedStatus
## 1 6 6 6 Never Ever Never Ever Female Single
## 2 5 5 3 Never Never Ever Never Female Single
## 3 6 2 4 Never Never Never Never Male Single
## 4 5 5 5 Never Ever Never Ever Male Single
## 5 6 2 3 Never Ever Never Ever Male Single
## 6 6 5 5 Never Ever Never Ever Female Single
## Occupation Education Income
## 1 Students/Pupils Undergraduate <5millions
## 2 Students/Pupils Undergraduate <5millions
## 3 Students/Pupils Undergraduate <5millions
## 4 Students/Pupils Undergraduate <5millions
## 5 Students/Pupils Undergraduate <5millions
## 6 Students/Pupils High.school <5millionsstr(SEM1)## 'data.frame': 863 obs. of 46 variables:
## $ MCCar : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ MCMotor : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 2 2 2 2 2 ...
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 1 1 1 1 1 1 1 3 1 1 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 2 2 2 2 2 2 2 2 2 2 ...
## $ FRE : Factor w/ 4 levels ">=3 days/week",..: 1 2 1 1 1 1 4 1 1 1 ...
## $ TripPurpose : Factor w/ 5 levels "Working","Studying",..: 2 4 2 2 2 2 4 1 2 2 ...
## $ Departure : Factor w/ 2 levels "Normal","Peak-Hour": 1 1 2 2 2 2 2 2 2 2 ...
## $ TimeUseonBus : Factor w/ 6 levels "Using.telephone",..: 4 4 4 1 4 6 1 4 4 3 ...
## $ TravelTime : num 3 2 0.17 4 2 2 1 2 2.5 1.5 ...
## $ PSB1 : int 6 3 2 5 2 2 1 6 2 4 ...
## $ PSB2 : int 6 3 6 6 2 5 1 6 4 4 ...
## $ PSB3 : int 4 4 6 6 5 4 5 6 4 7 ...
## $ PSB4 : int 6 4 4 5 5 4 1 6 4 4 ...
## $ LOY1 : int 4 4 2 5 4 4 2 6 6 6 ...
## $ LOY2 : int 6 5 6 5 5 3 1 6 5 6 ...
## $ LOY3 : int 5 4 6 5 5 2 1 6 6 6 ...
## $ LOY4 : int 7 4 4 5 4 5 2 7 5 6 ...
## $ LOY5 : int 6 4 4 5 4 5 2 7 5 4 ...
## $ LOY6 : int 6 4 2 5 4 6 1 7 5 6 ...
## $ LOY7 : int 6 5 2 5 6 6 2 7 5 7 ...
## $ IMA1 : int 6 4 4 5 5 4 2 6 5 6 ...
## $ IMA2 : int 6 5 2 5 4 3 3 6 6 7 ...
## $ IMA3 : int 6 6 5 5 5 2 2 5 6 7 ...
## $ IMA4 : int 4 5 2 5 4 2 2 6 6 7 ...
## $ IMA5 : int 6 4 2 5 5 4 2 6 6 7 ...
## $ PHB1 : int 6 3 5 5 5 4 3 6 6 7 ...
## $ PHB2 : int 7 4 2 5 6 6 3 7 5 7 ...
## $ PHB3 : int 6 3 6 5 6 7 1 4 3 7 ...
## $ PHB4 : int 7 4 6 5 6 6 4 6 7 7 ...
## $ PHB5 : int 7 6 6 5 5 4 4 6 7 7 ...
## $ PEV1 : int 4 5 6 5 6 7 4 6 7 7 ...
## $ PEV2 : int 4 6 5 5 6 7 2 7 7 7 ...
## $ PEV3 : int 4 6 6 5 6 5 2 6 6 7 ...
## $ PEV4 : int 4 4 6 5 6 2 2 6 6 7 ...
## $ PVA1 : int 6 5 6 5 6 6 6 6 6 6 ...
## $ PVA2 : int 6 5 2 5 2 5 4 5 4 6 ...
## $ PVA3 : int 6 3 4 5 3 5 5 6 5 6 ...
## $ EC_Stop : Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ WC_Stop : Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ EC_Bus : Factor w/ 2 levels "Never","Ever": 1 2 1 1 1 1 1 1 1 1 ...
## $ WC_Bus : Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 1 1 1 1 1 ...
## $ MarriedStatus: Factor w/ 2 levels "Married","Single": 2 2 2 2 2 2 2 1 2 2 ...
## $ Occupation : Factor w/ 7 levels "Students/Pupils",..: 1 1 1 1 1 1 1 2 1 1 ...
## $ Education : Factor w/ 5 levels "Secondary.school",..: 2 2 2 2 2 3 5 1 2 3 ...
## $ Income : Factor w/ 4 levels "<5millions","5-10millions",..: 1 1 1 1 1 1 1 1 1 1 ...dim(SEM1)## [1] 863 46names(SEM1)## [1] "MCCar" "MCMotor" "AGE" "CITY"
## [5] "FRE" "TripPurpose" "Departure" "TimeUseonBus"
## [9] "TravelTime" "PSB1" "PSB2" "PSB3"
## [13] "PSB4" "LOY1" "LOY2" "LOY3"
## [17] "LOY4" "LOY5" "LOY6" "LOY7"
## [21] "IMA1" "IMA2" "IMA3" "IMA4"
## [25] "IMA5" "PHB1" "PHB2" "PHB3"
## [29] "PHB4" "PHB5" "PEV1" "PEV2"
## [33] "PEV3" "PEV4" "PVA1" "PVA2"
## [37] "PVA3" "EC_Stop" "WC_Stop" "EC_Bus"
## [41] "WC_Bus" "Gender" "MarriedStatus" "Occupation"
## [45] "Education" "Income"SEM1_likert = SEM1[,c(3, 4, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42)] # BPE gom cac bien tu 10-37
head(SEM1_likert)## AGE CITY PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7 IMA1
## 1 16-25 HoChiMinh 6 6 4 6 4 6 5 7 6 6 6 6
## 2 16-25 HoChiMinh 3 3 4 4 4 5 4 4 4 4 5 4
## 3 16-25 HoChiMinh 2 6 6 4 2 6 6 4 4 2 2 4
## 4 16-25 HoChiMinh 5 6 6 5 5 5 5 5 5 5 5 5
## 5 16-25 HoChiMinh 2 2 5 5 4 5 5 4 4 4 6 5
## 6 16-25 HoChiMinh 2 5 4 4 4 3 2 5 5 6 6 4
## IMA2 IMA3 IMA4 IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEV1 PEV2 PEV3 PEV4 PVA1 PVA2
## 1 6 6 4 6 6 7 6 7 7 4 4 4 4 6 6
## 2 5 6 5 4 3 4 3 4 6 5 6 6 4 5 5
## 3 2 5 2 2 5 2 6 6 6 6 5 6 6 6 2
## 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 5 4 5 4 5 5 6 6 6 5 6 6 6 6 6 2
## 6 3 2 2 4 4 6 7 6 4 7 7 5 2 6 5
## PVA3 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 6 Never Ever Never Ever Female
## 2 3 Never Never Ever Never Female
## 3 4 Never Never Never Never Male
## 4 5 Never Ever Never Ever Male
## 5 3 Never Ever Never Ever Male
## 6 5 Never Ever Never Ever Femalestr(SEM1_likert)## 'data.frame': 863 obs. of 35 variables:
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 1 1 1 1 1 1 1 3 1 1 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 2 2 2 2 2 2 2 2 2 2 ...
## $ PSB1 : int 6 3 2 5 2 2 1 6 2 4 ...
## $ PSB2 : int 6 3 6 6 2 5 1 6 4 4 ...
## $ PSB3 : int 4 4 6 6 5 4 5 6 4 7 ...
## $ PSB4 : int 6 4 4 5 5 4 1 6 4 4 ...
## $ LOY1 : int 4 4 2 5 4 4 2 6 6 6 ...
## $ LOY2 : int 6 5 6 5 5 3 1 6 5 6 ...
## $ LOY3 : int 5 4 6 5 5 2 1 6 6 6 ...
## $ LOY4 : int 7 4 4 5 4 5 2 7 5 6 ...
## $ LOY5 : int 6 4 4 5 4 5 2 7 5 4 ...
## $ LOY6 : int 6 4 2 5 4 6 1 7 5 6 ...
## $ LOY7 : int 6 5 2 5 6 6 2 7 5 7 ...
## $ IMA1 : int 6 4 4 5 5 4 2 6 5 6 ...
## $ IMA2 : int 6 5 2 5 4 3 3 6 6 7 ...
## $ IMA3 : int 6 6 5 5 5 2 2 5 6 7 ...
## $ IMA4 : int 4 5 2 5 4 2 2 6 6 7 ...
## $ IMA5 : int 6 4 2 5 5 4 2 6 6 7 ...
## $ PHB1 : int 6 3 5 5 5 4 3 6 6 7 ...
## $ PHB2 : int 7 4 2 5 6 6 3 7 5 7 ...
## $ PHB3 : int 6 3 6 5 6 7 1 4 3 7 ...
## $ PHB4 : int 7 4 6 5 6 6 4 6 7 7 ...
## $ PHB5 : int 7 6 6 5 5 4 4 6 7 7 ...
## $ PEV1 : int 4 5 6 5 6 7 4 6 7 7 ...
## $ PEV2 : int 4 6 5 5 6 7 2 7 7 7 ...
## $ PEV3 : int 4 6 6 5 6 5 2 6 6 7 ...
## $ PEV4 : int 4 4 6 5 6 2 2 6 6 7 ...
## $ PVA1 : int 6 5 6 5 6 6 6 6 6 6 ...
## $ PVA2 : int 6 5 2 5 2 5 4 5 4 6 ...
## $ PVA3 : int 6 3 4 5 3 5 5 6 5 6 ...
## $ EC_Stop: Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ WC_Stop: Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ EC_Bus : Factor w/ 2 levels "Never","Ever": 1 2 1 1 1 1 1 1 1 1 ...
## $ WC_Bus : Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 1 1 1 1 1 ...dim(SEM1_likert)## [1] 863 35## Creat dat_PSB1 - cot 3 cua SEM1_likert
dat_PSB1 <- SEM1_likert [, c(1,2,3,31, 32, 33, 34, 35)]
head(dat_PSB1)## AGE CITY PSB1 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 2 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Femaledim(dat_PSB1)## [1] 863 8size <- 863
dat_PSB1$BPE <- rep("PSB1", size = size) # Tạo biến mới BPE
names(dat_PSB1)[names(dat_PSB1) == "PSB1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSB1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSB1
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PSB1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSB1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSB1
## 5 16-25 HoChiMinh 2 Never Ever Never Ever Male PSB1
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Female PSB1## Creat dat_PSB2 - cot 4 cua SEM1_likert
dat_PSB2 <- SEM1_likert [, c(1,2,4,31, 32, 33, 34, 35)]
head(dat_PSB2)## AGE CITY PSB2 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 2 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Femaledim(dat_PSB2)## [1] 863 8size <- 863
dat_PSB2$BPE <- rep("PSB2", size = size) # Tạo biến mới BPE
names(dat_PSB2)[names(dat_PSB2) == "PSB2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSB2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSB2
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PSB2
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PSB2
## 4 16-25 HoChiMinh 6 Never Ever Never Ever Male PSB2
## 5 16-25 HoChiMinh 2 Never Ever Never Ever Male PSB2
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female PSB2## Creat dat_PSB3 - cot 5 cua SEN1_likert
dat_PSB3 <- SEM1_likert [, c(1,2,5,31, 32, 33, 34, 35)]
head(dat_PSB3)## AGE CITY PSB3 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_PSB3)## [1] 863 8size <- 863
dat_PSB3$BPE <- rep("PSB3", size = size) # Tạo biến mới BPE
names(dat_PSB3)[names(dat_PSB3) == "PSB3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSB3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PSB3
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSB3
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PSB3
## 4 16-25 HoChiMinh 6 Never Ever Never Ever Male PSB3
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male PSB3
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PSB3## Creat dat_PSB4 - cot 6 cua SEM1_likert
dat_PSB4 <- SEM1_likert [, c(1,2,6,31, 32, 33, 34, 35)]
head(dat_PSB4)## AGE CITY PSB4 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 4 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_PSB4)## [1] 863 8size <- 863
dat_PSB4$BPE <- rep("PSB4", size = size) # Tạo biến mới BPE
names(dat_PSB4)[names(dat_PSB4) == "PSB4"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSB4)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSB4
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSB4
## 3 16-25 HoChiMinh 4 Never Never Never Never Male PSB4
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSB4
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male PSB4
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PSB4## Creat dat_LOY1 - cot 7 cua SEM1_likert
dat_LOY1 <- SEM1_likert [, c(1,2,7,31, 32, 33, 34, 35)]
head(dat_LOY1)## AGE CITY LOY1 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_LOY1)## [1] 863 8size <- 863
dat_LOY1$BPE <- rep("LOY1", size = size) # Tạo biến mới BPE
names(dat_LOY1)[names(dat_LOY1) == "LOY1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_LOY1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female LOY1
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female LOY1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male LOY1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male LOY1
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male LOY1
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female LOY1## Creat dat_LOY2 - cot 8 cua SEM1_likert
dat_LOY2 <- SEM1_likert [, c(1,2,8,31, 32, 33, 34, 35)]
head(dat_LOY2)## AGE CITY LOY2 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Femaledim(dat_LOY2)## [1] 863 8size <- 863
dat_LOY2$BPE <- rep("LOY2", size = size) # Tạo biến mới BPE
names(dat_LOY2)[names(dat_LOY2) == "LOY2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_LOY2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female LOY2
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female LOY2
## 3 16-25 HoChiMinh 6 Never Never Never Never Male LOY2
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male LOY2
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male LOY2
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Female LOY2## Creat dat_LOY3 - cot 9 cua SEM1_likert
dat_LOY3 <- SEM1_likert [, c(1,2,9,31, 32, 33, 34, 35)]
head(dat_LOY3)## AGE CITY LOY3 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 5 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Femaledim(dat_LOY3)## [1] 863 8size <- 863
dat_LOY3$BPE <- rep("LOY3", size = size) # Tạo biến mới BPE
names(dat_LOY3)[names(dat_LOY3) == "LOY3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_LOY3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 5 Never Ever Never Ever Female LOY3
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female LOY3
## 3 16-25 HoChiMinh 6 Never Never Never Never Male LOY3
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male LOY3
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male LOY3
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Female LOY3## Creat dat_LOY4 - cot 10 cua SEM1_likert
dat_LOY4 <- SEM1_likert [, c(1,2,10,31, 32, 33, 34, 35)]
head(dat_LOY4)## AGE CITY LOY4 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 7 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 4 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Femaledim(dat_LOY4)## [1] 863 8size <- 863
dat_LOY4$BPE <- rep("LOY4", size = size) # Tạo biến mới BPE
names(dat_LOY4)[names(dat_LOY4) == "LOY4"] <- "Res" # Đổi tên biến BST thành Res
head(dat_LOY4)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 7 Never Ever Never Ever Female LOY4
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female LOY4
## 3 16-25 HoChiMinh 4 Never Never Never Never Male LOY4
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male LOY4
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male LOY4
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female LOY4## Creat dat_LIN5 - cot 11 cua SEM1_likert
dat_LOY5 <- SEM1_likert [, c(1,2,11,31, 32, 33, 34, 35)]
head(dat_LOY5)## AGE CITY LOY5 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 4 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Femaledim(dat_LOY5)## [1] 863 8size <- 863
dat_LOY5$BPE <- rep("LOY5", size = size) # Tạo biến mới BPE
names(dat_LOY5)[names(dat_LOY5) == "LOY5"] <- "Res" # Đổi tên biến BST thành Res
head(dat_LOY5)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female LOY5
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female LOY5
## 3 16-25 HoChiMinh 4 Never Never Never Never Male LOY5
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male LOY5
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male LOY5
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female LOY5## Creat dat_LOY6 - cot 12 cua SEM1_likert
dat_LOY6 <- SEM1_likert [, c(1,2,12,31, 32, 33, 34, 35)]
head(dat_LOY6)## AGE CITY LOY6 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Femaledim(dat_LOY6)## [1] 863 8size <- 863
dat_LOY6$BPE <- rep("LOY6", size = size) # Tạo biến mới BPE
names(dat_LOY6)[names(dat_LOY6) == "LOY6"] <- "Res" # Đổi tên biến BST thành Res
head(dat_LOY6)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female LOY6
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female LOY6
## 3 16-25 HoChiMinh 2 Never Never Never Never Male LOY6
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male LOY6
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male LOY6
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Female LOY6## Creat dat_LOY7 - cot 13 cua SEM1_likert
dat_LOY7 <- SEM1_likert [, c(1,2,13,31, 32, 33, 34, 35)]
head(dat_LOY7)## AGE CITY LOY7 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Femaledim(dat_LOY7)## [1] 863 8size <- 863
dat_LOY7$BPE <- rep("LOY7", size = size) # Tạo biến mới BPE
names(dat_LOY7)[names(dat_LOY7) == "LOY7"] <- "Res" # Đổi tên biến BST thành Res
head(dat_LOY7)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female LOY7
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female LOY7
## 3 16-25 HoChiMinh 2 Never Never Never Never Male LOY7
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male LOY7
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male LOY7
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Female LOY7## Creat dat_IMA1 - cot 14 cua SEM1_likert
dat_IMA1 <- SEM1_likert [, c(1,2,14,31, 32, 33, 34, 35)]
head(dat_IMA1)## AGE CITY IMA1 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 4 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_IMA1)## [1] 863 8size <- 863
dat_IMA1$BPE <- rep("IMA1", size = size) # Tạo biến mới BPE
names(dat_IMA1)[names(dat_IMA1) == "IMA1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_IMA1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female IMA1
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female IMA1
## 3 16-25 HoChiMinh 4 Never Never Never Never Male IMA1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male IMA1
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male IMA1
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female IMA1## Creat dat_IMA2 - cot 15 cua SEM1_likert
dat_IMA2 <- SEM1_likert [, c(1,2,15,31, 32, 33, 34, 35)]
head(dat_IMA2)## AGE CITY IMA2 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Femaledim(dat_IMA2)## [1] 863 8size <- 863
dat_IMA2$BPE <- rep("IMA2", size = size) # Tạo biến mới BPE
names(dat_IMA2)[names(dat_IMA2) == "IMA2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_IMA2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female IMA2
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female IMA2
## 3 16-25 HoChiMinh 2 Never Never Never Never Male IMA2
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male IMA2
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male IMA2
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Female IMA2## Creat dat_IMA3 - cot 16 cua SEM1_likert
dat_IMA3 <- SEM1_likert [, c(1,2,16,31, 32, 33, 34, 35)]
head(dat_IMA3)## AGE CITY IMA3 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female
## 3 16-25 HoChiMinh 5 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Femaledim(dat_IMA3)## [1] 863 8size <- 863
dat_IMA3$BPE <- rep("IMA3", size = size) # Tạo biến mới BPE
names(dat_IMA3)[names(dat_IMA3) == "IMA3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_IMA3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female IMA3
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female IMA3
## 3 16-25 HoChiMinh 5 Never Never Never Never Male IMA3
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male IMA3
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male IMA3
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Female IMA3## Creat dat_IMA4 - cot 17 cua SEM1_likert
dat_IMA4 <- SEM1_likert [, c(1,2,17,31, 32, 33, 34, 35)]
head(dat_IMA4)## AGE CITY IMA4 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Femaledim(dat_IMA4)## [1] 863 8size <- 863
dat_IMA4$BPE <- rep("IMA4", size = size) # Tạo biến mới BPE
names(dat_IMA4)[names(dat_IMA4) == "IMA4"] <- "Res" # Đổi tên biến BST thành Res
head(dat_IMA4)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female IMA4
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female IMA4
## 3 16-25 HoChiMinh 2 Never Never Never Never Male IMA4
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male IMA4
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male IMA4
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Female IMA4## Creat dat_IMA5 - cot 18 cua SEM1_likert
dat_IMA5 <- SEM1_likert [, c(1,2,18,31, 32, 33, 34, 35)]
head(dat_IMA5)## AGE CITY IMA5 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_IMA5)## [1] 863 8size <- 863
dat_IMA5$BPE <- rep("IMA5", size = size) # Tạo biến mới BPE
names(dat_IMA5)[names(dat_IMA5) == "IMA5"] <- "Res" # Đổi tên biến BST thành Res
head(dat_IMA5)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female IMA5
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female IMA5
## 3 16-25 HoChiMinh 2 Never Never Never Never Male IMA5
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male IMA5
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male IMA5
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female IMA5## Creat dat_PHB1 - cot 19 cua SEM1_likert
dat_PHB1 <- SEM1_likert [, c(1,2,19,31, 32, 33, 34, 35)]
head(dat_PHB1)## AGE CITY PHB1 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female
## 3 16-25 HoChiMinh 5 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_PHB1)## [1] 863 8size <- 863
dat_PHB1$BPE <- rep("PHB1", size = size) # Tạo biến mới BPE
names(dat_PHB1)[names(dat_PHB1) == "PHB1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PHB1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PHB1
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PHB1
## 3 16-25 HoChiMinh 5 Never Never Never Never Male PHB1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB1
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB1
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PHB1## Creat dat_PHB2 - cot 20 cua SEM1_likert
dat_PHB2 <- SEM1_likert [, c(1,2,20,31, 32, 33, 34, 35)]
head(dat_PHB2)## AGE CITY PHB2 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 7 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Femaledim(dat_PHB2)## [1] 863 8size <- 863
dat_PHB2$BPE <- rep("PHB2", size = size) # Tạo biến mới BPE
names(dat_PHB2)[names(dat_PHB2) == "PHB2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PHB2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 7 Never Ever Never Ever Female PHB2
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PHB2
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PHB2
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB2
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PHB2
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Female PHB2## Creat dat_PHB3 - cot 21 cua SEN1_likert
dat_PHB3 <- SEM1_likert [, c(1,2,21,31, 32, 33, 34, 35)]
head(dat_PHB3)## AGE CITY PHB3 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 7 Never Ever Never Ever Femaledim(dat_PHB3)## [1] 863 8size <- 863
dat_PHB3$BPE <- rep("PHB3", size = size) # Tạo biến mới BPE
