R中数据帧中低频数据过滤的有效方法

我有一个包含几列的 data.frame，想要根据变量的组合过滤低频数据。这个例子就像性别变量中的男性/女性和胆固醇变量中的高/低。那么我的数据框将是这样的：

set.seed(123)
Sex = sample(c('Male','Female'),size = 20,replace = TRUE)
Age = sample(c('Low','High'),size = 20,replace = TRUE)
Index = 1:20
df = data.frame(index = Index,Sex=Sex,Age=Age)
df


  index    Sex  Age
1      1   Male High
2      2 Female High
3      3   Male High
4      4 Female High
5      5 Female High
6      6   Male High
7      7 Female High
8      8 Female High
9      9 Female  Low
10    10   Male  Low
11    11 Female High
12    12   Male High
13    13 Female High
14    14 Female High
15    15   Male  Low
16    16 Female  Low
17    17   Male High
18    18   Male  Low
19    19   Male  Low
20    20 Female  Low

现在我想过滤频率高于3的性别/年龄组合

table(df[,2:3])
        Age
Sex      High Low
  Female    8   3
  Male      5   4

换句话说，我想保留女性高、男性低和男性高的指数。

Notice1）我的数据框有几个变量（不像上面的例子）和2）我有not want使用任何第三个 R 包并且 3) 我希望它速度快。

这是 R 基础上的一个简单方法：

lvls <- interaction(df$Sex, df$Age)
counts <- table(lvls)
df[lvls %in% names(counts)[counts > 3], ]

#   index    Sex  Age
#1      1   Male High
#2      2 Female High
#3      3   Male High
#4      4 Female High
#5      5 Female High
#6      6   Male High
#7      7 Female High
#8      8 Female High
#10    10   Male  Low
#11    11 Female High
#12    12   Male High
#13    13 Female High
#14    14 Female High
#15    15   Male  Low
#17    17   Male High
#18    18   Male  Low
#19    19   Male  Low

如果变量数量较多，可以将它们存储在向量中：

vars <- c("Age", "Sex") # add more
lvls <- interaction(df[, vars])
counts <- table(lvls)
df[lvls %in% names(counts)[counts > 3], ]

这是第二个基本 R 方法，使用ave:

subset(df, ave(as.integer(factor(Sex)), Sex, Age, FUN = "length") > 3)