I found it easy to use R when typing data manually into it. Now I need to
read data from a file, and I get the following errors:
> refdata =
> read.table("K:\\MerchantData\\RiskModel\\refund_distribution.csv", header
> = TRUE)
Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings,
:
line 1 did not have 42 elements
> refdata =
> read.table("K:\\MerchantData\\RiskModel\\refund_distribution.csv")
Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings,
:
line 2 did not have 42 elements
>
(I'd tried the first version above because the first record has column
names.)
First, I don't know why R expects 42 elements in a record.
There is one column for a time variable (weeks since a given week of samples
were taken) and one for each week of sampling in the data file (Week 18
through Week 37 inclusive). And there is only 19 rows.
The samples represented by the columns are independant, and the numbers in
the columns are the fraction of events sampled that result in an event of
another kind in the week since the sample was taken.
The samples are not the same size, and starting with week 20, the number of
values progressively gets smaller since there have been fewer than 37 weeks
since the samples were taken.
I can show you the contents of the data file if you wish. It is
unremarkable, csv, with strings used for column names enclosed in double
quotes.
I don't have to manually separate the samples into their own files do I? I
was hoping to write a function that estimates the density function that best
fits each sample individually, and then iterate of the columns, applying
that function to each in turn.
What is the best way to handle this?
Thanks
Ted
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