文章信息: 作者： kaiwu; 日期：2022年12月06日; 点击数：540

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Rstudio	https://posit.co/

R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinformaticians and statisticians for data analysis and developing statistical software.^[6] Users have created packages to augment the functions of the R language.

According to user surveys and studies of scholarly literature databases, R is one of the most commonly used programming languages used in data mining.^[7] As of March 2022, R ranks 11th in the TIOBE index, a measure of programming language popularity, in which the language peaked in 8th place in August 2020.^[8]^[9]

The official R software environment is an open-source free software environment within the GNU package, available under the GNU General Public License. It is written primarily in C, Fortran, and R itself (partially self-hosting). Precompiled executables are provided for various operating systems. R has a command line interface.^[10] Multiple third-party graphical user interfaces are also available, such as RStudio, an integrated development environment, and Jupyter, a notebook interface.

https://posit.co/download/rstudio-desktop/

R 4.2.2 for windows

https://od.lk/d/178336078_aJAHZ/R-4.2.2-win%20%281%29.exe

Rstudio for windows

https://od.lk/d/178336079_xwuA8/RStudio-2022.07.2-576%20%281%29.exe

1.about the dataset (tourist satisfaction)

1.1questionnaire

https://od.lk/d/178312136_WyH9N/tourist_satisfaction_questionnaire_en.docx

https://od.lk/d/178312135_ZW7sa/tourist_satisfaction_questionnaire_en.pdf

1.2 data for general purpose (without variable labels and value labels)

https://od.lk/d/178313264_Tr9Ti/data_tourist_satisfaction.csv

https://od.lk/d/178313263_MVBwn/data_tourist_satisfaction_en.xlsx

1.3 R markdown file

https://od.lk/d/178342466_urRFT/tourist2022December6EN_with_task.Rmd

leaflet

Leaflet is the leading open-source JavaScript library for mobile-friendly interactive maps. Weighing just about 42 KB of JS, it has all the mapping features most developers ever need.

https://leafletjs.com/

openstreet map

https://www.openstreetmap.org/

Importance-Performance Analysis

https://www.sciencepublishinggroup.com/journal/paperinfo?journalid=175&doi=10.11648/j.sjbm.20200802.11

tourist satisfaction survey dataset(simulated data，45 variables，373 cases)

###(1)questionnaire https://od.lk/d/178312136_WyH9N/tourist_satisfaction_questionnaire_en.docx https://od.lk/d/178312135_ZW7sa/tourist_satisfaction_questionnaire_en.pdf

###(2)DATA https://od.lk/d/178313264_Tr9Ti/data_tourist_satisfaction.csv
https://od.lk/d/178313263_MVBwn/data_tourist_satisfaction_en.xlsx

###(3)Rmarkdown file(codes for data analysis)

1.data preparation

alternative terms of data preparation: data wrangling、data munging、data cleaning https://theappsolutions.com/blog/development/data-wrangling-guide-to-data-preparation/

Data Wrangling in R (1)Dplyr - essential data-munging R package. Supreme data framing tool. Especially useful for data management operating by categories. (2)Purrr - good for list function operations and error-checking. (3)Splitstackshape - an oldie but goldie. Good for shaping complex data sets and simplifying the visualization. (4)JSOnline - nice and easy parsing tool. (5)Magrittr - good for wrangling scattered sets and putting them into a more coherent form.

1.1 load packages

if(!isTRUE(require("Hmisc"))){install.packages("Hmisc")}#statistical analysis

## 载入需要的程辑包：Hmisc

## 载入需要的程辑包：lattice

## 载入需要的程辑包：survival

## 载入需要的程辑包：Formula

## 载入需要的程辑包：ggplot2

## 
## 载入程辑包：'Hmisc'

## The following objects are masked from 'package:base':
## 
##     format.pval, units

if(!isTRUE(require("psych"))){install.packages("psych")}#statistical analysis

## 载入需要的程辑包：psych

## 
## 载入程辑包：'psych'

## The following object is masked from 'package:Hmisc':
## 
##     describe

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

if(!isTRUE(require("lavaan"))){install.packages("lavaan")} #The lavaan package is developed to provide useRs, researchers and teachers a free open-source, but commercial-quality package for latent variable modeling

## 载入需要的程辑包：lavaan

## This is lavaan 0.6-12
## lavaan is FREE software! Please report any bugs.

## 
## 载入程辑包：'lavaan'

## The following object is masked from 'package:psych':
## 
##     cor2cov

if(!isTRUE(require("broom"))){install.packages("broom")}# tidy report

## 载入需要的程辑包：broom

if(!isTRUE(require("ggplot2"))){install.packages("ggplot2")}# data visualization
if(!isTRUE(require("ggrepel"))){install.packages("ggrepel")} # provides geoms for ggplot2 to repel overlapping text labels

## 载入需要的程辑包：ggrepel

if(!isTRUE(require("corrplot"))){install.packages("corrplot")} #A visualization of a correlation matrix

## 载入需要的程辑包：corrplot

## corrplot 0.92 loaded

if(!isTRUE(require("semPlot"))){install.packages("semPlot")} #Description Path diagrams and visual analysis of various SEM packages' output

## 载入需要的程辑包：semPlot

if(!isTRUE(require("leaflet"))){install.packages("leaflet")}# maps based on openstreet data

## 载入需要的程辑包：leaflet

install packages

#install.packages(Hmisc) 
#install.packages(psych) 
#install.packages(lavaan)

#install.packages(broom) 

#install.packages(ggplot2)
#install.packages(ggrepel)
#install.packages(corrplot)
 #install.packages(semPlot)

#install.packages(leaflet)

1.2 import dataset

datafolder is your working folder, you can modify this by yourself

#datafolder is your working folder, you can modify this by yourself
datafolder<-'D:/pdata/rdata/tourist_en/'

#tourist<-read.csv('https://od.lk/d/178313264_Tr9Ti/data_tourist_satisfaction.csv',header=TRUE,encoding = 'UTF-8')

tourist<-read.csv(paste(datafolder,'data_tourist_satisfaction.csv',sep=""),header=TRUE,encoding = 'UTF-8')



tail(tourist)

##        sid gender byear region income expense type3 type2 thotel sat1 sat2 sat3
## 368 rec368      2  1962      1   1826     269     2     1      1    4    4    3
## 369 rec369      2  1982      6   1869     301     3     2      4    5    5    3
## 370 rec370      2  1959      5   2766     261     2     2      1    5    4    4
## 371 rec371      2  1989      7   2023     210     3     2      1    4    3    4
## 372 rec372      2  1996      3   2062     266     2     1      1    5    5    4
## 373 rec373      2  2000      6   3311     444     3     2      4    5    3    4
##     sat4 sat5 sat6 ri1 ri2 ri3 ri4 ri5 rp1 rp2 rp3 rp4 rp5 te1 te2 te3 te4 te5
## 368    2    4    3   5   5   5   5   5   5   5   5   5   5   4   4   4   4   4
## 369    4    3    3   5   5   5   5   5   5   5   4   5   5   5   4   3   4   4
## 370    4    5    3   4   4   5   5   5   4   4   5   5   5   4   3   4   4   4
## 371    3    5    5   5   5   5   5   5   5   5   4   5   5   4   5   4   5   3
## 372    4    4    3   5   5   5   5   5   5   5   5   5   5   5   5   5   5   4
## 373    5    4    4   5   5   4   5   5   5   5   4   5   5   4   4   4   4   5
##     te6 te7 te8 zh1 zh2 zh3 zh4 zh5 zh6 zh7 latitude longitude
## 368   4   4   4   4   4   5   4   2   4   4 31.24039  121.4264
## 369   5   4   4   5   5   5   5   5   5   5 31.17127  121.4191
## 370   4   3   4   3   4   3   4   4   3   4 31.14341  121.6599
## 371   5   3   4   1   1   2   1   1   1   1 31.20616  121.4589
## 372   5   5   3   1   1   1   1   1   1   1 31.23762  121.5243
## 373   5   5   4   4   4   3   1   2   1   1 31.23350  121.4776

##### practice 1 ： download the data and import the data into the working space

1.3 value labels

add value labels to categorical variables.

