如何找到列表元素的平均值而不在R中不列出它们？
在大多数情况下，非列表用于查找列表元素的均值，但出于相同的目的，我们也可以使用双方括号。双方括号基本上用于访问列表元素中的值，因此均值函数直接使用这些值。查看下面的示例以了解其工作原理。
示例
请看以下列表：
> x<-rpois(50,5) > x
输出结果
[1] 3 3 3 5 3 1 4 7 5 4 5 9 9 7 4 3 6 2 4 3 3 4 7 4 4 [26] 4 5 3 4 4 3 5 7 2 3 4 7 9 6 3 2 7 12 8 3 2 4 3 7 7
示例
> y<-rpois(75,8) > y
输出结果
[1] 10 13 3 4 8 13 9 7 6 6 10 8 6 6 2 8 11 18 7 6 9 9 4 11 6 [26] 15 7 6 8 6 7 3 6 7 8 10 5 5 5 6 9 11 11 9 4 9 7 4 11 7 [51] 7 8 8 6 5 4 10 10 10 11 8 5 9 13 12 6 15 12 9 10 9 10 8 9 11
示例
> z<-sample(0:10,70,replace=TRUE) > z
输出结果
[1] 1 2 5 1 5 10 3 3 0 3 4 6 1 10 10 10 7 9 4 4 6 8 8 2 10 [26] 3 10 7 5 7 7 10 8 4 5 0 1 7 4 3 6 7 3 6 9 4 6 2 1 2 [51] 8 3 4 3 7 1 5 0 2 1 6 1 8 7 2 1 0 9 1 5
示例
> a<-rnorm(70,5,1) > a
输出结果
[1] 5.628975 5.573691 5.006051 5.077979 5.871554 5.385300 4.657602 4.735646 [9] 4.442138 4.760412 5.017734 4.280068 7.686220 3.875272 4.067216 5.701463 [17] 5.593412 5.775659 5.879765 5.016738 3.994980 5.155600 6.712727 4.389124 [25] 6.122981 4.864763 3.368189 5.707907 3.795154 5.352383 5.916437 4.626415 [33] 3.880298 4.557015 4.811892 6.055774 7.219117 4.646261 4.674638 6.131624 [41] 4.965561 4.821740 3.863569 3.450784 5.343807 3.379690 4.184890 5.120902 [49] 5.729253 4.493643 4.401883 3.231263 4.858176 2.979016 3.687358 4.505526 [57] 4.562880 6.275943 6.123173 7.069203 3.584879 4.142615 5.263056 4.817530 [65] 5.623996 4.190001 3.751704 2.850223 3.585672 4.894851
示例
> b<-runif(80,3,8) > b
输出结果
[1] 3.442903 4.001856 6.432766 4.891293 5.044795 5.218431 3.171832 4.397526 [9] 6.285000 6.379085 5.709033 6.291581 4.218582 4.236623 4.174158 5.074241 [17] 6.673121 7.885147 5.019942 6.679726 3.431944 3.822653 5.507297 5.496158 [25] 5.514768 4.815769 3.499057 4.669202 4.775597 7.990668 6.883988 6.130633 [33] 6.777758 3.015941 6.189278 4.884390 6.961173 4.642663 6.739331 4.135479 [41] 7.120461 6.584457 4.597551 4.988885 3.520279 6.762352 5.131450 7.817774 [49] 7.881543 4.903794 5.692226 4.195919 4.629501 3.668161 3.138173 4.645494 [57] 7.514812 5.957177 6.617235 6.490214 5.175956 4.944264 6.886052 7.902101 [65] 6.308360 3.615881 5.029082 6.779920 7.993889 4.205757 3.185573 3.111127 [73] 7.556962 3.458693 7.766828 7.935259 5.964028 4.831651 6.164589 6.658785
示例
> c<-rexp(60,3.12) > c
输出结果
