how to calculate 5 days cumulative using apply family in R

Question

i have a matrix data frame 6940 rows and 100 columns. I need to find 5 days cumulative at a time on the data set. Right now I was able to build a for loop code for this as follows :

cum<- matrix(data=q1,nrow=6940,ncol=100)
for (j in 1:100){
  for (i in 1:6940){
    cum[i,j]<-sum(q1[i,j],q1[i+1,j],q1[i+2,j],q1[i+3,j],q1[i+4,j],na.rm=T)
  }
}

I wanted to know whether there is any function in apply family to do the same, as this code is very time consuming.

for example if i generate a data frame using the command

 ens <- matrix(rnorm(200),20)

I want cumulative sum of 5 rows a time. i.e sum of row1:row5, row2:row6, row3:row7 and so on in a form of data frame.

i tried using apply function in this form :

apply(apply(apply(apply( apply(m, 2, cumsum),2, cumsum), 2, cumsum),2,cumsum),2,cumsum)

but the problem is I don't get the cumulative in blocks of 5, only an overall cumulative.

Answer 1

Here is one approach using the stats::filter function to calculate the rolling sums and apply to loop over the columns:

m <- matrix(1:48, ncol = 4)
#      [,1] [,2] [,3] [,4]
# [1,]    1   13   25   37
# [2,]    2   14   26   38
# [3,]    3   15   27   39
# [4,]    4   16   28   40
# [5,]    5   17   29   41
# [6,]    6   18   30   42
# [7,]    7   19   31   43
# [8,]    8   20   32   44
# [9,]    9   21   33   45
#[10,]   10   22   34   46
#[11,]   11   23   35   47
#[12,]   12   24   36   48

apply(m, 2, filter, filter = rep(1, 5), sides = 1)
#      [,1] [,2] [,3] [,4]
# [1,]   NA   NA   NA   NA
# [2,]   NA   NA   NA   NA
# [3,]   NA   NA   NA   NA
# [4,]   NA   NA   NA   NA
# [5,]   15   75  135  195
# [6,]   20   80  140  200
# [7,]   25   85  145  205
# [8,]   30   90  150  210
# [9,]   35   95  155  215
#[10,]   40  100  160  220
#[11,]   45  105  165  225
#[12,]   50  110  170  230

This might have to be adjusted depending on how you want to handle windows with less than 5 values (e.g., here in the beginning).

Answer 2

Another option is roll_sum (Data from @Roland's post)

library(RcppRoll)
apply(m, 2, roll_sumr, 5)
#       [,1] [,2] [,3] [,4]
# [1,]   NA   NA   NA   NA
# [2,]   NA   NA   NA   NA
# [3,]   NA   NA   NA   NA
# [4,]   NA   NA   NA   NA
# [5,]   15   75  135  195
# [6,]   20   80  140  200
# [7,]   25   85  145  205
# [8,]   30   90  150  210
# [9,]   35   95  155  215
#[10,]   40  100  160  220
#[11,]   45  105  165  225
#[12,]   50  110  170  230

As @alexis_laz mentioned in the comments, roll_sumr can take matrix as well. It is more efficient.

roll_sumr(m, 5, by = 1)

Benchmarks

set.seed(24)
m1 <- matrix(sample(1:50, 5000*5000, replace=TRUE), ncol=5000)
system.time(apply(m1, 2, roll_sumr, 5))
# user  system elapsed 
# 1.84    0.16    1.99 

system.time(roll_sumr(m1, 5, by = 1))
#  user  system elapsed 
#  0.59    0.15    0.74 

system.time(apply(m1, 2, stats::filter, filter = rep(1, 5), sides = 1))
#  user  system elapsed 
#  4.46    0.20    4.68