#value labels
tourist$gender <- factor(tourist$gender,levels = c(1,2),labels = c("male", "female"))
tourist$thotel <- factor(tourist$thotel,levels = c(1,2,3,4),labels = c("budget hotel", "luxury hotel", "bed and breakfast", "apartment hotel"))

tourist$type3 <- factor(tourist$type3,levels = c(1,2,3),labels = c("natural scenery", "historical scenery", "mixed scenery"))
 
tourist$type2 <- factor(tourist$type2,levels = c(1,2),labels = c("sightseeing", "participation"))



head(tourist)

##      sid gender byear region income expense              type3         type2
## 1 rec001 female  1971      4   2708     432 historical scenery participation
## 2 rec002 female  1995      6   1884     238 historical scenery participation
## 3 rec003   male  1990      3   2458     399      mixed scenery   sightseeing
## 4 rec004   male  1970      6   2726     245    natural scenery   sightseeing
## 5 rec005 female  1964      7   3084     287 historical scenery participation
## 6 rec006 female  1965      2   2184     216    natural scenery   sightseeing
##              thotel sat1 sat2 sat3 sat4 sat5 sat6 ri1 ri2 ri3 ri4 ri5 rp1 rp2
## 1 bed and breakfast    4    2    2    3    4    4   4   4   4   5   4   4   5
## 2      budget hotel    5    5    4    5    3    5   4   3   4   4   5   4   3
## 3      budget hotel    3    4    5    3    3    2   5   5   5   5   5   5   5
## 4      luxury hotel    5    2    3    5    4    4   4   5   5   5   5   4   5
## 5      budget hotel    5    3    5    2    4    5   5   5   5   5   5   5   5
## 6 bed and breakfast    4    3    3    2    5    3   4   4   5   5   5   4   4
##   rp3 rp4 rp5 te1 te2 te3 te4 te5 te6 te7 te8 zh1 zh2 zh3 zh4 zh5 zh6 zh7
## 1   4   5   4   4   4   4   4   4   4   4   4   3   1   1   2   3   2   2
## 2   4   4   5   5   5   4   4   4   5   3   4   4   4   5   3   5   5   5
## 3   4   5   5   3   4   4   4   4   4   4   4   1   1   2   2   1   3   1
## 4   5   5   5   4   4   4   4   5   4   2   4   3   3   3   3   2   2   2
## 5   5   5   5   4   5   4   4   5   5   4   5   1   1   1   4   2   1   1
## 6   5   5   5   1   3   3   3   1   1   3   2   4   3   3   4   2   4   4
##   latitude longitude
## 1 31.14558  121.7142
## 2 31.14376  121.6605
## 3 31.27034  121.4915
## 4 31.18496  121.3085
## 5 31.17656  121.3151
## 6 31.19191  121.4547

##### practice02
##### add value labels to variable of region

##### 1 Central China
##### 2 East China
##### 3 North China
##### 4 Northeast China
##### 5 Northwest China
##### 6 Southwest China
##### 7 West China

1.4 compute variables

compute variables

#compute the overall of satisfaction
tourist<-transform(tourist,sat=(sat1+sat2+sat3+sat4+sat5+sat6)/6)

summary(tourist$sat)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.667   3.667   4.000   3.994   4.333   5.000

#compute variables
# practice 03 ： compute the age
# age = 2022 - byear

#compute variables
# practice 03 ： compute the age
# age = 2022 - byear
tourist<-transform(tourist,age = 2022 - byear)

1.5 recode

# recode income3 (income range)
attach(tourist)
tourist$income3[income  < 2000] <- 1
tourist$income3[income >= 2000 & income  <3000] <- 2
tourist$income3[income >= 3000] <- 3
detach(tourist)

# add value labels
tourist$income3 <- factor(tourist$income3,levels = c(1,2,3),labels = c("below 2000", "2000-3000", "above 3000"))


summary(tourist$income3)

## below 2000  2000-3000 above 3000 
##         76        191        106

# recode tourism expense
attach(tourist)
tourist$expense3[expense  < 300] <- 1
tourist$expense3[expense >= 300 & expense  < 400] <- 2
tourist$expense3[expense > 400] <- 3
detach(tourist)
# add value labels
tourist$expense3 <- factor(tourist$expense3,levels = c(1,2,3),labels = c("below 300", "300-399", "above 400"))
summary(tourist$expense3)

## below 300   300-399 above 400 
##       152       144        77

# recode age range
# practice 04 ： recode age

# recode age range 
attach(tourist)
tourist$age4[age  < 20] <- 1
tourist$age4[age >= 20 & age  < 40] <- 2
tourist$age4[age >= 40 & age  < 60] <- 3
tourist$age4[age >= 60] <- 4
detach(tourist)
# add value labels
tourist$age4 <- factor(tourist$age4,levels = c(1,2,3,4),labels = c("below 20", "20-39","40-59", "above 60"))
summary(tourist$age4)

## below 20    20-39    40-59 above 60 
##       30      127      147       69

1.6 summary()

look through the dataset

summary(tourist)

