convertFile(
  filename,
  symbol1 = NULL,
  symbol2 = NULL,
  newsymbol1 = "",
  newsymbol2 = "",
  sep = ";",
  newsep = NULL,
  header = TRUE,
  columns = NULL,
  outputfile = gsub("^(.*)(\\.)([^\\.]*)$", "\\1_new.\\3", filename),
  fixed.s1 = TRUE,
  fixed.s2 = TRUE,
  fixed.sep = TRUE,
  ...
)

## Not run: 
# normally, the function call would look something like this:
convertFile('example1.csv', symbol1=',', symbol2='.', sep='\t', 
    newsymbol1='.', newsymbol2='')
# But as we are not sure that the file example1.csv is available,
# we need to do something a little more complicated to point to 
# the file 'example1.csv' that comes with the package:

# finding one of the example files from the package:
file1 <- system.file('extdata', 'example1.csv', package = 'plotfunctions')

# example 1: 
system.time({
    convertFile(file1, symbol1=',', symbol2='.', 
    newsymbol1='.', newsymbol2='', outputfile='example1_new.csv')
})
# example 2: type 'yes' to overwrite the previous output file, 
# or specify a different filename in outputfile.
system.time({
    convertFile(file1, symbol1=',', symbol2='.', sep='\t', 
    newsymbol1='.', newsymbol2='', columns=1:2, outputfile='example1_new.csv')
})
# Example 1 takes less  time, as it does not use read.table, 
# but just reads the file as text lines. However, the column 
# version could be useful when symbols should be replaced only 
# in specific columns.
# Note that Example 2 writes the output with quotes, but this is 
# not a problem for read.table:
dat <- read.table('example1_new.csv', header=TRUE, sep='\t', 
    stringsAsFactors=FALSE)

## End(Not run)