如何计算R中矢量元素的两个周期移动平均值？
如果要轻松找到两个周期的移动平均值，则无法在基数R中完成。我们需要使用Zoo包的rollmean函数，以单行代码解决此问题。例如，如果我们有一个向量x，它包含从1到100的百分之一，那么可以使用rollmean(x,2)找到x的两个周期
加载动物园包-
library(zoo)
例子
x1<−1:10 x1
输出结果
[1] 1 2 3 4 5 6 7 8 9 10
示例
rollmean(x1,2) [1] 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 x2<−1:120 rollmean(x2,2) [1] 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 [13] 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 [25] 25.5 26.5 27.5 28.5 29.5 30.5 31.5 32.5 33.5 34.5 35.5 36.5 [37] 37.5 38.5 39.5 40.5 41.5 42.5 43.5 44.5 45.5 46.5 47.5 48.5 [49] 49.5 50.5 51.5 52.5 53.5 54.5 55.5 56.5 57.5 58.5 59.5 60.5 [61] 61.5 62.5 63.5 64.5 65.5 66.5 67.5 68.5 69.5 70.5 71.5 72.5 [73] 73.5 74.5 75.5 76.5 77.5 78.5 79.5 80.5 81.5 82.5 83.5 84.5 [85] 85.5 86.5 87.5 88.5 89.5 90.5 91.5 92.5 93.5 94.5 95.5 96.5 [97] 97.5 98.5 99.5 100.5 101.5 102.5 103.5 104.5 105.5 106.5 107.5 108.5 [109] 109.5 110.5 111.5 112.5 113.5 114.5 115.5 116.5 117.5 118.5 119.5 x3<−sample(0:5,111,replace=TRUE) rollmean(x3,2) [1] 3.0 4.5 5.0 4.5 2.5 1.0 2.0 1.5 0.5 2.5 4.0 2.0 0.0 0.5 1.0 3.0 3.0 2.0 [19] 3.0 3.0 1.5 0.5 3.0 2.5 1.0 2.0 3.5 5.0 3.0 2.0 2.5 3.5 4.0 1.5 0.5 2.5 [37] 4.5 3.5 1.5 1.0 2.0 3.0 2.5 1.5 1.5 2.5 3.0 2.0 1.5 2.5 3.0 2.5 2.0 2.5 [55] 2.0 2.5 3.5 2.5 2.0 1.5 2.0 2.5 1.5 1.5 1.5 2.0 3.5 2.5 1.0 2.0 4.0 5.0 [73] 4.5 4.0 4.5 4.5 4.0 2.5 2.5 4.5 4.0 2.5 1.0 2.5 2.5 0.5 1.0 1.0 1.5 2.5 [91] 3.0 1.5 0.5 1.0 1.5 3.0 3.5 2.0 1.0 2.5 3.5 3.0 2.5 3.5 5.0 5.0 4.5 2.5 [109] 3.0 2.5 x4<−sample(1:5,111,replace=TRUE) rollmean(x4,2) [1] 5.0 3.0 2.5 4.0 2.5 2.5 3.0 1.5 3.0 4.0 2.0 2.5 2.5 2.0 4.0 5.0 4.0 3.0 [19] 4.0 3.0 2.0 3.0 4.0 4.5 3.5 3.5 2.5 1.0 3.0 4.5 4.0 3.0 1.5 1.0 3.0 5.0 [37] 3.0 1.0 1.5 2.5 2.5 3.0 2.5 2.0 3.5 3.5 3.5 3.5 2.0 2.0 4.0 4.0 4.0 5.0 [55] 3.5 3.0 3.0 2.0 3.0 2.5 1.5 1.5 3.0 3.0 1.5 2.0 2.5 4.0 5.0 4.5 3.0 1.5 [73] 2.0 4.0 3.5 2.5 2.0 2.0 3.5 4.0 4.0 4.0 3.0 2.0 2.5 4.0 4.5 2.5 1.5 2.5 [91] 2.5 3.5 3.0 1.0 1.5 2.0 2.5 2.0 1.0 2.5 2.5 3.0 4.5 3.5 4.0 5.0 3.5 2.0 [109] 3.0 4.0 x5<−sample(1:50,120,replace=TRUE) rollmean(x5,2) [1] 37.5 32.0 32.5 34.5 29.0 34.0 30.0 33.0 30.0 30.0 34.5 13.5 13.0 16.0 27.0 [16] 29.0 28.5 35.5 37.5 34.5 29.0 28.0 31.0 43.5 46.0 27.5 16.5 32.5 33.5 32.5 [31] 37.5 31.0 15.5 10.5 26.0 21.5 21.0 30.0 27.5 21.0 27.5 37.0 33.0 43.0 38.5 [46] 21.0 20.0 27.0 25.5 33.5 34.5 28.0 16.5 10.5 26.5 24.0 25.0 32.0 16.5 21.0 [61] 20.5 13.0 25.0 35.5 23.0 14.5 21.5 10.5 18.0 17.5 7.5 11.0 10.5 27.5 23.0 [76] 13.5 15.5 21.5 35.0 38.0 29.5 21.0 17.5 25.0 22.0 14.0 18.0 13.5 12.5 29.5 [91] 47.0 27.0 26.5 43.0 45.5 39.5 33.5 30.5 16.0 5.0 26.5 42.0 28.5 24.5 21.0 [106] 8.5 6.5 27.5 35.5 25.5 33.5 39.0 31.0 26.5 32.5 21.0 10.5 11.5 13.0 x6<−sample(91:100,120,replace=TRUE) rollmean(x6,2) [1] 95.0 94.0 95.0 94.5 96.5 95.0 92.5 