r - 从数据框中按类别选择随机行？

Question

我有一个数据框如下：

Category Name Value

我将如何选择每个类别 5 个随机名称？Usingsample使用所有行作为可能的候选返回随机行。但是，我想指定每个类别的随机行数。有什么建议么？

更新：我愿意使用ddply

Answer 1

没有测试用例的最佳猜测：

  do.call( rbind, lapply( split(dfrm, df$cat) ,
                         function(df) df[sample(nrow(df), 5) , ] )
          )

用乔纳森的数据测试：

> do.call( rbind, lapply( split(df, df$Category) ,
+                          function(df) df[sample(nrow(df), 5) , ] )
+           )

      Category Name      Value   
1.8          1    8 -0.2496109   #  useful side-effect of labeling source group
1.15         1   15 -0.4037368
1.17         1   17 -0.4223724
1.12         1   12 -0.9359026
1.18         1   18  0.3741184
2.37         2   37  0.3033610
2.34         2   34 -0.4517738
2.36         2   36 -0.7695923
snipped remainder

Answer 2

如果您想要每个类别中相同数量的项目，这很容易：

df[unlist(tapply(1:nrow(df),df$Category,function(x) sample(x,3))),]

例如，我生成df如下：

df <- data.frame(Category=rep(1:5,each=20),Name=1:100,Value=rnorm(100))

然后我从我的代码中得到以下信息：

> df[unlist(tapply(1:nrow(df),df$Category,function(x) sample(x,3))),]
    Category Name       Value
5          1    5  0.25151044
20         1   20  1.52486482
18         1   18  0.69313462
30         2   30  0.73444185
27         2   27  0.24000427
39         2   39 -0.10108203
46         3   46 -0.37200574
49         3   49 -1.84920469
43         3   43  0.35976388
68         4   68  0.57879516
76         4   76 -0.11049302
64         4   64 -0.13471303
100        5  100  0.95979408
95         5   95 -0.01928741
99         5   99  0.85725242

如果您希望每个类别的行数不同，那将更加复杂。

Answer 3

过去，我使用了我为“采样”包中的一些函数编写的一个小包装器。

这是功能：

strata.sampling <- function(data, group, size, method = NULL) {
  #  USE: 
  #   * Specify a data.frame and grouping variable.
  #   * Decide on your sample size. For a sample proportional to the 
  #     population, enter "size" as a decimal. For an equal number of 
  #     samples from each group, enter "size" as a whole number. For
  #     a specific number of samples from each group, enter the numbers
  #     required as a vector.

  require(sampling)
  if (is.null(method)) method <- "srswor"
  if (!method %in% c("srswor", "srswr")) 
    stop('method must be "srswor" or "srswr"')
  temp <- data[order(data[[group]]), ]
  ifelse(length(size) > 1,
         size <- size, 
         ifelse(size < 1,
                size <- round(table(temp[group]) * size),
                size <- rep(size, times=length(table(temp[group])))))
  strat = strata(temp, stratanames = names(temp[group]), 
                 size = size, method = method)
  getdata(temp, strat)
}

以下是您可以使用它的方法：

# Sample data --- Note each category has a different number of observations
df <- data.frame(Category = rep(1:5, times = c(40, 15, 7, 13, 25)), 
                 Name = 1:100, Value = rnorm(100))

# Sample 5 from each "Category" group
strata.sampling(df, "Category", 5)
# Sample 2 from the first category, 3 from the next, and so on
strata.sampling(df, "Category", c(2, 3, 4, 5, 2))
# Sample 15% from each group
strata.sampling(df, "Category", .15)

还有一个我在这里写的增强功能。该函数可以优雅地处理一组观察值可能少于指定样本数的情况，并且还允许您按多个变量进行分层。有关几个示例，请参阅文档。