is this all ok? 
library(data.table)

data <- fread('/Users/roberto.ortiz/Documents/ProjectosGrowth/NewProductRappiPlatinum/PRODUCTB/productBData.csv')

#'Cleaning year column
unique(data$YEAR)
data$YEAR <- as.numeric(data$YEAR)
sort(unique(data$YEAR))
data$YEAR[data$YEAR < 1950 | data$YEAR > 2001] <- NA
data$YEAR[is.na(data$YEAR)] <- round(mean(data$YEAR, na.rm = TRUE),0)
hist(data$YEAR)

#'Cleaning income column
incomeLevels <- c('Prefiero no responder','Actualmente no tengo ingresos',
 'Menos que 10,000 MXN','De 10,001 hasta 15,000 MXN',
 'De 15,001 hasta 20,000 MXN','De 20,001 hasta 25,000 MXN',
 'De 25,001 hasta 30,000 MXN','De 30,001 hasta 35,000 MXN',
 'De 35,001 hasta 40,000 MXN','De 40,001 hasta 45,000 MXN',
 'De 45,001 hasta 50,000 MXN','De 50,001 hasta 55,000 MXN',
 'De 55,001 hasta 60,000 MXN','De 60,001 hasta 65,000 MXN',
 'De 65,001 hasta 70,000 MXN','Más que 70,000 MNX')
data$INCOME <- factor(data$INCOME, levels = incomeLevels)
summary(data$INCOME)
ggplot(data.frame(data), aes(x=INCOME)) +
 geom\_bar(color = 'red', fill = 'red') + 
 theme(axis.text.x = element\_text(angle = 90, vjust = 0.5, hjust=1))

#'Fixing Credit Cards Column
unique(data$CREDIT\_CARDS)
creditCardLevels <- c('Ninguna','Solo 1','2 - 3','4 - 5','6 - 7','Más de 7')
data$CREDIT\_CARDS <- factor(data$CREDIT\_CARDS, levels = creditCardLevels)
ggplot(data.frame(data), aes(x=CREDIT\_CARDS)) +
 geom\_bar(color = 'red', fill = 'red') + 
 theme(axis.text.x = element\_text(angle = 90, vjust = 0.5, hjust=1))

colnames(data)

productB1 <- data.frame(HBO=1,Cash=0,Rest=1,Movies=1,Assistance=0,Cost=1000) 
productB2 <- data.frame(HBO=1,Cash=0,Rest=1,Movies=1,Assistance=1,Cost=1800) 
productB3 <- data.frame(HBO=0,Cash=1,Rest=0,Movies=1,Assistance=1,Cost=1400) 
productB4 <- data.frame(HBO=0,Cash=1,Rest=1,Movies=1,Assistance=1,Cost=1800) 
productB5 <- data.frame(HBO=1,Cash=1,Rest=0,Movies=1,Assistance=1,Cost=1000) 
productB6 <- data.frame(HBO=1,Cash=1,Rest=1,Movies=0,Assistance=1,Cost=1400) 
productB7 <- data.frame(HBO=1,Cash=1,Rest=1,Movies=0,Assistance=1,Cost=1800) 
productB8 <- data.frame(HBO=1,Cash=1,Rest=1,Movies=1,Assistance=0,Cost=1800) 
productB9 <- data.frame(HBO=1,Cash=1,Rest=1,Movies=1,Assistance=1,Cost=1400) 
productB10 <- data.frame(HBO=1,Cash=2,Rest=0,Movies=1,Assistance=1,Cost=1400) 
productB11 <- data.frame(HBO=1,Cash=2,Rest=0,Movies=1,Assistance=1,Cost=1800) 
productB12 <- data.frame(HBO=0,Cash=2,Rest=1,Movies=0,Assistance=1,Cost=1400) 
productB13 <- data.frame(HBO=1,Cash=2,Rest=1,Movies=0,Assistance=1,Cost=1400) 
productB14 <- data.frame(HBO=1,Cash=2,Rest=0,Movies=1,Assistance=0,Cost=1400) 
productB15 <- data.frame(HBO=1,Cash=2,Rest=1,Movies=1,Assistance=0,Cost=1500) 
productB16 <- data.frame(HBO=0,Cash=2,Rest=1,Movies=1,Assistance=1,Cost=1400) 

transform\_data <- function(data, products\_list) {
 # Create an empty data frame to store the transformed data
 transformed\_data <- data.frame()
 