names(dat_PHB3)[names(dat_PHB3) == "PHB3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PHB3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PHB3
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PHB3
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PHB3
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB3
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PHB3
## 6 16-25 HoChiMinh 7 Never Ever Never Ever Female PHB3## Creat dat_PHB4 - cot 22 cua SEM1_likert
dat_PHB4 <- SEM1_likert [, c(1,2,22,31, 32, 33, 34, 35)]
head(dat_PHB4)## AGE CITY PHB4 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 7 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Femaledim(dat_PHB4)## [1] 863 8size <- 863
dat_PHB4$BPE <- rep("PHB4", size = size) # Tạo biến mới BPE
names(dat_PHB4)[names(dat_PHB4) == "PHB4"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PHB4)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 7 Never Ever Never Ever Female PHB4
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PHB4
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PHB4
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB4
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PHB4
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Female PHB4## Creat dat_PHB5 - cot 23 cua SEM1_likert
dat_PHB5 <- SEM1_likert [, c(1,2,23,31, 32, 33, 34, 35)]
head(dat_PHB5)## AGE CITY PHB5 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 7 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_PHB5)## [1] 863 8size <- 863
dat_PHB5$BPE <- rep("PHB5", size = size) # Tạo biến mới BPE
names(dat_PHB5)[names(dat_PHB5) == "PHB5"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PHB5)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 7 Never Ever Never Ever Female PHB5
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female PHB5
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PHB5
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB5
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB5
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PHB5## Creat dat_PEV1 - cot 24 cua SEM1_likert
dat_PEV1 <- SEM1_likert [, c(1,2,24,31, 32, 33, 34, 35)]
head(dat_PEV1)## AGE CITY PEV1 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 7 Never Ever Never Ever Femaledim(dat_PEV1)## [1] 863 8size <- 863
dat_PEV1$BPE <- rep("PEV1", size = size) # Tạo biến mới BPE
names(dat_PEV1)[names(dat_PEV1) == "PEV1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PEV1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PEV1
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female PEV1
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PEV1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PEV1
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PEV1
## 6 16-25 HoChiMinh 7 Never Ever Never Ever Female PEV1dim(dat_PEV1)## [1] 863 9## Creat dat_PEV2 - cot 25 cua SEM1_likert
dat_PEV2 <- SEM1_likert [, c(1,2,25,31, 32, 33, 34, 35)]
head(dat_PEV2)## AGE CITY PEV2 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female
## 3 16-25 HoChiMinh 5 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 7 Never Ever Never Ever Femaledim(dat_PEV2)## [1] 863 8size <- 863
dat_PEV2$BPE <- rep("PEV2", size = size) # Tạo biến mới BPE
names(dat_PEV2)[names(dat_PEV2) == "PEV2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PEV2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PEV2
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female PEV2
## 3 16-25 HoChiMinh 5 Never Never Never Never Male PEV2
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PEV2
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PEV2
## 6 16-25 HoChiMinh 7 Never Ever Never Ever Female PEV2dim(dat_PEV2)## [1] 863 9## Creat dat_PEV3 - cot 26 cua SEM1_likert
dat_PEV3 <- SEM1_likert [, c(1,2,26,31, 32, 33, 34, 35)]
head(dat_PEV3)## AGE CITY PEV3 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Femaledim(dat_PEV3)## [1] 863 8size <- 863
dat_PEV3$BPE <- rep("PEV3", size = size) # Tạo biến mới BPE
names(dat_PEV3)[names(dat_PEV3) == "PEV3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PEV3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PEV3
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female PEV3
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PEV3
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PEV3
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PEV3
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female PEV3dim(dat_PEV3)## [1] 863 9## Creat dat_PEV4 - cot 27 cua SEM1_likert
dat_PEV4 <- SEM1_likert [, c(1,2,27,31, 32, 33, 34, 35)]
head(dat_PEV4)## AGE CITY PEV4 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Femaledim(dat_PEV4)## [1] 863 8size <- 863
dat_PEV4$BPE <- rep("PEV4", size = size) # Tạo biến mới BPE
names(dat_PEV4)[names(dat_PEV4) == "PEV4"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PEV4)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PEV4
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PEV4
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PEV4
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PEV4
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PEV4
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Female PEV4dim(PEV4)## NULL## Creat dat_PVA1 - cot 28 cua SEM1_likert
dat_PVA1 <- SEM1_likert [, c(1,2,28,31, 32, 33, 34, 35)]
head(dat_PVA1)## AGE CITY PVA1 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Femaledim(dat_PVA1)## [1] 863 8size <- 863
dat_PVA1$BPE <- rep("PVA1", size = size) # Tạo biến mới BPE
names(dat_PVA1)[names(dat_PVA1) == "PVA1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PVA1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PVA1
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female PVA1
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PVA1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PVA1
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PVA1
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Female PVA1## Creat dat_PVA2 - cot 29 cua SEM1_likert
dat_PVA2 <- SEM1_likert [, c(1,2,29,31, 32, 33, 34, 35)]
head(dat_PVA2)## AGE CITY PVA2 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 2 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Femaledim(dat_PVA2)## [1] 863 8size <- 863
dat_PVA2$BPE <- rep("PVA2", size = size) # Tạo biến mới BPE
names(dat_PVA2)[names(dat_PVA2) == "PVA2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PVA2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PVA2
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female PVA2
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PVA2
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PVA2
## 5 16-25 HoChiMinh 2 Never Ever Never Ever Male PVA2
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female PVA2## Creat dat_PVA3 - cot 30 cua SEM1_likert
dat_PVA3 <- SEM1_likert [, c(1,2,30,31, 32, 33, 34, 35)]
head(dat_PVA3)## AGE CITY PVA3 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female
## 3 16-25 HoChiMinh 4 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 3 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Femaledim(dat_PVA3)## [1] 863 8size <- 863
dat_PVA3$BPE <- rep("PVA3", size = size) # Tạo biến mới BPE
names(dat_PVA3)[names(dat_PVA3) == "PVA3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PVA3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PVA3
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PVA3
## 3 16-25 HoChiMinh 4 Never Never Never Never Male PVA3
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PVA3
## 5 16-25 HoChiMinh 3 Never Ever Never Ever Male PVA3
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female PVA3## Tạo dữ liệu likert từ 28 dữ liệu: dat_PSA1-4, dat_LOY1-7, dat_IMA1-5, dat_PHB1-5, dat_PEV1-4, dat_PVA1-3
B.Per <- rbind(dat_PSB1, dat_PSB2, dat_PSB3, dat_PSB4, dat_LOY1, dat_LOY2, dat_LOY3, dat_LOY4, dat_LOY5, dat_LOY6, dat_LOY7, dat_IMA1, dat_IMA2, dat_IMA3, dat_IMA4, dat_IMA5, dat_PHB1, dat_PHB2, dat_PHB3, dat_PHB4, dat_PHB5, dat_PEV1, dat_PEV2, dat_PEV3, dat_PEV4, dat_PVA1, dat_PVA2, dat_PVA3)
head(B.Per)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSB1
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PSB1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSB1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSB1
## 5 16-25 HoChiMinh 2 Never Ever Never Ever Male PSB1
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Female PSB1str(B.Per)## 'data.frame': 24164 obs. of 9 variables:
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 1 1 1 1 1 1 1 3 1 1 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 2 2 2 2 2 2 2 2 2 2 ...
## $ Res : int 6 3 2 5 2 2 1 6 2 4 ...
## $ EC_Stop: Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ WC_Stop: Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ EC_Bus : Factor w/ 2 levels "Never","Ever": 1 2 1 1 1 1 1 1 1 1 ...
## $ WC_Bus : Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 1 1 1 1 1 ...
## $ BPE : chr "PSB1" "PSB1" "PSB1" "PSB1" ...dim(B.Per)## [1] 24164 9## Tạo dữ liệu likert theo constructs
B.Per_PSB <- rbind(dat_PSB1, dat_PSB2, dat_PSB3, dat_PSB4)
head(B.Per_PSB)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSB1
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PSB1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSB1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSB1
## 5 16-25 HoChiMinh 2 Never Ever Never Ever Male PSB1
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Female PSB1str(B.Per_PSB)## 'data.frame': 3452 obs. of 9 variables:
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 1 1 1 1 1 1 1 3 1 1 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 2 2 2 2 2 2 2 2 2 2 ...
## $ Res : int 6 3 2 5 2 2 1 6 2 4 ...
## $ EC_Stop: Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ WC_Stop: Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ EC_Bus : Factor w/ 2 levels "Never","Ever": 1 2 1 1 1 1 1 1 1 1 ...
## $ WC_Bus : Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 1 1 1 1 1 ...
## $ BPE : chr "PSB1" "PSB1" "PSB1" "PSB1" ...dim(B.Per_PSB)## [1] 3452 9B.Per_LOY <- rbind(dat_LOY1, dat_LOY2, dat_LOY3, dat_LOY4, dat_LOY5, dat_LOY6, dat_LOY7)
head(B.Per_LOY)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female LOY1
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female LOY1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male LOY1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male LOY1
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male LOY1
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female LOY1str(B.Per_LOY)## 'data.frame': 6041 obs. of 9 variables:
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 1 1 1 1 1 1 1 3 1 1 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 2 2 2 2 2 2 2 2 2 2 ...
## $ Res : int 4 4 2 5 4 4 2 6 6 6 ...
## $ EC_Stop: Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ WC_Stop: Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ EC_Bus : Factor w/ 2 levels "Never","Ever": 1 2 1 1 1 1 1 1 1 1 ...
## $ WC_Bus : Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 1 1 1 1 1 ...
## $ BPE : chr "LOY1" "LOY1" "LOY1" "LOY1" ...dim(B.Per_LOY)## [1] 6041 9B.Per_IMA <- rbind(dat_IMA1, dat_IMA2, dat_IMA3, dat_IMA4, dat_IMA5)
head(B.Per_IMA)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female IMA1
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female IMA1
## 3 16-25 HoChiMinh 4 Never Never Never Never Male IMA1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male IMA1
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male IMA1
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female IMA1str(B.Per_IMA)## 'data.frame': 4315 obs. of 9 variables:
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 1 1 1 1 1 1 1 3 1 1 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 2 2 2 2 2 2 2 2 2 2 ...
## $ Res : int 6 4 4 5 5 4 2 6 5 6 ...
## $ EC_Stop: Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ WC_Stop: Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ EC_Bus : Factor w/ 2 levels "Never","Ever": 1 2 1 1 1 1 1 1 1 1 ...
## $ WC_Bus : Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 1 1 1 1 1 ...
## $ BPE : chr "IMA1" "IMA1" "IMA1" "IMA1" ...dim(B.Per_IMA)## [1] 4315 9B.Per_PHB <- rbind(dat_PHB1, dat_PHB2, dat_PHB3, dat_PHB4, dat_PHB5)
head(B.Per_PHB)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PHB1
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PHB1
## 3 16-25 HoChiMinh 5 Never Never Never Never Male PHB1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB1
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB1
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PHB1str(B.Per_PHB)## 'data.frame': 4315 obs. of 9 variables:
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 1 1 1 1 1 1 1 3 1 1 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 2 2 2 2 2 2 2 2 2 2 ...
## $ Res : int 6 3 5 5 5 4 3 6 6 7 ...
## $ EC_Stop: Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ WC_Stop: Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ EC_Bus : Factor w/ 2 levels "Never","Ever": 1 2 1 1 1 1 1 1 1 1 ...
## $ WC_Bus : Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 1 1 1 1 1 ...
## $ BPE : chr "PHB1" "PHB1" "PHB1" "PHB1" ...dim(B.Per_PHB)## [1] 4315 9B.Per_PEV <- rbind(dat_PEV1, dat_PEV2, dat_PEV3, dat_PEV4)
head(B.Per_PEV)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PEV1
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female PEV1
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PEV1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PEV1
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PEV1
## 6 16-25 HoChiMinh 7 Never Ever Never Ever Female PEV1str(B.Per_PEV)## 'data.frame': 3452 obs. of 9 variables:
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 1 1 1 1 1 1 1 3 1 1 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 2 2 2 2 2 2 2 2 2 2 ...
## $ Res : int 4 5 6 5 6 7 4 6 7 7 ...
## $ EC_Stop: Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ WC_Stop: Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ EC_Bus : Factor w/ 2 levels "Never","Ever": 1 2 1 1 1 1 1 1 1 1 ...
## $ WC_Bus : Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 1 1 1 1 1 ...
## $ BPE : chr "PEV1" "PEV1" "PEV1" "PEV1" ...dim(B.Per_PEV)## [1] 3452 9B.Per_PVA <- rbind(dat_PVA1, dat_PVA2, dat_PVA3)
head(B.Per_PVA)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PVA1
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female PVA1
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PVA1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PVA1
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PVA1
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Female PVA1str(B.Per_PVA)## 'data.frame': 2589 obs. of 9 variables:
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 1 1 1 1 1 1 1 3 1 1 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 2 2 2 2 2 2 2 2 2 2 ...
## $ Res : int 6 5 6 5 6 6 6 6 6 6 ...
## $ EC_Stop: Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ WC_Stop: Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ EC_Bus : Factor w/ 2 levels "Never","Ever": 1 2 1 1 1 1 1 1 1 1 ...
## $ WC_Bus : Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 1 1 1 1 1 ...
## $ BPE : chr "PVA1" "PVA1" "PVA1" "PVA1" ...dim(B.Per_PVA)## [1] 2589 9# Cách 2: Tạo dữ liệu thủ công trên excel với các cột BPE, Res, BUB, MSI, SBM, SBS: t_likert = "F:\\NGHIEN CUU SINH\\NCS - PHUONG ANH\\Part 3-Mode shift\\So lieu mode choice tu phan 1\\8. MS-Bieu do Likert.csv" ; BP_likert = read.csv(t_likert, header = T, sep = ";")
# Data Bus perception of people in Da Nang
DN_SEM1 = subset(B.Per, CITY == "DaNang")
head(DN_SEM1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 449 26-35 DaNang 6 Never Never Never Never Male PSB1
## 450 46-55 DaNang 6 Never Never Never Never Female PSB1
## 451 46-55 DaNang 6 Never Never Never Never Female PSB1
## 452 26-35 DaNang 5 Never Never Never Never Female PSB1
## 453 46-55 DaNang 6 Never Never Never Never Female PSB1
## 454 16-25 DaNang 6 Never Never Never Never Female PSB1str(DN_SEM1)## 'data.frame': 11620 obs. of 9 variables:
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 2 4 4 2 4 1 1 2 1 2 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 1 1 1 1 1 1 1 1 1 1 ...
## $ Res : int 6 6 6 5 6 6 6 7 4 6 ...
## $ EC_Stop: Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ WC_Stop: Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ EC_Bus : Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ WC_Bus : Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 2 1 1 1 1 1 1 2 2 1 ...
## $ BPE : chr "PSB1" "PSB1" "PSB1" "PSB1" ...dim(DN_SEM1)## [1] 11620 9DN_SEM1 = DN_SEM1[ , c(3, 9)]
dim(DN_SEM1)## [1] 11620 2head(DN_SEM1)## Res BPE
## 449 6 PSB1
## 450 6 PSB1
## 451 6 PSB1
## 452 5 PSB1
## 453 6 PSB1
## 454 6 PSB1# Data Bus perception of people in Ho Chi Minh
HCM_SEM1 = subset(B.Per, CITY == "HoChiMinh")
head(HCM_SEM1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSB1
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PSB1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSB1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSB1
## 5 16-25 HoChiMinh 2 Never Ever Never Ever Male PSB1
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Female PSB1str(HCM_SEM1)## 'data.frame': 12544 obs. of 9 variables:
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 1 1 1 1 1 1 1 3 1 1 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 2 2 2 2 2 2 2 2 2 2 ...
## $ Res : int 6 3 2 5 2 2 1 6 2 4 ...
## $ EC_Stop: Factor w/ 2 levels "Never","Ever": 1 1 1 1 1 1 1 1 1 1 ...
## $ WC_Stop: Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ EC_Bus : Factor w/ 2 levels "Never","Ever": 1 2 1 1 1 1 1 1 1 1 ...
## $ WC_Bus : Factor w/ 2 levels "Never","Ever": 2 1 1 2 2 2 1 1 2 1 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 1 1 1 1 1 ...
## $ BPE : chr "PSB1" "PSB1" "PSB1" "PSB1" ...dim(HCM_SEM1)## [1] 12544 9HCM_SEM1 = HCM_SEM1[ , c(3, 9)]
dim(HCM_SEM1)## [1] 12544 2head(HCM_SEM1)## Res BPE
## 1 6 PSB1
## 2 3 PSB1
## 3 2 PSB1
## 4 5 PSB1
## 5 2 PSB1
## 6 2 PSB1# Data Bus perception of people in both Da Nang and Ho Chi Minh cities
DN_HCM_SEM1 = B.Per[, c(3, 9)]
str(DN_HCM_SEM1)## 'data.frame': 24164 obs. of 2 variables:
## $ Res: int 6 3 2 5 2 2 1 6 2 4 ...
## $ BPE: chr "PSB1" "PSB1" "PSB1" "PSB1" ...dim(DN_HCM_SEM1)## [1] 24164 2head(DN_HCM_SEM1)## Res BPE
## 1 6 PSB1
## 2 3 PSB1
## 3 2 PSB1
## 4 5 PSB1
## 5 2 PSB1
## 6 2 PSB1# Data bus perception of people in both Da Nang and Ho Chi Minh cities - according constructs
## Construct PSA
DN_HCM_SEM1_PSB = B.Per_PSB[ , c(3, 9)]
dim(DN_HCM_SEM1_PSB)## [1] 3452 2head(DN_HCM_SEM1_PSB)## Res BPE
## 1 6 PSB1
## 2 3 PSB1
## 3 2 PSB1
## 4 5 PSB1
## 5 2 PSB1
## 6 2 PSB1## Construct LOY
DN_HCM_SEM1_LOY = B.Per_LOY[ , c(3, 9)]
dim(DN_HCM_SEM1_LOY)## [1] 6041 2head(DN_HCM_SEM1_LOY)## Res BPE
## 1 4 LOY1
## 2 4 LOY1
## 3 2 LOY1
## 4 5 LOY1
## 5 4 LOY1
## 6 4 LOY1## Construct IMA
DN_HCM_SEM1_IMA = B.Per_IMA[ , c(3, 9)]
dim(DN_HCM_SEM1_IMA)## [1] 4315 2head(DN_HCM_SEM1_IMA)## Res BPE
## 1 6 IMA1
## 2 4 IMA1
## 3 4 IMA1
## 4 5 IMA1
## 5 5 IMA1
## 6 4 IMA1## Construct PHB
DN_HCM_SEM1_PHB = B.Per_PHB[ , c(3, 9)]
dim(DN_HCM_SEM1_PHB)## [1] 4315 2head(DN_HCM_SEM1_PHB)## Res BPE
## 1 6 PHB1
## 2 3 PHB1
## 3 5 PHB1
## 4 5 PHB1
## 5 5 PHB1
## 6 4 PHB1## Construct PEV
DN_HCM_SEM1_PEV = B.Per_PEV[ , c(3, 9)]
dim(DN_HCM_SEM1_PEV)## [1] 3452 2head(DN_HCM_SEM1_PEV)## Res BPE
## 1 4 PEV1
## 2 5 PEV1
## 3 6 PEV1
## 4 5 PEV1
## 5 6 PEV1
## 6 7 PEV1## Construct PVA
DN_HCM_SEM1_PVA = B.Per_PVA[ , c(3, 9)]
dim(DN_HCM_SEM1_PVA)## [1] 2589 2head(DN_HCM_SEM1_PVA)## Res BPE
## 1 6 PVA1
## 2 5 PVA1
## 3 6 PVA1
## 4 5 PVA1
## 5 6 PVA1
## 6 6 PVA13.2. Vẽ biểu đồ likert với dữ liệu chung Đà Nẵng và Hồ Chí Minh - Nhãn tiếng anh
# Plot Likert graph for people in both Da Nang and Ho Chi Minh cities
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.4 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x tidyr::extract() masks magrittr::extract()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x purrr::set_names() masks magrittr::set_names()library(compareGroups)
head(DN_HCM_SEM1)## Res BPE
## 1 6 PSB1
## 2 3 PSB1
## 3 2 PSB1
## 4 5 PSB1
## 5 2 PSB1
## 6 2 PSB1dim(DN_HCM_SEM1)## [1] 24164 2attach(DN_HCM_SEM1)
DN_HCM_SEM1 = within(DN_HCM_SEM1, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("PSB1", "PSB2", "PSB3", "PSB4", "LOY1", "LOY2", "LOY3", "LOY4", "LOY5", "LOY6", "LOY7", "IMA1", "IMA2", "IMA3", "IMA4", "IMA5", "PHB1", "PHB2", "PHB3", "PHB4", "PHB5", "PEV1", "PEV2", "PEV3", "PEV4", "PVA1", "PVA2", "PVA3"))
})
str(DN_HCM_SEM1)## 'data.frame': 24164 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 3 2 5 2 2 1 6 2 4 ...
