858-SEM5-Descriptive Analyse
Phương Anh
17/05/2022
1. Read and set up Data Loyalty for SEM4
# Read Data directly
t = "E:/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-27.2.2022/858-Loyaltyofbuspassenger_NonBus_Mising-outliersSEM5.csv"
SEM5 = read.csv(t, header = T)
head(SEM5)## 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 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1
## 1 4 6 6 4 6 5 6 6 6 6 6 4 4 6
## 2 2 3 3 4 4 6 6 6 6 4 6 5 4 4
## 3 2 2 6 6 4 6 6 4 2 2 2 2 2 2
## 4 6 5 6 6 5 5 6 7 6 6 5 4 5 5
## 5 2 2 2 5 5 5 6 4 3 6 3 6 3 6
## 6 2 2 5 4 4 4 6 4 2 4 4 5 4 5
## PSQC2 PSQC3 PSQC4 PSQC5 PSQP1 PSQP2 PSQP3 PSQP4 PSQR1 PSQR2 PSQR3 PSQR4 PSQC6
## 1 4 4 4 6 6 6 5 6 4 4 6 4 2
## 2 5 6 4 6 6 4 4 5 4 3 4 3 6
## 3 4 1 2 1 1 1 6 6 2 6 2 5 2
## 4 5 5 4 4 5 5 5 5 4 5 5 5 5
## 5 6 4 3 6 4 4 4 5 5 3 5 3 5
## 6 5 4 3 3 4 4 4 5 3 3 3 3 4
## SAT1 SAT2 SAT3 LOY1 LOY2 LOY3 LOY4 LOY5 LOY6 LOY7 IMA1 IMA2 IMA3 IMA4 IMA5
## 1 6 6 6 4 6 5 7 6 6 6 6 6 6 4 6
## 2 5 5 4 4 5 4 4 4 4 5 4 5 6 5 4
## 3 2 2 2 2 6 6 4 4 2 2 4 2 5 2 2
## 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 5 4 4 4 4 5 5 4 4 4 6 5 4 5 4 5
## 6 4 5 5 4 3 2 5 5 6 6 4 3 2 2 4
## PHB1 PHB2 PHB3 PHB4 PHB5 PEB1 PEB2 PEB3 PEB4 ATM1 ATM2 ATM3 ATM4 ATM5 ATM6
## 1 6 7 6 7 7 4 4 4 4 3 5 6 4 2 4
## 2 3 4 3 4 6 5 6 6 4 3 5 4 4 4 4
## 3 5 2 6 6 6 6 5 6 6 2 6 4 2 2 4
## 4 5 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
## 6 4 6 7 6 4 7 7 5 2 1 4 5 2 1 4
## ATM7 PPI1 PPI2 PPI3 SIM1 SIM2 SIM3 SIM4 PPA1 PPA2 PPA3 PPA4 SBE1 SBE2 SBE3
## 1 6 6 6 6 4 4 4 2 4 6 4 4 4 4 4
## 2 5 4 4 4 4 4 4 4 4 4 4 4 5 4 5
## 3 2 4 2 2 4 4 4 2 4 6 6 2 6 6 6
## 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 5 3 2 3 4 6 6 5 5 5 5 5 5 5 5 5
## 6 2 5 2 2 2 2 2 2 2 3 3 2 2 2 2
## SBE4 EXB1 EXB2 EXB3 PVA1 PVA2 PVA3 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 4 4 4 3 6 6 6 0 1 0 1 0
## 2 5 4 5 3 5 5 3 0 0 1 0 0
## 3 6 4 6 2 6 2 4 0 0 0 0 1
## 4 5 5 5 5 5 5 5 0 1 0 1 1
## 5 5 5 5 4 6 2 3 0 1 0 1 1
## 6 2 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(SEM5)## [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" "PSQT1" "PSQT2" "PSQT3"
## [37] "PSQT4" "PSQC1" "PSQC2" "PSQC3"
## [41] "PSQC4" "PSQC5" "PSQP1" "PSQP2"
## [45] "PSQP3" "PSQP4" "PSQR1" "PSQR2"
## [49] "PSQR3" "PSQR4" "PSQC6" "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" "PEB1"
## [73] "PEB2" "PEB3" "PEB4" "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(SEM5)## [1] 858 1122. Desscriptive statistic
# 2.1. Subset Data SEM1 (6 constructs and 42 items)
SEM5 <- SEM5[, c(2, 3, 5, 6, 7, 8, 9, 10, 11, 26, 27, 28, 29, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 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(SEM5)## 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 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 PSQC3 PSQC4 PSQC5 PSQP1
## 1 6 4 6 6 6 4 4 6 4 4 4 6 6
## 2 3 4 4 4 6 5 4 4 5 6 4 6 6
## 3 6 6 4 2 2 2 2 2 4 1 2 1 1
## 4 6 6 5 6 5 4 5 5 5 5 4 4 5
## 5 2 5 5 6 3 6 3 6 6 4 3 6 4
## 6 5 4 4 4 4 5 4 5 5 4 3 3 4
## PSQP2 PSQP3 PSQP4 PSQR1 PSQR2 PSQR3 PSQR4 PSQC6 SAT1 SAT2 SAT3 IMA1 IMA2 IMA3
## 1 6 5 6 4 4 6 4 2 6 6 6 6 6 6
## 2 4 4 5 4 3 4 3 6 5 5 4 4 5 6
## 3 1 6 6 2 6 2 5 2 2 2 2 4 2 5
## 4 5 5 5 4 5 5 5 5 5 5 5 5 5 5
## 5 4 4 5 5 3 5 3 5 4 4 4 5 4 5
## 6 4 4 5 3 3 3 3 4 4 5 5 4 3 2
## IMA4 IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEB1 PEB2 PEB3 PEB4 PVA1 PVA2 PVA3 EC_Stop
## 1 4 6 6 7 6 7 7 4 4 4 4 6 6 6 0
## 2 5 4 3 4 3 4 6 5 6 6 4 5 5 3 0
## 3 2 2 5 2 6 6 6 6 5 6 6 6 2 4 0
## 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 0
## 5 4 5 5 6 6 6 5 6 6 6 6 6 2 3 0
## 6 2 4 4 6 7 6 4 7 7 5 2 6 5 5 0
## WC_Stop EC_Bus WC_Bus Gender MarriedStatus Occupation Education Income
## 1 1 0 1 0 1 1 2 1
## 2 0 1 0 0 1 1 2 1
## 3 0 0 0 1 1 1 2 1
## 4 1 0 1 1 1 1 2 1
## 5 1 0 1 1 1 1 2 1
## 6 1 0 1 0 1 1 3 1names(SEM5)## [1] "MCCar" "MCMotor" "AGE" "CITY"
## [5] "FRE" "TripPurpose" "Departure" "TimeUseonBus"
## [9] "TravelTime" "PSB1" "PSB2" "PSB3"
## [13] "PSB4" "PSQT1" "PSQT2" "PSQT3"
## [17] "PSQT4" "PSQC1" "PSQC2" "PSQC3"
## [21] "PSQC4" "PSQC5" "PSQP1" "PSQP2"
## [25] "PSQP3" "PSQP4" "PSQR1" "PSQR2"
## [29] "PSQR3" "PSQR4" "PSQC6" "SAT1"
## [33] "SAT2" "SAT3" "IMA1" "IMA2"
## [37] "IMA3" "IMA4" "IMA5" "PHB1"
## [41] "PHB2" "PHB3" "PHB4" "PHB5"
## [45] "PEB1" "PEB2" "PEB3" "PEB4"
## [49] "PVA1" "PVA2" "PVA3" "EC_Stop"
## [53] "WC_Stop" "EC_Bus" "WC_Bus" "Gender"
## [57] "MarriedStatus" "Occupation" "Education" "Income"dim(SEM5)## [1] 858 60# Data coding
str(SEM5)## 'data.frame': 858 obs. of 60 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 ...
## $ PSQT1 : int 6 4 2 6 6 4 2 5 5 7 ...
## $ PSQT2 : int 6 6 2 5 3 4 5 6 4 7 ...
## $ PSQT3 : int 4 5 2 4 6 5 5 4 5 7 ...
## $ PSQT4 : int 4 4 2 5 3 4 2 4 3 7 ...
## $ PSQC1 : int 6 4 2 5 6 5 2 6 3 7 ...
## $ PSQC2 : int 4 5 4 5 6 5 2 6 4 7 ...
## $ PSQC3 : int 4 6 1 5 4 4 2 3 4 7 ...
## $ PSQC4 : int 4 4 2 4 3 3 2 4 4 4 ...
## $ PSQC5 : int 6 6 1 4 6 3 2 3 3 4 ...
## $ PSQP1 : int 6 6 1 5 4 4 4 4 3 4 ...
## $ PSQP2 : int 6 4 1 5 4 4 4 4 4 4 ...
## $ PSQP3 : int 5 4 6 5 4 4 3 4 5 7 ...
## $ PSQP4 : int 6 5 6 5 5 5 4 4 6 7 ...
## $ PSQR1 : int 4 4 2 4 5 3 2 5 3 4 ...
## $ PSQR2 : int 4 3 6 5 3 3 1 3 4 7 ...
## $ PSQR3 : int 6 4 2 5 5 3 1 3 5 4 ...
## $ PSQR4 : int 4 3 5 5 3 3 2 2 4 7 ...
## $ PSQC6 : int 2 6 2 5 5 4 4 6 4 7 ...
## $ SAT1 : int 6 5 2 5 4 4 2 7 5 7 ...
## $ SAT2 : int 6 5 2 5 4 5 2 7 6 7 ...
## $ SAT3 : int 6 4 2 5 4 5 3 6 4 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 ...
## $ PEB1 : int 4 5 6 5 6 7 4 6 7 7 ...
## $ PEB2 : int 4 6 5 5 6 7 2 7 7 7 ...
## $ PEB3 : int 4 6 6 5 6 5 2 6 6 7 ...
## $ PEB4 : 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(SEM5)
SEM5 = within(SEM5, {
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(SEM5)## 'data.frame': 858 obs. of 60 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 ...
## $ PSQT1 : int 6 4 2 6 6 4 2 5 5 7 ...
## $ PSQT2 : int 6 6 2 5 3 4 5 6 4 7 ...
## $ PSQT3 : int 4 5 2 4 6 5 5 4 5 7 ...
## $ PSQT4 : int 4 4 2 5 3 4 2 4 3 7 ...
## $ PSQC1 : int 6 4 2 5 6 5 2 6 3 7 ...
## $ PSQC2 : int 4 5 4 5 6 5 2 6 4 7 ...
## $ PSQC3 : int 4 6 1 5 4 4 2 3 4 7 ...
## $ PSQC4 : int 4 4 2 4 3 3 2 4 4 4 ...
## $ PSQC5 : int 6 6 1 4 6 3 2 3 3 4 ...
## $ PSQP1 : int 6 6 1 5 4 4 4 4 3 4 ...
## $ PSQP2 : int 6 4 1 5 4 4 4 4 4 4 ...
## $ PSQP3 : int 5 4 6 5 4 4 3 4 5 7 ...
## $ PSQP4 : int 6 5 6 5 5 5 4 4 6 7 ...
## $ PSQR1 : int 4 4 2 4 5 3 2 5 3 4 ...
## $ PSQR2 : int 4 3 6 5 3 3 1 3 4 7 ...
## $ PSQR3 : int 6 4 2 5 5 3 1 3 5 4 ...
## $ PSQR4 : int 4 3 5 5 3 3 2 2 4 7 ...
## $ PSQC6 : int 2 6 2 5 5 4 4 6 4 7 ...
## $ SAT1 : int 6 5 2 5 4 4 2 7 5 7 ...
## $ SAT2 : int 6 5 2 5 4 5 2 7 6 7 ...
## $ SAT3 : int 6 4 2 5 4 5 3 6 4 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 ...
## $ PEB1 : int 4 5 6 5 6 7 4 6 7 7 ...
## $ PEB2 : int 4 6 5 5 6 7 2 7 7 7 ...
## $ PEB3 : int 4 6 6 5 6 5 2 6 6 7 ...
## $ PEB4 : 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(SEM5)## [1] 858 60# 2.2. Descritive Table
library(tableone)
require(tableone)
library(magrittr)
summary(SEM5)## MCCar MCMotor AGE CITY FRE
## No :838 No :696 16-25:417 DaNang :410 >=3 days/week :498
## Yes: 20 Yes:162 26-35:169 HoChiMinh:448 2days/month-2days/week:163
## 36-45:101 2days/year-1day/month : 98
## 46-55: 77 <2 days/year : 99
## >55 : 94
##
##
## TripPurpose Departure TimeUseonBus TravelTime
## Working :304 Normal :217 Using.telephone:198 Min. : 0.000
## Studying :295 Peak-Hour:641 Reading : 52 1st Qu.: 0.500
## Shopping : 58 Listening :137 Median : 1.000
## Entertaining: 98 Nothing :418 Mean : 1.273
## Others :103 Talking : 33 3rd Qu.: 2.000
## Others : 20 Max. :20.000
##
## PSB1 PSB2 PSB3 PSB4
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:4.000
## Median :5.000 Median :6.000 Median :6.000 Median :5.000
## Mean :4.638 Mean :5.079 Mean :5.499 Mean :4.818
## 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
##
## PSQT1 PSQT2 PSQT3 PSQT4
## 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 :5.351 Mean :5.392 Mean :5.203 Mean :5.183
## 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
##
## PSQC1 PSQC2 PSQC3 PSQC4 PSQC5
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:5.00 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :6.000 Median :6.00 Median :6.000 Median :5.000 Median :5.000
## Mean :5.138 Mean :5.58 Mean :5.119 Mean :4.773 Mean :5.033
## 3rd Qu.:6.000 3rd Qu.:6.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.00 Max. :7.000 Max. :7.000 Max. :7.000
##
## PSQP1 PSQP2 PSQP3 PSQP4
## 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 :5.321 Mean :5.308 Mean :5.382 Mean :5.558
## 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
##
## PSQR1 PSQR2 PSQR3 PSQR4 PSQC6
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.00 Min. :1.000
## 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:4.00 1st Qu.:4.00 1st Qu.:5.000
## Median :5.000 Median :6.000 Median :5.00 Median :5.00 Median :6.000
## Mean :4.951 Mean :5.234 Mean :5.02 Mean :4.85 Mean :5.531
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.00 3rd Qu.:6.00 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.00 Max. :7.00 Max. :7.000
##
## SAT1 SAT2 SAT3 IMA1 IMA2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.00
## 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.00 1st Qu.:5.00
## Median :6.000 Median :6.000 Median :6.000 Median :5.00 Median :6.00
## Mean :5.326 Mean :5.497 Mean :5.495 Mean :5.21 Mean :5.22
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.00 3rd Qu.:6.00
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.00 Max. :7.00
##
## IMA3 IMA4 IMA5 PHB1 PHB2
## 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.:5.000
## Median :6.000 Median :6.000 Median :6.00 Median :6.000 Median :6.000
## Mean :5.367 Mean :5.364 Mean :5.42 Mean :5.297 Mean :5.604
## 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
##
## PHB3 PHB4 PHB5 PEB1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:6.000 1st Qu.:5.000
## Median :6.000 Median :6.000 Median :6.000 Median :6.000
## Mean :5.446 Mean :5.872 Mean :5.802 Mean :5.763
## 3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## PEB2 PEB3 PEB4 PVA1
## 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 :5.586 Mean :5.533 Mean :5.487 Mean :5.846
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## PVA2 PVA3 EC_Stop WC_Stop EC_Bus
## Min. :1.000 Min. :1.000 Never:822 Never:737 Never:818
## 1st Qu.:5.000 1st Qu.:5.000 Ever : 36 Ever :121 Ever : 40
## Median :6.000 Median :6.000
## Mean :5.436 Mean :5.709
## 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000
##
## WC_Bus Gender MarriedStatus Occupation
## Never:743 Female:502 Married:331 Students/Pupils:361
## Ever :115 Male :356 Single :527 Full.time.job :304
## Part.time.job : 65
## Retirement : 46
## No.job : 4
## Housewife : 52
## Others : 26
## Education Income
## Secondary.school: 59 <5millions :458
## Undergraduate :276 5-10millions :248
## High.school :365 10-15millions:116
## Postgraduate :103 >15millions : 36
## Others : 55
##
## library(table1)##
## Attaching package: 'table1'## The following objects are masked from 'package:base':
##
## units, units<-Tab1_SEM5 <- table1(~ PSB1 + PSB2 + PSB3 + PSB4 + PSQT1 + PSQT2 + PSQT3 + PSQT4 + PSQC1 + PSQC2 + PSQC3 + PSQC4 + PSQC5 + PSQC6 + PSQP1 + PSQP2 + PSQP3 + PSQP4 + PSQR1 + PSQR2 + PSQR3 + PSQR4 + SAT1 + SAT2 + SAT3 + IMA1 + IMA2 + IMA3 + IMA4 + IMA5 + PHB1 + PHB2 + PHB3 + PHB4 + PHB5 + PEB1 + PEB2 + PEB3 + PEB4 + 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 = SEM5)
Tab1_SEM5| DaNang (N=410) |
HoChiMinh (N=448) |
Overall (N=858) |
|
|---|---|---|---|
| PSB1 | |||
| Mean (SD) | 5.57 (1.24) | 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) | 5.62 (1.19) | 4.58 (1.77) | 5.08 (1.60) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSB3 | |||
| Mean (SD) | 5.77 (0.964) | 5.25 (1.39) | 5.50 (1.23) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSB4 | |||
| Mean (SD) | 5.40 (1.22) | 4.28 (1.70) | 4.82 (1.59) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 4.00 [1.00, 7.00] | 5.00 [1.00, 7.00] |
| PSQT1 | |||
| Mean (SD) | 5.74 (1.06) | 5.00 (1.44) | 5.35 (1.32) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSQT2 | |||
| Mean (SD) | 5.91 (1.08) | 4.91 (1.56) | 5.39 (1.44) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSQT3 | |||
| Mean (SD) | 5.73 (1.19) | 4.72 (1.63) | 5.20 (1.52) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSQT4 | |||
| Mean (SD) | 5.50 (1.14) | 4.89 (1.51) | 5.18 (1.38) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSQC1 | |||
| Mean (SD) | 5.21 (1.27) | 5.07 (1.49) | 5.14 (1.39) |
| Median [Min, Max] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSQC2 | |||
| Mean (SD) | 5.77 (0.995) | 5.41 (1.40) | 5.58 (1.24) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSQC3 | |||
| Mean (SD) | 5.10 (1.41) | 5.14 (1.38) | 5.12 (1.40) |
| Median [Min, Max] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSQC4 | |||
| Mean (SD) | 4.81 (1.34) | 4.73 (1.40) | 4.77 (1.37) |
| Median [Min, Max] | 5.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 5.00 [1.00, 7.00] |
| PSQC5 | |||
| Mean (SD) | 5.17 (1.30) | 4.91 (1.46) | 5.03 (1.39) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 5.00 [1.00, 7.00] |
| PSQC6 | |||
| Mean (SD) | 5.57 (1.19) | 5.49 (1.28) | 5.53 (1.24) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSQP1 | |||
| Mean (SD) | 5.70 (0.984) | 4.97 (1.45) | 5.32 (1.30) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSQP2 | |||
| Mean (SD) | 5.71 (0.982) | 4.94 (1.46) | 5.31 (1.31) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSQP3 | |||
| Mean (SD) | 5.72 (0.884) | 5.07 (1.38) | 5.38 (1.22) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSQP4 | |||
| Mean (SD) | 5.85 (0.809) | 5.29 (1.32) | 5.56 (1.14) |
| Median [Min, Max] | 6.00 [3.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSQR1 | |||
| Mean (SD) | 5.06 (1.41) | 4.85 (1.46) | 4.95 (1.44) |
| Median [Min, Max] | 5.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 5.00 [1.00, 7.00] |
| PSQR2 | |||
| Mean (SD) | 5.68 (1.02) | 4.83 (1.45) | 5.23 (1.33) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PSQR3 | |||
| Mean (SD) | 5.19 (1.30) | 4.86 (1.46) | 5.02 (1.39) |
| Median [Min, Max] | 5.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 5.00 [1.00, 7.00] |
| PSQR4 | |||
| Mean (SD) | 5.29 (1.29) | 4.45 (1.72) | 4.85 (1.58) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 5.00 [1.00, 7.00] | 5.00 [1.00, 7.00] |
| SAT1 | |||
| Mean (SD) | 5.52 (1.06) | 5.15 (1.35) | 5.33 (1.23) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| SAT2 | |||
| Mean (SD) | 5.68 (1.00) | 5.33 (1.25) | 5.50 (1.15) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| SAT3 | |||
| Mean (SD) | 5.74 (0.947) | 5.27 (1.28) | 5.50 (1.15) |
| 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 (0.970) | 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) | 5.52 (0.874) | 4.95 (1.34) | 5.22 (1.18) |
| Median [Min, Max] | 6.00 [3.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| IMA3 | |||
| Mean (SD) | 5.62 (0.843) | 5.14 (1.28) | 5.37 (1.12) |
| Median [Min, Max] | 6.00 [3.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| IMA4 | |||
| Mean (SD) | 5.68 (0.858) | 5.07 (1.34) | 5.36 (1.18) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| IMA5 | |||
| Mean (SD) | 5.74 (0.795) | 5.13 (1.27) | 5.42 (1.11) |
| Median [Min, Max] | 6.00 [3.00, 7.00] | 5.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PHB1 | |||
| Mean (SD) | 5.42 (1.09) | 5.19 (1.53) | 5.30 (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 (1.00) | 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) | 5.47 (1.18) | 5.42 (1.38) | 5.45 (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) | 6.07 (0.843) | 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) | 6.04 (0.846) | 5.59 (1.46) | 5.80 (1.23) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PEB1 | |||
| Mean (SD) | 5.97 (0.841) | 5.58 (1.55) | 5.76 (1.28) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PEB2 | |||
| Mean (SD) | 5.79 (1.00) | 5.40 (1.60) | 5.59 (1.36) |
| Median [Min, Max] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PEB3 | |||
| Mean (SD) | 5.72 (1.06) | 5.36 (1.59) | 5.53 (1.38) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PEB4 | |||
| Mean (SD) | 5.70 (1.05) | 5.29 (1.57) | 5.49 (1.36) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| PVA1 | |||
| Mean (SD) | 5.94 (0.933) | 5.76 (1.19) | 5.85 (1.08) |
| 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 (1.20) | 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) | 5.89 (0.846) | 5.54 (1.25) | 5.71 (1.09) |
| Median [Min, Max] | 6.00 [2.00, 7.00] | 6.00 [1.00, 7.00] | 6.00 [1.00, 7.00] |
| EC_Stop | |||
| Never | 406 (99.0%) | 416 (92.9%) | 822 (95.8%) |
| Ever | 4 (1.0%) | 32 (7.1%) | 36 (4.2%) |
| WC_Stop | |||
| Never | 388 (94.6%) | 349 (77.9%) | 737 (85.9%) |
| Ever | 22 (5.4%) | 99 (22.1%) | 121 (14.1%) |
| EC_Bus | |||
| Never | 407 (99.3%) | 411 (91.7%) | 818 (95.3%) |
| Ever | 3 (0.7%) | 37 (8.3%) | 40 (4.7%) |
| WC_Bus | |||
| Never | 393 (95.9%) | 350 (78.1%) | 743 (86.6%) |
| Ever | 17 (4.1%) | 98 (21.9%) | 115 (13.4%) |
| Gender | |||
| Female | 230 (56.1%) | 272 (60.7%) | 502 (58.5%) |
| Male | 180 (43.9%) | 176 (39.3%) | 356 (41.5%) |
| MarriedStatus | |||
| Married | 147 (35.9%) | 184 (41.1%) | 331 (38.6%) |
| Single | 263 (64.1%) | 264 (58.9%) | 527 (61.4%) |
| Occupation | |||
| Students/Pupils | 201 (49.0%) | 160 (35.7%) | 361 (42.1%) |
| Full.time.job | 115 (28.0%) | 189 (42.2%) | 304 (35.4%) |
| Part.time.job | 25 (6.1%) | 40 (8.9%) | 65 (7.6%) |
| Retirement | 33 (8.0%) | 13 (2.9%) | 46 (5.4%) |
| No.job | 3 (0.7%) | 1 (0.2%) | 4 (0.5%) |
| Housewife | 27 (6.6%) | 25 (5.6%) | 52 (6.1%) |
| Others | 6 (1.5%) | 20 (4.5%) | 26 (3.0%) |
| Education | |||
| Secondary.school | 30 (7.3%) | 29 (6.5%) | 59 (6.9%) |
| Undergraduate | 128 (31.2%) | 148 (33.0%) | 276 (32.2%) |
| High.school | 173 (42.2%) | 192 (42.9%) | 365 (42.5%) |
| Postgraduate | 61 (14.9%) | 42 (9.4%) | 103 (12.0%) |
| Others | 18 (4.4%) | 37 (8.3%) | 55 (6.4%) |
| Income | |||
| <5millions | 270 (65.9%) | 188 (42.0%) | 458 (53.4%) |
| 5-10millions | 83 (20.2%) | 165 (36.8%) | 248 (28.9%) |
| 10-15millions | 49 (12.0%) | 67 (15.0%) | 116 (13.5%) |
| >15millions | 8 (2.0%) | 28 (6.2%) | 36 (4.2%) |
| MCCar | |||
| No | 410 (100%) | 428 (95.5%) | 838 (97.7%) |
| Yes | 0 (0%) | 20 (4.5%) | 20 (2.3%) |
| MCMotor | |||
| No | 409 (99.8%) | 287 (64.1%) | 696 (81.1%) |
| Yes | 1 (0.2%) | 161 (35.9%) | 162 (18.9%) |
| AGE | |||
| 16-25 | 217 (52.9%) | 200 (44.6%) | 417 (48.6%) |
| 26-35 | 76 (18.5%) | 93 (20.8%) | 169 (19.7%) |
| 36-45 | 35 (8.5%) | 66 (14.7%) | 101 (11.8%) |
| 46-55 | 35 (8.5%) | 42 (9.4%) | 77 (9.0%) |
| >55 | 47 (11.5%) | 47 (10.5%) | 94 (11.0%) |
| CITY | |||
| DaNang | 410 (100%) | 0 (0%) | 410 (47.8%) |
| HoChiMinh | 0 (0%) | 448 (100%) | 448 (52.2%) |
| FRE | |||
| >=3 days/week | 261 (63.7%) | 237 (52.9%) | 498 (58.0%) |
| 2days/month-2days/week | 83 (20.2%) | 80 (17.9%) | 163 (19.0%) |
| 2days/year-1day/month | 28 (6.8%) | 70 (15.6%) | 98 (11.4%) |
| <2 days/year | 38 (9.3%) | 61 (13.6%) | 99 (11.5%) |
| TripPurpose | |||
| Working | 115 (28.0%) | 189 (42.2%) | 304 (35.4%) |
| Studying | 161 (39.3%) | 134 (29.9%) | 295 (34.4%) |
| Shopping | 45 (11.0%) | 13 (2.9%) | 58 (6.8%) |
| Entertaining | 51 (12.4%) | 47 (10.5%) | 98 (11.4%) |
| Others | 38 (9.3%) | 65 (14.5%) | 103 (12.0%) |
| Departure | |||
| Normal | 145 (35.4%) | 72 (16.1%) | 217 (25.3%) |
| Peak-Hour | 265 (64.6%) | 376 (83.9%) | 641 (74.7%) |
| TimeUseonBus | |||
| Using.telephone | 104 (25.4%) | 94 (21.0%) | 198 (23.1%) |
| Reading | 21 (5.1%) | 31 (6.9%) | 52 (6.1%) |
| Listening | 54 (13.2%) | 83 (18.5%) | 137 (16.0%) |
| Nothing | 202 (49.3%) | 216 (48.2%) | 418 (48.7%) |
| Talking | 24 (5.9%) | 9 (2.0%) | 33 (3.8%) |
| Others | 5 (1.2%) | 15 (3.3%) | 20 (2.3%) |
| TravelTime | |||
| Mean (SD) | 1.12 (0.827) | 1.41 (1.81) | 1.27 (1.44) |
| Median [Min, Max] | 1.00 [0, 6.00] | 1.00 [0, 20.0] | 1.00 [0, 20.0] |
#2.3 Histogram
hist(SEM5$PEB1)
hist(SEM5$PEB2)
hist(SEM5$PEB3)
hist(SEM5$PEB4)
hist(SEM5$SAT1)
hist(SEM5$SAT2)
hist(SEM5$SAT3)
## Loại phân bố dữ liệu không thuộc phân bố chuẩn3. Perception of bus user 3.1. Tạo dữ liệu biểu đồ likert
# Create data for Likert graph by BS (remove ID and TM =7-Bus)
head(SEM5)## 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 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1
## 1 Nothing 3.00 6 6 4 6 6 6 4 4 6
## 2 Nothing 2.00 3 3 4 4 4 6 5 4 4
## 3 Nothing 0.17 2 6 6 4 2 2 2 2 2
## 4 Using.telephone 4.00 5 6 6 5 6 5 4 5 5
## 5 Nothing 2.00 2 2 5 5 6 3 6 3 6
## 6 Others 2.00 2 5 4 4 4 4 5 4 5
## PSQC2 PSQC3 PSQC4 PSQC5 PSQP1 PSQP2 PSQP3 PSQP4 PSQR1 PSQR2 PSQR3 PSQR4 PSQC6
## 1 4 4 4 6 6 6 5 6 4 4 6 4 2
## 2 5 6 4 6 6 4 4 5 4 3 4 3 6
## 3 4 1 2 1 1 1 6 6 2 6 2 5 2
## 4 5 5 4 4 5 5 5 5 4 5 5 5 5
## 5 6 4 3 6 4 4 4 5 5 3 5 3 5
## 6 5 4 3 3 4 4 4 5 3 3 3 3 4
## SAT1 SAT2 SAT3 IMA1 IMA2 IMA3 IMA4 IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEB1 PEB2
## 1 6 6 6 6 6 6 4 6 6 7 6 7 7 4 4
## 2 5 5 4 4 5 6 5 4 3 4 3 4 6 5 6
## 3 2 2 2 4 2 5 2 2 5 2 6 6 6 6 5
## 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 5 4 4 4 5 4 5 4 5 5 6 6 6 5 6 6
## 6 4 5 5 4 3 2 2 4 4 6 7 6 4 7 7
## PEB3 PEB4 PVA1 PVA2 PVA3 EC_Stop WC_Stop EC_Bus WC_Bus Gender MarriedStatus
## 1 4 4 6 6 6 Never Ever Never Ever Female Single
## 2 6 4 5 5 3 Never Never Ever Never Female Single
## 3 6 6 6 2 4 Never Never Never Never Male Single
## 4 5 5 5 5 5 Never Ever Never Ever Male Single
## 5 6 6 6 2 3 Never Ever Never Ever Male Single
## 6 5 2 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(SEM5)## 'data.frame': 858 obs. of 60 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 ...
