r - 重复时如何根据优先级重新分类/替换值

Question

我有一个 df，其中 value 表示 drug 的状态:

g1 = data.frame ( 
    drug = c('a','a','a','d','d'),
    value = c('fda','trial','case','case','pre')
)

drug value
1    a   fda
2    a trial
3    a  case
4    d  case
5    d   pre

所以对于药物，我想根据以下 value 的优先级顺序替换任何重复的 drug:

fda > trial > case > pre 

例如，如果药物 d 是“case”和“pre”，则 d 的所有发生率都将被重新分类为“case”。决赛 table 应该是这样的。

  drug value
1    a   fda
2    a   fda
3    a   fda
4    d  case
5    d  case

如何在不必遍历每种药物并先确定优先级然后替换的情况下执行此操作？

Answer 1

由于这是一个序数变量，您可以使 g1$value 成为一个 ordered 因子作为相应的 class。然后您可以像使用数字一样使用 min 和 max 等函数:

g1$value <- ordered(g1$value, levels = c("fda", "trial", "case", "pre"))
g1$value
#[1] fda   trial case  case  pre  
#Levels: fda < trial < case < pre
g1$value <- ave(g1$value, g1$drug, FUN=min)
g1
#  drug value
#1    a   fda
#2    a   fda
#3    a   fda
#4    d  case
#5    d  case

或者在 dplyr 中说:

g1 %>%
  mutate(value = ordered(value, levels = c("fda", "trial", "case", "pre"))) %>%
  group_by(drug) %>%
  mutate(value = min(value))

数据集中的顺序和任何 drug 组中存在的值范围不应影响此结果:

g2 = data.frame ( 
    drug = c( "a","a","a","d","d","e","e","e"),
    value = c("fda","trial","case","case","pre","pre","fda","case")
)

#  drug value
#1    a   fda
#2    a trial
#3    a  case
#4    d  case
#5    d   pre
#6    e   pre
#7    e   fda
#8    e  case

g2 %>%
  mutate(value = ordered(value, levels = c("fda", "trial", "case", "pre"))) %>%
  group_by(drug) %>%
  mutate(value = min(value))

## A tibble: 8 x 2
## Groups:   drug [3]
#  drug  value
#  <fct> <ord>
#1 a     fda  
#2 a     fda  
#3 a     fda  
#4 d     case 
#5 d     case 
#6 e     fda  
#7 e     fda  
#8 e     fda