[R] indexing into a data.frame using another data.frame that also contains values for replacement

[David Winsemius]

On Apr 30, 2011, at 4:18 PM, Alice Wines wrote:

> Hello all,
>
>     I have a quandry I have been scratching my head about for a
> while. I've searched the manual and the web and have not been able to
> find an acceptable result, so I am hoping for some help.
>
>     I have two data frames and I want to index into the first using
> the second, and replace the specific values I have indexed with more
> values from the second data.frame. I can do this using a loop, but I
> wanted a quicker solution with no loops involved.
>
> Although my data set is much larger than this, a small example of what
> I am trying to do is as follows:
>
> df1 <- data.frame(rows=c("A","B","C", "B", "C", "A"),
> columns=c("21_2", "22_2", "23_2", "21_2", "22_2", "23_2"),
> values=c(3.3, 2.5, 67.2, 44.3, 53, 66))
> df2 <- data.frame(matrix(rep(NA, length(df1$values)),nrow=3, ncol=3))
> names(df2) <- c("21_2", "22_2", "23_2")
> row.names(df2) <- c("A", "B", "C")
>
>> df1
>  rows columns values
> 1    A    21_2    3.3
> 2    B    22_2    2.5
> 3    C    23_2   67.2
> 4    B    21_2   44.3
> 5    C    22_2   53.0
> 6    A    23_2   66.0
>
 > require(Matrix)
 > xtabs(values~rows+columns, data=df1, sparse=TRUE)
3 x 3 sparse Matrix of class "dgCMatrix"
   21_2 22_2 23_2
A  3.3  .   66.0
B 44.3  2.5  .
C  .   53.0 67.2

>
>
>> df2
>  21_2 22_2 23_2
> A   NA   NA   NA
> B   NA   NA   NA
> C   NA   NA   NA
>
>
>     Note that none of the same locations in df2 are specified twice
> in df2, so I'm not worried about over-writing it.
>
>    I have tried 'mapply' and 'replace', but apparently either they do
> not work well for this or I don't understand how to use them properly
> for this purpose. My understanding is that 'replace' needs a vector
> input and that one cannot create a vector of vectors, so I couldn't
> pass my indices to 'replace'.
>
>     When I tried mapply, the code I used was something like what  
> follows:
>
> df3 <- mapply('[<-' , df2, paste(as.character(df1$rows),
> as.character(df1$columns), sep=', '), df1$values)
>
> but it yields the following strange result
>
>> df3
>        21_2 22_2 23_2 <NA> <NA> <NA>
>          NA   NA   NA   NA   NA   NA
>          NA   NA   NA   NA   NA   NA
>          NA   NA   NA   NA   NA   NA
> A, 21_2  3.3  2.5 67.2 44.3   53   66
>
>
> What I want to see is the following:
>
>> df3
>    21_2   22_2  23_2
> A   3.3    NA    66.0
> B   44.3  2.5    NA
> C   NA   53.0   67.2


-- 
David Winsemius, MD
Heritage Laboratories
West Hartford, CT