# 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

[1] 55 60 65 70 75

## Example

colSums(M1)


## Output

[1] 15 40 65 90 115

## Example

sum(M1)

## Output

[1] 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

[1] 39 39 36 43 41 40 37 42

## Example

colSums(M2)


## Output

[1] 41 46 37 38 47 40 40 28

## Example

sum(M2)

## Output

[1] 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

[1] 44 41 52 38 30 37 42 33 35 43 48 41 48 32 40 43 42 34 26 45

## Example

colSums(M3)


## Output

[1] 167 152 155 157 163

## Example

sum(M3)

## Output

[1] 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

[1] 2.1752264 1.9589749 −1.5758848 −1.1522107 −0.5897728 −1.5381445
[7] −2.1757735 −2.1965824 1.3698855 1.4401988 1.3346037 −1.2024817
[13] 1.8627125 −1.0381517 0.7401427 0.8689281 −1.2052297 −1.6775759
[19] 1.6349182 2.2698128

## Example

colSums(M4)

## Output

[1] −6.044709 6.024496 −2.417949 3.741758

## Example

sum(M4)


## Output

[1] 1.303596