即使在R中包含NA的情况下，如何找到多个向量中对应元素的总和？
如果我们有多个向量，则可以使用rowSums函数找到对应元素的总和，并可以使用cbind组合向量，以便R可以轻松读取对应的元素。但是，如果一个或多个向量中包含NA值，则我们还需要添加na.rm=TRUE参数。
示例
set.seed(100) x1<-sample(c(0,1,NA),100,replace=TRUE) x1
输出结果
[1] 0 0 1 0 1 1 NA 1 1 0 1 NA 0 1 NA NA 0 1 1 NA 1 NA 1 NA 1 [26] 0 NA NA 1 0 1 NA 1 NA NA NA 0 1 NA 0 0 NA NA NA 1 1 NA NA 0 0 [51] 0 0 0 0 1 0 0 0 1 0 1 1 NA NA 1 1 1 1 0 NA 1 0 1 NA 1 [76] 1 NA NA NA 0 1 1 NA NA 0 1 NA 0 0 NA NA 0 1 1 NA 1 1 0 0 NA
示例
x2<-sample(c(5,10,NA),100,replace=TRUE) x2
输出结果
[1] 5 10 NA 5 NA NA 10 10 NA NA 5 5 10 10 NA 10 10 5 10 5 5 5 10 5 NA [26] 10 10 NA 5 10 5 NA NA NA NA 10 NA 10 10 10 10 5 NA 5 10 5 NA 5 10 10 [51] 5 10 5 NA 5 5 NA NA 10 NA NA 10 NA NA 10 10 5 5 10 NA 10 5 10 NA NA [76] 10 10 NA NA 10 10 10 NA 10 NA NA NA 10 NA NA NA 5 10 NA 5 NA NA 10 5 5
示例
x3<-sample(c(1:4,NA),100,replace=TRUE) x3
输出结果
[1] 2 3 1 4 1 NA 2 NA 4 NA NA 4 1 3 3 4 NA 4 2 2 1 2 3 NA 3 [26] 1 4 1 3 2 3 NA 3 NA 2 4 NA NA 3 3 NA 3 2 3 3 NA 4 2 3 1 [51] 1 3 1 2 2 4 3 4 1 1 NA 2 1 4 2 2 4 3 3 4 3 4 2 4 NA [76] 1 4 2 1 NA NA 2 2 2 2 1 1 1 3 NA 3 1 3 1 2 4 1 4 2 NA
示例
x4<-sample(c(1:10,NA),100,replace=TRUE) x4
输出结果
[1] 9 5 9 1 2 10 6 5 4 4 NA 6 5 2 3 7 9 5 2 2 8 1 3 5 10 [26] 10 8 7 2 4 6 4 3 3 8 10 NA 2 4 2 1 2 3 5 8 1 NA 5 4 9 [51] 8 7 8 8 4 10 3 8 1 10 9 6 9 3 4 4 8 NA 10 6 10 1 4 NA 1 [76] 5 4 NA 2 2 9 2 9 NA 8 3 10 1 6 4 3 4 4 1 1 9 NA 2 8 9
示例
rowSums(cbind(x1,x2,x3,x4),na.rm=TRUE)
输出结果
[1] 11 9 14 16 12 12 20 1 20 8 5 14 25 14 3 11 6 13 21 8 15 18 3 17 14 [26] 20 12 11 13 20 13 22 7 18 12 20 8 22 3 11 22 4 8 9 7 10 24 21 11 7 [51] 21 18 10 8 18 8 17 7 19 6 20 10 18 16 15 7 10 22 7 16 20 19 14 4 8 [76] 14 12 1 8 24 15 23 7 9 2 16 18 11 6 10 22 8 19 12 18 1 8 4 22 16
示例
y1<-sample(c(0:9,NA),100,replace=TRUE) y1
输出结果
[1] 8 7 3 9 2 9 2 5 6 0 4 2 4 1 2 9 0 5 5 3 4 7 5 6 3 [26] NA 3 3 5 2 4 7 9 3 2 7 7 2 9 5 3 NA 1 3 3 2 5 8 3 9 [51] 9 6 2 2 NA NA 3 0 5 NA 6 NA 4 8 7 3 9 6 9 3 2 7 3 2 7 [76] NA 6 4 4 5 5 6 5 2 1 8 7 1 5 9 2 1 7 5 2 6 8 1 5 0
示例
y2<-sample(c(16:20,NA),100,replace=TRUE) y2
输出结果
[1] 16 20 20 20 19 16 17 NA 19 20 18 18 16 19 20 NA 19 18 16 18 20 16 18 NA 18 [26] 16 16 NA 17 NA 16 20 17 18 16 18 20 18 18 18 17 17 16 18 17 20 18 19 19 18 [51] 19 18 18 18 16 17 17 19 NA 17 16 18 18 17 18 20 19 16 19 20 16 19 20 16 NA [76] 19 18 17 17 16 18 16 18 19 NA NA NA 18 20 19 16 20 18 17 18 19 16 16 18 19
示例
y3<-sample(c(1:10,NA),100,replace=TRUE) y3
输出结果
[1] 9 2 4 4 9 10 2 2 NA 4 10 6 3 9 3 8 5 6 3 8 10 3 10 6 5 [26] 1 5 7 NA 7 6 2 6 9 9 2 7 4 2 2 3 6 9 9 5 9 8 8 9 3 [51] 9 8 1 6 8 6 9 10 1 4 6 1 7 9 9 3 1 2 7 4 9 3 8 NA NA [76] 6 NA 9 1 6 NA 10 4 5 5 7 6 9 10 5 10 5 4 6 5 10 10 9 10 3
示例
y4<-sample(c(0:1,NA),100,replace=TRUE) y4
输出结果
[1] 0 0 1 0 NA 1 0 0 1 1 1 1 NA 0 NA 1 0 1 1 0 NA 0 0 0 1 [26] NA 1 NA 0 1 NA 0 0 NA 1 NA 1 NA NA 0 NA 0 NA 1 0 1 0 0 1 1 [51] 1 1 NA 0 NA NA 0 NA 1 1 0 1 1 NA NA 0 NA NA 1 NA 1 1 0 1 NA [76] 1 0 NA NA 0 0 0 0 NA NA NA 0 NA 1 0 NA NA 0 0 1 0 0 1 0 NA
示例
rowSums(cbind(y1,y2,y3,y4),na.rm=TRUE)
输出结果
