如何在每组中找到连续n天的平均值r

Question

I have a dataframe that contains id(contains duplicate),date(contains duplicate),value. the values are recorded for different consecutive days. now what i want is to group the dataframe with id and date(as n consecutive days) and find mean of values. and return NA if the last group does not contain n days.

id  date          value
 1  2016-10-5       2
 1  2016-10-6       3
 1  2016-10-7       1
 1  2016-10-8       2
 1  2016-10-9       5
 2  2013-10-6       2
 .  .               .
 .  .               .
 .  .               .
 20 2012-2-6        10

desired output with n-consecutive days as 3

  id  date      value  group_n_consecutive_days     mean_n_consecutive_days
   1  2016-10-5  2         1                        2
   1  2016-10-6  3         1                        2
   1  2016-10-7  1         1                        2
   1  2016-10-8  2         2                        NA
   1  2016-10-9  5         2                        NA
   2  2013-10-6  2         1                        4
   .
   .
   .
   .
    20 2012-2-6  10         6                       25         

Answer 1

The data in the question is sorted and consecutive within id so we assume that that is the case. Also when the question refers to duplicate dates we assume that that means that different id values can have the same date but within id the dates are unique and consecutive. Now, using the data shown reproducibly in Note 2 at the end group by id and compute the group numbers using gl . Then grouping by id and group_no take the mean of each group of 3 or NA for smaller groups.

library(dplyr)

DF %>% 
  group_by(id) %>%
  mutate(group_no = c(gl(n(), 3, n()))) %>%
  group_by(group_no, add = TRUE) %>%
  mutate(mean = if (n() == 3) mean(value) else NA) %>%
  ungroup

giving:

# A tibble: 6 x 5
     id date       value group_no  mean
  <int> <date>     <int>    <int> <dbl>
1     1 2016-10-05     2        1     2
2     1 2016-10-06     3        1     2
3     1 2016-10-07     1        1     2
4     1 2016-10-08     2        2    NA
5     1 2016-10-09     5        2    NA
6     2 2013-10-06     2        1    NA

Note 1

An alternative to gl(...) could be cumsum(rep(1:3, length = n()) == 1) and an alternative to if (n() = 3) mean(value) else NA could be mean(head(c(value, NA, NA), 3)) .

Note 2

The input data in reproducible form was assumed to be:

Lines <- "id  date          value
 1  2016-10-5       2
 1  2016-10-6       3
 1  2016-10-7       1
 1  2016-10-8       2
 1  2016-10-9       5
 2  2013-10-6       2"
DF <- read.table(text = Lines, header = TRUE)
DF$date <- as.Date(DF$date)