My dataset is as below: http://dl.dropbox.com/u/822467/Data.csv
My situation is this. I have a series of questions (27 in all) where the response is binary in nature. 0=No, 1=Yes, 999=Missing.
My first problem is how to turn all columns into factors. I can do them one by one using as.factors but it takes forever.
My 2nd problem is that I need a summary with the Questions as headers and Yes and No as the first column and the cells are filled with the frequency of Yes and No for each question.
I would also need another dataframe with the %. Greatly appreciate any help I can have. I've looked into Hmisc's package summarize and summary and so onto no avail.
Four lines of code...
dat <- read.csv("http://dl.dropbox.com/u/822467/Data.csv")
dat[, -1] <- lapply(dat[, -1], factor, levels=c(0, 1, 999),
labels=c("No", "Yes", NA))
xx <- do.call(rbind, lapply(dat[, -1], table, useNA="always"))
cbind(xx, sum=rowSums(xx), prop.table(xx, margin=1))
... produces this result:
No Yes <NA> sum No Yes <NA>
Q1 7 57 0 64 0.109375 0.890625 0.000000
Q2 40 22 2 64 0.625000 0.343750 0.031250
Q3 28 36 0 64 0.437500 0.562500 0.000000
Q4 43 18 3 64 0.671875 0.281250 0.046875
Q5 24 39 1 64 0.375000 0.609375 0.015625
Q6 21 42 1 64 0.328125 0.656250 0.015625
Q7 15 49 0 64 0.234375 0.765625 0.000000
Q8 4 60 0 64 0.062500 0.937500 0.000000
Q9 60 4 0 64 0.937500 0.062500 0.000000
Q10 39 25 0 64 0.609375 0.390625 0.000000
Q11 55 8 1 64 0.859375 0.125000 0.015625
Q12 20 44 0 64 0.312500 0.687500 0.000000
Q13 49 15 0 64 0.765625 0.234375 0.000000
Q14 49 15 0 64 0.765625 0.234375 0.000000
Q15 51 13 0 64 0.796875 0.203125 0.000000
Q16 61 3 0 64 0.953125 0.046875 0.000000
Q17 41 23 0 64 0.640625 0.359375 0.000000
Q18 60 4 0 64 0.937500 0.062500 0.000000
Q19 64 0 0 64 1.000000 0.000000 0.000000
Q20 60 4 0 64 0.937500 0.062500 0.000000
Q21 60 4 0 64 0.937500 0.062500 0.000000
Q22 43 21 0 64 0.671875 0.328125 0.000000
Q23 59 4 1 64 0.921875 0.062500 0.015625
Q24 10 54 0 64 0.156250 0.843750 0.000000
Q25 54 9 1 64 0.843750 0.140625 0.015625
Q26 24 39 1 64 0.375000 0.609375 0.015625
Q27 0 0 64 64 0.000000 0.000000 1.000000