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demographic.R
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demographic.R
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library(ggpubr)
library(multcomp)
library(car)
library(FSA)
library(ggplot2)
library(phia)
#library(IRdisplay)
#library(repr)
library(plyr)
library(dplyr)
library(tidyr)
library(psych)
library(likert)
#data_x=read.csv("vip_clean.csv", sep=",", stringsAsFactors=FALSE)[ -c(1)]
#female_data=data_x[data_x$gender =="F", ]
#male_data=data_x[data_x$gender =="M", ]
#write.csv(female_data, file="female_data.csv") # Write in a new csv file
#write.csv(male_data, file="male_data.csv") # Write in a new csv file
# ---------------------------- Mean calculation -----------------------
mean_sd_ci_calculate <- function(list) {
list_mean=mean(list, na.rm=TRUE)
list_sd=sd(list, na.rm=TRUE)
error <- qt(0.975,df=length(list)-1)*sd(list)/sqrt(length(list))
# error
list_left_err <- mean(list)-error
list_right_err <- mean(list)+error
result = c(list_mean, list_sd , list_left_err, list_right_err)
return(result)
}
# ---------------------------- Mean calculation -----------------------
######################## Function
data_gen <- function(file_name, cond, degree) {
v1=paste(cond,"activity", degree, sep="_" )
v2= paste(cond,"distance", degree, sep="_")
v3=paste(cond,"attire", degree, sep="_")
v4=paste(cond,"conversation", degree, sep="_")
v5=paste(cond,"expression", degree, sep="_")
v6=paste(cond,"looking", degree, sep="_")
v7=paste(cond,"gender", degree, sep="_")
v8=paste(cond,"age", degree, sep="_")
v9=paste(cond,"ethnicity", degree, sep="_")
v10=paste(cond,"height", degree, sep="_")
v11=paste(cond,"weight", degree, sep="_")
data_x=read.csv(file_name, sep=",", stringsAsFactors=FALSE)[ -c(1),c(v1,v2,v3,v4,v5,v6,v7,v8,v9,v10,v11, "gender", "blind_level", "age", "blind_since")]
colnames(data_x) <- c("activity", "distance", "attire", "conversation", "expression", "looking", "gender",
"age", "ethnicity", "height", "weight", "M_F", "blind_level", "age_p", "blind_since" )
data_x=data_x[!apply(data_x == "", 1, all),]
data_x$activity = factor(data_x$activity,
levels = c("1", "2", "3", "4", "5"),
ordered = TRUE)
data_x$distance = factor(data_x$distance,
levels = c("1", "2", "3", "4", "5"),
ordered = TRUE)
data_x$attire = factor(data_x$attire,
levels = c("1", "2", "3", "4", "5"),
ordered = TRUE)
data_x$conversation = factor(data_x$conversation,
levels = c("1", "2", "3", "4", "5"),
ordered = TRUE)
data_x$expression = factor(data_x$expression,
levels = c("1", "2", "3", "4", "5"),
ordered = TRUE)
data_x$looking = factor(data_x$looking,
levels = c("1", "2", "3", "4", "5"),
ordered = TRUE)
data_x$gender = factor(data_x$gender,
levels = c("1", "2", "3", "4", "5"),
ordered = TRUE)
data_x$age = factor(data_x$age,
levels = c("1", "2", "3", "4", "5"),
ordered = TRUE)
data_x$ethnicity = factor(data_x$ethnicity,
levels = c("1", "2", "3", "4", "5"),
ordered = TRUE)
data_x$height = factor(data_x$height,
levels = c("1", "2", "3", "4", "5"),
ordered = TRUE)
data_x$weight = factor(data_x$weight,
levels = c("1", "2", "3", "4", "5"),
ordered = TRUE)
nrow_matrix=nrow(data_x)
degree_label= rep(degree,nrow_matrix)
data_x = cbind(degree=degree_label, data_x)
# colnames(data_x) <- c("audience", "credit_card", "face", "food", "prescription", "mess", "laptop_screen")
return(data_x)
}
group_information <- function(dataset) {
dataset$information[dataset$information=="activity" |
dataset$information=="looking"|
dataset$information=="conversation" |
dataset$information=="expression"] <- "Behavior"
dataset$information[dataset$information=="age" |
