Question

我的数据集具有以下功能：玩家ID，周和点。

我想计算前几周的积分平均值，但不是过去几周的平均值，只计算最后5周或更少（如果当前周数小于5）。

示例：对于player_id = 5，周= 7，结果将是player_id = 5和第2,3,4,5和6周的POINTS的平均值。

以下代码已经完成了前一周的平均值，因此我需要进行调整以使其仅在前一周进行。

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

Answer 1

HAVB的方法很棒，但根据你想要的，这是另一个。此方法从this answer改为不同的问题，但根据您的具体情况进行了更改：

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)

然后我们可以查看一个子集来证明它有效：

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

所以我们每次都可以看到 t ，{em> 的rolling_mean将是玩家points观察值的平均值>我有时{ t - 1，...，min（1， t - 5）}。

Answer 2

您可以使用slice为每个组选择最近5周。试试这个：

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

该行

slice( (n()-4):n() )

为每个组

选择[（最后一行 - 4）：最后一行]范围内的行

编辑：为避免当前周数小于5时出现问题，请使用ifelse语句进行验证：

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

计算最近观察的累积平均值

2 个答案: