Get Value of last non-empty column for each row

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盖世英雄少女心
盖世英雄少女心 2021-01-05 21:27

Take this sample data:

data.frame(a_1=c(\"Apple\",\"Grapes\",\"Melon\",\"Peach\"),a_2=c(\"Nuts\",\"Kiwi\",\"Lime\",\"Honey\"),a_3=c(\"Plum\",\"Apple\",NA,NA)         


        
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  • 2021-01-05 22:03

    There's no need for regex here. Just use apply + tail + na.omit:

    > apply(mydf, 1, function(x) tail(na.omit(x), 1))
    [1] "Cucumber" "Apple"    "Lime"     "Honey" 
    

    I don't know how this compares in terms of speed, but you You can also use a combination of "data.table" and "reshape2", like this:

    library(data.table)
    library(reshape2)
    na.omit(melt(as.data.table(mydf, keep.rownames = TRUE), 
                 id.vars = "rn"))[, value[.N], by = rn]
    #    rn       V1
    # 1:  1 Cucumber
    # 2:  2    Apple
    # 3:  3     Lime
    # 4:  4    Honey
    

    Or, even better:

    melt(as.data.table(df, keep.rownames = TRUE), 
         id.vars = "rn", na.rm = TRUE)[, value[.N], by = rn]
    #    rn       V1
    # 1:  1 Cucumber
    # 2:  2    Apple
    # 3:  3     Lime
    # 4:  4    Honey
    

    This would be much faster. On an 800k-row dataset, apply took ~ 50 seconds while the data.table approach took about 2.5 seconds.

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  • 2021-01-05 22:04

    Another alternative that might be pretty fast:

    DF[cbind(seq_len(nrow(DF)), max.col(!is.na(DF), "last"))]
    #[1] "Cucumber" "Apple"    "Lime"     "Honey"
    

    Where "DF":

    DF = structure(list(a_1 = structure(1:4, .Label = c("Apple", "Grapes", 
    "Melon", "Peach"), class = "factor"), a_2 = structure(c(4L, 2L, 
    3L, 1L), .Label = c("Honey", "Kiwi", "Lime", "Nuts"), class = "factor"), 
        a_3 = structure(c(2L, 1L, NA, NA), .Label = c("Apple", "Plum"
        ), class = "factor"), a_4 = structure(c(1L, NA, NA, NA), .Label = "Cucumber", class = "factor")), .Names = c("a_1", 
    "a_2", "a_3", "a_4"), row.names = c(NA, -4L), class = "data.frame")
    
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  • 2021-01-05 22:07

    You could also try: (df1 is the dataset)

     indx <- which(!is.na(df1), arr.ind=TRUE)
     df1[cbind(1:nrow(df1),tapply(indx[,2], indx[,1], FUN=max))]
     #[1] "Cucumber" "Apple"    "Lime"     "Honey"  
    
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