## $ PSQT1 : int 6 4 2 6 6 4 2 5 5 7 ...
## $ PSQT2 : int 6 6 2 5 3 4 5 6 4 7 ...
## $ PSQT3 : int 4 5 2 4 6 5 5 4 5 7 ...
## $ PSQT4 : int 4 4 2 5 3 4 2 4 3 7 ...
## $ PSQC1 : int 6 4 2 5 6 5 2 6 3 7 ...
## $ PSQC2 : int 4 5 4 5 6 5 2 6 4 7 ...
## $ PSQC3 : int 4 6 1 5 4 4 2 3 4 7 ...
## $ PSQC4 : int 4 4 2 4 3 3 2 4 4 4 ...
## $ PSQC5 : int 6 6 1 4 6 3 2 3 3 4 ...
## $ PSQP1 : int 6 6 1 5 4 4 4 4 3 4 ...
## $ PSQP2 : int 6 4 1 5 4 4 4 4 4 4 ...
## $ PSQP3 : int 5 4 6 5 4 4 3 4 5 7 ...
## $ PSQP4 : int 6 5 6 5 5 5 4 4 6 7 ...
## $ PSQR1 : int 4 4 2 4 5 3 2 5 3 4 ...
## $ PSQR2 : int 4 3 6 5 3 3 1 3 4 7 ...
## $ PSQR3 : int 6 4 2 5 5 3 1 3 5 4 ...
## $ PSQR4 : int 4 3 5 5 3 3 2 2 4 7 ...
## $ PSQC6 : int 2 6 2 5 5 4 4 6 4 7 ...
## $ SAT1 : int 6 5 2 5 4 4 2 7 5 7 ...
## $ SAT2 : int 6 5 2 5 4 5 2 7 6 7 ...
## $ SAT3 : int 6 4 2 5 4 5 3 6 4 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 ...
## $ PEB1 : int 4 5 6 5 6 7 4 6 7 7 ...
## $ PEB2 : int 4 6 5 5 6 7 2 7 7 7 ...
## $ PEB3 : int 4 6 6 5 6 5 2 6 6 7 ...
## $ PEB4 : 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(SEM5)## [1] 858 60names(SEM5)## [1] "MCCar" "MCMotor" "AGE" "CITY"
## [5] "FRE" "TripPurpose" "Departure" "TimeUseonBus"
## [9] "TravelTime" "PSB1" "PSB2" "PSB3"
## [13] "PSB4" "PSQT1" "PSQT2" "PSQT3"
## [17] "PSQT4" "PSQC1" "PSQC2" "PSQC3"
## [21] "PSQC4" "PSQC5" "PSQP1" "PSQP2"
## [25] "PSQP3" "PSQP4" "PSQR1" "PSQR2"
## [29] "PSQR3" "PSQR4" "PSQC6" "SAT1"
## [33] "SAT2" "SAT3" "IMA1" "IMA2"
## [37] "IMA3" "IMA4" "IMA5" "PHB1"
## [41] "PHB2" "PHB3" "PHB4" "PHB5"
## [45] "PEB1" "PEB2" "PEB3" "PEB4"
## [49] "PVA1" "PVA2" "PVA3" "EC_Stop"
## [53] "WC_Stop" "EC_Bus" "WC_Bus" "Gender"
## [57] "MarriedStatus" "Occupation" "Education" "Income"SEM5_likert = SEM5[,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, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56)] # BPE gom cac bien tu 10-51
head(SEM5_likert)## AGE CITY PSB1 PSB2 PSB3 PSB4 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 PSQC3
## 1 16-25 HoChiMinh 6 6 4 6 6 6 4 4 6 4 4
## 2 16-25 HoChiMinh 3 3 4 4 4 6 5 4 4 5 6
## 3 16-25 HoChiMinh 2 6 6 4 2 2 2 2 2 4 1
## 4 16-25 HoChiMinh 5 6 6 5 6 5 4 5 5 5 5
## 5 16-25 HoChiMinh 2 2 5 5 6 3 6 3 6 6 4
## 6 16-25 HoChiMinh 2 5 4 4 4 4 5 4 5 5 4
## PSQC4 PSQC5 PSQP1 PSQP2 PSQP3 PSQP4 PSQR1 PSQR2 PSQR3 PSQR4 PSQC6 SAT1 SAT2
## 1 4 6 6 6 5 6 4 4 6 4 2 6 6
## 2 4 6 6 4 4 5 4 3 4 3 6 5 5
## 3 2 1 1 1 6 6 2 6 2 5 2 2 2
## 4 4 4 5 5 5 5 4 5 5 5 5 5 5
## 5 3 6 4 4 4 5 5 3 5 3 5 4 4
## 6 3 3 4 4 4 5 3 3 3 3 4 4 5
## SAT3 IMA1 IMA2 IMA3 IMA4 IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEB1 PEB2 PEB3 PEB4
## 1 6 6 6 6 4 6 6 7 6 7 7 4 4 4 4
## 2 4 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 4 5 4 5 4 5 5 6 6 6 5 6 6 6 6
## 6 5 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
## 1 6 6 6 Never Ever Never Ever Female
## 2 5 5 3 Never Never Ever Never Female
## 3 6 2 4 Never Never Never Never Male
## 4 5 5 5 Never Ever Never Ever Male
## 5 6 2 3 Never Ever Never Ever Male
## 6 6 5 5 Never Ever Never Ever Femalestr(SEM5_likert)## 'data.frame': 858 obs. of 49 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 ...
## $ PSQT1 : int 6 4 2 6 6 4 2 5 5 7 ...
## $ PSQT2 : int 6 6 2 5 3 4 5 6 4 7 ...
## $ PSQT3 : int 4 5 2 4 6 5 5 4 5 7 ...
## $ PSQT4 : int 4 4 2 5 3 4 2 4 3 7 ...
## $ PSQC1 : int 6 4 2 5 6 5 2 6 3 7 ...
## $ PSQC2 : int 4 5 4 5 6 5 2 6 4 7 ...
## $ PSQC3 : int 4 6 1 5 4 4 2 3 4 7 ...
## $ PSQC4 : int 4 4 2 4 3 3 2 4 4 4 ...
## $ PSQC5 : int 6 6 1 4 6 3 2 3 3 4 ...
## $ PSQP1 : int 6 6 1 5 4 4 4 4 3 4 ...
## $ PSQP2 : int 6 4 1 5 4 4 4 4 4 4 ...
## $ PSQP3 : int 5 4 6 5 4 4 3 4 5 7 ...
## $ PSQP4 : int 6 5 6 5 5 5 4 4 6 7 ...
## $ PSQR1 : int 4 4 2 4 5 3 2 5 3 4 ...
## $ PSQR2 : int 4 3 6 5 3 3 1 3 4 7 ...
## $ PSQR3 : int 6 4 2 5 5 3 1 3 5 4 ...
## $ PSQR4 : int 4 3 5 5 3 3 2 2 4 7 ...
## $ PSQC6 : int 2 6 2 5 5 4 4 6 4 7 ...
## $ SAT1 : int 6 5 2 5 4 4 2 7 5 7 ...
## $ SAT2 : int 6 5 2 5 4 5 2 7 6 7 ...
## $ SAT3 : int 6 4 2 5 4 5 3 6 4 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 ...
## $ PEB1 : int 4 5 6 5 6 7 4 6 7 7 ...
## $ PEB2 : int 4 6 5 5 6 7 2 7 7 7 ...
## $ PEB3 : int 4 6 6 5 6 5 2 6 6 7 ...
## $ PEB4 : 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(SEM5_likert)## [1] 858 49## Creat dat_PSB1 - cot 3 cua SEM5_likert
dat_PSB1 <- SEM5_likert [, c(1,2,3,45, 46, 47, 48, 49)]
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] 858 8size <- 858
dat_PSB1$BPE <- rep("PSB1", size = size) # Tạo biến mới BPE
names(dat_PSB1)[names(dat_PSB1) == "PSB1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSB1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSB1
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PSB1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSB1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSB1
## 5 16-25 HoChiMinh 2 Never Ever Never Ever Male PSB1
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Female PSB1## Creat dat_PSB2 - cot 4 cua SEM5_likert
dat_PSB2 <- SEM5_likert [, c(1,2,4,45, 46, 47, 48, 49)]
head(dat_PSB2)## AGE CITY PSB2 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 2 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Femaledim(dat_PSB2)## [1] 858 8size <- 858
dat_PSB2$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 SEN5_likert
dat_PSB3 <- SEM5_likert [, c(1,2,5,45, 46, 47, 48, 49)]
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] 858 8size <- 858
dat_PSB3$BPE <- rep("PSB3", size = size) # Tạo biến mới BPE
names(dat_PSB3)[names(dat_PSB3) == "PSB3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSB3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PSB3
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSB3
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PSB3
## 4 16-25 HoChiMinh 6 Never Ever Never Ever Male PSB3
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male PSB3
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PSB3## Creat dat_PSB4 - cot 6 cua SEM5_likert
dat_PSB4 <- SEM5_likert [, c(1,2,6,45, 46, 47, 48, 49)]
head(dat_PSB4)## AGE CITY PSB4 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 4 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_PSB4)## [1] 858 8size <- 858
dat_PSB4$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_PSQT1 - cot 7 cua SEM5_likert
dat_PSQT1 <- SEM5_likert [, c(1,2,7,45, 46, 47, 48, 49)]
head(dat_PSQT1)## AGE CITY PSQT1 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 6 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_PSQT1)## [1] 858 8size <- 858
dat_PSQT1$BPE <- rep("PSQT1", size = size) # Tạo biến mới BPE
names(dat_PSQT1)[names(dat_PSQT1) == "PSQT1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQT1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSQT1
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSQT1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSQT1
## 4 16-25 HoChiMinh 6 Never Ever Never Ever Male PSQT1
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PSQT1
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQT1## Creat dat_PSQT2 - cot 8 cua SEM5_likert
dat_PSQT2 <- SEM5_likert [, c(1,2,8,45, 46, 47, 48, 49)]
head(dat_PSQT2)## AGE CITY PSQT2 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 3 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_PSQT2)## [1] 858 8size <- 858
dat_PSQT2$BPE <- rep("PSQT2", size = size) # Tạo biến mới BPE
names(dat_PSQT2)[names(dat_PSQT2) == "PSQT2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQT2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSQT2
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female PSQT2
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSQT2
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQT2
## 5 16-25 HoChiMinh 3 Never Ever Never Ever Male PSQT2
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQT2## Creat dat_PSQT3 - cot 9 cua SEM5_likert
dat_PSQT3 <- SEM5_likert [, c(1,2,9,45, 46, 47, 48, 49)]
head(dat_PSQT3)## AGE CITY PSQT3 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 4 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_PSQT3)## [1] 858 8size <- 858
dat_PSQT3$BPE <- rep("PSQT3", size = size) # Tạo biến mới BPE
names(dat_PSQT3)[names(dat_PSQT3) == "PSQT3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQT3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQT3
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female PSQT3
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSQT3
## 4 16-25 HoChiMinh 4 Never Ever Never Ever Male PSQT3
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PSQT3
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female PSQT3## Creat dat_PSQT4 - cot 10 cua SEM5_likert
dat_PSQT4 <- SEM5_likert [, c(1,2,10,45, 46, 47, 48, 49)]
head(dat_PSQT4)## AGE CITY PSQT4 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 3 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_PSQT4)## [1] 858 8size <- 858
dat_PSQT4$BPE <- rep("PSQT4", size = size) # Tạo biến mới BPE
names(dat_PSQT4)[names(dat_PSQT4) == "PSQT4"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQT4)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQT4
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSQT4
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSQT4
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQT4
## 5 16-25 HoChiMinh 3 Never Ever Never Ever Male PSQT4
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQT4## Creat dat_PSQC1 - cot 11 cua SEM5_likert
dat_PSQC1 <- SEM5_likert [, c(1,2,11,45, 46, 47, 48, 49)]
head(dat_PSQC1)## AGE CITY PSQC1 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 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Femaledim(dat_PSQC1)## [1] 858 8size <- 858
dat_PSQC1$BPE <- rep("PSQC1", size = size) # Tạo biến mới BPE
names(dat_PSQC1)[names(dat_PSQC1) == "PSQC1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQC1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSQC1
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSQC1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSQC1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQC1
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PSQC1
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female PSQC1## Creat dat_PSQC2 - cot 12 cua SEM5_likert
dat_PSQC2 <- SEM5_likert [, c(1,2,12,45, 46, 47, 48, 49)]
head(dat_PSQC2)## AGE CITY PSQC2 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female
## 3 16-25 HoChiMinh 4 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Femaledim(dat_PSQC2)## [1] 858 8size <- 858
dat_PSQC2$BPE <- rep("PSQC2", size = size) # Tạo biến mới BPE
names(dat_PSQC2)[names(dat_PSQC2) == "PSQC2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQC2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQC2
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female PSQC2
## 3 16-25 HoChiMinh 4 Never Never Never Never Male PSQC2
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQC2
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PSQC2
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female PSQC2## Creat dat_PSQC3 - cot 13 cua SEM5_likert
dat_PSQC3 <- SEM5_likert [, c(1,2,13,45, 46, 47, 48, 49)]
head(dat_PSQC3)## AGE CITY PSQC3 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 1 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_PSQC3)## [1] 858 8size <- 858
dat_PSQC3$BPE <- rep("PSQC3", size = size) # Tạo biến mới BPE
names(dat_PSQC3)[names(dat_PSQC3) == "PSQC3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQC3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQC3
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female PSQC3
## 3 16-25 HoChiMinh 1 Never Never Never Never Male PSQC3
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQC3
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male PSQC3
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQC3## Creat dat_PSQC4 - cot 14 cua SEM5_likert
dat_PSQC4 <- SEM5_likert [, c(1,2,14,45, 46, 47, 48, 49)]
head(dat_PSQC4)## AGE CITY PSQC4 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 4 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 3 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Femaledim(dat_PSQC4)## [1] 858 8size <- 858
dat_PSQC4$BPE <- rep("PSQC4", size = size) # Tạo biến mới BPE
names(dat_PSQC4)[names(dat_PSQC4) == "PSQC4"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQC4)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQC4
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSQC4
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSQC4
## 4 16-25 HoChiMinh 4 Never Ever Never Ever Male PSQC4
## 5 16-25 HoChiMinh 3 Never Ever Never Ever Male PSQC4
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Female PSQC4## Creat dat_PSQC5 - cot 15 cua SEM5_likert
dat_PSQC5 <- SEM5_likert [, c(1,2,15,45, 46, 47, 48, 49)]
head(dat_PSQC5)## AGE CITY PSQC5 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 1 Never Never Never Never Male
## 4 16-25 HoChiMinh 4 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Femaledim(dat_PSQC5)## [1] 858 8size <- 858
dat_PSQC5$BPE <- rep("PSQC5", size = size) # Tạo biến mới BPE
names(dat_PSQC5)[names(dat_PSQC5) == "PSQC5"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQC5)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSQC5
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female PSQC5
## 3 16-25 HoChiMinh 1 Never Never Never Never Male PSQC5
## 4 16-25 HoChiMinh 4 Never Ever Never Ever Male PSQC5
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PSQC5
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Female PSQC5## Creat dat_PSQC6 - cot 24 cua SEM5_likert
dat_PSQC6 <- SEM5_likert [, c(1,2,24,45, 46, 47, 48, 49)]
head(dat_PSQC6)## AGE CITY PSQC6 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 2 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_PSQC6)## [1] 858 8size <- 858
dat_PSQC6$BPE <- rep("PSQC6", size = size) # Tạo biến mới BPE
names(dat_PSQC6)[names(dat_PSQC6) == "PSQC6"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQC6)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 2 Never Ever Never Ever Female PSQC6
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female PSQC6
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSQC6
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQC6
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQC6
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQC6## Creat dat_PSQP1 - cot 16 cua SEM5_likert
dat_PSQP1 <- SEM5_likert [, c(1,2,16,45, 46, 47, 48, 49)]
head(dat_PSQP1)## AGE CITY PSQP1 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 1 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_PSQP1)## [1] 858 8size <- 858
dat_PSQP1$BPE <- rep("PSQP1", size = size) # Tạo biến mới BPE
names(dat_PSQP1)[names(dat_PSQP1) == "PSQP1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQP1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSQP1
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female PSQP1
## 3 16-25 HoChiMinh 1 Never Never Never Never Male PSQP1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQP1
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male PSQP1
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQP1## Creat dat_PSQP2 - cot 17 cua SEM5_likert
dat_PSQP2 <- SEM5_likert [, c(1,2,17,45, 46, 47, 48, 49)]
head(dat_PSQP2)## AGE CITY PSQP2 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 1 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_PSQP2)## [1] 858 8size <- 858
dat_PSQP2$BPE <- rep("PSQP2", size = size) # Tạo biến mới BPE
names(dat_PSQP2)[names(dat_PSQP2) == "PSQP2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQP2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSQP2
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSQP2
## 3 16-25 HoChiMinh 1 Never Never Never Never Male PSQP2
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQP2
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male PSQP2
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQP2## Creat dat_PSQP3 - cot 18 cua SEM5_likert
dat_PSQP3 <- SEM5_likert [, c(1,2,18,45, 46, 47, 48, 49)]
head(dat_PSQP3)## AGE CITY PSQP3 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 4 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_PSQP3)## [1] 858 8size <- 858
dat_PSQP3$BPE <- rep("PSQP3", size = size) # Tạo biến mới BPE
names(dat_PSQP3)[names(dat_PSQP3) == "PSQP3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQP3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 5 Never Ever Never Ever Female PSQP3
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSQP3
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PSQP3
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQP3
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male PSQP3
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQP3## Creat dat_PSQP4 - cot 19 cua SEM5_likert
dat_PSQP4 <- SEM5_likert [, c(1,2,19,45, 46, 47, 48, 49)]
head(dat_PSQP4)## AGE CITY PSQP4 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Femaledim(dat_PSQP4)## [1] 858 8size <- 858
dat_PSQP4$BPE <- rep("PSQP4", size = size) # Tạo biến mới BPE
names(dat_PSQP4)[names(dat_PSQP4) == "PSQP4"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQP4)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSQP4
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female PSQP4
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PSQP4
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQP4
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQP4
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female PSQP4## Creat dat_PSQR1 - cot 20 cua SEM5_likert
dat_PSQR1 <- SEM5_likert [, c(1,2,20,45, 46, 47, 48, 49)]
head(dat_PSQR1)## AGE CITY PSQR1 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 4 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_PSQR1)## [1] 858 8size <- 858
dat_PSQR1$BPE <- rep("PSQR1", size = size) # Tạo biến mới BPE
names(dat_PSQR1)[names(dat_PSQR1) == "PSQR1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQR1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQR1
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSQR1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSQR1
## 4 16-25 HoChiMinh 4 Never Ever Never Ever Male PSQR1
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQR1
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Female PSQR1## Creat dat_PSQR2 - cot 21 cua SEM5_likert
dat_PSQR2 <- SEM5_likert [, c(1,2,21,45, 46, 47, 48, 49)]
head(dat_PSQR2)## AGE CITY PSQR2 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 3 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Femaledim(dat_PSQR2)## [1] 858 8size <- 858
dat_PSQR2$BPE <- rep("PSQR2", size = size) # Tạo biến mới BPE
names(dat_PSQR2)[names(dat_PSQR2) == "PSQR2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQR2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQR2
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PSQR2
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PSQR2
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQR2
## 5 16-25 HoChiMinh 3 Never Ever Never Ever Male PSQR2
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Female PSQR2## Creat dat_PSQR3 - cot 22 cua SEM5_likert
dat_PSQR3 <- SEM5_likert [, c(1,2,22,45, 46, 47, 48, 49)]
head(dat_PSQR3)## AGE CITY PSQR3 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 3 Never Ever Never Ever Femaledim(dat_PSQR3)## [1] 858 8size <- 858
dat_PSQR3$BPE <- rep("PSQR3", size = size) # Tạo biến mới BPE
names(dat_PSQR3)[names(dat_PSQR3) == "PSQR3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQR3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSQR3
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSQR3
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSQR3
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQR3
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQR3
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Female PSQR3## Creat dat_PSQR4 - cot 23 cua SEM5_likert
dat_PSQR4 <- SEM5_likert [, c(1,2,23,45, 46, 47, 48, 49)]
head(dat_PSQR4)## AGE CITY PSQR4 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female
## 3 16-25 HoChiMinh 5 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 3 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Femaledim(dat_PSQR4)## [1] 858 8size <- 858
dat_PSQR4$BPE <- rep("PSQR4", size = size) # Tạo biến mới BPE
names(dat_PSQR4)[names(dat_PSQR4) == "PSQR4"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PSQR4)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQR4
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PSQR4
## 3 16-25 HoChiMinh 5 Never Never Never Never Male PSQR4
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQR4
## 5 16-25 HoChiMinh 3 Never Ever Never Ever Male PSQR4
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Female PSQR4## Creat dat_SAT1 - cot 25 cua SEM5_likert
dat_SAT1 <- SEM5_likert [, c(1,2,25,45, 46, 47, 48, 49)]