dataset$information=="gender"|
dataset$information=="attire" |
dataset$information=="height"|
dataset$information=="weight" |
dataset$information=="ethnicity"] <- "Physical attributes"
#dataset$information[dataset$information=="gender"| dataset$information=="ethnicity"] <- "Social construct"
dataset$information[dataset$information=="distance"] <- "Distance"
return(dataset)
}
# ----------------------- comfort data ---------------------------
comfortable_120=data_gen("vip_clean.csv", "comf", "120")
comfortable_360=data_gen("vip_clean.csv", "comf", "360")
comf_combine=rbind(comfortable_120,comfortable_360)
comf_combine = gather(comf_combine, "information", "comfort", 2:12 )
comfort_data_gather=comf_combine
comfort_data_gather=group_information(comfort_data_gather)
table(comfort_data_gather$information)
comfort_data_gather$comfort = as.numeric(as.character(comfort_data_gather$comfort))
comfort_data_gather=na.omit(comfort_data_gather)
physical_comfort_data=comfort_data_gather[comfort_data_gather$information =="Physical attributes", ]
distance_comfort_data=comfort_data_gather[comfort_data_gather$information =="Distance", ]
behavior_comfort_data=comfort_data_gather[comfort_data_gather$information =="Behavior", ]
#social_comfort_data=comfort_data_gather[comfort_data_gather$information =="Social construct", ]
comfort_data_gather$degree <- ordered(comfort_data_gather$degree,
levels = c("120", "360"))
#comfort_data_gather_120 = comfort_data_gather[comfort_data_gather$degree =="120", ]
#comfort_data_gather_360 = comfort_data_gather[comfort_data_gather$degree =="360", ]
# ---------------------------- Blind Level -----------------------
comfort_data_gather$blind_level <- ordered(comfort_data_gather$blind_level,
levels = c("Total blind", "Low vision"))
wilcox.test(comfort~ blind_level, data = comfort_data_gather)
cohensD(comfort_data_gather$comfort ~ comfort_data_gather$blind_level)
total_blind_data=comfort_data_gather[comfort_data_gather$blind_level =="Total blind", ]
mean_sd_ci_calculate(total_blind_data$comfort)
low_vision_blind_data=comfort_data_gather[comfort_data_gather$blind_level =="Low vision", ]
mean_sd_ci_calculate(low_vision_blind_data$comfort)
ggline(comfort_data_gather, x = "blind_level", y = "comfort", color = "blind_level",
add = c("mean_ci"), ylab="Comfort level", xlab="blind_level"
#palette = c("#FF0000", "#006400", "#0000CD", "#8B008B", "#556B2F")
)
# ---------------------------- Blind since -----------------------
comfort_data_gather$blind_since <- ordered(comfort_data_gather$blind_since,
levels = c("Since birth", "Blind later"))
wilcox.test(comfort~ blind_since, data = comfort_data_gather)
ggline(comfort_data_gather, x = "blind_since", y = "comfort", color = "blind_since",
add = c("mean_ci"), ylab="Comfort level", xlab="blind_since"
#palette = c("#FF0000", "#006400", "#0000CD", "#8B008B", "#556B2F")
)
# ---------------------------- Gender -----------------------
comfort_data_gather$M_F <- ordered(comfort_data_gather$M_F,
levels = c("M", "F"))
wilcox.test(comfort~ M_F, data = comfort_data_gather)
cohensD(comfort_data_gather$comfort ~ comfort_data_gather$M_F)
female_data=comfort_data_gather[comfort_data_gather$M_F =="F", ]
mean_sd_ci_calculate(female_data$comfort)
male_data=comfort_data_gather[comfort_data_gather$M_F =="M", ]
mean_sd_ci_calculate(male_data$comfort)
wilcox.test(comfort~ M_F, data = physical_comfort_data)
wilcox.test(comfort~ M_F, data = distance_comfort_data)
wilcox.test(comfort~ M_F, data = behavior_comfort_data)
wilcox.test(comfort~ M_F, data = social_comfort_data)
wilcox.test(comfort~ degree, data = female_data)
wilcox.test(comfort~ degree, data = male_data)