## $ BPE: Factor w/ 28 levels "PSB1","PSB2",..: 22 22 22 22 22 22 22 22 22 22 ...summary(DN_HCM_SEM1)## Res BPE
## Very Disagree : 460 PSB1 : 863
## Disagree : 746 PSB2 : 863
## Quite Disagree: 768 PSB3 : 863
## Normal : 2719 PSB4 : 863
## Quite Agree : 4804 LOY1 : 863
## Agree :10731 LOY2 : 863
## Very Agree : 3936 (Other):18986 ## t <- compareGroups(Res ~ BPE, data = DN_HCM_SEM1)
## createTable(t)
DN_HCM_SEM1 %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
# arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7 IMA1 IMA2 IMA3 IMA4
## [16] IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEV1 PEV2 PEV3 PEV4 PVA1 PVA2 PVA3
## 28 Levels: PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7 IMA1 ... PVA3# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 196 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Disagree 6 0.7 0.7%
## 2 PSB1 Disagree 20 2.32 2.32%
## 3 PSB1 Quite Disagree 32 3.71 3.71%
## 4 PSB1 Normal 156 18.1 18.08%
## 5 PSB1 Quite Agree 230 26.6 26.65%
## 6 PSB1 Agree 354 41.0 41.02%
## 7 PSB1 Very Agree 65 7.53 7.53%
## 8 PSB2 Very Disagree 11 1.27 1.27%
## 9 PSB2 Disagree 19 2.2 2.2%
## 10 PSB2 Quite Disagree 26 3.01 3.01%
## # ... with 186 more rowslibrary(extrafont)## Registering fonts with Rtheme_set(theme_minimal())
gg <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "Perception of passengers towards bus systems in urban areas of Viet Nam",
subtitle = "Likert scale is a type of rating scale commonly used in surveys. When responding to a Likert type question,\nrespondents simply state their level of agreement or disagreement on a symmetric agree-disagree scale.")
gg## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "Very Disagree") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Disagree 6 0.7 0.7%
## 2 PSB2 Very Disagree 11 1.27 1.27%
## 3 PSB3 Very Disagree 11 1.27 1.27%
## 4 PSB4 Very Disagree 14 1.62 1.62%
## 5 LOY1 Very Disagree 6 0.7 0.7%
## 6 LOY2 Very Disagree 4 0.46 0.46%
## 7 LOY3 Very Disagree 6 0.7 0.7%
## 8 LOY4 Very Disagree 3 0.35 0.35%
## 9 LOY5 Very Disagree 13 1.51 1.51%
## 10 LOY6 Very Disagree 12 1.39 1.39%
## # ... with 18 more rows# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Disagree 20 2.32 2.32%
## 2 PSB2 Disagree 19 2.2 2.2%
## 3 PSB3 Disagree 11 1.27 1.27%
## 4 PSB4 Disagree 14 1.62 1.62%
## 5 LOY1 Disagree 21 2.43 2.43%
## 6 LOY2 Disagree 18 2.09 2.09%
## 7 LOY3 Disagree 22 2.55 2.55%
## 8 LOY4 Disagree 16 1.85 1.85%
## 9 LOY5 Disagree 18 2.09 2.09%
## 10 LOY6 Disagree 24 2.78 2.78%
## # ... with 18 more rows# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Quite Disagree") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Quite Disagree 32 3.71 3.71%
## 2 PSB2 Quite Disagree 26 3.01 3.01%
## 3 PSB3 Quite Disagree 20 2.32 2.32%
## 4 PSB4 Quite Disagree 26 3.01 3.01%
## 5 LOY1 Quite Disagree 21 2.43 2.43%
## 6 LOY2 Quite Disagree 20 2.32 2.32%
## 7 LOY3 Quite Disagree 17 1.97 1.97%
## 8 LOY4 Quite Disagree 19 2.2 2.2%
## 9 LOY5 Quite Disagree 32 3.71 3.71%
## 10 LOY6 Quite Disagree 26 3.01 3.01%
## # ... with 18 more rows# For displaying percent of "Normal":
df_odered %>%
filter(Res == "Normal") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Normal 156 18.1 18.08%
## 2 PSB2 Normal 160 18.5 18.54%
## 3 PSB3 Normal 130 15.1 15.06%
## 4 PSB4 Normal 114 13.2 13.21%
## 5 LOY1 Normal 106 12.3 12.28%
## 6 LOY2 Normal 114 13.2 13.21%
## 7 LOY3 Normal 99 11.5 11.47%
## 8 LOY4 Normal 92 10.7 10.66%
## 9 LOY5 Normal 130 15.1 15.06%
## 10 LOY6 Normal 111 12.9 12.86%
## # ... with 18 more rows# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Quite Agree") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Quite Agree 230 26.6 26.65%
## 2 PSB2 Quite Agree 213 24.7 24.68%
## 3 PSB3 Quite Agree 209 24.2 24.22%
## 4 PSB4 Quite Agree 201 23.3 23.29%
## 5 LOY1 Quite Agree 205 23.8 23.75%
## 6 LOY2 Quite Agree 168 19.5 19.47%
## 7 LOY3 Quite Agree 171 19.8 19.81%
## 8 LOY4 Quite Agree 161 18.7 18.66%
## 9 LOY5 Quite Agree 212 24.6 24.57%
## 10 LOY6 Quite Agree 214 24.8 24.8%
## # ... with 18 more rows# For displaying percent of "Agree":
df_odered %>%
filter(Res == "Agree") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Agree 354 41.0 41.02%
## 2 PSB2 Agree 368 42.6 42.64%
## 3 PSB3 Agree 403 46.7 46.7%
## 4 PSB4 Agree 408 47.3 47.28%
## 5 LOY1 Agree 420 48.7 48.67%
## 6 LOY2 Agree 433 50.2 50.17%
## 7 LOY3 Agree 430 49.8 49.83%
## 8 LOY4 Agree 440 51.0 50.98%
## 9 LOY5 Agree 340 39.4 39.4%
## 10 LOY6 Agree 347 40.2 40.21%
## # ... with 18 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") %>%
filter(percent >= 0) -> df_for_text7
df_for_text7## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Agree 65 7.53 7.53%
## 2 PSB2 Very Agree 66 7.65 7.65%
## 3 PSB3 Very Agree 79 9.15 9.15%
## 4 PSB4 Very Agree 86 9.97 9.97%
## 5 LOY1 Very Agree 84 9.73 9.73%
## 6 LOY2 Very Agree 106 12.3 12.28%
## 7 LOY3 Very Agree 118 13.7 13.67%
## 8 LOY4 Very Agree 132 15.3 15.3%
## 9 LOY5 Very Agree 118 13.7 13.67%
## 10 LOY6 Very Agree 129 15.0 14.95%
## # ... with 18 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") -> df_for_text
df_for_text## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Agree 65 7.53 7.53%
## 2 PSB2 Very Agree 66 7.65 7.65%
## 3 PSB3 Very Agree 79 9.15 9.15%
## 4 PSB4 Very Agree 86 9.97 9.97%
## 5 LOY1 Very Agree 84 9.73 9.73%
## 6 LOY2 Very Agree 106 12.3 12.28%
## 7 LOY3 Very Agree 118 13.7 13.67%
## 8 LOY4 Very Agree 132 15.3 15.3%
## 9 LOY5 Very Agree 118 13.7 13.67%
## 10 LOY6 Very Agree 129 15.0 14.95%
## # ... with 18 more rows# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg +
geom_text(data = df_for_text1, aes(x = BPE, y = 2, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-35, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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3.3. Vẽ biểu đồ likert với dữ liệu chung Đà Nẵng và Hồ Chí Minh - Nhãn tiếng việt
# Nhan tieng viet
DN_HCM_SEM1_v = within(DN_HCM_SEM1, {
Res = factor(Res, labels = c("R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD", "B\u00ECnh th\u01B0\u1EDDng", "Kh\u00E1 \u0111\u1ED3ng \u00FD", "\u0110\u1ED3ng \u00FD", "R\u1EA5t \u0111\u1ED3ng \u00FD"))
BPE = factor(BPE, labels = c("PSB1", "PSB2", "PSB3", "PSB4", "LOY1", "LOY2", "LOY3", "LOY4", "LOY5", "LOY6", "LOY7", "IMA1", "IMA2", "IMA3", "IMA4", "IMA5", "PHB1", "PHB2", "PHB3", "PHB4", "PHB5", "PEV1", "PEV2", "PEV3", "PEV4", "PVA1", "PVA2", "PVA3"))
})
str(DN_HCM_SEM1_v)## 'data.frame': 24164 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 6 3 2 5 2 2 1 6 2 4 ...
## $ BPE: Factor w/ 28 levels "PSB1","PSB2",..: 22 22 22 22 22 22 22 22 22 22 ...summary(DN_HCM_SEM1_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 460 PSB1 : 863
## Không d<U+1ED3>ng ý : 746 PSB2 : 863
## Khá không d<U+1ED3>ng ý : 768 PSB3 : 863
## Bình thu<U+1EDD>ng : 2719 PSB4 : 863
## Khá d<U+1ED3>ng ý : 4804 LOY1 : 863
## Ð<U+1ED3>ng ý :10731 LOY2 : 863
## R<U+1EA5>t d<U+1ED3>ng ý : 3936 (Other):18986 ## t <- compareGroups(Res ~ BPE, data = DN_HCM_SEM1)
## createTable(t)
DN_HCM_SEM1_v %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
# arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7 IMA1 IMA2 IMA3 IMA4
## [16] IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEV1 PEV2 PEV3 PEV4 PVA1 PVA2 PVA3
## 28 Levels: PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7 IMA1 ... PVA3# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 196 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t không d<U+1ED3>ng ý 6 0.7 0.7%
## 2 PSB1 Không d<U+1ED3>ng ý 20 2.32 2.32%
## 3 PSB1 Khá không d<U+1ED3>ng ý 32 3.71 3.71%
## 4 PSB1 Bình thu<U+1EDD>ng 156 18.1 18.08%
## 5 PSB1 Khá d<U+1ED3>ng ý 230 26.6 26.65%
## 6 PSB1 Ð<U+1ED3>ng ý 354 41.0 41.02%
## 7 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 65 7.53 7.53%
## 8 PSB2 R<U+1EA5>t không d<U+1ED3>ng ý 11 1.27 1.27%
## 9 PSB2 Không d<U+1ED3>ng ý 19 2.2 2.2%
## 10 PSB2 Khá không d<U+1ED3>ng ý 26 3.01 3.01%
## # ... with 186 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg_viet <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "C\u1EA3m nh\u1EADn c\u1EE7a h\u00E0nh kh\u00E1ch v\u1EC1 h\u1EC7 th\u1ED1ng xe bu\u00FDt \u0111\u00F4 th\u1ECB Vi\u1EC7t Nam",
subtitle = "Thang \u0111o Likert l\u00E0 lo\u1EA1i thang \u0111o \u0111i\u1EC3m \u0111\u00E1nh gi\u00E1 \u0111\u01B0\u1EE3c s\u1EED d\u1EE5ng ph\u1ED5 bi\u1EBFn trong kh\u1EA3o s\u00E1t. Nghi\u00EAn c\u1EE9u s\u1EED d\u1EE5ng thang \u0111o 7 m\u1EE9c \u0111\u1ED9 t\u01B0\u01A1ng \u1EE9ng v\u1EDBi 1 - R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD \u0111\u1EBFn 7 - R\u1EA5t \u0111\u1ED3ng \u00FD.")
gg_viet## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t không d<U+1ED3>ng ý 6 0.7 0.7%
## 2 PSB2 R<U+1EA5>t không d<U+1ED3>ng ý 11 1.27 1.27%
## 3 PSB3 R<U+1EA5>t không d<U+1ED3>ng ý 11 1.27 1.27%
## 4 PSB4 R<U+1EA5>t không d<U+1ED3>ng ý 14 1.62 1.62%
## 5 LOY1 R<U+1EA5>t không d<U+1ED3>ng ý 6 0.7 0.7%
## 6 LOY2 R<U+1EA5>t không d<U+1ED3>ng ý 4 0.46 0.46%
## 7 LOY3 R<U+1EA5>t không d<U+1ED3>ng ý 6 0.7 0.7%
## 8 LOY4 R<U+1EA5>t không d<U+1ED3>ng ý 3 0.35 0.35%
## 9 LOY5 R<U+1EA5>t không d<U+1ED3>ng ý 13 1.51 1.51%
## 10 LOY6 R<U+1EA5>t không d<U+1ED3>ng ý 12 1.39 1.39%
## # ... with 18 more rows# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Không d<U+1ED3>ng ý 20 2.32 2.32%
## 2 PSB2 Không d<U+1ED3>ng ý 19 2.2 2.2%
## 3 PSB3 Không d<U+1ED3>ng ý 11 1.27 1.27%
## 4 PSB4 Không d<U+1ED3>ng ý 14 1.62 1.62%
## 5 LOY1 Không d<U+1ED3>ng ý 21 2.43 2.43%
## 6 LOY2 Không d<U+1ED3>ng ý 18 2.09 2.09%
## 7 LOY3 Không d<U+1ED3>ng ý 22 2.55 2.55%
## 8 LOY4 Không d<U+1ED3>ng ý 16 1.85 1.85%
## 9 LOY5 Không d<U+1ED3>ng ý 18 2.09 2.09%
## 10 LOY6 Không d<U+1ED3>ng ý 24 2.78 2.78%
## # ... with 18 more rows# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Khá không d<U+1ED3>ng ý 32 3.71 3.71%
## 2 PSB2 Khá không d<U+1ED3>ng ý 26 3.01 3.01%
## 3 PSB3 Khá không d<U+1ED3>ng ý 20 2.32 2.32%
## 4 PSB4 Khá không d<U+1ED3>ng ý 26 3.01 3.01%
## 5 LOY1 Khá không d<U+1ED3>ng ý 21 2.43 2.43%
## 6 LOY2 Khá không d<U+1ED3>ng ý 20 2.32 2.32%
## 7 LOY3 Khá không d<U+1ED3>ng ý 17 1.97 1.97%
## 8 LOY4 Khá không d<U+1ED3>ng ý 19 2.2 2.2%
## 9 LOY5 Khá không d<U+1ED3>ng ý 32 3.71 3.71%
## 10 LOY6 Khá không d<U+1ED3>ng ý 26 3.01 3.01%
## # ... with 18 more rows# For displaying percent of "Normal":
df_odered %>%
filter(Res == "B\u00ECnh th\u01B0\u1EDDng") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Bình thu<U+1EDD>ng 156 18.1 18.08%
## 2 PSB2 Bình thu<U+1EDD>ng 160 18.5 18.54%
## 3 PSB3 Bình thu<U+1EDD>ng 130 15.1 15.06%
## 4 PSB4 Bình thu<U+1EDD>ng 114 13.2 13.21%
## 5 LOY1 Bình thu<U+1EDD>ng 106 12.3 12.28%
## 6 LOY2 Bình thu<U+1EDD>ng 114 13.2 13.21%
## 7 LOY3 Bình thu<U+1EDD>ng 99 11.5 11.47%
## 8 LOY4 Bình thu<U+1EDD>ng 92 10.7 10.66%
## 9 LOY5 Bình thu<U+1EDD>ng 130 15.1 15.06%
## 10 LOY6 Bình thu<U+1EDD>ng 111 12.9 12.86%
## # ... with 18 more rows# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Kh\u00E1 \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Khá d<U+1ED3>ng ý 230 26.6 26.65%
## 2 PSB2 Khá d<U+1ED3>ng ý 213 24.7 24.68%
## 3 PSB3 Khá d<U+1ED3>ng ý 209 24.2 24.22%
## 4 PSB4 Khá d<U+1ED3>ng ý 201 23.3 23.29%
## 5 LOY1 Khá d<U+1ED3>ng ý 205 23.8 23.75%
## 6 LOY2 Khá d<U+1ED3>ng ý 168 19.5 19.47%
## 7 LOY3 Khá d<U+1ED3>ng ý 171 19.8 19.81%
## 8 LOY4 Khá d<U+1ED3>ng ý 161 18.7 18.66%
## 9 LOY5 Khá d<U+1ED3>ng ý 212 24.6 24.57%
## 10 LOY6 Khá d<U+1ED3>ng ý 214 24.8 24.8%
## # ... with 18 more rows# For displaying percent of "Agree":
df_odered %>%
filter(Res == "\u0110\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Ð<U+1ED3>ng ý 354 41.0 41.02%
## 2 PSB2 Ð<U+1ED3>ng ý 368 42.6 42.64%
## 3 PSB3 Ð<U+1ED3>ng ý 403 46.7 46.7%
## 4 PSB4 Ð<U+1ED3>ng ý 408 47.3 47.28%
## 5 LOY1 Ð<U+1ED3>ng ý 420 48.7 48.67%
## 6 LOY2 Ð<U+1ED3>ng ý 433 50.2 50.17%
## 7 LOY3 Ð<U+1ED3>ng ý 430 49.8 49.83%
## 8 LOY4 Ð<U+1ED3>ng ý 440 51.0 50.98%
## 9 LOY5 Ð<U+1ED3>ng ý 340 39.4 39.4%
## 10 LOY6 Ð<U+1ED3>ng ý 347 40.2 40.21%
## # ... with 18 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text7
df_for_text7## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 65 7.53 7.53%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 66 7.65 7.65%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 79 9.15 9.15%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 86 9.97 9.97%
## 5 LOY1 R<U+1EA5>t d<U+1ED3>ng ý 84 9.73 9.73%
## 6 LOY2 R<U+1EA5>t d<U+1ED3>ng ý 106 12.3 12.28%
## 7 LOY3 R<U+1EA5>t d<U+1ED3>ng ý 118 13.7 13.67%
## 8 LOY4 R<U+1EA5>t d<U+1ED3>ng ý 132 15.3 15.3%
## 9 LOY5 R<U+1EA5>t d<U+1ED3>ng ý 118 13.7 13.67%
## 10 LOY6 R<U+1EA5>t d<U+1ED3>ng ý 129 15.0 14.95%
## # ... with 18 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") -> df_for_text
df_for_text## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 65 7.53 7.53%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 66 7.65 7.65%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 79 9.15 9.15%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 86 9.97 9.97%
## 5 LOY1 R<U+1EA5>t d<U+1ED3>ng ý 84 9.73 9.73%
## 6 LOY2 R<U+1EA5>t d<U+1ED3>ng ý 106 12.3 12.28%
## 7 LOY3 R<U+1EA5>t d<U+1ED3>ng ý 118 13.7 13.67%
## 8 LOY4 R<U+1EA5>t d<U+1ED3>ng ý 132 15.3 15.3%
## 9 LOY5 R<U+1EA5>t d<U+1ED3>ng ý 118 13.7 13.67%
## 10 LOY6 R<U+1EA5>t d<U+1ED3>ng ý 129 15.0 14.95%
## # ... with 18 more rows# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg_viet +
geom_text(data = df_for_text1, aes(x = BPE, y = 2, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-35, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
3.4. Vẽ biểu đồ likert cho dữ liệu Đà Nẵng - Nhãn tiếng anh
# Plot Likert graph for people in Da Nang
library(tidyverse)
library(compareGroups)
head(DN_SEM1)## Res BPE
## 449 6 PSB1
## 450 6 PSB1
## 451 6 PSB1
## 452 5 PSB1
## 453 6 PSB1
## 454 6 PSB1dim(DN_SEM1)## [1] 11620 2attach(DN_SEM1)## The following objects are masked from DN_HCM_SEM1:
##
## BPE, ResDN_SEM1 = within(DN_SEM1, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("PSB1", "PSB2", "PSB3", "PSB4", "LOY1", "LOY2", "LOY3", "LOY4", "LOy5", "LOY6", "LOY7", "IMA1", "IMA2", "IMA3", "IMA4", "IMA5", "PHB1", "PHB2", "PHB3", "PHB4", "PHB5", "PEV1", "PEV2", "PEV3", "PEV4", "PVA1", "PVA2", "PVA3"))
})
str(DN_SEM1)## 'data.frame': 11620 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 6 6 5 6 6 6 7 4 6 ...
