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
different outcomes using read.table vs read.csv
3 messages · jatwood, Barry Rowlingson, Jason Rupert
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
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
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.