##      sid               gender        byear          region          income    
##  Length:373         male  :214   Min.   :1952   Min.   :1.000   Min.   :1133  
##  Class :character   female:159   1st Qu.:1965   1st Qu.:2.000   1st Qu.:2121  
##  Mode  :character                Median :1978   Median :4.000   Median :2654  
##                                  Mean   :1979   Mean   :3.761   Mean   :2590  
##                                  3rd Qu.:1993   3rd Qu.:6.000   3rd Qu.:3084  
##                                  Max.   :2005   Max.   :7.000   Max.   :3773  
##     expense                     type3               type2    
##  Min.   :199.0   natural scenery   : 58   sightseeing  :178  
##  1st Qu.:262.0   historical scenery:132   participation:195  
##  Median :326.0   mixed scenery     :183                      
##  Mean   :330.6                                               
##  3rd Qu.:391.0                                               
##  Max.   :556.0                                               
##                thotel         sat1            sat2            sat3      
##  budget hotel     :163   Min.   :3.000   Min.   :2.000   Min.   :1.000  
##  luxury hotel     : 35   1st Qu.:4.000   1st Qu.:3.000   1st Qu.:3.000  
##  bed and breakfast:121   Median :5.000   Median :4.000   Median :4.000  
##  apartment hotel  : 54   Mean   :4.349   Mean   :3.743   Mean   :3.676  
##                          3rd Qu.:5.000   3rd Qu.:5.000   3rd Qu.:5.000  
##                          Max.   :5.000   Max.   :5.000   Max.   :5.000  
##       sat4            sat5            sat6            ri1       
##  Min.   :2.000   Min.   :3.000   Min.   :2.000   Min.   :3.000  
##  1st Qu.:4.000   1st Qu.:3.000   1st Qu.:3.000   1st Qu.:4.000  
##  Median :4.000   Median :4.000   Median :4.000   Median :4.000  
##  Mean   :4.214   Mean   :4.097   Mean   :3.887   Mean   :4.351  
##  3rd Qu.:5.000   3rd Qu.:5.000   3rd Qu.:5.000   3rd Qu.:5.000  
##  Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
##       ri2             ri3             ri4             ri5       
##  Min.   :3.000   Min.   :3.000   Min.   :3.000   Min.   :3.000  
##  1st Qu.:4.000   1st Qu.:4.000   1st Qu.:5.000   1st Qu.:5.000  
##  Median :5.000   Median :5.000   Median :5.000   Median :5.000  
##  Mean   :4.491   Mean   :4.499   Mean   :4.708   Mean   :4.727  
##  3rd Qu.:5.000   3rd Qu.:5.000   3rd Qu.:5.000   3rd Qu.:5.000  
##  Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
##       rp1             rp2             rp3             rp4       
##  Min.   :3.000   Min.   :3.000   Min.   :2.000   Min.   :3.000  
##  1st Qu.:4.000   1st Qu.:4.000   1st Qu.:4.000   1st Qu.:5.000  
##  Median :4.000   Median :5.000   Median :5.000   Median :5.000  
##  Mean   :4.316   Mean   :4.515   Mean   :4.383   Mean   :4.721  
##  3rd Qu.:5.000   3rd Qu.:5.000   3rd Qu.:5.000   3rd Qu.:5.000  
##  Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
##       rp5             te1             te2             te3       
##  Min.   :3.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:4.000   1st Qu.:4.000   1st Qu.:3.000   1st Qu.:4.000  
##  Median :5.000   Median :4.000   Median :4.000   Median :4.000  
##  Mean   :4.643   Mean   :3.912   Mean   :3.791   Mean   :4.131  
##  3rd Qu.:5.000   3rd Qu.:5.000   3rd Qu.:4.000   3rd Qu.:5.000  
##  Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
##       te4             te5             te6             te7       
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:4.000   1st Qu.:3.000   1st Qu.:3.000   1st Qu.:3.000  
##  Median :4.000   Median :4.000   Median :4.000   Median :4.000  
##  Mean   :4.078   Mean   :3.756   Mean   :3.898   Mean   :3.729  
##  3rd Qu.:5.000   3rd Qu.:4.000   3rd Qu.:5.000   3rd Qu.:4.000  
##  Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
##       te8             zh1             zh2             zh3       
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:3.000   1st Qu.:2.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :4.000   Median :3.000   Median :3.000   Median :3.000  
##  Mean   :3.657   Mean   :2.836   Mean   :2.627   Mean   :2.702  
##  3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:4.000  
##  Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
##       zh4             zh5             zh6             zh7       
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :2.000   Median :3.000   Median :3.000   Median :3.000  
##  Mean   :2.552   Mean   :2.598   Mean   :2.528   Mean   :2.555  
##  3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:4.000  
##  Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
##     latitude       longitude          sat             age       
##  Min.   :30.79   Min.   :121.0   Min.   :2.667   Min.   :17.00  
##  1st Qu.:31.15   1st Qu.:121.4   1st Qu.:3.667   1st Qu.:29.00  
##  Median :31.20   Median :121.5   Median :4.000   Median :44.00  
##  Mean   :31.21   Mean   :121.5   Mean   :3.994   Mean   :43.12  
##  3rd Qu.:31.23   3rd Qu.:121.7   3rd Qu.:4.333   3rd Qu.:57.00  
##  Max.   :31.72   Max.   :121.9   Max.   :5.000   Max.   :70.00  
##        income3         expense3         age4    
##  below 2000: 76   below 300:152   below 20: 30  
##  2000-3000 :191   300-399  :144   20-39   :127  
##  above 3000:106   above 400: 77   40-59   :147  
##                                   above 60: 69  
##                                                 
##

1.7 distribution of the data

# density chart
plot(density(tourist$sat))

plot(density(tourist$sat),main="", xlab='overall satisfaction', ylab='frequenccy')

# histgram chart
hist(tourist$sat)

hist(tourist$sat,main="", xlab='overall satisfaction data', ylab='frequency')

# practice 05 ： check the distribution of income

for loop: distribution of 31 variables

#for (i in 10:40){
  # plot(density(tourist[,i]))
  # plot(density(tourist[,i]),main="", xlab=names(tourist[i]))
#}

for loop: distribution of 31 variables histgram chart

#for (i in 10:40){
  # hist(tourist[,i],main="", xlab=names(tourist[i]))
  

  # h<-hist(tourist[,i],plot=FALSE)
  # hist(tourist[,i],main="",xlab=names(tourist[i]),labels = TRUE,ylim=c(0,1.1*max(h$counts)))
  
  # https://stackoverflow.com/questions/9317948/how-to-label-histogram-bars-with-data-values-or-percents-in-r  
#}

1.8 save the dataset

save(tourist,file=paste(datafolder,'tourist_EN.rda',sep=""))

2. basic analysis

2.1 frequency analysis

#frequency table of gender
t1<-table(tourist$gender)
t1

## 
##   male female 
##    214    159

#percentage of gender
prop.table(t1)

## 
##      male    female 
## 0.5737265 0.4262735

round(prop.table(t1)*100,2)

## 
##   male female 
##  57.37  42.63

for tidy report,we can transfer the table into data frame

#t1b is on frequency
t1b<-as.data.frame(table(tourist$gender))


# t1c is on percentage
t1c<-as.data.frame(round(prop.table(t1)*100,2))

#combine t1b and t1c
t1d<-cbind(t1b,t1c$Freq)

#rename the variable names
colnames(t1d)<-c('gender','frequency','percent')

print(t1d)

##   gender frequency percent
## 1   male       214   57.37
## 2 female       159   42.63

#export the result as a csv file.
write.csv(t1d,paste(datafolder,'tourist_gender.csv',sep=""))

#frequency of hotel type
t2<-table(tourist$thotel)
t2

## 
##      budget hotel      luxury hotel bed and breakfast   apartment hotel 
##               163                35               121                54

round(prop.table(t2)*100,2)

## 
##      budget hotel      luxury hotel bed and breakfast   apartment hotel 
##             43.70              9.38             32.44             14.48

##### practice 06  frequency analysis on region

hist(tourist$byear)

  # with labels(counts)
h2<-hist(tourist$byear,plot=FALSE)
hist(tourist$byear,main="", xlab='birth year')
hist(tourist$byear,main="", xlab='birth year',labels = TRUE)

hist(tourist$byear,main="", xlab='birth year',labels = TRUE,ylim=c(0,50))

hist(tourist$byear,main="", xlab='birth year',labels = TRUE,ylim=c(0,50),col='lightblue')

hist(tourist$byear,main="", xlab='birth year',labels = TRUE,ylim=c(0,50),col='#ffb01f')