## $ BPE: Factor w/ 28 levels "PSB1","PSB2",..: 22 22 22 22 22 22 22 22 22 22 ...summary(DN_SEM1)## Res BPE
## Very Disagree : 37 PSB1 : 415
## Disagree : 125 PSB2 : 415
## Quite Disagree: 314 PSB3 : 415
## Normal : 790 PSB4 : 415
## Quite Agree :2636 LOY1 : 415
## Agree :5567 LOY2 : 415
## Very Agree :2151 (Other):9130 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_SEM1 %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
# arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOy5 LOY6 LOY7 IMA1 IMA2 IMA3 IMA4
## [16] IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEV1 PEV2 PEV3 PEV4 PVA1 PVA2 PVA3
## 28 Levels: PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOy5 LOY6 LOY7 IMA1 ... PVA3# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 181 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Disagree 1 0.24 0.24%
## 2 PSB1 Disagree 3 0.72 0.72%
## 3 PSB1 Quite Disagree 11 2.65 2.65%
## 4 PSB1 Normal 56 13.5 13.49%
## 5 PSB1 Quite Agree 137 33.0 33.01%
## 6 PSB1 Agree 174 41.9 41.93%
## 7 PSB1 Very Agree 33 7.95 7.95%
## 8 PSB2 Disagree 1 0.24 0.24%
## 9 PSB2 Quite Disagree 8 1.93 1.93%
## 10 PSB2 Normal 49 11.8 11.81%
## # ... with 171 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "Perception of passengers towards bus systems in Da Nang",
subtitle = "Likert scale is a type of rating scale commonly used in surveys. When responding to a Likert type question,\nrespondents simply state their level of agreement or disagreement on a symmetric agree-disagree scale.")
gg## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "Very Disagree") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 16 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Disagree 1 0.24 0.24%
## 2 PSB4 Very Disagree 1 0.24 0.24%
## 3 LOy5 Very Disagree 1 0.24 0.24%
## 4 LOY6 Very Disagree 1 0.24 0.24%
## 5 LOY7 Very Disagree 1 0.24 0.24%
## 6 IMA1 Very Disagree 1 0.24 0.24%
## 7 IMA2 Very Disagree 2 0.48 0.48%
## 8 IMA3 Very Disagree 2 0.48 0.48%
## 9 IMA4 Very Disagree 1 0.24 0.24%
## 10 PHB1 Very Disagree 1 0.24 0.24%
## 11 PHB5 Very Disagree 1 0.24 0.24%
## 12 PEV1 Very Disagree 9 2.17 2.17%
## 13 PEV2 Very Disagree 6 1.45 1.45%
## 14 PEV3 Very Disagree 4 0.96 0.96%
## 15 PEV4 Very Disagree 4 0.96 0.96%
## 16 PVA2 Very Disagree 1 0.24 0.24%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 25 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Disagree 3 0.72 0.72%
## 2 PSB2 Disagree 1 0.24 0.24%
## 3 PSB3 Disagree 1 0.24 0.24%
## 4 LOY1 Disagree 1 0.24 0.24%
## 5 LOY2 Disagree 2 0.48 0.48%
## 6 LOY3 Disagree 1 0.24 0.24%
## 7 LOY4 Disagree 1 0.24 0.24%
## 8 LOy5 Disagree 1 0.24 0.24%
## 9 LOY6 Disagree 3 0.72 0.72%
## 10 LOY7 Disagree 16 3.86 3.86%
## # ... with 15 more rows# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Quite Disagree") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Quite Disagree 11 2.65 2.65%
## 2 PSB2 Quite Disagree 8 1.93 1.93%
## 3 PSB3 Quite Disagree 4 0.96 0.96%
## 4 PSB4 Quite Disagree 8 1.93 1.93%
## 5 LOY1 Quite Disagree 6 1.45 1.45%
## 6 LOY2 Quite Disagree 7 1.69 1.69%
## 7 LOY3 Quite Disagree 7 1.69 1.69%
## 8 LOY4 Quite Disagree 5 1.2 1.2%
## 9 LOy5 Quite Disagree 18 4.34 4.34%
## 10 LOY6 Quite Disagree 13 3.13 3.13%
## # ... with 18 more rows# For displaying percent of "Normal":
df_odered %>%
filter(Res == "Normal") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Normal 56 13.5 13.49%
## 2 PSB2 Normal 49 11.8 11.81%
## 3 PSB3 Normal 41 9.88 9.88%
## 4 PSB4 Normal 26 6.27 6.27%
## 5 LOY1 Normal 25 6.02 6.02%
## 6 LOY2 Normal 27 6.51 6.51%
## 7 LOY3 Normal 24 5.78 5.78%
## 8 LOY4 Normal 31 7.47 7.47%
## 9 LOy5 Normal 40 9.64 9.64%
## 10 LOY6 Normal 33 7.95 7.95%
## # ... with 18 more rows# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Quite Agree") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Quite Agree 137 33.0 33.01%
## 2 PSB2 Quite Agree 113 27.2 27.23%
## 3 PSB3 Quite Agree 109 26.3 26.27%
## 4 PSB4 Quite Agree 104 25.1 25.06%
## 5 LOY1 Quite Agree 98 23.6 23.61%
## 6 LOY2 Quite Agree 92 22.2 22.17%
## 7 LOY3 Quite Agree 88 21.2 21.2%
## 8 LOY4 Quite Agree 75 18.1 18.07%
## 9 LOy5 Quite Agree 122 29.4 29.4%
## 10 LOY6 Quite Agree 115 27.7 27.71%
## # ... with 18 more rows# For displaying percent of "Agree":
df_odered %>%
filter(Res == "Agree") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Agree 174 41.9 41.93%
## 2 PSB2 Agree 210 50.6 50.6%
## 3 PSB3 Agree 216 52.0 52.05%
## 4 PSB4 Agree 223 53.7 53.73%
## 5 LOY1 Agree 233 56.1 56.14%
## 6 LOY2 Agree 221 53.2 53.25%
## 7 LOY3 Agree 224 54.0 53.98%
## 8 LOY4 Agree 223 53.7 53.73%
## 9 LOy5 Agree 163 39.3 39.28%
## 10 LOY6 Agree 170 41.0 40.96%
## # ... with 18 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") %>%
filter(percent >= 0) -> df_for_text7
df_for_text7## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Agree 33 7.95 7.95%
## 2 PSB2 Very Agree 34 8.19 8.19%
## 3 PSB3 Very Agree 44 10.6 10.6%
## 4 PSB4 Very Agree 53 12.8 12.77%
## 5 LOY1 Very Agree 52 12.5 12.53%
## 6 LOY2 Very Agree 66 15.9 15.9%
## 7 LOY3 Very Agree 71 17.1 17.11%
## 8 LOY4 Very Agree 80 19.3 19.28%
## 9 LOy5 Very Agree 70 16.9 16.87%
## 10 LOY6 Very Agree 80 19.3 19.28%
## # ... with 18 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") -> df_for_text
df_for_text## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Agree 33 7.95 7.95%
## 2 PSB2 Very Agree 34 8.19 8.19%
## 3 PSB3 Very Agree 44 10.6 10.6%
## 4 PSB4 Very Agree 53 12.8 12.77%
## 5 LOY1 Very Agree 52 12.5 12.53%
## 6 LOY2 Very Agree 66 15.9 15.9%
## 7 LOY3 Very Agree 71 17.1 17.11%
## 8 LOY4 Very Agree 80 19.3 19.28%
## 9 LOy5 Very Agree 70 16.9 16.87%
## 10 LOY6 Very Agree 80 19.3 19.28%
## # ... with 18 more rows# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg +
geom_text(data = df_for_text1, aes(x = BPE, y = 2, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-40, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
3.5. Vẽ biểu đồ likert cho dữ liệu Đà Nẵng - Nhãn tiếng việt
# Nhãm tiếng việt
DN_SEM1_viet = within(DN_SEM1, {
Res = factor(Res, labels = c("R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD", "B\u00ECnh th\u01B0\u1EDDng", "Kh\u00E1 \u0111\u1ED3ng \u00FD", "\u0110\u1ED3ng \u00FD", "R\u1EA5t \u0111\u1ED3ng \u00FD"))
BPE = factor(BPE, labels = c("PSB1", "PSB2", "PSB3", "PSB4", "LOY1", "LOY2", "LOY3", "LOY4", "LOy5", "LOY6", "LOY7", "IMA1", "IMA2", "IMA3", "IMA4", "IMA5", "PHB1", "PHB2", "PHB3", "PHB4", "PHB5", "PEV1", "PEV2", "PEV3", "PEV4", "PVA1", "PVA2", "PVA3"))
})
str(DN_SEM1_viet)## 'data.frame': 11620 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 6 6 6 5 6 6 6 7 4 6 ...
## $ BPE: Factor w/ 28 levels "PSB1","PSB2",..: 22 22 22 22 22 22 22 22 22 22 ...summary(DN_SEM1_viet)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 37 PSB1 : 415
## Không d<U+1ED3>ng ý : 125 PSB2 : 415
## Khá không d<U+1ED3>ng ý : 314 PSB3 : 415
## Bình thu<U+1EDD>ng : 790 PSB4 : 415
## Khá d<U+1ED3>ng ý :2636 LOY1 : 415
## Ð<U+1ED3>ng ý :5567 LOY2 : 415
## R<U+1EA5>t d<U+1ED3>ng ý :2151 (Other):9130 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_SEM1_viet %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
# arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOy5 LOY6 LOY7 IMA1 IMA2 IMA3 IMA4
## [16] IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEV1 PEV2 PEV3 PEV4 PVA1 PVA2 PVA3
## 28 Levels: PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOy5 LOY6 LOY7 IMA1 ... PVA3# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 181 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%
## 2 PSB1 Không d<U+1ED3>ng ý 3 0.72 0.72%
## 3 PSB1 Khá không d<U+1ED3>ng ý 11 2.65 2.65%
## 4 PSB1 Bình thu<U+1EDD>ng 56 13.5 13.49%
## 5 PSB1 Khá d<U+1ED3>ng ý 137 33.0 33.01%
## 6 PSB1 Ð<U+1ED3>ng ý 174 41.9 41.93%
## 7 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 33 7.95 7.95%
## 8 PSB2 Không d<U+1ED3>ng ý 1 0.24 0.24%
## 9 PSB2 Khá không d<U+1ED3>ng ý 8 1.93 1.93%
## 10 PSB2 Bình thu<U+1EDD>ng 49 11.8 11.81%
## # ... with 171 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg_viet <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "C\u1EA3m nh\u1EADn c\u1EE7a h\u00E0nh kh\u00E1ch v\u1EC1 h\u1EC7 th\u1ED1ng xe bu\u00FDt \u0111\u00F4 th\u1ECB \u0110\u00E0 N\u1EB5ng",
subtitle = "Thang \u0111o Likert l\u00E0 lo\u1EA1i thang \u0111o \u0111i\u1EC3m \u0111\u00E1nh gi\u00E1 \u0111\u01B0\u1EE3c s\u1EED d\u1EE5ng ph\u1ED5 bi\u1EBFn trong kh\u1EA3o s\u00E1t. Nghi\u00EAn c\u1EE9u s\u1EED d\u1EE5ng thang \u0111o 7 m\u1EE9c \u0111\u1ED9 t\u01B0\u01A1ng \u1EE9ng v\u1EDBi 1 - R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD \u0111\u1EBFn 7 - R\u1EA5t \u0111\u1ED3ng \u00FD.")
gg_viet## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 16 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%
## 2 PSB4 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%
## 3 LOy5 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%
## 4 LOY6 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%
## 5 LOY7 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%
## 6 IMA1 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%
## 7 IMA2 R<U+1EA5>t không d<U+1ED3>ng ý 2 0.48 0.48%
## 8 IMA3 R<U+1EA5>t không d<U+1ED3>ng ý 2 0.48 0.48%
## 9 IMA4 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%
## 10 PHB1 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%
## 11 PHB5 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%
## 12 PEV1 R<U+1EA5>t không d<U+1ED3>ng ý 9 2.17 2.17%
## 13 PEV2 R<U+1EA5>t không d<U+1ED3>ng ý 6 1.45 1.45%
## 14 PEV3 R<U+1EA5>t không d<U+1ED3>ng ý 4 0.96 0.96%
## 15 PEV4 R<U+1EA5>t không d<U+1ED3>ng ý 4 0.96 0.96%
## 16 PVA2 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 25 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Không d<U+1ED3>ng ý 3 0.72 0.72%
## 2 PSB2 Không d<U+1ED3>ng ý 1 0.24 0.24%
## 3 PSB3 Không d<U+1ED3>ng ý 1 0.24 0.24%
## 4 LOY1 Không d<U+1ED3>ng ý 1 0.24 0.24%
## 5 LOY2 Không d<U+1ED3>ng ý 2 0.48 0.48%
## 6 LOY3 Không d<U+1ED3>ng ý 1 0.24 0.24%
## 7 LOY4 Không d<U+1ED3>ng ý 1 0.24 0.24%
## 8 LOy5 Không d<U+1ED3>ng ý 1 0.24 0.24%
## 9 LOY6 Không d<U+1ED3>ng ý 3 0.72 0.72%
## 10 LOY7 Không d<U+1ED3>ng ý 16 3.86 3.86%
## # ... with 15 more rows# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Khá không d<U+1ED3>ng ý 11 2.65 2.65%
## 2 PSB2 Khá không d<U+1ED3>ng ý 8 1.93 1.93%
## 3 PSB3 Khá không d<U+1ED3>ng ý 4 0.96 0.96%
## 4 PSB4 Khá không d<U+1ED3>ng ý 8 1.93 1.93%
## 5 LOY1 Khá không d<U+1ED3>ng ý 6 1.45 1.45%
## 6 LOY2 Khá không d<U+1ED3>ng ý 7 1.69 1.69%
## 7 LOY3 Khá không d<U+1ED3>ng ý 7 1.69 1.69%
## 8 LOY4 Khá không d<U+1ED3>ng ý 5 1.2 1.2%
## 9 LOy5 Khá không d<U+1ED3>ng ý 18 4.34 4.34%
## 10 LOY6 Khá không d<U+1ED3>ng ý 13 3.13 3.13%
## # ... with 18 more rows# For displaying percent of "Normal":
df_odered %>%
filter(Res == "B\u00ECnh th\u01B0\u1EDDng") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Bình thu<U+1EDD>ng 56 13.5 13.49%
## 2 PSB2 Bình thu<U+1EDD>ng 49 11.8 11.81%
## 3 PSB3 Bình thu<U+1EDD>ng 41 9.88 9.88%
## 4 PSB4 Bình thu<U+1EDD>ng 26 6.27 6.27%
## 5 LOY1 Bình thu<U+1EDD>ng 25 6.02 6.02%
## 6 LOY2 Bình thu<U+1EDD>ng 27 6.51 6.51%
## 7 LOY3 Bình thu<U+1EDD>ng 24 5.78 5.78%
## 8 LOY4 Bình thu<U+1EDD>ng 31 7.47 7.47%
## 9 LOy5 Bình thu<U+1EDD>ng 40 9.64 9.64%
## 10 LOY6 Bình thu<U+1EDD>ng 33 7.95 7.95%
## # ... with 18 more rows# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Kh\u00E1 \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Khá d<U+1ED3>ng ý 137 33.0 33.01%
## 2 PSB2 Khá d<U+1ED3>ng ý 113 27.2 27.23%
## 3 PSB3 Khá d<U+1ED3>ng ý 109 26.3 26.27%
## 4 PSB4 Khá d<U+1ED3>ng ý 104 25.1 25.06%
## 5 LOY1 Khá d<U+1ED3>ng ý 98 23.6 23.61%
## 6 LOY2 Khá d<U+1ED3>ng ý 92 22.2 22.17%
## 7 LOY3 Khá d<U+1ED3>ng ý 88 21.2 21.2%
## 8 LOY4 Khá d<U+1ED3>ng ý 75 18.1 18.07%
## 9 LOy5 Khá d<U+1ED3>ng ý 122 29.4 29.4%
## 10 LOY6 Khá d<U+1ED3>ng ý 115 27.7 27.71%
## # ... with 18 more rows# For displaying percent of "Agree":
df_odered %>%
filter(Res == "\u0110\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Ð<U+1ED3>ng ý 174 41.9 41.93%
## 2 PSB2 Ð<U+1ED3>ng ý 210 50.6 50.6%
## 3 PSB3 Ð<U+1ED3>ng ý 216 52.0 52.05%
## 4 PSB4 Ð<U+1ED3>ng ý 223 53.7 53.73%
## 5 LOY1 Ð<U+1ED3>ng ý 233 56.1 56.14%
## 6 LOY2 Ð<U+1ED3>ng ý 221 53.2 53.25%
## 7 LOY3 Ð<U+1ED3>ng ý 224 54.0 53.98%
## 8 LOY4 Ð<U+1ED3>ng ý 223 53.7 53.73%
## 9 LOy5 Ð<U+1ED3>ng ý 163 39.3 39.28%
## 10 LOY6 Ð<U+1ED3>ng ý 170 41.0 40.96%
## # ... with 18 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text7
df_for_text7## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 33 7.95 7.95%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 34 8.19 8.19%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 44 10.6 10.6%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 53 12.8 12.77%
## 5 LOY1 R<U+1EA5>t d<U+1ED3>ng ý 52 12.5 12.53%
## 6 LOY2 R<U+1EA5>t d<U+1ED3>ng ý 66 15.9 15.9%
## 7 LOY3 R<U+1EA5>t d<U+1ED3>ng ý 71 17.1 17.11%
## 8 LOY4 R<U+1EA5>t d<U+1ED3>ng ý 80 19.3 19.28%
## 9 LOy5 R<U+1EA5>t d<U+1ED3>ng ý 70 16.9 16.87%
## 10 LOY6 R<U+1EA5>t d<U+1ED3>ng ý 80 19.3 19.28%
## # ... with 18 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") -> df_for_text
df_for_text## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 33 7.95 7.95%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 34 8.19 8.19%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 44 10.6 10.6%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 53 12.8 12.77%
## 5 LOY1 R<U+1EA5>t d<U+1ED3>ng ý 52 12.5 12.53%
## 6 LOY2 R<U+1EA5>t d<U+1ED3>ng ý 66 15.9 15.9%
## 7 LOY3 R<U+1EA5>t d<U+1ED3>ng ý 71 17.1 17.11%
## 8 LOY4 R<U+1EA5>t d<U+1ED3>ng ý 80 19.3 19.28%
## 9 LOy5 R<U+1EA5>t d<U+1ED3>ng ý 70 16.9 16.87%
## 10 LOY6 R<U+1EA5>t d<U+1ED3>ng ý 80 19.3 19.28%
## # ... with 18 more rows# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg_viet +
geom_text(data = df_for_text1, aes(x = BPE, y = 2, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-40, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
3.6. Vẽ biểu đồ likert cho dữ liệu Hồ Chí Minh-Nhãn tiếng anh
# Plot Likert graph for people in Ho Chi Minh
library(tidyverse)
library(compareGroups)
head(HCM_SEM1)## Res BPE
## 1 6 PSB1
## 2 3 PSB1
## 3 2 PSB1
## 4 5 PSB1
## 5 2 PSB1
## 6 2 PSB1dim(HCM_SEM1)## [1] 12544 2attach(HCM_SEM1)## The following objects are masked from DN_SEM1:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1:
##
## BPE, ResHCM_SEM1 = within(HCM_SEM1, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("PSB1", "PSB2", "PSB3", "PSB4", "LOY1", "LOY2", "LOY3", "LOY4", "LOY5", "LOY6", "LOY7", "IMA1", "IMA2", "IMA3", "IMA4", "IMA5", "PHB1", "PHB2", "PHB3", "PHB4", "PHB5", "PEV1", "PEV2", "PEV3", "PEV4", "PVA1", "PVA2", "PVA3"))
})
str(HCM_SEM1)## 'data.frame': 12544 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 3 2 5 2 2 1 6 2 4 ...
## $ BPE: Factor w/ 28 levels "PSB1","PSB2",..: 22 22 22 22 22 22 22 22 22 22 ...summary(HCM_SEM1)## Res BPE
## Very Disagree : 423 PSB1 : 448
## Disagree : 621 PSB2 : 448
## Quite Disagree: 454 PSB3 : 448
## Normal :1929 PSB4 : 448
## Quite Agree :2168 LOY1 : 448
## Agree :5164 LOY2 : 448
## Very Agree :1785 (Other):9856 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
HCM_SEM1 %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
# arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7 IMA1 IMA2 IMA3 IMA4
## [16] IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEV1 PEV2 PEV3 PEV4 PVA1 PVA2 PVA3
## 28 Levels: PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7 IMA1 ... PVA3# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 196 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Disagree 5 1.12 1.12%
## 2 PSB1 Disagree 17 3.79 3.79%
## 3 PSB1 Quite Disagree 21 4.69 4.69%
## 4 PSB1 Normal 100 22.3 22.32%
## 5 PSB1 Quite Agree 93 20.8 20.76%
## 6 PSB1 Agree 180 40.2 40.18%
## 7 PSB1 Very Agree 32 7.14 7.14%
## 8 PSB2 Very Disagree 11 2.46 2.46%
## 9 PSB2 Disagree 18 4.02 4.02%
## 10 PSB2 Quite Disagree 18 4.02 4.02%
## # ... with 186 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "Perception of passengers towards bus systems in Ho Chi Minh",
subtitle = "Likert scale is a type of rating scale commonly used in surveys. When responding to a Likert type question,\nrespondents simply state their level of agreement or disagreement on a symmetric agree-disagree scale.")
