根据变量是否匹配向量中的项将值替换为NA (

Question

我的问题是如何改进我怀疑是低效的代码。

我有两个数据:一个包含县级灾害信息数据，另一个包含县级人均收入数据。作为第一步，我感兴趣的是找出哪些县的人均收入数据缺失。下面是示例数据格式的内容：

counties <- data.frame(polyname = c("alabama,autauga","alabama,autauga",
                                    "alabama,baldwin","alabama,baldwin",
                                    "alabama,barbour","alabama,barbour", 
                                    "alabama,bibb", "alabama,bibb"), 
                       indAnyDisaster_frequency = c("1-2", "1-2", "0", "0", 
                                                   "3-5", "3-5", "1-2","1-2"))
counties_persinc_1980 <- data.frame(polyname = c("alabama,autauga","alabama,autauga",
                                                 "alabama,baldwin","alabama,baldwin",
                                                 "alabama,barbour","alabama,barbour", 
                                                 "alabama,bibb", "alabama,bibb"), 
                                    persinc_1980 = c(NA, NA, NA, NA, 25, 30, 32, 28))
no_persinc_1980 <- unique(counties_persinc_1980$polyname[is.na(counties_persinc_1980$persinc_1980)])

现在，如果相同索引的县$polyname与向量的元素匹配，我希望使用缺失县名的向量来替换counties$indAnyDisaster_frequency。我相信我已经能够通过一个for循环来实现这一点，但我不认为它是非常有效的。然而，我一直未能弄清楚如何使用来实现同样的结果。我已经包括了循环的代码和我尝试使用lapply的一次尝试。

for(i in 1:length(no_persinc_1980)){
  counties$indAnyDisaster_frequency[counties$polyname==no_persinc_1980[i]] <- NA
}
lapply(1:length(no_persinc_1980), function(x) counties$indAnyDisaster_frequency[counties$polyname==no_persinc_1980[x]] <- NA)

如能就如何改进这一办法提供任何指导，将不胜感激。

Answer 1

idx <- which(counties$polyname %in% no_persinc_1980)
counties[ idx, 'indAnyDisaster_frequency' ] <- NA

Answer 2

不需要循环。我会调查一下%in%

counties$indAnyDisaster_frequency[counties$polyname %in% no_persinc_1980] <- NA

counties
#>          polyname indAnyDisaster_frequency
#> 1 alabama,autauga                     <NA>
#> 2 alabama,autauga                     <NA>
#> 3 alabama,baldwin                     <NA>
#> 4 alabama,baldwin                     <NA>
#> 5 alabama,barbour                      3-5
#> 6 alabama,barbour                      3-5
#> 7    alabama,bibb                      1-2
#> 8    alabama,bibb                      1-2

Answer 3

library(tidyverse)

用NA在persinc_1980列中提取县名

counties_nas <- counties_persinc_1980 %>% 
  filter(is.na(persinc_1980)) %>% 
  unique() %>% 
  pull(polyname)

如果向量中存在多个名称，则将indAnyDisaster_frequency转换为NA

counties %>%  
  mutate(indAnyDisaster_frequency = case_when(polyname %in% counties_nas ~ NA_character_, 
                                              TRUE ~ indAnyDisaster_frequency))

  polyname        indAnyDisaster_frequency
  <chr>           <chr>                   
1 alabama,autauga NA                      
2 alabama,autauga NA                      
3 alabama,baldwin NA                      
4 alabama,baldwin NA                      
5 alabama,barbour 3-5                     
6 alabama,barbour 3-5                     
7 alabama,bibb    1-2                     
8 alabama,bibb    1-2