vectorise rows of a dataframe, apply vector function, return to original dataframe r -


given following df:

a=c('a','b','c') b=c(1,2,5) c=c(2,3,4) d=c(2,1,6) df=data.frame(a,b,c,d)    b c d 1 1 2 2 2 b 2 3 1 3 c 5 4 6

i'd apply function takes vector (and returns vector) cummax row row columns in position b d.

then, i'd have output in df, either vector in new column of df, or replacing original data.

i'd avoid writing for loop iterate every row, pull out content of cells vector, thing , put back.

is there more efficient way? i've given apply family functions go, i'm struggling first way vectorise content of columns row , right output.

the final output (imagining i've applied cummax() function).

    b c d  1   1 2 2 2   b 2 3 3 3   c 5 5 6

or

    b c d output 1   1 2 2 (1,2,2) 2   b 2 3 1 (2,3,3) 3   c 5 4 6 (5,5,6)

where output vector.

seems simple apply problem want cbind df:

> cbind(df, apply(df[ , 4:2]   # work columns in reverse order                      , 1,      # row-by-row                       cummax) )   b c d 1 2 3 d 1 2 2 2 1 6 c b 2 3 1 2 3 6 b c 5 4 6 2 3 6

ouch. bitten failing notice returned in column oriented matrix , need transpose result; such newbie mistake. show value of having question reproducible dataset suppose.

> cbind(df, t(apply(df[ , 4:2] , 1, cummax) ) )   b c d d c b 1 1 2 2 2 2 2 2 b 2 3 1 1 3 3 3 c 5 4 6 6 6 6

to destructively assign result df use:

df <-   # .... code.

this concatenation commas (and result no longer needs transposed:

> cbind(df, output=apply(df[ , 4:2] , 1, function(x) paste( cummax(x), collapse=",") ) )   b c d output 1 1 2 2  2,2,2 2 b 2 3 1  1,3,3 3 c 5 4 6  6,6,6

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