gg## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "Very Disagree") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Disagree 5 1.12 1.12%
## 2 PSB2 Very Disagree 11 2.46 2.46%
## 3 PSB3 Very Disagree 11 2.46 2.46%
## 4 PSB4 Very Disagree 13 2.9 2.9%
## 5 LOY1 Very Disagree 6 1.34 1.34%
## 6 LOY2 Very Disagree 4 0.89 0.89%
## 7 LOY3 Very Disagree 6 1.34 1.34%
## 8 LOY4 Very Disagree 3 0.67 0.67%
## 9 LOY5 Very Disagree 12 2.68 2.68%
## 10 LOY6 Very Disagree 11 2.46 2.46%
## # ... with 18 more rows# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Disagree 17 3.79 3.79%
## 2 PSB2 Disagree 18 4.02 4.02%
## 3 PSB3 Disagree 10 2.23 2.23%
## 4 PSB4 Disagree 14 3.12 3.12%
## 5 LOY1 Disagree 20 4.46 4.46%
## 6 LOY2 Disagree 16 3.57 3.57%
## 7 LOY3 Disagree 21 4.69 4.69%
## 8 LOY4 Disagree 15 3.35 3.35%
## 9 LOY5 Disagree 17 3.79 3.79%
## 10 LOY6 Disagree 21 4.69 4.69%
## # ... with 18 more rows# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Quite Disagree") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Quite Disagree 21 4.69 4.69%
## 2 PSB2 Quite Disagree 18 4.02 4.02%
## 3 PSB3 Quite Disagree 16 3.57 3.57%
## 4 PSB4 Quite Disagree 18 4.02 4.02%
## 5 LOY1 Quite Disagree 15 3.35 3.35%
## 6 LOY2 Quite Disagree 13 2.9 2.9%
## 7 LOY3 Quite Disagree 10 2.23 2.23%
## 8 LOY4 Quite Disagree 14 3.12 3.12%
## 9 LOY5 Quite Disagree 14 3.12 3.12%
## 10 LOY6 Quite Disagree 13 2.9 2.9%
## # ... with 18 more rows# For displaying percent of "Normal":
df_odered %>%
filter(Res == "Normal") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Normal 100 22.3 22.32%
## 2 PSB2 Normal 111 24.8 24.78%
## 3 PSB3 Normal 89 19.9 19.87%
## 4 PSB4 Normal 88 19.6 19.64%
## 5 LOY1 Normal 81 18.1 18.08%
## 6 LOY2 Normal 87 19.4 19.42%
## 7 LOY3 Normal 75 16.7 16.74%
## 8 LOY4 Normal 61 13.6 13.62%
## 9 LOY5 Normal 90 20.1 20.09%
## 10 LOY6 Normal 78 17.4 17.41%
## # ... with 18 more rows# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Quite Agree") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Quite Agree 93 20.8 20.76%
## 2 PSB2 Quite Agree 100 22.3 22.32%
## 3 PSB3 Quite Agree 100 22.3 22.32%
## 4 PSB4 Quite Agree 97 21.6 21.65%
## 5 LOY1 Quite Agree 107 23.9 23.88%
## 6 LOY2 Quite Agree 76 17.0 16.96%
## 7 LOY3 Quite Agree 83 18.5 18.53%
## 8 LOY4 Quite Agree 86 19.2 19.2%
## 9 LOY5 Quite Agree 90 20.1 20.09%
## 10 LOY6 Quite Agree 99 22.1 22.1%
## # ... with 18 more rows# For displaying percent of "Agree":
df_odered %>%
filter(Res == "Agree") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Agree 180 40.2 40.18%
## 2 PSB2 Agree 158 35.3 35.27%
## 3 PSB3 Agree 187 41.7 41.74%
## 4 PSB4 Agree 185 41.3 41.29%
## 5 LOY1 Agree 187 41.7 41.74%
## 6 LOY2 Agree 212 47.3 47.32%
## 7 LOY3 Agree 206 46.0 45.98%
## 8 LOY4 Agree 217 48.4 48.44%
## 9 LOY5 Agree 177 39.5 39.51%
## 10 LOY6 Agree 177 39.5 39.51%
## # ... with 18 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") %>%
filter(percent >= 0) -> df_for_text7
df_for_text7## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Agree 32 7.14 7.14%
## 2 PSB2 Very Agree 32 7.14 7.14%
## 3 PSB3 Very Agree 35 7.81 7.81%
## 4 PSB4 Very Agree 33 7.37 7.37%
## 5 LOY1 Very Agree 32 7.14 7.14%
## 6 LOY2 Very Agree 40 8.93 8.93%
## 7 LOY3 Very Agree 47 10.5 10.49%
## 8 LOY4 Very Agree 52 11.6 11.61%
## 9 LOY5 Very Agree 48 10.7 10.71%
## 10 LOY6 Very Agree 49 10.9 10.94%
## # ... with 18 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") -> df_for_text
df_for_text## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Agree 32 7.14 7.14%
## 2 PSB2 Very Agree 32 7.14 7.14%
## 3 PSB3 Very Agree 35 7.81 7.81%
## 4 PSB4 Very Agree 33 7.37 7.37%
## 5 LOY1 Very Agree 32 7.14 7.14%
## 6 LOY2 Very Agree 40 8.93 8.93%
## 7 LOY3 Very Agree 47 10.5 10.49%
## 8 LOY4 Very Agree 52 11.6 11.61%
## 9 LOY5 Very Agree 48 10.7 10.71%
## 10 LOY6 Very Agree 49 10.9 10.94%
## # ... with 18 more rows# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg +
geom_text(data = df_for_text1, aes(x = BPE, y = 4, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-30, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-2, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
3.7. Vẽ biểu đồ likert cho dữ liệu Hồ Chí Minh-Nhãn tiếng việt
HCM_SEM1_viet = within(HCM_SEM1, {
Res = factor(Res, labels = c("R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD", "B\u00ECnh th\u01B0\u1EDDng", "Kh\u00E1 \u0111\u1ED3ng \u00FD", "\u0110\u1ED3ng \u00FD", "R\u1EA5t \u0111\u1ED3ng \u00FD"))
BPE = factor(BPE, labels = c("PSB1", "PSB2", "PSB3", "PSB4", "LOY1", "LOY2", "LOY3", "LOY4", "LOY5", "LOY6", "LOY7", "IMA1", "IMA2", "IMA3", "IMA4", "IMA5", "PHB1", "PHB2", "PHB3", "PHB4", "PHB5", "PEV1", "PEV2", "PEV3", "PEV4", "PVA1", "PVA2", "PVA3"))
})
str(HCM_SEM1_viet)## 'data.frame': 12544 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 6 3 2 5 2 2 1 6 2 4 ...
## $ BPE: Factor w/ 28 levels "PSB1","PSB2",..: 22 22 22 22 22 22 22 22 22 22 ...summary(HCM_SEM1_viet)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 423 PSB1 : 448
## Không d<U+1ED3>ng ý : 621 PSB2 : 448
## Khá không d<U+1ED3>ng ý : 454 PSB3 : 448
## Bình thu<U+1EDD>ng :1929 PSB4 : 448
## Khá d<U+1ED3>ng ý :2168 LOY1 : 448
## Ð<U+1ED3>ng ý :5164 LOY2 : 448
## R<U+1EA5>t d<U+1ED3>ng ý :1785 (Other):9856 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
HCM_SEM1_viet %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
# arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7 IMA1 IMA2 IMA3 IMA4
## [16] IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEV1 PEV2 PEV3 PEV4 PVA1 PVA2 PVA3
## 28 Levels: PSB1 PSB2 PSB3 PSB4 LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7 IMA1 ... PVA3# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 196 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t không d<U+1ED3>ng ý 5 1.12 1.12%
## 2 PSB1 Không d<U+1ED3>ng ý 17 3.79 3.79%
## 3 PSB1 Khá không d<U+1ED3>ng ý 21 4.69 4.69%
## 4 PSB1 Bình thu<U+1EDD>ng 100 22.3 22.32%
## 5 PSB1 Khá d<U+1ED3>ng ý 93 20.8 20.76%
## 6 PSB1 Ð<U+1ED3>ng ý 180 40.2 40.18%
## 7 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 32 7.14 7.14%
## 8 PSB2 R<U+1EA5>t không d<U+1ED3>ng ý 11 2.46 2.46%
## 9 PSB2 Không d<U+1ED3>ng ý 18 4.02 4.02%
## 10 PSB2 Khá không d<U+1ED3>ng ý 18 4.02 4.02%
## # ... with 186 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg_viet <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "C\u1EA3m nh\u1EADn c\u1EE7a h\u00E0nh kh\u00E1ch v\u1EC1 h\u1EC7 th\u1ED1ng xe bu\u00FDt \u0111\u00F4 th\u1ECB Th\u00E0nh ph\u1ED1 H\u1ED3 Ch\u00ED Minh",
subtitle = "Thang \u0111o Likert l\u00E0 lo\u1EA1i thang \u0111o \u0111i\u1EC3m \u0111\u00E1nh gi\u00E1 \u0111\u01B0\u1EE3c s\u1EED d\u1EE5ng ph\u1ED5 bi\u1EBFn trong kh\u1EA3o s\u00E1t. Nghi\u00EAn c\u1EE9u s\u1EED d\u1EE5ng thang \u0111o 7 m\u1EE9c \u0111\u1ED9 t\u01B0\u01A1ng \u1EE9ng v\u1EDBi 1 - R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD \u0111\u1EBFn 7 - R\u1EA5t \u0111\u1ED3ng \u00FD.")
gg_viet## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t không d<U+1ED3>ng ý 5 1.12 1.12%
## 2 PSB2 R<U+1EA5>t không d<U+1ED3>ng ý 11 2.46 2.46%
## 3 PSB3 R<U+1EA5>t không d<U+1ED3>ng ý 11 2.46 2.46%
## 4 PSB4 R<U+1EA5>t không d<U+1ED3>ng ý 13 2.9 2.9%
## 5 LOY1 R<U+1EA5>t không d<U+1ED3>ng ý 6 1.34 1.34%
## 6 LOY2 R<U+1EA5>t không d<U+1ED3>ng ý 4 0.89 0.89%
## 7 LOY3 R<U+1EA5>t không d<U+1ED3>ng ý 6 1.34 1.34%
## 8 LOY4 R<U+1EA5>t không d<U+1ED3>ng ý 3 0.67 0.67%
## 9 LOY5 R<U+1EA5>t không d<U+1ED3>ng ý 12 2.68 2.68%
## 10 LOY6 R<U+1EA5>t không d<U+1ED3>ng ý 11 2.46 2.46%
## # ... with 18 more rows# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Không d<U+1ED3>ng ý 17 3.79 3.79%
## 2 PSB2 Không d<U+1ED3>ng ý 18 4.02 4.02%
## 3 PSB3 Không d<U+1ED3>ng ý 10 2.23 2.23%
## 4 PSB4 Không d<U+1ED3>ng ý 14 3.12 3.12%
## 5 LOY1 Không d<U+1ED3>ng ý 20 4.46 4.46%
## 6 LOY2 Không d<U+1ED3>ng ý 16 3.57 3.57%
## 7 LOY3 Không d<U+1ED3>ng ý 21 4.69 4.69%
## 8 LOY4 Không d<U+1ED3>ng ý 15 3.35 3.35%
## 9 LOY5 Không d<U+1ED3>ng ý 17 3.79 3.79%
## 10 LOY6 Không d<U+1ED3>ng ý 21 4.69 4.69%
## # ... with 18 more rows# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Khá không d<U+1ED3>ng ý 21 4.69 4.69%
## 2 PSB2 Khá không d<U+1ED3>ng ý 18 4.02 4.02%
## 3 PSB3 Khá không d<U+1ED3>ng ý 16 3.57 3.57%
## 4 PSB4 Khá không d<U+1ED3>ng ý 18 4.02 4.02%
## 5 LOY1 Khá không d<U+1ED3>ng ý 15 3.35 3.35%
## 6 LOY2 Khá không d<U+1ED3>ng ý 13 2.9 2.9%
## 7 LOY3 Khá không d<U+1ED3>ng ý 10 2.23 2.23%
## 8 LOY4 Khá không d<U+1ED3>ng ý 14 3.12 3.12%
## 9 LOY5 Khá không d<U+1ED3>ng ý 14 3.12 3.12%
## 10 LOY6 Khá không d<U+1ED3>ng ý 13 2.9 2.9%
## # ... with 18 more rows# For displaying percent of "Normal":
df_odered %>%
filter(Res == "B\u00ECnh th\u01B0\u1EDDng") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Bình thu<U+1EDD>ng 100 22.3 22.32%
## 2 PSB2 Bình thu<U+1EDD>ng 111 24.8 24.78%
## 3 PSB3 Bình thu<U+1EDD>ng 89 19.9 19.87%
## 4 PSB4 Bình thu<U+1EDD>ng 88 19.6 19.64%
## 5 LOY1 Bình thu<U+1EDD>ng 81 18.1 18.08%
## 6 LOY2 Bình thu<U+1EDD>ng 87 19.4 19.42%
## 7 LOY3 Bình thu<U+1EDD>ng 75 16.7 16.74%
## 8 LOY4 Bình thu<U+1EDD>ng 61 13.6 13.62%
## 9 LOY5 Bình thu<U+1EDD>ng 90 20.1 20.09%
## 10 LOY6 Bình thu<U+1EDD>ng 78 17.4 17.41%
## # ... with 18 more rows# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Kh\u00E1 \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Khá d<U+1ED3>ng ý 93 20.8 20.76%
## 2 PSB2 Khá d<U+1ED3>ng ý 100 22.3 22.32%
## 3 PSB3 Khá d<U+1ED3>ng ý 100 22.3 22.32%
## 4 PSB4 Khá d<U+1ED3>ng ý 97 21.6 21.65%
## 5 LOY1 Khá d<U+1ED3>ng ý 107 23.9 23.88%
## 6 LOY2 Khá d<U+1ED3>ng ý 76 17.0 16.96%
## 7 LOY3 Khá d<U+1ED3>ng ý 83 18.5 18.53%
## 8 LOY4 Khá d<U+1ED3>ng ý 86 19.2 19.2%
## 9 LOY5 Khá d<U+1ED3>ng ý 90 20.1 20.09%
## 10 LOY6 Khá d<U+1ED3>ng ý 99 22.1 22.1%
## # ... with 18 more rows# For displaying percent of "Agree":
df_odered %>%
filter(Res == "\u0110\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Ð<U+1ED3>ng ý 180 40.2 40.18%
## 2 PSB2 Ð<U+1ED3>ng ý 158 35.3 35.27%
## 3 PSB3 Ð<U+1ED3>ng ý 187 41.7 41.74%
## 4 PSB4 Ð<U+1ED3>ng ý 185 41.3 41.29%
## 5 LOY1 Ð<U+1ED3>ng ý 187 41.7 41.74%
## 6 LOY2 Ð<U+1ED3>ng ý 212 47.3 47.32%
## 7 LOY3 Ð<U+1ED3>ng ý 206 46.0 45.98%
## 8 LOY4 Ð<U+1ED3>ng ý 217 48.4 48.44%
## 9 LOY5 Ð<U+1ED3>ng ý 177 39.5 39.51%
## 10 LOY6 Ð<U+1ED3>ng ý 177 39.5 39.51%
## # ... with 18 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text7
df_for_text7## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 32 7.14 7.14%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 32 7.14 7.14%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 35 7.81 7.81%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 33 7.37 7.37%
## 5 LOY1 R<U+1EA5>t d<U+1ED3>ng ý 32 7.14 7.14%
## 6 LOY2 R<U+1EA5>t d<U+1ED3>ng ý 40 8.93 8.93%
## 7 LOY3 R<U+1EA5>t d<U+1ED3>ng ý 47 10.5 10.49%
## 8 LOY4 R<U+1EA5>t d<U+1ED3>ng ý 52 11.6 11.61%
## 9 LOY5 R<U+1EA5>t d<U+1ED3>ng ý 48 10.7 10.71%
## 10 LOY6 R<U+1EA5>t d<U+1ED3>ng ý 49 10.9 10.94%
## # ... with 18 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") -> df_for_text
df_for_text## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 32 7.14 7.14%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 32 7.14 7.14%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 35 7.81 7.81%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 33 7.37 7.37%
## 5 LOY1 R<U+1EA5>t d<U+1ED3>ng ý 32 7.14 7.14%
## 6 LOY2 R<U+1EA5>t d<U+1ED3>ng ý 40 8.93 8.93%
## 7 LOY3 R<U+1EA5>t d<U+1ED3>ng ý 47 10.5 10.49%
## 8 LOY4 R<U+1EA5>t d<U+1ED3>ng ý 52 11.6 11.61%
## 9 LOY5 R<U+1EA5>t d<U+1ED3>ng ý 48 10.7 10.71%
## 10 LOY6 R<U+1EA5>t d<U+1ED3>ng ý 49 10.9 10.94%
## # ... with 18 more rows# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg_viet +
geom_text(data = df_for_text1, aes(x = BPE, y = 4, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-30, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-2, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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3.8. Vẽ biểu đồ likert cho dữ liệu của 2 thành phố theo từng constructs 3.8.1. Construct PSA - Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct PSA
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM1_PSB)## Res BPE
## 1 6 PSB1
## 2 3 PSB1
## 3 2 PSB1
## 4 5 PSB1
## 5 2 PSB1
## 6 2 PSB1dim(DN_HCM_SEM1_PSB)## [1] 3452 2attach(DN_HCM_SEM1_PSB)## The following objects are masked from HCM_SEM1:
##
## BPE, Res## The following objects are masked from DN_SEM1:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1:
##
## BPE, ResDN_HCM_SEM1_PSB = within(DN_HCM_SEM1_PSB, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("PSB1", "PSB2", "PSB3", "PSB4"))
})
str(DN_HCM_SEM1_PSB)## 'data.frame': 3452 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 3 2 5 2 2 1 6 2 4 ...
## $ BPE: Factor w/ 4 levels "PSB1","PSB2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM1_PSB)## Res BPE
## Very Disagree : 146 PSB1:863
## Disagree : 296 PSB2:863
## Quite Disagree: 190 PSB3:863
## Normal : 381 PSB4:863
## Quite Agree : 563
## Agree :1518
## Very Agree : 358 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM1_PSB %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PSB4 PSB1 PSB2 PSB3
## Levels: PSB1 PSB2 PSB3 PSB4# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Disagree 59 6.84 6.84%
## 2 PSB1 Disagree 129 15.0 14.95%
## 3 PSB1 Quite Disagree 53 6.14 6.14%
## 4 PSB1 Normal 80 9.27 9.27%
## 5 PSB1 Quite Agree 127 14.7 14.72%
## 6 PSB1 Agree 332 38.5 38.47%
## 7 PSB1 Very Agree 83 9.62 9.62%
## 8 PSB2 Very Disagree 37 4.29 4.29%
## 9 PSB2 Disagree 65 7.53 7.53%
## 10 PSB2 Quite Disagree 51 5.91 5.91%
## # ... with 18 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "Perceived safety (PSA) on bus of passengers in urban areas of Viet Nam",
subtitle = "Likert scale is a type of rating scale commonly used in surveys. When responding to a Likert type question,\nrespondents simply state their level of agreement or disagreement on a symmetric agree-disagree scale.")
gg## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "Very Disagree") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Disagree 59 6.84 6.84%
## 2 PSB2 Very Disagree 37 4.29 4.29%
## 3 PSB3 Very Disagree 18 2.09 2.09%
## 4 PSB4 Very Disagree 32 3.71 3.71%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 3) -> df_for_text2
df_for_text2## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Disagree 129 15.0 14.95%
## 2 PSB2 Disagree 65 7.53 7.53%
## 3 PSB4 Disagree 79 9.15 9.15%# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Quite Disagree") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Quite Disagree 53 6.14 6.14%
## 2 PSB2 Quite Disagree 51 5.91 5.91%
## 3 PSB3 Quite Disagree 29 3.36 3.36%
## 4 PSB4 Quite Disagree 57 6.6 6.6%# For displaying percent of "Normal":
df_odered %>%
filter(Res == "Normal") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Normal 80 9.27 9.27%
## 2 PSB2 Normal 87 10.1 10.08%
## 3 PSB3 Normal 66 7.65 7.65%
## 4 PSB4 Normal 148 17.2 17.15%# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Quite Agree") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Quite Agree 127 14.7 14.72%
## 2 PSB2 Quite Agree 127 14.7 14.72%
## 3 PSB3 Quite Agree 148 17.2 17.15%
## 4 PSB4 Quite Agree 161 18.7 18.66%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "Agree") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Agree 332 38.5 38.47%
## 2 PSB2 Agree 402 46.6 46.58%
## 3 PSB3 Agree 475 55.0 55.04%
## 4 PSB4 Agree 309 35.8 35.81%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") %>%
filter(percent >= 0) -> df_for_text7 # Chỉ thể hiện giá trị có % lựa chọn >0
df_for_text7## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Agree 83 9.62 9.62%
## 2 PSB2 Very Agree 94 10.9 10.89%
## 3 PSB3 Very Agree 104 12.0 12.05%
## 4 PSB4 Very Agree 77 8.92 8.92%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") -> df_for_text
df_for_text## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Agree 83 9.62 9.62%
## 2 PSB2 Very Agree 94 10.9 10.89%
## 3 PSB3 Very Agree 104 12.0 12.05%
## 4 PSB4 Very Agree 77 8.92 8.92%# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg +
geom_text(data = df_for_text1, aes(x = BPE, y = 2, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-30, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-2, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
3.8.2. Construct PSA - Nhãn tiếng việt
DN_HCM_SEM1_PSB_v = within(DN_HCM_SEM1_PSB, {
Res = factor(Res, labels = c("R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD", "B\u00ECnh th\u01B0\u1EDDng", "Kh\u00E1 \u0111\u1ED3ng \u00FD", "\u0110\u1ED3ng \u00FD", "R\u1EA5t \u0111\u1ED3ng \u00FD"))
BPE = factor(BPE, labels = c("PSB1", "PSB2", "PSB3", "PSB4"))
})
str(DN_HCM_SEM1_PSB_v)## 'data.frame': 3452 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 6 3 2 5 2 2 1 6 2 4 ...
