如何在R中生成四舍五入到小数点后四位的随机样本？
可以以多种方式生成随机样本，例如使用离散和连续分布，使用整数向量，使用数字向量，使用字符向量和/或因子向量以及数据集的列。如果我们的样本本质上是连续的，则这些值很可能在小数点后包含许多值，我们可以将这些值限制为4或使用舍入函数使用任何其他限制。
示例
x1<-round(rnorm(10),4) x1
输出结果
[1] 0.1667 -0.5536 -0.2748 -0.1064 -0.7740 0.0009 0.3966 -0.2256 -0.4090 [10] 0.4069
示例
x2<-round(rnorm(50,545,18.5),4) x2
输出结果
[1] 534.2164 562.2330 508.1712 544.9145 568.2658 566.7066 520.1372 555.8063 [9] 490.2302 530.0890 550.1564 552.7664 512.8081 539.7952 536.8150 527.6695 [17] 543.5097 556.8944 528.4357 519.7085 554.9833 541.6425 580.0051 559.1235 [25] 558.1846 549.6800 581.8289 555.0069 560.3540 528.3715 523.2465 526.7506 [33] 513.3090 569.5984 504.2076 565.5712 594.9353 551.5697 550.7176 560.9044 [41] 553.6006 548.2351 571.0769 513.0627 560.5451 529.1586 552.1659 541.0974 [49] 557.9535 535.8345
示例
x3<-round(runif(100,2,10),4) x3
输出结果
[1] 3.3254 4.2719 2.5700 6.6529 2.3641 5.7524 3.5321 4.0652 8.7108 9.5321 [11] 4.8742 2.6012 5.9765 4.1423 6.1051 2.3037 3.0290 2.2835 4.3078 6.9569 [21] 3.0007 9.8222 7.3561 7.8823 6.9002 9.8261 3.9888 9.4723 7.6115 3.5430 [31] 3.5425 2.5960 6.5738 6.5245 4.6534 5.0535 2.4721 9.9349 3.3694 2.1342 [41] 9.8047 3.2214 4.1357 5.9450 7.5481 9.9704 9.1236 6.6892 4.2183 4.2098 [51] 9.1609 3.3085 3.8390 5.6355 3.2058 3.1597 9.0767 4.7739 3.4916 6.7490 [61] 8.5052 3.1381 4.4015 2.5416 9.0075 8.8939 4.2335 7.5611 4.3264 6.2221 [71] 2.6467 3.4608 3.3156 6.6610 9.0668 7.0933 9.2373 6.7479 6.3007 9.3724 [81] 5.5539 9.2073 3.7334 2.8943 4.1278 6.9277 4.0263 7.8080 3.2616 5.0656 [91] 4.7286 9.8659 4.3485 5.7590 4.8930 5.5345 3.5390 9.2349 2.0937 6.1323
示例
x4<-round(rexp(100,3.5),4) x4
输出结果
[1] 0.0671 0.1094 0.0880 0.0271 0.0188 0.0594 0.9127 0.4143 0.2443 0.1643 [11] 0.2951 0.0381 0.1241 0.3050 0.1466 0.3556 0.1066 1.0008 0.0905 0.0861 [21] 0.2225 0.0188 0.8789 0.1757 0.4530 0.0884 0.2754 0.2594 0.2853 0.2232 [31] 0.1717 0.3329 0.5694 0.1007 1.5399 0.3020 0.1537 0.0227 0.3811 0.1305 [41] 0.2021 0.0148 0.1565 0.3016 0.2054 0.0474 0.4245 0.2189 0.0746 0.0540 [51] 0.1479 0.1197 0.0670 0.5775 0.4953 0.9968 0.1305 0.2396 0.1523 0.0392 [61] 0.2877 0.0349 0.1656 0.2726 0.4670 0.2336 0.0932 0.2731 0.3326 0.0030 [71] 0.3336 0.5345 0.1917 0.3915 0.6645 0.0015 0.3518 0.7542 0.0054 0.2730 [81] 0.5271 0.0723 0.0439 0.2569 0.4438 1.0872 0.1163 0.5161 0.0244 0.0762 [91] 0.1537 0.0876 0.2585 0.0544 0.1379 0.3796 0.1431 0.2645 0.5104 0.3769