head(dat_SAT1)## AGE CITY SAT1 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 4 Never Ever Never Ever Femaledim(dat_SAT1)## [1] 858 8size <- 858
dat_SAT1$BPE <- rep("SAT1", size = size) # Tạo biến mới BPE
names(dat_SAT1)[names(dat_SAT1) == "SAT1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_SAT1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female SAT1
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female SAT1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male SAT1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male SAT1
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male SAT1
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female SAT1## Creat dat_SAT2 - cot 26 cua SEM5_likert
dat_SAT2 <- SEM5_likert [, c(1,2,26,45, 46, 47, 48, 49)]
head(dat_SAT2)## AGE CITY SAT2 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Femaledim(dat_SAT2)## [1] 858 8size <- 858
dat_SAT2$BPE <- rep("SAT2", size = size) # Tạo biến mới BPE
names(dat_SAT2)[names(dat_SAT2) == "SAT2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_SAT2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female SAT2
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female SAT2
## 3 16-25 HoChiMinh 2 Never Never Never Never Male SAT2
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male SAT2
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male SAT2
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female SAT2## Creat dat_SAT3 - cot 27 cua SEM5_likert
dat_SAT3 <- SEM5_likert [, c(1,2,27,45, 46, 47, 48, 49)]
head(dat_SAT3)## AGE CITY SAT3 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 5 Never Ever Never Ever Femaledim(dat_SAT3)## [1] 858 8size <- 858
dat_SAT3$BPE <- rep("SAT3", size = size) # Tạo biến mới BPE
names(dat_SAT3)[names(dat_SAT3) == "SAT3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_SAT3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female SAT3
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female SAT3
## 3 16-25 HoChiMinh 2 Never Never Never Never Male SAT3
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male SAT3
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male SAT3
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female SAT3## Creat dat_IMA1 - cot 28 cua SEN5_likert
dat_IMA1 <- SEM5_likert [, c(1,2,28,45, 46, 47, 48, 49)]
head(dat_IMA1)## AGE CITY IMA1 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 4 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_IMA1)## [1] 858 8size <- 858
dat_IMA1$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 29 cua SEM5_likert
dat_IMA2 <- SEM5_likert [, c(1,2,29,45, 46, 47, 48, 49)]
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] 858 8size <- 858
dat_IMA2$BPE <- rep("IMA2", size = size) # Tạo biến mới BPE
names(dat_IMA2)[names(dat_IMA2) == "IMA2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_IMA2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female IMA2
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female IMA2
## 3 16-25 HoChiMinh 2 Never Never Never Never Male IMA2
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male IMA2
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male IMA2
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Female IMA2## Creat dat_IMA3 - cot 30 cua SEM5_likert
dat_IMA3 <- SEM5_likert [, c(1,2,30,45, 46, 47, 48, 49)]
head(dat_IMA3)## AGE CITY IMA3 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female
## 3 16-25 HoChiMinh 5 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Femaledim(dat_IMA3)## [1] 858 8size <- 858
dat_IMA3$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 31 cua SEM5_likert
dat_IMA4 <- SEM5_likert [, c(1,2,31,45, 46, 47, 48, 49)]
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] 858 8size <- 858
dat_IMA4$BPE <- rep("IMA4", size = size) # Tạo biến mới BPE
names(dat_IMA4)[names(dat_IMA4) == "IMA4"] <- "Res" # Đổi tên biến BST thành Res
head(dat_IMA4)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female IMA4
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female IMA4
## 3 16-25 HoChiMinh 2 Never Never Never Never Male IMA4
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male IMA4
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male IMA4
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Female IMA4dim(dat_IMA4)## [1] 858 9## Creat dat_IMA5 - cot 32 cua SEM5_likert
dat_IMA5 <- SEM5_likert [, c(1,2,32,45, 46, 47, 48, 49)]
head(dat_IMA5)## AGE CITY IMA5 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Femaledim(dat_IMA5)## [1] 858 8size <- 858
dat_IMA5$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 IMA5dim(dat_IMA5)## [1] 858 9## Creat dat_PHB1 - cot 33 cua SEM5_likert
dat_PHB1 <- SEM5_likert [, c(1,2,33,45, 46, 47, 48, 49)]
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] 858 8size <- 858
dat_PHB1$BPE <- rep("PHB1", size = size) # Tạo biến mới BPE
names(dat_PHB1)[names(dat_PHB1) == "PHB1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PHB1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PHB1
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PHB1
## 3 16-25 HoChiMinh 5 Never Never Never Never Male PHB1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB1
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB1
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PHB1dim(dat_PHB1)## [1] 858 9## Creat dat_PHB2 - cot 34 cua SEM5_likert
dat_PHB2 <- SEM5_likert [, c(1,2,34,45, 46, 47, 48, 49)]
head(dat_PHB2)## AGE CITY PHB2 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 7 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 2 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Femaledim(dat_PHB2)## [1] 858 8size <- 858
dat_PHB2$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 PHB2dim(dat_PHB2)## [1] 858 9## Creat dat_PHB3 - cot 35 cua SEM5_likert
dat_PHB3 <- SEM5_likert [, c(1,2,35,45, 46, 47, 48, 49)]
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] 858 8size <- 858
dat_PHB3$BPE <- rep("PHB3", size = size) # Tạo biến mới BPE
names(dat_PHB3)[names(dat_PHB3) == "PHB3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PHB3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PHB3
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female PHB3
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PHB3
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB3
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PHB3
## 6 16-25 HoChiMinh 7 Never Ever Never Ever Female PHB3dim(dat_PHB3)## [1] 858 9## Creat dat_PHB4 - cot 36 cua SEM5_likert
dat_PHB4 <- SEM5_likert [, c(1,2,36,45, 46, 47, 48, 49)]
head(dat_PHB4)## AGE CITY PHB4 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 7 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Femaledim(dat_PHB4)## [1] 858 8size <- 858
dat_PHB4$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 PHB4dim(dat_PHB4)## [1] 858 9## Creat dat_PHB5 - cot 37 cua SEM5_likert
dat_PHB5 <- SEM5_likert [, c(1,2,37,45, 46, 47, 48, 49)]
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] 858 8size <- 858
dat_PHB5$BPE <- rep("PHB5", size = size) # Tạo biến mới BPE
names(dat_PHB5)[names(dat_PHB5) == "PHB5"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PHB5)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 7 Never Ever Never Ever Female PHB5
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female PHB5
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PHB5
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB5
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male PHB5
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PHB5dim(dat_PHB5)## [1] 858 9## Creat dat_PEB1 - cot 38 cua SEM5_likert
dat_PEB1 <- SEM5_likert [, c(1,2,38,45, 46, 47, 48, 49)]
head(dat_PEB1)## AGE CITY PEB1 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 7 Never Ever Never Ever Femaledim(dat_PEB1)## [1] 858 8size <- 858
dat_PEB1$BPE <- rep("PEB1", size = size) # Tạo biến mới BPE
names(dat_PEB1)[names(dat_PEB1) == "PEB1"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PEB1)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PEB1
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female PEB1
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PEB1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PEB1
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PEB1
## 6 16-25 HoChiMinh 7 Never Ever Never Ever Female PEB1dim(dat_PEB1)## [1] 858 9## Creat dat_PEB2 - cot 39 cua SEM5_likert
dat_PEB2 <- SEM5_likert [, c(1,2,39,45, 46, 47, 48, 49)]
head(dat_PEB2)## AGE CITY PEB2 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_PEB2)## [1] 858 8size <- 858
dat_PEB2$BPE <- rep("PEB2", size = size) # Tạo biến mới BPE
names(dat_PEB2)[names(dat_PEB2) == "PEB2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PEB2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PEB2
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female PEB2
## 3 16-25 HoChiMinh 5 Never Never Never Never Male PEB2
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PEB2
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PEB2
## 6 16-25 HoChiMinh 7 Never Ever Never Ever Female PEB2dim(dat_PEB2)## [1] 858 9## Creat dat_PEB3 - cot 40 cua SEM5_likert
dat_PEB3 <- SEM5_likert [, c(1,2,40,45, 46, 47, 48, 49)]
head(dat_PEB3)## AGE CITY PEB3 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Femaledim(dat_PEB3)## [1] 858 8size <- 858
dat_PEB3$BPE <- rep("PEB3", size = size) # Tạo biến mới BPE
names(dat_PEB3)[names(dat_PEB3) == "PEB3"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PEB3)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PEB3
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female PEB3
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PEB3
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PEB3
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PEB3
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female PEB3dim(dat_PEB3)## [1] 858 9## Creat dat_PEB4 - cot 41 cua SEM5_likert
dat_PEB4 <- SEM5_likert [, c(1,2,41,45, 46, 47, 48, 49)]
head(dat_PEB4)## AGE CITY PEB4 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_PEB4)## [1] 858 8size <- 858
dat_PEB4$BPE <- rep("PEB4", size = size) # Tạo biến mới BPE
names(dat_PEB4)[names(dat_PEB4) == "PEB4"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PEB4)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PEB4
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PEB4
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PEB4
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PEB4
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PEB4
## 6 16-25 HoChiMinh 2 Never Ever Never Ever Female PEB4dim(dat_PEB4)## [1] 858 9## Creat dat_PVA1 - cot 42 cua SEM5_likert
dat_PVA1 <- SEM5_likert [, c(1,2,42,45, 46, 47, 48, 49)]
head(dat_PVA1)## AGE CITY PVA1 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female
## 3 16-25 HoChiMinh 6 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 6 Never Ever Never Ever Femaledim(dat_PVA1)## [1] 858 8size <- 858
dat_PVA1$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 PVA1dim(dat_PVA1)## [1] 858 9## Creat dat_PVA2 - cot 43 cua SEM5_likert
dat_PVA2 <- SEM5_likert [, c(1,2,43,45, 46, 47, 48, 49)]
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] 858 8size <- 858
dat_PVA2$BPE <- rep("PVA2", size = size) # Tạo biến mới BPE
names(dat_PVA2)[names(dat_PVA2) == "PVA2"] <- "Res" # Đổi tên biến BST thành Res
head(dat_PVA2)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PVA2
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female PVA2
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PVA2
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PVA2
## 5 16-25 HoChiMinh 2 Never Ever Never Ever Male PVA2
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female PVA2dim(dat_PVA2)## [1] 858 9## Creat dat_PVA3 - cot 44 cua SEM5_likert
dat_PVA3 <- SEM5_likert [, c(1,2,44,45, 46, 47, 48, 49)]
head(dat_PVA3)## AGE CITY PVA3 EC_Stop WC_Stop EC_Bus WC_Bus Gender
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female
## 2 16-25 HoChiMinh 3 Never Never Ever Never Female
## 3 16-25 HoChiMinh 4 Never Never Never Never Male
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male
## 5 16-25 HoChiMinh 3 Never Ever Never Ever Male
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Femaledim(dat_PVA3)## [1] 858 8size <- 858
dat_PVA3$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 PVA3dim(dat_PVA3)## [1] 858 9## Tạo dữ liệu likert từ 42 dữ liệu: dat_PSB1-4,dat_PSQT1-4, dat_PSQTC-6, dat_PSQP1-4, dat_PSQR1-4, dat_SAT1-3, dat_IMA1-5, dat_PHB1-5, dat_PEB1-4, dat_PVA1-3
B.Per <- rbind(dat_PSB1, dat_PSB2, dat_PSB3, dat_PSB4, dat_PSQT1, dat_PSQT2, dat_PSQT3, dat_PSQT4, dat_PSQC1, dat_PSQC2, dat_PSQC3, dat_PSQC4, dat_PSQC5, dat_PSQC6, dat_PSQP1, dat_PSQP2, dat_PSQP3, dat_PSQP4, dat_PSQR1, dat_PSQR2, dat_PSQR3, dat_PSQR4, dat_SAT1, dat_SAT2, dat_SAT3, dat_IMA1, dat_IMA2, dat_IMA3, dat_IMA4, dat_IMA5, dat_PHB1, dat_PHB2, dat_PHB3, dat_PHB4, dat_PHB5, dat_PEB1, dat_PEB2, dat_PEB3, dat_PEB4, 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': 36036 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] 36036 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': 3432 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] 3432 9B.Per_PSQT <- rbind(dat_PSQT1, dat_PSQT2, dat_PSQT3, dat_PSQT4)
head(B.Per_PSQT)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSQT1
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSQT1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSQT1
## 4 16-25 HoChiMinh 6 Never Ever Never Ever Male PSQT1
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PSQT1
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQT1str(B.Per_PSQT)## 'data.frame': 3432 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 2 6 6 4 2 5 5 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 "PSQT1" "PSQT1" "PSQT1" "PSQT1" ...dim(B.Per_PSQT)## [1] 3432 9B.Per_PSQC <- rbind(dat_PSQC1, dat_PSQC2, dat_PSQC3, dat_PSQC4, dat_PSQC5, dat_PSQC6)
head(B.Per_PSQC)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSQC1
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSQC1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSQC1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQC1
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PSQC1
## 6 16-25 HoChiMinh 5 Never Ever Never Ever Female PSQC1str(B.Per_PSQC)## 'data.frame': 5148 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 2 5 6 5 2 6 3 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 "PSQC1" "PSQC1" "PSQC1" "PSQC1" ...dim(B.Per_PSQC)## [1] 5148 9B.Per_PSQP <- rbind(dat_PSQP1, dat_PSQP2, dat_PSQP3, dat_PSQP4)
head(B.Per_PSQP)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female PSQP1
## 2 16-25 HoChiMinh 6 Never Never Ever Never Female PSQP1
## 3 16-25 HoChiMinh 1 Never Never Never Never Male PSQP1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQP1
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male PSQP1
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQP1str(B.Per_PSQP)## 'data.frame': 3432 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 6 1 5 4 4 4 4 3 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 "PSQP1" "PSQP1" "PSQP1" "PSQP1" ...dim(B.Per_PSQP)## [1] 3432 9B.Per_PSQR <- rbind(dat_PSQR1, dat_PSQR2, dat_PSQR3, dat_PSQR4)
head(B.Per_PSQR)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PSQR1
## 2 16-25 HoChiMinh 4 Never Never Ever Never Female PSQR1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male PSQR1
## 4 16-25 HoChiMinh 4 Never Ever Never Ever Male PSQR1
## 5 16-25 HoChiMinh 5 Never Ever Never Ever Male PSQR1
## 6 16-25 HoChiMinh 3 Never Ever Never Ever Female PSQR1str(B.Per_PSQR)## 'data.frame': 3432 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 4 5 3 2 5 3 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 "PSQR1" "PSQR1" "PSQR1" "PSQR1" ...dim(B.Per_PSQR)## [1] 3432 9B.Per_SAT <- rbind(dat_SAT1, dat_SAT2, dat_SAT3)
head(B.Per_SAT)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 6 Never Ever Never Ever Female SAT1
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female SAT1
## 3 16-25 HoChiMinh 2 Never Never Never Never Male SAT1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male SAT1
## 5 16-25 HoChiMinh 4 Never Ever Never Ever Male SAT1
## 6 16-25 HoChiMinh 4 Never Ever Never Ever Female SAT1str(B.Per_SAT)## 'data.frame': 2574 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 2 5 4 4 2 7 5 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 "SAT1" "SAT1" "SAT1" "SAT1" ...dim(B.Per_SAT)## [1] 2574 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': 4290 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] 4290 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': 4290 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] 4290 9B.Per_PEB <- rbind(dat_PEB1, dat_PEB2, dat_PEB3, dat_PEB4)
head(B.Per_PEB)## AGE CITY Res EC_Stop WC_Stop EC_Bus WC_Bus Gender BPE
## 1 16-25 HoChiMinh 4 Never Ever Never Ever Female PEB1
## 2 16-25 HoChiMinh 5 Never Never Ever Never Female PEB1
## 3 16-25 HoChiMinh 6 Never Never Never Never Male PEB1
## 4 16-25 HoChiMinh 5 Never Ever Never Ever Male PEB1
## 5 16-25 HoChiMinh 6 Never Ever Never Ever Male PEB1
## 6 16-25 HoChiMinh 7 Never Ever Never Ever Female PEB1str(B.Per_PEB)## 'data.frame': 3432 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 "PEB1" "PEB1" "PEB1" "PEB1" ...dim(B.Per_PEB)## [1] 3432 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': 2574 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] 2574 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_SEM5 = subset(B.Per, CITY == "DaNang")
head(DN_SEM5)## 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_SEM5)## 'data.frame': 17220 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_SEM5)## [1] 17220 9DN_SEM5 = DN_SEM5[ , c(3, 9)]
dim(DN_SEM5)## [1] 17220 2head(DN_SEM5)## 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_SEM5 = subset(B.Per, CITY == "HoChiMinh")
head(HCM_SEM5)## 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_SEM5)## 'data.frame': 18816 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_SEM5)## [1] 18816 9HCM_SEM5 = HCM_SEM5[ , c(3, 9)]
dim(HCM_SEM5)## [1] 18816 2head(HCM_SEM5)## 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_SEM5 = B.Per[, c(3, 9)]
str(DN_HCM_SEM5)## 'data.frame': 36036 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_SEM5)## [1] 36036 2head(DN_HCM_SEM5)## 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 PSB
DN_HCM_SEM5_PSB = B.Per_PSB[ , c(3, 9)]
dim(DN_HCM_SEM5_PSB)## [1] 3432 2head(DN_HCM_SEM5_PSB)## Res BPE
## 1 6 PSB1
## 2 3 PSB1
## 3 2 PSB1
## 4 5 PSB1
## 5 2 PSB1
## 6 2 PSB1## Construct PSQT
DN_HCM_SEM5_PSQT = B.Per_PSQT[ , c(3, 9)]
dim(DN_HCM_SEM5_PSQT)## [1] 3432 2head(DN_HCM_SEM5_PSQT)## Res BPE
## 1 6 PSQT1
## 2 4 PSQT1
## 3 2 PSQT1
## 4 6 PSQT1
## 5 6 PSQT1
## 6 4 PSQT1## Construct PSQC
DN_HCM_SEM5_PSQC = B.Per_PSQC[ , c(3, 9)]
dim(DN_HCM_SEM5_PSQC)## [1] 5148 2head(DN_HCM_SEM5_PSQC)## Res BPE
## 1 6 PSQC1
## 2 4 PSQC1
## 3 2 PSQC1
## 4 5 PSQC1
## 5 6 PSQC1
## 6 5 PSQC1## Construct PSQP
DN_HCM_SEM5_PSQP = B.Per_PSQP[ , c(3, 9)]
dim(DN_HCM_SEM5_PSQP)## [1] 3432 2head(DN_HCM_SEM5_PSQP)## Res BPE
## 1 6 PSQP1
## 2 6 PSQP1
## 3 1 PSQP1
## 4 5 PSQP1
## 5 4 PSQP1
## 6 4 PSQP1## Construct PSQR
DN_HCM_SEM5_PSQR = B.Per_PSQR[ , c(3, 9)]
dim(DN_HCM_SEM5_PSQR)## [1] 3432 2head(DN_HCM_SEM5_PSQR)## Res BPE
## 1 4 PSQR1
## 2 4 PSQR1
## 3 2 PSQR1
## 4 4 PSQR1
## 5 5 PSQR1
## 6 3 PSQR1## Construct SAT
DN_HCM_SEM5_SAT = B.Per_SAT[ , c(3, 9)]
dim(DN_HCM_SEM5_SAT)## [1] 2574 2head(DN_HCM_SEM5_SAT)## Res BPE
## 1 6 SAT1
## 2 5 SAT1
## 3 2 SAT1
## 4 5 SAT1
## 5 4 SAT1
## 6 4 SAT1## Construct IMA
DN_HCM_SEM5_IMA = B.Per_IMA[ , c(3, 9)]
dim(DN_HCM_SEM5_IMA)## [1] 4290 2head(DN_HCM_SEM5_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_SEM5_PHB = B.Per_PHB[ , c(3, 9)]
dim(DN_HCM_SEM5_PHB)## [1] 4290 2head(DN_HCM_SEM5_PHB)## Res BPE
## 1 6 PHB1
## 2 3 PHB1
## 3 5 PHB1
## 4 5 PHB1
## 5 5 PHB1
## 6 4 PHB1## Construct PEB
DN_HCM_SEM5_PEB = B.Per_PEB[ , c(3, 9)]
dim(DN_HCM_SEM5_PEB)## [1] 3432 2head(DN_HCM_SEM5_PEB)## Res BPE
## 1 4 PEB1
## 2 5 PEB1
## 3 6 PEB1
## 4 5 PEB1
## 5 6 PEB1
## 6 7 PEB1## Construct PVA
DN_HCM_SEM5_PVA = B.Per_PVA[ , c(3, 9)]
dim(DN_HCM_SEM5_PVA)## [1] 2574 2head(DN_HCM_SEM5_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_SEM5)## Res BPE
## 1 6 PSB1
## 2 3 PSB1
## 3 2 PSB1
## 4 5 PSB1
## 5 2 PSB1
## 6 2 PSB1dim(DN_HCM_SEM5)## [1] 36036 2attach(DN_HCM_SEM5)
DN_HCM_SEM5 = within(DN_HCM_SEM5, {
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", "PSQT1", "PSQT2", "PSQT3", "PSQT4", "PSQC1", "PSQC2", "PSQC3", "PSQC4", "PSQC5", "PSQC6", "PSQP1", "PSQP2", "PSQP3", "PSQP4", "PSQR1", "PSQR2", "PSQR3", "PSQR4", "SAT1", "SAT2", "SAT3", "IMA1", "IMA2", "IMA3", "IMA4", "IMA5", "PHB1", "PHB2", "PHB3", "PHB4", "PHB5", "PEB1", "PEB2", "PEB3", "PEB4", "PVA1", "PVA2", "PVA3"))
})
str(DN_HCM_SEM5)## 'data.frame': 36036 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 3 2 5 2 2 1 6 2 4 ...