## $ BPE: Factor w/ 4 levels "PSB1","PSB2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM1_PSB_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 146 PSB1:863
## Không d<U+1ED3>ng ý : 296 PSB2:863
## Khá không d<U+1ED3>ng ý : 190 PSB3:863
## Bình thu<U+1EDD>ng : 381 PSB4:863
## Khá d<U+1ED3>ng ý : 563
## Ð<U+1ED3>ng ý :1518
## R<U+1EA5>t d<U+1ED3>ng ý : 358 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM1_PSB_v %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PSB4 PSB1 PSB2 PSB3
## Levels: PSB1 PSB2 PSB3 PSB4# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t không d<U+1ED3>ng ý 59 6.84 6.84%
## 2 PSB1 Không d<U+1ED3>ng ý 129 15.0 14.95%
## 3 PSB1 Khá không d<U+1ED3>ng ý 53 6.14 6.14%
## 4 PSB1 Bình thu<U+1EDD>ng 80 9.27 9.27%
## 5 PSB1 Khá d<U+1ED3>ng ý 127 14.7 14.72%
## 6 PSB1 Ð<U+1ED3>ng ý 332 38.5 38.47%
## 7 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 83 9.62 9.62%
## 8 PSB2 R<U+1EA5>t không d<U+1ED3>ng ý 37 4.29 4.29%
## 9 PSB2 Không d<U+1ED3>ng ý 65 7.53 7.53%
## 10 PSB2 Khá không d<U+1ED3>ng ý 51 5.91 5.91%
## # ... with 18 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg_v <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "C\u1EA3m nh\u1EADn v\u1EC1 an to\u00E0n (PSA) c\u1EE7a h\u00E0nh kh\u00E1ch \u0111\u1ED1i v\u1EDBi h\u1EC7 th\u1ED1ng xe bu\u00FDt \u0111\u00F4 thi Vi\u1EC7t Nam ",
subtitle = "Thang \u0111o Likert l\u00E0 lo\u1EA1i thang \u0111o \u0111i\u1EC3m \u0111\u00E1nh gi\u00E1 \u0111\u01B0\u1EE3c s\u1EED d\u1EE5ng ph\u1ED5 bi\u1EBFn trong kh\u1EA3o s\u00E1t. Nghi\u00EAn c\u1EE9u s\u1EED d\u1EE5ng thang \u0111o 7 m\u1EE9c \u0111\u1ED9 t\u01B0\u01A1ng \u1EE9ng v\u1EDBi 1 - R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD \u0111\u1EBFn 7 - R\u1EA5t \u0111\u1ED3ng \u00FD.")
gg_v## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t không d<U+1ED3>ng ý 59 6.84 6.84%
## 2 PSB2 R<U+1EA5>t không d<U+1ED3>ng ý 37 4.29 4.29%
## 3 PSB3 R<U+1EA5>t không d<U+1ED3>ng ý 18 2.09 2.09%
## 4 PSB4 R<U+1EA5>t không d<U+1ED3>ng ý 32 3.71 3.71%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Không d<U+1ED3>ng ý 129 15.0 14.95%
## 2 PSB2 Không d<U+1ED3>ng ý 65 7.53 7.53%
## 3 PSB3 Không d<U+1ED3>ng ý 23 2.67 2.67%
## 4 PSB4 Không d<U+1ED3>ng ý 79 9.15 9.15%# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Khá không d<U+1ED3>ng ý 53 6.14 6.14%
## 2 PSB2 Khá không d<U+1ED3>ng ý 51 5.91 5.91%
## 3 PSB3 Khá không d<U+1ED3>ng ý 29 3.36 3.36%
## 4 PSB4 Khá không d<U+1ED3>ng ý 57 6.6 6.6%# For displaying percent of "Normal":
df_odered %>%
filter(Res == "B\u00ECnh th\u01B0\u1EDDng") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Bình thu<U+1EDD>ng 80 9.27 9.27%
## 2 PSB2 Bình thu<U+1EDD>ng 87 10.1 10.08%
## 3 PSB3 Bình thu<U+1EDD>ng 66 7.65 7.65%
## 4 PSB4 Bình thu<U+1EDD>ng 148 17.2 17.15%# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Kh\u00E1 \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Khá d<U+1ED3>ng ý 127 14.7 14.72%
## 2 PSB2 Khá d<U+1ED3>ng ý 127 14.7 14.72%
## 3 PSB3 Khá d<U+1ED3>ng ý 148 17.2 17.15%
## 4 PSB4 Khá d<U+1ED3>ng ý 161 18.7 18.66%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "\u0110\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Ð<U+1ED3>ng ý 332 38.5 38.47%
## 2 PSB2 Ð<U+1ED3>ng ý 402 46.6 46.58%
## 3 PSB3 Ð<U+1ED3>ng ý 475 55.0 55.04%
## 4 PSB4 Ð<U+1ED3>ng ý 309 35.8 35.81%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text7
df_for_text7## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 83 9.62 9.62%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 94 10.9 10.89%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 104 12.0 12.05%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 77 8.92 8.92%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") -> df_for_text
df_for_text## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 83 9.62 9.62%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 94 10.9 10.89%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 104 12.0 12.05%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 77 8.92 8.92%# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg_v +
geom_text(data = df_for_text1, aes(x = BPE, y = 2, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-30, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-2, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
3.8.3. Construct LOY-Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct LIN
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM1_LOY)## Res BPE
## 1 4 LOY1
## 2 4 LOY1
## 3 2 LOY1
## 4 5 LOY1
## 5 4 LOY1
## 6 4 LOY1dim(DN_HCM_SEM1_LOY)## [1] 6041 2attach(DN_HCM_SEM1_LOY)## The following objects are masked from DN_HCM_SEM1_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM1:
##
## BPE, Res## The following objects are masked from DN_SEM1:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1:
##
## BPE, ResDN_HCM_SEM1_LOY = within(DN_HCM_SEM1_LOY, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("LOY1", "LOY2", "LOY3", "LOY4", "LOY5", "LOY6", "LOY7"))
})
str(DN_HCM_SEM1_LOY)## 'data.frame': 6041 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 4 4 2 5 4 4 2 6 6 6 ...
## $ BPE: Factor w/ 7 levels "LOY1","LOY2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM1_LOY)## Res BPE
## Very Disagree : 66 LOY1:863
## Disagree : 174 LOY2:863
## Quite Disagree: 183 LOY3:863
## Normal : 767 LOY4:863
## Quite Agree :1255 LOY5:863
## Agree :2692 LOY6:863
## Very Agree : 904 LOY7:863 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM1_LOY %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] LOY1 LOY2 LOY4 LOY6 LOY5 LOY3 LOY7
## Levels: LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 49 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 Very Disagree 4 0.46 0.46%
## 2 LOY1 Disagree 18 2.09 2.09%
## 3 LOY1 Quite Disagree 20 2.32 2.32%
## 4 LOY1 Normal 114 13.2 13.21%
## 5 LOY1 Quite Agree 168 19.5 19.47%
## 6 LOY1 Agree 433 50.2 50.17%
## 7 LOY1 Very Agree 106 12.3 12.28%
## 8 LOY2 Very Disagree 6 0.7 0.7%
## 9 LOY2 Disagree 22 2.55 2.55%
## 10 LOY2 Quite Disagree 17 1.97 1.97%
## # ... with 39 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "Perceived loylty (LOY) of passengers in urban areas of Viet Nam",
subtitle = "Likert scale is a type of rating scale commonly used in surveys. When responding to a Likert type question,\nrespondents simply state their level of agreement or disagreement on a symmetric agree-disagree scale.")
gg## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "Very Disagree") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 Very Disagree 4 0.46 0.46%
## 2 LOY2 Very Disagree 6 0.7 0.7%
## 3 LOY3 Very Disagree 3 0.35 0.35%
## 4 LOY4 Very Disagree 13 1.51 1.51%
## 5 LOY5 Very Disagree 12 1.39 1.39%
## 6 LOY6 Very Disagree 15 1.74 1.74%
## 7 LOY7 Very Disagree 13 1.51 1.51%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 3) -> df_for_text2
df_for_text2## # A tibble: 1 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY6 Disagree 60 6.95 6.95%# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Quite Disagree") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 Quite Disagree 20 2.32 2.32%
## 2 LOY2 Quite Disagree 17 1.97 1.97%
## 3 LOY3 Quite Disagree 19 2.2 2.2%
## 4 LOY4 Quite Disagree 32 3.71 3.71%
## 5 LOY5 Quite Disagree 26 3.01 3.01%
## 6 LOY6 Quite Disagree 52 6.03 6.03%
## 7 LOY7 Quite Disagree 17 1.97 1.97%# For displaying percent of "Normal":
df_odered %>%
filter(Res == "Normal") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 Normal 114 13.2 13.21%
## 2 LOY2 Normal 99 11.5 11.47%
## 3 LOY3 Normal 92 10.7 10.66%
## 4 LOY4 Normal 130 15.1 15.06%
## 5 LOY5 Normal 111 12.9 12.86%
## 6 LOY6 Normal 141 16.3 16.34%
## 7 LOY7 Normal 80 9.27 9.27%# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Quite Agree") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 Quite Agree 168 19.5 19.47%
## 2 LOY2 Quite Agree 171 19.8 19.81%
## 3 LOY3 Quite Agree 161 18.7 18.66%
## 4 LOY4 Quite Agree 212 24.6 24.57%
## 5 LOY5 Quite Agree 214 24.8 24.8%
## 6 LOY6 Quite Agree 172 19.9 19.93%
## 7 LOY7 Quite Agree 157 18.2 18.19%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "Agree") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 Agree 433 50.2 50.17%
## 2 LOY2 Agree 430 49.8 49.83%
## 3 LOY3 Agree 440 51.0 50.98%
## 4 LOY4 Agree 340 39.4 39.4%
## 5 LOY5 Agree 347 40.2 40.21%
## 6 LOY6 Agree 296 34.3 34.3%
## 7 LOY7 Agree 406 47.0 47.05%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") %>%
filter(percent >= 0) -> df_for_text7 # Chỉ thể hiện giá trị có % lựa chọn >0
df_for_text7## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 Very Agree 106 12.3 12.28%
## 2 LOY2 Very Agree 118 13.7 13.67%
## 3 LOY3 Very Agree 132 15.3 15.3%
## 4 LOY4 Very Agree 118 13.7 13.67%
## 5 LOY5 Very Agree 129 15.0 14.95%
## 6 LOY6 Very Agree 127 14.7 14.72%
## 7 LOY7 Very Agree 174 20.2 20.16%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") -> df_for_text
df_for_text## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 Very Agree 106 12.3 12.28%
## 2 LOY2 Very Agree 118 13.7 13.67%
## 3 LOY3 Very Agree 132 15.3 15.3%
## 4 LOY4 Very Agree 118 13.7 13.67%
## 5 LOY5 Very Agree 129 15.0 14.95%
## 6 LOY6 Very Agree 127 14.7 14.72%
## 7 LOY7 Very Agree 174 20.2 20.16%# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg +
geom_text(data = df_for_text1, aes(x = BPE, y = 3, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text2, aes(x = BPE, y = 9, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text5, aes(x = BPE, y = 32, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-30, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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3.8.4. Construct LOY-Nhãn tiếng việt
DN_HCM_SEM1_LOY_v = within(DN_HCM_SEM1_LOY, {
Res = factor(Res, labels = c("R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD", "B\u00ECnh th\u01B0\u1EDDng", "Kh\u00E1 \u0111\u1ED3ng \u00FD", "\u0110\u1ED3ng \u00FD", "R\u1EA5t \u0111\u1ED3ng \u00FD"))
BPE = factor(BPE, labels = c("LOY1", "LOY2", "LOY3", "LOY4", "LOY5", "LOY6", "LOY7"))
})
str(DN_HCM_SEM1_LOY_v)## 'data.frame': 6041 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 4 4 2 5 4 4 2 6 6 6 ...
## $ BPE: Factor w/ 7 levels "LOY1","LOY2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM1_LOY_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 66 LOY1:863
## Không d<U+1ED3>ng ý : 174 LOY2:863
## Khá không d<U+1ED3>ng ý : 183 LOY3:863
## Bình thu<U+1EDD>ng : 767 LOY4:863
## Khá d<U+1ED3>ng ý :1255 LOY5:863
## Ð<U+1ED3>ng ý :2692 LOY6:863
## R<U+1EA5>t d<U+1ED3>ng ý : 904 LOY7:863 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM1_LOY_v %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] LOY1 LOY2 LOY4 LOY6 LOY5 LOY3 LOY7
## Levels: LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 49 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 R<U+1EA5>t không d<U+1ED3>ng ý 4 0.46 0.46%
## 2 LOY1 Không d<U+1ED3>ng ý 18 2.09 2.09%
## 3 LOY1 Khá không d<U+1ED3>ng ý 20 2.32 2.32%
## 4 LOY1 Bình thu<U+1EDD>ng 114 13.2 13.21%
## 5 LOY1 Khá d<U+1ED3>ng ý 168 19.5 19.47%
## 6 LOY1 Ð<U+1ED3>ng ý 433 50.2 50.17%
## 7 LOY1 R<U+1EA5>t d<U+1ED3>ng ý 106 12.3 12.28%
## 8 LOY2 R<U+1EA5>t không d<U+1ED3>ng ý 6 0.7 0.7%
## 9 LOY2 Không d<U+1ED3>ng ý 22 2.55 2.55%
## 10 LOY2 Khá không d<U+1ED3>ng ý 17 1.97 1.97%
## # ... with 39 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg_v <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "C\u1EA3m nh\u1EADn l\u00F2ng trung th\u00E0nh (LOY) c\u1EE7a h\u00E0nh kh\u00E1ch s\u1EED d\u1EE5ng xe bu\u00FDt \u0111\u00F4 thi \u1EDF Vi\u1EC7t Nam ",
subtitle = "Thang \u0111o Likert l\u00E0 lo\u1EA1i thang \u0111o \u0111i\u1EC3m \u0111\u00E1nh gi\u00E1 \u0111\u01B0\u1EE3c s\u1EED d\u1EE5ng ph\u1ED5 bi\u1EBFn trong kh\u1EA3o s\u00E1t. Nghi\u00EAn c\u1EE9u s\u1EED d\u1EE5ng thang \u0111o 7 m\u1EE9c \u0111\u1ED9 t\u01B0\u01A1ng \u1EE9ng v\u1EDBi 1 - R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD \u0111\u1EBFn 7 - R\u1EA5t \u0111\u1ED3ng \u00FD.")
gg_v## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 R<U+1EA5>t không d<U+1ED3>ng ý 4 0.46 0.46%
## 2 LOY2 R<U+1EA5>t không d<U+1ED3>ng ý 6 0.7 0.7%
## 3 LOY3 R<U+1EA5>t không d<U+1ED3>ng ý 3 0.35 0.35%
## 4 LOY4 R<U+1EA5>t không d<U+1ED3>ng ý 13 1.51 1.51%
## 5 LOY5 R<U+1EA5>t không d<U+1ED3>ng ý 12 1.39 1.39%
## 6 LOY6 R<U+1EA5>t không d<U+1ED3>ng ý 15 1.74 1.74%
## 7 LOY7 R<U+1EA5>t không d<U+1ED3>ng ý 13 1.51 1.51%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 Không d<U+1ED3>ng ý 18 2.09 2.09%
## 2 LOY2 Không d<U+1ED3>ng ý 22 2.55 2.55%
## 3 LOY3 Không d<U+1ED3>ng ý 16 1.85 1.85%
## 4 LOY4 Không d<U+1ED3>ng ý 18 2.09 2.09%
## 5 LOY5 Không d<U+1ED3>ng ý 24 2.78 2.78%
## 6 LOY6 Không d<U+1ED3>ng ý 60 6.95 6.95%
## 7 LOY7 Không d<U+1ED3>ng ý 16 1.85 1.85%# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 Khá không d<U+1ED3>ng ý 20 2.32 2.32%
## 2 LOY2 Khá không d<U+1ED3>ng ý 17 1.97 1.97%
## 3 LOY3 Khá không d<U+1ED3>ng ý 19 2.2 2.2%
## 4 LOY4 Khá không d<U+1ED3>ng ý 32 3.71 3.71%
## 5 LOY5 Khá không d<U+1ED3>ng ý 26 3.01 3.01%
## 6 LOY6 Khá không d<U+1ED3>ng ý 52 6.03 6.03%
## 7 LOY7 Khá không d<U+1ED3>ng ý 17 1.97 1.97%# For displaying percent of "Normal":
df_odered %>%
filter(Res == "B\u00ECnh th\u01B0\u1EDDng") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 Bình thu<U+1EDD>ng 114 13.2 13.21%
## 2 LOY2 Bình thu<U+1EDD>ng 99 11.5 11.47%
## 3 LOY3 Bình thu<U+1EDD>ng 92 10.7 10.66%
## 4 LOY4 Bình thu<U+1EDD>ng 130 15.1 15.06%
## 5 LOY5 Bình thu<U+1EDD>ng 111 12.9 12.86%
## 6 LOY6 Bình thu<U+1EDD>ng 141 16.3 16.34%
## 7 LOY7 Bình thu<U+1EDD>ng 80 9.27 9.27%# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Kh\u00E1 \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 Khá d<U+1ED3>ng ý 168 19.5 19.47%
## 2 LOY2 Khá d<U+1ED3>ng ý 171 19.8 19.81%
## 3 LOY3 Khá d<U+1ED3>ng ý 161 18.7 18.66%
## 4 LOY4 Khá d<U+1ED3>ng ý 212 24.6 24.57%
## 5 LOY5 Khá d<U+1ED3>ng ý 214 24.8 24.8%
## 6 LOY6 Khá d<U+1ED3>ng ý 172 19.9 19.93%
## 7 LOY7 Khá d<U+1ED3>ng ý 157 18.2 18.19%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "\u0110\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 Ð<U+1ED3>ng ý 433 50.2 50.17%
## 2 LOY2 Ð<U+1ED3>ng ý 430 49.8 49.83%
## 3 LOY3 Ð<U+1ED3>ng ý 440 51.0 50.98%
## 4 LOY4 Ð<U+1ED3>ng ý 340 39.4 39.4%
## 5 LOY5 Ð<U+1ED3>ng ý 347 40.2 40.21%
## 6 LOY6 Ð<U+1ED3>ng ý 296 34.3 34.3%
## 7 LOY7 Ð<U+1ED3>ng ý 406 47.0 47.05%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text7
df_for_text7## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 R<U+1EA5>t d<U+1ED3>ng ý 106 12.3 12.28%
## 2 LOY2 R<U+1EA5>t d<U+1ED3>ng ý 118 13.7 13.67%
## 3 LOY3 R<U+1EA5>t d<U+1ED3>ng ý 132 15.3 15.3%
## 4 LOY4 R<U+1EA5>t d<U+1ED3>ng ý 118 13.7 13.67%
## 5 LOY5 R<U+1EA5>t d<U+1ED3>ng ý 129 15.0 14.95%
## 6 LOY6 R<U+1EA5>t d<U+1ED3>ng ý 127 14.7 14.72%
## 7 LOY7 R<U+1EA5>t d<U+1ED3>ng ý 174 20.2 20.16%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") -> df_for_text
df_for_text## # A tibble: 7 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 LOY1 R<U+1EA5>t d<U+1ED3>ng ý 106 12.3 12.28%
## 2 LOY2 R<U+1EA5>t d<U+1ED3>ng ý 118 13.7 13.67%
## 3 LOY3 R<U+1EA5>t d<U+1ED3>ng ý 132 15.3 15.3%
## 4 LOY4 R<U+1EA5>t d<U+1ED3>ng ý 118 13.7 13.67%
## 5 LOY5 R<U+1EA5>t d<U+1ED3>ng ý 129 15.0 14.95%
## 6 LOY6 R<U+1EA5>t d<U+1ED3>ng ý 127 14.7 14.72%
## 7 LOY7 R<U+1EA5>t d<U+1ED3>ng ý 174 20.2 20.16%# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg_v +
geom_text(data = df_for_text1, aes(x = BPE, y = 3, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text2, aes(x = BPE, y = 9, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text5, aes(x = BPE, y = 32, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-30, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
3.8.5. Construct IMA- Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct IMA
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM1_IMA)## Res BPE
## 1 6 IMA1
## 2 4 IMA1
## 3 4 IMA1
## 4 5 IMA1
## 5 5 IMA1
## 6 4 IMA1dim(DN_HCM_SEM1_IMA)## [1] 4315 2attach(DN_HCM_SEM1_IMA)## The following objects are masked from DN_HCM_SEM1_LOY:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM1:
##
## BPE, Res## The following objects are masked from DN_SEM1:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1:
##
## BPE, ResDN_HCM_SEM1_IMA = within(DN_HCM_SEM1_IMA, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("IMA1", "IMA2", "IMA3", "IMA4", "IMA5"))
})
str(DN_HCM_SEM1_IMA)## 'data.frame': 4315 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 4 4 5 5 4 2 6 5 6 ...
## $ BPE: Factor w/ 5 levels "IMA1","IMA2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM1_IMA)## Res BPE
## Very Disagree : 48 IMA1:863
## Disagree : 85 IMA2:863
## Quite Disagree: 125 IMA3:863
## Normal : 666 IMA4:863
## Quite Agree :1058 IMA5:863
## Agree :1953
## Very Agree : 380 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM1_IMA %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] IMA1 IMA2 IMA3 IMA5 IMA4
## Levels: IMA1 IMA2 IMA3 IMA4 IMA5# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 35 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Very Disagree 6 0.7 0.7%
## 2 IMA1 Disagree 20 2.32 2.32%
## 3 IMA1 Quite Disagree 32 3.71 3.71%
## 4 IMA1 Normal 156 18.1 18.08%
## 5 IMA1 Quite Agree 230 26.6 26.65%
## 6 IMA1 Agree 354 41.0 41.02%
## 7 IMA1 Very Agree 65 7.53 7.53%
## 8 IMA2 Very Disagree 11 1.27 1.27%
## 9 IMA2 Disagree 19 2.2 2.2%
## 10 IMA2 Quite Disagree 26 3.01 3.01%
## # ... with 25 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "Perceived image (IMA) of passengers in urban areas of Viet Nam",
subtitle = "Likert scale is a type of rating scale commonly used in surveys. When responding to a Likert type question,\nrespondents simply state their level of agreement or disagreement on a symmetric agree-disagree scale.")
gg## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "Very Disagree") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Very Disagree 6 0.7 0.7%
## 2 IMA2 Very Disagree 11 1.27 1.27%
## 3 IMA3 Very Disagree 11 1.27 1.27%
## 4 IMA4 Very Disagree 14 1.62 1.62%
## 5 IMA5 Very Disagree 6 0.7 0.7%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 3) -> df_for_text2
df_for_text2## # A tibble: 0 x 5
## # ... with 5 variables: BPE <fct>, Res <fct>, n <int>, percent <dbl>,
## # bar_text <chr># For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Quite Disagree") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Quite Disagree 32 3.71 3.71%
## 2 IMA2 Quite Disagree 26 3.01 3.01%
## 3 IMA3 Quite Disagree 20 2.32 2.32%
## 4 IMA4 Quite Disagree 26 3.01 3.01%
## 5 IMA5 Quite Disagree 21 2.43 2.43%# For displaying percent of "Normal":
df_odered %>%
filter(Res == "Normal") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Normal 156 18.1 18.08%
## 2 IMA2 Normal 160 18.5 18.54%
## 3 IMA3 Normal 130 15.1 15.06%
## 4 IMA4 Normal 114 13.2 13.21%
## 5 IMA5 Normal 106 12.3 12.28%# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Quite Agree") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Quite Agree 230 26.6 26.65%
## 2 IMA2 Quite Agree 213 24.7 24.68%
## 3 IMA3 Quite Agree 209 24.2 24.22%
## 4 IMA4 Quite Agree 201 23.3 23.29%
## 5 IMA5 Quite Agree 205 23.8 23.75%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "Agree") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Agree 354 41.0 41.02%
## 2 IMA2 Agree 368 42.6 42.64%
## 3 IMA3 Agree 403 46.7 46.7%
## 4 IMA4 Agree 408 47.3 47.28%
## 5 IMA5 Agree 420 48.7 48.67%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") %>%
filter(percent >= 0) -> df_for_text7 # Chỉ thể hiện giá trị có % lựa chọn >0
df_for_text7## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Very Agree 65 7.53 7.53%
## 2 IMA2 Very Agree 66 7.65 7.65%
## 3 IMA3 Very Agree 79 9.15 9.15%
## 4 IMA4 Very Agree 86 9.97 9.97%
## 5 IMA5 Very Agree 84 9.73 9.73%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") -> df_for_text
df_for_text## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Very Agree 65 7.53 7.53%
## 2 IMA2 Very Agree 66 7.65 7.65%
## 3 IMA3 Very Agree 79 9.15 9.15%
## 4 IMA4 Very Agree 86 9.97 9.97%
## 5 IMA5 Very Agree 84 9.73 9.73%# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg +
geom_text(data = df_for_text1, aes(x = BPE, y = 3, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text4, aes(x = BPE, y = 13, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text5, aes(x = BPE, y = 32, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-30, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
3.8.6. Construct IMA- Nhãn tiếng việt
DN_HCM_SEM1_IMA_v = within(DN_HCM_SEM1_IMA, {
Res = factor(Res, labels = c("R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD", "B\u00ECnh th\u01B0\u1EDDng", "Kh\u00E1 \u0111\u1ED3ng \u00FD", "\u0110\u1ED3ng \u00FD", "R\u1EA5t \u0111\u1ED3ng \u00FD"))
BPE = factor(BPE, labels = c("IMA1", "IMA2", "IMA3", "IMA4", "IMA5"))
})
str(DN_HCM_SEM1_IMA_v)## 'data.frame': 4315 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 6 4 4 5 5 4 2 6 5 6 ...
