R，计算最后 3 个平均值
R, calculating last 3 average
我在R中有以下数据框。它包含奥运会篮球比赛中每个球员的统计数据
示例 Table
每个游戏在游戏栏中用一个数字表示。我想用最近 3 场比赛的平均值创建一个新列。在关注类似帖子中的示例时，我最大的失败是使用游戏编号而不是其他方法似乎需要的实际日期。
如有任何帮助，我们将不胜感激。
谢谢
编辑：
根据一些解决方案和建议进一步澄清。对于每一行，我希望新列显示最近 3 场比赛的平均分钟数或得分。到目前为止，建议让每一行显示第 3、4 和 5 场比赛的平均值。
举个例子。
玩家 A，游戏 = 3
平均得分 = 平均值（比赛 1 分，比赛 2 分，比赛 3 分）
玩家 B，游戏 = 4
平均分 = 均值（分游戏 2，分游戏 3，分游戏 4）
我希望一切顺利。
谢谢
数据：
我对此很陌生。我希望这是共享数据的合适方法。
structure(list(Player = structure(c(1L, 2L, 6L, 8L, 17L, 21L,
23L, 24L, 24L, 24L, 24L, 25L, 26L, 15L, 20L, 20L, 12L, 15L, 11L,
5L, 15L, 16L, 14L, 9L, 20L, 11L, 18L, 4L, 12L, 9L, 4L, 9L, 20L,
12L, 5L, 13L, 22L, 7L, 11L, 20L, 4L, 5L, 10L, 11L, 14L, 19L,
3L, 7L, 14L, 5L), .Label = c("Adas Juskevicius", "Alex Abrines",
"Andrew Bogut", "Bojan Bogdanovic", "Boris Diaw", "Brock Motum",
"Dario Saric", "Dwight Lewis", "Facundo Campazzo", "Ike Diogu",
"Jianlian Yi", "Jonas Maciulis", "Kevin Durant", "Luis Scola",
"Mantas Kalnietis", "Matt Dellavedova", "Miguel Marriaga", "Milos Teodosic",
"Nikola Mirotic", "Pau Gasol", "Rafa Luz", "Ricky Rubio", "Roberto Acuna",
"Vaidas Kariniauskas", "Windi Graterol", "Zeljko Sakic"), class = "factor"),
Team = structure(c(8L, 6L, 2L, 12L, 12L, 3L, 1L, 8L, 8L,
8L, 8L, 12L, 5L, 8L, 6L, 6L, 8L, 8L, 4L, 7L, 8L, 2L, 1L,
1L, 6L, 4L, 10L, 5L, 8L, 1L, 5L, 1L, 6L, 8L, 7L, 11L, 6L,
5L, 4L, 6L, 5L, 7L, 9L, 4L, 1L, 6L, 2L, 5L, 1L, 7L), .Label = c("ARG",
"AUS", "BRZ", "CHN", "CRO", "ESP", "FRA", "LTU", "NGR", "SRB",
"USA", "VEN"), class = "factor"), Pos = structure(c(3L, 4L,
2L, 5L, 2L, 5L, 1L, 2L, 2L, 2L, 2L, 1L, 4L, 3L, 1L, 1L, 4L,
5L, 2L, 2L, 5L, 3L, 2L, 3L, 1L, 4L, 5L, 2L, 2L, 3L, 2L, 3L,
1L, 2L, 2L, 4L, 3L, 4L, 4L, 1L, 2L, 2L, 2L, 4L, 1L, 2L, 1L,
4L, 1L, 2L), .Label = c("C", "PF", "PG", "SF", "SG"), class = "factor"),
game = c(4L, 5L, 4L, 5L, 3L, 4L, 3L, 1L, 2L, 3L, 4L, 5L,
5L, 3L, 2L, 3L, 3L, 4L, 3L, 3L, 2L, 4L, 3L, 3L, 5L, 5L, 5L,
4L, 2L, 2L, 2L, 5L, 4L, 4L, 2L, 2L, 1L, 4L, 4L, 1L, 5L, 4L,
3L, 2L, 4L, 2L, 2L, 3L, 2L, 1L), Status = c(0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), Drafted = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 85,
82, 80, 78, 77, 74, 68, 68, 68, 65, 64, 63, 62, 62, 61, 61,
60, 59, 59, 59, 58, 57, 57, 57, 56, 56, 56, 55, 55, 55, 55,
54, 54, 53, 53, 52, 51), Min = c(11.04, 1.44, 16.56, 2.88,
