reshape2

I hope that I'm not recreating the wheel, and do not think that the following can be answered using reshape . I have messy longitudinal survey data, that I want to convert from wide to long format. By messy I mean: I have a mixture of variable types (numeric, factor, logical) Not all variables have been collected at every timepoint. For example: data <- read.table(header=T, text=' id inlove.1 inlove.2 income.2 income.3 mood.1 mood.3 random 1 TRUE FALSE 87717.76 82281.25 happy happy filler 2 TRUE TRUE 70795.53 54995.19 so-so happy filler 3 FALSE FALSE 48012.77 47650.47 sad so-so filler ') I

This question already has an answer here: Reshaping multiple sets of measurement columns (wide format) into single columns (long format) 7 answers Apologies if this is a simple question, but I haven't been able to find a simple solution after searching. I'm fairly new to R, and am having trouble converting wide format to long format using either the melt (reshape2) or gather(tidyr) functions. The dataset that I'm working with contains 22 different time variables that are each 3 time periods. The problem occurs when I try to convert all of these from wide to long format at once. I have had

How can I do this reshape faster and so that it takes up less memory? My aim is to reshape a dataframe that is 500,000 rows by 500 columns with 4 Gb RAM. Here's a function that will make some reproducible data: make_example <- function(ndoc, ntop){ # doc numbers V1 = seq(1:ndoc) # filenames V2 <- list("vector", size = ndoc) for (i in 1:ndoc){ V2[i] <- paste(sample(c(rep(0:9,each=5),LETTERS,letters),5,replace=TRUE),collapse='') } # topic proportions tvals <- data.frame(matrix(runif(1:(ndoc*ntop)), ncol = ntop)) # topic number tnumvals <- data.frame(matrix(sample(1:ntop, size = ndoc*ntop,

This question already has an answer here: Transpose / reshape dataframe without “timevar” from long to wide format 6 answers I have a data frame similar to the following example: > df <- data.frame(imp = c("Johny", "Johny", "Lisa", "Max"), item = c(5025, 1101, 2057, 1619)) > df imp item [1,] "Johny" "5025" [2,] "Johny" "1101" [3,] "Lisa" "2057" [4,] "Max" "1619" I would like to have an unique row for each user . The final result should be something like this: > df imp item1 item2 [1,] "Johny" "5025" "1101" [2,] "Lisa" "2057" NA [3,] "Max" "1619" NA ## Add an ID column to distinguish multiple

I have some data in long form that looks like this: dat1 = data.frame( id = rep(LETTERS[1:2], each=4), value = 1:8 ) In table form: id value A 1 A 2 A 3 A 4 B 5 B 6 B 7 B 8 And I want it to be in short form and look like this: dat1 = data.frame(A = 1:4, B = 5:8) In table form: A B 1 5 2 6 3 7 4 8 Now I could solve this by looping with cbind() and stuff, but I want to use some kind of reshape/melt function as these are the best way to do this kind of thing I think. However, from spending >30 minutes trying to get melt() and reshape() to work, reading answers on SO, it seems that these functions

Whis this data frame, df <- expand.grid(id="01", parameter=c("blood", "saliva"), visit=c("V1", "V2", "V3")) df$value <- c(1:6) df$sex <- rep("f", 6) df > df id parameter visit value sex 1 01 blood V1 1 f 2 01 saliva V1 2 f 3 01 blood V2 3 f 4 01 saliva V2 4 f 5 01 blood V3 5 f 6 01 saliva V3 6 f When I reshape it in the "wide" format, I get identical results with both the base reshape function and the dcast function from reshape2 . reshape(df, timevar="visit", idvar=c("id", "parameter", "sex"), direction="wide") id parameter sex value.V1 value.V2 value.V3 1 01 blood f 1 3 5 2 01 saliva f 2 4 6