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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
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
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
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
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
Updated on: 09-Feb-2021
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