Handling NA values while modifying output tables

[str_guru]

I am looking to hide values from the output table if the frequency of data in the respective variable is less than 4 .

lets say if the number of records in column hp, mpg, qsec is less than 4 than the mean or median should be masked with "--"

i am trying like below but not working showing some error due to NA in database

library(expss)
data <- data.frame(
  gender = c(1, 2, 1),
  sector = c(3, 3, 1),
  col1 = c(12, 15, 22),
  col2 = c(33, NA, 41),
  col3 = c(1, 1, 0),
  col4 = c(NA,NA,NA),
  col5 = c(1, 2, 1)
)

data$col3 <- factor(data$col3, levels = 1, labels = "Management")
data$col4 <- factor(data$col4, levels = 1, labels = "HR")

lst <- list(data$col4,data$col3)

fun1 <- function(dataset,var_list,banner1){
  perc_25 <- function(x, ...){unname(quantile(x, .25, na.rm=TRUE))}
  perc_75 <- function(x, ...){unname(quantile(x, .75, na.rm=TRUE))}
  
  dataset<-dataset[var_list] %>% as.data.frame() 
  first_col_param <- head(var_list,1)
  second_col_param <- tail(var_list,1)
  var_lab(colnames(dataset)[ncol(dataset)]) <- ""
  mr <- parse(text=paste0("mrset(",
                          first_col_param ," %to% ",second_col_param,")"))
  fun_replace_valid_n <- function(x, n) {
    dat <- dplyr::cur_data_all() %>% replace(is.na(.),0)
    func_name <- dat$func_name
    if(x[func_name == "Valid N"] < n) {
      replace(x, func_name %in% c("Mean", "Median"), "--") 
      
    } else x
  }
  
  
  t1<- cross_fun(dataset, 
                 eval(mr),
                 col_vars = banner1,
                 fun = combine_functions("Mean" =  mean, 
                                         "Median" = median,
                                         "Max"= max,
                                         "Min"=min,
                                         "25th Perc" = perc_25,
                                         "75th Perc" = perc_75,
                                         "Valid N" = valid_n
                 ))
  
  t1 <- as.data.frame(t1)
  t1 <- t1 %>%  tidyr::separate(row_labels, into = c('grp', 'func_name'), sep = "\\|")
  t1 <- t1 %>%   dplyr::group_by(grp) 
  t1 <- t1 %>%  dplyr::mutate(dplyr::across(where(is.numeric), fun_replace_valid_n, n = 4)) %>%
    dplyr::ungroup()
  t1 <- t1 %>% tidyr::unite(row_labels, grp, func_name, sep = "|") %>%
    as.etable
  t1
  
}

debugonce(fun1)
t1 <- fun1(dataset=data,"col1",banner1=lst)

THe output should be look like

[Flm]

library(tidyverse)

df <- tibble(
  x = c("A", "B", "A", "B", "A", "A"),
  y = c(NA, 1, 4, 5, NA, 7)
)

To summarise the mean

df %>% 
  group_by(x) %>% 
  summarise(mean = mean(y))

Mean in group A can't be computed because of missing values, so:

df %>% 
  group_by(x) %>% 
  summarise(mean = mean(y, na.rm = T))

if you just want to replace NA with --, then:

df %>% 
  group_by(x) %>% 
  summarise(mean = mean(y)) %>% 
  mutate_all(~replace(., is.na(.), "--"))

[str_guru]

what i am doing wrong in my code...???