## $ BPE: Factor w/ 42 levels "PSB1","PSB2",..: 15 15 15 15 15 15 15 15 15 15 ...summary(DN_HCM_SEM5)## Res BPE
## Very Disagree : 697 PSB1 : 858
## Disagree : 1317 PSB2 : 858
## Quite Disagree: 1502 PSB3 : 858
## Normal : 4150 PSB4 : 858
## Quite Agree : 7478 PSQT1 : 858
## Agree :15674 PSQT2 : 858
## Very Agree : 5218 (Other):30888 ## t <- compareGroups(Res ~ BPE, data = DN_HCM_SEM1)
## createTable(t)
DN_HCM_SEM5 %>%
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 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 PSQC3 PSQC4
## [13] PSQC5 PSQC6 PSQP1 PSQP2 PSQP3 PSQP4 PSQR1 PSQR2 PSQR3 PSQR4 SAT1 SAT2
## [25] SAT3 IMA1 IMA2 IMA3 IMA4 IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEB1
## [37] PEB2 PEB3 PEB4 PVA1 PVA2 PVA3
## 42 Levels: PSB1 PSB2 PSB3 PSB4 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 ... 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: 294 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 19 2.21 2.21%
## 3 PSB1 Quite Disagree 33 3.85 3.85%
## 4 PSB1 Normal 154 18.0 17.95%
## 5 PSB1 Quite Agree 230 26.8 26.81%
## 6 PSB1 Agree 351 40.9 40.91%
## 7 PSB1 Very Agree 65 7.58 7.58%
## 8 PSB2 Very Disagree 11 1.28 1.28%
## 9 PSB2 Disagree 18 2.1 2.1%
## 10 PSB2 Quite Disagree 26 3.03 3.03%
## # ... with 284 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$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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
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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
# 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: 42 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 1.28 1.28%
## 3 PSB3 Very Disagree 11 1.28 1.28%
## 4 PSB4 Very Disagree 13 1.52 1.52%
## 5 PSQT1 Very Disagree 6 0.7 0.7%
## 6 PSQT2 Very Disagree 24 2.8 2.8%
## 7 PSQT3 Very Disagree 25 2.91 2.91%
## 8 PSQT4 Very Disagree 25 2.91 2.91%
## 9 PSQC1 Very Disagree 24 2.8 2.8%
## 10 PSQC2 Very Disagree 25 2.91 2.91%
## # ... with 32 more rows# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Disagree 19 2.21 2.21%
## 2 PSB2 Disagree 18 2.1 2.1%
## 3 PSB3 Disagree 10 1.17 1.17%
## 4 PSB4 Disagree 15 1.75 1.75%
## 5 PSQT1 Disagree 20 2.33 2.33%
## 6 PSQT2 Disagree 8 0.93 0.93%
## 7 PSQT3 Disagree 17 1.98 1.98%
## 8 PSQT4 Disagree 20 2.33 2.33%
## 9 PSQC1 Disagree 19 2.21 2.21%
## 10 PSQC2 Disagree 18 2.1 2.1%
## # ... with 32 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Quite Disagree 33 3.85 3.85%
## 2 PSB2 Quite Disagree 26 3.03 3.03%
## 3 PSB3 Quite Disagree 21 2.45 2.45%
## 4 PSB4 Quite Disagree 26 3.03 3.03%
## 5 PSQT1 Quite Disagree 20 2.33 2.33%
## 6 PSQT2 Quite Disagree 13 1.52 1.52%
## 7 PSQT3 Quite Disagree 21 2.45 2.45%
## 8 PSQT4 Quite Disagree 22 2.56 2.56%
## 9 PSQC1 Quite Disagree 22 2.56 2.56%
## 10 PSQC2 Quite Disagree 37 4.31 4.31%
## # ... with 32 more rows# For displaying percent of "Normal":
df_odered %>%
filter(Res == "Normal") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Normal 154 18.0 17.95%
## 2 PSB2 Normal 158 18.4 18.41%
## 3 PSB3 Normal 128 14.9 14.92%
## 4 PSB4 Normal 113 13.2 13.17%
## 5 PSQT1 Normal 104 12.1 12.12%
## 6 PSQT2 Normal 60 6.99 6.99%
## 7 PSQT3 Normal 67 7.81 7.81%
## 8 PSQT4 Normal 77 8.97 8.97%
## 9 PSQC1 Normal 93 10.8 10.84%
## 10 PSQC2 Normal 97 11.3 11.31%
## # ... with 32 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Quite Agree 230 26.8 26.81%
## 2 PSB2 Quite Agree 214 24.9 24.94%
## 3 PSB3 Quite Agree 207 24.1 24.13%
## 4 PSB4 Quite Agree 201 23.4 23.43%
## 5 PSQT1 Quite Agree 204 23.8 23.78%
## 6 PSQT2 Quite Agree 127 14.8 14.8%
## 7 PSQT3 Quite Agree 170 19.8 19.81%
## 8 PSQT4 Quite Agree 168 19.6 19.58%
## 9 PSQC1 Quite Agree 167 19.5 19.46%
## 10 PSQC2 Quite Agree 215 25.1 25.06%
## # ... with 32 more rows# For displaying percent of "Agree":
df_odered %>%
filter(Res == "Agree") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Agree 351 40.9 40.91%
## 2 PSB2 Agree 365 42.5 42.54%
## 3 PSB3 Agree 403 47.0 46.97%
## 4 PSB4 Agree 406 47.3 47.32%
## 5 PSQT1 Agree 420 49.0 48.95%
## 6 PSQT2 Agree 391 45.6 45.57%
## 7 PSQT3 Agree 353 41.1 41.14%
## 8 PSQT4 Agree 354 41.3 41.26%
## 9 PSQC1 Agree 358 41.7 41.72%
## 10 PSQC2 Agree 352 41.0 41.03%
## # ... with 32 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Agree 65 7.58 7.58%
## 2 PSB2 Very Agree 66 7.69 7.69%
## 3 PSB3 Very Agree 78 9.09 9.09%
## 4 PSB4 Very Agree 84 9.79 9.79%
## 5 PSQT1 Very Agree 84 9.79 9.79%
## 6 PSQT2 Very Agree 235 27.4 27.39%
## 7 PSQT3 Very Agree 205 23.9 23.89%
## 8 PSQT4 Very Agree 192 22.4 22.38%
## 9 PSQC1 Very Agree 175 20.4 20.4%
## 10 PSQC2 Very Agree 114 13.3 13.29%
## # ... with 32 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") -> df_for_text
df_for_text## # A tibble: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Agree 65 7.58 7.58%
## 2 PSB2 Very Agree 66 7.69 7.69%
## 3 PSB3 Very Agree 78 9.09 9.09%
## 4 PSB4 Very Agree 84 9.79 9.79%
## 5 PSQT1 Very Agree 84 9.79 9.79%
## 6 PSQT2 Very Agree 235 27.4 27.39%
## 7 PSQT3 Very Agree 205 23.9 23.89%
## 8 PSQT4 Very Agree 192 22.4 22.38%
## 9 PSQC1 Very Agree 175 20.4 20.4%
## 10 PSQC2 Very Agree 114 13.3 13.29%
## # ... with 32 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
## 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
3.3. Vẽ biểu đồ likert với dữ liệu chung Đà Nẵng và Hồ Chí Minh - Nhãn tiếng việt
# Nhan tieng viet
DN_HCM_SEM5_v = within(DN_HCM_SEM5, {
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", "PSQT1", "PSQT2", "PSQT3", "PSQT4", "PSQC1", "PSQC2", "PSQC3", "PSQC4", "PSQC5", "PSQC6", "PSQP1", "PSQP2", "PSQP3", "PSQP4", "PSQR1", "PSQR2", "PSQR3", "PSQR4", "SAT1", "SAT2", "SAT3", "IMA1", "IMA2", "IMA3", "IMA4", "IMA5", "PHB1", "PHB2", "PHB3", "PHB4", "PHB5", "PEB1", "PEB2", "PEB3", "PEB4", "PVA1", "PVA2", "PVA3"))
})
str(DN_HCM_SEM5_v)## 'data.frame': 36036 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/ 42 levels "PSB1","PSB2",..: 15 15 15 15 15 15 15 15 15 15 ...summary(DN_HCM_SEM5_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 697 PSB1 : 858
## Không d<U+1ED3>ng ý : 1317 PSB2 : 858
## Khá không d<U+1ED3>ng ý : 1502 PSB3 : 858
## Bình thu<U+1EDD>ng : 4150 PSB4 : 858
## Khá d<U+1ED3>ng ý : 7478 PSQT1 : 858
## Ð<U+1ED3>ng ý :15674 PSQT2 : 858
## R<U+1EA5>t d<U+1ED3>ng ý : 5218 (Other):30888 ## t <- compareGroups(Res ~ BPE, data = DN_HCM_SEM1)
## createTable(t)
DN_HCM_SEM5_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 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 PSQC3 PSQC4
## [13] PSQC5 PSQC6 PSQP1 PSQP2 PSQP3 PSQP4 PSQR1 PSQR2 PSQR3 PSQR4 SAT1 SAT2
## [25] SAT3 IMA1 IMA2 IMA3 IMA4 IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEB1
## [37] PEB2 PEB3 PEB4 PVA1 PVA2 PVA3
## 42 Levels: PSB1 PSB2 PSB3 PSB4 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 ... 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: 294 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 19 2.21 2.21%
## 3 PSB1 Khá không d<U+1ED3>ng ý 33 3.85 3.85%
## 4 PSB1 Bình thu<U+1EDD>ng 154 18.0 17.95%
## 5 PSB1 Khá d<U+1ED3>ng ý 230 26.8 26.81%
## 6 PSB1 Ð<U+1ED3>ng ý 351 40.9 40.91%
## 7 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 65 7.58 7.58%
## 8 PSB2 R<U+1EA5>t không d<U+1ED3>ng ý 11 1.28 1.28%
## 9 PSB2 Không d<U+1ED3>ng ý 18 2.1 2.1%
## 10 PSB2 Khá không d<U+1ED3>ng ý 26 3.03 3.03%
## # ... with 284 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: 42 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 1.28 1.28%
## 3 PSB3 R<U+1EA5>t không d<U+1ED3>ng ý 11 1.28 1.28%
## 4 PSB4 R<U+1EA5>t không d<U+1ED3>ng ý 13 1.52 1.52%
## 5 PSQT1 R<U+1EA5>t không d<U+1ED3>ng ý 6 0.7 0.7%
## 6 PSQT2 R<U+1EA5>t không d<U+1ED3>ng ý 24 2.8 2.8%
## 7 PSQT3 R<U+1EA5>t không d<U+1ED3>ng ý 25 2.91 2.91%
## 8 PSQT4 R<U+1EA5>t không d<U+1ED3>ng ý 25 2.91 2.91%
## 9 PSQC1 R<U+1EA5>t không d<U+1ED3>ng ý 24 2.8 2.8%
## 10 PSQC2 R<U+1EA5>t không d<U+1ED3>ng ý 25 2.91 2.91%
## # ... with 32 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Không d<U+1ED3>ng ý 19 2.21 2.21%
## 2 PSB2 Không d<U+1ED3>ng ý 18 2.1 2.1%
## 3 PSB3 Không d<U+1ED3>ng ý 10 1.17 1.17%
## 4 PSB4 Không d<U+1ED3>ng ý 15 1.75 1.75%
## 5 PSQT1 Không d<U+1ED3>ng ý 20 2.33 2.33%
## 6 PSQT2 Không d<U+1ED3>ng ý 8 0.93 0.93%
## 7 PSQT3 Không d<U+1ED3>ng ý 17 1.98 1.98%
## 8 PSQT4 Không d<U+1ED3>ng ý 20 2.33 2.33%
## 9 PSQC1 Không d<U+1ED3>ng ý 19 2.21 2.21%
## 10 PSQC2 Không d<U+1ED3>ng ý 18 2.1 2.1%
## # ... with 32 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Khá không d<U+1ED3>ng ý 33 3.85 3.85%
## 2 PSB2 Khá không d<U+1ED3>ng ý 26 3.03 3.03%
## 3 PSB3 Khá không d<U+1ED3>ng ý 21 2.45 2.45%
## 4 PSB4 Khá không d<U+1ED3>ng ý 26 3.03 3.03%
## 5 PSQT1 Khá không d<U+1ED3>ng ý 20 2.33 2.33%
## 6 PSQT2 Khá không d<U+1ED3>ng ý 13 1.52 1.52%
## 7 PSQT3 Khá không d<U+1ED3>ng ý 21 2.45 2.45%
## 8 PSQT4 Khá không d<U+1ED3>ng ý 22 2.56 2.56%
## 9 PSQC1 Khá không d<U+1ED3>ng ý 22 2.56 2.56%
## 10 PSQC2 Khá không d<U+1ED3>ng ý 37 4.31 4.31%
## # ... with 32 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Bình thu<U+1EDD>ng 154 18.0 17.95%
## 2 PSB2 Bình thu<U+1EDD>ng 158 18.4 18.41%
## 3 PSB3 Bình thu<U+1EDD>ng 128 14.9 14.92%
## 4 PSB4 Bình thu<U+1EDD>ng 113 13.2 13.17%
## 5 PSQT1 Bình thu<U+1EDD>ng 104 12.1 12.12%
## 6 PSQT2 Bình thu<U+1EDD>ng 60 6.99 6.99%
## 7 PSQT3 Bình thu<U+1EDD>ng 67 7.81 7.81%
## 8 PSQT4 Bình thu<U+1EDD>ng 77 8.97 8.97%
## 9 PSQC1 Bình thu<U+1EDD>ng 93 10.8 10.84%
## 10 PSQC2 Bình thu<U+1EDD>ng 97 11.3 11.31%
## # ... with 32 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Khá d<U+1ED3>ng ý 230 26.8 26.81%
## 2 PSB2 Khá d<U+1ED3>ng ý 214 24.9 24.94%
## 3 PSB3 Khá d<U+1ED3>ng ý 207 24.1 24.13%
## 4 PSB4 Khá d<U+1ED3>ng ý 201 23.4 23.43%
## 5 PSQT1 Khá d<U+1ED3>ng ý 204 23.8 23.78%
## 6 PSQT2 Khá d<U+1ED3>ng ý 127 14.8 14.8%
## 7 PSQT3 Khá d<U+1ED3>ng ý 170 19.8 19.81%
## 8 PSQT4 Khá d<U+1ED3>ng ý 168 19.6 19.58%
## 9 PSQC1 Khá d<U+1ED3>ng ý 167 19.5 19.46%
## 10 PSQC2 Khá d<U+1ED3>ng ý 215 25.1 25.06%
## # ... with 32 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Ð<U+1ED3>ng ý 351 40.9 40.91%
## 2 PSB2 Ð<U+1ED3>ng ý 365 42.5 42.54%
## 3 PSB3 Ð<U+1ED3>ng ý 403 47.0 46.97%
## 4 PSB4 Ð<U+1ED3>ng ý 406 47.3 47.32%
## 5 PSQT1 Ð<U+1ED3>ng ý 420 49.0 48.95%
## 6 PSQT2 Ð<U+1ED3>ng ý 391 45.6 45.57%
## 7 PSQT3 Ð<U+1ED3>ng ý 353 41.1 41.14%
## 8 PSQT4 Ð<U+1ED3>ng ý 354 41.3 41.26%
## 9 PSQC1 Ð<U+1ED3>ng ý 358 41.7 41.72%
## 10 PSQC2 Ð<U+1ED3>ng ý 352 41.0 41.03%
## # ... with 32 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: 42 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 7.58 7.58%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 66 7.69 7.69%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 78 9.09 9.09%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 84 9.79 9.79%
## 5 PSQT1 R<U+1EA5>t d<U+1ED3>ng ý 84 9.79 9.79%
## 6 PSQT2 R<U+1EA5>t d<U+1ED3>ng ý 235 27.4 27.39%
## 7 PSQT3 R<U+1EA5>t d<U+1ED3>ng ý 205 23.9 23.89%
## 8 PSQT4 R<U+1EA5>t d<U+1ED3>ng ý 192 22.4 22.38%
## 9 PSQC1 R<U+1EA5>t d<U+1ED3>ng ý 175 20.4 20.4%
## 10 PSQC2 R<U+1EA5>t d<U+1ED3>ng ý 114 13.3 13.29%
## # ... with 32 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: 42 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 7.58 7.58%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 66 7.69 7.69%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 78 9.09 9.09%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 84 9.79 9.79%
## 5 PSQT1 R<U+1EA5>t d<U+1ED3>ng ý 84 9.79 9.79%
## 6 PSQT2 R<U+1EA5>t d<U+1ED3>ng ý 235 27.4 27.39%
## 7 PSQT3 R<U+1EA5>t d<U+1ED3>ng ý 205 23.9 23.89%
## 8 PSQT4 R<U+1EA5>t d<U+1ED3>ng ý 192 22.4 22.38%
## 9 PSQC1 R<U+1EA5>t d<U+1ED3>ng ý 175 20.4 20.4%
## 10 PSQC2 R<U+1EA5>t d<U+1ED3>ng ý 114 13.3 13.29%
## # ... with 32 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_SEM5)## Res BPE
## 449 6 PSB1
## 450 6 PSB1
## 451 6 PSB1
## 452 5 PSB1
## 453 6 PSB1
## 454 6 PSB1dim(DN_SEM5)## [1] 17220 2attach(DN_SEM5)## The following objects are masked from DN_HCM_SEM5:
##
## BPE, ResDN_SEM5 = within(DN_SEM5, {
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", "PSQT1", "PSQT2", "PSQT3", "PSQT4", "PSQC1", "PSQC2", "PSQC3", "PSQC4", "PSQC5", "PSQC6", "PSQP1", "PSQP2", "PSQP3", "PSQP4", "PSQR1", "PSQR2", "PSQR3", "PSQR4", "SAT1", "SAT2", "SAT3", "IMA1", "IMA2", "IMA3", "IMA4", "IMA5", "PHB1", "PHB2", "PHB3", "PHB4", "PHB5", "PEB1", "PEB2", "PEB3", "PEB4", "PVA1", "PVA2", "PVA3"))
})
str(DN_SEM5)## 'data.frame': 17220 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 6 6 5 6 6 6 7 4 6 ...
## $ BPE: Factor w/ 42 levels "PSB1","PSB2",..: 15 15 15 15 15 15 15 15 15 15 ...summary(DN_SEM5)## Res BPE
## Very Disagree : 93 PSB1 : 410
## Disagree : 237 PSB2 : 410
## Quite Disagree: 617 PSB3 : 410
## Normal :1286 PSB4 : 410
## Quite Agree :4005 PSQT1 : 410
## Agree :7994 PSQT2 : 410
## Very Agree :2988 (Other):14760 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_SEM5 %>%
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 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 PSQC3 PSQC4
## [13] PSQC5 PSQC6 PSQP1 PSQP2 PSQP3 PSQP4 PSQR1 PSQR2 PSQR3 PSQR4 SAT1 SAT2
## [25] SAT3 IMA1 IMA2 IMA3 IMA4 IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEB1
## [37] PEB2 PEB3 PEB4 PVA1 PVA2 PVA3
## 42 Levels: PSB1 PSB2 PSB3 PSB4 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 ... 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: 272 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 2 0.49 0.49%
## 3 PSB1 Quite Disagree 12 2.93 2.93%
## 4 PSB1 Normal 54 13.2 13.17%
## 5 PSB1 Quite Agree 137 33.4 33.41%
## 6 PSB1 Agree 171 41.7 41.71%
## 7 PSB1 Very Agree 33 8.05 8.05%
## 8 PSB2 Quite Disagree 8 1.95 1.95%
## 9 PSB2 Normal 47 11.5 11.46%
## 10 PSB2 Quite Agree 114 27.8 27.8%
## # ... with 262 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: 27 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Disagree 1 0.24 0.24%
## 2 PSQT2 Very Disagree 1 0.24 0.24%
## 3 PSQT3 Very Disagree 1 0.24 0.24%
## 4 PSQC2 Very Disagree 2 0.49 0.49%
## 5 PSQP1 Very Disagree 7 1.71 1.71%
## 6 PSQP2 Very Disagree 4 0.98 0.98%
## 7 PSQP3 Very Disagree 1 0.24 0.24%
## 8 PSQP4 Very Disagree 4 0.98 0.98%
## 9 PSQR1 Very Disagree 4 0.98 0.98%
## 10 PSQR2 Very Disagree 3 0.73 0.73%
## # ... with 17 more rows# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 35 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Disagree 2 0.49 0.49%
## 2 PSB4 Disagree 1 0.24 0.24%
## 3 PSQT3 Disagree 4 0.98 0.98%
## 4 PSQT4 Disagree 6 1.46 1.46%
## 5 PSQC1 Disagree 5 1.22 1.22%
## 6 PSQC2 Disagree 6 1.46 1.46%
## 7 PSQC3 Disagree 5 1.22 1.22%
## 8 PSQC4 Disagree 11 2.68 2.68%
## 9 PSQC6 Disagree 1 0.24 0.24%
## 10 PSQP1 Disagree 9 2.2 2.2%
## # ... with 25 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Quite Disagree 12 2.93 2.93%
## 2 PSB2 Quite Disagree 8 1.95 1.95%
## 3 PSB3 Quite Disagree 5 1.22 1.22%
## 4 PSB4 Quite Disagree 8 1.95 1.95%
## 5 PSQT1 Quite Disagree 5 1.22 1.22%
## 6 PSQT2 Quite Disagree 3 0.73 0.73%
## 7 PSQT3 Quite Disagree 9 2.2 2.2%
## 8 PSQT4 Quite Disagree 14 3.41 3.41%
## 9 PSQC1 Quite Disagree 15 3.66 3.66%
## 10 PSQC2 Quite Disagree 17 4.15 4.15%
## # ... with 32 more rows# For displaying percent of "Normal":
df_odered %>%
filter(Res == "Normal") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Normal 54 13.2 13.17%
## 2 PSB2 Normal 47 11.5 11.46%
## 3 PSB3 Normal 39 9.51 9.51%
## 4 PSB4 Normal 25 6.1 6.1%
## 5 PSQT1 Normal 23 5.61 5.61%
## 6 PSQT2 Normal 14 3.41 3.41%
## 7 PSQT3 Normal 17 4.15 4.15%
## 8 PSQT4 Normal 25 6.1 6.1%
## 9 PSQC1 Normal 26 6.34 6.34%
## 10 PSQC2 Normal 34 8.29 8.29%
## # ... with 32 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Quite Agree 137 33.4 33.41%
## 2 PSB2 Quite Agree 114 27.8 27.8%
## 3 PSB3 Quite Agree 107 26.1 26.1%
## 4 PSB4 Quite Agree 104 25.4 25.37%
## 5 PSQT1 Quite Agree 97 23.7 23.66%
## 6 PSQT2 Quite Agree 77 18.8 18.78%
## 7 PSQT3 Quite Agree 98 23.9 23.9%
## 8 PSQT4 Quite Agree 86 21.0 20.98%
## 9 PSQC1 Quite Agree 89 21.7 21.71%
## 10 PSQC2 Quite Agree 139 33.9 33.9%
## # ... with 32 more rows# For displaying percent of "Agree":
df_odered %>%
filter(Res == "Agree") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Agree 171 41.7 41.71%
## 2 PSB2 Agree 207 50.5 50.49%
## 3 PSB3 Agree 216 52.7 52.68%
## 4 PSB4 Agree 221 53.9 53.9%
## 5 PSQT1 Agree 233 56.8 56.83%
## 6 PSQT2 Agree 210 51.2 51.22%
## 7 PSQT3 Agree 187 45.6 45.61%
## 8 PSQT4 Agree 193 47.1 47.07%
## 9 PSQC1 Agree 193 47.1 47.07%
## 10 PSQC2 Agree 159 38.8 38.78%
## # ... with 32 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Agree 33 8.05 8.05%
## 2 PSB2 Very Agree 34 8.29 8.29%
## 3 PSB3 Very Agree 43 10.5 10.49%
## 4 PSB4 Very Agree 51 12.4 12.44%
## 5 PSQT1 Very Agree 52 12.7 12.68%
## 6 PSQT2 Very Agree 105 25.6 25.61%
## 7 PSQT3 Very Agree 94 22.9 22.93%
## 8 PSQT4 Very Agree 86 21.0 20.98%
## 9 PSQC1 Very Agree 82 20 20%
## 10 PSQC2 Very Agree 53 12.9 12.93%
## # ... with 32 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") -> df_for_text
df_for_text## # A tibble: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Agree 33 8.05 8.05%
## 2 PSB2 Very Agree 34 8.29 8.29%
## 3 PSB3 Very Agree 43 10.5 10.49%
## 4 PSB4 Very Agree 51 12.4 12.44%
## 5 PSQT1 Very Agree 52 12.7 12.68%
## 6 PSQT2 Very Agree 105 25.6 25.61%
## 7 PSQT3 Very Agree 94 22.9 22.93%
## 8 PSQT4 Very Agree 86 21.0 20.98%
## 9 PSQC1 Very Agree 82 20 20%
## 10 PSQC2 Very Agree 53 12.9 12.93%
## # ... with 32 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_SEM5_viet = within(DN_SEM5, {
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", "PSQT1", "PSQT2", "PSQT3", "PSQT4", "PSQC1", "PSQC2", "PSQC3", "PSQC4", "PSQC5", "PSQC6", "PSQP1", "PSQP2", "PSQP3", "PSQP4", "PSQR1", "PSQR2", "PSQR3", "PSQR4", "SAT1", "SAT2", "SAT3", "IMA1", "IMA2", "IMA3", "IMA4", "IMA5", "PHB1", "PHB2", "PHB3", "PHB4", "PHB5", "PEB1", "PEB2", "PEB3", "PEB4", "PVA1", "PVA2", "PVA3"))
})
str(DN_SEM5_viet)## 'data.frame': 17220 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/ 42 levels "PSB1","PSB2",..: 15 15 15 15 15 15 15 15 15 15 ...summary(DN_SEM5_viet)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 93 PSB1 : 410
## Không d<U+1ED3>ng ý : 237 PSB2 : 410
## Khá không d<U+1ED3>ng ý : 617 PSB3 : 410
## Bình thu<U+1EDD>ng :1286 PSB4 : 410
## Khá d<U+1ED3>ng ý :4005 PSQT1 : 410
## Ð<U+1ED3>ng ý :7994 PSQT2 : 410
## R<U+1EA5>t d<U+1ED3>ng ý :2988 (Other):14760 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_SEM5_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 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 PSQC3 PSQC4
## [13] PSQC5 PSQC6 PSQP1 PSQP2 PSQP3 PSQP4 PSQR1 PSQR2 PSQR3 PSQR4 SAT1 SAT2
## [25] SAT3 IMA1 IMA2 IMA3 IMA4 IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEB1
## [37] PEB2 PEB3 PEB4 PVA1 PVA2 PVA3
## 42 Levels: PSB1 PSB2 PSB3 PSB4 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 ... 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: 272 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 2 0.49 0.49%
## 3 PSB1 Khá không d<U+1ED3>ng ý 12 2.93 2.93%
## 4 PSB1 Bình thu<U+1EDD>ng 54 13.2 13.17%
## 5 PSB1 Khá d<U+1ED3>ng ý 137 33.4 33.41%
## 6 PSB1 Ð<U+1ED3>ng ý 171 41.7 41.71%
## 7 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 33 8.05 8.05%
## 8 PSB2 Khá không d<U+1ED3>ng ý 8 1.95 1.95%
## 9 PSB2 Bình thu<U+1EDD>ng 47 11.5 11.46%
## 10 PSB2 Khá d<U+1ED3>ng ý 114 27.8 27.8%
## # ... with 262 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 \u0111\u1ED1i v\u1EDBi 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: 27 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 PSQT2 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%
## 3 PSQT3 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%
## 4 PSQC2 R<U+1EA5>t không d<U+1ED3>ng ý 2 0.49 0.49%
## 5 PSQP1 R<U+1EA5>t không d<U+1ED3>ng ý 7 1.71 1.71%
## 6 PSQP2 R<U+1EA5>t không d<U+1ED3>ng ý 4 0.98 0.98%
## 7 PSQP3 R<U+1EA5>t không d<U+1ED3>ng ý 1 0.24 0.24%
## 8 PSQP4 R<U+1EA5>t không d<U+1ED3>ng ý 4 0.98 0.98%
## 9 PSQR1 R<U+1EA5>t không d<U+1ED3>ng ý 4 0.98 0.98%
## 10 PSQR2 R<U+1EA5>t không d<U+1ED3>ng ý 3 0.73 0.73%
## # ... with 17 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: 35 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Không d<U+1ED3>ng ý 2 0.49 0.49%
## 2 PSB4 Không d<U+1ED3>ng ý 1 0.24 0.24%
## 3 PSQT3 Không d<U+1ED3>ng ý 4 0.98 0.98%
## 4 PSQT4 Không d<U+1ED3>ng ý 6 1.46 1.46%
## 5 PSQC1 Không d<U+1ED3>ng ý 5 1.22 1.22%
## 6 PSQC2 Không d<U+1ED3>ng ý 6 1.46 1.46%
## 7 PSQC3 Không d<U+1ED3>ng ý 5 1.22 1.22%
## 8 PSQC4 Không d<U+1ED3>ng ý 11 2.68 2.68%
## 9 PSQC6 Không d<U+1ED3>ng ý 1 0.24 0.24%
## 10 PSQP1 Không d<U+1ED3>ng ý 9 2.2 2.2%
## # ... with 25 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Khá không d<U+1ED3>ng ý 12 2.93 2.93%
## 2 PSB2 Khá không d<U+1ED3>ng ý 8 1.95 1.95%
## 3 PSB3 Khá không d<U+1ED3>ng ý 5 1.22 1.22%
## 4 PSB4 Khá không d<U+1ED3>ng ý 8 1.95 1.95%
## 5 PSQT1 Khá không d<U+1ED3>ng ý 5 1.22 1.22%
## 6 PSQT2 Khá không d<U+1ED3>ng ý 3 0.73 0.73%
## 7 PSQT3 Khá không d<U+1ED3>ng ý 9 2.2 2.2%
## 8 PSQT4 Khá không d<U+1ED3>ng ý 14 3.41 3.41%
## 9 PSQC1 Khá không d<U+1ED3>ng ý 15 3.66 3.66%
## 10 PSQC2 Khá không d<U+1ED3>ng ý 17 4.15 4.15%
## # ... with 32 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Bình thu<U+1EDD>ng 54 13.2 13.17%
## 2 PSB2 Bình thu<U+1EDD>ng 47 11.5 11.46%
## 3 PSB3 Bình thu<U+1EDD>ng 39 9.51 9.51%
## 4 PSB4 Bình thu<U+1EDD>ng 25 6.1 6.1%
## 5 PSQT1 Bình thu<U+1EDD>ng 23 5.61 5.61%
## 6 PSQT2 Bình thu<U+1EDD>ng 14 3.41 3.41%
## 7 PSQT3 Bình thu<U+1EDD>ng 17 4.15 4.15%
## 8 PSQT4 Bình thu<U+1EDD>ng 25 6.1 6.1%
## 9 PSQC1 Bình thu<U+1EDD>ng 26 6.34 6.34%
## 10 PSQC2 Bình thu<U+1EDD>ng 34 8.29 8.29%
## # ... with 32 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Khá d<U+1ED3>ng ý 137 33.4 33.41%
## 2 PSB2 Khá d<U+1ED3>ng ý 114 27.8 27.8%
## 3 PSB3 Khá d<U+1ED3>ng ý 107 26.1 26.1%
## 4 PSB4 Khá d<U+1ED3>ng ý 104 25.4 25.37%
## 5 PSQT1 Khá d<U+1ED3>ng ý 97 23.7 23.66%
## 6 PSQT2 Khá d<U+1ED3>ng ý 77 18.8 18.78%
## 7 PSQT3 Khá d<U+1ED3>ng ý 98 23.9 23.9%
## 8 PSQT4 Khá d<U+1ED3>ng ý 86 21.0 20.98%
## 9 PSQC1 Khá d<U+1ED3>ng ý 89 21.7 21.71%
## 10 PSQC2 Khá d<U+1ED3>ng ý 139 33.9 33.9%
## # ... with 32 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Ð<U+1ED3>ng ý 171 41.7 41.71%
## 2 PSB2 Ð<U+1ED3>ng ý 207 50.5 50.49%
## 3 PSB3 Ð<U+1ED3>ng ý 216 52.7 52.68%
## 4 PSB4 Ð<U+1ED3>ng ý 221 53.9 53.9%
## 5 PSQT1 Ð<U+1ED3>ng ý 233 56.8 56.83%
## 6 PSQT2 Ð<U+1ED3>ng ý 210 51.2 51.22%
## 7 PSQT3 Ð<U+1ED3>ng ý 187 45.6 45.61%
## 8 PSQT4 Ð<U+1ED3>ng ý 193 47.1 47.07%
## 9 PSQC1 Ð<U+1ED3>ng ý 193 47.1 47.07%
## 10 PSQC2 Ð<U+1ED3>ng ý 159 38.8 38.78%
## # ... with 32 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: 42 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 8.05 8.05%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 34 8.29 8.29%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 43 10.5 10.49%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 51 12.4 12.44%
## 5 PSQT1 R<U+1EA5>t d<U+1ED3>ng ý 52 12.7 12.68%
## 6 PSQT2 R<U+1EA5>t d<U+1ED3>ng ý 105 25.6 25.61%
## 7 PSQT3 R<U+1EA5>t d<U+1ED3>ng ý 94 22.9 22.93%
## 8 PSQT4 R<U+1EA5>t d<U+1ED3>ng ý 86 21.0 20.98%
## 9 PSQC1 R<U+1EA5>t d<U+1ED3>ng ý 82 20 20%
## 10 PSQC2 R<U+1EA5>t d<U+1ED3>ng ý 53 12.9 12.93%
## # ... with 32 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: 42 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 8.05 8.05%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 34 8.29 8.29%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 43 10.5 10.49%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 51 12.4 12.44%
## 5 PSQT1 R<U+1EA5>t d<U+1ED3>ng ý 52 12.7 12.68%
## 6 PSQT2 R<U+1EA5>t d<U+1ED3>ng ý 105 25.6 25.61%
## 7 PSQT3 R<U+1EA5>t d<U+1ED3>ng ý 94 22.9 22.93%
## 8 PSQT4 R<U+1EA5>t d<U+1ED3>ng ý 86 21.0 20.98%
## 9 PSQC1 R<U+1EA5>t d<U+1ED3>ng ý 82 20 20%
## 10 PSQC2 R<U+1EA5>t d<U+1ED3>ng ý 53 12.9 12.93%
## # ... with 32 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_SEM5)## Res BPE
## 1 6 PSB1
## 2 3 PSB1
## 3 2 PSB1
## 4 5 PSB1
## 5 2 PSB1
## 6 2 PSB1dim(HCM_SEM5)## [1] 18816 2attach(HCM_SEM5)## The following objects are masked from DN_SEM5:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5:
##
## BPE, ResHCM_SEM5 = within(HCM_SEM5, {
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", "PSQT1", "PSQT2", "PSQT3", "PSQT4", "PSQC1", "PSQC2", "PSQC3", "PSQC4", "PSQC5", "PSQC6", "PSQP1", "PSQP2", "PSQP3", "PSQP4", "PSQR1", "PSQR2", "PSQR3", "PSQR4", "SAT1", "SAT2", "SAT3", "IMA1", "IMA2", "IMA3", "IMA4", "IMA5", "PHB1", "PHB2", "PHB3", "PHB4", "PHB5", "PEB1", "PEB2", "PEB3", "PEB4", "PVA1", "PVA2", "PVA3"))
})
str(HCM_SEM5)## 'data.frame': 18816 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 3 2 5 2 2 1 6 2 4 ...