#html color codes
#https://html-color.codes/
# r colors names
# http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
# https://www.datamentor.io/r-programming/color/

##### practice 7 ： 
##### step1 : 1.4 compute age based on birth year
##### step2 : 1.7 check the distribution of age
##### step3 : 1.5 record age variable age6
#####            (below 20,20-29,30-39,40-49,50-59,above 60)
##### step4 : 2.1 conduct frequency analysis of age6

# solutions to practice 07
age<-2022-tourist$byear
hist(age)

attach(tourist)

## The following object is masked _by_ .GlobalEnv:
## 
##     age

tourist$age6[age  < 20] <- 1
tourist$age6[age >= 20 & age < 30] <- 2
tourist$age6[age >= 30 & age < 40] <- 3
tourist$age6[age >= 40 & age < 50] <- 4
tourist$age6[age >= 50 & age < 60] <- 5
tourist$age6[age >= 60] <- 6
detach(tourist)

tourist$age6 <- factor(tourist$age6,levels = c(1,2,3,4,5,6),labels = c("below 20", "20-29","30-39","40-49", "50-59","above 60"))

t3<-table(tourist$age6)
t3

## 
## below 20    20-29    30-39    40-49    50-59 above 60 
##       30       65       62       73       74       69

round(prop.table(t3)*100,2)

## 
## below 20    20-29    30-39    40-49    50-59 above 60 
##     8.04    17.43    16.62    19.57    19.84    18.50

2.2 crosstable and chi-square test

### crosstable 
ct1<-table(tourist$thotel,tourist$gender)
prop.table(ct1)

##                    
##                           male     female
##   budget hotel      0.23592493 0.20107239
##   luxury hotel      0.05898123 0.03485255
##   bed and breakfast 0.20375335 0.12064343
##   apartment hotel   0.07506702 0.06970509

prop.table(ct1,1)

##                    
##                          male    female
##   budget hotel      0.5398773 0.4601227
##   luxury hotel      0.6285714 0.3714286
##   bed and breakfast 0.6280992 0.3719008
##   apartment hotel   0.5185185 0.4814815

prop.table(ct1,2)

##                    
##                           male     female
##   budget hotel      0.41121495 0.47169811
##   luxury hotel      0.10280374 0.08176101
##   bed and breakfast 0.35514019 0.28301887
##   apartment hotel   0.13084112 0.16352201

round(prop.table(ct1,1)*100,2)

##                    
##                      male female
##   budget hotel      53.99  46.01
##   luxury hotel      62.86  37.14
##   bed and breakfast 62.81  37.19
##   apartment hotel   51.85  48.15

# summary  of chi-square test
summary(ct1)

## Number of cases in table: 373 
## Number of factors: 2 
## Test for independence of all factors:
##  Chisq = 3.33, df = 3, p-value = 0.3435

#
#transfer ct1 into data frame
ct1b<-as.data.frame(ct1)


#
ct1c<-as.data.frame(round(prop.table(ct1,1)*100,2))

#combine
ct1d<-cbind(ct1b,ct1c$Freq)

#rename
colnames(ct1d)<-c('hotel','gender','frequency','percent')

print(ct1d)

##               hotel gender frequency percent
## 1      budget hotel   male        88   53.99
## 2      luxury hotel   male        22   62.86
## 3 bed and breakfast   male        76   62.81
## 4   apartment hotel   male        28   51.85
## 5      budget hotel female        75   46.01
## 6      luxury hotel female        13   37.14
## 7 bed and breakfast female        45   37.19
## 8   apartment hotel female        26   48.15

#export
write.csv(ct1d,paste(datafolder,'tourist_hotel.csv',sep=""))

##### practice 08 ： 
##### is there significant relationship between income3 and thotel?

2.3 independent T-test

t.test(tourist$sat1~tourist$gender)

## 
##  Welch Two Sample t-test
## 
## data:  tourist$sat1 by tourist$gender
## t = 0.89559, df = 332.88, p-value = 0.3711
## alternative hypothesis: true difference in means between group male and group female is not equal to 0
## 95 percent confidence interval:
##  -0.08414446  0.22480161
## sample estimates:
##   mean in group male mean in group female 
##             4.378505             4.308176

#tidy report
ttest1<-t.test(tourist$sat1~tourist$gender)
ttest12<-tidy(ttest1)
ttest12

## # A tibble: 1 × 10
##   estim…¹ estim…² estim…³ stati…⁴ p.value param…⁵ conf.…⁶ conf.…⁷ method alter…⁸
##     <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <chr>  <chr>  
## 1  0.0703    4.38    4.31   0.896   0.371    333. -0.0841   0.225 Welch… two.si…
## # … with abbreviated variable names ¹estimate, ²estimate1, ³estimate2,
## #   ⁴statistic, ⁵parameter, ⁶conf.low, ⁷conf.high, ⁸alternative

#export
write.csv(ttest12,paste(datafolder,'sat1_gender.csv',sep=""))

##### practice 09 ： 
##### H3 female is more satisfied with scenery than male

2.4 ANOVA

fit<-aov(sat1~income3,tourist)
summary(fit)

##              Df Sum Sq Mean Sq F value Pr(>F)
## income3       2   0.72  0.3594   0.646  0.525
## Residuals   370 205.97  0.5567

tidy(fit)

## # A tibble: 2 × 6
##   term         df   sumsq meansq statistic p.value
##   <chr>     <dbl>   <dbl>  <dbl>     <dbl>   <dbl>
## 1 income3       2   0.719  0.359     0.646   0.525
## 2 Residuals   370 206.     0.557    NA      NA

2.5 correlation

#scatter chart
plot(tourist$income,tourist$expense)

cor.test(tourist$income,tourist$expense)

## 
##  Pearson's product-moment correlation
## 
## data:  tourist$income and tourist$expense
## t = 9.5162, df = 371, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3574822 0.5210526
## sample estimates:
##       cor 
## 0.4429459

cor(tourist$income,tourist$expense,method="spearman")

## [1] 0.4027449

tidy report

cor_income_expense<-cor.test(tourist$income,tourist$expense)
cor_income_expense<-tidy(cor_income_expense)
cor_income_expense

## # A tibble: 1 × 8
##   estimate statistic  p.value parameter conf.low conf.high method        alter…¹
##      <dbl>     <dbl>    <dbl>     <int>    <dbl>     <dbl> <chr>         <chr>  
## 1    0.443      9.52 2.35e-19       371    0.357     0.521 Pearson's pr… two.si…
## # … with abbreviated variable name ¹alternative

