Apply a function to every row of a matrix or a data frame

Question

Suppose I have a n by 2 matrix and a function that takes a 2-vector as one of its arguments  I would like to apply the function to each row of the matrix and get a n-vector  How to do this in R   For example  I would like to compute the density of a 2D standard Normal distribution on three points   bivariate density x   c 0  0   mu   c 0  0   sigma   c 1  1   rho   0       exp -1  2  1-rho 2    x 1  2 sigma 1  2 x 2  2 sigma 2  2-2 rho x 1  x 2   sigma 1  sigma 2       1  2 pi sigma 1  sigma 2  sqrt 1-rho 2      out  lt - rbind c 1  2   c 3  4   c 5  6     How to apply the function to each row of out   How to pass values for the other arguments besides the points to the function in the way you specify

User · Answer

First step would be making the function object, then applying it. If you want a matrix object that has the same number of rows, you can predefine it and use the object[] form as illustrated (otherwise the returned value will be simplified to a vector):

bvnormdens <- function(x=c(0,0),mu=c(0,0), sigma=c(1,1), rho=0){
     exp(-1/(2*(1-rho^2))*(x[1]^2/sigma[1]^2+
                           x[2]^2/sigma[2]^2-
                           2*rho*x[1]*x[2]/(sigma[1]*sigma[2]))) * 
     1/(2*pi*sigma[1]*sigma[2]*sqrt(1-rho^2))
     }
 out=rbind(c(1,2),c(3,4),c(5,6));

 bvout<-matrix(NA, ncol=1, nrow=3)
 bvout[] <-apply(out, 1, bvnormdens)
 bvout
             [,1]
[1,] 1.306423e-02
[2,] 5.931153e-07
[3,] 9.033134e-15

If you wanted to use other than your default parameters then the call should include named arguments after the function:

bvout[] <-apply(out, 1, FUN=bvnormdens, mu=c(-1,1), rho=0.6)

apply() can also be used on higher dimensional arrays and the MARGIN argument can be a vector as well as a single integer.

User · Answer

Another approach if you want to use a varying portion of the dataset instead of a single value is to use rollapply data  width  FUN         Using a vector of widths allows you to apply a function on a varying window of the dataset   I ve used this to build an adaptive filtering routine  though it isn t very efficient

User · Answer

Apply does the job well  but is quite slow   Using sapply and vapply could be useful  dplyr s rowwise could also be useful Let s see an example of how to do row wise product of any data frame   a   data frame t iris 1 10 1 3    vapply a  prod  0  sapply a  prod    Note that assigning to variable before using vapply sapply  apply is good practice as it reduces time a lot  Let s see microbenchmark results  a   data frame t iris 1 10 1 3    b   iris 1 10 1 3  microbenchmark  microbenchmark      apply b  1   prod       vapply a  prod  0       sapply a  prod         apply iris 1 10 1 3   1   prod       vapply data frame t iris 1 10 1 3     prod  0       sapply data frame t iris 1 10 1 3     prod        b   gt    rowwise     gt           summarise p   prod Sepal Length Sepal Width Petal Length       Have a careful look at how t   is being used

User · Answer

You simply use the apply   function   R gt  M  lt - matrix 1 6  nrow 3  byrow TRUE  R gt  M        1    2   1      1    2  2      3    4  3      5    6 R gt  apply M  1  function x  2 x 1  x 2    1   4 10 16 R gt     This takes a matrix and applies a  silly  function to each row  You pass extra arguments to the function as fourth  fifth      arguments to apply

User · Answer

Here is a short example of applying a function to each row of a matrix   Here  the function applied normalizes every row to 1    Note  The result from the apply   had to be transposed using t   to get the same layout as the input matrix A   A  lt - matrix c    0  1  1  2    0  0  1  3    0  0  1  3    nrow   3  byrow   TRUE   t apply A  1  function x  x   sum x       Result          1    2    3    4   1      0 0 25 0 25 0 50  2      0 0 00 0 25 0 75  3      0 0 00 0 25 0 75

User · Answer

In case you want to apply common functions such as sum or mean  you should use rowSums or rowMeans since they re faster than apply data  1  sum  approach  Otherwise  stick with apply data  1  fun   You can pass additional arguments after FUN argument  as Dirk already suggested    set seed 1  m  lt - matrix round runif 20  1  5    ncol 4  diag m   lt - NA m        1    2    3    4   1     NA    5    2    3  2      2   NA    2    4  3      3    4   NA    5  4      5    4    3   NA  5      2    1    4    4   Then you can do something like this   apply m  1  quantile  probs c  25  5   75   na rm TRUE        1    2    3    4    5  25   2 5    2  3 5  3 5 1 75 50   3 0    2  4 0  4 0 3 00 75   4 0    3  4 5  4 5 4 00

[r] Apply a function to every row of a matrix or a data frame

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