通常,我们从R数据帧中提取列作为矢量,但是有时我们可能需要将列作为数据帧,因此,我们可以使用as.data.frame提取要提取为单平方的数据帧的列括号。其背后的目的可能是将该列与另一个数据框合并。
请看以下数据帧-
set.seed(9) x1<-rnorm(20) x2<-rnorm(20,0.2) x3<-rnorm(20,0.5) x4<-rnorm(20,2.5) x5<-rpois(20,5) x6<-runif(20,2,5) df<-data.frame(x1,x2,x3,x4,x5,x6) df
输出结果
x1 x2 x3 x4 x5 x6 1 -0.76679604 1.95699294 -0.30845634 2.7812222 5 3.087890 2 -0.81645834 0.38225214 -1.51938169 1.2972914 8 3.559316 3 -0.14153519 -0.06688875 -0.23872407 2.9651637 3 2.710724 4 -0.27760503 1.12642163 0.88288656 2.8520164 8 3.417152 5 0.43630690 -0.49333188 2.23086367 1.9101438 5 2.520602 6 -1.18687252 2.88199007 0.29691805 1.6464000 8 4.958743 7 1.19198691 0.42252448 -0.49639735 2.2532679 8 4.302067 8 -0.01819034 -0.50667241 -0.80653629 4.0393386 6 3.447421 9 -0.24808460 0.61721325 -0.49783160 3.0460777 2 2.992220 10 -0.36293689 0.56955678 -0.06502873 4.0649619 4 2.563962 11 1.27757055 -0.71376435 2.25205784 2.7496702 5 3.204598 12 -0.46889715 -0.11691475 -0.04777135 0.5375814 4 2.962441 13 0.07105410 1.24905921 -0.35852571 1.6909398 7 2.752308 14 -0.26603845 0.36811181 0.54929453 2.0013149 5 3.582086 15 1.84525720 0.23144021 0.29995552 2.8051218 3 4.365315 16 -0.83944966 -0.81033054 -0.60395445 2.2107928 3 3.258313 17 -0.07744806 0.58275153 0.74058804 3.9577142 2 2.204786 18 -2.61770553 -0.61969653 0.88111362 3.3737555 9 3.329696 19 0.88788403 0.56171109 2.73045895 1.5470440 7 4.025269 20 -0.70749145 0.29337136 1.69920239 2.4683245 4 4.254372
提取一些列作为单独的数据帧-
x2 <-as.data.frame(df[,2]) x2
输出结果
df[, 2] 1 1.95699294 2 0.38225214 3 -0.06688875 4 1.12642163 5 -0.49333188 6 2.88199007 7 0.42252448 8 -0.50667241 9 0.61721325 10 0.56955678 11 -0.71376435 12 -0.11691475 13 1.24905921 14 0.36811181 15 0.23144021 16 -0.81033054 17 0.58275153 18 -0.61969653 19 0.56171109 20 0.29337136
is.data.frame(x2) [1] TRUE x5 <-as.data.frame(df[,5]) x5 df[, 5]
输出结果
1 5 2 8 3 3 4 8 5 5 6 8 7 8 8 6 9 2 10 4 11 5 12 4 13 7 14 5 15 3 16 3 17 2 18 9 19 7 20 4
is.data.frame(x5) [1] TRUE x3 <-as.data.frame(df[,3]) x3 df[, 3]
输出结果
1 -0.30845634 2 -1.51938169 3 -0.23872407 4 0.88288656 5 2.23086367 6 0.29691805 7 -0.49639735 8 -0.80653629 9 -0.49783160 10 -0.06502873 11 2.25205784 12 -0.04777135 13 -0.35852571 14 0.54929453 15 0.29995552 16 -0.60395445 17 0.74058804 18 0.88111362 19 2.73045895 20 1.69920239 is.data.frame(x3) [1] TRUE