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.b...@sci.monash.edu.au
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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