#export
write.csv(cor_income_expense,paste(datafolder,'cor_income_expense.csv',sep=""))

correlation matrix

cor_data<-tourist[,c(5,6,10:15)]
mydata.cor = cor(cor_data)
mydata.cor

##              income      expense         sat1        sat2        sat3
## income   1.00000000  0.442945941  0.026740705 -0.01573717 -0.03478183
## expense  0.44294594  1.000000000  0.002191373 -0.02832285 -0.09644656
## sat1     0.02674071  0.002191373  1.000000000  0.19670856  0.10043646
## sat2    -0.01573717 -0.028322850  0.196708559  1.00000000 -0.04452174
## sat3    -0.03478183 -0.096446564  0.100436456 -0.04452174  1.00000000
## sat4     0.01931803 -0.088759307  0.044004353  0.06493726  0.04694267
## sat5    -0.03642428 -0.060271195 -0.039155116  0.09324654  0.09942254
## sat6    -0.04216386  0.073107112  0.054031745 -0.01636401 -0.11162753
##                sat4        sat5        sat6
## income   0.01931803 -0.03642428 -0.04216386
## expense -0.08875931 -0.06027119  0.07310711
## sat1     0.04400435 -0.03915512  0.05403174
## sat2     0.06493726  0.09324654 -0.01636401
## sat3     0.04694267  0.09942254 -0.11162753
## sat4     1.00000000 -0.09264920  0.10743228
## sat5    -0.09264920  1.00000000  0.02634869
## sat6     0.10743228  0.02634869  1.00000000

#rcorr is from the Hmisc package
mydata.rcorr <- rcorr(as.matrix(cor_data))
mydata.coeff <- mydata.rcorr$r
mydata.coeff

##              income      expense         sat1        sat2        sat3
## income   1.00000000  0.442945941  0.026740705 -0.01573717 -0.03478183
## expense  0.44294594  1.000000000  0.002191373 -0.02832285 -0.09644656
## sat1     0.02674071  0.002191373  1.000000000  0.19670856  0.10043646
## sat2    -0.01573717 -0.028322850  0.196708559  1.00000000 -0.04452174
## sat3    -0.03478183 -0.096446564  0.100436456 -0.04452174  1.00000000
## sat4     0.01931803 -0.088759307  0.044004353  0.06493726  0.04694267
## sat5    -0.03642428 -0.060271195 -0.039155116  0.09324654  0.09942254
## sat6    -0.04216386  0.073107112  0.054031745 -0.01636401 -0.11162753
##                sat4        sat5        sat6
## income   0.01931803 -0.03642428 -0.04216386
## expense -0.08875931 -0.06027119  0.07310711
## sat1     0.04400435 -0.03915512  0.05403174
## sat2     0.06493726  0.09324654 -0.01636401
## sat3     0.04694267  0.09942254 -0.11162753
## sat4     1.00000000 -0.09264920  0.10743228
## sat5    -0.09264920  1.00000000  0.02634869
## sat6     0.10743228  0.02634869  1.00000000

mydata.p <- mydata.rcorr$P
mydata.p

##            income    expense         sat1         sat2       sat3       sat4
## income         NA 0.00000000 0.6066875674 0.7619402861 0.50305111 0.70998546
## expense 0.0000000         NA 0.9663548597 0.5855619683 0.06277516 0.08692367
## sat1    0.6066876 0.96635486           NA 0.0001314393 0.05260579 0.39675922
## sat2    0.7619403 0.58556197 0.0001314393           NA 0.39122559 0.21084024
## sat3    0.5030511 0.06277516 0.0526057891 0.3912255868         NA 0.36595875
## sat4    0.7099855 0.08692367 0.3967592185 0.2108402357 0.36595875         NA
## sat5    0.4830900 0.24556908 0.4508720781 0.0720556601 0.05505029 0.07390625
## sat6    0.4168218 0.15881152 0.2979756738 0.7527602339 0.03113131 0.03808825
##               sat5       sat6
## income  0.48309000 0.41682182
## expense 0.24556908 0.15881152
## sat1    0.45087208 0.29797567
## sat2    0.07205566 0.75276023
## sat3    0.05505029 0.03113131
## sat4    0.07390625 0.03808825
## sat5            NA 0.61197396
## sat6    0.61197396         NA

tidy(mydata.rcorr)

## # A tibble: 28 × 5
##    column1 column2 estimate     n  p.value
##    <chr>   <chr>      <dbl> <int>    <dbl>
##  1 expense income   0.443     373 0       
##  2 sat1    income   0.0267    373 0.607   
##  3 sat1    expense  0.00219   373 0.966   
##  4 sat2    income  -0.0157    373 0.762   
##  5 sat2    expense -0.0283    373 0.586   
##  6 sat2    sat1     0.197     373 0.000131
##  7 sat3    income  -0.0348    373 0.503   
##  8 sat3    expense -0.0964    373 0.0628  
##  9 sat3    sat1     0.100     373 0.0526  
## 10 sat3    sat2    -0.0445    373 0.391   
## # … with 18 more rows

#export
write.csv(tidy(mydata.rcorr),paste(datafolder,'correlation_matrix.csv',sep=""))

chart of correlation matrix

corrplot(mydata.cor)

3.1 ggplot2

###plot

qplot(tourist$income,tourist$expense)

## Warning: `qplot()` was deprecated in ggplot2 3.4.0.

ggplot(tourist,aes(x=income,y=expense))+
  geom_point(size=1)

ggplot(tourist,aes(x=income,y=expense))+
  geom_point(size=2,aes(colour=gender))

ggplot(tourist,aes(x=income,y=expense))+
  geom_point(size=2,aes(colour=thotel))

ggplot(tourist,aes(x=income,y=expense))+
  geom_point(size=2)+
  facet_grid(type3 ~.)

ggplot(tourist,aes(x=income,y=expense))+
  geom_point(size=2,aes(colour=type3))+
  facet_grid(type3 ~.)

ggplot(tourist,aes(x=income,y=expense))+
  geom_point(size=2,aes(colour=type3))+
  facet_grid(type3 ~type2)

ggplot(tourist,aes(x=sat2,fill=gender))+
  geom_histogram(binwidth=1,colour="white")+
  facet_grid(gender ~.)

ggplot(tourist,aes(x=sat2,fill=type3))+
  geom_histogram(binwidth=1,colour="white")+
  facet_grid(type2 ~type3)

4 map

leaflet

Leaflet is the leading open-source JavaScript library for mobile-friendly interactive maps. Weighing just about 42 KB of JS, it has all the mapping features most developers ever need.

https://leafletjs.com/

openstreet map

https://www.openstreetmap.org/

center_lon = median(tourist$longitude,na.rm = TRUE)
center_lat = median(tourist$latitude,na.rm = TRUE)

leaflet(tourist) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
  addCircles(lng = ~longitude, lat = ~latitude,radius = ~sqrt(income/100))  %>%
  
  # center,zoom level
  setView(lng=center_lon, lat=center_lat,zoom = 10)

5 IPA (importance-performance analysis)

Liu, X., & Zhang, N. (2020). Research on Customer Satisfaction of Budget Hotels Based on Revised IPA and Online Reviews. Science Journal of Business and Management, 8(2), 50–56. https://doi.org/10.11648/j.sjbm.20200802.11

https://www.sciencepublishinggroup.com/journal/paperinfo?journalid=175&doi=10.11648/j.sjbm.20200802.11

5.1 data wrangling

review_label<-as.data.frame(c('amount', 'publishing date',  'relevance',    'positive', 'credibility'))

colnames(review_label)<-c("label")
tourist1<-tourist[,c("ri1","ri2","ri3","ri4","ri5","rp1","rp2","rp3","rp4","rp5")]
hdata<-colMeans(tourist1)


im<-tourist[,c("ri1","ri2","ri3","ri4","ri5")]
pe<-tourist[,c("rp1","rp2","rp3","rp4","rp5")]


imdata<-as.data.frame(colMeans(im))
colnames(imdata)<-c("importance")

pedata<-as.data.frame(colMeans(pe))
colnames(pedata)<-c("performance")
ipa<-cbind(imdata,pedata,review_label)