## $ BPE: Factor w/ 42 levels "PSB1","PSB2",..: 15 15 15 15 15 15 15 15 15 15 ...summary(HCM_SEM5)## Res BPE
## Very Disagree : 604 PSB1 : 448
## Disagree :1080 PSB2 : 448
## Quite Disagree: 885 PSB3 : 448
## Normal :2864 PSB4 : 448
## Quite Agree :3473 PSQT1 : 448
## Agree :7680 PSQT2 : 448
## Very Agree :2230 (Other):16128 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
HCM_SEM5 %>%
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 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 PSQC3 PSQC4
## [13] PSQC5 PSQC6 PSQP1 PSQP2 PSQP3 PSQP4 PSQR1 PSQR2 PSQR3 PSQR4 SAT1 SAT2
## [25] SAT3 IMA1 IMA2 IMA3 IMA4 IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEB1
## [37] PEB2 PEB3 PEB4 PVA1 PVA2 PVA3
## 42 Levels: PSB1 PSB2 PSB3 PSB4 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 ... 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: 294 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 284 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: 42 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 PSQT1 Very Disagree 6 1.34 1.34%
## 6 PSQT2 Very Disagree 23 5.13 5.13%
## 7 PSQT3 Very Disagree 24 5.36 5.36%
## 8 PSQT4 Very Disagree 25 5.58 5.58%
## 9 PSQC1 Very Disagree 24 5.36 5.36%
## 10 PSQC2 Very Disagree 23 5.13 5.13%
## # ... with 32 more rows# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 0) -> df_for_text2
df_for_text2## # A tibble: 42 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 PSQT1 Disagree 20 4.46 4.46%
## 6 PSQT2 Disagree 8 1.79 1.79%
## 7 PSQT3 Disagree 13 2.9 2.9%
## 8 PSQT4 Disagree 14 3.12 3.12%
## 9 PSQC1 Disagree 14 3.12 3.12%
## 10 PSQC2 Disagree 12 2.68 2.68%
## # ... with 32 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: 42 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 PSQT1 Quite Disagree 15 3.35 3.35%
## 6 PSQT2 Quite Disagree 10 2.23 2.23%
## 7 PSQT3 Quite Disagree 12 2.68 2.68%
## 8 PSQT4 Quite Disagree 8 1.79 1.79%
## 9 PSQC1 Quite Disagree 7 1.56 1.56%
## 10 PSQC2 Quite Disagree 20 4.46 4.46%
## # ... with 32 more rows# For displaying percent of "Normal":
df_odered %>%
filter(Res == "Normal") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 42 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 PSQT1 Normal 81 18.1 18.08%
## 6 PSQT2 Normal 46 10.3 10.27%
## 7 PSQT3 Normal 50 11.2 11.16%
## 8 PSQT4 Normal 52 11.6 11.61%
## 9 PSQC1 Normal 67 15.0 14.96%
## 10 PSQC2 Normal 63 14.1 14.06%
## # ... with 32 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: 42 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 PSQT1 Quite Agree 107 23.9 23.88%
## 6 PSQT2 Quite Agree 50 11.2 11.16%
## 7 PSQT3 Quite Agree 72 16.1 16.07%
## 8 PSQT4 Quite Agree 82 18.3 18.3%
## 9 PSQC1 Quite Agree 78 17.4 17.41%
## 10 PSQC2 Quite Agree 76 17.0 16.96%
## # ... with 32 more rows# For displaying percent of "Agree":
df_odered %>%
filter(Res == "Agree") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 42 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 PSQT1 Agree 187 41.7 41.74%
## 6 PSQT2 Agree 181 40.4 40.4%
## 7 PSQT3 Agree 166 37.0 37.05%
## 8 PSQT4 Agree 161 35.9 35.94%
## 9 PSQC1 Agree 165 36.8 36.83%
## 10 PSQC2 Agree 193 43.1 43.08%
## # ... with 32 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: 42 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 PSQT1 Very Agree 32 7.14 7.14%
## 6 PSQT2 Very Agree 130 29.0 29.02%
## 7 PSQT3 Very Agree 111 24.8 24.78%
## 8 PSQT4 Very Agree 106 23.7 23.66%
## 9 PSQC1 Very Agree 93 20.8 20.76%
## 10 PSQC2 Very Agree 61 13.6 13.62%
## # ... with 32 more rows# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") -> df_for_text
df_for_text## # A tibble: 42 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 PSQT1 Very Agree 32 7.14 7.14%
## 6 PSQT2 Very Agree 130 29.0 29.02%
## 7 PSQT3 Very Agree 111 24.8 24.78%
## 8 PSQT4 Very Agree 106 23.7 23.66%
## 9 PSQC1 Very Agree 93 20.8 20.76%
## 10 PSQC2 Very Agree 61 13.6 13.62%
## # ... with 32 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_SEM5_viet = within(HCM_SEM5, {
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", "PSQT1", "PSQT2", "PSQT3", "PSQT4", "PSQC1", "PSQC2", "PSQC3", "PSQC4", "PSQC5", "PSQC6", "PSQP1", "PSQP2", "PSQP3", "PSQP4", "PSQR1", "PSQR2", "PSQR3", "PSQR4", "SAT1", "SAT2", "SAT3", "IMA1", "IMA2", "IMA3", "IMA4", "IMA5", "PHB1", "PHB2", "PHB3", "PHB4", "PHB5", "PEB1", "PEB2", "PEB3", "PEB4", "PVA1", "PVA2", "PVA3"))
})
str(HCM_SEM5_viet)## 'data.frame': 18816 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/ 42 levels "PSB1","PSB2",..: 15 15 15 15 15 15 15 15 15 15 ...summary(HCM_SEM5_viet)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 604 PSB1 : 448
## Không d<U+1ED3>ng ý :1080 PSB2 : 448
## Khá không d<U+1ED3>ng ý : 885 PSB3 : 448
## Bình thu<U+1EDD>ng :2864 PSB4 : 448
## Khá d<U+1ED3>ng ý :3473 PSQT1 : 448
## Ð<U+1ED3>ng ý :7680 PSQT2 : 448
## R<U+1EA5>t d<U+1ED3>ng ý :2230 (Other):16128 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
HCM_SEM5_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 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 PSQC3 PSQC4
## [13] PSQC5 PSQC6 PSQP1 PSQP2 PSQP3 PSQP4 PSQR1 PSQR2 PSQR3 PSQR4 SAT1 SAT2
## [25] SAT3 IMA1 IMA2 IMA3 IMA4 IMA5 PHB1 PHB2 PHB3 PHB4 PHB5 PEB1
## [37] PEB2 PEB3 PEB4 PVA1 PVA2 PVA3
## 42 Levels: PSB1 PSB2 PSB3 PSB4 PSQT1 PSQT2 PSQT3 PSQT4 PSQC1 PSQC2 ... 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: 294 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 284 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 \u1EDF 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$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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: 42 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 PSQT1 R<U+1EA5>t không d<U+1ED3>ng ý 6 1.34 1.34%
## 6 PSQT2 R<U+1EA5>t không d<U+1ED3>ng ý 23 5.13 5.13%
## 7 PSQT3 R<U+1EA5>t không d<U+1ED3>ng ý 24 5.36 5.36%
## 8 PSQT4 R<U+1EA5>t không d<U+1ED3>ng ý 25 5.58 5.58%
## 9 PSQC1 R<U+1EA5>t không d<U+1ED3>ng ý 24 5.36 5.36%
## 10 PSQC2 R<U+1EA5>t không d<U+1ED3>ng ý 23 5.13 5.13%
## # ... with 32 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: 42 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 PSQT1 Không d<U+1ED3>ng ý 20 4.46 4.46%
## 6 PSQT2 Không d<U+1ED3>ng ý 8 1.79 1.79%
## 7 PSQT3 Không d<U+1ED3>ng ý 13 2.9 2.9%
## 8 PSQT4 Không d<U+1ED3>ng ý 14 3.12 3.12%
## 9 PSQC1 Không d<U+1ED3>ng ý 14 3.12 3.12%
## 10 PSQC2 Không d<U+1ED3>ng ý 12 2.68 2.68%
## # ... with 32 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: 42 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 PSQT1 Khá không d<U+1ED3>ng ý 15 3.35 3.35%
## 6 PSQT2 Khá không d<U+1ED3>ng ý 10 2.23 2.23%
## 7 PSQT3 Khá không d<U+1ED3>ng ý 12 2.68 2.68%
## 8 PSQT4 Khá không d<U+1ED3>ng ý 8 1.79 1.79%
## 9 PSQC1 Khá không d<U+1ED3>ng ý 7 1.56 1.56%
## 10 PSQC2 Khá không d<U+1ED3>ng ý 20 4.46 4.46%
## # ... with 32 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: 42 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 PSQT1 Bình thu<U+1EDD>ng 81 18.1 18.08%
## 6 PSQT2 Bình thu<U+1EDD>ng 46 10.3 10.27%
## 7 PSQT3 Bình thu<U+1EDD>ng 50 11.2 11.16%
## 8 PSQT4 Bình thu<U+1EDD>ng 52 11.6 11.61%
## 9 PSQC1 Bình thu<U+1EDD>ng 67 15.0 14.96%
## 10 PSQC2 Bình thu<U+1EDD>ng 63 14.1 14.06%
## # ... with 32 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: 42 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 PSQT1 Khá d<U+1ED3>ng ý 107 23.9 23.88%
## 6 PSQT2 Khá d<U+1ED3>ng ý 50 11.2 11.16%
## 7 PSQT3 Khá d<U+1ED3>ng ý 72 16.1 16.07%
## 8 PSQT4 Khá d<U+1ED3>ng ý 82 18.3 18.3%
## 9 PSQC1 Khá d<U+1ED3>ng ý 78 17.4 17.41%
## 10 PSQC2 Khá d<U+1ED3>ng ý 76 17.0 16.96%
## # ... with 32 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: 42 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 PSQT1 Ð<U+1ED3>ng ý 187 41.7 41.74%
## 6 PSQT2 Ð<U+1ED3>ng ý 181 40.4 40.4%
## 7 PSQT3 Ð<U+1ED3>ng ý 166 37.0 37.05%
## 8 PSQT4 Ð<U+1ED3>ng ý 161 35.9 35.94%
## 9 PSQC1 Ð<U+1ED3>ng ý 165 36.8 36.83%
## 10 PSQC2 Ð<U+1ED3>ng ý 193 43.1 43.08%
## # ... with 32 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: 42 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 PSQT1 R<U+1EA5>t d<U+1ED3>ng ý 32 7.14 7.14%
## 6 PSQT2 R<U+1EA5>t d<U+1ED3>ng ý 130 29.0 29.02%
## 7 PSQT3 R<U+1EA5>t d<U+1ED3>ng ý 111 24.8 24.78%
## 8 PSQT4 R<U+1EA5>t d<U+1ED3>ng ý 106 23.7 23.66%
## 9 PSQC1 R<U+1EA5>t d<U+1ED3>ng ý 93 20.8 20.76%
## 10 PSQC2 R<U+1EA5>t d<U+1ED3>ng ý 61 13.6 13.62%
## # ... with 32 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: 42 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 PSQT1 R<U+1EA5>t d<U+1ED3>ng ý 32 7.14 7.14%
## 6 PSQT2 R<U+1EA5>t d<U+1ED3>ng ý 130 29.0 29.02%
## 7 PSQT3 R<U+1EA5>t d<U+1ED3>ng ý 111 24.8 24.78%
## 8 PSQT4 R<U+1EA5>t d<U+1ED3>ng ý 106 23.7 23.66%
## 9 PSQC1 R<U+1EA5>t d<U+1ED3>ng ý 93 20.8 20.76%
## 10 PSQC2 R<U+1EA5>t d<U+1ED3>ng ý 61 13.6 13.62%
## # ... with 32 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$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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. Vẽ biểu đồ likert cho dữ liệu của 2 thành phố theo từng constructs 3.8.1. Construct PSB - Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct PSQ
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM5_PSB)## Res BPE
## 1 6 PSB1
## 2 3 PSB1
## 3 2 PSB1
## 4 5 PSB1
## 5 2 PSB1
## 6 2 PSB1dim(DN_HCM_SEM5_PSB)## [1] 3432 2attach(DN_HCM_SEM5_PSB)## The following objects are masked from HCM_SEM5:
##
## BPE, Res## The following objects are masked from DN_SEM5:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5:
##
## BPE, ResDN_HCM_SEM5_PSB = within(DN_HCM_SEM5_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_SEM5_PSB)## 'data.frame': 3432 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_SEM5_PSB)## Res BPE
## Very Disagree : 139 PSB1:858
## Disagree : 295 PSB2:858
## Quite Disagree: 190 PSB3:858
## Normal : 376 PSB4:858
## Quite Agree : 562
## Agree :1514
## Very Agree : 356 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_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 57 6.64 6.64%
## 2 PSB1 Disagree 130 15.2 15.15%
## 3 PSB1 Quite Disagree 53 6.18 6.18%
## 4 PSB1 Normal 80 9.32 9.32%
## 5 PSB1 Quite Agree 127 14.8 14.8%
## 6 PSB1 Agree 329 38.3 38.34%
## 7 PSB1 Very Agree 82 9.56 9.56%
## 8 PSB2 Very Disagree 35 4.08 4.08%
## 9 PSB2 Disagree 65 7.58 7.58%
## 10 PSB2 Quite Disagree 51 5.94 5.94%
## # ... 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 on Bus (PSQT) 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$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSB1 Very Disagree 57 6.64 6.64%
## 2 PSB2 Very Disagree 35 4.08 4.08%
## 3 PSB3 Very Disagree 15 1.75 1.75%
## 4 PSB4 Very Disagree 32 3.73 3.73%# 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 130 15.2 15.15%
## 2 PSB2 Disagree 65 7.58 7.58%
## 3 PSB4 Disagree 78 9.09 9.09%# 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 6.18 6.18%
## 2 PSB2 Quite Disagree 51 5.94 5.94%
## 3 PSB3 Quite Disagree 30 3.5 3.5%
## 4 PSB4 Quite Disagree 56 6.53 6.53%# 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 9.32 9.32%
## 2 PSB2 Normal 85 9.91 9.91%
## 3 PSB3 Normal 66 7.69 7.69%
## 4 PSB4 Normal 145 16.9 16.9%# 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 14.8 14.8%
## 2 PSB2 Quite Agree 126 14.7 14.69%
## 3 PSB3 Quite Agree 148 17.2 17.25%
## 4 PSB4 Quite Agree 161 18.8 18.76%# 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 329 38.3 38.34%
## 2 PSB2 Agree 402 46.8 46.85%
## 3 PSB3 Agree 474 55.2 55.24%
## 4 PSB4 Agree 309 36.0 36.01%# 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 82 9.56 9.56%
## 2 PSB2 Very Agree 94 11.0 10.96%
## 3 PSB3 Very Agree 103 12 12%
## 4 PSB4 Very Agree 77 8.97 8.97%# 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 82 9.56 9.56%
## 2 PSB2 Very Agree 94 11.0 10.96%
## 3 PSB3 Very Agree 103 12 12%
## 4 PSB4 Very Agree 77 8.97 8.97%# 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 = 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 PSB - Nhãn tiếng việt
DN_HCM_SEM5_PSB_v = within(DN_HCM_SEM5_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_SEM5_PSB_v)## 'data.frame': 3432 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_SEM5_PSB_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 139 PSB1:858
## Không d<U+1ED3>ng ý : 295 PSB2:858
## Khá không d<U+1ED3>ng ý : 190 PSB3:858
## Bình thu<U+1EDD>ng : 376 PSB4:858
## Khá d<U+1ED3>ng ý : 562
## Ð<U+1ED3>ng ý :1514
## R<U+1EA5>t d<U+1ED3>ng ý : 356 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_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 57 6.64 6.64%
## 2 PSB1 Không d<U+1ED3>ng ý 130 15.2 15.15%
## 3 PSB1 Khá không d<U+1ED3>ng ý 53 6.18 6.18%
## 4 PSB1 Bình thu<U+1EDD>ng 80 9.32 9.32%
## 5 PSB1 Khá d<U+1ED3>ng ý 127 14.8 14.8%
## 6 PSB1 Ð<U+1ED3>ng ý 329 38.3 38.34%
## 7 PSB1 R<U+1EA5>t d<U+1ED3>ng ý 82 9.56 9.56%
## 8 PSB2 R<U+1EA5>t không d<U+1ED3>ng ý 35 4.08 4.08%
## 9 PSB2 Không d<U+1ED3>ng ý 65 7.58 7.58%
## 10 PSB2 Khá không d<U+1ED3>ng ý 51 5.94 5.94%
## # ... 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 c\u1EE7a h\u00E0nh kh\u00E1ch v\u1EC1 an to\u00E0n tr\u00EAn xe (PSB) \u0111\u1ED1i v\u1EDBi 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_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 57 6.64 6.64%
## 2 PSB2 R<U+1EA5>t không d<U+1ED3>ng ý 35 4.08 4.08%
## 3 PSB3 R<U+1EA5>t không d<U+1ED3>ng ý 15 1.75 1.75%
## 4 PSB4 R<U+1EA5>t không d<U+1ED3>ng ý 32 3.73 3.73%# 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 130 15.2 15.15%
## 2 PSB2 Không d<U+1ED3>ng ý 65 7.58 7.58%
## 3 PSB3 Không d<U+1ED3>ng ý 22 2.56 2.56%
## 4 PSB4 Không d<U+1ED3>ng ý 78 9.09 9.09%# 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 6.18 6.18%
## 2 PSB2 Khá không d<U+1ED3>ng ý 51 5.94 5.94%
## 3 PSB3 Khá không d<U+1ED3>ng ý 30 3.5 3.5%
## 4 PSB4 Khá không d<U+1ED3>ng ý 56 6.53 6.53%# 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 9.32 9.32%
## 2 PSB2 Bình thu<U+1EDD>ng 85 9.91 9.91%
## 3 PSB3 Bình thu<U+1EDD>ng 66 7.69 7.69%
## 4 PSB4 Bình thu<U+1EDD>ng 145 16.9 16.9%# 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 14.8 14.8%
## 2 PSB2 Khá d<U+1ED3>ng ý 126 14.7 14.69%
## 3 PSB3 Khá d<U+1ED3>ng ý 148 17.2 17.25%
## 4 PSB4 Khá d<U+1ED3>ng ý 161 18.8 18.76%# 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 329 38.3 38.34%
## 2 PSB2 Ð<U+1ED3>ng ý 402 46.8 46.85%
## 3 PSB3 Ð<U+1ED3>ng ý 474 55.2 55.24%
## 4 PSB4 Ð<U+1ED3>ng ý 309 36.0 36.01%# 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 82 9.56 9.56%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 94 11.0 10.96%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 103 12 12%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 77 8.97 8.97%# 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 82 9.56 9.56%
## 2 PSB2 R<U+1EA5>t d<U+1ED3>ng ý 94 11.0 10.96%
## 3 PSB3 R<U+1EA5>t d<U+1ED3>ng ý 103 12 12%
## 4 PSB4 R<U+1EA5>t d<U+1ED3>ng ý 77 8.97 8.97%# 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 = 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$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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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3.8.3. Construct PSQT - Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct PSQ
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM5_PSQT)## Res BPE
## 1 6 PSQT1
## 2 4 PSQT1
## 3 2 PSQT1
## 4 6 PSQT1
## 5 6 PSQT1
## 6 4 PSQT1dim(DN_HCM_SEM5_PSQT)## [1] 3432 2attach(DN_HCM_SEM5_PSQT)## The following objects are masked from DN_HCM_SEM5_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM5:
##
## BPE, Res## The following objects are masked from DN_SEM5:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5:
##
## BPE, ResDN_HCM_SEM5_PSQT = within(DN_HCM_SEM5_PSQT, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("PSQT1", "PSQT2", "PSQT3", "PSQT4"))
})
str(DN_HCM_SEM5_PSQT)## 'data.frame': 3432 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 4 2 6 6 4 2 5 5 7 ...
## $ BPE: Factor w/ 4 levels "PSQT1","PSQT2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM5_PSQT)## Res BPE
## Very Disagree : 76 PSQT1:858
## Disagree : 166 PSQT2:858
## Quite Disagree: 169 PSQT3:858
## Normal : 341 PSQT4:858
## Quite Agree : 740
## Agree :1431
## Very Agree : 509 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_PSQT %>%
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] PSQT4 PSQT1 PSQT3 PSQT2
## Levels: PSQT1 PSQT2 PSQT3 PSQT4# 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 PSQT1 Very Disagree 12 1.4 1.4%
## 2 PSQT1 Disagree 33 3.85 3.85%
## 3 PSQT1 Quite Disagree 38 4.43 4.43%
## 4 PSQT1 Normal 92 10.7 10.72%
## 5 PSQT1 Quite Agree 194 22.6 22.61%
## 6 PSQT1 Agree 362 42.2 42.19%
## 7 PSQT1 Very Agree 127 14.8 14.8%
## 8 PSQT2 Very Disagree 18 2.1 2.1%
## 9 PSQT2 Disagree 45 5.24 5.24%
## 10 PSQT2 Quite Disagree 32 3.73 3.73%
## # ... 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 Service Quality of tangibility (PSQT) 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
## 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: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQT1 Very Disagree 12 1.4 1.4%
## 2 PSQT2 Very Disagree 18 2.1 2.1%
## 3 PSQT3 Very Disagree 30 3.5 3.5%
## 4 PSQT4 Very Disagree 16 1.86 1.86%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 3) -> df_for_text2
df_for_text2## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQT1 Disagree 33 3.85 3.85%
## 2 PSQT2 Disagree 45 5.24 5.24%
## 3 PSQT3 Disagree 45 5.24 5.24%
## 4 PSQT4 Disagree 43 5.01 5.01%# 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 PSQT1 Quite Disagree 38 4.43 4.43%
## 2 PSQT2 Quite Disagree 32 3.73 3.73%
## 3 PSQT3 Quite Disagree 50 5.83 5.83%
## 4 PSQT4 Quite Disagree 49 5.71 5.71%# 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 PSQT1 Normal 92 10.7 10.72%
## 2 PSQT2 Normal 85 9.91 9.91%
## 3 PSQT3 Normal 76 8.86 8.86%
## 4 PSQT4 Normal 88 10.3 10.26%# 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 PSQT1 Quite Agree 194 22.6 22.61%
## 2 PSQT2 Quite Agree 143 16.7 16.67%
## 3 PSQT3 Quite Agree 180 21.0 20.98%
## 4 PSQT4 Quite Agree 223 26.0 25.99%# 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 PSQT1 Agree 362 42.2 42.19%
## 2 PSQT2 Agree 378 44.1 44.06%
## 3 PSQT3 Agree 349 40.7 40.68%
## 4 PSQT4 Agree 342 39.9 39.86%# 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 PSQT1 Very Agree 127 14.8 14.8%
## 2 PSQT2 Very Agree 157 18.3 18.3%
## 3 PSQT3 Very Agree 128 14.9 14.92%
## 4 PSQT4 Very Agree 97 11.3 11.31%# 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 PSQT1 Very Agree 127 14.8 14.8%
## 2 PSQT2 Very Agree 157 18.3 18.3%
## 3 PSQT3 Very Agree 128 14.9 14.92%
## 4 PSQT4 Very Agree 97 11.3 11.31%# 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
3.8.4. Construct PSQT - Nhãn tiếng việt
DN_HCM_SEM5_PSQT_v = within(DN_HCM_SEM5_PSQT, {
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("PSQT1", "PSQT2", "PSQT3", "PSQT4"))
})
str(DN_HCM_SEM5_PSQT_v)## 'data.frame': 3432 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 6 4 2 6 6 4 2 5 5 7 ...