[1] 30 28 28 23 33 11 31 25 9 13 30 32 1 23 9 31 28 36 20 19 11 34 25 34 22 [26] 24 33 20 33 30 23 32 26 3 24 20 25 25 19 23 34 12 35 25 29 30 19 32 25 8 [51] 28 25 22 33 28 25 30 5 22 29 20 12 30 30 30 37 32 25 18 12 6 28 25 30 24 [76] 30 16 37 13 26 36 12 25 34 28 36 23 26 26 29 35 11 27 26 29 25 29 29 18 32
示例
z1<-rnorm(50,2,1) z1
输出结果
[1] 0.9390446 1.8841026 0.8087676 0.1962055 1.4784851 1.2412525 [7] 3.6371884 2.6628671 1.9350691 1.7370759 0.9465960 2.5413368 [13] 2.4258287 2.8816722 1.3528497 2.2549404 0.2872082 1.0979457 [19] 2.8332518 0.9191813 0.9455777 3.7688646 2.4412353 2.4766955 [25] 3.6113515 1.5080660 1.5711290 0.2477241 1.7384521 2.1096380 [31] 0.7545868 2.8332222 2.8187682 3.0912341 -0.1257987 2.6147647 [37] -0.4006146 1.2134572 2.3096535 0.6461462 1.0135814 3.4684232 [43] 0.5147140 2.6046640 1.4192276 2.7918187 1.3309162 3.1853232 [49] 2.2843582 0.4706695
示例
z2<-rnorm(50,5,1) z2
输出结果
[1] 5.194030 3.659833 6.463705 4.058468 3.753853 7.025124 4.360630 3.764877 [9] 4.203693 5.497098 4.798063 3.839269 5.313773 3.739678 5.147129 4.646849 [17] 5.368790 3.020165 6.553668 4.956129 5.255372 5.690039 4.483388 6.234553 [25] 7.198351 6.886410 4.595667 5.211127 4.925852 6.680891 5.288206 6.860453 [33] 5.802522 5.717358 5.927209 3.892963 3.774943 4.742993 4.745586 4.863254 [41] 6.604966 3.766678 3.995291 5.265133 4.197123 3.350861 5.282620 5.661311 [49] 3.747484 5.096677
示例
z3<-rnorm(50,10,1) z3
输出结果
[1] 9.763367 9.580911 10.348878 9.345549 9.722785 9.969824 9.765327 [8] 11.663498 8.961884 9.606949 9.031431 10.761535 11.570366 9.555245 [15] 10.977628 10.625502 10.392293 10.128622 10.149368 9.966646 9.286093 [22] 12.026304 9.701265 9.738910 8.869799 9.065077 11.659160 10.745537 [29] 10.095464 9.371354 10.270817 10.621408 9.796881 10.455191 11.025334 [36] 9.912442 9.275351 9.334389 10.687026 10.064154 8.855830 8.800893 [43] 9.511070 11.068484 9.432597 9.525493 9.489726 10.794717 7.277489 [50] 8.777386
示例
z4<-rnorm(50,12,1) z4
输出结果
[1] 9.996957 10.218267 9.826439 11.906861 13.069571 11.481097 12.467620 [8] 11.889104 13.010052 11.185881 12.684369 9.749237 11.323544 11.476971 [15] 11.067891 12.924520 14.284396 11.494215 10.214767 10.580071 10.178868 [22] 13.288666 12.467862 11.423686 12.146402 11.443403 13.539518 11.176606 [29] 14.230201 10.965661 10.241278 13.162762 11.863015 10.614357 13.261041 [36] 12.370224 12.048375 13.118374 11.697336 11.376205 12.656320 11.178566 [43] 10.832501 11.015387 11.016804 11.357585 13.443464 13.807832 12.021377 [50] 12.202021
示例
rowSums(cbind(z1,z2,z3,z4),na.rm=TRUE)
输出结果
[1] 30.44584 31.54599 28.98250 26.92679 29.40666 28.20746 32.78253 27.98593 [9] 30.71212 30.81943 27.75039 29.63786 29.35340 25.96591 29.01252 28.83205 [17] 31.40262 29.33605 30.50771 33.15974 29.79469 28.08797 29.56919 26.35508 [25] 29.59622 28.42052 26.37487 29.12010 30.86113 27.35858 30.30758 33.52172 [33] 25.13615 28.12270 27.55533 28.59861 30.45220 30.08791 32.59381 30.67407 [41] 29.72042 29.40276 29.71063 33.81690 31.77828 34.39260 27.38295 30.76655 [49] 28.85173 27.17768