95.0 96.5 97.0 96.0 97.0 [13] 98.0 96.5 98.0 99.5 99.5 97.5 94.5 97.0 99.5 97.0 93.5 95.5 [25] 99.5 98.0 96.0 98.0 97.0 92.5 95.5 100.0 100.0 99.0 99.0 99.5 [37] 97.5 94.5 96.5 98.0 93.5 95.0 97.0 93.5 95.0 97.0 95.5 93.0 [49] 92.0 93.0 96.0 97.0 96.0 97.0 94.0 94.5 98.0 98.5 95.0 92.5 [61] 95.0 94.5 96.0 96.0 95.5 97.5 97.0 96.0 94.5 93.0 91.5 92.0 [73] 95.0 97.5 97.5 95.0 95.0 97.0 95.5 95.0 93.5 94.0 94.0 93.5 [85] 96.0 94.5 95.0 97.5 99.0 95.5 95.5 95.5 95.5 96.5 92.5 95.0 [97] 94.5 95.0 95.5 92.0 91.5 93.5 95.0 95.5 96.5 96.5 94.5 93.5 [109] 97.0 95.0 92.5 92.5 91.5 94.0 95.0 95.5 98.5 96.0 95.5 x7<−rnorm(50,5,0.5) rollmean(x7,2) [1] 5.480386 5.447735 5.411541 4.920386 5.179600 5.283988 5.171337 5.249699 [9] 5.191743 5.259015 5.007683 4.996620 4.829728 4.294174 4.718041 5.287286 [17] 5.300202 5.306356 5.328401 5.122863 5.064679 4.806393 4.620227 4.946723 [25] 5.273818 5.262808 5.224571 5.105804 5.115384 5.720728 4.859122 4.447057 [33] 5.122608 4.862555 4.868280 4.399689 4.053471 4.768004 4.767681 5.001960 [41] 5.081844 4.530953 4.181293 3.973446 4.100880 4.184364 4.364118 4.598242 [49] 4.764706 x8<−runif(50,5,10) rollmean(x8,2) [1] 8.058824 8.243607 8.555279 8.167787 7.318688 8.031011 8.259745 7.551773 [9] 6.461240 7.416219 9.041726 7.248550 6.468459 7.297782 7.023565 6.675229 [17] 6.787259 7.667929 8.520316 7.144133 7.185223 7.089798 7.531991 8.460906 [25] 8.486284 8.251683 6.947352 7.645662 6.853478 6.889086 7.891153 8.025121 [33] 7.420661 7.357484 6.920305 5.854457 6.961225 8.196131 8.013994 7.056246 [41] 8.132790 8.148006 8.128420 7.191846 7.159202 9.603268 8.572502 7.001365 [49] 6.500651 x9<−rexp(50,2.1) rollmean(x9,2) [1] 0.50955472 0.47625696 0.44801966 0.54031908 0.44315428 0.30659278 [7] 0.12889859 0.06259362 0.60524099 0.59404019 0.20083159 0.26323887 [13] 0.17771971 0.80555530 0.76444119 0.87520681 0.97853392 0.58382596 [19] 0.48346306 0.13819934 0.15771218 0.24265242 0.39860430 0.72997709 [25] 0.55388338 0.07809430 0.10359228 0.17891867 0.40828716 0.33985932 [31] 0.21839668 0.40999751 0.33016602 0.14308831 0.63506390 1.05480764 [37] 1.29507319 1.23082710 0.59804633 0.39464417 0.31223577 0.14141055 [43] 0.04557646 0.29261665 0.61306151 0.55907498 0.46214800 0.73766138 [49] 0.55396346 x10<−rlnorm(50,2,1.5) rollmean(x10,2) [1] 4.871013 5.354362 1.862284 31.728599 31.585093 1.676455 15.009501 [8] 17.861997 6.556540 37.194718 34.591386 6.580697 10.248382 37.585671 [15] 34.945119 8.400405 13.329515 6.899100 17.023316 24.452512 53.928439 [22] 48.656265 2.999969 4.237573 7.059769 4.747940 4.274969 4.085395 [29] 3.502757 6.556758 18.719148 19.074384 7.633016 10.052852 80.373963 [36] 85.713024 18.683646 7.845827 4.430249 11.650022 26.635095 18.275929 [43] 1.449896 2.641906 4.318882 8.366553 9.616981 19.477526 17.515400