在数据分析中，我们经常需要查找小于，小于，大于或大于等于的值，以将它们与某个阈值进行比较。有时我们还需要这些值的频率。因此，我们可以将sum函数用于此目的。例如，如果向量x具有10个整数值，则要检查其中有多少个整数值大于或等于10，我们可以使用命令sum（x> = 10）。
x1<−rpois(150,5) x1输出结果
[1] 4 5 3 3 5 4 9 4 7 1 6 15 3 3 2 3 5 5 3 6 9 6 5 2 1 [26] 4 2 1 6 3 8 2 6 7 6 5 5 7 6 3 3 4 5 3 8 11 0 3 7 5 [51] 5 4 7 3 4 6 4 4 9 8 3 6 10 4 3 7 3 6 5 4 7 5 3 1 3 [76] 6 4 5 2 11 5 6 5 2 4 3 6 8 3 5 8 8 2 7 10 5 4 4 3 3 [101] 8 7 5 7 3 5 5 3 9 3 3 8 5 5 5 5 7 5 7 5 4 7 4 8 5 [126] 4 4 7 7 4 2 1 7 7 4 6 7 6 6 3 2 3 7 4 3 6 8 4 3 8
sum(x1>=5)输出结果
[1] 83
x2<−rpois(150,2) x2输出结果
[1] 0 6 3 2 3 2 1 1 0 3 0 5 2 0 3 1 3 1 2 3 2 3 2 5 0 3 0 3 0 3 3 1 2 3 3 1 3 [38] 2 2 2 2 2 2 1 3 2 0 2 1 4 2 1 1 2 4 0 2 2 1 0 1 4 2 1 2 4 1 2 0 3 2 2 1 0 [75] 0 3 1 2 1 1 4 4 1 2 2 0 1 3 3 5 5 1 5 2 1 2 2 3 3 3 2 3 2 1 2 1 5 0 3 2 1 [112] 3 2 1 3 4 2 4 3 4 1 3 4 1 1 1 2 3 1 3 2 0 2 2 2 2 3 2 3 1 1 4 1 5 3 3 1 1 [149] 0 0
sum(x2>=5)输出结果
[1] 8
x3<−round(rnorm(100),2) x3输出结果
[1] −1.45 0.32 −0.04 0.96 0.73 −0.23 −0.74 0.53 −1.78 2.12 1.52 1.51 [13] 1.98 −0.57 −0.88 −2.06 0.44 −0.33 1.03 1.63 1.55 0.34 −0.17 −0.79 [25] 0.68 −0.64 −0.82 1.04 −1.02 0.05 1.07 −1.50 −2.70 0.99 0.07 −0.97 [37] −0.08 −1.32 0.51 0.01 0.92 −1.11 −0.32 1.05 0.19 0.49 −0.06 1.38 [49] 0.04 0.06 1.57 −0.09 1.35 0.83 0.54 1.70 −1.36 0.31 −1.53 −0.07 [61] 0.07 0.61 0.13 −0.82 −0.24 −1.40 0.71 −0.77 −0.08 −0.06 0.10 −1.07 [73] −0.75 0.29 −0.11 −0.58 −1.01 0.06 −1.06 0.32 −0.21 −0.61 0.98 0.12 [85] −0.58 −0.56 1.11 −0.29 0.21 0.53 −0.29 1.13 0.03 −0.04 1.85 −1.22 [97] −0.23 −1.45 −1.68 0.27
sum(x3>=0.25)输出结果
[1] 38
x4<−rnorm(100,10,3) x4输出结果
[1] 7.597491 7.949568 8.126105 7.463872 6.617113 8.354158 8.803647 [8] 9.424731 14.517557 9.776073 14.094525 14.051761 6.694324 10.161498 [15] 6.684266 8.487643 13.053916 8.888167 8.633980 12.356086 11.405917 [22] 10.707835 8.051093 2.950978 12.248564 7.022646 10.238748 13.662946 [29] 10.866488 7.355724 16.510802 13.140203 7.632991 8.343411 7.714010 [36] 10.462814 6.513682 5.024443 9.283309 9.174802 10.994014 11.732416 [43] 10.227234 7.312629 8.476452 9.001631 11.709300 9.743112 12.810862 [50] 12.298896 9.539788 13.943619 11.345357 9.963804 13.229894 5.094773 [57] 10.099810 7.648431 11.704927 8.428569 4.880670 14.992570 10.566891 [64] 12.549172 13.232136 4.453650 8.361071 11.638091 12.893968 8.072887 [71] 14.663789 7.899722 10.907741 4.416071 12.089033 14.545859 19.397030 [78] 6.023658 8.473864 8.215500 2.549976 6.273917 17.355490 7.472054 [85] 11.106132 9.293263 10.283507 12.538926 17.778788 12.705169 7.679622 [92] 10.750032 6.393891 12.565089 5.556969 12.610292 17.343324 15.736677 [99] 9.505158 11.343209输出结果
sum(x4>=10)输出结果
[1] 49
x5<−runif(100,2,5) x5输出结果
[1] 3.636398 3.898034 4.296205 2.249604 2.379428 3.233782 4.798916 3.150175 [9] 3.526049 3.318808 2.092628 3.640334 4.776549 2.145952 4.751566 3.429172 [17] 2.197853 2.783967 2.018186 2.002520 2.579248 3.083350 3.924225 3.271760 [25] 2.556557 2.416284 3.140659 4.329581 2.354663 2.951233 2.386200 3.322287 [33] 3.178507 2.592847 4.553768 4.011444 2.467380 2.325412 3.087714 2.945327 [41] 4.635870 3.877260 2.586140 3.005279 4.753057 3.766665 2.614960 3.958394 [49] 2.167482 3.436171 4.089592 2.031541 3.491617 4.199338 2.635759 3.255678 [57] 3.798499 3.738574 2.895809 3.674032 4.137452 4.003497 2.231915 4.378317 [65] 2.938005 3.695278 3.475259 3.673781 3.564739 4.816054 3.876752 2.127490 [73] 2.341472 4.316100 3.912831 4.377490 3.600701 3.091522 3.442127 2.196780 [81] 3.584403 3.917059 3.135924 3.425142 3.590885 3.860618 2.778151 2.195585 [89] 3.199603 2.247892 4.830438 4.290870 2.662993 2.749634 4.931301 2.567786 [97] 3.551937 4.484705 2.348458 2.189373
sum(x5>=4)输出结果
[1] 21