如何使用dplyr在R数据框中选择具有分组分组的变量的最小值或最大值的行？
如果R数据帧包含具有许多组级别的组变量，则很难根据组级别找到离散变量或连续变量的最小值和最大值。但这可以通过dplyr包中的slice函数来完成。
考虑下面的数据帧，该数据帧具有一组变量，连续变量和离散变量-
> set.seed(2) > x1<-sample(1:100,20,replace=TRUE) > x2<-sample(1:10,20,replace=TRUE) > x3<-rpois(20,10) > x4<-rpois(20,5) > x5<-rpois(20,5) > x6<-runif(20,2,5) > x7<-sample(200:1000,20,replace=TRUE) > Group<-rep(c(1,2,3,4),times=5) > df<-data.frame(x1,x2,x3,x4,x5,x6,x7,Group) > df x1 x2 x3 x4 x5 x6 x7 Group 1 85 8 14 7 8 2.900301 749 1 2 79 7 12 4 3 3.331022 200 2 3 70 1 17 5 6 4.190603 883 3 4 6 6 11 8 5 4.004491 649 4 5 32 9 13 5 4 2.934971 641 1 6 8 4 7 3 6 3.435734 699 2 7 17 6 9 3 4 2.874230 679 3 8 93 9 7 1 3 2.546523 642 4 9 81 8 8 6 8 3.082288 496 1 10 76 6 7 4 1 4.711400 570 2 11 41 3 8 5 9 3.182143 847 3 12 50 9 9 7 4 4.339642 707 4 13 75 7 8 7 6 2.852477 805 1 14 65 8 16 8 5 4.561162 233 2 15 3 6 8 2 7 2.516727 783 3 16 80 2 9 9 3 2.237793 788 4 17 96 7 10 5 4 2.876195 792 1 18 50 2 7 1 3 4.521114 375 2 19 55 3 6 3 4 4.835804 942 3 20 63 4 9 10 6 2.134896 228 4
加载dplyr软件包-
> library(dplyr)
查找特定变量的最小值和最大值的分组行-
> df %>% group_by(Group) %>% slice(which.min(x5)) # A tibble: 4 x 8 # Groups: Group [4] x1 x2 x3 x4 x5 x6 x7 Group <int> <int> <int> <int> <int> <dbl> <int> <dbl> 1 32 9 13 5 4 2.93 641 1 2 76 6 7 4 1 4.71 570 2 3 17 6 9 3 4 2.87 679 3 4 93 9 7 1 3 2.55 642 4 > df %>% group_by(Group) %>% slice(which.max(x5)) # A tibble: 4 x 8 # Groups: Group [4] x1 x2 x3 x4 x5 x6 x7 Group <int> <int> <int> <int> <int> <dbl> <int> <dbl> 1 85 8 14 7 8 2.90 749 1 2 8 4 7 3 6 3.44 699 2 3 41 3 8 5 9 3.18 847 3 4 63 4 9 10 6 2.13 228 4 > df %>% group_by(Group) %>% slice(which.max(x7)) # A tibble: 4 x 8 # Groups: Group [4] x1 x2 x3 x4 x5 x6 x7 Group <int> <int> <int> <int> <int> <dbl> <int> <dbl> 1 75 7 8 7 6 2.85 805 1 2 8 4 7 3 6 3.44 699 2 3 55 3 6 3 4 4.84 942 3 4 80 2 9 9 3 2.24 788 4 > df %>% group_by(Group) %>% slice(which.min(x1)) # A tibble: 4 x 8 # Groups: Group [4] x1 x2 x3 x4 x5 x6 x7 Group <int> <int> <int> <int> <int> <dbl> <int> <dbl> 1 32 9 13 5 4 2.93 641 1 2 8 4 7 3 6 3.44 699 2 3 3 6 8 2 7 2.52 783 3 4 6 6 11 8 5 4.00 649 4 > df %>% group_by(Group) %>% slice(which.max(x1)) # A tibble: 4 x 8 # Groups: Group [4] x1 x2 x3 x4 x5 x6 x7 Group <int> <int> <int> <int> <int> <dbl> <int> <dbl> 1 96 7 10 5 4 2.88 792 1 2 79 7 12 4 3 3.33 200 2 3 70 1 17 5 6 4.19 883 3 4 93 9 7 1 3 2.55 642 4 > df %>% group_by(Group) %>% slice(which.max(x6)) # A tibble: 4 x 8 # Groups: Group [4] x1 x2 x3 x4 x5 x6 x7 Group <int> <int> <int> <int> <int> <dbl> <int> <dbl> 1 81 8 8 6 8 3.08 496 1 2 76 6 7 4 1 4.71 570 2 3 55 3 6 3 4 4.84 942 3 4 50 9 9 7 4 4.34 707 4 > df %>% group_by(Group) %>% slice(which.min(x6)) # A tibble: 4 x 8 # Groups: Group [4] x1 x2 x3 x4 x5 x6 x7 Group <int> <int> <int> <int> <int> <dbl> <int> <dbl> 1 75 7 8 7 6 2.85 805 1 2 79 7 12 4 3 3.33 200 2 3 3 6 8 2 7 2.52 783 3 4 63 4 9 10 6 2.13 228 4