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QUESTIONNAIRE_1_transform_data_to_numeric.R
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QUESTIONNAIRE_1_transform_data_to_numeric.R
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setwd("data/")
sample_info_csv <- read.table("INPUT_curated_questionaire.csv", header = T, sep = ",", stringsAsFactors = F)
rownames(sample_info_csv) <- sample_info_csv$ID_PEBC
# I remove physical traits and of place of origin
physical_or_origin_traits <- c("Country",
"Gender",
"Language",
"Race",
"Current.Recidence.Category",
"Weight",
"Height",
"Hair.Color",
"Hair.Shape",
"Eyes.Color",
"Age_turning_in_2016",
"Country_born",
"City_village_born",
"Country_residence",
"City_village_residence",
"Lived_elsewhere")
sample_info_csv <- sample_info_csv[, !colnames(sample_info_csv) %in% physical_or_origin_traits]
### ORDERED FACTORS: (ordering and assigning numbers to ordered factors)
### Glasses
sample_info_csv$Use.Glasses. <- as.numeric(factor(sample_info_csv$Use.Glasses., levels = c("No", "Sometimes", "Yes"), ordered = T))
### Education
sample_info_csv$Education <- as.factor(sample_info_csv$Education )
levels(sample_info_csv$Education) # "Basic School" "Elementary School" "High School"
# "Master" "Other" "Other (Upper level training)" "PhD" "University"
levels(sample_info_csv$Education) <- c(1,1,2,4,2.5,2.5,5,3)
sample_info_csv$Education <- as.numeric(as.character(sample_info_csv$Education))
### Smoking
sample_info_csv$Smoking <- as.factor(sample_info_csv$Smoking )
levels(sample_info_csv$Smoking) # "Former" "Heavy" "Light" "Never"
levels(sample_info_csv$Smoking) <- c(3,4,2,1)
sample_info_csv$Smoking <- as.numeric(as.character(sample_info_csv$Smoking))
### "Diet" # From more plant-based to less plant-based
sample_info_csv$Diet <- as.factor(sample_info_csv$Diet )
levels(sample_info_csv$Diet) # "Not Vegetarian" "Not Vegetarian (former vegetarian)" "Vegan" "Vegetarian" "Vegetarian (mostly)"
levels(sample_info_csv$Diet) <- c(5, 4, 1, 2,3)
sample_info_csv$Diet <- as.numeric(as.character(sample_info_csv$Diet))
### Alcohol
sample_info_csv$Drinking.Alcohol <- as.factor(sample_info_csv$Drinking.Alcohol )
levels(sample_info_csv$Drinking.Alcohol) # "Never" "Often" "Sometimes" "Sometimes-Often"
levels(sample_info_csv$Drinking.Alcohol) <- c(1,3,2,2.5)
sample_info_csv$Drinking.Alcohol <- as.numeric(as.character(sample_info_csv$Drinking.Alcohol))
### Soft-drinks
sample_info_csv$Drinking.Soft.drinks <- as.factor(sample_info_csv$Drinking.Soft.drinks )
levels(sample_info_csv$Drinking.Soft.drinks) # "Never" "Often" "Sometimes"
levels(sample_info_csv$Drinking.Soft.drinks) <- c(1,3,2)
sample_info_csv$Drinking.Soft.drinks <- as.numeric(as.character(sample_info_csv$Drinking.Soft.drinks))
# Work indoor-outdoor
sample_info_csv$Employment_in_outdoor <- as.factor(sample_info_csv$Employment_in_outdoor )
levels(sample_info_csv$Employment_in_outdoor) # "Indoor" "Indoor/Outdoor" "Outdoor"
levels(sample_info_csv$Employment_in_outdoor) <- c(0,1,2)
sample_info_csv$Employment_in_outdoor <- as.numeric(as.character(sample_info_csv$Employment_in_outdoor))
## RH
sample_info_csv$RH <- as.factor(sample_info_csv$RH )
levels(sample_info_csv$RH) # "-", "+"
levels(sample_info_csv$RH) <- c(0,1)
sample_info_csv$RH <- as.numeric(as.character(sample_info_csv$RH))
## Handwriting
sample_info_csv$Handwriting <- as.factor(sample_info_csv$Handwriting )
levels(sample_info_csv$Handwriting) # Left, Right
levels(sample_info_csv$Handwriting) <- c(0,1)
sample_info_csv$Handwriting <- as.numeric(as.character(sample_info_csv$Handwriting))
###### Transform LOGICAL or Yes/No to numeric
for(i in 1:ncol(sample_info_csv)) {