## $ BPE: Factor w/ 5 levels "IMA1","IMA2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM1_IMA_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 48 IMA1:863
## Không d<U+1ED3>ng ý : 85 IMA2:863
## Khá không d<U+1ED3>ng ý : 125 IMA3:863
## Bình thu<U+1EDD>ng : 666 IMA4:863
## Khá d<U+1ED3>ng ý :1058 IMA5:863
## Ð<U+1ED3>ng ý :1953
## R<U+1EA5>t d<U+1ED3>ng ý : 380 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM1_IMA_v %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] IMA1 IMA2 IMA3 IMA5 IMA4
## Levels: IMA1 IMA2 IMA3 IMA4 IMA5# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 35 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 R<U+1EA5>t không d<U+1ED3>ng ý 6 0.7 0.7%
## 2 IMA1 Không d<U+1ED3>ng ý 20 2.32 2.32%
## 3 IMA1 Khá không d<U+1ED3>ng ý 32 3.71 3.71%
## 4 IMA1 Bình thu<U+1EDD>ng 156 18.1 18.08%
## 5 IMA1 Khá d<U+1ED3>ng ý 230 26.6 26.65%
## 6 IMA1 Ð<U+1ED3>ng ý 354 41.0 41.02%
## 7 IMA1 R<U+1EA5>t d<U+1ED3>ng ý 65 7.53 7.53%
## 8 IMA2 R<U+1EA5>t không d<U+1ED3>ng ý 11 1.27 1.27%
## 9 IMA2 Không d<U+1ED3>ng ý 19 2.2 2.2%
## 10 IMA2 Khá không d<U+1ED3>ng ý 26 3.01 3.01%
## # ... with 25 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg_v <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "C\u1EA3m nh\u1EADn v\u1EC1 h\u00ECnh \u1EA3nh (IMA) c\u1EE7a h\u00E0nh kh\u00E1ch \u0111\u1ED1i v\u1EDBi h\u1EC7 th\u1ED1ng xe bu\u00FDt \u0111\u00F4 thi Vi\u1EC7t Nam ",
subtitle = "Thang \u0111o Likert l\u00E0 lo\u1EA1i thang \u0111o \u0111i\u1EC3m \u0111\u00E1nh gi\u00E1 \u0111\u01B0\u1EE3c s\u1EED d\u1EE5ng ph\u1ED5 bi\u1EBFn trong kh\u1EA3o s\u00E1t. Nghi\u00EAn c\u1EE9u s\u1EED d\u1EE5ng thang \u0111o 7 m\u1EE9c \u0111\u1ED9 t\u01B0\u01A1ng \u1EE9ng v\u1EDBi 1 - R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD \u0111\u1EBFn 7 - R\u1EA5t \u0111\u1ED3ng \u00FD.")
gg_v## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 R<U+1EA5>t không d<U+1ED3>ng ý 6 0.7 0.7%
## 2 IMA2 R<U+1EA5>t không d<U+1ED3>ng ý 11 1.27 1.27%
## 3 IMA3 R<U+1EA5>t không d<U+1ED3>ng ý 11 1.27 1.27%
## 4 IMA4 R<U+1EA5>t không d<U+1ED3>ng ý 14 1.62 1.62%
## 5 IMA5 R<U+1EA5>t không d<U+1ED3>ng ý 6 0.7 0.7%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Không d<U+1ED3>ng ý 20 2.32 2.32%
## 2 IMA2 Không d<U+1ED3>ng ý 19 2.2 2.2%
## 3 IMA3 Không d<U+1ED3>ng ý 11 1.27 1.27%
## 4 IMA4 Không d<U+1ED3>ng ý 14 1.62 1.62%
## 5 IMA5 Không d<U+1ED3>ng ý 21 2.43 2.43%# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Khá không d<U+1ED3>ng ý 32 3.71 3.71%
## 2 IMA2 Khá không d<U+1ED3>ng ý 26 3.01 3.01%
## 3 IMA3 Khá không d<U+1ED3>ng ý 20 2.32 2.32%
## 4 IMA4 Khá không d<U+1ED3>ng ý 26 3.01 3.01%
## 5 IMA5 Khá không d<U+1ED3>ng ý 21 2.43 2.43%# For displaying percent of "Normal":
df_odered %>%
filter(Res == "B\u00ECnh th\u01B0\u1EDDng") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Bình thu<U+1EDD>ng 156 18.1 18.08%
## 2 IMA2 Bình thu<U+1EDD>ng 160 18.5 18.54%
## 3 IMA3 Bình thu<U+1EDD>ng 130 15.1 15.06%
## 4 IMA4 Bình thu<U+1EDD>ng 114 13.2 13.21%
## 5 IMA5 Bình thu<U+1EDD>ng 106 12.3 12.28%# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Kh\u00E1 \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Khá d<U+1ED3>ng ý 230 26.6 26.65%
## 2 IMA2 Khá d<U+1ED3>ng ý 213 24.7 24.68%
## 3 IMA3 Khá d<U+1ED3>ng ý 209 24.2 24.22%
## 4 IMA4 Khá d<U+1ED3>ng ý 201 23.3 23.29%
## 5 IMA5 Khá d<U+1ED3>ng ý 205 23.8 23.75%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "\u0110\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Ð<U+1ED3>ng ý 354 41.0 41.02%
## 2 IMA2 Ð<U+1ED3>ng ý 368 42.6 42.64%
## 3 IMA3 Ð<U+1ED3>ng ý 403 46.7 46.7%
## 4 IMA4 Ð<U+1ED3>ng ý 408 47.3 47.28%
## 5 IMA5 Ð<U+1ED3>ng ý 420 48.7 48.67%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text7
df_for_text7## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 R<U+1EA5>t d<U+1ED3>ng ý 65 7.53 7.53%
## 2 IMA2 R<U+1EA5>t d<U+1ED3>ng ý 66 7.65 7.65%
## 3 IMA3 R<U+1EA5>t d<U+1ED3>ng ý 79 9.15 9.15%
## 4 IMA4 R<U+1EA5>t d<U+1ED3>ng ý 86 9.97 9.97%
## 5 IMA5 R<U+1EA5>t d<U+1ED3>ng ý 84 9.73 9.73%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") -> df_for_text
df_for_text## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 R<U+1EA5>t d<U+1ED3>ng ý 65 7.53 7.53%
## 2 IMA2 R<U+1EA5>t d<U+1ED3>ng ý 66 7.65 7.65%
## 3 IMA3 R<U+1EA5>t d<U+1ED3>ng ý 79 9.15 9.15%
## 4 IMA4 R<U+1EA5>t d<U+1ED3>ng ý 86 9.97 9.97%
## 5 IMA5 R<U+1EA5>t d<U+1ED3>ng ý 84 9.73 9.73%# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg_v +
geom_text(data = df_for_text1, aes(x = BPE, y = 3, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text4, aes(x = BPE, y = 13, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text5, aes(x = BPE, y = 32, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-30, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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3.8.7. Construct PHB - Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct PHB
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM1_PHB)## Res BPE
## 1 6 PHB1
## 2 3 PHB1
## 3 5 PHB1
## 4 5 PHB1
## 5 5 PHB1
## 6 4 PHB1dim(DN_HCM_SEM1_PHB)## [1] 4315 2attach(DN_HCM_SEM1_PHB)## The following objects are masked from DN_HCM_SEM1_IMA:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1_LOY:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM1:
##
## BPE, Res## The following objects are masked from DN_SEM1:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1:
##
## BPE, ResDN_HCM_SEM1_PHB = within(DN_HCM_SEM1_PHB, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("PHB1", "PHB2", "PHB3", "PHB4", "PHB5"))
})
str(DN_HCM_SEM1_PHB)## 'data.frame': 4315 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 3 5 5 5 4 3 6 6 7 ...
## $ BPE: Factor w/ 5 levels "PHB1","PHB2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM1_PHB)## Res BPE
## Very Disagree : 88 PHB1:863
## Disagree : 76 PHB2:863
## Quite Disagree: 115 PHB3:863
## Normal : 377 PHB4:863
## Quite Agree : 807 PHB5:863
## Agree :1928
## Very Agree : 924 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM1_PHB %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PHB1 PHB3 PHB2 PHB5 PHB4
## Levels: PHB1 PHB2 PHB3 PHB4 PHB5# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 35 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 Very Disagree 24 2.78 2.78%
## 2 PHB1 Disagree 20 2.32 2.32%
## 3 PHB1 Quite Disagree 37 4.29 4.29%
## 4 PHB1 Normal 99 11.5 11.47%
## 5 PHB1 Quite Agree 216 25.0 25.03%
## 6 PHB1 Agree 353 40.9 40.9%
## 7 PHB1 Very Agree 114 13.2 13.21%
## 8 PHB2 Very Disagree 13 1.51 1.51%
## 9 PHB2 Disagree 22 2.55 2.55%
## 10 PHB2 Quite Disagree 25 2.9 2.9%
## # ... with 25 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "Perceived health benefits (PHB) of passengers towards urban bus systems of Viet Nam",
subtitle = "Likert scale is a type of rating scale commonly used in surveys. When responding to a Likert type question,\nrespondents simply state their level of agreement or disagreement on a symmetric agree-disagree scale.")
gg## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "Very Disagree") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 Very Disagree 24 2.78 2.78%
## 2 PHB2 Very Disagree 13 1.51 1.51%
## 3 PHB3 Very Disagree 15 1.74 1.74%
## 4 PHB4 Very Disagree 13 1.51 1.51%
## 5 PHB5 Very Disagree 23 2.67 2.67%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 3) -> df_for_text2
df_for_text2## # A tibble: 0 x 5
## # ... with 5 variables: BPE <fct>, Res <fct>, n <int>, percent <dbl>,
## # bar_text <chr># For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Quite Disagree") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 Quite Disagree 37 4.29 4.29%
## 2 PHB2 Quite Disagree 25 2.9 2.9%
## 3 PHB3 Quite Disagree 38 4.4 4.4%
## 4 PHB4 Quite Disagree 10 1.16 1.16%
## 5 PHB5 Quite Disagree 5 0.580 0.58%# For displaying percent of "Normal":
df_odered %>%
filter(Res == "Normal") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 Normal 99 11.5 11.47%
## 2 PHB2 Normal 70 8.11 8.11%
## 3 PHB3 Normal 89 10.3 10.31%
## 4 PHB4 Normal 55 6.37 6.37%
## 5 PHB5 Normal 64 7.42 7.42%# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Quite Agree") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 Quite Agree 216 25.0 25.03%
## 2 PHB2 Quite Agree 164 19 19%
## 3 PHB3 Quite Agree 176 20.4 20.39%
## 4 PHB4 Quite Agree 134 15.5 15.53%
## 5 PHB5 Quite Agree 117 13.6 13.56%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "Agree") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 Agree 353 40.9 40.9%
## 2 PHB2 Agree 381 44.2 44.15%
## 3 PHB3 Agree 385 44.6 44.61%
## 4 PHB4 Agree 391 45.3 45.31%
## 5 PHB5 Agree 418 48.4 48.44%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") %>%
filter(percent >= 0) -> df_for_text7 # Chỉ thể hiện giá trị có % lựa chọn >0
df_for_text7## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 Very Agree 114 13.2 13.21%
## 2 PHB2 Very Agree 188 21.8 21.78%
## 3 PHB3 Very Agree 140 16.2 16.22%
## 4 PHB4 Very Agree 253 29.3 29.32%
## 5 PHB5 Very Agree 229 26.5 26.54%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") -> df_for_text
df_for_text## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 Very Agree 114 13.2 13.21%
## 2 PHB2 Very Agree 188 21.8 21.78%
## 3 PHB3 Very Agree 140 16.2 16.22%
## 4 PHB4 Very Agree 253 29.3 29.32%
## 5 PHB5 Very Agree 229 26.5 26.54%# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg +
geom_text(data = df_for_text1, aes(x = BPE, y = 3, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text5, aes(x = BPE, y = 22, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-40, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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3.8.8. Construct PHB - Nhãn tiếng việt
DN_HCM_SEM1_PHB_v = within(DN_HCM_SEM1_PHB, {
Res = factor(Res, labels = c("R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD", "B\u00ECnh th\u01B0\u1EDDng", "Kh\u00E1 \u0111\u1ED3ng \u00FD", "\u0110\u1ED3ng \u00FD", "R\u1EA5t \u0111\u1ED3ng \u00FD"))
BPE = factor(BPE, labels = c("PHB1", "PHB2", "PHB3", "PHB4", "PHB5"))
})
str(DN_HCM_SEM1_PHB_v)## 'data.frame': 4315 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 6 3 5 5 5 4 3 6 6 7 ...
## $ BPE: Factor w/ 5 levels "PHB1","PHB2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM1_PHB_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 88 PHB1:863
## Không d<U+1ED3>ng ý : 76 PHB2:863
## Khá không d<U+1ED3>ng ý : 115 PHB3:863
## Bình thu<U+1EDD>ng : 377 PHB4:863
## Khá d<U+1ED3>ng ý : 807 PHB5:863
## Ð<U+1ED3>ng ý :1928
## R<U+1EA5>t d<U+1ED3>ng ý : 924 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM1_PHB_v %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PHB1 PHB3 PHB2 PHB5 PHB4
## Levels: PHB1 PHB2 PHB3 PHB4 PHB5# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 35 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 R<U+1EA5>t không d<U+1ED3>ng ý 24 2.78 2.78%
## 2 PHB1 Không d<U+1ED3>ng ý 20 2.32 2.32%
## 3 PHB1 Khá không d<U+1ED3>ng ý 37 4.29 4.29%
## 4 PHB1 Bình thu<U+1EDD>ng 99 11.5 11.47%
## 5 PHB1 Khá d<U+1ED3>ng ý 216 25.0 25.03%
## 6 PHB1 Ð<U+1ED3>ng ý 353 40.9 40.9%
## 7 PHB1 R<U+1EA5>t d<U+1ED3>ng ý 114 13.2 13.21%
## 8 PHB2 R<U+1EA5>t không d<U+1ED3>ng ý 13 1.51 1.51%
## 9 PHB2 Không d<U+1ED3>ng ý 22 2.55 2.55%
## 10 PHB2 Khá không d<U+1ED3>ng ý 25 2.9 2.9%
## # ... with 25 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg_v <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "C\u1EA3m nh\u1EADn v\u1EC1 l\u1EE3i \u00EDch s\u1EE9c kh\u1ECFe (PHB) c\u1EE7a h\u00E0nh kh\u00E1ch \u0111\u1ED1i v\u1EDBi h\u1EC7 th\u1ED1ng xe bu\u00FDt \u0111\u00F4 thi Vi\u1EC7t Nam ",
subtitle = "Thang \u0111o Likert l\u00E0 lo\u1EA1i thang \u0111o \u0111i\u1EC3m \u0111\u00E1nh gi\u00E1 \u0111\u01B0\u1EE3c s\u1EED d\u1EE5ng ph\u1ED5 bi\u1EBFn trong kh\u1EA3o s\u00E1t. Nghi\u00EAn c\u1EE9u s\u1EED d\u1EE5ng thang \u0111o 7 m\u1EE9c \u0111\u1ED9 t\u01B0\u01A1ng \u1EE9ng v\u1EDBi 1 - R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD \u0111\u1EBFn 7 - R\u1EA5t \u0111\u1ED3ng \u00FD.")
gg_v## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 R<U+1EA5>t không d<U+1ED3>ng ý 24 2.78 2.78%
## 2 PHB2 R<U+1EA5>t không d<U+1ED3>ng ý 13 1.51 1.51%
## 3 PHB3 R<U+1EA5>t không d<U+1ED3>ng ý 15 1.74 1.74%
## 4 PHB4 R<U+1EA5>t không d<U+1ED3>ng ý 13 1.51 1.51%
## 5 PHB5 R<U+1EA5>t không d<U+1ED3>ng ý 23 2.67 2.67%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 Không d<U+1ED3>ng ý 20 2.32 2.32%
## 2 PHB2 Không d<U+1ED3>ng ý 22 2.55 2.55%
## 3 PHB3 Không d<U+1ED3>ng ý 20 2.32 2.32%
## 4 PHB4 Không d<U+1ED3>ng ý 7 0.81 0.81%
## 5 PHB5 Không d<U+1ED3>ng ý 7 0.81 0.81%# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 Khá không d<U+1ED3>ng ý 37 4.29 4.29%
## 2 PHB2 Khá không d<U+1ED3>ng ý 25 2.9 2.9%
## 3 PHB3 Khá không d<U+1ED3>ng ý 38 4.4 4.4%
## 4 PHB4 Khá không d<U+1ED3>ng ý 10 1.16 1.16%
## 5 PHB5 Khá không d<U+1ED3>ng ý 5 0.580 0.58%# For displaying percent of "Normal":
df_odered %>%
filter(Res == "B\u00ECnh th\u01B0\u1EDDng") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 Bình thu<U+1EDD>ng 99 11.5 11.47%
## 2 PHB2 Bình thu<U+1EDD>ng 70 8.11 8.11%
## 3 PHB3 Bình thu<U+1EDD>ng 89 10.3 10.31%
## 4 PHB4 Bình thu<U+1EDD>ng 55 6.37 6.37%
## 5 PHB5 Bình thu<U+1EDD>ng 64 7.42 7.42%# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Kh\u00E1 \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 Khá d<U+1ED3>ng ý 216 25.0 25.03%
## 2 PHB2 Khá d<U+1ED3>ng ý 164 19 19%
## 3 PHB3 Khá d<U+1ED3>ng ý 176 20.4 20.39%
## 4 PHB4 Khá d<U+1ED3>ng ý 134 15.5 15.53%
## 5 PHB5 Khá d<U+1ED3>ng ý 117 13.6 13.56%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "\u0110\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 Ð<U+1ED3>ng ý 353 40.9 40.9%
## 2 PHB2 Ð<U+1ED3>ng ý 381 44.2 44.15%
## 3 PHB3 Ð<U+1ED3>ng ý 385 44.6 44.61%
## 4 PHB4 Ð<U+1ED3>ng ý 391 45.3 45.31%
## 5 PHB5 Ð<U+1ED3>ng ý 418 48.4 48.44%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text7
df_for_text7## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 R<U+1EA5>t d<U+1ED3>ng ý 114 13.2 13.21%
## 2 PHB2 R<U+1EA5>t d<U+1ED3>ng ý 188 21.8 21.78%
## 3 PHB3 R<U+1EA5>t d<U+1ED3>ng ý 140 16.2 16.22%
## 4 PHB4 R<U+1EA5>t d<U+1ED3>ng ý 253 29.3 29.32%
## 5 PHB5 R<U+1EA5>t d<U+1ED3>ng ý 229 26.5 26.54%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") -> df_for_text
df_for_text## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PHB1 R<U+1EA5>t d<U+1ED3>ng ý 114 13.2 13.21%
## 2 PHB2 R<U+1EA5>t d<U+1ED3>ng ý 188 21.8 21.78%
## 3 PHB3 R<U+1EA5>t d<U+1ED3>ng ý 140 16.2 16.22%
## 4 PHB4 R<U+1EA5>t d<U+1ED3>ng ý 253 29.3 29.32%
## 5 PHB5 R<U+1EA5>t d<U+1ED3>ng ý 229 26.5 26.54%# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg_v +
geom_text(data = df_for_text1, aes(x = BPE, y = 3, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text5, aes(x = BPE, y = 22, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-40, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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3.8.9. Construct PEV - Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct PEV
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM1_PEV)## Res BPE
## 1 4 PEV1
## 2 5 PEV1
## 3 6 PEV1
## 4 5 PEV1
## 5 6 PEV1
## 6 7 PEV1dim(DN_HCM_SEM1_PEV)## [1] 3452 2attach(DN_HCM_SEM1_PEV)## The following objects are masked from DN_HCM_SEM1_PHB:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1_IMA:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1_LOY:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM1:
##
## BPE, Res## The following objects are masked from DN_SEM1:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1:
##
## BPE, ResDN_HCM_SEM1_PEV = within(DN_HCM_SEM1_PEV, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("PEV1", "PEV2", "PEV3", "PEV4"))
})
str(DN_HCM_SEM1_PEV)## 'data.frame': 3452 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 4 5 6 5 6 7 4 6 7 7 ...