## $ BPE: Factor w/ 4 levels "PSQT1","PSQT2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM5_PSQT_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 76 PSQT1:858
## Không d<U+1ED3>ng ý : 166 PSQT2:858
## Khá không d<U+1ED3>ng ý : 169 PSQT3:858
## Bình thu<U+1EDD>ng : 341 PSQT4:858
## Khá d<U+1ED3>ng ý : 740
## Ð<U+1ED3>ng ý :1431
## R<U+1EA5>t d<U+1ED3>ng ý : 509 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_PSQT_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] PSQT4 PSQT1 PSQT3 PSQT2
## Levels: PSQT1 PSQT2 PSQT3 PSQT4# 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 PSQT1 R<U+1EA5>t không d<U+1ED3>ng ý 12 1.4 1.4%
## 2 PSQT1 Không d<U+1ED3>ng ý 33 3.85 3.85%
## 3 PSQT1 Khá không d<U+1ED3>ng ý 38 4.43 4.43%
## 4 PSQT1 Bình thu<U+1EDD>ng 92 10.7 10.72%
## 5 PSQT1 Khá d<U+1ED3>ng ý 194 22.6 22.61%
## 6 PSQT1 Ð<U+1ED3>ng ý 362 42.2 42.19%
## 7 PSQT1 R<U+1EA5>t d<U+1ED3>ng ý 127 14.8 14.8%
## 8 PSQT2 R<U+1EA5>t không d<U+1ED3>ng ý 18 2.1 2.1%
## 9 PSQT2 Không d<U+1ED3>ng ý 45 5.24 5.24%
## 10 PSQT2 Khá không d<U+1ED3>ng ý 32 3.73 3.73%
## # ... 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 c\u1EE7a h\u00E0nh kh\u00E1ch v\u1EC1 ch\u1EA5t l\u01B0\u1EE3ng d\u1ECBch v\u1EE5 h\u1EEFu h\u00ECnh (PSQT) \u0111\u1ED1i v\u1EDBi 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_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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# 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 PSQT1 R<U+1EA5>t không d<U+1ED3>ng ý 12 1.4 1.4%
## 2 PSQT2 R<U+1EA5>t không d<U+1ED3>ng ý 18 2.1 2.1%
## 3 PSQT3 R<U+1EA5>t không d<U+1ED3>ng ý 30 3.5 3.5%
## 4 PSQT4 R<U+1EA5>t không d<U+1ED3>ng ý 16 1.86 1.86%# 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 PSQT1 Không d<U+1ED3>ng ý 33 3.85 3.85%
## 2 PSQT2 Không d<U+1ED3>ng ý 45 5.24 5.24%
## 3 PSQT3 Không d<U+1ED3>ng ý 45 5.24 5.24%
## 4 PSQT4 Không d<U+1ED3>ng ý 43 5.01 5.01%# 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 PSQT1 Khá không d<U+1ED3>ng ý 38 4.43 4.43%
## 2 PSQT2 Khá không d<U+1ED3>ng ý 32 3.73 3.73%
## 3 PSQT3 Khá không d<U+1ED3>ng ý 50 5.83 5.83%
## 4 PSQT4 Khá không d<U+1ED3>ng ý 49 5.71 5.71%# 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 PSQT1 Bình thu<U+1EDD>ng 92 10.7 10.72%
## 2 PSQT2 Bình thu<U+1EDD>ng 85 9.91 9.91%
## 3 PSQT3 Bình thu<U+1EDD>ng 76 8.86 8.86%
## 4 PSQT4 Bình thu<U+1EDD>ng 88 10.3 10.26%# 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 PSQT1 Khá d<U+1ED3>ng ý 194 22.6 22.61%
## 2 PSQT2 Khá d<U+1ED3>ng ý 143 16.7 16.67%
## 3 PSQT3 Khá d<U+1ED3>ng ý 180 21.0 20.98%
## 4 PSQT4 Khá d<U+1ED3>ng ý 223 26.0 25.99%# 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 PSQT1 Ð<U+1ED3>ng ý 362 42.2 42.19%
## 2 PSQT2 Ð<U+1ED3>ng ý 378 44.1 44.06%
## 3 PSQT3 Ð<U+1ED3>ng ý 349 40.7 40.68%
## 4 PSQT4 Ð<U+1ED3>ng ý 342 39.9 39.86%# 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 PSQT1 R<U+1EA5>t d<U+1ED3>ng ý 127 14.8 14.8%
## 2 PSQT2 R<U+1EA5>t d<U+1ED3>ng ý 157 18.3 18.3%
## 3 PSQT3 R<U+1EA5>t d<U+1ED3>ng ý 128 14.9 14.92%
## 4 PSQT4 R<U+1EA5>t d<U+1ED3>ng ý 97 11.3 11.31%# 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 PSQT1 R<U+1EA5>t d<U+1ED3>ng ý 127 14.8 14.8%
## 2 PSQT2 R<U+1EA5>t d<U+1ED3>ng ý 157 18.3 18.3%
## 3 PSQT3 R<U+1EA5>t d<U+1ED3>ng ý 128 14.9 14.92%
## 4 PSQT4 R<U+1EA5>t d<U+1ED3>ng ý 97 11.3 11.31%# 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
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3.8.5. Construct PSQC-Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct LIN
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM5_PSQC)## Res BPE
## 1 6 PSQC1
## 2 4 PSQC1
## 3 2 PSQC1
## 4 5 PSQC1
## 5 6 PSQC1
## 6 5 PSQC1dim(DN_HCM_SEM5_PSQC)## [1] 5148 2attach(DN_HCM_SEM5_PSQC)## The following objects are masked from DN_HCM_SEM5_PSQT:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM5:
##
## BPE, Res## The following objects are masked from DN_SEM5:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5:
##
## BPE, ResDN_HCM_SEM5_PSQC = within(DN_HCM_SEM5_PSQC, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("PSQC1", "PSQC2", "PSQC3", "PSQC4", "PSQC5", "PSQC6"))
})
str(DN_HCM_SEM5_PSQC)## 'data.frame': 5148 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 4 2 5 6 5 2 6 3 7 ...
## $ BPE: Factor w/ 6 levels "PSQC1","PSQC2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM5_PSQC)## Res BPE
## Very Disagree : 84 PSQC1:858
## Disagree : 233 PSQC2:858
## Quite Disagree: 268 PSQC3:858
## Normal : 773 PSQC4:858
## Quite Agree :1030 PSQC5:858
## Agree :2169 PSQC6:858
## Very Agree : 591 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_PSQC %>%
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] PSQC4 PSQC5 PSQC3 PSQC1 PSQC2 PSQC6
## Levels: PSQC1 PSQC2 PSQC3 PSQC4 PSQC5 PSQC6# 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Very Disagree 17 1.98 1.98%
## 2 PSQC1 Disagree 38 4.43 4.43%
## 3 PSQC1 Quite Disagree 62 7.23 7.23%
## 4 PSQC1 Normal 100 11.7 11.66%
## 5 PSQC1 Quite Agree 205 23.9 23.89%
## 6 PSQC1 Agree 348 40.6 40.56%
## 7 PSQC1 Very Agree 88 10.3 10.26%
## 8 PSQC2 Very Disagree 10 1.17 1.17%
## 9 PSQC2 Disagree 32 3.73 3.73%
## 10 PSQC2 Quite Disagree 16 1.86 1.86%
## # ... with 32 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 service quality of convenience (PSQC) 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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# 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: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Very Disagree 17 1.98 1.98%
## 2 PSQC2 Very Disagree 10 1.17 1.17%
## 3 PSQC3 Very Disagree 16 1.86 1.86%
## 4 PSQC4 Very Disagree 16 1.86 1.86%
## 5 PSQC5 Very Disagree 15 1.75 1.75%
## 6 PSQC6 Very Disagree 10 1.17 1.17%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 3) -> df_for_text2
df_for_text2## # A tibble: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Disagree 38 4.43 4.43%
## 2 PSQC2 Disagree 32 3.73 3.73%
## 3 PSQC3 Disagree 43 5.01 5.01%
## 4 PSQC4 Disagree 49 5.71 5.71%
## 5 PSQC5 Disagree 47 5.48 5.48%# For displaying percent of "Quite Disagree":
df_odered %>%
filter(Res == "Quite Disagree") %>%
filter(percent >= 0) -> df_for_text3
df_for_text3## # A tibble: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Quite Disagree 62 7.23 7.23%
## 2 PSQC2 Quite Disagree 16 1.86 1.86%
## 3 PSQC3 Quite Disagree 62 7.23 7.23%
## 4 PSQC4 Quite Disagree 54 6.29 6.29%
## 5 PSQC5 Quite Disagree 42 4.9 4.9%
## 6 PSQC6 Quite Disagree 32 3.73 3.73%# For displaying percent of "Normal":
df_odered %>%
filter(Res == "Normal") %>%
filter(percent >= 0) -> df_for_text4
df_for_text4## # A tibble: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Normal 100 11.7 11.66%
## 2 PSQC2 Normal 69 8.04 8.04%
## 3 PSQC3 Normal 101 11.8 11.77%
## 4 PSQC4 Normal 255 29.7 29.72%
## 5 PSQC5 Normal 182 21.2 21.21%
## 6 PSQC6 Normal 66 7.69 7.69%# For displaying percent of "Quite Agree":
df_odered %>%
filter(Res == "Quite Agree") %>%
filter(percent >= 0) -> df_for_text5
df_for_text5## # A tibble: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Quite Agree 205 23.9 23.89%
## 2 PSQC2 Quite Agree 139 16.2 16.2%
## 3 PSQC3 Quite Agree 202 23.5 23.54%
## 4 PSQC4 Quite Agree 162 18.9 18.88%
## 5 PSQC5 Quite Agree 150 17.5 17.48%
## 6 PSQC6 Quite Agree 172 20.0 20.05%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "Agree") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Agree 348 40.6 40.56%
## 2 PSQC2 Agree 449 52.3 52.33%
## 3 PSQC3 Agree 348 40.6 40.56%
## 4 PSQC4 Agree 265 30.9 30.89%
## 5 PSQC5 Agree 349 40.7 40.68%
## 6 PSQC6 Agree 410 47.8 47.79%# 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: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Very Agree 88 10.3 10.26%
## 2 PSQC2 Very Agree 143 16.7 16.67%
## 3 PSQC3 Very Agree 86 10.0 10.02%
## 4 PSQC4 Very Agree 57 6.64 6.64%
## 5 PSQC5 Very Agree 73 8.51 8.51%
## 6 PSQC6 Very Agree 144 16.8 16.78%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "Very Agree") -> df_for_text
df_for_text## # A tibble: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Very Agree 88 10.3 10.26%
## 2 PSQC2 Very Agree 143 16.7 16.67%
## 3 PSQC3 Very Agree 86 10.0 10.02%
## 4 PSQC4 Very Agree 57 6.64 6.64%
## 5 PSQC5 Very Agree 73 8.51 8.51%
## 6 PSQC6 Very Agree 144 16.8 16.78%# 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$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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 PSQC-Nhãn tiếng việt
DN_HCM_SEM5_PSQC_v = within(DN_HCM_SEM5_PSQC, {
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("PSQC1", "PSQC2", "PSQC3", "PSQC4", "PSQC5", "PSQC6"))
})
str(DN_HCM_SEM5_PSQC_v)## 'data.frame': 5148 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 6 4 2 5 6 5 2 6 3 7 ...
## $ BPE: Factor w/ 6 levels "PSQC1","PSQC2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM5_PSQC_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 84 PSQC1:858
## Không d<U+1ED3>ng ý : 233 PSQC2:858
## Khá không d<U+1ED3>ng ý : 268 PSQC3:858
## Bình thu<U+1EDD>ng : 773 PSQC4:858
## Khá d<U+1ED3>ng ý :1030 PSQC5:858
## Ð<U+1ED3>ng ý :2169 PSQC6:858
## R<U+1EA5>t d<U+1ED3>ng ý : 591 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_PSQC_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] PSQC4 PSQC5 PSQC3 PSQC1 PSQC2 PSQC6
## Levels: PSQC1 PSQC2 PSQC3 PSQC4 PSQC5 PSQC6# 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: 42 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 R<U+1EA5>t không d<U+1ED3>ng ý 17 1.98 1.98%
## 2 PSQC1 Không d<U+1ED3>ng ý 38 4.43 4.43%
## 3 PSQC1 Khá không d<U+1ED3>ng ý 62 7.23 7.23%
## 4 PSQC1 Bình thu<U+1EDD>ng 100 11.7 11.66%
## 5 PSQC1 Khá d<U+1ED3>ng ý 205 23.9 23.89%
## 6 PSQC1 Ð<U+1ED3>ng ý 348 40.6 40.56%
## 7 PSQC1 R<U+1EA5>t d<U+1ED3>ng ý 88 10.3 10.26%
## 8 PSQC2 R<U+1EA5>t không d<U+1ED3>ng ý 10 1.17 1.17%
## 9 PSQC2 Không d<U+1ED3>ng ý 32 3.73 3.73%
## 10 PSQC2 Khá không d<U+1ED3>ng ý 16 1.86 1.86%
## # ... with 32 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 ch\u1EA5t l\u01B0\u1EE3ng d\u1ECBch v\u1EE5 ti\u1EC7n nghi (PSQC) \u0111\u1ED1i v\u1EDBi 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_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: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 R<U+1EA5>t không d<U+1ED3>ng ý 17 1.98 1.98%
## 2 PSQC2 R<U+1EA5>t không d<U+1ED3>ng ý 10 1.17 1.17%
## 3 PSQC3 R<U+1EA5>t không d<U+1ED3>ng ý 16 1.86 1.86%
## 4 PSQC4 R<U+1EA5>t không d<U+1ED3>ng ý 16 1.86 1.86%
## 5 PSQC5 R<U+1EA5>t không d<U+1ED3>ng ý 15 1.75 1.75%
## 6 PSQC6 R<U+1EA5>t không d<U+1ED3>ng ý 10 1.17 1.17%# 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: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Không d<U+1ED3>ng ý 38 4.43 4.43%
## 2 PSQC2 Không d<U+1ED3>ng ý 32 3.73 3.73%
## 3 PSQC3 Không d<U+1ED3>ng ý 43 5.01 5.01%
## 4 PSQC4 Không d<U+1ED3>ng ý 49 5.71 5.71%
## 5 PSQC5 Không d<U+1ED3>ng ý 47 5.48 5.48%
## 6 PSQC6 Không d<U+1ED3>ng ý 24 2.8 2.8%# 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: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Khá không d<U+1ED3>ng ý 62 7.23 7.23%
## 2 PSQC2 Khá không d<U+1ED3>ng ý 16 1.86 1.86%
## 3 PSQC3 Khá không d<U+1ED3>ng ý 62 7.23 7.23%
## 4 PSQC4 Khá không d<U+1ED3>ng ý 54 6.29 6.29%
## 5 PSQC5 Khá không d<U+1ED3>ng ý 42 4.9 4.9%
## 6 PSQC6 Khá không d<U+1ED3>ng ý 32 3.73 3.73%# 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: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Bình thu<U+1EDD>ng 100 11.7 11.66%
## 2 PSQC2 Bình thu<U+1EDD>ng 69 8.04 8.04%
## 3 PSQC3 Bình thu<U+1EDD>ng 101 11.8 11.77%
## 4 PSQC4 Bình thu<U+1EDD>ng 255 29.7 29.72%
## 5 PSQC5 Bình thu<U+1EDD>ng 182 21.2 21.21%
## 6 PSQC6 Bình thu<U+1EDD>ng 66 7.69 7.69%# 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: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Khá d<U+1ED3>ng ý 205 23.9 23.89%
## 2 PSQC2 Khá d<U+1ED3>ng ý 139 16.2 16.2%
## 3 PSQC3 Khá d<U+1ED3>ng ý 202 23.5 23.54%
## 4 PSQC4 Khá d<U+1ED3>ng ý 162 18.9 18.88%
## 5 PSQC5 Khá d<U+1ED3>ng ý 150 17.5 17.48%
## 6 PSQC6 Khá d<U+1ED3>ng ý 172 20.0 20.05%# For displaying percent of "Agree":
df_odered %>%
filter(Res == "\u0110\u1ED3ng \u00FD") %>%
filter(percent >= 0) -> df_for_text6
df_for_text6## # A tibble: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 Ð<U+1ED3>ng ý 348 40.6 40.56%
## 2 PSQC2 Ð<U+1ED3>ng ý 449 52.3 52.33%
## 3 PSQC3 Ð<U+1ED3>ng ý 348 40.6 40.56%
## 4 PSQC4 Ð<U+1ED3>ng ý 265 30.9 30.89%
## 5 PSQC5 Ð<U+1ED3>ng ý 349 40.7 40.68%
## 6 PSQC6 Ð<U+1ED3>ng ý 410 47.8 47.79%# 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: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 R<U+1EA5>t d<U+1ED3>ng ý 88 10.3 10.26%
## 2 PSQC2 R<U+1EA5>t d<U+1ED3>ng ý 143 16.7 16.67%
## 3 PSQC3 R<U+1EA5>t d<U+1ED3>ng ý 86 10.0 10.02%
## 4 PSQC4 R<U+1EA5>t d<U+1ED3>ng ý 57 6.64 6.64%
## 5 PSQC5 R<U+1EA5>t d<U+1ED3>ng ý 73 8.51 8.51%
## 6 PSQC6 R<U+1EA5>t d<U+1ED3>ng ý 144 16.8 16.78%# For displaying percent of "Very Agree":
df_odered %>%
filter(Res == "R\u1EA5t \u0111\u1ED3ng \u00FD") -> df_for_text
df_for_text## # A tibble: 6 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQC1 R<U+1EA5>t d<U+1ED3>ng ý 88 10.3 10.26%
## 2 PSQC2 R<U+1EA5>t d<U+1ED3>ng ý 143 16.7 16.67%
## 3 PSQC3 R<U+1EA5>t d<U+1ED3>ng ý 86 10.0 10.02%
## 4 PSQC4 R<U+1EA5>t d<U+1ED3>ng ý 57 6.64 6.64%
## 5 PSQC5 R<U+1EA5>t d<U+1ED3>ng ý 73 8.51 8.51%
## 6 PSQC6 R<U+1EA5>t d<U+1ED3>ng ý 144 16.8 16.78%# 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
## 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
3.8.7. Construct PSQP- Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct IMA
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM5_PSQP)## Res BPE
## 1 6 PSQP1
## 2 6 PSQP1
## 3 1 PSQP1
## 4 5 PSQP1
## 5 4 PSQP1
## 6 4 PSQP1dim(DN_HCM_SEM5_PSQP)## [1] 3432 2attach(DN_HCM_SEM5_PSQP)## The following objects are masked from DN_HCM_SEM5_PSQC:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQT:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM5:
##
## BPE, Res## The following objects are masked from DN_SEM5:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5:
##
## BPE, ResDN_HCM_SEM5_PSQP = within(DN_HCM_SEM5_PSQP, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("PSQP1", "PSQP2", "PSQP3", "PSQP4"))
})
str(DN_HCM_SEM5_PSQP)## 'data.frame': 3432 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 6 1 5 4 4 4 4 3 4 ...
## $ BPE: Factor w/ 4 levels "PSQP1","PSQP2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM5_PSQP)## Res BPE
## Very Disagree : 47 PSQP1:858
## Disagree : 105 PSQP2:858
## Quite Disagree: 120 PSQP3:858
## Normal : 393 PSQP4:858
## Quite Agree : 700
## Agree :1652
## Very Agree : 415 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_PSQP %>%
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] PSQP3 PSQP1 PSQP2 PSQP4
## Levels: PSQP1 PSQP2 PSQP3 PSQP4# 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 PSQP1 Very Disagree 14 1.63 1.63%
## 2 PSQP1 Disagree 27 3.15 3.15%
## 3 PSQP1 Quite Disagree 40 4.66 4.66%
## 4 PSQP1 Normal 110 12.8 12.82%
## 5 PSQP1 Quite Agree 167 19.5 19.46%
## 6 PSQP1 Agree 398 46.4 46.39%
## 7 PSQP1 Very Agree 102 11.9 11.89%
## 8 PSQP2 Very Disagree 13 1.52 1.52%
## 9 PSQP2 Disagree 31 3.61 3.61%
## 10 PSQP2 Quite Disagree 42 4.9 4.9%
## # ... 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 service quality of personel (PSQP) 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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# 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 PSQP1 Very Disagree 14 1.63 1.63%
## 2 PSQP2 Very Disagree 13 1.52 1.52%
## 3 PSQP3 Very Disagree 11 1.28 1.28%
## 4 PSQP4 Very Disagree 9 1.05 1.05%# 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 PSQP1 Disagree 27 3.15 3.15%
## 2 PSQP2 Disagree 31 3.61 3.61%
## 3 PSQP3 Disagree 26 3.03 3.03%# 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 PSQP1 Quite Disagree 40 4.66 4.66%
## 2 PSQP2 Quite Disagree 42 4.9 4.9%
## 3 PSQP3 Quite Disagree 25 2.91 2.91%
## 4 PSQP4 Quite Disagree 13 1.52 1.52%# 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 PSQP1 Normal 110 12.8 12.82%
## 2 PSQP2 Normal 99 11.5 11.54%
## 3 PSQP3 Normal 100 11.7 11.66%
## 4 PSQP4 Normal 84 9.79 9.79%# 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 PSQP1 Quite Agree 167 19.5 19.46%
## 2 PSQP2 Quite Agree 188 21.9 21.91%
## 3 PSQP3 Quite Agree 187 21.8 21.79%
## 4 PSQP4 Quite Agree 158 18.4 18.41%# 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 PSQP1 Agree 398 46.4 46.39%
## 2 PSQP2 Agree 378 44.1 44.06%
## 3 PSQP3 Agree 418 48.7 48.72%
## 4 PSQP4 Agree 458 53.4 53.38%# 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 PSQP1 Very Agree 102 11.9 11.89%
## 2 PSQP2 Very Agree 107 12.5 12.47%
## 3 PSQP3 Very Agree 91 10.6 10.61%
## 4 PSQP4 Very Agree 115 13.4 13.4%# 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 PSQP1 Very Agree 102 11.9 11.89%
## 2 PSQP2 Very Agree 107 12.5 12.47%
## 3 PSQP3 Very Agree 91 10.6 10.61%
## 4 PSQP4 Very Agree 115 13.4 13.4%# 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
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3.8.8. Construct PSQP - Nhãn tiếng việt
DN_HCM_SEM5_PSQP_v = within(DN_HCM_SEM5_PSQP, {
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("PSQP1", "PSQP2", "PSQP3", "PSQP4"))
})
str(DN_HCM_SEM5_PSQP_v)## 'data.frame': 3432 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 6 6 1 5 4 4 4 4 3 4 ...