示例
x5<-round(rexp(100,10),4) x5
输出结果
[1] 0.0389 0.0984 0.0092 0.0384 0.1462 0.2182 0.2277 0.0102 0.0589 0.0148 [11] 0.0952 0.1037 0.6899 0.0279 0.0562 0.0555 0.3258 0.0199 0.0649 0.0214 [21] 0.1385 0.0805 0.0707 0.0129 0.0227 0.0688 0.0681 0.4176 0.2576 0.4757 [31] 0.0199 0.1240 0.0096 0.0349 0.0394 0.0025 0.0233 0.0495 0.0408 0.1025 [41] 0.0480 0.2670 0.0988 0.1490 0.0547 0.0152 0.0390 0.0286 0.0113 0.0384 [51] 0.1952 0.0361 0.0834 0.0852 0.0246 0.0099 0.1016 0.0028 0.0340 0.3052 [61] 0.1962 0.1240 0.0688 0.0276 0.0665 0.1007 0.1076 0.0231 0.0344 0.0634 [71] 0.1471 0.0151 0.0515 0.2178 0.1477 0.0004 0.0597 0.1680 0.1433 0.0205 [81] 0.0958 0.1006 0.0436 0.0225 0.2576 0.0816 0.2283 0.0376 0.0981 0.0226 [91] 0.0263 0.0126 0.0322 0.0307 0.1616 0.0353 0.0021 0.0073 0.1099 0.0533
示例
x6<-round(rnorm(100,5,1),4) x6
输出结果
[1] 5.6763 6.0442 3.8460 6.9212 5.8802 4.5369 5.7293 5.4419 4.8554 4.8552 [11] 6.3626 3.0416 5.5835 3.3639 3.5631 4.4367 6.1987 6.5645 4.4211 4.5338 [21] 5.1488 2.3234 6.7397 4.6025 4.6126 4.3689 5.3708 6.2197 4.7341 5.7758 [31] 3.7592 6.2968 5.8481 4.9489 4.8234 4.9166 3.5623 4.4833 3.4248 5.2370 [41] 5.0679 6.2172 4.6400 5.7530 6.9294 5.3914 5.2008 3.8987 5.8946 3.6129 [51] 3.6497 5.5797 3.7814 4.9368 4.0392 5.5592 3.1513 5.3439 5.1348 4.6515 [61] 5.3077 4.8295 3.8849 4.8448 3.3433 3.2978 4.0765 7.9617 5.1990 4.1103 [71] 4.5910 4.3942 6.5974 6.1603 4.2415 4.2938 7.0490 4.5537 4.9323 5.6792 [81] 6.3582 4.3443 4.0635 4.3988 7.5974 5.1550 6.2474 4.1624 4.0028 4.1772 [91] 3.7348 5.1781 5.4563 4.9231 6.7859 3.9984 4.8556 4.1362 5.0205 6.1024
示例
x7<-round(rt(100,35),4) x7
输出结果
[1] 0.3204 -1.3065 -1.0071 -2.0756 -0.0839 0.7791 -0.0810 -1.5429 0.6007 [10] -0.3293 1.4005 -2.2854 -2.9662 -0.1180 1.0510 -0.0574 -0.5469 -1.1538 [19] -0.0302 0.3048 1.3370 1.3374 2.1286 -1.0568 0.1238 -0.2602 -1.4643 [28] 0.9552 -0.0340 -0.3254 -2.4275 -0.4864 2.8389 -1.0984 -1.0615 -2.2571 [37] -1.7641 -1.0414 -0.8122 -0.9599 1.0389 -2.0130 1.4173 0.6212 -0.2002 [46] 1.0792 0.8220 -0.4070 -0.4462 