 # Iterate through each row in the original data frame
 for (i in 1:nrow(data)) {
 # Extract user-specific information
 user\_info <- data[i, c("YEAR", "INCOME", "CREDIT\_CARDS", "USER\_ID", "SUBMITTED\_AT", "TOKEN", "BLOCK")]
 
 # Iterate through each choice column
 for (choice\_col in c("CHOICE\_1", "CHOICE\_2", "CHOICE\_3", "CHOICE\_4", "CHOICE\_5", "CHOICE\_6")) {
 # Get the choice value (Tarjeta A or Tarjeta B)
 choice\_value <- data[[choice\_col]][i]
 
 # Find the corresponding product index based on the block and question
 block <- as.integer(gsub("B", "", data$BLOCK[i]))
 question <- as.integer(gsub("CHOICE\_", "", choice\_col))
 
 # Get the product index for Tarjeta A and Tarjeta B
 product\_a\_index <- 4 \* (question - 1) + block
 product\_b\_index <- product\_a\_index + 4
 
 # Choose the product based on the choice value
 chosen\_product\_index <- ifelse(choice\_value == "Tarjeta A", product\_a\_index, product\_b\_index)
 
 # Make sure the index is within the bounds of the products\_list
 if (chosen\_product\_index >= 1 && chosen\_product\_index <= length(products\_list)) {
 # Get the corresponding product features
 chosen\_product\_features <- products\_list[[chosen\_product\_index]]
 
 # Add the target variable 'CHOICE' based on the choice the respondent made
 choice\_column <- ifelse(choice\_value == "Tarjeta A", 1, 0)
 
 # Combine the user information, product features, and choice into a single row
 transformed\_row <- cbind(user\_info, chosen\_product\_features, CHOICE = choice\_column)
 
 # Append the transformed row to the transformed data frame
 transformed\_data <- rbind(transformed\_data, transformed\_row)
 }
 }
 }
 
 return(transformed\_data)
}

products\_list <- list(productB1, productB2, productB3, productB4, productB5, productB6, productB7, productB8, productB9, productB10, productB11, productB12, productB13, productB14, productB15, productB16)

transformed\_data <- transform\_data(data, products\_list)

colnames(transformed\_data)

formula <- as.formula('CHOICE~0+HBO+Cash+Rest+Movies+Assistance+Cost')

model <- lm(formula, data = transformed\_data)