4.8, 1.92, 13.68, 3.84, 9.36, 2.64, 21.12, 17.04, 0.24, 36.48,
32.16, 23.28, 26.88, 17.28, 33.6, 28.56, 30.48, 19.92, 30.24,
25.92, 27.84, 34.8, 15.12, 36, 28.8, 29.04, 29.28, 21.36,
23.04, 18.72, 21.12, 25.2, 12.24, 27.12, 32.88, 31.92, 34.08,
18.24, 27.6, 32.64, 33.6, 32.88, 24.72, 34.8, 35.76, 31.44
), FIC = c(3.8, 1.5, 10.2, 1, 0, -1, 0.2, 0.5, -3.2, -1,
0.6, 4.5, -0.5, 15.6, 9.5, 11.1, 0.5, 7.8, 17, 16.8, 25.2,
10.5, 10, 6, 14.4, 6, 7.5, 15.5, 14.8, 6.2, 7.9, 3, 26.9,
0.8, 11.4, 16, -1, 4.9, 14.1, 18.5, 5.9, 6.5, 10, 10, 10,
8, 19, 9, 12.1, 7.5), FP = c(8, 4, 21.75, 2, 2.75, -0.5,
4.75, 1.5, 2.5, 1.25, 8.5, 13, 0, 35.25, 37, 32.25, 17, 18.5,
39.5, 34.25, 49, 19.25, 28.75, 20.25, 41.25, 27.5, 16.5,
39.25, 33.5, 29, 30.75, 13.25, 47.25, 9, 24.5, 28.5, 6.25,
19.5, 38.25, 40.25, 27.5, 17, 21.75, 37.5, 29, 21, 38.5,
30.75, 37.75, 25.75), FPM = c(0.72463768115942, 2.77777777777778,
1.31340579710145, 0.694444444444444, 0.572916666666667, -0.260416666666667,
0.347222222222222, 0.390625, 0.267094017094017, 0.473484848484848,
0.402462121212121, 0.762910798122066, 0, 0.966282894736842,
1.15049751243781, 1.38530927835052, 0.632440476190476, 1.07060185185185,
1.17559523809524, 1.19922969187675, 1.60761154855643, 0.96636546184739,
0.950727513227513, 0.78125, 1.48168103448276, 0.790229885057471,
1.09126984126984, 1.09027777777778, 1.16319444444444, 0.99862258953168,
1.05020491803279, 0.620318352059925, 2.05078125, 0.480769230769231,
1.16003787878788, 1.13095238095238, 0.51062091503268, 0.719026548672566,
1.16332116788321, 1.2609649122807, 0.806924882629108, 0.932017543859649,
0.78804347826087, 1.14889705882353, 0.863095238095238, 0.638686131386861,
1.55744336569579, 0.883620689655172, 1.05564876957494, 0.819020356234097
), PTS = c(5L, 2L, 15L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 6L, 9L,
0L, 17L, 13L, 16L, 10L, 16L, 18L, 11L, 21L, 6L, 12L, 10L,
19L, 20L, 7L, 28L, 21L, 10L, 18L, 10L, 23L, 4L, 7L, 16L,
0L, 7L, 20L, 26L, 22L, 10L, 7L, 19L, 14L, 6L, 9L, 15L, 23L,
9L), TPM = c(1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 2L, 0L, 0L, 2L, 2L, 0L, 0L, 1L, 0L, 1L, 2L, 1L, 0L,
1L, 3L, 3L, 1L, 1L, 1L, 5L, 0L, 1L, 2L, 0L, 1L, 2L, 3L, 4L,
0L, 0L, 3L, 2L, 0L, 1L, 3L, 3L, 1L), Ast = c(2L, 0L, 2L,
0L, 1L, 0L, 0L, 1L, 2L, 0L, 1L, 1L, 0L, 7L, 1L, 3L, 1L, 2L,
1L, 9L, 12L, 8L, 4L, 1L, 1L, 2L, 5L, 2L, 2L, 8L, 2L, 1L,