#5.2 compute means for IPA

print("------------importance----------")

## [1] "------------importance----------"

print(paste("min :",round(min(ipa[1]),2)))

## [1] "min : 4.35"

print(paste("max :",round(max(ipa[1]),2)))

## [1] "max : 4.73"

print("------------performance----------")

## [1] "------------performance----------"

print(paste("min :",round(min(ipa[2]),2)))

## [1] "min : 4.32"

print(paste("max :",round(max(ipa[2]),2)))

## [1] "max : 4.72"

print(colMeans(ipa[,1:2]))

##  importance performance 
##    4.554960    4.515818

5.3 IPA chart

#2 basic of

ipa_chart<-ggplot(ipa,aes(x=performance,y=importance,))+
  geom_point(size=2)+ 
  xlim(4.2, 4.8)+ylim(4.2, 4.8)+
  labs(x = "performance",y="importance")+
  geom_vline(xintercept=4.516,color = "red",linetype="dashed")+
  geom_hline(yintercept=4.555,color = "red",linetype="dashed")+
  geom_abline(intercept = 0, slope = 1, color="blue",size=0.5)+
  geom_text_repel(aes(label=label),size=4,hjust=0.3,vjust=0.3)

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.

ipa_chart

ggsave(paste(datafolder,'ipa_chart.png',sep=""),ipa_chart, width = 7)

## Saving 7 x 5 in image

6.EFA(explortary factor analysis)

Chapman, C., & Feit, E. M. (2019). R For Marketing Research and Analytics (2nd ). Springer. https://link.springer.com/book/10.1007%2F978-3-030-14316-9

https://link.springer.com/book/10.1007/978-3-030-14316-9

#load(url('https://od.lk/d/170336656_2dwPO/tourist_EN.rda'))
load(paste(datafolder,'tourist_EN.rda',sep=""))

6.1 datasets

satdata<-tourist[,c(10:15)]
idata<-tourist[,c(16:20)]
pdata<-tourist[,c(21:25)]
tmdata<-tourist[,c(26:33)]
zhdata<-tourist[,c(34:40)]
#zhdata<-tourist[,c('zh1','zh2','zh3','zh4','zh5','zh6','zh7')]
head(satdata)

##   sat1 sat2 sat3 sat4 sat5 sat6
## 1    4    2    2    3    4    4
## 2    5    5    4    5    3    5
## 3    3    4    5    3    3    2
## 4    5    2    3    5    4    4
## 5    5    3    5    2    4    5
## 6    4    3    3    2    5    3

head(idata)

##   ri1 ri2 ri3 ri4 ri5
## 1   4   4   4   5   4
## 2   4   3   4   4   5
## 3   5   5   5   5   5
## 4   4   5   5   5   5
## 5   5   5   5   5   5
## 6   4   4   5   5   5

head(pdata)

##   rp1 rp2 rp3 rp4 rp5
## 1   4   5   4   5   4
## 2   4   3   4   4   5
## 3   5   5   4   5   5
## 4   4   5   5   5   5
## 5   5   5   5   5   5
## 6   4   4   5   5   5

head(tmdata)

##   te1 te2 te3 te4 te5 te6 te7 te8
## 1   4   4   4   4   4   4   4   4
## 2   5   5   4   4   4   5   3   4
## 3   3   4   4   4   4   4   4   4
## 4   4   4   4   4   5   4   2   4
## 5   4   5   4   4   5   5   4   5
## 6   1   3   3   3   1   1   3   2

head(zhdata)

##   zh1 zh2 zh3 zh4 zh5 zh6 zh7
## 1   3   1   1   2   3   2   2
## 2   4   4   5   3   5   5   5
## 3   1   1   2   2   1   3   1
## 4   3   3   3   3   2   2   2
## 5   1   1   1   4   2   1   1
## 6   4   3   3   4   2   4   4

6.2 kmo test

https://www.statisticshowto.com/kaiser-meyer-olkin/

KMO(Kaiser-Meyer-Olkin) The Kaiser-Meyer-Olkin (KMO) Test is a measure of how suited your data is for Factor Analysis. The test measures sampling adequacy for each variable in the model and for the complete model.

KMO returns values between 0 and 1.

KMO values between 0.8 and 1 indicate the sampling is adequate.

kmo_test<-KMO(zhdata)
print(kmo_test, stats = c("both", "MSA", "KMO"), vars = "all", sort = FALSE, show = "all", digits = 3)

## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = zhdata)
## Overall MSA =  0.872
## MSA for each item = 
##   zh1   zh2   zh3   zh4   zh5   zh6   zh7 
## 0.894 0.881 0.887 0.897 0.907 0.821 0.823

6.3 Bartlett’s test

https://www.statology.org/bartletts-test-of-sphericity/

Bartlett’s test of sphericity

Bartlett’s Test of Sphericity compares an observed correlation matrix to the identity matrix. Essentially it checks to see if there is a certain redundancy between the variables that we can summarize with a few number of factors.

If the p-value from Bartlett’s Test of Sphericity is lower than our chosen significance level (common choices are 0.10, 0.05, and 0.01), then our dataset is suitable for a data reduction technique.

cortest.bartlett(zhdata)

## R was not square, finding R from data

## $chisq
## [1] 1820.569
## 
## $p.value
## [1] 0
## 
## $df
## [1] 21

6.4 estimate the number of factors

Chapman, C., & Feit, E. M. (2019). R For Marketing Research and Analytics. Springer. chapter 8.p206-213 https://link.springer.com/book/10.1007%2F978-3-030-14316-9

parallel <- fa.parallel(zhdata, fm = 'minres', fa = 'both',n.iter=100)

## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.

## Parallel analysis suggests that the number of factors =  2  and the number of components =  1

6.5 attrive 2 factors

efa2 <- fa(zhdata,nfactors = 2,rotate = "oblimin",fm="minres")

## 载入需要的名字空间：GPArotation

print(efa2)

## Factor Analysis using method =  minres
## Call: fa(r = zhdata, nfactors = 2, rotate = "oblimin", fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
##       MR1   MR2   h2   u2 com
## zh1  0.82  0.02 0.69 0.31   1
## zh2  0.91 -0.06 0.76 0.24   1
## zh3  0.88  0.02 0.78 0.22   1
## zh4  0.73  0.09 0.64 0.36   1
## zh5  0.09  0.74 0.64 0.36   1
## zh6 -0.03  0.87 0.72 0.28   1
## zh7 -0.01  0.91 0.81 0.19   1
## 
##                        MR1  MR2
## SS loadings           2.86 2.18
## Proportion Var        0.41 0.31
## Cumulative Var        0.41 0.72
## Proportion Explained  0.57 0.43
## Cumulative Proportion 0.57 1.00
## 
##  With factor correlations of 
##      MR1  MR2
## MR1 1.00 0.66
## MR2 0.66 1.00
## 
## Mean item complexity =  1
## Test of the hypothesis that 2 factors are sufficient.
## 
## The degrees of freedom for the null model are  21  and the objective function was  4.94 with Chi Square of  1820.57
## The degrees of freedom for the model are 8  and the objective function was  0.11 
## 
## The root mean square of the residuals (RMSR) is  0.02 
## The df corrected root mean square of the residuals is  0.03 
## 
## The harmonic number of observations is  373 with the empirical chi square  6.95  with prob <  0.54 
## The total number of observations was  373  with Likelihood Chi Square =  39.38  with prob <  4.2e-06 
## 
## Tucker Lewis Index of factoring reliability =  0.954
## RMSEA index =  0.103  and the 90 % confidence intervals are  0.072 0.136
## BIC =  -7.99
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy             
##                                                    MR1  MR2
## Correlation of (regression) scores with factors   0.96 0.95
## Multiple R square of scores with factors          0.92 0.90
## Minimum correlation of possible factor scores     0.84 0.81

print(efa2$loadings,cutoff = 0.5)

