rbind

I need to rbind two large data frames. Right now I use df <- rbind(df, df.extension) but I (almost) instantly run out of memory. I guess its because df is held in the memory twice. I might see even bigger data frames in the future, so I need some kind of in-place rbind. So my question is: Is there a way to avoid data duplication in memory when using rbind? I found this question , which uses SqlLite, but I really want to avoid using the hard drive as a cache. Ari B. Friedman data.table is your friend! C.f. http://www.mail-archive.com/r-help@r-project.org/msg175877.html Following up on nikola's

I have 3 lists, each with 500 elements. Here for demonstrative purposes, I have 2 lists with 1 element each: structure(list(timeseries = c(1, 7, 59), t = c(1, 3, 7)), .Names = c("timeseries", "t"), row.names = c(NA, 3L), class = "data.frame") structure(list(timeseries = c(5, 6, 7), t = c(8, 9, 10)), .Names = c("timeseries", "t"), row.names = c(NA, 3L), class = "data.frame") My aim is to rbind the first element in list 1 with the first element in list 2 and 3. Then, the second element in list 1 with the second element in list 2 and 3. And so on. In my example, I would end up with a list of this

First off, this is related to a homework question for the Coursera R programming course. I have found other ways to do what I want to do but my research has led me to a question I'm curious about. I have a variable number of csv files that I need to pull data from and then take the mean of the "pollutant" column in said files. The files are listed in their directory with an id number. I put together the following code which works fine for a single csv file but doesn't work for multiple csv files: pollutantmean <- function (directory, pollutant, id = 1:332) { id <- formatC(id, width=3, flag="0"

This is my sample dataset: vector1 <- data.frame( "name" = "a", "age" = 10, "fruit" = c("orange", "cherry", "apple"), "count" = c(1, 1, 1), "tag" = c(1, 1, 2) ) vector2 <- data.frame( "name" = "b", "age" = 33, "fruit" = c("apple", "mango"), "count" = c(1, 1), "tag" = c(2, 2) ) vector3 <- data.frame( "name" = "c", "age" = 58, "fruit" = c("cherry", "apple"), "count" = c(1, 1), "tag" = c(1, 1) ) list <- list(vector1, vector2, vector3) print(list) This is my test: default <- c("cherry", "orange", "apple", "mango") for (num in 1:length(list)) { #print(list[[num]]) list[[num]] <- rbind( list[[num]],

I have a big data.frame of flowers and fruits in a plant for a 30 years survey. I want to add zeros (0) in some rows which represent individuals in specific months where the plant did not have flowers or fruits (because it is a seasonal species). Example: Year Month Flowers Fruits 2004 6 25 2 2004 7 48 4 2005 7 20 1 2005 8 16 1 I want to add the months that are not included with values of zero so I was thinking in a function that recognize the missing months and fill them with 0. Thanks. ## x is the data frame you gave in the question x <- data.frame( Year = c(2004, 2004, 2005, 2005), Month =