[1] 0.1047474880 0.1613035476 0.2720461048 0.3714672737 0.1450241494 [6] 0.1172384887 0.0929828154 0.0664085124 0.1664490039 0.1463586694 [11] 0.5154090083 0.3250343993 0.0129845417 0.1228420892 0.0005871378 [16] 0.4522178841 0.0405094619 0.0640481666 0.1504493882 0.0456359051 [21] 0.3061571805 0.2142645778 0.0006100014 0.8694030410 0.1857693661 [26] 0.8603525043 0.5723388268 0.1471301074 0.3497110378 0.0989022598 [31] 0.7969995083 0.1855059460 0.3252937372 0.0620924534 0.5784960475 [36] 0.5985177050 0.0473140977 0.4036286207 0.0482094750 0.0045920645 [41] 0.1998384091 0.3897105935 0.1562889333 0.3647960092 0.2741774798 [46] 0.0256578746 0.3201171403 0.2401259122 0.7529862278 0.3503083498 [51] 0.1387969009 0.0194571765 0.0571447048 0.4248323683 0.3069464950 [56] 0.2832176146 0.0090127322 0.0379236670 0.3523537577 0.2085964980
示例
> w<-sample(1:100,65) > w
输出结果
[1] 58 94 72 56 33 29 9 28 60 42 97 40 67 98 78 68 43 2 10 [20] 53 79 27 47 1 41 84 34 99 92 19 93 37 13 64 51 3 46 52 [39] 59 35 73 30 80 17 95 76 15 44 81 21 55 74 83 6 77 36 8 [58] 65 63 45 14 71 100 61 90
示例
> q<-round(rnorm(150),0) > q
输出结果
[1] -1 -1 0 0 0 1 0 -2 -1 0 1 0 0 -1 1 1 1 2 2 -1 -1 0 1 1 1 [26] -3 -1 0 0 2 0 1 0 -1 1 0 0 1 2 2 1 0 1 0 1 1 0 1 0 1 [51] 0 1 0 0 1 1 2 0 1 1 -1 0 1 1 0 1 0 0 0 0 0 -1 0 1 0 [76] 1 1 1 -2 -1 1 0 0 0 2 -1 1 0 0 0 -1 0 0 -2 2 0 1 1 -1 1 [101] -3 -1 -1 1 1 -2 0 0 1 0 0 1 -1 0 2 -1 -1 1 0 1 -1 0 0 -1 0 [126] 0 1 -1 -2 1 0 1 -1 0 -1 0 -2 -1 0 1 -1 1 1 -1 0 -2 -1 2 -2 0
示例
> List<-list(x,y,z,a,b,c,w,q) > List
输出结果
[[1]] [1] 3 3 3 5 3 1 4 7 5 4 5 9 9 7 4 3 6 2 4 3 3 4 7 4 4 [26] 4 5 3 4 4 3 5 7 2 3 4 7 9 6 3 2 7 12 8 3 2 4 3 7 7 [[2]] [1] 10 13 3 4 8 13 9 7 6 6 10 8 6 6 2 8 11 18 7 6 9 9 4 11 6 [26] 15 7 6 8 6 7 3 6 7 8 10 5 5 5 6 9 11 11 9 4 9 7 4 11 7 [51] 7 8 8 6 5 4 10 10 10 11 8 5 9 13 12 6 15 12 9 10 9 10 8 9 11 [[3]] [1] 1 2 5 1 5 10 3 3 0 3 4 6 1 10 10 10 7 9 4 4 6 8 8 2 10 [26] 3 10 7 5 7 7 10 8 4 5 0 1 7 4 3 6 7 3 6 9 4 6 2 1 2 [51] 8 3 4 3 7 1 5 0 2 1 6 1 8 7 2 1 0 9 1 5 [[4]] [1] 5.628975 5.573691 5.006051 5.077979 5.871554 5.385300 4.657602 4.735646 [9] 4.442138 4.760412 5.017734 4.280068 7.686220 3.875272 4.067216 5.701463 [17] 5.593412 5.775659 5.879765 5.016738 3.994980 5.155600 6.712727 4.389124 [25] 6.122981 4.864763 3.368189 5.707907 3.795154 5.352383 5.916437 4.626415 [33] 3.880298 4.557015 4.811892 6.055774 7.219117 4.646261 4.674638 6.131624 [41] 4.965561 4.821740 3.863569 3.450784 5.343807 3.379690 4.184890 5.120902 [49] 5.729253 4.493643 4.401883 3.231263 4.858176 2.979016 3.687358 4.505526 [57] 4.562880 6.275943 6.123173 7.069203 3.584879 4.142615 5.263056 4.817530 [65] 5.623996 4.190001 3.751704 2.850223 3.585672 4.894851 [[5]] [1] 3.442903 4.001856 6.432766 4.891293 