creating frequency table for labelled data

[ashley21]

I am trying to create a table for multiple response database but its not showing the all output.
its showing only total frequency.

library(expss)
data<-data.frame(
  gender = c(1,2,1,2,1,2,1,2,2,2,2,1,1,2,2,2,2,1,1,1,1,1,2,1,2,1,2,2,2,1,2,1,2,1,2,1,2,2,2),
  sector = c(3,3,1,2,5,4,4,4,4,3,3,4,3,4,2,1,4,2,3,4,4,4,3,1,2,1,5,5,4,3,1,4,5,2,3,4,5,1,4),
  col1=c(1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,1,1,0,0,0,0,1,0,0,0,0,1,0,1),
  col2=c(1,1,1,1,1,0,0,0,0,1,1,1,1,1,0,0,0,1,1,1,0,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,0,0,0),
  col3=c(1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,0,0,1),
  col4=c(1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),
  col5=c(1,2,1,1,1,2,1,2,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,1,1,1,2,2,2,1,1,2,2,1,1,1,2,2,2)
)

data$gender<-factor(data$gender, levels=c(1,2), labels=c("Male","female"))
data$sector<-factor(data$sector, levels=c(1,2,3,4,5), labels=c("TX","CA","NY","LA","WA"))
data$col1<-factor(data$col1, levels=1, labels="Sales")
data$col2<-factor(data$col2, levels=1, labels="OPS")
data$col3<-factor(data$col3, levels=1, labels="Management")
data$col4<-factor(data$col4, levels=1, labels="HR")
data$col5<-factor(data$col5, levels=c(1,2), labels=c("National","Overseas"))

data$gender1 <- ifelse(data$gender == "Male",1,0)
data$total <- ifelse(data$col5 == "National",1,0)

val_lab(data$gender1)<-c("GENDER"=1)
val_lab(data$total)<-c("All Market"=1)
lst <- list(data$gender1,data$total)

data <- data
var_list <- c("col1","col2","col3")
my_fre <- function(curr_var) setNames(expss::fre(curr_var)[, 1:3],
                                      c("row_labels", "Count N", "Valid percent"))

cross_fun_df(data, list(as.category(col1 %to% col3)),lst, fun = my_fre)

this should show frequency for all variables but showing only total

[nirgrahamuk]

which variables are the ones to group by and which others are the ones to get counts of ?

[ashley21]

@ashley21

col1 %to% col3)),lst

col1 %to% col3)),lst
col1 to col3 are ones to get count of and lst variables are group by

[ashley21]

@ashley21

col1 %to% col3)),lst

col1 %to% col3)),lst
col1 to col3 are ones to get count of and lst variables are group by