R help - May 2010 - conditional calculations per row (loop versus apply)

Hi all,
I'm hoping someone might help with a query about conditionally applying
formulas to a dataframe.

In essence I have 3 lookup tables (Table A, B & C) and a dataframe with a
variable Type.Code, which identifies the Lookup Table to which each record
belongs. The lookup tables reference different sensor types for which I need
apply a different formula to values in Column3 in each row to calculate a value
for Column 4. I have no problem writing a for loop to handle this, but with over
1M rows in the df this is very slow.


Is there a way to solve this using a function such as sapply or would I be
better off just subsetting and applying each function separately on the
appropriate subset before rejoining.

Thanks in advance.

Nick


Table.A
1
3
5
13

Table.B
4
6
10
20

Table.C

5321
3233
4532

Dataframe
Record	  Type.code	Column3	Column4
1			1		0.4			
2			3		0.25			
3			4		100			
4			20		150			
5			5		0.4			
6			4532	NA		


I have no problem writing a foor loop to do this
	for (i in 1:nrow(dataframe) ) {
	
			if (Type.code[i]%in%Table.A) Reading[i]<-function 1		
			else
			if (Type.code[i]%in%tTable.B) Reading[i]<-function 2
			else						
			if (Type.code[i]%in%Table.C) Reading[i]<-function 3		
			}


~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Dr Nick Bond
Research Fellow
Monash University
Victoria, Australia, 38000
Email: Nick.Bond@sci.monash.edu.au
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


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try this:

Reading <- ifesle (Type.code%in%Table.A, function 1,
           ifelse (Type.code%in%tTable.B, function 2,
           ifelse (Type.code%in%Table.C, function 3,NA)))


On Sat, May 15, 2010 at 5:21 AM, Nick Bond
<Nick.Bond@sci.monash.edu.au>wrote:
> Hi all,
> I'm hoping someone might help with a query about conditionally applying
> formulas to a dataframe.
>
> In essence I have 3 lookup tables (Table A, B & C) and a dataframe with
a
> variable Type.Code, which identifies the Lookup Table to which each record
> belongs. The lookup tables reference different sensor types for which I
need
> apply a different formula to values in Column3 in each row to calculate a
> value for Column 4. I have no problem writing a for loop to handle this,
but
> with over 1M rows in the df this is very slow.
>
>
> Is there a way to solve this using a function such as sapply or would I be
> better off just subsetting and applying each function separately on the
> appropriate subset before rejoining.
>
> Thanks in advance.
>
> Nick
>
>
> Table.A
> 1
> 3
> 5
> 13
>
> Table.B
> 4
> 6
> 10
> 20
>
> Table.C
>
> 5321
> 3233
> 4532
>
> Dataframe
> Record    Type.code     Column3 Column4
> 1                       1               0.4
> 2                       3               0.25
> 3                       4               100
> 4                       20              150
> 5                       5               0.4
> 6                       4532    NA
>
>
> I have no problem writing a foor loop to do this
>        for (i in 1:nrow(dataframe) ) {
>
>                        if (Type.code[i]%in%Table.A) Reading[i]<-function
1
>                        else
>                        if (Type.code[i]%in%tTable.B)
Reading[i]<-function 2
>                        else
>                        if (Type.code[i]%in%Table.C) Reading[i]<-function
3
>                        }
>
>
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> Dr Nick Bond
> Research Fellow
> Monash University
> Victoria, Australia, 38000
> Email: Nick.Bond@sci.monash.edu.au
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>
>
>        [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@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<http://www.r-project.org/posting-guide.html>
> and provide commented, minimal, self-contained, reproducible code.
>


-- 
Jim Holtman
Cincinnati, OH
+1 513 646 9390

What is the problem that you are trying to solve?

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