5L, 2L, 5L, 5L, 1L, 0L, 2L, 1L, 1L, 0L, 3L, 0L, 1L, 2L, 6L,
3L, 0L, 2L), Reb = c(0L, 0L, 3L, 0L, 1L, 0L, 1L, 0L, 0L,
1L, 2L, 2L, 0L, 5L, 10L, 7L, 6L, 0L, 10L, 9L, 4L, 1L, 7L,
3L, 13L, 2L, 0L, 3L, 4L, 4L, 7L, 1L, 5L, 0L, 4L, 2L, 1L,
6L, 9L, 9L, 2L, 4L, 7L, 6L, 10L, 8L, 12L, 7L, 9L, 5L), BLK = c(0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 2L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 2L, 0L, 0L, 1L, 0L, 1L,
0L, 2L, 0L, 3L, 1L, 0L, 1L, 2L, 0L, 0L, 0L, 1L, 1L, 0L, 1L,
3L, 1L, 1L, 2L), STL = c(0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 3L, 0L, 1L, 0L, 1L,
2L, 0L, 0L, 1L, 2L, 1L, 2L, 0L, 0L, 2L, 1L, 0L, 0L, 2L, 2L,
0L, 0L, 1L, 1L, 0L, 4L, 0L, 0L, 0L, 1L, 1L, 2L), TO = c(1L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 3L, 1L, 0L, 3L, 3L, 2L,
6L, 3L, 0L, 3L, 2L, 0L, 1L, 0L, 3L, 4L, 1L, 2L, 2L, 5L, 3L,
0L, 0L, 0L, 3L, 1L, 1L, 3L, 2L, 0L, 5L, 0L, 1L, 1L, 3L, 0L,
2L, 6L, 4L, 2L)), .Names = c("Player", "Team", "Pos", "game",
"Status", "Drafted", "Min", "FIC", "FP", "FPM", "PTS", "TPM",
"Ast", "Reb", "BLK", "STL", "TO"), row.names = c(NA, 50L), class = "data.frame")
将 dplyr 与 mtcars 示例数据结合使用：
library(dplyr)
mtcars %>%
group_by(cyl) %>%
mutate(last3mean = mean(tail(mpg, 3)))
在您的情况下，使用 Player 和列而不是 cyl 和 mpg聚合。
使用data.table，（由@akrun 建议）：
data.table as.data.table(mtcars)[, .(last3mean = mean(tail(mpg,3))), by = cyl]
首先将数据框按玩家拆分
playerDFs <- split(origdata, origdata["Player"])
然后对最后 3 场比赛进行子集化
playerLast3 <- lapply(playerDFs, function(x) x[tail(order(x[["game"]]),3), ])
终于拿到手了
vapply(playerLast3, colMeans, numeric(ncol(origdata)))
您可以将 zoo 包中的 rollmeanr 与 dplyr 一起使用。它的特点是不仅计算一名球员最近三场比赛的平均值，而且计算每个球员最近三场比赛的移动平均值。代码如下：
library(dplyr)
library(zoo)
avg.last.3 <- function (x) if (length(x) < 3) rep(NA, length(x)) else rollmeanr(x, 3, fill = NA) ## 1.
res <- df %>% group_by(Player) %>% arrange(game) %>% ## 2.
mutate(Avg.Pts=avg.last.3(PTS)) %>% ## 3.
ungroup() %>% arrange(Player,game) ## 4.
备注：
- 定义一个函数
avg.last.3，它应用函数 rollmeanr，window 长度为 3。 rollmeanr 指定 align="right" 对 最后 三场比赛进行平均，我们用 NA 填充任何没有三天平均的结果。请注意，需要此函数中的 if 条件，以便：
-
x 的长度至少是 rollmeanr 的 window 长度，如 rollmeanr 所要求
avg.last.3 returns 一个与其输入长度相同的向量，符合 mutate 的要求。
- 先
group_by再Player。因为我注意到 game 列不一定对每个 Player 进行排序，所以我们按 game 升序排序。
- 使用
mutate 创建一个新列 Avg.Pts，这是通过对列应用 avg.last.3 函数而产生的，例如 PTS.
