Btukfyl

I have a problem in writing ifelse statement ,I have three columns as shown below:

Team 1     Winner

  T1        T1

  T2        T1

  T2        NA

  T3        NA

I want another column : Result such that if Team=Winner it should be Winner else losser and If Team=anything & winner=NA then it should be no result...

Team 1     Winner   result

  T1        T1       winner

  T2        T1       losser

  T2        NA       noresult

  T3        NA       noresult

Any help would be appreciated.

Use -

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

Output

  Team.1 Winner   result

1     T1     T1   winner

2     T2     T1    loser

3     T2   <NA> noresult

4     T3   <NA> noresult

Another possibility is with case_when from 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

Data:

tt <- "Team     Winner

  T1        T1

T2        T1

T2        NA

T3        NA"



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

You can use dplyr::if_else(), as I learned, it is strict, because it checks the data type and it handles the NAs, making code simpler:

 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

Despite, looking at the one-liner solution here, for your example data, it's not the fastest (the winner is the @Tim Biegeleisen 's answer, +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

So I can figure out a trade off between simplicity (that is subjective, of course) and more control (that is objective, due the nature of the functions), and velocity (if it's an issue to you, looking your real data, but it's more objective).

Try this logic:

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