I have a dataframe like below.I want add rows for each name with year and missing week of the year with a total value of 0.
Name Year Week Total
John 2021 1 3
John 2021 2 2
John 2021 5 1
John 2021 10 2
Mary 2020 3 1
Mary 2021 5 2
Expected result
John 2021 1 3
John 2021 2 2
John 2021 3 0
John 2021 4 0
John 2021 5 1
John 2021 6 0
.
.
.
John 2021 53 0
This is what I am trying to do
data1<-data %>%
complete(Week = seq(min(Week), max(Week), by = 'week')) %>%
mutate_each(funs(ifelse(is.na(.),0,.)))
CodePudding user response:
You were on the right track:
data %>%
complete(Week = 1:53, Name, fill=list(Total=0))
Gives this:
Week Name Year Total
<int> <chr> <int> <dbl>
1 1 John 2021 3
2 2 John 2021 2
3 3 John NA 0
4 4 John NA 0
5 5 John 2021 1
6 6 John NA 0
7 7 John NA 0
8 8 John NA 0
9 9 John NA 0
10 10 John 2021 2
# ... with 96 more rows
You can use fill(Year)
to replace NAs of the column Year by the previous non-NA value. If you also want to do it for multiple years, then you can group_by
Year, and it will automatically fill the Year value.
CodePudding user response:
I think this is what you're asking for:
x1 %>%
group_by(Name) %>%
complete(Week = seq(min(Week), max(Week), by = 1), fill = list(Total = 0))