R help - May 2012 - read.table() vs read.delim() any difference??

Hi,

I have a tab seperated file with 206 rows and 30 columns.

I read in the file into R using read.table() function. I checked the dim()
of the data frame created in R, it had only 103 rows (exactly half), 30
columns. Then I tried reading in the file using read.delim() function and
this time the dim() showed to be 206 rows, 30 columns as expected.
Reading the read.table() R-help documentation, I came across count.fields()
function. On using that on the tab seperated file, I got to learn that the
header line alone has 30 fields and rest of the rows have 9 fields. I am
now just wondering why read.delim() function was able to read in the file
correctly and read.table() wasn't able to read the file completely ?

Could anyone please throw some light on this?

Thanks for your valuable time,

Regards
Sashi

	[[alternative HTML version deleted]]

read.delim calls read.table so any differences between the two are  
caused by differences in the default values of some of the parameters.  
Take a look at the help file ?read.table

read.table uses white space as separator; read.delim tabs
read.table uses " and ' as quotes; read.delim just "
etc.

Jan


Rameswara Sashi Kiran Challa <schalla at umail.iu.edu> schreef:
> Hi,
>
> I have a tab seperated file with 206 rows and 30 columns.
>
> I read in the file into R using read.table() function. I checked the dim()
> of the data frame created in R, it had only 103 rows (exactly half), 30
> columns. Then I tried reading in the file using read.delim() function and
> this time the dim() showed to be 206 rows, 30 columns as expected.
> Reading the read.table() R-help documentation, I came across count.fields()
> function. On using that on the tab seperated file, I got to learn that the
> header line alone has 30 fields and rest of the rows have 9 fields. I am
> now just wondering why read.delim() function was able to read in the file
> correctly and read.table() wasn't able to read the file completely ?
>
> Could anyone please throw some light on this?
>
> Thanks for your valuable time,
>
> Regards
> Sashi
>
> 	[[alternative HTML version deleted]]
>
> ______________________________________________
> 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.

On May 4, 2012, at 08:16 , Rameswara Sashi Kiran Challa wrote:
> Hi,
> 
> I have a tab seperated file with 206 rows and 30 columns.
> 
> I read in the file into R using read.table() function. I checked the dim()
> of the data frame created in R, it had only 103 rows (exactly half), 30
> columns. Then I tried reading in the file using read.delim() function and
> this time the dim() showed to be 206 rows, 30 columns as expected.
> Reading the read.table() R-help documentation, I came across count.fields()
> function. On using that on the tab seperated file, I got to learn that the
> header line alone has 30 fields and rest of the rows have 9 fields. I am
> now just wondering why read.delim() function was able to read in the file
> correctly and read.table() wasn't able to read the file completely ?
> 
> Could anyone please throw some light on this?
This can't be answered in abstractum. However, all that read.delim does is
to call read.table with a specific set of arguments, so you should be able to
get the right result from

read.table(......., header = TRUE, sep = "\t", quote =
"\"", dec = ".",
    fill = TRUE, comment.char = "")

So check that it works. If you are curious as to what is causing the difference,
just knock out the arguments one by one.

> 
> Thanks for your valuable time,
> 
> Regards
> Sashi
> 
> 	[[alternative HTML version deleted]]
> 
> ______________________________________________
> 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.
-- 
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com