问题
I've got a data like below:
data <- structure(list(seq = c(1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L,
7L, 7L, 8L, 8L, 9L, 9L, 9L, 10L, 10L, 10L), new_seq = c(2, 2,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
2, 2, 2, 2, NA, NA, NA, NA, NA, 4, 4, 4, 4, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 6, 6, 6, 6, 6, NA, NA, 8, 8, 8, NA, NA, NA), value = c(2L,
0L, 0L, 1L, 0L, 5L, 5L, 3L, 0L, 3L, 2L, 3L, 2L, 3L, 4L, 1L, 0L,
0L, 0L, 1L, 1L, 0L, 2L, 5L, 3L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 3L,
5L, 3L, 1L, 1L, 1L, 0L, 1L, 0L, 4L, 3L, 0L, 3L, 1L, 3L, 0L, 0L,
1L, 0L, 0L, 3L, 4L, 5L, 3L, 5L, 3L, 5L, 0L, 1L, 1L, 3L, 2L, 1L,
0L, 0L, 0L, 0L, 5L, 1L, 1L, 0L, 4L, 1L, 5L, 0L, 3L, 1L, 2L, 1L,
0L, 3L, 0L, 1L, 1L, 3L, 0L, 1L, 1L, 2L, 2L, 1L, 0L, 4L, 0L, 0L,
3L, 0L, 0L)), row.names = c(NA, -100L), class = c("tbl_df", "tbl",
"data.frame"))
Column new_seq
refers to the value of seq
. For each value in new_seq
which is not NA
I would like to calculate a average of last 2
rows of value
from respective seq
. So, for example rows 1:2
of a new column should have a value of 0.5
(average of rows 49:50
), rows 51:54
also should have a value of 0.5
(average of rows 49:50
as well), but rows 60:63
should have value of 4
(average of rows 58:59
). How can I do that with tidyverse
?
回答1:
Something like this?
# calculate the mean value based on the last two rows of each seq
lookup <- data %>%
group_by(seq) %>%
mutate(rank = seq(n(), 1)) %>%
filter(rank <= 2) %>%
summarise(new_column = mean(value)) %>%
ungroup()
# match back to original dataset (only non-NA values of new_seq can be matched)
left_join(data, lookup, by = c("new_seq" = "seq"))
Results in:
# A tibble: 100 x 4
seq new_seq value new.column
<int> <dbl> <int> <dbl>
1 1 2 2 0.5
2 1 2 0 0.5
3 2 NA 0 NA
4 2 NA 1 NA
...
回答2:
Well, it's only half tidyverse
and I'm sure someone can do better, but here's an attempt.
group_by
and mutate
make it easy to calculate the average of the last 2 rows of the group, but I couldn't figure out how to get the connection between seq
and new_seq
so I did that in base R.
dat2 <- dat %>%
group_by(seq) %>%
mutate(end_val = (nth(value, -1L) + nth(value, -2L))/2)
dat3$result <- apply(dat2, 1, function(x) {
dat2[dat2$seq == x['new_seq'], 'end_val'][[1]][1]
})
Here's the result. I subsetted the relevant rows (since otherwise it'd be too long to see on the screen at once) but added the original row numbers as the rowid
column:
dat3 %>% tibble::rowid_to_column() %>% .[c(1:3,50:55,59:64),]
# A tibble: 15 x 6
# Groups: seq [6]
rowid seq new_seq value end_val result
<int> <int> <dbl> <int> <dbl> <dbl>
1 1 1 2 2 1 0.5
2 2 1 2 0 1 0.5
3 3 2 NA 0 0.5 NA
4 50 2 NA 1 0.5 NA
5 51 3 2 0 3.5 0.5
6 52 3 2 0 3.5 0.5
7 53 3 2 3 3.5 0.5
8 54 3 2 4 3.5 0.5
9 55 4 NA 5 4 NA
10 59 4 NA 5 4 NA
11 60 5 4 0 2 4
12 61 5 4 1 2 4
13 62 5 4 1 2 4
14 63 5 4 3 2 4
15 64 6 NA 2 2 NA
来源:https://stackoverflow.com/questions/52334683/how-to-return-an-average-of-last-n-rows-from-different-group-indicated-by-a-var