## $ BPE: Factor w/ 4 levels "PSQP1","PSQP2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM5_PSQP_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 47 PSQP1:858
## Không d<U+1ED3>ng ý : 105 PSQP2:858
## Khá không d<U+1ED3>ng ý : 120 PSQP3:858
## Bình thu<U+1EDD>ng : 393 PSQP4:858
## Khá d<U+1ED3>ng ý : 700
## Ð<U+1ED3>ng ý :1652
## R<U+1EA5>t d<U+1ED3>ng ý : 415 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_PSQP_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] PSQP3 PSQP1 PSQP2 PSQP4
## Levels: PSQP1 PSQP2 PSQP3 PSQP4# 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 PSQP1 R<U+1EA5>t không d<U+1ED3>ng ý 14 1.63 1.63%
## 2 PSQP1 Không d<U+1ED3>ng ý 27 3.15 3.15%
## 3 PSQP1 Khá không d<U+1ED3>ng ý 40 4.66 4.66%
## 4 PSQP1 Bình thu<U+1EDD>ng 110 12.8 12.82%
## 5 PSQP1 Khá d<U+1ED3>ng ý 167 19.5 19.46%
## 6 PSQP1 Ð<U+1ED3>ng ý 398 46.4 46.39%
## 7 PSQP1 R<U+1EA5>t d<U+1ED3>ng ý 102 11.9 11.89%
## 8 PSQP2 R<U+1EA5>t không d<U+1ED3>ng ý 13 1.52 1.52%
## 9 PSQP2 Không d<U+1ED3>ng ý 31 3.61 3.61%
## 10 PSQP2 Khá không d<U+1ED3>ng ý 42 4.9 4.9%
## # ... 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 c\u1EE7a h\u00E0nh kh\u00E1ch v\u1EC1 ch\u1EA5t l\u01B0\u1EE3ng d\u1ECBch v\u1EE5 c\u00E1 nh\u00E2n (PSQC) \u0111\u1ED1i v\u1EDBi 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_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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# 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 PSQP1 R<U+1EA5>t không d<U+1ED3>ng ý 14 1.63 1.63%
## 2 PSQP2 R<U+1EA5>t không d<U+1ED3>ng ý 13 1.52 1.52%
## 3 PSQP3 R<U+1EA5>t không d<U+1ED3>ng ý 11 1.28 1.28%
## 4 PSQP4 R<U+1EA5>t không d<U+1ED3>ng ý 9 1.05 1.05%# 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 PSQP1 Không d<U+1ED3>ng ý 27 3.15 3.15%
## 2 PSQP2 Không d<U+1ED3>ng ý 31 3.61 3.61%
## 3 PSQP3 Không d<U+1ED3>ng ý 26 3.03 3.03%
## 4 PSQP4 Không d<U+1ED3>ng ý 21 2.45 2.45%# 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 PSQP1 Khá không d<U+1ED3>ng ý 40 4.66 4.66%
## 2 PSQP2 Khá không d<U+1ED3>ng ý 42 4.9 4.9%
## 3 PSQP3 Khá không d<U+1ED3>ng ý 25 2.91 2.91%
## 4 PSQP4 Khá không d<U+1ED3>ng ý 13 1.52 1.52%# 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 PSQP1 Bình thu<U+1EDD>ng 110 12.8 12.82%
## 2 PSQP2 Bình thu<U+1EDD>ng 99 11.5 11.54%
## 3 PSQP3 Bình thu<U+1EDD>ng 100 11.7 11.66%
## 4 PSQP4 Bình thu<U+1EDD>ng 84 9.79 9.79%# 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 PSQP1 Khá d<U+1ED3>ng ý 167 19.5 19.46%
## 2 PSQP2 Khá d<U+1ED3>ng ý 188 21.9 21.91%
## 3 PSQP3 Khá d<U+1ED3>ng ý 187 21.8 21.79%
## 4 PSQP4 Khá d<U+1ED3>ng ý 158 18.4 18.41%# 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 PSQP1 Ð<U+1ED3>ng ý 398 46.4 46.39%
## 2 PSQP2 Ð<U+1ED3>ng ý 378 44.1 44.06%
## 3 PSQP3 Ð<U+1ED3>ng ý 418 48.7 48.72%
## 4 PSQP4 Ð<U+1ED3>ng ý 458 53.4 53.38%# 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 PSQP1 R<U+1EA5>t d<U+1ED3>ng ý 102 11.9 11.89%
## 2 PSQP2 R<U+1EA5>t d<U+1ED3>ng ý 107 12.5 12.47%
## 3 PSQP3 R<U+1EA5>t d<U+1ED3>ng ý 91 10.6 10.61%
## 4 PSQP4 R<U+1EA5>t d<U+1ED3>ng ý 115 13.4 13.4%# 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 PSQP1 R<U+1EA5>t d<U+1ED3>ng ý 102 11.9 11.89%
## 2 PSQP2 R<U+1EA5>t d<U+1ED3>ng ý 107 12.5 12.47%
## 3 PSQP3 R<U+1EA5>t d<U+1ED3>ng ý 91 10.6 10.61%
## 4 PSQP4 R<U+1EA5>t d<U+1ED3>ng ý 115 13.4 13.4%# 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
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3.8.9. Construct PSQR- Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct IMA
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM5_PSQR)## Res BPE
## 1 4 PSQR1
## 2 4 PSQR1
## 3 2 PSQR1
## 4 4 PSQR1
## 5 5 PSQR1
## 6 3 PSQR1dim(DN_HCM_SEM5_PSQR)## [1] 3432 2attach(DN_HCM_SEM5_PSQR)## The following objects are masked from DN_HCM_SEM5_PSQP:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQC:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQT:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM5:
##
## BPE, Res## The following objects are masked from DN_SEM5:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5:
##
## BPE, ResDN_HCM_SEM5_PSQR = within(DN_HCM_SEM5_PSQR, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("PSQR1", "PSQR2", "PSQR3", "PSQR4"))
})
str(DN_HCM_SEM5_PSQR)## 'data.frame': 3432 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 4 4 2 4 5 3 2 5 3 4 ...
## $ BPE: Factor w/ 4 levels "PSQR1","PSQR2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM5_PSQR)## Res BPE
## Very Disagree : 84 PSQR1:858
## Disagree : 196 PSQR2:858
## Quite Disagree: 266 PSQR3:858
## Normal : 429 PSQR4:858
## Quite Agree : 847
## Agree :1288
## Very Agree : 322 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_PSQR %>%
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] PSQR1 PSQR3 PSQR4 PSQR2
## Levels: PSQR1 PSQR2 PSQR3 PSQR4# 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 PSQR1 Very Disagree 24 2.8 2.8%
## 2 PSQR1 Disagree 48 5.59 5.59%
## 3 PSQR1 Quite Disagree 73 8.51 8.51%
## 4 PSQR1 Normal 97 11.3 11.31%
## 5 PSQR1 Quite Agree 244 28.4 28.44%
## 6 PSQR1 Agree 303 35.3 35.31%
## 7 PSQR1 Very Agree 69 8.04 8.04%
## 8 PSQR2 Very Disagree 12 1.4 1.4%
## 9 PSQR2 Disagree 31 3.61 3.61%
## 10 PSQR2 Quite Disagree 56 6.53 6.53%
## # ... 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 service quality of reliability (PSQR) 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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# 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 PSQR1 Very Disagree 24 2.8 2.8%
## 2 PSQR2 Very Disagree 12 1.4 1.4%
## 3 PSQR3 Very Disagree 19 2.21 2.21%
## 4 PSQR4 Very Disagree 29 3.38 3.38%# For displaying percent of "Disagree":
df_odered %>%
filter(Res == "Disagree") %>%
filter(percent >= 3) -> df_for_text2
df_for_text2## # A tibble: 4 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 PSQR1 Disagree 48 5.59 5.59%
## 2 PSQR2 Disagree 31 3.61 3.61%
## 3 PSQR3 Disagree 42 4.9 4.9%
## 4 PSQR4 Disagree 75 8.74 8.74%# 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 PSQR1 Quite Disagree 73 8.51 8.51%
## 2 PSQR2 Quite Disagree 56 6.53 6.53%
## 3 PSQR3 Quite Disagree 61 7.11 7.11%
## 4 PSQR4 Quite Disagree 76 8.86 8.86%# 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 PSQR1 Normal 97 11.3 11.31%
## 2 PSQR2 Normal 107 12.5 12.47%
## 3 PSQR3 Normal 126 14.7 14.69%
## 4 PSQR4 Normal 99 11.5 11.54%# 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 PSQR1 Quite Agree 244 28.4 28.44%
## 2 PSQR2 Quite Agree 190 22.1 22.14%
## 3 PSQR3 Quite Agree 217 25.3 25.29%
## 4 PSQR4 Quite Agree 196 22.8 22.84%# 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 PSQR1 Agree 303 35.3 35.31%
## 2 PSQR2 Agree 363 42.3 42.31%
## 3 PSQR3 Agree 319 37.2 37.18%
## 4 PSQR4 Agree 303 35.3 35.31%# 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 PSQR1 Very Agree 69 8.04 8.04%
## 2 PSQR2 Very Agree 99 11.5 11.54%
## 3 PSQR3 Very Agree 74 8.62 8.62%
## 4 PSQR4 Very Agree 80 9.32 9.32%# 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 PSQR1 Very Agree 69 8.04 8.04%
## 2 PSQR2 Very Agree 99 11.5 11.54%
## 3 PSQR3 Very Agree 74 8.62 8.62%
## 4 PSQR4 Very Agree 80 9.32 9.32%# 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
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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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3.8.10. Construct PSQP - Nhãn tiếng việt
DN_HCM_SEM5_PSQR_v = within(DN_HCM_SEM5_PSQR, {
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("PSQR1", "PSQR2", "PSQR3", "PSQR4"))
})
str(DN_HCM_SEM5_PSQR_v)## 'data.frame': 3432 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 4 4 2 4 5 3 2 5 3 4 ...
## $ BPE: Factor w/ 4 levels "PSQR1","PSQR2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM5_PSQR_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 84 PSQR1:858
## Không d<U+1ED3>ng ý : 196 PSQR2:858
## Khá không d<U+1ED3>ng ý : 266 PSQR3:858
## Bình thu<U+1EDD>ng : 429 PSQR4:858
## Khá d<U+1ED3>ng ý : 847
## Ð<U+1ED3>ng ý :1288
## R<U+1EA5>t d<U+1ED3>ng ý : 322 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_PSQR_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] PSQR1 PSQR3 PSQR4 PSQR2
## Levels: PSQR1 PSQR2 PSQR3 PSQR4# 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 PSQR1 R<U+1EA5>t không d<U+1ED3>ng ý 24 2.8 2.8%
## 2 PSQR1 Không d<U+1ED3>ng ý 48 5.59 5.59%
## 3 PSQR1 Khá không d<U+1ED3>ng ý 73 8.51 8.51%
## 4 PSQR1 Bình thu<U+1EDD>ng 97 11.3 11.31%
## 5 PSQR1 Khá d<U+1ED3>ng ý 244 28.4 28.44%
## 6 PSQR1 Ð<U+1ED3>ng ý 303 35.3 35.31%
## 7 PSQR1 R<U+1EA5>t d<U+1ED3>ng ý 69 8.04 8.04%
## 8 PSQR2 R<U+1EA5>t không d<U+1ED3>ng ý 12 1.4 1.4%
## 9 PSQR2 Không d<U+1ED3>ng ý 31 3.61 3.61%
## 10 PSQR2 Khá không d<U+1ED3>ng ý 56 6.53 6.53%
## # ... 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 c\u1EE7a h\u00E0nh kh\u00E1ch v\u1EC1 ch\u1EA5t l\u01B0\u1EE3ng d\u1ECBch v\u1EE5 li\u00EAn quan \u0111\u1EBFn \u0111\u1ED9 tin c\u1EADy (PSQR) \u0111\u1ED1i v\u1EDBi 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_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
## 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 PSQR1 R<U+1EA5>t không d<U+1ED3>ng ý 24 2.8 2.8%
## 2 PSQR2 R<U+1EA5>t không d<U+1ED3>ng ý 12 1.4 1.4%
## 3 PSQR3 R<U+1EA5>t không d<U+1ED3>ng ý 19 2.21 2.21%
## 4 PSQR4 R<U+1EA5>t không d<U+1ED3>ng ý 29 3.38 3.38%# 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 PSQR1 Không d<U+1ED3>ng ý 48 5.59 5.59%
## 2 PSQR2 Không d<U+1ED3>ng ý 31 3.61 3.61%
## 3 PSQR3 Không d<U+1ED3>ng ý 42 4.9 4.9%
## 4 PSQR4 Không d<U+1ED3>ng ý 75 8.74 8.74%# 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 PSQR1 Khá không d<U+1ED3>ng ý 73 8.51 8.51%
## 2 PSQR2 Khá không d<U+1ED3>ng ý 56 6.53 6.53%
## 3 PSQR3 Khá không d<U+1ED3>ng ý 61 7.11 7.11%
## 4 PSQR4 Khá không d<U+1ED3>ng ý 76 8.86 8.86%# 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 PSQR1 Bình thu<U+1EDD>ng 97 11.3 11.31%
## 2 PSQR2 Bình thu<U+1EDD>ng 107 12.5 12.47%
## 3 PSQR3 Bình thu<U+1EDD>ng 126 14.7 14.69%
## 4 PSQR4 Bình thu<U+1EDD>ng 99 11.5 11.54%# 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 PSQR1 Khá d<U+1ED3>ng ý 244 28.4 28.44%
## 2 PSQR2 Khá d<U+1ED3>ng ý 190 22.1 22.14%
## 3 PSQR3 Khá d<U+1ED3>ng ý 217 25.3 25.29%
## 4 PSQR4 Khá d<U+1ED3>ng ý 196 22.8 22.84%# 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 PSQR1 Ð<U+1ED3>ng ý 303 35.3 35.31%
## 2 PSQR2 Ð<U+1ED3>ng ý 363 42.3 42.31%
## 3 PSQR3 Ð<U+1ED3>ng ý 319 37.2 37.18%
## 4 PSQR4 Ð<U+1ED3>ng ý 303 35.3 35.31%# 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 PSQR1 R<U+1EA5>t d<U+1ED3>ng ý 69 8.04 8.04%
## 2 PSQR2 R<U+1EA5>t d<U+1ED3>ng ý 99 11.5 11.54%
## 3 PSQR3 R<U+1EA5>t d<U+1ED3>ng ý 74 8.62 8.62%
## 4 PSQR4 R<U+1EA5>t d<U+1ED3>ng ý 80 9.32 9.32%# 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 PSQR1 R<U+1EA5>t d<U+1ED3>ng ý 69 8.04 8.04%
## 2 PSQR2 R<U+1EA5>t d<U+1ED3>ng ý 99 11.5 11.54%
## 3 PSQR3 R<U+1EA5>t d<U+1ED3>ng ý 74 8.62 8.62%
## 4 PSQR4 R<U+1EA5>t d<U+1ED3>ng ý 80 9.32 9.32%# 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
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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$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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 SAT- Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct IMA
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM5_SAT)## Res BPE
## 1 6 SAT1
## 2 5 SAT1
## 3 2 SAT1
## 4 5 SAT1
## 5 4 SAT1
## 6 4 SAT1dim(DN_HCM_SEM5_SAT)## [1] 2574 2attach(DN_HCM_SEM5_SAT)## The following objects are masked from DN_HCM_SEM5_PSQR:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQP:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQC:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQT:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM5:
##
## BPE, Res## The following objects are masked from DN_SEM5:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5:
##
## BPE, ResDN_HCM_SEM5_SAT = within(DN_HCM_SEM5_SAT, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("SAT1", "SAT2", "SAT3"))
})
str(DN_HCM_SEM5_SAT)## 'data.frame': 2574 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "Very Disagree",..: 6 5 2 5 4 4 2 7 5 7 ...
## $ BPE: Factor w/ 3 levels "SAT1","SAT2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM5_SAT)## Res BPE
## Very Disagree : 23 SAT1:858
## Disagree : 54 SAT2:858
## Quite Disagree: 90 SAT3:858
## Normal : 289
## Quite Agree : 625
## Agree :1132
## Very Agree : 361 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_SAT %>%
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] SAT1 SAT3 SAT2
## Levels: SAT1 SAT2 SAT3# 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 SAT1 Very Disagree 9 1.05 1.05%
## 2 SAT1 Disagree 23 2.68 2.68%
## 3 SAT1 Quite Disagree 37 4.31 4.31%
## 4 SAT1 Normal 110 12.8 12.82%
## 5 SAT1 Quite Agree 215 25.1 25.06%
## 6 SAT1 Agree 359 41.8 41.84%
## 7 SAT1 Very Agree 105 12.2 12.24%
## 8 SAT2 Very Disagree 6 0.7 0.7%
## 9 SAT2 Disagree 14 1.63 1.63%
## 10 SAT2 Quite Disagree 27 3.15 3.15%
## # ... 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 satisfaction (SAT) 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$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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
## 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: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 SAT1 Very Disagree 9 1.05 1.05%
## 2 SAT2 Very Disagree 6 0.7 0.7%
## 3 SAT3 Very Disagree 8 0.93 0.93%# 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: 3 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 SAT1 Quite Disagree 37 4.31 4.31%
## 2 SAT2 Quite Disagree 27 3.15 3.15%
## 3 SAT3 Quite Disagree 26 3.03 3.03%# 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 SAT1 Normal 110 12.8 12.82%
## 2 SAT2 Normal 100 11.7 11.66%
## 3 SAT3 Normal 79 9.21 9.21%# 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 SAT1 Quite Agree 215 25.1 25.06%
## 2 SAT2 Quite Agree 197 23.0 22.96%
## 3 SAT3 Quite Agree 213 24.8 24.83%# 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 SAT1 Agree 359 41.8 41.84%
## 2 SAT2 Agree 382 44.5 44.52%
## 3 SAT3 Agree 391 45.6 45.57%# 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 SAT1 Very Agree 105 12.2 12.24%
## 2 SAT2 Very Agree 132 15.4 15.38%
## 3 SAT3 Very Agree 124 14.4 14.45%# 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 SAT1 Very Agree 105 12.2 12.24%
## 2 SAT2 Very Agree 132 15.4 15.38%
## 3 SAT3 Very Agree 124 14.4 14.45%# 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
3.8.12. Construct SAT- Nhãn tiếng việt
DN_HCM_SEM5_SAT_v = within(DN_HCM_SEM5_SAT, {
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("SAT1", "SAT2", "SAT3"))
})
str(DN_HCM_SEM5_SAT_v)## 'data.frame': 2574 obs. of 2 variables:
## $ Res: Factor w/ 7 levels "R<U+1EA5>t không d<U+1ED3>ng ý",..: 6 5 2 5 4 4 2 7 5 7 ...
## $ BPE: Factor w/ 3 levels "SAT1","SAT2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM5_SAT_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 23 SAT1:858
## Không d<U+1ED3>ng ý : 54 SAT2:858
## Khá không d<U+1ED3>ng ý : 90 SAT3:858
## Bình thu<U+1EDD>ng : 289
## Khá d<U+1ED3>ng ý : 625
## Ð<U+1ED3>ng ý :1132
## R<U+1EA5>t d<U+1ED3>ng ý : 361 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_SAT_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] SAT1 SAT3 SAT2
## Levels: SAT1 SAT2 SAT3# 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 SAT1 R<U+1EA5>t không d<U+1ED3>ng ý 9 1.05 1.05%
## 2 SAT1 Không d<U+1ED3>ng ý 23 2.68 2.68%
## 3 SAT1 Khá không d<U+1ED3>ng ý 37 4.31 4.31%
## 4 SAT1 Bình thu<U+1EDD>ng 110 12.8 12.82%
## 5 SAT1 Khá d<U+1ED3>ng ý 215 25.1 25.06%
## 6 SAT1 Ð<U+1ED3>ng ý 359 41.8 41.84%
## 7 SAT1 R<U+1EA5>t d<U+1ED3>ng ý 105 12.2 12.24%
## 8 SAT2 R<U+1EA5>t không d<U+1ED3>ng ý 6 0.7 0.7%
## 9 SAT2 Không d<U+1ED3>ng ý 14 1.63 1.63%
## 10 SAT2 Khá không d<U+1ED3>ng ý 27 3.15 3.15%
## # ... 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 h\u00E0i l\u00F2ng c\u1EE7a h\u00E0nh kh\u00E1ch (SAT) \u0111\u1ED1i v\u1EDBi 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_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$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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 == "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 SAT1 R<U+1EA5>t không d<U+1ED3>ng ý 9 1.05 1.05%
## 2 SAT2 R<U+1EA5>t không d<U+1ED3>ng ý 6 0.7 0.7%
## 3 SAT3 R<U+1EA5>t không d<U+1ED3>ng ý 8 0.93 0.93%# 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 SAT1 Không d<U+1ED3>ng ý 23 2.68 2.68%
## 2 SAT2 Không d<U+1ED3>ng ý 14 1.63 1.63%
## 3 SAT3 Không d<U+1ED3>ng ý 17 1.98 1.98%# 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 SAT1 Khá không d<U+1ED3>ng ý 37 4.31 4.31%
## 2 SAT2 Khá không d<U+1ED3>ng ý 27 3.15 3.15%
## 3 SAT3 Khá không d<U+1ED3>ng ý 26 3.03 3.03%# 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 SAT1 Bình thu<U+1EDD>ng 110 12.8 12.82%
## 2 SAT2 Bình thu<U+1EDD>ng 100 11.7 11.66%
## 3 SAT3 Bình thu<U+1EDD>ng 79 9.21 9.21%# 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 SAT1 Khá d<U+1ED3>ng ý 215 25.1 25.06%
## 2 SAT2 Khá d<U+1ED3>ng ý 197 23.0 22.96%
## 3 SAT3 Khá d<U+1ED3>ng ý 213 24.8 24.83%# 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 SAT1 Ð<U+1ED3>ng ý 359 41.8 41.84%
## 2 SAT2 Ð<U+1ED3>ng ý 382 44.5 44.52%
## 3 SAT3 Ð<U+1ED3>ng ý 391 45.6 45.57%# 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 SAT1 R<U+1EA5>t d<U+1ED3>ng ý 105 12.2 12.24%
## 2 SAT2 R<U+1EA5>t d<U+1ED3>ng ý 132 15.4 15.38%
## 3 SAT3 R<U+1EA5>t d<U+1ED3>ng ý 124 14.4 14.45%# 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 SAT1 R<U+1EA5>t d<U+1ED3>ng ý 105 12.2 12.24%
## 2 SAT2 R<U+1EA5>t d<U+1ED3>ng ý 132 15.4 15.38%
## 3 SAT3 R<U+1EA5>t d<U+1ED3>ng ý 124 14.4 14.45%# 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$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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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3.8.13. Construct IMA- Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct IMA
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM5_IMA)## Res BPE
## 1 6 IMA1
## 2 4 IMA1
## 3 4 IMA1
## 4 5 IMA1
## 5 5 IMA1
## 6 4 IMA1dim(DN_HCM_SEM5_IMA)## [1] 4290 2attach(DN_HCM_SEM5_IMA)## The following objects are masked from DN_HCM_SEM5_SAT:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQR:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQP:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQC:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQT:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM5:
##
## BPE, Res## The following objects are masked from DN_SEM5:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5:
##
## BPE, ResDN_HCM_SEM5_IMA = within(DN_HCM_SEM5_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_SEM5_IMA)## 'data.frame': 4290 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_SEM5_IMA)## Res BPE
## Very Disagree : 47 IMA1:858
## Disagree : 82 IMA2:858
## Quite Disagree: 126 IMA3:858
## Normal : 657 IMA4:858
## Quite Agree :1056 IMA5:858
## Agree :1945
## Very Agree : 377 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_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 IMA4 IMA5
## 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 19 2.21 2.21%
## 3 IMA1 Quite Disagree 33 3.85 3.85%
## 4 IMA1 Normal 154 18.0 17.95%
## 5 IMA1 Quite Agree 230 26.8 26.81%
## 6 IMA1 Agree 351 40.9 40.91%
## 7 IMA1 Very Agree 65 7.58 7.58%
## 8 IMA2 Very Disagree 11 1.28 1.28%
## 9 IMA2 Disagree 18 2.1 2.1%
## 10 IMA2 Quite Disagree 26 3.03 3.03%
## # ... 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$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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: 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 1.28 1.28%
## 3 IMA3 Very Disagree 11 1.28 1.28%
## 4 IMA4 Very Disagree 13 1.52 1.52%
## 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 33 3.85 3.85%
## 2 IMA2 Quite Disagree 26 3.03 3.03%
## 3 IMA3 Quite Disagree 21 2.45 2.45%
## 4 IMA4 Quite Disagree 26 3.03 3.03%
## 5 IMA5 Quite Disagree 20 2.33 2.33%# 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 154 18.0 17.95%
## 2 IMA2 Normal 158 18.4 18.41%
## 3 IMA3 Normal 128 14.9 14.92%
## 4 IMA4 Normal 113 13.2 13.17%
## 5 IMA5 Normal 104 12.1 12.12%# 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 26.8 26.81%
## 2 IMA2 Quite Agree 214 24.9 24.94%
## 3 IMA3 Quite Agree 207 24.1 24.13%
## 4 IMA4 Quite Agree 201 23.4 23.43%
## 5 IMA5 Quite Agree 204 23.8 23.78%# 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 351 40.9 40.91%
## 2 IMA2 Agree 365 42.5 42.54%
## 3 IMA3 Agree 403 47.0 46.97%
## 4 IMA4 Agree 406 47.3 47.32%
## 5 IMA5 Agree 420 49.0 48.95%# 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 7.58 7.58%
## 2 IMA2 Very Agree 66 7.69 7.69%
## 3 IMA3 Very Agree 78 9.09 9.09%
## 4 IMA4 Very Agree 84 9.79 9.79%
## 5 IMA5 Very Agree 84 9.79 9.79%# 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 7.58 7.58%
## 2 IMA2 Very Agree 66 7.69 7.69%
## 3 IMA3 Very Agree 78 9.09 9.09%
## 4 IMA4 Very Agree 84 9.79 9.79%
## 5 IMA5 Very Agree 84 9.79 9.79%# 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$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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$y, :
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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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3.8.14. Construct IMA- Nhãn tiếng việt
DN_HCM_SEM5_IMA_v = within(DN_HCM_SEM5_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_SEM5_IMA_v)## 'data.frame': 4290 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_SEM5_IMA_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 47 IMA1:858