if(identical(sort(unique(sample_info_csv[, i])), c("No", "Yes"))) {
sample_info_csv[, i] <- ifelse(sample_info_csv[, i] == "Yes", yes = 1, no = 0)
}
if(class(sample_info_csv[, i]) == "logical") sample_info_csv[, i] <- as.character(sample_info_csv[, i])
if(identical(sort(unique(sample_info_csv[, i])), c("FALSE", "TRUE"))) {
sample_info_csv[, i] <- ifelse(sample_info_csv[, i] == "TRUE", yes = 1, no = 0)
}
if(identical(sort(unique(sample_info_csv[, i])), c("FALSE"))) {
sample_info_csv[, i][sample_info_csv[, i] == "FALSE"] <- 0
sample_info_csv[, i] <- as.numeric(sample_info_csv[, i])
}
}
### SPLITTING NON-ORDERED FACTORS into separate columns
### Why.Use.Glasses
unique(sample_info_csv$Why.Use.Glasses)
for(x in c("Myopia", "Presbyopia", "Hyperopia", "Fatigue", "Astigmatism")) {
sample_info_csv[, x] <- as.numeric(grepl(x, sample_info_csv$Why.Use.Glasses))
}
sample_info_csv <- sample_info_csv[!colnames(sample_info_csv) %in% "Why.Use.Glasses"] # Remove original column
### Employment
for(x in c("Medical leave", "Employee", "Autonomous", "Unemployed", "Retired" )) {
sample_info_csv[, x] <- as.numeric(grepl(x, sample_info_csv$Employment))
}
sample_info_csv <- sample_info_csv[!colnames(sample_info_csv) %in% "Employment"] # Remove original column
### Employment_physical_office_travel
for(x in c("Office", "Travel", "Physical" )) {
sample_info_csv[, x] <- as.numeric(grepl(x, sample_info_csv$Employment_physical_office_travel))
}
sample_info_csv <- sample_info_csv[!colnames(sample_info_csv) %in% "Employment_physical_office_travel"] # Remove original column
### Employment_category
for(x in c("Executive", "Salaried", "Own business" )) {
sample_info_csv[, x] <- as.numeric(grepl(x, sample_info_csv$Employment.Category))
}
sample_info_csv <- sample_info_csv[!colnames(sample_info_csv) %in% "Employment.Category"] # Remove original column
## Family_status
for(x in c("Divorced/separated/widowed", "Married./.In.couple", "Single" )) {
sample_info_csv[, x] <- as.numeric(grepl(x, sample_info_csv$Family_status))
}
sample_info_csv <- sample_info_csv[!colnames(sample_info_csv) %in% "Family_status"] # Remove original column
## Exercice indoor-outdoor
for(x in c("Indoor", "Outdoor")) {
sample_info_csv[, x] <- as.numeric(grepl(x, sample_info_csv$Exercise..Indoor.Outdoor.))
}
sample_info_csv <- sample_info_csv[!colnames(sample_info_csv) %in% "Exercise..Indoor.Outdoor."] # Remove original column
## Blood group
sample_info_csv$Blood_group_A <- as.numeric(grepl("A", sample_info_csv$Blood.Group))
sample_info_csv$Blood_group_B <- as.numeric(grepl("B", sample_info_csv$Blood.Group))
sample_info_csv$Blood_group_A[is.na(sample_info_csv$Blood.Group)] <- NA
sample_info_csv$Blood_group_B[is.na(sample_info_csv$Blood.Group)] <- NA
sample_info_csv <- sample_info_csv[!colnames(sample_info_csv) %in% "Blood.Group"] # Remove original column
# I change NAs for median or for mode
# But first I remove column "Last.cigarrete..years". Because to non-smokers I should actually asign an Inf.
# I also remove Time married, because otherwise I have to put a number for married, single and widowed people.
sample_info_csv <- sample_info_csv[, !colnames(sample_info_csv) %in% c("Time.Married...In.couple..years.", "Last.cigarrette..years.")]
# I change NAs for mode
mode_fun =function(x) {
q=table(x)
q=sort(q, decreasing = TRUE)
return(as.numeric(names(q[1])))
}
for(i in 3:ncol(sample_info_csv)) {
sample_info_csv[, i][is.na(sample_info_csv[, i])] <- mode_fun(sample_info_csv[, i])
}
write.table(sample_info_csv, sep = ",", quote = F, row.names = F,
file = "OUTPUT_lookalike_form_numeric.csv")