带条件的累积平均值

我的 df 的小代表：

PTS_TeamHome <- c(101,87,94,110,95)
PTS_TeamAway <- c(95,89,105,111,121)
TeamHome <- c("LAL", "HOU", "SAS", "MIA", "LAL")
TeamAway <- c("IND", "LAL", "LAL", "HOU", "NOP")
df <- data.frame(cbind(TeamHome, TeamAway,PTS_TeamHome,PTS_TeamAway))
df

TeamHome TeamAway PTS_TeamHome PTS_TeamAway
  LAL      IND          101           95
  HOU      LAL           87           89
  SAS      LAL           94          105
  MIA      HOU          110          111
  LAL      NOP           95          121

想象一下，这是一个赛季 1230 场比赛的前四场比赛。我想计算主队和客队在任何给定时间每场比赛的累积积分（平均值）。

输出如下所示：

  TeamHome TeamAway PTS_TeamHome PTS_TeamAway HOMETEAM_AVGCUMPTS ROADTEAM_AVGCUMPTS
1  LAL      IND          101           95                101                 95
2  HOU      LAL           87           89                 87                 95
3  SAS      LAL           94          105                 94              98.33
4  MIA      HOU          110          111                110                 99
5  LAL      NOP           95          121               97.5                121

请注意该公式对主队第五场比赛的作用。由于 LAL 是主队，因此会查看 LAL 在主场或客场比赛时得分多少。在这种情况下 (101 + 89 + 105 + 95) / 4 = 97.5

这是我尝试过但没有取得多大成功的方法：

lst <- list()
for(i in 1:nrow(df)) lst[[i]] <- ( cumsum(df[which(df$TEAM1[1:i]==df$TEAM1[i]),df$PTS_TeamAway,0]) 
                                 + cumsum(df[which(df$TEAM2[1:i]==df$TEAM1[i]),df$PTS_TeamHome,0]) ) 
                             / #divided by number of games
  df$HOMETEAM_AVGCUMPTS <- unlist(lst)

我想计算累积 PTS，然后用游戏数量来除以它，但这些都不起作用。

我认为你应该重组你的数据更整齐的格式 https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html每场比赛有两排：客队一排，主队一排。处理整齐/长格式的数据要容易得多。

library(dplyr)
library(tidyr)

df %>%
  mutate(game = row_number()) %>%
  gather(location, team, TeamHome, TeamAway) %>%
  gather(location2, points, PTS_TeamHome, PTS_TeamAway) %>%
  filter(
    (location == "TeamHome" & location2 == "PTS_TeamHome") | 
      (location == "TeamAway" & location2 == "PTS_TeamAway")
  ) %>%
  select(-location2) %>%
  arrange(game) %>%
  group_by(team) %>%
  mutate(run_mean_points = cummean(points))

data

# note that cbind() is removed.

df <- data.frame(TeamHome, TeamAway,PTS_TeamHome,PTS_TeamAway, stringsAsFactors = FALSE)

Source: local data frame [10 x 5]
Groups: team

   game location team points run_mean_points
1     1 TeamHome  LAL    101       101.00000
2     1 TeamAway  IND     95        95.00000
3     2 TeamHome  HOU     87        87.00000
4     2 TeamAway  LAL     89        95.00000
5     3 TeamHome  SAS     94        94.00000
6     3 TeamAway  LAL    105        98.33333
7     4 TeamHome  MIA    110       110.00000
8     4 TeamAway  HOU    111        99.00000
9     5 TeamHome  LAL     95        97.50000
10    5 TeamAway  NOP    121       121.00000