- 最后，
ungroup 并显示按 Player 排序的结果，然后是 game
当然，您可以通过以下方式获得任意数量列的平均值：
mutate(Avg.Pts=avg.last.3(PTS), Avg.Min=avg.last.3(Min), Avg.Ast=avg.last.3(Ast), ...)
仅对 PTS 列进行平均的结果由（仅打印前六列加上 PTS 和 Avg.Pts）给出：
print(res[,c(colnames(res)[1:6],"PTS","Avg.Pts")],n=50)
### A tibble: 50 x 8
## Player Team Pos game Status Drafted PTS Avg.Pts
## <fctr> <fctr> <fctr> <int> <int> <dbl> <int> <dbl>
##1 Adas Juskevicius LTU PG 4 0 0 5 NA
##2 Alex Abrines ESP SF 5 0 0 2 NA
##3 Andrew Bogut AUS C 2 1 53 9 NA
##4 Bojan Bogdanovic CRO PF 2 1 59 18 NA
##5 Bojan Bogdanovic CRO PF 4 1 61 28 NA
##6 Bojan Bogdanovic CRO PF 5 1 55 22 22.666667
##7 Boris Diaw FRA PF 1 1 51 9 NA
##8 Boris Diaw FRA PF 2 1 57 7 NA
##9 Boris Diaw FRA PF 3 1 68 11 9.000000
##10 Boris Diaw FRA PF 4 1 55 10 9.333333
##11 Brock Motum AUS PF 4 0 0 15 NA
##12 Dario Saric CRO SF 3 1 53 15 NA
##13 Dario Saric CRO SF 4 1 56 7 NA
##14 Dwight Lewis VEN SG 5 0 0 0 NA
##15 Facundo Campazzo ARG PG 2 1 60 10 NA
##16 Facundo Campazzo ARG PG 3 1 64 10 NA
##17 Facundo Campazzo ARG PG 5 0 59 10 10.000000
##18 Ike Diogu NGR PF 3 1 55 7 NA
##19 Jianlian Yi CHN SF 2 1 55 19 NA
##20 Jianlian Yi CHN PF 3 1 74 18 NA
##21 Jianlian Yi CHN SF 4 1 56 20 19.000000
##22 Jianlian Yi CHN SF 5 1 62 20 19.333333
##23 Jonas Maciulis LTU PF 2 1 61 21 NA
##24 Jonas Maciulis LTU SF 3 1 78 10 NA
##25 Jonas Maciulis LTU PF 4 1 58 4 11.666667
##26 Kevin Durant USA SF 2 1 57 16 NA
##27 Luis Scola ARG C 2 1 52 23 NA
##28 Luis Scola ARG PF 3 1 65 12 NA
##29 Luis Scola ARG C 4 1 54 14 16.333333
##30 Mantas Kalnietis LTU SG 2 1 68 21 NA
##31 Mantas Kalnietis LTU PG 3 1 85 17 NA
##32 Mantas Kalnietis LTU SG 4 1 77 16 18.000000
##33 Matt Dellavedova AUS PG 4 1 68 6 NA
##34 Miguel Marriaga VEN PF 3 0 0 0 NA
##35 Milos Teodosic SRB SG 5 0 62 7 NA
##36 Nikola Mirotic ESP PF 2 1 54 6 NA
##37 Pau Gasol ESP C 1 1 56 26 NA
##38 Pau Gasol ESP C 2 1 82 13 NA
##39 Pau Gasol ESP C 3 1 80 16 18.333333
##40 Pau Gasol ESP C 4 1 59 23 17.333333
##41 Pau Gasol ESP C 5 1 63 19 19.333333
##42 Rafa Luz BRZ SG 4 0 0 0 NA
##43 Ricky Rubio ESP PG 1 1 57 0 NA
##44 Roberto Acuna ARG C 3 1 0 2 NA
##45 Vaidas Kariniauskas LTU PF 1 0 0 0 NA
##46 Vaidas Kariniauskas LTU PF 2 0 0 0 NA
##47 Vaidas Kariniauskas LTU PF 3 0 0 0 0.000000
##48 Vaidas Kariniauskas LTU PF 4 0 0 6 2.000000
##49 Windi Graterol VEN C 5 0 0 9 NA
##50 Zeljko Sakic CRO SF 5 0 0 0 NA
