要替换R数据框中的完整列，我们可以使用delta运算符将原始列设置为新值。例如，如果我们有一个名为df的数据框，其中包含一列x，其中x的列具有500个来自正态分布的值，则可以用df $x <-rnorm（500,5 ）。
考虑以下数据帧-
x1<−rpois(20,2) x2<−rpois(20,2) x3<−rpois(20,3) df1<−data.frame(x1,x2,x3) df1输出结果
x1 x2 x3 1 1 3 1 2 0 3 1 3 1 4 3 4 4 3 2 5 0 4 1 6 2 3 6 7 2 2 4 8 4 1 2 9 0 4 5 10 1 1 4 11 3 3 1 12 1 3 3 13 1 1 10 14 2 1 3 15 2 3 3 16 1 1 3 17 2 4 8 18 1 3 2 19 1 3 0 20 1 1 0
用具有lambda 2的泊松分布替换x3-
df1$x3<−rpois(20,2) df1输出结果
x1 x2 x3 1 1 3 1 2 0 3 2 3 1 4 0 4 4 3 1 5 0 4 3 6 2 3 2 7 2 2 2 8 4 1 3 9 0 4 2 10 1 1 3 11 3 3 1 12 1 3 2 13 1 1 3 14 2 1 0 15 2 3 0 16 1 1 1 17 2 4 2 18 1 3 3 19 1 3 4 20 1 1 1
y1<−rnorm(20,1,0.05) y2<−rnorm(20,1,0.75) y3<−rnorm(20,1,0.05) y4<−rnorm(20,1,0.05) df2<−data.frame(y1,y2,y3,y4) df2输出结果
y1 y2 y3 y4 1 1.0141018 0.71738148 0.9420311 1.0009205 2 1.0258060 1.03202326 1.0183309 1.0953612 3 0.9743657 1.58046651 1.0517233 1.0596325 4 1.0199483 1.08089945 0.9873335 0.9910522 5 1.0740019 2.13191506 1.0805077 1.0352464 6 1.0504327 1.55207108 1.0105741 0.9503119 7 0.9656107 1.51496959 1.0856465 1.0721738 8 1.0314142 −0.62997358 0.9007254 0.9555474 9 0.9688579 2.05252761 0.9920891 0.9693772 10 0.9811555 1.58630688 0.9550110 0.9611265 11 0.9594506 1.49768858 0.9792084 0.9442541 12 0.9891804 0.50237995 0.8821927 1.0816134 13 1.0939416 0.16319086 1.0682660 0.9552987 14 1.0437989 2.06159460 1.0034599 0.9708994 15 0.9660916 1.21363074 0.9780202 0.9961647 16 1.0634504 0.82467522 1.0184935 1.0586482 17 0.9907623 1.06935013 1.0507246 0.9516461 18 1.0336085 2.07268738 0.9972536 0.9815386 19 1.0366192 2.20583375 1.0393763 0.9332535 20 1.0861114 0.02966648 1.0502028 0.9452250
用均值1和标准差0.05-的正态分布替换y2
df2$y2<−rnorm(20,1,0.05) df2输出结果
y1 y2 y3 y4 1 1.0141018 0.9238429 0.9420311 1.0009205 2 1.0258060 0.9940841 1.0183309 1.0953612 3 0.9743657 0.9705115 1.0517233 1.0596325 4 1.0199483 0.9521452 0.9873335 0.9910522 5 1.0740019 0.9531263 1.0805077 1.0352464 6 1.0504327 1.0587658 1.0105741 0.9503119 7 0.9656107 0.9558315 1.0856465 1.0721738 8 1.0314142 1.0368435 0.9007254 0.9555474 9 0.9688579 0.9117594 0.9920891 0.9693772 10 0.9811555 1.0072615 0.9550110 0.9611265 11 0.9594506 0.9935137 0.9792084 0.9442541 12 0.9891804 1.0018355 0.8821927 1.0816134 13 1.0939416 0.9531882 1.0682660 0.9552987 14 1.0437989 0.8805634 1.0034599 0.9708994 15 0.9660916 1.0378592 0.9780202 0.9961647 16 1.0634504 1.0431174 1.0184935 1.0586482 17 0.9907623 1.0666330 1.0507246 0.9516461 18 1.0336085 0.9449561 0.9972536 0.9815386 19 1.0366192 0.9172270 1.0393763 0.9332535 20 1.0861114 0.9739211 1.0502028 0.9452250