Reputation: 15373
Consider the following data:
library(tidyverse)
GameID <- c('Bos001', 'Bos002', 'Bos003', 'Pit001', 'Pit002', 'Pit003')
Stadium <- c("Fenway Park", NA, "Fenway Park", NA, NA, "PNC Park")
GameData <- data.frame(GameID, Stadium)
GameData
GameID Stadium
1 Bos001 Fenway Park
2 Bos002 <NA>
3 Bos003 Fenway Park
4 Pit001 <NA>
5 Pit002 <NA>
6 Pit003 PNC Park
The Stadium column has a relationship with the GameID column. In this contrived example:
Tidied Data:
GameID Stadium
1 Bos001 Fenway Park
2 Bos002 Fenway Park
3 Bos003 Fenway Park
4 Pit001 PNC Park
5 Pit002 PNC Park
6 Pit003 PNC Park
How can I fill in these values?
Should I use a combination of dplyr:arrange()
and tidyr:fill()
?
Upvotes: 0
Views: 54
Reputation: 5415
Using @markus' suggestion from comments, will happily remove answer if they submit answer:
library(tidyverse)
GameData %>%
group_by(GamePrefix = substr(GameID, 1, 3)) %>%
mutate(Stadium = first(Stadium[!is.na(Stadium)])) %>%
ungroup() %>%
select(-GamePrefix)
# A tibble: 6 x 2
GameID Stadium
<fct> <fct>
1 Bos001 Fenway Park
2 Bos002 Fenway Park
3 Bos003 Fenway Park
4 Pit001 PNC Park
5 Pit002 PNC Park
6 Pit003 PNC Park
The ungroup
is needed in order to remove the temporary grouping column which consists of the first 3 characters of the GameID
values.
Upvotes: 2
Reputation: 4390
You are on the right track and this worked for me:
gd2 <- GameData %>%
mutate(nev =substr(GameID,1,3)) %>%
arrange(Stadium) %>%
group_by(nev) %>%
fill(Stadium)
Upvotes: 0
Reputation: 3183
You can use multiple if
or a lookup dataframe ; something like this:
> GameData %>%
+ mutate(Stadium = ifelse(grepl("^Bos", GameID), "Fenway Park",
+ ifelse(grepl("^Pit", GameID), "PNC Park", NA))
+ )
GameID Stadium
1 Bos001 Fenway Park
2 Bos002 Fenway Park
3 Bos003 Fenway Park
4 Pit001 PNC Park
5 Pit002 PNC Park
6 Pit003 PNC Park
Upvotes: 0