我在R中有以下数据框。它包含奥运会篮球比赛中每个球员的统计数据 示例 Table
每个游戏在游戏栏中用一个数字表示。我想用最近 3 场比赛的平均值创建一个新列。在关注类似帖子中的示例时，我最大的失败是使用游戏编号而不是其他方法似乎需要的实际日期。
如有任何帮助，我们将不胜感激。 谢谢
编辑： 根据一些解决方案和建议进一步澄清。对于每一行，我希望新列显示最近 3 场比赛的平均分钟数或得分。到目前为止，建议让每一行显示第 3、4 和 5 场比赛的平均值。
举个例子。 玩家 A，游戏 = 3 平均得分 = 平均值（比赛 1 分，比赛 2 分，比赛 3 分）
玩家 B，游戏 = 4 平均分 = 均值（分游戏 2，分游戏 3，分游戏 4）
我希望一切顺利。 谢谢
数据：
我对此很陌生。我希望这是共享数据的合适方法。
structure(list(Player = structure(c(1L, 2L, 6L, 8L, 17L, 21L,
23L, 24L, 24L, 24L, 24L, 25L, 26L, 15L, 20L, 20L, 12L, 15L, 11L,
5L, 15L, 16L, 14L, 9L, 20L, 11L, 18L, 4L, 12L, 9L, 4L, 9L, 20L,
12L, 5L, 13L, 22L, 7L, 11L, 20L, 4L, 5L, 10L, 11L, 14L, 19L,
3L, 7L, 14L, 5L), .Label = c("Adas Juskevicius", "Alex Abrines",
"Andrew Bogut", "Bojan Bogdanovic", "Boris Diaw", "Brock Motum",
"Dario Saric", "Dwight Lewis", "Facundo Campazzo", "Ike Diogu",
"Jianlian Yi", "Jonas Maciulis", "Kevin Durant", "Luis Scola",
"Mantas Kalnietis", "Matt Dellavedova", "Miguel Marriaga", "Milos Teodosic",
"Nikola Mirotic", "Pau Gasol", "Rafa Luz", "Ricky Rubio", "Roberto Acuna",
"Vaidas Kariniauskas", "Windi Graterol", "Zeljko Sakic"), class = "factor"),
Team = structure(c(8L, 6L, 2L, 12L, 12L, 3L, 1L, 8L, 8L,
8L, 8L, 12L, 5L, 8L, 6L, 6L, 8L, 8L, 4L, 7L, 8L, 2L, 1L,
1L, 6L, 4L, 10L, 5L, 8L, 1L, 5L, 1L, 6L, 8L, 7L, 11L, 6L,
5L, 4L, 6L, 5L, 7L, 9L, 4L, 1L, 6L, 2L, 5L, 1L, 7L), .Label = c("ARG",
"AUS", "BRZ", "CHN", "CRO", "ESP", "FRA", "LTU", "NGR", "SRB",
"USA", "VEN"), class = "factor"), Pos = structure(c(3L, 4L,
2L, 5L, 2L, 5L, 1L, 2L, 2L, 2L, 2L, 1L, 4L, 3L, 1L, 1L, 4L,
5L, 2L, 2L, 5L, 3L, 2L, 3L, 1L, 4L, 5L, 2L, 2L, 3L, 2L, 3L,
1L, 2L, 2L, 4L, 3L, 4L, 4L, 1L, 2L, 2L, 2L, 4L, 1L, 2L, 1L,
4L, 1L, 2L), .Label = c("C", "PF", "PG", "SF", "SG"), class = "factor"),
game = c(4L, 5L, 4L, 5L, 3L, 4L, 3L, 1L, 2L, 3L, 4L, 5L,
5L, 3L, 2L, 3L, 3L, 4L, 3L, 3L, 2L, 4L, 3L, 3L, 5L, 5L, 5L,
4L, 2L, 2L, 2L, 5L, 4L, 4L, 2L, 2L, 1L, 4L, 4L, 1L, 5L, 4L,
3L, 2L, 4L, 2L, 2L, 3L, 2L, 1L), Status = c(0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), Drafted = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 85,
82, 80, 78, 77, 74, 68, 68, 68, 65, 64, 63, 62, 62, 61, 61,
60, 59, 59, 59, 58, 57, 57, 57, 56, 56, 56, 55, 55, 55, 55,
54, 54, 53, 53, 52, 51), Min = c(11.04, 1.44, 16.56, 2.88,
4.8, 1.92, 13.68, 3.84, 9.36, 2.64, 21.12, 17.04, 0.24, 36.48,