## $ BPE: Factor w/ 4 levels "PEV1","PEV2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM1_PEV)## Res BPE
## Very Disagree : 101 PEV1:863
## Disagree : 68 PEV2:863
## Quite Disagree: 77 PEV3:863
## Normal : 301 PEV4:863
## Quite Agree : 634
## Agree :1462
## Very Agree : 809 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM1_PEV %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PEV4 PEV3 PEV2 PEV1
## Levels: PEV1 PEV2 PEV3 PEV4# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 Very Disagree 25 2.9 2.9%
## 2 PEV1 Disagree 9 1.04 1.04%
## 3 PEV1 Quite Disagree 13 1.51 1.51%
## 4 PEV1 Normal 59 6.84 6.84%
## 5 PEV1 Quite Agree 128 14.8 14.83%
## 6 PEV1 Agree 393 45.5 45.54%
## 7 PEV1 Very Agree 236 27.4 27.35%
## 8 PEV2 Very Disagree 26 3.01 3.01%
## 9 PEV2 Disagree 18 2.09 2.09%
## 10 PEV2 Quite Disagree 20 2.32 2.32%
## # ... with 18 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "Perceived environmental values (PEV) towards urban bus systems of Viet Nam",
subtitle = "Likert scale is a type of rating scale commonly used in surveys. When responding to a Likert type question,\nrespondents simply state their level of agreement or disagreement on a symmetric agree-disagree scale.")
gg## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "Very Disagree") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 Very Disagree 25 2.9 2.9%
## 2 PEV2 Very Disagree 26 3.01 3.01%
## 3 PEV3 Very Disagree 26 3.01 3.01%
## 4 PEV4 Very Disagree 24 2.78 2.78%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 3) -> df_for_text2
df_for_text2## # A tibble: 0 x 5
## # ... with 5 variables: BPE <fct>, Res <fct>, n <int>, percent <dbl>,
## # bar_text <chr># For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Quite Disagree") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 Quite Disagree 13 1.51 1.51%
## 2 PEV2 Quite Disagree 20 2.32 2.32%
## 3 PEV3 Quite Disagree 22 2.55 2.55%
## 4 PEV4 Quite Disagree 22 2.55 2.55%# For displaying percent of "Normal":
df_odered %>%
filter(Res == "Normal") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 Normal 59 6.84 6.84%
## 2 PEV2 Normal 68 7.88 7.88%
## 3 PEV3 Normal 78 9.04 9.04%
## 4 PEV4 Normal 96 11.1 11.12%# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Quite Agree") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 Quite Agree 128 14.8 14.83%
## 2 PEV2 Quite Agree 171 19.8 19.81%
## 3 PEV3 Quite Agree 168 19.5 19.47%
## 4 PEV4 Quite Agree 167 19.4 19.35%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "Agree") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 Agree 393 45.5 45.54%
## 2 PEV2 Agree 355 41.1 41.14%
## 3 PEV3 Agree 355 41.1 41.14%
## 4 PEV4 Agree 359 41.6 41.6%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") %>%
filter(percent >= 0) -> df_for_text7 # Chỉ thể hiện giá trị có % lựa chọn >0
df_for_text7## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 Very Agree 236 27.4 27.35%
## 2 PEV2 Very Agree 205 23.8 23.75%
## 3 PEV3 Very Agree 193 22.4 22.36%
## 4 PEV4 Very Agree 175 20.3 20.28%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") -> df_for_text
df_for_text## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 Very Agree 236 27.4 27.35%
## 2 PEV2 Very Agree 205 23.8 23.75%
## 3 PEV3 Very Agree 193 22.4 22.36%
## 4 PEV4 Very Agree 175 20.3 20.28%# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg +
geom_text(data = df_for_text1, aes(x = BPE, y = 3, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text5, aes(x = BPE, y = 22, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-40, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
3.8.10. Construct PEV - Nhãn tiếng việt
DN_HCM_SEM1_PEV_v = within(DN_HCM_SEM1_PEV, {
Res = factor(Res, labels = c("R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD", "B\u00ECnh th\u01B0\u1EDDng", "Kh\u00E1 \u0111\u1ED3ng \u00FD", "\u0110\u1ED3ng \u00FD", "R\u1EA5t \u0111\u1ED3ng \u00FD"))
BPE = factor(BPE, labels = c("PEV1", "PEV2", "PEV3", "PEV4"))
})
str(DN_HCM_SEM1_PEV_v)## 'data.frame': 3452 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 4 5 6 5 6 7 4 6 7 7 ...
## $ BPE: Factor w/ 4 levels "PEV1","PEV2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM1_PEV_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 101 PEV1:863
## Không d<U+1ED3>ng ý : 68 PEV2:863
## Khá không d<U+1ED3>ng ý : 77 PEV3:863
## Bình thu<U+1EDD>ng : 301 PEV4:863
## Khá d<U+1ED3>ng ý : 634
## Ð<U+1ED3>ng ý :1462
## R<U+1EA5>t d<U+1ED3>ng ý : 809 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM1_PEV_v %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PEV4 PEV3 PEV2 PEV1
## Levels: PEV1 PEV2 PEV3 PEV4# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 28 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 R<U+1EA5>t không d<U+1ED3>ng ý 25 2.9 2.9%
## 2 PEV1 Không d<U+1ED3>ng ý 9 1.04 1.04%
## 3 PEV1 Khá không d<U+1ED3>ng ý 13 1.51 1.51%
## 4 PEV1 Bình thu<U+1EDD>ng 59 6.84 6.84%
## 5 PEV1 Khá d<U+1ED3>ng ý 128 14.8 14.83%
## 6 PEV1 Ð<U+1ED3>ng ý 393 45.5 45.54%
## 7 PEV1 R<U+1EA5>t d<U+1ED3>ng ý 236 27.4 27.35%
## 8 PEV2 R<U+1EA5>t không d<U+1ED3>ng ý 26 3.01 3.01%
## 9 PEV2 Không d<U+1ED3>ng ý 18 2.09 2.09%
## 10 PEV2 Khá không d<U+1ED3>ng ý 20 2.32 2.32%
## # ... with 18 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg_v <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "C\u1EA3m nh\u1EADn v\u1EC1 gi\u00E1 tr\u1ECB m\u00F4i tr\u01B0\u1EDDng (PEV) c\u1EE7a h\u00E0nh kh\u00E1ch \u0111\u1ED1i v\u1EDBi h\u1EC7 th\u1ED1ng xe bu\u00FDt \u0111\u00F4 thi Vi\u1EC7t Nam ",
subtitle = "Thang \u0111o Likert l\u00E0 lo\u1EA1i thang \u0111o \u0111i\u1EC3m \u0111\u00E1nh gi\u00E1 \u0111\u01B0\u1EE3c s\u1EED d\u1EE5ng ph\u1ED5 bi\u1EBFn trong kh\u1EA3o s\u00E1t. Nghi\u00EAn c\u1EE9u s\u1EED d\u1EE5ng thang \u0111o 7 m\u1EE9c \u0111\u1ED9 t\u01B0\u01A1ng \u1EE9ng v\u1EDBi 1 - R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD \u0111\u1EBFn 7 - R\u1EA5t \u0111\u1ED3ng \u00FD.")
gg_v## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 R<U+1EA5>t không d<U+1ED3>ng ý 25 2.9 2.9%
## 2 PEV2 R<U+1EA5>t không d<U+1ED3>ng ý 26 3.01 3.01%
## 3 PEV3 R<U+1EA5>t không d<U+1ED3>ng ý 26 3.01 3.01%
## 4 PEV4 R<U+1EA5>t không d<U+1ED3>ng ý 24 2.78 2.78%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 Không d<U+1ED3>ng ý 9 1.04 1.04%
## 2 PEV2 Không d<U+1ED3>ng ý 18 2.09 2.09%
## 3 PEV3 Không d<U+1ED3>ng ý 21 2.43 2.43%
## 4 PEV4 Không d<U+1ED3>ng ý 20 2.32 2.32%# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 Khá không d<U+1ED3>ng ý 13 1.51 1.51%
## 2 PEV2 Khá không d<U+1ED3>ng ý 20 2.32 2.32%
## 3 PEV3 Khá không d<U+1ED3>ng ý 22 2.55 2.55%
## 4 PEV4 Khá không d<U+1ED3>ng ý 22 2.55 2.55%# For displaying percent of "Normal":
df_odered %>%
filter(Res == "B\u00ECnh th\u01B0\u1EDDng") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 Bình thu<U+1EDD>ng 59 6.84 6.84%
## 2 PEV2 Bình thu<U+1EDD>ng 68 7.88 7.88%
## 3 PEV3 Bình thu<U+1EDD>ng 78 9.04 9.04%
## 4 PEV4 Bình thu<U+1EDD>ng 96 11.1 11.12%# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Kh\u00E1 \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 Khá d<U+1ED3>ng ý 128 14.8 14.83%
## 2 PEV2 Khá d<U+1ED3>ng ý 171 19.8 19.81%
## 3 PEV3 Khá d<U+1ED3>ng ý 168 19.5 19.47%
## 4 PEV4 Khá d<U+1ED3>ng ý 167 19.4 19.35%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "\u0110\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 Ð<U+1ED3>ng ý 393 45.5 45.54%
## 2 PEV2 Ð<U+1ED3>ng ý 355 41.1 41.14%
## 3 PEV3 Ð<U+1ED3>ng ý 355 41.1 41.14%
## 4 PEV4 Ð<U+1ED3>ng ý 359 41.6 41.6%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text7
df_for_text7## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 R<U+1EA5>t d<U+1ED3>ng ý 236 27.4 27.35%
## 2 PEV2 R<U+1EA5>t d<U+1ED3>ng ý 205 23.8 23.75%
## 3 PEV3 R<U+1EA5>t d<U+1ED3>ng ý 193 22.4 22.36%
## 4 PEV4 R<U+1EA5>t d<U+1ED3>ng ý 175 20.3 20.28%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") -> df_for_text
df_for_text## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PEV1 R<U+1EA5>t d<U+1ED3>ng ý 236 27.4 27.35%
## 2 PEV2 R<U+1EA5>t d<U+1ED3>ng ý 205 23.8 23.75%
## 3 PEV3 R<U+1EA5>t d<U+1ED3>ng ý 193 22.4 22.36%
## 4 PEV4 R<U+1EA5>t d<U+1ED3>ng ý 175 20.3 20.28%# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg_v +
geom_text(data = df_for_text1, aes(x = BPE, y = 3, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text5, aes(x = BPE, y = 22, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-40, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
3.8.11. Construct PVA - Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct PVA
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM1_PVA)## Res BPE
## 1 6 PVA1
## 2 5 PVA1
## 3 6 PVA1
## 4 5 PVA1
## 5 6 PVA1
## 6 6 PVA1dim(DN_HCM_SEM1_PVA)## [1] 2589 2attach(DN_HCM_SEM1_PVA)## The following objects are masked from DN_HCM_SEM1_PEV:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1_PHB:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1_IMA:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1_LOY:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM1:
##
## BPE, Res## The following objects are masked from DN_SEM1:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM1:
##
## BPE, ResDN_HCM_SEM1_PVA = within(DN_HCM_SEM1_PVA, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("PVA1", "PVA2", "PVA3"))
})
str(DN_HCM_SEM1_PVA)## 'data.frame': 2589 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 5 6 5 6 6 6 6 6 6 ...
## $ BPE: Factor w/ 3 levels "PVA1","PVA2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM1_PVA)## Res BPE
## Very Disagree : 11 PVA1:863
## Disagree : 47 PVA2:863
## Quite Disagree: 78 PVA3:863
## Normal : 227
## Quite Agree : 487
## Agree :1178
## Very Agree : 561 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM1_PVA %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PVA2 PVA3 PVA1
## Levels: PVA1 PVA2 PVA3# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 21 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 Very Disagree 5 0.580 0.58%
## 2 PVA1 Disagree 8 0.93 0.93%
## 3 PVA1 Quite Disagree 18 2.09 2.09%
## 4 PVA1 Normal 56 6.49 6.49%
## 5 PVA1 Quite Agree 130 15.1 15.06%
## 6 PVA1 Agree 418 48.4 48.44%
## 7 PVA1 Very Agree 228 26.4 26.42%
## 8 PVA2 Very Disagree 4 0.46 0.46%
## 9 PVA2 Disagree 26 3.01 3.01%
## 10 PVA2 Quite Disagree 39 4.52 4.52%
## # ... with 11 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "Perceived values (PVA) towards urban bus systems of Viet Nam",
subtitle = "Likert scale is a type of rating scale commonly used in surveys. When responding to a Likert type question,\nrespondents simply state their level of agreement or disagreement on a symmetric agree-disagree scale.")
gg## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "Very Disagree") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 Very Disagree 5 0.580 0.58%
## 2 PVA2 Very Disagree 4 0.46 0.46%
## 3 PVA3 Very Disagree 2 0.23 0.23%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 3) -> df_for_text2
df_for_text2## # A tibble: 1 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA2 Disagree 26 3.01 3.01%# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Quite Disagree") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 Quite Disagree 18 2.09 2.09%
## 2 PVA2 Quite Disagree 39 4.52 4.52%
## 3 PVA3 Quite Disagree 21 2.43 2.43%# For displaying percent of "Normal":
df_odered %>%
filter(Res == "Normal") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 Normal 56 6.49 6.49%
## 2 PVA2 Normal 101 11.7 11.7%
## 3 PVA3 Normal 70 8.11 8.11%# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Quite Agree") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 Quite Agree 130 15.1 15.06%
## 2 PVA2 Quite Agree 196 22.7 22.71%
## 3 PVA3 Quite Agree 161 18.7 18.66%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "Agree") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 Agree 418 48.4 48.44%
## 2 PVA2 Agree 345 40.0 39.98%
## 3 PVA3 Agree 415 48.1 48.09%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") %>%
filter(percent >= 0) -> df_for_text7 # Chỉ thể hiện giá trị có % lựa chọn >0
df_for_text7## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 Very Agree 228 26.4 26.42%
## 2 PVA2 Very Agree 152 17.6 17.61%
## 3 PVA3 Very Agree 181 21.0 20.97%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") -> df_for_text
df_for_text## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 Very Agree 228 26.4 26.42%
## 2 PVA2 Very Agree 152 17.6 17.61%
## 3 PVA3 Very Agree 181 21.0 20.97%# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg +
geom_text(data = df_for_text1, aes(x = BPE, y = 3, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text5, aes(x = BPE, y = 23, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-40, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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3.8.12. Construct PVA - Nhãn tiếng anh
DN_HCM_SEM1_PVA_v = within(DN_HCM_SEM1_PVA, {
Res = factor(Res, labels = c("R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00F4ng \u0111\u1ED3ng \u00FD", "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD", "B\u00ECnh th\u01B0\u1EDDng", "Kh\u00E1 \u0111\u1ED3ng \u00FD", "\u0110\u1ED3ng \u00FD", "R\u1EA5t \u0111\u1ED3ng \u00FD"))
BPE = factor(BPE, labels = c("PVA1", "PVA2", "PVA3"))
})
str(DN_HCM_SEM1_PVA_v)## 'data.frame': 2589 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 6 5 6 5 6 6 6 6 6 6 ...
## $ BPE: Factor w/ 3 levels "PVA1","PVA2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM1_PVA_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 11 PVA1:863
## Không d<U+1ED3>ng ý : 47 PVA2:863
## Khá không d<U+1ED3>ng ý : 78 PVA3:863
## Bình thu<U+1EDD>ng : 227
## Khá d<U+1ED3>ng ý : 487
## Ð<U+1ED3>ng ý :1178
## R<U+1EA5>t d<U+1ED3>ng ý : 561 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM1_PVA_v %>%
group_by(BPE, Res) %>%
count() %>%
ungroup() %>%
group_by(BPE) %>%
mutate(percent = 100*n / sum(n)) %>%
mutate(percent = round(percent, 2)) %>%
mutate(bar_text = paste0(percent, "%")) %>%
ungroup() -> df_for_ploting
df_for_ploting %>%
filter(Res == Res[7]) %>%
arrange(percent) %>% # sắp xếp theo %
pull(BPE) -> order_x
order_x## [1] PVA2 PVA3 PVA1
## Levels: PVA1 PVA2 PVA3# Make a draft plot:
my_colors <- c("#3e6487", "#829cb2", "pink", "pink3", "palevioletred", "rosybrown2", "rosybrown3")
my_font <- "Roboto Condensed"
df_for_ploting %>%
mutate(BPE = factor(BPE, levels = order_x), Res = factor(Res, levels = Res[7:1])) -> df_odered
df_odered## # A tibble: 21 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 R<U+1EA5>t không d<U+1ED3>ng ý 5 0.580 0.58%
## 2 PVA1 Không d<U+1ED3>ng ý 8 0.93 0.93%
## 3 PVA1 Khá không d<U+1ED3>ng ý 18 2.09 2.09%
## 4 PVA1 Bình thu<U+1EDD>ng 56 6.49 6.49%
## 5 PVA1 Khá d<U+1ED3>ng ý 130 15.1 15.06%
## 6 PVA1 Ð<U+1ED3>ng ý 418 48.4 48.44%
## 7 PVA1 R<U+1EA5>t d<U+1ED3>ng ý 228 26.4 26.42%
## 8 PVA2 R<U+1EA5>t không d<U+1ED3>ng ý 4 0.46 0.46%
## 9 PVA2 Không d<U+1ED3>ng ý 26 3.01 3.01%
## 10 PVA2 Khá không d<U+1ED3>ng ý 39 4.52 4.52%
## # ... with 11 more rowslibrary(extrafont)
theme_set(theme_minimal())
gg_v <- df_odered %>%
ggplot(aes(x = BPE, y = percent, fill = Res)) +
geom_col(width = 0.8) +
coord_flip() +
scale_fill_manual(values = my_colors[7:1], name = "") +
theme(legend.position = "top") +
theme(text = element_text(family = my_font)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_y_continuous(labels = paste0(seq(0, 100, 25), "%"), expand = c(0, 0)) +
theme(plot.title = element_text(size = 18), plot.subtitle = element_text(size = 11, color = "grey20")) +
theme(axis.text = element_text(color = "grey20", size = 10.2)) +
theme(plot.margin = unit(rep(0.7, 4), "cm")) +
theme(panel.grid.major.y = element_blank(), panel.grid.minor.x = element_blank()) +
theme(legend.key.height = unit(0.15, "mm")) +
labs(x = NULL, y = NULL,
title = "C\u1EA3m nh\u1EADn c\u1EE7a h\u00E0nh kh\u00E1ch v\u1EC1 gi\u00E1 tr\u1ECB (PVA) \u0111\u1ED1i v\u1EDBi h\u1EC7 th\u1ED1ng xe bu\u00FDt \u0111\u00F4 thi Vi\u1EC7t Nam ",
subtitle = "Thang \u0111o Likert l\u00E0 lo\u1EA1i thang \u0111o \u0111i\u1EC3m \u0111\u00E1nh gi\u00E1 \u0111\u01B0\u1EE3c s\u1EED d\u1EE5ng ph\u1ED5 bi\u1EBFn trong kh\u1EA3o s\u00E1t. Nghi\u00EAn c\u1EE9u s\u1EED d\u1EE5ng thang \u0111o 7 m\u1EE9c \u0111\u1ED9 t\u01B0\u01A1ng \u1EE9ng v\u1EDBi 1 - R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD \u0111\u1EBFn 7 - R\u1EA5t \u0111\u1ED3ng \u00FD.")
gg_v## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
# For displaying percent of "Very Disagree": Điều chỉnh percent để thể hiện giá trị của %
df_odered %>%
filter(Res == "R\u1EA5t kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text1
df_for_text1## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 R<U+1EA5>t không d<U+1ED3>ng ý 5 0.580 0.58%
## 2 PVA2 R<U+1EA5>t không d<U+1ED3>ng ý 4 0.46 0.46%
## 3 PVA3 R<U+1EA5>t không d<U+1ED3>ng ý 2 0.23 0.23%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 Không d<U+1ED3>ng ý 8 0.93 0.93%
## 2 PVA2 Không d<U+1ED3>ng ý 26 3.01 3.01%
## 3 PVA3 Không d<U+1ED3>ng ý 13 1.51 1.51%# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Kh\u00E1 kh\u00F4ng \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 Khá không d<U+1ED3>ng ý 18 2.09 2.09%
## 2 PVA2 Khá không d<U+1ED3>ng ý 39 4.52 4.52%
## 3 PVA3 Khá không d<U+1ED3>ng ý 21 2.43 2.43%# For displaying percent of "Normal":
df_odered %>%
filter(Res == "B\u00ECnh th\u01B0\u1EDDng") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 Bình thu<U+1EDD>ng 56 6.49 6.49%
## 2 PVA2 Bình thu<U+1EDD>ng 101 11.7 11.7%
## 3 PVA3 Bình thu<U+1EDD>ng 70 8.11 8.11%# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Kh\u00E1 \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 Khá d<U+1ED3>ng ý 130 15.1 15.06%
## 2 PVA2 Khá d<U+1ED3>ng ý 196 22.7 22.71%
## 3 PVA3 Khá d<U+1ED3>ng ý 161 18.7 18.66%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "\u0110\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 Ð<U+1ED3>ng ý 418 48.4 48.44%
## 2 PVA2 Ð<U+1ED3>ng ý 345 40.0 39.98%
## 3 PVA3 Ð<U+1ED3>ng ý 415 48.1 48.09%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text7
df_for_text7## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 R<U+1EA5>t d<U+1ED3>ng ý 228 26.4 26.42%
## 2 PVA2 R<U+1EA5>t d<U+1ED3>ng ý 152 17.6 17.61%
## 3 PVA3 R<U+1EA5>t d<U+1ED3>ng ý 181 21.0 20.97%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") -> df_for_text
df_for_text## # A tibble: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PVA1 R<U+1EA5>t d<U+1ED3>ng ý 228 26.4 26.42%
## 2 PVA2 R<U+1EA5>t d<U+1ED3>ng ý 152 17.6 17.61%
## 3 PVA3 R<U+1EA5>t d<U+1ED3>ng ý 181 21.0 20.97%# Ad text layers-thay đổi giá trị y để thể hiện vị trí ghi text phù hợp
gg_v +
geom_text(data = df_for_text1, aes(x = BPE, y = 3, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text5, aes(x = BPE, y = 22, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text6, aes(x = BPE, y = 100-40, label = bar_text), size = 4, color = "white", family = my_font) +
geom_text(data = df_for_text7, aes(x = BPE, y = 100-4, label = bar_text), size = 4, color = "white", family = my_font)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database