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 -- View this message in context: http://www.nabble.com/Novice-question-about-getting-data-into-R-tp19576065p19576065.html Sent from the R help mailing list archive at Nabble.com.
On 9/19/2008 1:01 PM, Ted Byers wrote:> 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)If your file is really a comma separated file, use read.csv, not read.table (which defaults to white space separators). Duncan Murdoch> 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 > >
Ted Byers wrote:> 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. >Hard to tell. One guess is that you have 42 header names. Spaces inside any of them? Is this really a CSV file? (As in Comma Separated Values). If so, you at least need to set the sep= argument, but how about read.csv()? or if TAB separated, read.delim().> 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. >You might well have to. One man's "unremarkable" can be remarkably different from others'...> 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 > > >-- O__ ---- Peter Dalgaard ?ster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907
> 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>R interprets that you have 42 columns from the variable names. Do you? See if removing spaces between column names helps (e.g., "week.1" instead of "week 1"). Also, because yours is a csv file, fields are separated by comas. You can either use the "read.csv" command instead of the "read.table" (see ?read.table for details), or add the argument sep="," to tell R that fields are separated by comas. You might also need to specify, if you have empty cells, what to do with them (e.g., na.strings="") -- View this message in context: http://www.nabble.com/Novice-question-about-getting-data-into-R-tp19576065p19576350.html Sent from the R help mailing list archive at Nabble.com.
Try
read.csv("K:\\MerchantData\\RiskModel\\refund_distribution.csv",header
= TRUE)
--- On Fri, 9/19/08, Ted Byers <r.ted.byers at gmail.com> wrote:
> From: Ted Byers <r.ted.byers at gmail.com>
> Subject: [R] Novice question about getting data into R
> To: r-help at r-project.org
> Received: Friday, September 19, 2008, 1:01 PM
> 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
>
>
> --
> View this message in context:
>
http://www.nabble.com/Novice-question-about-getting-data-into-R-tp19576065p19576065.html
> Sent from the R help mailing list archive at Nabble.com.
>
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