32.16, 23.28, 26.88, 17.28, 33.6, 28.56, 30.48, 19.92, 30.24,
25.92, 27.84, 34.8, 15.12, 36, 28.8, 29.04, 29.28, 21.36,
23.04, 18.72, 21.12, 25.2, 12.24, 27.12, 32.88, 31.92, 34.08,
18.24, 27.6, 32.64, 33.6, 32.88, 24.72, 34.8, 35.76, 31.44
), FIC = c(3.8, 1.5, 10.2, 1, 0, -1, 0.2, 0.5, -3.2, -1,
0.6, 4.5, -0.5, 15.6, 9.5, 11.1, 0.5, 7.8, 17, 16.8, 25.2,
10.5, 10, 6, 14.4, 6, 7.5, 15.5, 14.8, 6.2, 7.9, 3, 26.9,
0.8, 11.4, 16, -1, 4.9, 14.1, 18.5, 5.9, 6.5, 10, 10, 10,
8, 19, 9, 12.1, 7.5), FP = c(8, 4, 21.75, 2, 2.75, -0.5,
4.75, 1.5, 2.5, 1.25, 8.5, 13, 0, 35.25, 37, 32.25, 17, 18.5,
39.5, 34.25, 49, 19.25, 28.75, 20.25, 41.25, 27.5, 16.5,
39.25, 33.5, 29, 30.75, 13.25, 47.25, 9, 24.5, 28.5, 6.25,
19.5, 38.25, 40.25, 27.5, 17, 21.75, 37.5, 29, 21, 38.5,
30.75, 37.75, 25.75), FPM = c(0.72463768115942, 2.77777777777778,
1.31340579710145, 0.694444444444444, 0.572916666666667, -0.260416666666667,
0.347222222222222, 0.390625, 0.267094017094017, 0.473484848484848,
0.402462121212121, 0.762910798122066, 0, 0.966282894736842,
1.15049751243781, 1.38530927835052, 0.632440476190476, 1.07060185185185,
1.17559523809524, 1.19922969187675, 1.60761154855643, 0.96636546184739,
0.950727513227513, 0.78125, 1.48168103448276, 0.790229885057471,
1.09126984126984, 1.09027777777778, 1.16319444444444, 0.99862258953168,
1.05020491803279, 0.620318352059925, 2.05078125, 0.480769230769231,
1.16003787878788, 1.13095238095238, 0.51062091503268, 0.719026548672566,
1.16332116788321, 1.2609649122807, 0.806924882629108, 0.932017543859649,
0.78804347826087, 1.14889705882353, 0.863095238095238, 0.638686131386861,
1.55744336569579, 0.883620689655172, 1.05564876957494, 0.819020356234097
), PTS = c(5L, 2L, 15L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 6L, 9L,
0L, 17L, 13L, 16L, 10L, 16L, 18L, 11L, 21L, 6L, 12L, 10L,
19L, 20L, 7L, 28L, 21L, 10L, 18L, 10L, 23L, 4L, 7L, 16L,
0L, 7L, 20L, 26L, 22L, 10L, 7L, 19L, 14L, 6L, 9L, 15L, 23L,
9L), TPM = c(1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 2L, 0L, 0L, 2L, 2L, 0L, 0L, 1L, 0L, 1L, 2L, 1L, 0L,
1L, 3L, 3L, 1L, 1L, 1L, 5L, 0L, 1L, 2L, 0L, 1L, 2L, 3L, 4L,
0L, 0L, 3L, 2L, 0L, 1L, 3L, 3L, 1L), Ast = c(2L, 0L, 2L,
0L, 1L, 0L, 0L, 1L, 2L, 0L, 1L, 1L, 0L, 7L, 1L, 3L, 1L, 2L,
1L, 9L, 12L, 8L, 4L, 1L, 1L, 2L, 5L, 2L, 2L, 8L, 2L, 1L,
5L, 2L, 5L, 5L, 1L, 0L, 2L, 1L, 1L, 0L, 3L, 0L, 1L, 2L, 6L,
