plyr

I'm trying to merge a directory full of comma delimited text files using R, while also incorporating the file name of each file as a new variable in the data set. I've been using the following: library(plyr) file_list <- list.files() dataset <- ldply(file_list, read.table, header=FALSE, sep=",") Can anyone shed any light on how I'd add the file name for each file read as a new variable within dataset? Many thanks, -Jon You can just make a wrapper around the read.table() function that adds in your filename variable. Something like this should work: read.data <- function(file){ dat <- read.table

i have a data frame with 2 groups 1 timevariable and an dependent variable. e.g.: name <- c("a", "a", "a", "a", "a", "a","a", "a", "a", "b", "b", "b","b", "b", "b","b", "b", "b") class <- c("c1", "c1", "c1", "c2", "c2", "c2", "c3", "c3", "c3","c1", "c1", "c1", "c2", "c2", "c2", "c3", "c3", "c3") year <- c("2010", "2009", "2008", "2010", "2009", "2008", "2010", "2009", "2008", "2010", "2009", "2008", "2010", "2009", "2008", "2010", "2009", "2008") value <- c(100, 33, 80, 90, 80, 100, 100, 90, 80, 90, 80, 100, 100, 90, 80, 99, 80, 100) df <- data.frame(name, class, year, value) df and would like

I have the following data.frame d from an experiment: - Variable y (response, continuous) - Factor f (500 levels) - Time t (posixct) In the last 8 years, y was measured roughly once a month (exact date in t) for each level of f. Sometimes there are 2 measures per month, sometimes a couple of month passed without any measures. Sorry for not providing example data, but making up unregular time series goes beyond my R knowledge. ;) I'd like to do the following with this data: make a regression using the loess() function (y ~ t) , for each level of f make a prediction of y for the first day of

Below is the piece of code. It gives percentile of the trade price level for rolling 15-minute(historical) window. It runs quickly if the length is 500 or 1000, but as you can see there are 45K observations, and for the entire data its very slow. Can I apply any of the plyr functions? Any other suggestions are welcome. This is how trade data looks like: > str(trade) 'data.frame': 45571 obs. of 5 variables: $ time : chr "2013-10-20 22:00:00.489" "2013-10-20 22:00:00.807" "2013-10-20 22:00:00.811" "2013-10-20 22:00:00.811" ... $ prc : num 121 121 121 121 121 ... $ siz : int 1 4 1 2 3 3 2 2 3 4 .

Hi:I'm new to the plyr/dplyr family but enjoying it. I can see it's massive utility for my own work, but I'm stil trying to get my head around it. I have a data frame that looks like below. 1) How do I produce a table for each non-grouping variable that shows the distribution of responses within each value of the grouping variable? 2) Note: I do have some missing values and I would like to exclude them from the tabulation. I realize the summarize_each command will apply the function to each column, but I don't know how to handle the missing values issue in a simple way. I have seen some codes