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