R: slice rows with min date and additional criteria from other columns-CodePudding

I have the following dataframe

df =
id date           medication related_medication
1 2017-02-18      A          yes
1 2017-02-07      D          yes
2 2017-02-18      S          yes
2 2017-02-18      F          no
3 2017-02-18      A          yes
3 2017-02-01      F          yes

I want to take only the minimum date on which related medications appeared per id. In the above example we have only indivoduals 1 and 3 with 2 related_medication (sinc this variable take a value yes). For this reason I would love to take the minimum date of when the appear. The resulting table should look like the following:

df =
id date           medication related_medication
1 2017-02-07      D          yes
2 2017-02-18      S          yes
2 2017-02-18      F          no
3 2017-02-01      F          yes

so far I have tried:

df_final <- df %>%
   slice(which.min(date))

but I dont find the way to carry this operation only when a certain condition is met, i.e. related_medication == "yes"

cheers

CodePudding user response：

You can use slice_min

library(dplyr)
df %>% 
  group_by(id, related_medication) %>% 
  slice_min(date)

output

     id date       medication related_medication        
1     1 2017-02-07 D          yes               
2     2 2017-02-18 F          no                
3     2 2017-02-18 S          yes               
4     3 2017-02-01 F          yes

If one wants to keep all observations if related_medication == "no".

df %>% 
  group_by(id) %>% 
  filter(date[related_medication == "yes"] == min(date[related_medication == "yes"]) |
         related_medication == "no")

CodePudding user response：

Using data.table

library(data.table)
setDT(df)[, .SD[which.min(date)], .(id, related_medication)]

-output

     id related_medication       date medication
   <int>             <char>     <Date>     <char>
1:     1                yes 2017-02-07          D
2:     2                yes 2017-02-18          S
3:     2                 no 2017-02-18          F
4:     3                yes 2017-02-01          F