# How to find the sum of rows, columns, and total in a matrix in R?

To find the sum of row, columns, and total in a matrix can be simply done by using the functions rowSums, colSums, and sum respectively. The row sums, column sums, and total are mostly used comparative analysis tools such as analysis of variance, chi−square testing etc.

## Example1

Live Demo

M1<−matrix(1:25,nrow=5)
M1

## Output

[,1] [,2] [,3] [,4] [,5]
[1,] 1 6 11 16 21
[2,] 2 7 12 17 22
[3,] 3 8 13 18 23
[4,] 4 9 14 19 24
[5,] 5 10 15 20 25

## Example

rowSums(M1)

## Output

 55 60 65 70 75

## Example

colSums(M1)


## Output

 15 40 65 90 115

## Example

sum(M1)

## Output

 325

## Example2

Live Demo

M2<−matrix(rpois(64,5),nrow=8)
M2

## Output

   [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 3   7   6   3   8   4 4 4
[2,] 5   4   6   5   4   6 5 4
[3,] 7   4   3   6   4   2 6 4
[4,] 4   7   4   3   11 3 7 4
[5,] 3   4   11  9   3   4 4 3
[6,] 3   10  3   3   5 8 6 2
[7,] 9   2   3   4   6 6 3 4
[8,] 7   8   1   5   6 7 5 3

## Example

rowSums(M2)

## Output

 39 39 36 43 41 40 37 42

## Example

colSums(M2)


## Output

 41 46 37 38 47 40 40 28

## Example

sum(M2)

## Output

 317

## Example3

Live Demo

M3<−matrix(rpois(100,8),nrow=20)
M3

## Output

[,1] [,2] [,3] [,4] [,5]
[1,] 5 11 12 5 11
[2,] 7 7 9 8 10
[3,] 15 11 8 11 7
[4,] 9 3 7 12 7
[5,] 6 9 4 4 7
[6,] 10 8 7 8 4
[7,] 14 6 6 7 9
[8,] 6 5 5 10 7
[9,] 8 7 3 7 10
[10,] 12 7 5 11 8
[11,] 8 10 9 11 10
[12,] 4 7 9 9 12
[13,] 9 8 15 10 6
[14,] 6 4 9 7 6
[15,] 7 10 7 7 9
[16,] 10 11 8 7 7
[17,] 9 6 12 6 9
[18,] 5 8 8 7 6
[19,] 5 7 3 3 8
[20,] 12 7 9 7 10

## Output

rowSums(M3)

## Example

 44 41 52 38 30 37 42 33 35 43 48 41 48 32 40 43 42 34 26 45

## Example

colSums(M3)


## Output

 167 152 155 157 163

## Example

sum(M3)

## Output

 794

## Example4

Live Demo

M4<−matrix(rnorm(80),nrow=20)
M4

## Output

[,1] [,2] [,3] [,4]
[1,] 0.3188698 0.81972400 1.196668896 −0.1600363
[2,] 1.1626229 0.58996464 −0.936554585 1.1429420
[3,] −0.5603425 −1.76147185 0.287399797 0.4585298
[4,] −0.9019506 −0.23604072 −1.034877755 1.0206584
[5,] −0.5248356 1.05424991 −0.645217913 −0.4739691
[6,] −1.5610909 −0.20283113 0.996732180 −0.7709547
[7,] 0.2476689 0.76585019 −2.972610580 −0.2166821
[8,] −0.6014235 −0.80808451 −1.318205769 0.5311314
[9,] −0.5758808 1.00178363 0.030445943 0.9135367
[10,] 0.1207577 1.10829807 −0.057548495 0.2686915
[11,] 1.3505622 0.21916808 −0.521492576 0.2863660
[12,] −1.0845436 0.81589145 −0.316661626 −0.6171679
[13,] 0.6450188 1.22799788 −0.625778034 0.6154738
[14,] −2.9332291 1.09912124 0.274039557 0.5219165
[15,] 1.3656969 −1.91670352 1.099289293 0.1918600
[16,] −1.3845404 1.56674213 0.951188176 −0.2644617
[17,] −1.4644000 −0.02311353 0.006714121 0.2755697
[18,] −0.8878274 −1.08802913 1.098809046 −0.8005284
[19,] −0.6842826 0.05200371 0.488929737 1.7782674
[20,] 1.9084408 1.73997571 −0.419218542 −0.9593852

## Example

rowSums(M4)

## Output

 2.1752264 1.9589749 −1.5758848 −1.1522107 −0.5897728 −1.5381445
 −2.1757735 −2.1965824 1.3698855 1.4401988 1.3346037 −1.2024817
 1.8627125 −1.0381517 0.7401427 0.8689281 −1.2052297 −1.6775759
 1.6349182 2.2698128

## Example

colSums(M4)

## Output

 −6.044709 6.024496 −2.417949 3.741758

## Example

sum(M4)


## Output

 1.303596