3L, 0L, 2L), Reb = c(0L, 0L, 3L, 0L, 1L, 0L, 1L, 0L, 0L,
1L, 2L, 2L, 0L, 5L, 10L, 7L, 6L, 0L, 10L, 9L, 4L, 1L, 7L,
3L, 13L, 2L, 0L, 3L, 4L, 4L, 7L, 1L, 5L, 0L, 4L, 2L, 1L,
6L, 9L, 9L, 2L, 4L, 7L, 6L, 10L, 8L, 12L, 7L, 9L, 5L), BLK = c(0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 2L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 2L, 0L, 0L, 1L, 0L, 1L,
0L, 2L, 0L, 3L, 1L, 0L, 1L, 2L, 0L, 0L, 0L, 1L, 1L, 0L, 1L,
3L, 1L, 1L, 2L), STL = c(0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 3L, 0L, 1L, 0L, 1L,
2L, 0L, 0L, 1L, 2L, 1L, 2L, 0L, 0L, 2L, 1L, 0L, 0L, 2L, 2L,
0L, 0L, 1L, 1L, 0L, 4L, 0L, 0L, 0L, 1L, 1L, 2L), TO = c(1L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 3L, 1L, 0L, 3L, 3L, 2L,
6L, 3L, 0L, 3L, 2L, 0L, 1L, 0L, 3L, 4L, 1L, 2L, 2L, 5L, 3L,
0L, 0L, 0L, 3L, 1L, 1L, 3L, 2L, 0L, 5L, 0L, 1L, 1L, 3L, 0L,
2L, 6L, 4L, 2L)), .Names = c("Player", "Team", "Pos", "game",
"Status", "Drafted", "Min", "FIC", "FP", "FPM", "PTS", "TPM",
"Ast", "Reb", "BLK", "STL", "TO"), row.names = c(NA, 50L), class = "data.frame")
将 dplyr 与 mtcars 示例数据结合使用：
library(dplyr)
mtcars %>%
group_by(cyl) %>%
mutate(last3mean = mean(tail(mpg, 3)))
在您的情况下，使用 Player 和列而不是 cyl 和 mpg聚合。
使用data.table，（由@akrun 建议）：
data.table as.data.table(mtcars)[, .(last3mean = mean(tail(mpg,3))), by = cyl]
首先将数据框按玩家拆分
playerDFs <- split(origdata, origdata["Player"])
然后对最后 3 场比赛进行子集化
playerLast3 <- lapply(playerDFs, function(x) x[tail(order(x[["game"]]),3), ])
终于拿到手了
vapply(playerLast3, colMeans, numeric(ncol(origdata)))
您可以将 zoo 包中的 rollmeanr 与 dplyr 一起使用。它的特点是不仅计算一名球员最近三场比赛的平均值，而且计算每个球员最近三场比赛的移动平均值。代码如下：
library(dplyr)
library(zoo)
avg.last.3 <- function (x) if (length(x) < 3) rep(NA, length(x)) else rollmeanr(x, 3, fill = NA) ## 1.
res <- df %>% group_by(Player) %>% arrange(game) %>% ## 2.
mutate(Avg.Pts=avg.last.3(PTS)) %>% ## 3.
ungroup() %>% arrange(Player,game) ## 4.
备注：
- 定义一个函数
avg.last.3，它应用函数rollmeanr，window 长度为3。rollmeanr指定align="right"对 最后 三场比赛进行平均，我们用NA填充任何没有三天平均的结果。请注意，需要此函数中的if条件，以便：-
x的长度至少是rollmeanr的 window 长度，如rollmeanr 所要求
avg.last.3returns 一个与其输入长度相同的向量，符合mutate的要求。
-
- 先
group_by再Player。因为我注意到game列不一定对每个Player进行排序，所以我们按game升序排序。 - 使用
mutate创建一个新列Avg.Pts，这是通过对列应用avg.last.3函数而产生的，例如PTS. - 最后，
ungroup并显示按Player排序的结果，然后是game
当然，您可以通过以下方式获得任意数量列的平均值：
mutate(Avg.Pts=avg.last.3(PTS), Avg.Min=avg.last.3(Min), Avg.Ast=avg.last.3(Ast), ...)