## 
## Loadings:
##     MR1    MR2   
## zh1  0.821       
## zh2  0.911       
## zh3  0.875       
## zh4  0.733       
## zh5         0.737
## zh6         0.868
## zh7         0.910
## 
##                  MR1   MR2
## SS loadings    2.816 2.136
## Proportion Var 0.402 0.305
## Cumulative Var 0.402 0.707

6.6 chart

factor.plot(efa2,labels=rownames(efa2$loadings))

#heatmap.2(efa2$loadings,
 #         col=brewer.pal(9, "Greens"), trace="none", key=FALSE, dend="none",
 #         Colv=FALSE, cexCol = 1.2)

fa.diagram(efa2)

#fa.diagram(efa4plog,simple=FALSE)

7.CFA, Confirmatory Factor Analysis

MR1    MR2

zh1 0.821
zh2 0.911
zh3 0.875
zh4 0.733
zh5 0.737 zh6 0.868 zh7 0.910

7.1 model setting

cfa_model <- " D1 =~ zh1 + zh2 + zh3 + zh4 
 D2 =~ zh5 + zh6 + zh7 "

7.2 model estimation

cfa.fit <- cfa(cfa_model, data=zhdata)
summary(cfa.fit, fit.measures=TRUE)

## lavaan 0.6-12 ended normally after 25 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        15
## 
##   Number of observations                           373
## 
## Model Test User Model:
##                                                       
##   Test statistic                                48.083
##   Degrees of freedom                                13
##   P-value (Chi-square)                           0.000
## 
## Model Test Baseline Model:
## 
##   Test statistic                              1841.136
##   Degrees of freedom                                21
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    0.981
##   Tucker-Lewis Index (TLI)                       0.969
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -3488.689
##   Loglikelihood unrestricted model (H1)      -3464.647
##                                                       
##   Akaike (AIC)                                7007.377
##   Bayesian (BIC)                              7066.201
##   Sample-size adjusted Bayesian (BIC)         7018.610
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.085
##   90 Percent confidence interval - lower         0.060
##   90 Percent confidence interval - upper         0.111
##   P-value RMSEA <= 0.05                          0.012
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.027
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   D1 =~                                               
##     zh1               1.000                           
##     zh2               1.058    0.053   19.985    0.000
##     zh3               1.097    0.053   20.885    0.000
##     zh4               0.999    0.055   18.218    0.000
##   D2 =~                                               
##     zh5               1.000                           
##     zh6               1.108    0.061   18.060    0.000
##     zh7               1.183    0.061   19.257    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   D1 ~~                                               
##     D2                0.714    0.080    8.978    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .zh1               0.512    0.047   10.851    0.000
##    .zh2               0.452    0.045   10.068    0.000
##    .zh3               0.374    0.042    9.008    0.000
##    .zh4               0.609    0.054   11.319    0.000
##    .zh5               0.534    0.050   10.684    0.000
##    .zh6               0.505    0.052    9.656    0.000
##    .zh7               0.316    0.047    6.751    0.000
##     D1                1.137    0.118    9.613    0.000
##     D2                0.988    0.109    9.074    0.000

7.3 chart

semPaths(cfa.fit, what="est", fade=FALSE, residuals=FALSE,edge.label.cex=0.75)

8.reliability analysis

zh_d1<-tourist[,c('zh1','zh2','zh3','zh4')]
alpha(zh_d1)

## 
## Reliability analysis   
## Call: alpha(x = zh_d1)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean  sd median_r
##       0.91      0.91    0.89      0.71  10 0.0077  2.7 1.2     0.73
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.89  0.91  0.92
## Duhachek  0.89  0.91  0.92
## 
##  Reliability if an item is dropped:
##     raw_alpha std.alpha G6(smc) average_r S/N alpha se   var.r med.r
## zh1      0.89      0.89    0.84      0.72 7.8   0.0102 0.00207  0.74
## zh2      0.88      0.88    0.83      0.70 7.1   0.0111 0.00202  0.72
## zh3      0.87      0.87    0.82      0.69 6.7   0.0118 0.00240  0.67
## zh4      0.90      0.90    0.85      0.74 8.6   0.0093 0.00043  0.74
## 
##  Item statistics 
##       n raw.r std.r r.cor r.drop mean  sd
## zh1 373  0.88  0.88  0.82   0.78  2.8 1.3
## zh2 373  0.90  0.90  0.85   0.81  2.6 1.3
## zh3 373  0.91  0.91  0.87   0.83  2.7 1.3
## zh4 373  0.86  0.86  0.79   0.75  2.6 1.3
## 
## Non missing response frequency for each item
##        1    2    3    4    5 miss
## zh1 0.24 0.14 0.25 0.30 0.07    0
## zh2 0.31 0.13 0.23 0.27 0.05    0
## zh3 0.28 0.14 0.26 0.24 0.08    0
## zh4 0.31 0.20 0.19 0.24 0.07    0

zh_d2<-tourist[,c('zh5','zh6','zh7')]
alpha(zh_d2)

## 
## Reliability analysis   
## Call: alpha(x = zh_d2)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N  ase mean  sd median_r
##       0.88      0.88    0.84      0.72 7.7 0.01  2.6 1.2     0.72
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.86  0.88  0.90
## Duhachek  0.86  0.88  0.91
## 
##  Reliability if an item is dropped:
##     raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## zh5      0.87      0.87    0.76      0.76 6.5    0.014    NA  0.76
## zh6      0.84      0.84    0.72      0.72 5.2    0.017    NA  0.72
## zh7      0.80      0.80    0.67      0.67 4.0    0.021    NA  0.67
## 
##  Item statistics 
##       n raw.r std.r r.cor r.drop mean  sd
## zh5 373  0.88  0.88  0.79   0.74  2.6 1.2
## zh6 373  0.90  0.90  0.83   0.77  2.5 1.3
## zh7 373  0.92  0.92  0.87   0.82  2.6 1.3
## 
## Non missing response frequency for each item
##        1    2    3    4    5 miss
## zh5 0.28 0.16 0.30 0.21 0.05    0
## zh6 0.34 0.15 0.19 0.28 0.04    0
## zh7 0.33 0.12 0.26 0.23 0.05    0

文章信息: 作者： kaiwu; 日期：2022年11月21日; 点击数：185

1.Microsoft Office

https://www.office.com/

2.google document

https://docs.google.com

logo googledocs

https://en.wikipedia.org/wiki/Google_Docs,_Sheets_and_Slides,_Sheets_and_Slides

3.Zoho office

https://www.zoho.com/

https://www.zoho.com.cn/docs/

4.libreoffice

https://www.libreoffice.org/

LibreOffice is a free and powerful office suite, and a successor to OpenOffice.org (commonly known as OpenOffice).

