如何总结缺失数据的分类变量？

Question

我正在尝试对分类变量 frailty 分数执行 group_by 总结。数据的结构使得每个主题都有多个观察结果，其中一些包含缺失数据，例如

Subject  Frailty
1        Managing well
1        NA
1        NA
2        NA
2        NA
2        Vulnerable
3        NA
3        NA
3        NA

我希望对数据进行汇总，以便在有可用的情况下显示脆弱性描述，如果没有则显示 NA，例如

Subject  Frailty
1        Managing well
2        Vulnerable 
3        NA

我尝试了以下两种方法都返回错误：

Mode <- function(x) {
ux <- na.omit(unique(x[!is.na(x)]))
tab <- tabulate(match(x, ux)); ux[tab == max(tab)]
}

data %>% 
group_by(Subject) %>% 
summarise(frailty = Mode(frailty)) %>% 

Error: Expecting a single value: [extent=2].

condense <- function(x){unique(x[!is.na(x)])}

data %>% 
group_by(subject) %>% 
summarise(frailty = condense(frailty))

Error: Column frailty must be length 1 (a summary value), not 0

Answer 1

如果只有一个非NA元素，则按'Subject'分组后，得到第一个非NA元素

library(dplyr)
data %>%
   group_by(Subject) %>%
   summarise(Frailty = Frailty[which(!is.na(Frailty))[1]])
# A tibble: 3 x 2
#  Subject Frailty      
#    <int> <chr>        
#1       1 Managing well
#2       2 Vulnerable   
#3       3 <NA>

如果有多个非 NA 独特元素，我们要么 paste 将它们放在一起，要么 return 作为 list

data %>%
    group_by(Subject) %>%
    summarise(Frailty = na_if(toString(unique(na.omit(Frailty))), ""))
# A tibble: 3 x 2
#  Subject Frailty      
#    <int> <chr>        
#1       1 Managing well
#2       2 Vulnerable   
#3       3 <NA>

数据

data <- structure(list(Subject = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L
), Frailty = c("Managing well", NA, NA, NA, NA, "Vulnerable", 
NA, NA, NA)), class = "data.frame", row.names = c(NA, -9L))

Answer 2

涉及 dplyr 的一个解决方案可能是：

df %>%
 group_by(Subject) %>%
 slice(which.min(is.na(Frailty)))

  Subject Frailty      
    <int> <chr>        
1       1 Managing_well
2       2 Vulnerable   
3       3 <NA>

如何总结缺失数据的分类变量？

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数据