Question

I have two R data frame with differing dimensions. However but data frames have an id column

df1:

nrow(df1)=22308

                     c1      c2       c3           pattern1.match
ENSMUSG00000000001_at 10.175115 10.175423 10.109524              0
ENSMUSG00000000003_at  2.133651  2.144733  2.106649              0
ENSMUSG00000000028_at  5.713781  5.714827  5.701983              0

df2:

                               Genes Pattern.Count
ENSMUSG00000000276 ENSMUSG00000000276_at             1
ENSMUSG00000000876 ENSMUSG00000000876_at             1
ENSMUSG00000001065 ENSMUSG00000001065_at             1
ENSMUSG00000001098 ENSMUSG00000001098_at             1

nrow(df2)=425

I would like to loop through df2, and find all genes that have pattern.count=1 and check it in df1$pattern1.match column.

Basically I would like to overwrite the fields GENES AND pattern1.match with the df2$Genes and df2$Pattern.Count. All the elements from df2$Pattern.Count are equal to one.

I wrote this function, but R freezes while looping through all these rows.

idcol <- ncol(df1)
return.frame.matches <- function(df1, df2, idcol) { 
    for (i in 1:nrow(df1)) { 
         for (j in 1:nrow(df2))
                 if(df1[i, 1] == df2[j, 1]) { 
                     df1[i, idcol] = 1
                     break
                 }    
     }
     return (df1) 
}

Is there another way of doing that without almost killing the computer?

Answer 1

I’m not sure I get exactly what you are doing, but the following should at least get you closer.

The first column of df1 doesn’t seem to have a name, are they rownames?

If so,

df1$Genes <- rownames(df1)

Then you could then do a merge to create a new dataframe with the genes you require:

merge(df1,subset(df2,Pattern.Count==1))

Note they are matching on the common column Genes. I’m not sure what you want to do with the pattern1.match column, but a subset on the df1 part of merge can incorporate conditions on that.

Edit

Going by the extra information in the comments,

df1$pattern1.match <- as.numeric(df1$Genes %in% df2$Genes)

should achieve what you are looking for.