R，dplyr：如何根据 group_by 的大小替换 0 个值

Question

我正在尝试根据 group_by 的大小将列中的 0 值替换为大型数据集的组的中值。

set.seed(10000)
Data <- data.frame(
    X = as.numeric(c(0,2,3,4,5,6,7,8,9,0)),
    Y = c("no","yes","yes","yes","yes","yes","yes","yes","yes","yes"),
    Z = c(F,T,T,T,T,F,F,F,T,T)
)

# change 0 in the 10 spot to median
Data <- Data %>%
    # group by Y and Z then
    group_by(Y,Z) %>%
    # if the size of the group is less than 2 and if X is NA change it to 10
    # else leave it as X else (if group size 2 or greater) leave value as NA then
    mutate(X = ifelse(n()<2,ifelse(X==0,median(X),X),X)) 

# change 0 in 1 spot to median
Data <- Data %>%
    # group by Y then
    group_by(Y) %>%
    # if the size of the group is larger than 2 and if X is NA change it to 1
    # else leave is as X else(if group size 3 or larger) leave value as X
    mutate(X = ifelse(n()<3,ifelse(X==0,median(X),X),X))

导致错误：

n > 1 中的错误：

比较 (6) 仅适用于原子和列表类型

我希望 X 列是上述代码之后 1:10 的序列。

这是我在使用大型数据集时遇到的问题的概括，我试图将 0 值作为不同组的中位数，以组的大小为条件，我得到与上述相同的错误。

Answer 1

我找不到如何回答您的确切问题，但我希望这可以为您指明正确的方向（这也是 data.table 解决方案）。

假设您想要列的 mean 而不是任何 NA，这取决于组的大小，zoo 包中的一个函数可以提供帮助：

# load libraries

library(zoo)
library(data.table)

# convert Data to a data.table

setDT(Data)

现在，我们将使用函数 zoo::na.aggregate 替换为 mean 任何 NA。但是我们需要引入组的大小作为条件。所以我先一步一步来：

# create a column with the number of elements in the group. It'll be removed later:

Data[, n:= .N, by = Y]

# Create a new X column with the NAs replaced by the mean, in case the group is larger than 2, or an arbitrary number -I choosed 100-, if the group is less or equal than 2:

Data[, newX := ifelse(n >2, na.aggregate(x), 100), by = Y]

# Now you can optionally copy newX to X:

Data[, X := newX]

# and delete n and newX:

Data[, c("n", "newX") := NULL]

当然，您可以通过直接分配给X 来跳过X := newX 部分，但它认为它比一步一步的过程更晦涩。

Answer 2

看看这是否适合你：

library(tidyverse)

set.seed(10000)
Data <- data.frame(
  X = c(NA,2,3,4,5,6,7,8,9,NA),
  Y = c("yes","yes","yes","yes","yes","yes","yes","yes","yes","no"),
  Z = c(T,F,F,F,F,F,F,F,F,T)
)

# change NA in the 10 spot to 10
Data %>%
  group_by(Y) %>%
  mutate(count = n()) %>%
  mutate(X = ifelse(count < 2, ifelse(is.na(X), 10, X), NA)) %>%
  select(-count)


# change NA in 1 spot to 1
Data %>%
  group_by(Y,Z) %>%
  mutate(count = n()) %>%
  mutate(X = ifelse(count < 3, ifelse(is.na(X), 1, X), X)) %>%
  select(-count)



# You can bypass the count column 
Data %>%
  group_by(Y) %>%
  mutate(X = ifelse(n() < 2, ifelse(is.na(X), 10, X), NA))