如何在R中编写具有多个条件的ifelse语句？

Question

我在编写ifelse语句时遇到问题，我有三列，如下所示：

Team 1     Winner
  T1        T1
  T2        T1
  T2        NA
  T3        NA

我想要另一列：结果，如果Team = Winner应该是Winner否则是失败者，而Team = anything和Winner = NA则应该没有结果...

Team 1     Winner   result
  T1        T1       winner
  T2        T1       losser
  T2        NA       noresult
  T3        NA       noresult

任何帮助，将不胜感激。

Answer 1

另一种可能性是case_when从dplyr ：

library(dplyr)

df %>% 
  mutate(Result = case_when(
    Team == Winner ~ "Winner",
    Team != Winner ~ "Loser",
    is.na(Winner) ~ "No result"
  ))


#   Team Winner    Result
# 1   T1     T1    Winner
# 2   T2     T1     Loser
# 3   T2   <NA> No result
# 4   T3   <NA> No result

数据：

tt <- "Team     Winner
  T1        T1
T2        T1
T2        NA
T3        NA"

df <- read.table(text=tt, header = T, stringsAsFactors = F)

Answer 2

您可以使用dplyr::if_else()因为我了解到 ，它是严格的，因为它会检查数据类型和它控制NAS，使得代码更简单：

 df %>% mutate(Result = if_else( Team==Winner, "Winner", "Loser", missing ='No result'))
  Team Winner    Result
1   T1     T1    Winner
2   T2     T1     Loser
3   T2   <NA> No result
4   T3   <NA> No result

尽管，在这里查看单线解决方案，对于您的示例数据，它并不是最快的（赢家是@Tim Biegeleisen的答案+1）：

Unit: microseconds
    expr      min        lq       mean    median        uq      max neval cld
 IF_ELSE  893.013  974.5060 1176.35331 1053.2260 1343.3590 2278.398   100  b 
  IFELSE   20.481   34.3475   49.57934   47.3605   58.0275  143.361   100 a  
    CASE 1067.946 1152.4255 1423.41426 1226.0255 1721.3850 4108.795   100   c

因此，我可以找出在简单性（当然是主观的）和更多控制（即由于函数的性质而客观的）和速度（如果对您来说是一个问题，请查看实际数据）之间的权衡问题。更加客观）。

Answer 3

尝试以下逻辑：

df$result <- ifelse(is.na(df$Winner), "no result",
    ifelse(df$Team==df$Winner, "winner", "loser"))
df

Team Winner    result
1   T1     T1    winner
2   T2     T1     loser
3   T2   <NA> no result
4   T3   <NA> no result

Answer 4

采用 -

df$Winner <- factor(df[,2], levels=unique(df$Team.1)) # avoid "level sets of factors are different" error
df$result <- ifelse(df$Team.1 == df$Winner, "winner", "loser")
df[is.na(df$result), "result"] <- "noresult"
df

输出量

  Team.1 Winner   result
1     T1     T1   winner
2     T2     T1    loser
3     T2   <NA> noresult
4     T3   <NA> noresult