Merging data from many files and plot them

Question

I have written application that is analyzing data and writing results in CSV file. It contains three columns: id, diff and count.
1. id is t

Answer 1

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:

myCSVs <- list.files("path/to/directory")

Then I would 'loop' over the list of file names using lapply, reading each file into a data frame using read.csv:

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,...)

Depending on the structure of your .csv's you may need to pass some additional arguments to read.csv. Finally, I would combine this list of data frames into a single data frame:

myData <- do.call(rbind, dataList)

Then you should have all your data in a single data frame, myData, that you can pass to ggplot.

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.