我的数据集有作为特征：玩家的ID，周和点。
我想计算点的平均值为之前的几个星期，但不是所有过去的几周，只是到了最后5个或更少（如果当前周小于5）。
例如：对于player_id = 5，周= 7，其结果将是点为player_id = 5和周2，3，4，5和6的平均值。
下面的代码已经做了平均历届星期，所以我需要一个适应，使之成为仅有5前一周持平。
player_id<-c(rep(1,30),rep(2,30),rep(3,30),rep(4,30),rep(5,30))
week<-1:30
points<-round(runif(150,1,10),0)
mydata<- data.frame(player_id=player_id,week=rep(week,5),points)
mydata<-mydata %>%
group_by(player_id) %>% # the group to perform the stat on
arrange(week) %>% # order the weeks within each group
mutate(previous_mean = cummean(points) ) %>% # for each week get the
cumulative mean
mutate(previous_mean = lag(previous_mean) ) %>% # shift cumulative
mean back one week
arrange(player_id) # sort by player_id
HAVB的做法是伟大的，但取决于你想要什么，这里是另一个。 这种方法是从适合这个答案到一个不同的问题，但改变了您的情况：
library(dplyr)
library(zoo)
# set the seed for reproducibility
set.seed(123)
player_id<-c(rep(1,30),rep(2,30),rep(3,30),rep(4,30),rep(5,30))
week<-1:30
points<-round(runif(150,1,10),0)
mydata<- data.frame(player_id=player_id,week=rep(week,5),points)
roll_mean <- function(x, k) {
result <- rollapplyr(x, k, mean, partial=TRUE, na.rm=TRUE)
result[is.nan(result)] <- NA
return( result )
}
mydata<- data.frame(player_id=player_id,week=rep(week,5),points)
mydata<-mydata %>%
group_by(player_id) %>%
arrange(week) %>%
mutate(rolling_mean = roll_mean(x=lag(points), k=5) ) %>%
arrange(player_id)
然后，我们可以看一个子集，以显示它的工作：
mydata[mydata$player_id %in% 1:2 & mydata$week %in% 1:6, ]
# A tibble: 12 x 4
# Groups: player_id [2]
player_id week points rolling_mean
<dbl> <int> <dbl> <dbl>
1 1 1 4 NA
2 1 2 8 4.000000
3 1 3 5 6.000000
4 1 4 9 5.666667
5 1 5 9 6.500000
6 1 6 1 7.000000
7 2 1 10 NA
8 2 2 9 10.000000
9 2 3 7 9.500000
10 2 4 8 8.666667
11 2 5 1 8.500000
12 2 6 5 7.000000
因此，我们可以在每个时间t看到， rolling_mean供玩家我将是平均points的球员的意见我有时【T - 1，...，分钟（1，T - 5）}。
您可以使用slice只选择最后5周各组。 尝试这个：
player_id<-c(rep(1,30),rep(2,30),rep(3,30),rep(4,30),rep(5,30))
week<-1:30
points<-round(runif(150,1,10),0)
mydata<- data.frame(player_id=player_id,week=rep(week,5),points)
library(dplyr)
mydata <- mydata %>%
group_by(player_id) %>% # the group to perform the stat on
arrange(week) %>% # order the weeks within each group
slice( (n()-4):n() ) %>% # "slice" the last 5 rows (weeks) of every group
mutate(previous_mean = cummean(points) ) %>% # for each week get the cumulative mean
mutate(previous_mean = lag(previous_mean) ) %>% # shift cumulative mean back one week
arrange(player_id) # sort by player_id
该生产线
slice( (n()-4):n() )
选择[（最后一行 - 4）：最后一行]的范围内的行，对每个组
编辑：为了避免麻烦，当本周小于5，使用ifelse语句来验证：
mydata %>%
group_by(player_id) %>% # the group to perform the stat on
arrange(week) %>% # order the weeks within each group
slice(ifelse(n() < 5, 1:n(), n()-4):n()) %>% # "slice" the last 5 rows (weeks) of every group
mutate(previous_mean = cummean(points) ) %>% # for each week get the cumulative mean
mutate(previous_mean = lag(previous_mean) ) %>% # shift cumulative mean back one week
arrange(player_id) # sort by player_id