summary(model)
이게 다 괜찮나요? library(data.table)data <- fread('/Users/roberto.ortiz/Documents/ProjectosGrowth/NewProductRappiPlatinum/PRODUCTB/productBData.csv')#'연도 열 정리unique(data$YEAR)data$YEAR <- as.numeric(data$YEAR)sort(unique(data$YEAR))data$YEAR[data$YEAR < 1950 | data$YEAR > 2001] <- NAdata$YEAR[is.na(data$YEAR)] <- round(mean(data$YEAR, na.rm = TRUE),0)hist(data$YEAR)#'소득 열 정리incomeLevels <- c('응답자 없음','현재 수입이 없음', '10,000 MXN 미만','최소 10,001에서 최대 15,000 MXN', '15,001에서 20,000 MXN까지','20,001에서 25,000 MXN까지', 'De 25,001에서 30,000 MXN','De 30,001에서 35,000 MXN', 'De 35,001에서 40,000 MXN','De 40,001에서 45,000 MXN', 'De 45,001부터 50,000 MXN','De 50,001부터 55,000 MXN', 'De 55,001부터 60,000 MXN','De 60,001부터 65,000 MXN', '65,001에서 70,000 MXN까지','70,000 MNX 이상')data$INCOME <- factor(data$INCOME, levels = incomeLevels)summary(data$INCOME)ggplot(data.frame(data), aes(x=INCOME)) + geom\_bar(color = 'red', fill = 'red') +.  theme(axis.text.x = element\_text(angle = 90, vjust = 0.5, hjust = 1))#'신용 카드 열 수정unique(data$CREDIT\_CARDS)creditCardLevels <- c('Ninguna','Solo 1','2 - 3','4 - 5','6 - 7','Más de 7')data$CREDIT\_CARDS <- factor(data$CREDIT\_CARDS, levels = creditCardLevels)ggplot(data.frame(data), aes(x=CREDIT\_CARDS)) + geom\_bar(color = 'red', fill = 'red') +.  theme(axis.text.x = element\_text(angle = 90, vjust = 0.5, hjust = 1))colnames(데이터)productB1 <- data.frame(HBO=1,현금=0,휴식=1,영화=1,지원=0,비용=1000) productB2 <- data.frame(HBO=1,Cash=0,Rest=1,Movies=1,Assistance=1,Cost=1800) productB3 <- data.frame(HBO=0,Cash=1,Rest=0,Movies=1,Assistance=1,Cost=1400) productB4 <- data.frame(HBO=0,Cash=1,Rest=1,Movies=1,Assistance=1,Cost=1800) productB5 <- data.frame(HBO=1,Cash=1,Rest=0,Movies=1,Assistance=1,Cost=1000) productB6 <- data.frame(HBO=1,Cash=1,Rest=1,Movies=0,Assistance=1,Cost=1400) productB7 <- data.frame(HBO=1,Cash=1,Rest=1,Movies=0,Assistance=1,Cost=1800) productB8 <- data.frame(HBO=1,Cash=1,Rest=1,Movies=1,Assistance=0,Cost=1800) productB9 <- data.frame(HBO=1,Cash=1,Rest=1,Movies=1,Assistance=1,Cost=1400) productB10 <- data.frame(HBO=1,Cash=2,Rest=0,Movies=1,Assistance=1,Cost=1400) productB11 <- data.frame(HBO=1,Cash=2,Rest=0,Movies=1,Assistance=1,Cost=1800) productB12 <- data.frame(HBO=0,현금=2,휴식=1,영화=0,지원=1,비용=1400) productB13 <- data.frame(HBO=1,Cash=2,Rest=1,Movies=0,Assistance=1,Cost=1400) productB14 <- data.frame(HBO=1,Cash=2,Rest=0,Movies=1,Assistance=0,Cost=1400) productB15 <- data.frame(HBO=1,Cash=2,Rest=1,Movies=1,Assistance=0,Cost=1500) productB16 <- data.frame(HBO=0,Cash=2,Rest=1,Movies=1,Assistance=1,Cost=1400) transform\_data <- function(data, products\_list) { # 변환된 데이터를 저장할 빈 데이터 프레임을 생성합니다. transformed\_data <- data.frame()  # 원본 데이터 프레임의 각 행을 반복합니다. for (i in 1:nrow(data)) { # 사용자별 정보 추출 user\_info <- data[i, c("YEAR", "INCOME", "CREDIT\_CARDS", "USER\_ID", "SUBMITTED\_AT", "TOKEN", "BLOCK")]  # 각 선택 열을 반복합니다. for (choice\_col in c("CHOICE\_1", "CHOICE\_2", "CHOICE\_3", "CHOICE\_4", "CHOICE\_5", "CHOICE\_6")) { # 선택 값 가져오기 (타르제타 A 또는 타르제타 B) choice\_value <- data[[choice\_col]][i]  # 블록과 문제를 기반으로 해당 제품 인덱스를 찾습니다. block <- as.integer(gsub("B", "", data$BLOCK[i])) question <- as.integer(gsub("CHOICE\_", "", choice\_col))  # 타겟 A와 타겟 B의 제품 인덱스 가져오기 product\_a\_index <- 4 \* (질문 - 1) + 블록 product\_b\_index <- product\_a\_index + 4  # 선택 값에 따라 제품 선택 selected\_product\_index <- ifelse(choice\_value == "Tarjeta A", product\_a\_index, product\_b\_index)  # 인덱스가 제품\_목록의 범위 내에 있는지 확인합니다. if (selected\_product\_index >= 1 && selected\_product\_index <= length(products\_list)) { # 해당 제품 기능 가져오기 chosen\_product\_features <- products\_list[[chosen\_product\_index]]  # 응답자의 선택에 따라 대상 변수 'CHOICE'를 추가합니다. choice\_column <- ifelse(choice\_value == "Tarjeta A", 1, 0)  # 사용자 정보, 제품 기능, 선택 항목을 하나의 행으로 결합합니다. transformed\_row <- cbind(user\_info, selected\_product\_features, CHOICE = choice\_column)  # 변환된 행을 변환된 데이터 프레임에 추가합니다. transformed\_data <- rbind(transformed\_data, transformed\_row) } } }  return(transformed\_data)}products\_list <- list(productB1, productB2, productB3, productB4, productB5, productB6, productB7, productB8, productB9, productB10, productB11, productB12, productB13, productB14, productB15, productB16)transformed\_data <- transform\_data(data, products\_list)colnames(transformed\_data)formula <- as.formula('CHOICE~0+HBO+현금+휴식+영화+지원+비용')model <- 