## Không d<U+1ED3>ng ý : 82 IMA2:858
## Khá không d<U+1ED3>ng ý : 126 IMA3:858
## Bình thu<U+1EDD>ng : 657 IMA4:858
## Khá d<U+1ED3>ng ý :1056 IMA5:858
## Ð<U+1ED3>ng ý :1945
## R<U+1EA5>t d<U+1ED3>ng ý : 377 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_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 IMA4 IMA5
## 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 19 2.21 2.21%
## 3 IMA1 Khá không d<U+1ED3>ng ý 33 3.85 3.85%
## 4 IMA1 Bình thu<U+1EDD>ng 154 18.0 17.95%
## 5 IMA1 Khá d<U+1ED3>ng ý 230 26.8 26.81%
## 6 IMA1 Ð<U+1ED3>ng ý 351 40.9 40.91%
## 7 IMA1 R<U+1EA5>t d<U+1ED3>ng ý 65 7.58 7.58%
## 8 IMA2 R<U+1EA5>t không d<U+1ED3>ng ý 11 1.28 1.28%
## 9 IMA2 Không d<U+1ED3>ng ý 18 2.1 2.1%
## 10 IMA2 Khá không d<U+1ED3>ng ý 26 3.03 3.03%
## # ... 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$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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 == "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 1.28 1.28%
## 3 IMA3 R<U+1EA5>t không d<U+1ED3>ng ý 11 1.28 1.28%
## 4 IMA4 R<U+1EA5>t không d<U+1ED3>ng ý 13 1.52 1.52%
## 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 19 2.21 2.21%
## 2 IMA2 Không d<U+1ED3>ng ý 18 2.1 2.1%
## 3 IMA3 Không d<U+1ED3>ng ý 10 1.17 1.17%
## 4 IMA4 Không d<U+1ED3>ng ý 15 1.75 1.75%
## 5 IMA5 Không d<U+1ED3>ng ý 20 2.33 2.33%# 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 33 3.85 3.85%
## 2 IMA2 Khá không d<U+1ED3>ng ý 26 3.03 3.03%
## 3 IMA3 Khá không d<U+1ED3>ng ý 21 2.45 2.45%
## 4 IMA4 Khá không d<U+1ED3>ng ý 26 3.03 3.03%
## 5 IMA5 Khá không d<U+1ED3>ng ý 20 2.33 2.33%# 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 154 18.0 17.95%
## 2 IMA2 Bình thu<U+1EDD>ng 158 18.4 18.41%
## 3 IMA3 Bình thu<U+1EDD>ng 128 14.9 14.92%
## 4 IMA4 Bình thu<U+1EDD>ng 113 13.2 13.17%
## 5 IMA5 Bình thu<U+1EDD>ng 104 12.1 12.12%# 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: 5 x 5
## BPE Res n percent bar_text
## <fct> <fct> <int> <dbl> <chr>
## 1 IMA1 Khá d<U+1ED3>ng ý 230 26.8 26.81%
## 2 IMA2 Khá d<U+1ED3>ng ý 214 24.9 24.94%
## 3 IMA3 Khá d<U+1ED3>ng ý 207 24.1 24.13%
## 4 IMA4 Khá d<U+1ED3>ng ý 201 23.4 23.43%
## 5 IMA5 Khá d<U+1ED3>ng ý 204 23.8 23.78%# 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 351 40.9 40.91%
## 2 IMA2 Ð<U+1ED3>ng ý 365 42.5 42.54%
## 3 IMA3 Ð<U+1ED3>ng ý 403 47.0 46.97%
## 4 IMA4 Ð<U+1ED3>ng ý 406 47.3 47.32%
## 5 IMA5 Ð<U+1ED3>ng ý 420 49.0 48.95%# 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 7.58 7.58%
## 2 IMA2 R<U+1EA5>t d<U+1ED3>ng ý 66 7.69 7.69%
## 3 IMA3 R<U+1EA5>t d<U+1ED3>ng ý 78 9.09 9.09%
## 4 IMA4 R<U+1EA5>t d<U+1ED3>ng ý 84 9.79 9.79%
## 5 IMA5 R<U+1EA5>t d<U+1ED3>ng ý 84 9.79 9.79%# 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 7.58 7.58%
## 2 IMA2 R<U+1EA5>t d<U+1ED3>ng ý 66 7.69 7.69%
## 3 IMA3 R<U+1EA5>t d<U+1ED3>ng ý 78 9.09 9.09%
## 4 IMA4 R<U+1EA5>t d<U+1ED3>ng ý 84 9.79 9.79%
## 5 IMA5 R<U+1EA5>t d<U+1ED3>ng ý 84 9.79 9.79%# 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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## 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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3.8.15. Construct PHB - Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct PHB
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM5_PHB)## Res BPE
## 1 6 PHB1
## 2 3 PHB1
## 3 5 PHB1
## 4 5 PHB1
## 5 5 PHB1
## 6 4 PHB1dim(DN_HCM_SEM5_PHB)## [1] 4290 2attach(DN_HCM_SEM5_PHB)## The following objects are masked from DN_HCM_SEM5_IMA:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_SAT:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQR:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQP:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQC:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQT:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM5:
##
## BPE, Res## The following objects are masked from DN_SEM5:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5:
##
## BPE, ResDN_HCM_SEM5_PHB = within(DN_HCM_SEM5_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_SEM5_PHB)## 'data.frame': 4290 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_SEM5_PHB)## Res BPE
## Very Disagree : 88 PHB1:858
## Disagree : 73 PHB2:858
## Quite Disagree: 116 PHB3:858
## Normal : 370 PHB4:858
## Quite Agree : 801 PHB5:858
## Agree :1919
## Very Agree : 923 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_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 25 2.91 2.91%
## 2 PHB1 Disagree 18 2.1 2.1%
## 3 PHB1 Quite Disagree 37 4.31 4.31%
## 4 PHB1 Normal 97 11.3 11.31%
## 5 PHB1 Quite Agree 215 25.1 25.06%
## 6 PHB1 Agree 352 41.0 41.03%
## 7 PHB1 Very Agree 114 13.3 13.29%
## 8 PHB2 Very Disagree 13 1.52 1.52%
## 9 PHB2 Disagree 21 2.45 2.45%
## 10 PHB2 Quite Disagree 26 3.03 3.03%
## # ... 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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## 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$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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 PHB1 Very Disagree 25 2.91 2.91%
## 2 PHB2 Very Disagree 13 1.52 1.52%
## 3 PHB3 Very Disagree 15 1.75 1.75%
## 4 PHB4 Very Disagree 13 1.52 1.52%
## 5 PHB5 Very Disagree 22 2.56 2.56%# 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 4.31 4.31%
## 2 PHB2 Quite Disagree 26 3.03 3.03%
## 3 PHB3 Quite Disagree 38 4.43 4.43%
## 4 PHB4 Quite Disagree 10 1.17 1.17%
## 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 97 11.3 11.31%
## 2 PHB2 Normal 69 8.04 8.04%
## 3 PHB3 Normal 88 10.3 10.26%
## 4 PHB4 Normal 53 6.18 6.18%
## 5 PHB5 Normal 63 7.34 7.34%# 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 215 25.1 25.06%
## 2 PHB2 Quite Agree 163 19 19%
## 3 PHB3 Quite Agree 174 20.3 20.28%
## 4 PHB4 Quite Agree 133 15.5 15.5%
## 5 PHB5 Quite Agree 116 13.5 13.52%# 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 352 41.0 41.03%
## 2 PHB2 Agree 378 44.1 44.06%
## 3 PHB3 Agree 384 44.8 44.76%
## 4 PHB4 Agree 390 45.4 45.45%
## 5 PHB5 Agree 415 48.4 48.37%# 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 13.3 13.29%
## 2 PHB2 Very Agree 188 21.9 21.91%
## 3 PHB3 Very Agree 140 16.3 16.32%
## 4 PHB4 Very Agree 252 29.4 29.37%
## 5 PHB5 Very Agree 229 26.7 26.69%# 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 13.3 13.29%
## 2 PHB2 Very Agree 188 21.9 21.91%
## 3 PHB3 Very Agree 140 16.3 16.32%
## 4 PHB4 Very Agree 252 29.4 29.37%
## 5 PHB5 Very Agree 229 26.7 26.69%# 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
3.8.16. Construct PHB - Nhãn tiếng việt
DN_HCM_SEM5_PHB_v = within(DN_HCM_SEM5_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_SEM5_PHB_v)## 'data.frame': 4290 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_SEM5_PHB_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 88 PHB1:858
## Không d<U+1ED3>ng ý : 73 PHB2:858
## Khá không d<U+1ED3>ng ý : 116 PHB3:858
## Bình thu<U+1EDD>ng : 370 PHB4:858
## Khá d<U+1ED3>ng ý : 801 PHB5:858
## Ð<U+1ED3>ng ý :1919
## R<U+1EA5>t d<U+1ED3>ng ý : 923 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_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 25 2.91 2.91%
## 2 PHB1 Không d<U+1ED3>ng ý 18 2.1 2.1%
## 3 PHB1 Khá không d<U+1ED3>ng ý 37 4.31 4.31%
## 4 PHB1 Bình thu<U+1EDD>ng 97 11.3 11.31%
## 5 PHB1 Khá d<U+1ED3>ng ý 215 25.1 25.06%
## 6 PHB1 Ð<U+1ED3>ng ý 352 41.0 41.03%
## 7 PHB1 R<U+1EA5>t d<U+1ED3>ng ý 114 13.3 13.29%
## 8 PHB2 R<U+1EA5>t không d<U+1ED3>ng ý 13 1.52 1.52%
## 9 PHB2 Không d<U+1ED3>ng ý 21 2.45 2.45%
## 10 PHB2 Khá không d<U+1ED3>ng ý 26 3.03 3.03%
## # ... 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$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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: 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 25 2.91 2.91%
## 2 PHB2 R<U+1EA5>t không d<U+1ED3>ng ý 13 1.52 1.52%
## 3 PHB3 R<U+1EA5>t không d<U+1ED3>ng ý 15 1.75 1.75%
## 4 PHB4 R<U+1EA5>t không d<U+1ED3>ng ý 13 1.52 1.52%
## 5 PHB5 R<U+1EA5>t không d<U+1ED3>ng ý 22 2.56 2.56%# 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 18 2.1 2.1%
## 2 PHB2 Không d<U+1ED3>ng ý 21 2.45 2.45%
## 3 PHB3 Không d<U+1ED3>ng ý 19 2.21 2.21%
## 4 PHB4 Không d<U+1ED3>ng ý 7 0.82 0.82%
## 5 PHB5 Không d<U+1ED3>ng ý 8 0.93 0.93%# 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 4.31 4.31%
## 2 PHB2 Khá không d<U+1ED3>ng ý 26 3.03 3.03%
## 3 PHB3 Khá không d<U+1ED3>ng ý 38 4.43 4.43%
## 4 PHB4 Khá không d<U+1ED3>ng ý 10 1.17 1.17%
## 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 97 11.3 11.31%
## 2 PHB2 Bình thu<U+1EDD>ng 69 8.04 8.04%
## 3 PHB3 Bình thu<U+1EDD>ng 88 10.3 10.26%
## 4 PHB4 Bình thu<U+1EDD>ng 53 6.18 6.18%
## 5 PHB5 Bình thu<U+1EDD>ng 63 7.34 7.34%# 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 215 25.1 25.06%
## 2 PHB2 Khá d<U+1ED3>ng ý 163 19 19%
## 3 PHB3 Khá d<U+1ED3>ng ý 174 20.3 20.28%
## 4 PHB4 Khá d<U+1ED3>ng ý 133 15.5 15.5%
## 5 PHB5 Khá d<U+1ED3>ng ý 116 13.5 13.52%# 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 352 41.0 41.03%
## 2 PHB2 Ð<U+1ED3>ng ý 378 44.1 44.06%
## 3 PHB3 Ð<U+1ED3>ng ý 384 44.8 44.76%
## 4 PHB4 Ð<U+1ED3>ng ý 390 45.4 45.45%
## 5 PHB5 Ð<U+1ED3>ng ý 415 48.4 48.37%# 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 13.3 13.29%
## 2 PHB2 R<U+1EA5>t d<U+1ED3>ng ý 188 21.9 21.91%
## 3 PHB3 R<U+1EA5>t d<U+1ED3>ng ý 140 16.3 16.32%
## 4 PHB4 R<U+1EA5>t d<U+1ED3>ng ý 252 29.4 29.37%
## 5 PHB5 R<U+1EA5>t d<U+1ED3>ng ý 229 26.7 26.69%# 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 13.3 13.29%
## 2 PHB2 R<U+1EA5>t d<U+1ED3>ng ý 188 21.9 21.91%
## 3 PHB3 R<U+1EA5>t d<U+1ED3>ng ý 140 16.3 16.32%
## 4 PHB4 R<U+1EA5>t d<U+1ED3>ng ý 252 29.4 29.37%
## 5 PHB5 R<U+1EA5>t d<U+1ED3>ng ý 229 26.7 26.69%# 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
3.8.17. Construct PEB - Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct PEV
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM5_PEB)## Res BPE
## 1 4 PEB1
## 2 5 PEB1
## 3 6 PEB1
## 4 5 PEB1
## 5 6 PEB1
## 6 7 PEB1dim(DN_HCM_SEM5_PEB)## [1] 3432 2attach(DN_HCM_SEM5_PEB)## The following objects are masked from DN_HCM_SEM5_PHB:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_IMA:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_SAT:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQR:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQP:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQC:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQT:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM5:
##
## BPE, Res## The following objects are masked from DN_SEM5:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5:
##
## BPE, ResDN_HCM_SEM5_PEB = within(DN_HCM_SEM5_PEB, {
Res = factor(Res, labels = c("Very Disagree", "Disagree", "Quite Disagree", "Normal", "Quite Agree", "Agree", "Very Agree"))
BPE = factor(BPE, labels = c("PEB1", "PEB2", "PEB3", "PEB4"))
})
str(DN_HCM_SEM5_PEB)## 'data.frame': 3432 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 "PEB1","PEB2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM5_PEB)## Res BPE
## Very Disagree : 98 PEB1:858
## Disagree : 64 PEB2:858
## Quite Disagree: 78 PEB3:858
## Normal : 297 PEB4:858
## Quite Agree : 632
## Agree :1456
## Very Agree : 807 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_PEB %>%
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] PEB4 PEB3 PEB2 PEB1
## Levels: PEB1 PEB2 PEB3 PEB4# 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 PEB1 Very Disagree 24 2.8 2.8%
## 2 PEB1 Disagree 8 0.93 0.93%
## 3 PEB1 Quite Disagree 13 1.52 1.52%
## 4 PEB1 Normal 60 6.99 6.99%
## 5 PEB1 Quite Agree 127 14.8 14.8%
## 6 PEB1 Agree 391 45.6 45.57%
## 7 PEB1 Very Agree 235 27.4 27.39%
## 8 PEB2 Very Disagree 25 2.91 2.91%
## 9 PEB2 Disagree 17 1.98 1.98%
## 10 PEB2 Quite Disagree 21 2.45 2.45%
## # ... 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 benefits (PEB) 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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# 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 PEB1 Very Disagree 24 2.8 2.8%
## 2 PEB2 Very Disagree 25 2.91 2.91%
## 3 PEB3 Very Disagree 25 2.91 2.91%
## 4 PEB4 Very Disagree 24 2.8 2.8%# 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 PEB1 Quite Disagree 13 1.52 1.52%
## 2 PEB2 Quite Disagree 21 2.45 2.45%
## 3 PEB3 Quite Disagree 22 2.56 2.56%
## 4 PEB4 Quite Disagree 22 2.56 2.56%# 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 PEB1 Normal 60 6.99 6.99%
## 2 PEB2 Normal 67 7.81 7.81%
## 3 PEB3 Normal 77 8.97 8.97%
## 4 PEB4 Normal 93 10.8 10.84%# 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 PEB1 Quite Agree 127 14.8 14.8%
## 2 PEB2 Quite Agree 170 19.8 19.81%
## 3 PEB3 Quite Agree 168 19.6 19.58%
## 4 PEB4 Quite Agree 167 19.5 19.46%# 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 PEB1 Agree 391 45.6 45.57%
## 2 PEB2 Agree 353 41.1 41.14%
## 3 PEB3 Agree 354 41.3 41.26%
## 4 PEB4 Agree 358 41.7 41.72%# 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 PEB1 Very Agree 235 27.4 27.39%
## 2 PEB2 Very Agree 205 23.9 23.89%
## 3 PEB3 Very Agree 192 22.4 22.38%
## 4 PEB4 Very Agree 175 20.4 20.4%# 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 PEB1 Very Agree 235 27.4 27.39%
## 2 PEB2 Very Agree 205 23.9 23.89%
## 3 PEB3 Very Agree 192 22.4 22.38%
## 4 PEB4 Very Agree 175 20.4 20.4%# 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$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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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3.8.18. Construct PEB - Nhãn tiếng việt
DN_HCM_SEM5_PEB_v = within(DN_HCM_SEM5_PEB, {
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("PEB1", "PEB2", "PEB3", "PEB4"))
})
str(DN_HCM_SEM5_PEB_v)## 'data.frame': 3432 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 "PEB1","PEB2",..: 1 1 1 1 1 1 1 1 1 1 ...summary(DN_HCM_SEM5_PEB_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 98 PEB1:858
## Không d<U+1ED3>ng ý : 64 PEB2:858
## Khá không d<U+1ED3>ng ý : 78 PEB3:858
## Bình thu<U+1EDD>ng : 297 PEB4:858
## Khá d<U+1ED3>ng ý : 632
## Ð<U+1ED3>ng ý :1456
## R<U+1EA5>t d<U+1ED3>ng ý : 807 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_PEB_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] PEB4 PEB3 PEB2 PEB1
## Levels: PEB1 PEB2 PEB3 PEB4# 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 PEB1 R<U+1EA5>t không d<U+1ED3>ng ý 24 2.8 2.8%
## 2 PEB1 Không d<U+1ED3>ng ý 8 0.93 0.93%
## 3 PEB1 Khá không d<U+1ED3>ng ý 13 1.52 1.52%
## 4 PEB1 Bình thu<U+1EDD>ng 60 6.99 6.99%
## 5 PEB1 Khá d<U+1ED3>ng ý 127 14.8 14.8%
## 6 PEB1 Ð<U+1ED3>ng ý 391 45.6 45.57%
## 7 PEB1 R<U+1EA5>t d<U+1ED3>ng ý 235 27.4 27.39%
## 8 PEB2 R<U+1EA5>t không d<U+1ED3>ng ý 25 2.91 2.91%
## 9 PEB2 Không d<U+1ED3>ng ý 17 1.98 1.98%
## 10 PEB2 Khá không d<U+1ED3>ng ý 21 2.45 2.45%
## # ... 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 (PEB) 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
## 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 PEB1 R<U+1EA5>t không d<U+1ED3>ng ý 24 2.8 2.8%
## 2 PEB2 R<U+1EA5>t không d<U+1ED3>ng ý 25 2.91 2.91%
## 3 PEB3 R<U+1EA5>t không d<U+1ED3>ng ý 25 2.91 2.91%
## 4 PEB4 R<U+1EA5>t không d<U+1ED3>ng ý 24 2.8 2.8%# 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 PEB1 Không d<U+1ED3>ng ý 8 0.93 0.93%
## 2 PEB2 Không d<U+1ED3>ng ý 17 1.98 1.98%
## 3 PEB3 Không d<U+1ED3>ng ý 20 2.33 2.33%
## 4 PEB4 Không d<U+1ED3>ng ý 19 2.21 2.21%# 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 PEB1 Khá không d<U+1ED3>ng ý 13 1.52 1.52%
## 2 PEB2 Khá không d<U+1ED3>ng ý 21 2.45 2.45%
## 3 PEB3 Khá không d<U+1ED3>ng ý 22 2.56 2.56%
## 4 PEB4 Khá không d<U+1ED3>ng ý 22 2.56 2.56%# 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 PEB1 Bình thu<U+1EDD>ng 60 6.99 6.99%
## 2 PEB2 Bình thu<U+1EDD>ng 67 7.81 7.81%
## 3 PEB3 Bình thu<U+1EDD>ng 77 8.97 8.97%
## 4 PEB4 Bình thu<U+1EDD>ng 93 10.8 10.84%# 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 PEB1 Khá d<U+1ED3>ng ý 127 14.8 14.8%
## 2 PEB2 Khá d<U+1ED3>ng ý 170 19.8 19.81%
## 3 PEB3 Khá d<U+1ED3>ng ý 168 19.6 19.58%
## 4 PEB4 Khá d<U+1ED3>ng ý 167 19.5 19.46%# 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 PEB1 Ð<U+1ED3>ng ý 391 45.6 45.57%
## 2 PEB2 Ð<U+1ED3>ng ý 353 41.1 41.14%
## 3 PEB3 Ð<U+1ED3>ng ý 354 41.3 41.26%
## 4 PEB4 Ð<U+1ED3>ng ý 358 41.7 41.72%# 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 PEB1 R<U+1EA5>t d<U+1ED3>ng ý 235 27.4 27.39%
## 2 PEB2 R<U+1EA5>t d<U+1ED3>ng ý 205 23.9 23.89%
## 3 PEB3 R<U+1EA5>t d<U+1ED3>ng ý 192 22.4 22.38%
## 4 PEB4 R<U+1EA5>t d<U+1ED3>ng ý 175 20.4 20.4%# 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 PEB1 R<U+1EA5>t d<U+1ED3>ng ý 235 27.4 27.39%
## 2 PEB2 R<U+1EA5>t d<U+1ED3>ng ý 205 23.9 23.89%
## 3 PEB3 R<U+1EA5>t d<U+1ED3>ng ý 192 22.4 22.38%
## 4 PEB4 R<U+1EA5>t d<U+1ED3>ng ý 175 20.4 20.4%# 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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3.8.19. Construct PVA - Nhãn tiếng anh
# Plot Likert graph for people in both cities for construct PVA
library(tidyverse)
library(compareGroups)
head(DN_HCM_SEM5_PVA)## Res BPE
## 1 6 PVA1
## 2 5 PVA1
## 3 6 PVA1
## 4 5 PVA1
## 5 6 PVA1
## 6 6 PVA1dim(DN_HCM_SEM5_PVA)## [1] 2574 2attach(DN_HCM_SEM5_PVA)## The following objects are masked from DN_HCM_SEM5_PEB:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PHB:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_IMA:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_SAT:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQR:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQP:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQC:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSQT:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5_PSB:
##
## BPE, Res## The following objects are masked from HCM_SEM5:
##
## BPE, Res## The following objects are masked from DN_SEM5:
##
## BPE, Res## The following objects are masked from DN_HCM_SEM5:
##
## BPE, ResDN_HCM_SEM5_PVA = within(DN_HCM_SEM5_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_SEM5_PVA)## 'data.frame': 2574 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_SEM5_PVA)## Res BPE
## Very Disagree : 11 PVA1:858
## Disagree : 49 PVA2:858
## Quite Disagree: 79 PVA3:858
## Normal : 225
## Quite Agree : 485
## Agree :1168
## Very Agree : 557 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_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 9 1.05 1.05%
## 3 PVA1 Quite Disagree 18 2.1 2.1%
## 4 PVA1 Normal 56 6.53 6.53%
## 5 PVA1 Quite Agree 131 15.3 15.27%
## 6 PVA1 Agree 413 48.1 48.14%
## 7 PVA1 Very Agree 226 26.3 26.34%
## 8 PVA2 Very Disagree 4 0.47 0.47%
## 9 PVA2 Disagree 26 3.03 3.03%
## 10 PVA2 Quite Disagree 39 4.55 4.55%
## # ... 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 value (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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# 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 0.47 0.47%
## 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 3.03 3.03%# 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 2.1 2.1%
## 2 PVA2 Quite Disagree 39 4.55 4.55%
## 3 PVA3 Quite Disagree 22 2.56 2.56%# 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 6.53 6.53%
## 2 PVA2 Normal 100 11.7 11.66%
## 3 PVA3 Normal 69 8.04 8.04%# 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 131 15.3 15.27%
## 2 PVA2 Quite Agree 194 22.6 22.61%
## 3 PVA3 Quite Agree 160 18.6 18.65%# 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 413 48.1 48.14%
## 2 PVA2 Agree 344 40.1 40.09%
## 3 PVA3 Agree 411 47.9 47.9%# 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 226 26.3 26.34%
## 2 PVA2 Very Agree 151 17.6 17.6%
## 3 PVA3 Very Agree 180 21.0 20.98%# 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 226 26.3 26.34%
## 2 PVA2 Very Agree 151 17.6 17.6%
## 3 PVA3 Very Agree 180 21.0 20.98%# 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$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$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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3.8.20. Construct PVA - Nhãn tiếng việt
DN_HCM_SEM5_PVA_v = within(DN_HCM_SEM5_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_SEM5_PVA_v)## 'data.frame': 2574 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_SEM5_PVA_v)## Res BPE
## R<U+1EA5>t không d<U+1ED3>ng ý: 11 PVA1:858
## Không d<U+1ED3>ng ý : 49 PVA2:858
## Khá không d<U+1ED3>ng ý : 79 PVA3:858
## Bình thu<U+1EDD>ng : 225
## Khá d<U+1ED3>ng ý : 485
## Ð<U+1ED3>ng ý :1168
## R<U+1EA5>t d<U+1ED3>ng ý : 557 ## t <- compareGroups(Res ~ BPE, data = DN_SEM1)
## createTable(t)
DN_HCM_SEM5_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 9 1.05 1.05%
## 3 PVA1 Khá không d<U+1ED3>ng ý 18 2.1 2.1%
## 4 PVA1 Bình thu<U+1EDD>ng 56 6.53 6.53%
## 5 PVA1 Khá d<U+1ED3>ng ý 131 15.3 15.27%
## 6 PVA1 Ð<U+1ED3>ng ý 413 48.1 48.14%
## 7 PVA1 R<U+1EA5>t d<U+1ED3>ng ý 226 26.3 26.34%
## 8 PVA2 R<U+1EA5>t không d<U+1ED3>ng ý 4 0.47 0.47%
## 9 PVA2 Không d<U+1ED3>ng ý 26 3.03 3.03%
## 10 PVA2 Khá không d<U+1ED3>ng ý 39 4.55 4.55%
## # ... 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 v\u1EC1 gi\u00E1 tr\u1ECB c\u1EE7a h\u00E0nh kh\u00E1ch (PVA) \u0111\u1ED1i v\u1EDBi 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_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
## 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 0.47 0.47%
## 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 9 1.05 1.05%
## 2 PVA2 Không d<U+1ED3>ng ý 26 3.03 3.03%
## 3 PVA3 Không d<U+1ED3>ng ý 14 1.63 1.63%# 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 2.1 2.1%
## 2 PVA2 Khá không d<U+1ED3>ng ý 39 4.55 4.55%
## 3 PVA3 Khá không d<U+1ED3>ng ý 22 2.56 2.56%# 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 6.53 6.53%
## 2 PVA2 Bình thu<U+1EDD>ng 100 11.7 11.66%
## 3 PVA3 Bình thu<U+1EDD>ng 69 8.04 8.04%# 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 131 15.3 15.27%
## 2 PVA2 Khá d<U+1ED3>ng ý 194 22.6 22.61%
## 3 PVA3 Khá d<U+1ED3>ng ý 160 18.6 18.65%# 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 413 48.1 48.14%
## 2 PVA2 Ð<U+1ED3>ng ý 344 40.1 40.09%
## 3 PVA3 Ð<U+1ED3>ng ý 411 47.9 47.9%# 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 226 26.3 26.34%
## 2 PVA2 R<U+1EA5>t d<U+1ED3>ng ý 151 17.6 17.6%
## 3 PVA3 R<U+1EA5>t d<U+1ED3>ng ý 180 21.0 20.98%# 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 226 26.3 26.34%
## 2 PVA2 R<U+1EA5>t d<U+1ED3>ng ý 151 17.6 17.6%
## 3 PVA3 R<U+1EA5>t d<U+1ED3>ng ý 180 21.0 20.98%# 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