r - 包含每个因子的至少一个实例的随机子集

Question

让我们定义一个data.frame3 列 10 行的 df。第三列是类，前两个是一些变量。

var1 <- rnorm(10)
var2 <- rnorm(10,2)
class<- as.factor(c(1,2,3,1,2,1,2,1,3,3))
df   <- data.frame(var1=var1,var2=var2,class=class)

如何df在两个子集中随机分组，以便 sub.df1 和 sub.df2 每个类至少有一个实例？

Answer 1

这有效：

set.seed(123)
partition <- function(x, n = 2) sample(c(1:n, sample(1:n, length(x) - n, TRUE)))
split(df, as.integer(ave(df$class, df$class, FUN = partition)))

# $`1`
#          var1      var2 class
# 4   1.6674610 3.3886789     1
# 7  -0.2245588 0.8284845     2
# 8  -1.1481185 4.1586492     1
# 10 -0.4712463 3.1846324     3
# 
# $`2`
#         var1       var2 class
# 1  0.9884264  3.3487054     1
# 2 -0.1549679 -0.5815586     2
# 3  1.4484692  0.3521933     3
# 5  0.5454097  2.0405363     2
# 6  1.0971626  0.6410492     1
# 9 -1.3042283  3.3235418     3

Answer 2

一种非常不优雅的方式可能是：

#changed the data to better check
var1 <- rnorm(21)
var2 <- rnorm(21,2)
class<- as.factor(c(1,2,3,1,2,1,2,1,3,3,4,1,2,4,4,5,1,2,1,2,5))
DF   <- data.frame(var1=var1,var2=var2,class=class)

#order DF by class
DF <- DF[order(DF$class),]                  

#add a column (like a second class) 
#so that every level of the "second class" contains all levels from original class
DF$col4 <- unlist((sapply(table(DF$class), 
                         function(x) { letters[1:x] })), use.names = F)

#order by the "second class"
DF <- DF[order(DF$col4),]  

#one df with all levels of original class
DF1 <- split(DF, DF$col4)$a
#another df with all levels of orignal class
DF2 <- split(DF, DF$col4)$b

#remaining levels of second class contain levels 
#of original class already present in DF1, DF2
#so, just add them to either DF1 or DF2
DF3 <- do.call(rbind, split(DF, DF$col4)[letters[3:max(table(DF$class))]])
DF1 <- rbind(DF1, DF3[1:(nrow(DF3)/2),])
DF2 <- rbind(DF2, DF3[(nrow(DF3)/2):nrow(DF3),])

#remove the second class
DF1 <- DF1[,1:3]
DF2 <- DF2[,1:3]

#> DF1
#            var1       var2 class
#1    -0.32872359  2.0055574     1
#2    -0.93543130  1.9035439     2
#3    -0.04343290  2.9213939     3
#11    1.15724846  3.0646201     4
#16    0.44848508  0.2414504     5
#c.6   0.14438547  3.5265833     1
#c.7   0.31614781  3.7160113     2
#c.10 -0.45882460 -0.2937924     3
#c.15  0.07145533  1.8942732     4
#d.8   0.61422896  1.8690204     1
#> DF2
#            var1      var2 class
#4     0.09097849 1.4701793     1
#5     1.19147818 3.4190744     2
#9    -0.37807035 3.4565437     3
#14   -1.35257981 3.6023453     4
#21   -1.07466815 0.2104640     5
#d.8   0.61422896 1.8690204     1
#d.13 -0.26357372 1.7867625     2
#e.12  0.05694470 3.1141126     1
#e.18 -0.51124304 0.8070597     2
#f.17 -2.94353989 1.6532037     1
#f.20  0.21011089 2.4029225     2