0.7189 1.7046 0.5914 -0.2832 0.2117 [55] 0.0152 0.9239 0.1632 -0.0780 -1.4047 1.0794 -0.8982 0.9582 -0.2948 [64] 0.3619 1.8029 -1.7045 -0.0908 1.6610 1.1330 -0.9057 1.6303 1.0317 [73] 0.9091 0.7074 0.5091 -0.0232 0.2435 1.4325 0.4925 0.6357 -1.3657 [82] 1.4857 0.5618 0.1661 -0.1686 -0.3427 -1.1928 -1.4164 -0.4323 0.2180 [91] 1.2018 -2.5645 1.5959 0.1095 -0.2138 -0.3220 -0.2515 1.5408 -1.7223 [100] 0.9292
示例
x8<-round(rf(100,2,36),4) x8
输出结果
[1] 0.8147 0.0376 0.1377 0.6935 2.3744 1.8458 0.2076 0.7661 2.1681 2.1221 [11] 0.2457 0.2045 2.8954 0.5044 0.3615 0.8056 1.0633 0.6306 1.1108 0.2749 [21] 0.2724 0.0587 1.8507 1.2245 0.7484 0.1565 0.3563 0.9754 1.1266 0.2427 [31] 0.1566 0.5620 1.0601 0.2009 6.3452 0.1189 1.5286 1.0583 1.1904 0.8433 [41] 0.4249 0.0324 0.0383 1.2045 1.7232 2.3048 3.0217 0.2014 0.3035 0.3601 [51] 0.4877 0.1151 0.2056 0.6065 0.9192 0.6845 3.7313 0.0209 1.3406 0.1404 [61] 0.7136 1.3781 0.0560 2.8054 1.3838 3.4458 0.3417 0.1298 1.3320 0.0430 [71] 2.5704 3.4338 0.8516 1.3397 1.5941 3.6932 0.1111 1.1202 1.7724 0.8752 [81] 3.8412 0.2798 0.4708 2.1400 0.8470 0.5233 0.7540 0.2193 0.1412 1.8143 [91] 0.9365 0.3903 2.3025 1.6087 0.2255 0.5314 1.8405 0.2321 0.8266 1.3261
示例
x9<-round(rlnorm(100,5,2),4) x9
输出结果
[1] 3663.4697 1177.2276 11.4535 815.7052 3066.3002 63.3265 [7] 6.8847 35.7552 236.8196 79.1767 689.2404 5.5008 [13] 344.2535 1138.8724 287.1132 49.1381 489.8662 158.1743 [19] 109.4748 355.1509 855.8181 191.1866 9.0679 314.2612 [25] 1008.5422 17.5288 20.0675 37.5216 2466.2132 1104.7948 [31] 570.9207 11.2588 3.1528 481.2571 354.3538 4.6479 [37] 928.3763 743.7015 1054.3399 253.3067 20.7045 352.9785 [43] 1849.3159 167.5804 0.9107 2489.4271 12994.5896 1119.0242 [49] 24.1053 1861.5244 754.3061 28.5280 24.2137 57.0343 [55] 203.8550 245.9501 582.4504 7571.2720 305.8121 3.3545 [61] 10962.3609 35055.4310 345.9599 50.3923 815.9937 0.1470 [67] 142.1961 310.8576 632.7251 929.2929 61.2483 166.1665 [73] 238.8496 5.8300 252.5437 93.1759 4494.1969 40.7933 [79] 2271.2802 743.6349 387.8769 2.0409 65.9566 64.4320 [85] 66.8955 1093.2168 24.1452 65.7903 31.2941 9.4564 [91] 5453.9356 27.8865 72.1684 216.2117 46.6497 72.9932 [97] 12.9700 217.6623 2230.6189 329.4752