在 r 中按行按组计算加权滚动平均值？

Question

我有一个数据框 games_h。这只是表格的一个片段，但它有很多球队，并按日期、球队、比赛编号排序。我正在尝试创建按团队分组的加权滚动平均值。我希望最近一场比赛的权重是两场以上之前的。因此权重将为 (Game_1 * 1+ Game_2 *2)/3 或权重等于 1，且比率相同，因此权重 = c(1-.667, .667)。

dput(games_h)
structure(list(GameId = c(16, 16, 37, 37, 57, 57), GameDate = structure(c(17905, 
17905, 17916, 17916, 17926, 17926), class = "Date"), NeutralSite = c(0, 
0, 0, 0, 0, 0), AwayTeam = c("Virginia Cavaliers", "Virginia Cavaliers", 
"Florida State Seminoles", "Florida State Seminoles", "Syracuse Orange", 
"Syracuse Orange"), HomeTeam = c("Boston College Eagles", "Boston College Eagles", 
"Boston College Eagles", "Boston College Eagles", "Boston College Eagles", 
"Boston College Eagles"), Team = c("Virginia Cavaliers", "Boston College Eagles", 
"Florida State Seminoles", "Boston College Eagles", "Syracuse Orange", 
"Boston College Eagles"), Home = c(0, 1, 0, 1, 0, 1), Score = c(83, 
56, 82, 87, 77, 71), AST = c(17, 6, 12, 16, 11, 13), TOV = c(10, 
8, 9, 13, 11, 11), STL = c(5, 4, 4, 6, 6, 5), BLK = c(6, 0, 4, 
4, 1, 0), Rebounds = c(38, 18, 36, 33, 23, 23), ORB = c(7, 4, 
16, 10, 7, 6), DRB = c(31, 14, 20, 23, 16, 17), FGA = c(55, 57, 
67, 55, 52, 45), FGM = c(33, 22, 28, 27, 29, 21), X3FGM = c(8, 
7, 8, 13, 11, 9), X3FGA = c(19, 25, 25, 21, 26, 22), FTA = c(14, 
9, 24, 28, 15, 23), FTM = c(9, 5, 18, 20, 8, 20), Fouls = c(16, 
12, 25, 20, 19, 19), Game_Number = 1:6, Count = c(1, 1, 1, 1, 
1, 1)), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -6L), groups = structure(list(HomeTeam = "Boston College Eagles", 
    .rows = structure(list(1:6), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -1L), .drop = TRUE))

以下是分数列的输出示例。

这是我失败的尝试。该函数工作正常，但我无法按组将其应用到整个数据框。

weighted_avg<-function(x, wt1, wt2) {
  
rs1 = rollsum(x,1,align = "right")
rs2 = rollsum(x,2,align = "right")
rs1=rs1[-1]
rs3 = rs2 - rs1
weighted_avg= ((rs3 * wt2)+ (rs1*wt1))/(wt1+wt2)
return(weighted_avg)
}



weighted_avg(csum$Score_Y, 2, 1)
apply(csum$Score_Y , 2, weighted_avg, wt1 = 2, wt2=1)
test<-csum %>%
  group_by(Team)%>%
  group_map(across(c(Score:Fouls), weighted_avg(.x$Team, 2, 1)  ))

test<-csum %>%
  group_by(Team)%>%
  group_walk(across(c(Score:Fouls),weighted_avg(.~,2,1)  ))

Answer 1

以下是有关代码的一些注释：

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• 我用的是slider::slide_dbl函数。首先，我们指定要计算移动平均值的向量Score。
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• 由于我们需要一个长度为 2 的滑动窗口，我.before在中使用了参数slide_dbl来使用前一个值和当前值来计算移动平均值。
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• 另外，我将.complete参数设置TRUE为 确保仅在有先前值时才计算移动平均值。换句话说，我们在第一行没有任何移动平均线。
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有关更多信息，请查看滑块包的文档。

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library(tidyverse)\nlibrary(slider)\n\ndf %>%\n  group_by(HomeTeam) %>%\n  summarise(Example = c(NA, slide_dbl(Score, .before = 1, .complete = TRUE, \n                                      .f = ~ (.x[1] * 1 + .x[2] * 2) / 3)))\n\n`summarise()` has grouped output by \'HomeTeam\'. You can override using the `.groups` argument.\n# A tibble: 7 \xc3\x97 2\n# Groups:   HomeTeam [1]\n  HomeTeam              Example\n  <chr>                   <dbl>\n1 Boston College Eagles    NA  \n2 Boston College Eagles    NA  \n3 Boston College Eagles    65  \n4 Boston College Eagles    73.3\n5 Boston College Eagles    85.3\n6 Boston College Eagles    80.3\n7 Boston College Eagles    73 \n

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如果要计算所有数字列的移动平均值，您可以尝试：

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df %>%\n  group_by(HomeTeam) %>%\n  summarise(across(where(is.numeric), ~ c(NA, slide_dbl(., .before = 1, .complete = TRUE, \n                                                      .f = ~ (.x[1] * 1 + .x[2] * 2) / 3)))) %>%\n  ungroup()\n\n`summarise()` has grouped output by \'HomeTeam\'. You can override using the `.groups` argument.\n# A tibble: 7 \xc3\x97 21\n  HomeTeam  GameId NeutralSite   Home Score   AST   TOV   STL    BLK Rebounds   ORB   DRB   FGA   FGM\n  <chr>      <dbl>       <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl> <dbl> <dbl> <dbl> <dbl>\n1 Boston C\xe2\x80\xa6   NA            NA NA      NA   NA    NA    NA    NA         NA   NA     NA    NA    NA  \n2 Boston C\xe2\x80\xa6   NA            NA NA      NA   NA    NA    NA    NA         NA   NA     NA    NA    NA  \n3 Boston C\xe2\x80\xa6   16             0  0.667  65    9.67  8.67  4.33  2         24.7  5     19.7  56.3  25.7\n4 Boston C\xe2\x80\xa6   30             0  0.333  73.3 10     8.67  4     2.67      30   12     18    63.7  26  \n5 Boston C\xe2\x80\xa6   37             0  0.667  85.3 14.7  11.7   5.33  4         34   12     22    59    27.3\n6 Boston C\xe2\x80\xa6   50.3           0  0.333  80.3 12.7  11.7   6     2         26.3  8     18.3  53    28.3\n7 Boston C\xe2\x80\xa6   57             0  0.667  73   12.3  11     5.33  0.333     23    6.33  16.7  47.3  23.7\n# \xe2\x80\xa6 with 7 more variables: X3FGM <dbl>, X3FGA <dbl>, FTA <dbl>, FTM <dbl>, Fouls <dbl>,\n#   Game_Number <dbl>, Count <dbl>\n

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