5.044795 5.218431 3.171832 4.397526 [9] 6.285000 6.379085 5.709033 6.291581 4.218582 4.236623 4.174158 5.074241 [17] 6.673121 7.885147 5.019942 6.679726 3.431944 3.822653 5.507297 5.496158 [25] 5.514768 4.815769 3.499057 4.669202 4.775597 7.990668 6.883988 6.130633 [33] 6.777758 3.015941 6.189278 4.884390 6.961173 4.642663 6.739331 4.135479 [41] 7.120461 6.584457 4.597551 4.988885 3.520279 6.762352 5.131450 7.817774 [49] 7.881543 4.903794 5.692226 4.195919 4.629501 3.668161 3.138173 4.645494 [57] 7.514812 5.957177 6.617235 6.490214 5.175956 4.944264 6.886052 7.902101 [65] 6.308360 3.615881 5.029082 6.779920 7.993889 4.205757 3.185573 3.111127 [73] 7.556962 3.458693 7.766828 7.935259 5.964028 4.831651 6.164589 6.658785 [[6]] [1] 0.1047474880 0.1613035476 0.2720461048 0.3714672737 0.1450241494 [6] 0.1172384887 0.0929828154 0.0664085124 0.1664490039 0.1463586694 [11] 0.5154090083 0.3250343993 0.0129845417 0.1228420892 0.0005871378 [16] 0.4522178841 0.0405094619 0.0640481666 0.1504493882 0.0456359051 [21] 0.3061571805 0.2142645778 0.0006100014 0.8694030410 0.1857693661 [26] 0.8603525043 0.5723388268 0.1471301074 0.3497110378 0.0989022598 [31] 0.7969995083 0.1855059460 0.3252937372 0.0620924534 0.5784960475 [36] 0.5985177050 0.0473140977 0.4036286207 0.0482094750 0.0045920645 [41] 0.1998384091 0.3897105935 0.1562889333 0.3647960092 0.2741774798 [46] 0.0256578746 0.3201171403 0.2401259122 0.7529862278 0.3503083498 [51] 0.1387969009 0.0194571765 0.0571447048 0.4248323683 0.3069464950 [56] 0.2832176146 0.0090127322 0.0379236670 0.3523537577 0.2085964980 [[7]] [1] 58 94 72 56 33 29 9 28 60 42 97 40 67 98 78 68 43 2 10 [20] 53 79 27 47 1 41 84 34 99 92 19 93 37 13 64 51 3 46 52 [39] 59 35 73 30 80 17 95 76 15 44 81 21 55 74 83 6 77 36 8 [58] 65 63 45 14 71 100 61 90 [[8]] [1] -1 -1 0 0 0 1 0 -2 -1 0 1 0 0 -1 1 1 1 2 2 -1 -1 0 1 1 1 [26] -3 -1 0 0 2 0 1 0 -1 1 0 0 1 2 2 1 0 1 0 1 1 0 1 0 1 [51] 0 1 0 0 1 1 2 0 1 1 -1 0 1 1 0 1 0 0 0 0 0 -1 0 1 0 [76] 1 1 1 -2 -1 1 0 0 0 2 -1 1 0 0 0 -1 0 0 -2 2 0 1 1 -1 1 [101] -3 -1 -1 1 1 -2 0 0 1 0 0 1 -1 0 2 -1 -1 1 0 1 -1 0 0 -1 0 [126] 0 1 -1 -2 1 0 1 -1 0 -1 0 -2 -1 0 1 -1 1 1 -1 0 -2 -1 2 -2 0
在列表中查找每个元素的均值：
示例
> mean(List[[1]])
输出结果
[1] 4.72
示例
> mean(List[[2]])
输出结果
[1] 8.146667
示例
> mean(List[[3]])
输出结果
[1] 4.757143
示例
> mean(List[[4]])
输出结果
[1] 4.882414
示例
> mean(List[[5]])
输出结果
[1] 5.480545
示例
> mean(List[[6]])
输出结果
[1] 0.249022
示例
> mean(List[[7]])
输出结果
[1] 52.2
示例
> mean(List[[8]])
输出结果
[1] 0.1066667