用列表删除组中具有特定值的变量的随机子集

Question

这是对删除一组具有一定值的变量中的随机观察子集问题的一个细微变化。我正在寻找的变体是如何删除行的子集，其中删除的行数在分组条件每次更改时都会更改。这是一个简单的示例数据集，其中有一列数值和一个数值分组列（分组列也可以是“AA1”、“AA2”等因子）。

set.seed(23)
df<-data.frame(a=round(rnorm(500,mean=20,sd=2)))
df$group<-seq(from = 1, to = length (df),by=5)

数据表（table（df$a））给出了以下结果：

group: 14 15 16 17 18 19 20 21 22 23 24 25 
count:  1  7 13 24 65 87 91 91 59 42 12  8 

例如：当分组值等于15时，我想随机删除4行;当组= 16时，随机删除7行;当组 = 17 时，随机删除 7 行。对于每个分组变量，此过程将继续。

以下是我目前的解决方案:

(dfindex<-which(df$a==15)) ##create index that meets the grouping variable criteria
(delete.df.index<-sample(dfindex,4)) ##select number of rows to randomly remove
dfnew<-df[-delete.df.index,] ##create a new data frame and delete the randomly selected rows

对新创建的数据帧重复上面的步骤：

(dfindex<-which(dfnew$a==16)) ##create another index from the grouping variable criteria 
(delete.df.index<-sample(dfindex,3)) ##select rows to randomly delete
dfnew<-dfnew[-delete.df.index,] ##delete rows

对要删除的分组变量和随机选择的行的每个组合重复。

(dfindex<-which(dfnew$a==17))
(delete.df.index<-sample(dfindex,7))
dfnew<-dfnew[-delete.df.index,]

在这个例子中，我有12个分组级别。简单但耗时的方法是复制/粘贴/编辑分组变量和行删除的每个组合的代码。我想知道是否可以使用一个表（或类似的东西）来指定特定分组变量的分组值和要删除的行数：

要删除的组和行的示例表。

Group  Number of rows to randomly remove
14          0
15          4
16          3
17          7
18          40
19          23

感谢您的任何意见。

Answer 1

在仔细阅读了Codoremifa提供的答案后，我注意到了一些小细节，这些细节可能值得其他人记录下来。使用Codoremifa提供的答案，我做了一些小的修改，并添加了一些额外的代码来说明一些重要的细节。基本上，注意合并步骤，并决定如何处理合并步骤生成的NA值。

set.seed(23)
df<-data.frame(a=round(rnorm(50,mean=20,sd=2)))

# create table of no of rows that need to be removed per each a
noofrowsremove <- read.table(textConnection(
 'a toremove
21 0  

17 1  
19 2  
20 2  
24 2  
16 1
22 1
18 3'), header = TRUE)

##excluded values 23 and 15 from the above df to illustrate an example below
#Keep value 21 and just assigned it a 0 (i.e., do not remove any values of 21).

library(data.table)

# assign random number in a new column, this will help in sampling
df$tosample <- runif(50) #can also use runif(nrow(df))

# convert data.frame to data.table, grouped operations are easier on data.table
dt <- data.table(df)

# rank the tosample column within each unique a value
dt[,samplerank := rank(tosample), by = 'a']

# merge the filtering no of rows with dt.  Be careful with merge options.
dt1 <- merge(dt,noofrowsremove, by = 'a') #46 rows
dt2 <- merge(dt,noofrowsremove, by = 'a',all=TRUE) #51 rows. 

#Notice slight differences in the number of rows between dt1 and dt2 
#In dt2, value 23 in the toremove column is "NA" because 23 was not included in noofrowsremove
nrow(dt1) #46 rows
nrow(dt2) #51 rows

##to keep values with "NA" change the "NA" to a 0
dt2$toremove[is.na(dt2$toremove)] <- 0 #assign NA to 0

# filter out rows that have samplerank columns <= the no of rows that need to be removed
dttrimmed1 <- dt1[samplerank > toremove] #36 rows.  toremove values with NA are exlcuded
dttrimmed2 <- dt2[samplerank > toremove] #40 rows.  Kept values with NA reasigned to 0

Answer 2

试着运行这个-

set.seed(23)
df<-data.frame(a=round(rnorm(50,mean=20,sd=2)))

# create table of no of rows that need to be removed per each a
noofrowsremove <- read.table(textConnection(
'a toremove
21 1  
23 2  
15 2  
17 1  
19 2  
20 2  
24 2  
16 1
22 1
18 3'), header = TRUE)

library(data.table)

# assign random number in a new column, this will help in sampling
df$tosample <- runif(50)

# convert data.frame to data.table, grouped operations are easier on data.table
dt <- data.table(df)
# rank the tosample column within each unique a value
dt[,samplerank := rank(tosample), by = 'a']
# merge the filtering no of rows with dt
dt <- merge(dt,noofrowsremove, by = 'a')
# filter out rows that have samplerank columns <= the no of rows that need to be removed
dttrimmed <- dt[samplerank > toremove]