仅对 PTS 列进行平均的结果由（仅打印前六列加上 PTS 和 Avg.Pts）给出：
print(res[,c(colnames(res)[1:6],"PTS","Avg.Pts")],n=50)
### A tibble: 50 x 8
## Player Team Pos game Status Drafted PTS Avg.Pts
## <fctr> <fctr> <fctr> <int> <int> <dbl> <int> <dbl>
##1 Adas Juskevicius LTU PG 4 0 0 5 NA
##2 Alex Abrines ESP SF 5 0 0 2 NA
##3 Andrew Bogut AUS C 2 1 53 9 NA
##4 Bojan Bogdanovic CRO PF 2 1 59 18 NA
##5 Bojan Bogdanovic CRO PF 4 1 61 28 NA
##6 Bojan Bogdanovic CRO PF 5 1 55 22 22.666667
##7 Boris Diaw FRA PF 1 1 51 9 NA
##8 Boris Diaw FRA PF 2 1 57 7 NA
##9 Boris Diaw FRA PF 3 1 68 11 9.000000
##10 Boris Diaw FRA PF 4 1 55 10 9.333333
##11 Brock Motum AUS PF 4 0 0 15 NA
##12 Dario Saric CRO SF 3 1 53 15 NA
##13 Dario Saric CRO SF 4 1 56 7 NA
##14 Dwight Lewis VEN SG 5 0 0 0 NA
##15 Facundo Campazzo ARG PG 2 1 60 10 NA
##16 Facundo Campazzo ARG PG 3 1 64 10 NA
##17 Facundo Campazzo ARG PG 5 0 59 10 10.000000
##18 Ike Diogu NGR PF 3 1 55 7 NA
##19 Jianlian Yi CHN SF 2 1 55 19 NA
##20 Jianlian Yi CHN PF 3 1 74 18 NA
##21 Jianlian Yi CHN SF 4 1 56 20 19.000000
##22 Jianlian Yi CHN SF 5 1 62 20 19.333333
##23 Jonas Maciulis LTU PF 2 1 61 21 NA
##24 Jonas Maciulis LTU SF 3 1 78 10 NA
##25 Jonas Maciulis LTU PF 4 1 58 4 11.666667
##26 Kevin Durant USA SF 2 1 57 16 NA
##27 Luis Scola ARG C 2 1 52 23 NA
##28 Luis Scola ARG PF 3 1 65 12 NA
##29 Luis Scola ARG C 4 1 54 14 16.333333
##30 Mantas Kalnietis LTU SG 2 1 68 21 NA
##31 Mantas Kalnietis LTU PG 3 1 85 17 NA
##32 Mantas Kalnietis LTU SG 4 1 77 16 18.000000
##33 Matt Dellavedova AUS PG 4 1 68 6 NA
##34 Miguel Marriaga VEN PF 3 0 0 0 NA
##35 Milos Teodosic SRB SG 5 0 62 7 NA
##36 Nikola Mirotic ESP PF 2 1 54 6 NA
##37 Pau Gasol ESP C 1 1 56 26 NA
##38 Pau Gasol ESP C 2 1 82 13 NA
##39 Pau Gasol ESP C 3 1 80 16 18.333333
##40 Pau Gasol ESP C 4 1 59 23 17.333333
##41 Pau Gasol ESP C 5 1 63 19 19.333333
##42 Rafa Luz BRZ SG 4 0 0 0 NA
##43 Ricky Rubio ESP PG 1 1 57 0 NA
##44 Roberto Acuna ARG C 3 1 0 2 NA
##45 Vaidas Kariniauskas LTU PF 1 0 0 0 NA
##46 Vaidas Kariniauskas LTU PF 2 0 0 0 NA
##47 Vaidas Kariniauskas LTU PF 3 0 0 0 0.000000
##48 Vaidas Kariniauskas LTU PF 4 0 0 6 2.000000
##49 Windi Graterol VEN C 5 0 0 9 NA
##50 Zeljko Sakic CRO SF 5 0 0 0 NA