[R] different outcomes using read.table vs read.csv

[Jason Rupert]

Without data it is a bit difficult.  However, you may want to check out the following:

library(prob)

That is from:
http://finzi.psych.upenn.edu/R/R-devel/archive/26683.html

It allows you to diff the data.frames, so you can see what is missing. 

This should allow you to find out what rows are missing.  Maybe some NA rows were automatically removed. 


--- On Fri, 3/13/09, jatwood <jatwood at montana.edu> wrote:

> From: jatwood <jatwood at montana.edu>
> Subject: [R] different outcomes using read.table vs read.csv
> To: r-help at r-project.org
> Date: Friday, March 13, 2009, 3:32 PM
> 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.
> 
> Joe A
> 
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