Message-ID: <d8ad40b50903131347g2caa5b68vff6a891213d8abd@mail.gmail.com>
Date: 2009-03-13T20:47:04Z
From: Barry Rowlingson
Subject: different outcomes using read.table vs read.csv
In-Reply-To: <200903132032.n2DKWe3I649873@pollux.msu.montana.edu>
2009/3/13 jatwood <jatwood at montana.edu>:
> Good Afternoon
> I have noticed results similar to the following several times as I have used
> R over the past several years.
> My .csv file has a header row and 3073 rows of data.
>
>> rskreg<-read.table('D:/data/riskregions.csv',header=T,sep=",")
>> dim(rskreg)
> [1] 2722 ? 13
>> rskreg<-read.csv('D:/data/riskregions.csv',header=T)
>> dim(rskreg)
> [1] 3073 ? 13
>>
>
> Does someone know what could be causing the read.table and read.csv
> functions to give different results on some occasions? ?The riskregions.csv
> file was generated with and saved from MS.Excel.
read.table has 'comment.char="#"', so if a line starts with # it gets
ignored. read.csv doesn't have this set, so it might explain why
read.csv gets more than read.table...
Do you have lines starting with #? Try read.table with
comment.char="" and see if you get the right number. See the help for
read.table for more info.
I'd not seen this before, hope it hasn't bitten me...
Barry