删除组内具有特定值的随机变量子集和列表

Question

这是问题Deleting random subset of observations within a group of variables that have a certain value 的一个细微变化。 我正在寻找的变化是如何删除行子集，每次分组标准更改时，删除的行数都会更改。这是一个简单的示例数据集，其中包含一列数值和一个数字分组列（分组列也可以是“AA1”、“AA2”等因素）。

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 group = 16 时，随机删除 7 行；当 group = 17 时，随机删除 7 行。对于每个分组变量，此过程继续进行。

这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

在新创建的数据框上重复上述步骤：

(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

重复分组变量的每个组合和要删除的随机选择的行。

(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表格（或类似的东西）来指定分组值和要删除的特定分组变量的行数：

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

提前感谢您的任何意见。

Answer 1

尝试运行这个 -

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]

Answer 2

在研究了 Codoremifa 提供的答案后，我注意到一些小细节可能值得记录下来，以供其他发现这篇文章的人使用。使用 Codoremifa 提供的答案，我做了一些小的更改，并包含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