传递给 map_dfr 的向量中的 NA 值

我试图将向量传递给map()函数，每个向量都有不同数量的NA值，但返回错误.

我有一个由N个数字列和1个分类列组成的Tibble.我想将每个数字列的分布与按分类列的值拆分的其他列的分布进行比较.我使用overlapping::overlap()来计算分布的重叠，并将数字列提供给map_dfr函数进行迭代.例如:

require(overlapping)
require(dplyr)
require(purrr)

set.seed( 1 )
n <- 100
G1 <- sample( 0:30, size = n, replace = TRUE )
G2 <- sample( 0:30, size = n, replace = TRUE, prob = dbinom( 0:30, 31, .55 ))
G3 <- sample( 0:30, size = n, replace = TRUE, prob = dbinom( 0:30, 41, .65 ))
Data <- data.frame(y = G1, x = G2, z = G3, group = rep(c("G1","G2", "G3"), each = n), class = rep(c("C1","C2", "C3"), each = 1)) %>% as_tibble()
Data 

overlap_fcn <- function(.x) {
        ## construct list of vectors
    dist_list <- list(
                "C1" = Data %>% 
                        filter(class == 'C1', !is.na(.x)) %>% 
                        pull(.x), 
                "C2" = Data %>% 
                        filter(class == 'C2', !is.na(.x)) %>% 
                        pull(.x),
                "C3" = Data %>% 
                        filter(class == 'C3', !is.na(.x)) %>% 
                        pull(.x)
                )
## calculate distribution overlaps
    return(
        enframe(
                overlapping::overlap(dist_list)$OV*100
        ) %>% 
        mutate(value = paste0(round(value, 2), "%"),
                class = .x) %>%
        rename(comparison = name, overlap = value) %>%
        relocate(class)
    )

}

overlap_table <- purrr::map_dfr(
  .x = c('y', 'x', "z"),
  .f = ~overlap_fcn(.x))

overlap_table

以上工作达到了预期的效果.然而，在实践中，我对x、y和z中的每一个都有不同程度的思念.我试着用!is.na(.x)上的过滤器来解释这个问题，但它不工作.例如:

Data$x[1:3] <- NA
Data$y[10:20] <- NA
Data$z[100:150] <- NA

overlap_table <- purrr::map_dfr(
  .x = c('x', 'y', "z"),
  .f = ~overlap_fcn(.x))

返回此错误:

Error in density.default(x[[j]], n = nbins, ...): 'x' contains missing values
Error in density.default(x[[j]], n = nbins, ...): 'x' contains missing values
Traceback:
1. purrr::map_dfr(.x = c("x", "y", "z"), .f = ~overlap_fcn(.x))
2. map(.x, .f, ...)
3. .f(.x[[i]], ...)
4. overlap_fcn(.x)
5. enframe(overlapping::overlap(dist_list)$OV * 100) %>% mutate(value = paste0(round(value, 
 .     2), "%"), class = .x) %>% rename(comparison = name, overlap = value) %>% 
 .     relocate(class)   # at line 25-33 of file <text>
6. relocate(., class)
7. rename(., comparison = name, overlap = value)
8. mutate(., value = paste0(round(value, 2), "%"), class = .x)
9. enframe(overlapping::overlap(dist_list)$OV * 100)
10. overlapping::overlap(dist_list)
11. density(x[[j]], n = nbins, ...)
12. density.default(x[[j]], n = nbins, ...)
13. stop("'x' contains missing values")

有谁能帮帮我吗？我肯定我错过了一些非常明显的东西；我只是看不出是什么！