Its clean interface and feature-rich tools help you unleash your creativity and enhance your productivity.

5.openoffice

https://www.openoffice.org/

Apache OpenOffice is the leading open-source office software suite for word processing, spreadsheets, presentations, graphics, databases and more. It is available in many languages and works on all common computers. It stores all your data in an international open standard format and can also read and write files from other common office software packages.

online office platforms in China mainland.

6.WPS office

https://www.wps.com/

https://drive.wps.cn/ 或者https://docs.wps.cn

https://www.wps.cn/

WPS Office is available on multiple platforms, including Windows, macOS, Linux, Android, and iOS. You can work anytime and anywhere on your mobile phone or computer. Work-study-life balance is no longer a far-fetched dream but at your fingertips.

7.tengxun document

https://docs.qq.com

8.dingtalk document

https://docs.dingtalk.com/

9.feishu document

https://docs.feishu.cn/

10. Pandoc支持多种格式文本之间的转换

About pandoc

If you need to convert files from one markup format into another, pandoc is your swiss-army knife. Pandoc can convert between the following formats:

(← = conversion from; → = conversion to; ↔︎ = conversion from and to)

Lightweight markup formats: ↔︎ Markdown (including CommonMark and GitHub-flavored Markdown)
↔︎ reStructuredText
→ AsciiDoc
↔︎ Emacs Org-Mode
↔︎ Emacs Muse
↔︎ Textile
→ Markua
← txt2tags
HTML formats: ↔︎ (X)HTML 4
↔︎ HTML5
Ebooks: ↔︎ EPUB version 2 or 3
↔︎ FictionBook2
Documentation formats: → GNU TexInfo
↔︎ Haddock markup
Roff formats: ↔︎ roff man
→ roff ms
TeX formats: ↔︎ LaTeX
→ ConTeXt
XML formats: ↔︎ DocBook version 4 or 5
↔︎ JATS
→ TEI Simple
→ OpenDocument XML
Outline formats: ↔︎ OPML
Bibliography formats: ↔︎ BibTeX
↔︎ BibLaTeX
↔︎ CSL JSON
↔︎ CSL YAML
← RIS
← EndNote XML

Word processor formats: ↔︎ Microsoft Word docx
↔︎ Rich Text Format RTF
↔︎ OpenOffice/LibreOffice ODT
Interactive notebook formats: ↔︎ Jupyter notebook (ipynb)
Page layout formats: → InDesign ICML
Wiki markup formats: ↔︎ MediaWiki markup
↔︎ DokuWiki markup
← TikiWiki markup
← TWiki markup
← Vimwiki markup
→ XWiki markup
→ ZimWiki markup
↔︎ Jira wiki markup
← Creole
Slide show formats: → LaTeX Beamer
→ Microsoft PowerPoint
→ Slidy
→ reveal.js
→ Slideous
→ S5
→ DZSlides
Data formats: ← CSV tables
Custom formats: ↔︎ custom readers and writers can be written in Lua
PDF: → via pdflatex, lualatex, xelatex, latexmk, tectonic, wkhtmltopdf, weasyprint, prince, pagedjs-cli, context, or pdfroff.

Pandoc understands a number of useful markdown syntax extensions, including document metadata (title, author, date); footnotes; tables; definition lists; superscript and subscript; strikeout; enhanced ordered lists (start number and numbering style are significant); running example lists; delimited code blocks with syntax highlighting; smart quotes, dashes, and ellipses; markdown inside HTML blocks; and inline LaTeX. If strict markdown compatibility is desired, all of these extensions can be turned off.

LaTeX math (and even macros) can be used in markdown documents. Several different methods of rendering math in HTML are provided, including MathJax and translation to MathML. LaTeX math is converted (as needed by the output format) to unicode, native Word equation objects, MathML, or roff eqn.

Pandoc includes a powerful system for automatic citations and bibliographies. This means that you can write a citation like

[see @doe99, pp. 33-35; also @smith04, ch. 1]

and pandoc will convert it into a properly formatted citation using any of hundreds of CSL styles (including footnote styles, numerical styles, and author-date styles), and add a properly formatted bibliography at the end of the document. The bibliographic data may be in BibTeX, BibLaTeX, CSL JSON, or CSL YAML format. Citations work in every output format.

There are many ways to customize pandoc to fit your needs, including a template system and a powerful system for writing filters.

Pandoc includes a Haskell library and a standalone command-line program. The library includes separate modules for each input and output format, so adding a new input or output format just requires adding a new module.

文章信息: 作者： kaiwu; 日期：2022年11月17日; 点击数：265

听了得到app，卓克.科技参考2 274 | 衰老：我们会在34岁、60岁、78岁突然老去

https://www.dedao.cn/course/article?id=5Mr9mzb36pP4JL5d3PXkWqB2EYNegL

衰老的进程并不是均匀的，而是有 3 个时间节点——34 岁、60 岁、78 岁。

这 3 个时间节点对大部分人来说都是如此。大家都是在这 3 个年龄节点，在可测的指标上突然大幅衰老。而在其他时间段，衰老的进程要慢得

Tony 团队把年轻小鼠的血液输送给老年小鼠后，很明显的改善了老年小鼠的大脑功能。这就提示 Tony 团队，血液中肯定存在着很多与年龄相关的物质，如果把这些物质的浓度、种类、配比准确测量出来，说不定就能用它来评估衰老程度。所以从 2015 年开始，他们就把注意力集中在血浆蛋白上。

血浆蛋白是个统称，可测的蛋白质有几千种，有的负责维持渗透压，有的负责稳定 pH 值，有的是免疫蛋白，有的起催化作用，有的起运输作用，有的起凝血作用等等。每升血液里的血浆蛋白大概有 70 多克。

Tony 团队一共召集了 4263 个年龄从 18-95 岁的人，测量了他们体内 2925 种血浆蛋白，然后在海量数据里筛选出与年龄变化高度相关的蛋白质，就得出了以上的结论。

Lehallier, B., Gate, D., Schaum, N., Nanasi, T., Lee, S. E., Yousef, H., Moran Losada, P., Berdnik, D., Keller, A., Verghese, J., Sathyan, S., Franceschi, C., Milman, S., Barzilai, N., & Wyss-Coray, T. (2019). Undulating Changes in Human Plasma Proteome Profiles Across the Lifespan. Nature Medicine, 25(12), Article 12. https://doi.org/10.1038/s41591-019-0673-2

Wang, T., Ma, J., Hogan, A. N., Fong, S., Licon, K., Tsui, B., Kreisberg, J. F., Adams, P. D., Carvunis, A.-R., Bannasch, D. L., Ostrander, E. A., & Ideker, T. (2019). Quantitative Translation of Dog-to-Human Aging by Conserved Remodeling of Epigenetic Networks (p. 829192). bioRxiv. https://doi.org/10.1101/829192

https://www.biorxiv.org/content/10.1101/829192v2

https://www.biorxiv.org/content/10.1101/751115v1.full.pdf

data analysis using R and Rstudio

Tourist2022EN

Kai Wu

2022/12/5