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    <p>I find a for loop and lists is well enough for stuff like this. Once you have a working set of code it's easy enough to move from a loop into a function which can be <code>sapply</code>ied or similar, but that kind of vectorization is idiosyncratic anyway and probably not useful outside of private one-liners. </p> <p>You probably want to avoid assigning to multiple objects with different names in the workspace (this a FAQ which usually comes up as "how do I assign() . . ."). </p> <p>Please beware my untested code. </p> <p>A vector of file names, and a list with a named element for each file. </p> <pre><code>files &lt;- c("AA01.csv", "AA02.csv") lst &lt;- vector("list", length(files)) names(lst) &lt;- files </code></pre> <p>Loop over each file. </p> <pre><code>library(timeSeries) for (i in 1:length(files)) { ## read strings as character tmp &lt;- read.csv(files[i], stringsAsFactors = FALSE) ## convert to 'timeDate' tmp$tfrm &lt;- timeDate(paste(tmp$cdt, tmp$ctm),format ="%Y/%m/%d %H:%M:%S")) ## create timeSeries object obj &lt;- timeSeries(as.matrix(tmp$Value), tmp$tfrm) ## store object in the list, by name lst[[files[i]]] &lt;- as.xts(obj) } ## clean up rm(tmp, files, obj) </code></pre> <p>Now all the read objects are in <code>lst</code>, but you'll want to test that the file is available, that it was read correctly, and you may want to modify the names to be more sensible than just the file name. </p> <p>Print out the first object by name index from the list: </p> <pre><code>lst[[files[1]]] </code></pre>
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    1. POWhich is the best method to apply a script repetitively to n .csv files in R?
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    1. POWhich is the best method to apply a script repetitively to n .csv files in R?
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    1. COWhen appropriately edited, i get this error: 'Error: object 'tfrm' not found Error in timeSeries(as.matrix(tmp$VALUE), tfrm) : error in evaluating the argument 'charvec' in selecting a method for function 'timeSeries''.// tfrm in the original snippet was meant to house the appropriately converted timeSeries index. ie, cdt was the Date in column 1, ctm was the Time in column 2 and both needed to be pasted together as characters per the requirement for timeSeries to work.
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    2. COie tfrm doesn't exist in the original CSV; just DATE, TIME, VALUE are the columns in the original CSV. tfrm is the correctly formatted combination of DATE & TIME.
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    3. COeven converting: obj <- timeSeries(as.matrix(tmp$Value), tfrm) to obj <- timeSeries(as.matrix(tmp$Value), tmp$tfrm) then gives the error// Error in xts(as.matrix(x@.Data), order.by = order.by, ...) : NROW(x) must match length(order.by)
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