ggline(comfort_data_gather, x = "information", y = "comfort", color = "M_F",
add = c("mean_ci"), ylab="Comfort level", xlab="Information type"
#palette = c("#FF0000", "#006400", "#0000CD", "#8B008B")
)
# ---------------------------- Age -----------------------
comfort_data_gather$age_p <- ordered(comfort_data_gather$age_p,
levels = c("18-29", "30-39", "40-49", "50-64", "65 or Older"))
#wilcox.test(comfort~ age_p, data = comfort_data_gather)
kruskal.test(comfort ~ age_p, data = comfort_data_gather)
pairwise.wilcox.test(comfort_data_gather$comfort, comfort_data_gather$age_p,
p.adjust.method = "BH")
ggline(comfort_data_gather, x = "age_p", y = "comfort", color = "age_p",
add = c("mean_ci"), ylab="Comfort level", xlab="Age"
#palette = c("#FF0000", "#006400", "#0000CD", "#8B008B", "#556B2F")
)
# ---------------------------- Useful data -----------------------
useful_120=data_gen("vip_clean.csv", "useful", "120")
useful_360=data_gen("vip_clean.csv", "useful", "360")
useful_combine=rbind(useful_120,useful_360)
useful_combine = gather(useful_combine, "information", "useful", 2:12 )
useful_combine=na.omit(useful_combine)
useful_combine=group_information(useful_combine)
#useful_combine$information[useful_combine$information=="age" | useful_combine$information=="gender"|
# useful_combine$information=="height" |useful_combine$information=="weight" |
# useful_combine$information=="ethnicity" | useful_combine$information=="attire"] <- "Physical_attributes"
#useful_combine$information[useful_combine$information=="activity" | useful_combine$information=="looking"|
# useful_combine$information=="conversation" | useful_combine$information=="expression"] <- "Activity"
#useful_combine$information[useful_combine$information=="expression" ] <- "Behavior"
#useful_combine$information[useful_combine$information=="distnace" ] <- "Distance"
table(useful_combine$information)
useful_combine$useful = as.numeric(as.character(useful_combine$useful))
# ---------------------------- Blind Level -----------------------
useful_combine$blind_level <- ordered(useful_combine$blind_level,
levels = c("Total blind", "Low vision"))
wilcox.test(useful~ blind_level, data = useful_combine)
total_blind_data=useful_combine[useful_combine$blind_level =="Total blind", ]
mean_sd_ci_calculate(total_blind_data$useful)
low_vision_blind_data=useful_combine[useful_combine$blind_level =="Low vision", ]
mean_sd_ci_calculate(low_vision_blind_data$useful)
ggline(useful_combine, x = "blind_level", y = "useful", color = "blind_level",
add = c("mean_ci"), ylab="useful level", xlab="blind_level"
#palette = c("#FF0000", "#006400", "#0000CD", "#8B008B", "#556B2F")
)
# ---------------------------- Blind since -----------------------
useful_combine$blind_since <- ordered(useful_combine$blind_since,
levels = c("Since birth", "Blind later"))
wilcox.test(useful~ blind_since, data = useful_combine)
ggline(useful_combine, x = "blind_since", y = "useful", color = "blind_since",
add = c("mean_ci"), ylab="useful level", xlab="blind_since"
#palette = c("#FF0000", "#006400", "#0000CD", "#8B008B", "#556B2F")
)
# ---------------------------- Gender -----------------------
useful_combine$M_F <- ordered(useful_combine$M_F,
levels = c("M", "F"))
wilcox.test(useful~ M_F, data = useful_combine)
female_data=useful_combine[useful_combine$M_F =="F", ]
mean_sd_ci_calculate(female_data$useful)
male_data=useful_combine[useful_combine$M_F =="M", ]
mean_sd_ci_calculate(male_data$useful)
ggline(useful_combine, x = "M_F", y = "useful", color = "M_F",
add = c("mean_ci"), ylab="Comfort level", xlab="Information type"
#palette = c("#FF0000", "#006400", "#0000CD", "#8B008B")
)