reshape2

I have dataframe df with x,y,and monthly.year data for each x,y point. I am trying to get the seasonal aggregate. I need to calculate seasonal means i.e. For winter mean of (December,January,February); for Spring mean of (March,April,May), for Summer mean of (June,July,August) and for autumn mean of (September,October,November). The data looks similar to: set.seed(1) df <- data.frame(x=1:3,y=1:3, matrix(rnorm(72),nrow=3) ) names(df)[3:26] <- paste(month.abb,rep(2009:2010,each=12),sep=".") x y Jan.2009 Feb.2009 ... Dec.2010 1 1 1 -0.6264538 1.5952808 ... 2.1726117 2 2 2 0.1836433 0.3295078 ...

This question already has an answer here: Converting multiple boolean columns to single factor column 4 answers Trying to melt or collapse a dataframe with multiple boolean columns into a two column database with an id column and a column for the collapsed values BUT each value results in a new row. Example beginning: A S1 S2 S3 S4 1 ex1 1 0 0 0 2 ex2 0 1 0 0 3 ex3 0 0 1 0 4 ex4 1 1 0 0 5 ex5 0 1 0 1 6 ex6 0 1 0 0 7 ex7 1 1 1 0 8 ex8 0 1 1 0 9 ex9 0 0 1 0 10 ex10 1 0 0 0 Desired output: A Type ex1 S1 ex2 S2 ex3 S3 ex4 S1 ex4 S2 ex5 S2 ex5 S4 ex6 S2 ex7 S1 ex7 S2 ex7 S3 ex8 S2 ex8 S3 ex9 S3

I'm trying to convert a data frame from wide to long format using the melt formula. The challenge is that I have multiple column names that are labeled the same. When I use the melt function, it drops the values from the repeat column. I have read similar questions and it was advised to use the reshape function, however I was not able to get it work. To reproduce my starting data frame: conversion.id<-c("1", "2", "3") interaction.num<-c("1","1","1") interaction.num2<-c("2","2","2") conversion.id<-as.data.frame(conversion.id) interaction.num<-as.data.frame(interaction.num) interaction.num2<-as

I'm trying to use dcast from the latest reshape2 package (1.2.1) to denormalize a data frame (or data.table) where the value.var is a POSIXct type, but in the resulting data frame, the date values have lost their POSIXct class and become numeric. Do I really have to as.POSIXct() every generated column if I want the values back as POSIXct's, or am I missing something? x <- c("a","b"); y <- c("c","d"); z <- as.POSIXct(c("2012-01-01 01:01:01","2012-02-02 02:02:02")); d <- data.frame(x, y, z, stringsAsFactors=FALSE); str(d); library(reshape2); e <- dcast(d, formula = x ~ y, value.var = "z"); str(e

Must one melt a data frame prior to having it cast? From ?melt : data molten data frame, see melt. In other words, is it absolutely necessary to have a data frame molten prior to any acast or dcast operation? Consider the following: library("reshape2") library("MASS") xb <- dcast(Cars93, Manufacturer ~ Type, mean, value.var="Price") m.Cars93 <- melt(Cars93, id.vars=c("Manufacturer", "Type"), measure.vars="Price") xc <- dcast(m.Cars93, Manufacturer ~ Type, mean, value.var="value") Then: > identical(xb, xc) [1] TRUE So in this case the melt operation seems to have been redundant. What are the