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7311591
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2011-09-05T18:30:01.777
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2011-09-05T18:58:55.213
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2011-09-05T18:58:55.213
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Edited to clean up some typos and address the multiple K value issue. I'm going to assume that you've placed all your .csv files in a single directory (and there's nothing else in this directory). I will also assume that each .csv really do have the same structure (same number of columns, in the same order). I would begin by generating a list of the file names: <pre><code>myCSVs <- list.files("path/to/directory") </code></pre> Then I would 'loop' over the list of file names using <code>lapply</code>, reading each file into a data frame using <code>read.csv</code>: <pre><code>setwd("path/to/directory") #This function just reads in the file and # appends a column with the K val taken from the file # name. You may need to tinker with the particulars here. myFun <- function(fn){ tmp <- read.csv(fn) tmp$K <- strsplit(fn,".",fixed = TRUE)[[1]][1] tmp } dataList <- lapply(myCSVs, FUN = myFun,...) </code></pre> Depending on the structure of your .csv's you may need to pass some additional arguments to <code>read.csv</code>. Finally, I would combine this list of data frames into a single data frame: <pre><code>myData <- do.call(rbind, dataList) </code></pre> Then you should have all your data in a single data frame, <code>myData</code>, that you can pass to <code>ggplot</code>. As for the statistical aspect of your question, it's a little difficult to offer an opinion without concrete examples of your data. Once you've figured the programming part out, you could ask a separate question that provides some sample data (either here, or on stats.stackexchange.com) and folks will be able to suggest some visualization or analysis techniques that may help.
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1. COthat worked perfectly!
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