R help - Oct 2009 - Recommendation for dealing with mixed input types in CSV

Currently I have a CSV with mixed input types that I am trying to read in and
reformat without having to list off all the column names.? Below is an example
of the data:

HouseColor, HouseSize, HouseCost
Blue, 1600, 160e3
Blue, 1600, 160e3

Actually I have about 60 columns like this, so imagine the above repeated about
30 times column-wise.?

Luckily the ones in scientific notation are grouped together, i.e. columns
11-56.

Using read.csv or as.numeric, is there?a way to convert all those in?scientific
format over to?general numeric syntax??

Right now I have something like the following
input_df<-read.csv(InputFile, skip=0, header=TRUE, strip.white = TRUE)

I tried:
as.numeric(input_df[, 11:56])
but this returns an error 
Error: (list) object cannot be coerced to type 'double'

Oddly it does appear to work successfully row-wiseas.numeric(input_df[1, 11:56])
as.numeric(input_df[2, 11:56])
etc.

However, trying it on multiple rows produces the same error as above:
as.numeric(input_df[1:2, 11:56])

After a bit, I became a bit frustrated that this was not working so I tried just
deleting the columns:
input_df[1, 11:56]<-NULL

This also failed, so are there any suggestions about how to convert the values
in scientific notation over to standard numeric syntaix?

Thank you again again for all your insights and feedback.

On Oct 29, 2009, at 3:26 PM, Jason Rupert wrote:
> Currently I have a CSV with mixed input types that I am trying to  
> read in and reformat without having to list off all the column  
> names.  Below is an example of the data:
>
> HouseColor, HouseSize, HouseCost
> Blue, 1600, 160e3
> Blue, 1600, 160e3
>
> Actually I have about 60 columns like this, so imagine the above  
> repeated about 30 times column-wise.
>
> Luckily the ones in scientific notation are grouped together, i.e.  
> columns 11-56.
>
> Using read.csv or as.numeric, is there a way to convert all those in  
> scientific format over to general numeric syntax?
Option 1: do it in the read step. (in my experience the more dificult  
and error-prone method when you are starting out.)

?read.table

see section on colClasses, and define your columns as "character" or  
"numeric" appropriately.

Option 2:
Read them in with as.is=TRUE, and stringsAsFactors=FALSE

convert them in a loop

for (i in 11:56) DFhouses[, i] <- as.numeric( DFhouses[, i] )

>
> Right now I have something like the following
> input_df<-read.csv(InputFile, skip=0, header=TRUE, strip.white = TRUE)
>
> I tried:
> as.numeric(input_df[, 11:56])
> but this returns an error
> Error: (list) object cannot be coerced to type 'double'
>
> Oddly it does appear to work successfully row- 
> wiseas.numeric(input_df[1, 11:56])
> as.numeric(input_df[2, 11:56])
> etc.
>
> However, trying it on multiple rows produces the same error as above:
> as.numeric(input_df[1:2, 11:56])
>
> After a bit, I became a bit frustrated that this was not working so  
> I tried just deleting the columns:
> input_df[1, 11:56]<-NULL
>
> This also failed, so are there any suggestions about how to convert  
> the values in scientific notation over to standard numeric syntaix?
>
> Thank you again again for all your insights and feedback.
>
>
>
>
> ______________________________________________
> 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.
David Winsemius, MD
Heritage Laboratories
West Hartford, CT

Jason Rupert wrote:
> 
> Currently I have a CSV with mixed input types that I am trying to read in
> and reformat without having to list off all the column names.? Below is an
> example of the data:
> 
> HouseColor, HouseSize, HouseCost
> Blue, 1600, 160e3
> Blue, 1600, 160e3
> 
> [snip]
> 
I'm a little surprised that read.csv is *not* automatically converting your
scientific notation to numeric.  When I save the three lines above to
the file "house.tmp", I get numeric values ...
> read.csv("house.tmp",header=TRUE)
  HouseColor HouseSize HouseCost
1       Blue      1600    160000
2       Blue      1600    160000

  Are you sure there isn't something else funny about those columns?
-- 
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