R 循环花费的时间太长，我怎样才能用另一种方式处理这个问题？
R loop takes too long, how can I approach this another way?
我正在从收集 NFL 数据的特定网站上抓取和清理数据，所以我最终得到了不同长度的数据框，如下所示：
现在我想以特定方式总结数据，从球队客场和主场平均数开始。所以我使用 NFL 团队向量并循环这些行，如下所示：
teamList <- as.matrix(c("New England Patriots", "Dallas Cowboys", "Denver Broncos",
"Pittsburgh Steelers", "Seattle Seahawks","Oakland Raiders", "Philadelphia Eagles", "Green Bay Packers"
,"San Francisco 49ers", "New York Giants","Chicago Bears", "Minnesota Vikings", "Washington Redskins",
"Carolina Panthers", "Carolina Panthers", "New Orleans Saints", "St. Louis Rams", "New York Jets",
"Baltimore Ravens", "San Diego Chargers", "Indianapolis Colts", "Houston Texans", "Arizona Cardinals",
"Detroit Lions", "Cleveland Browns", "Atlanta Falcons", "Buffalo Bills", "Jacksonville Jaguars", "Cincinnati Bengals",
"Kansas City Chiefs", "Tampa Bay Buccaneers", "Tennesee Titans", "Miami Dolphins"))
#Calcuating average yards per game vs any opponent for each team (home and away)
for (i in 1:nrow(teamList)){
for (y in 1:nrow(grossM)){
homeV1 <- matrix(0,1000000,1)
awayV1 <- matrix(0,1000000,1)
homeV2 <- matrix(0,1000000,1)
awayV2 <- matrix(0,1000000,1)
if (teamList[i,]==grossM[y,4]&grossM[y,5]=="@")(awayV1[y,1] <- grossM[y,9]) else 0
if (teamList[i,]==grossM[y,4]&grossM[y,5]=="")(homeV1[y,1] <- grossM[y,9]) else 0
if (teamList[i,]==grossM[y,6]&grossM[y,5]=="")(awayV2[y,1] <- grossM[y,11]) else 0
if (teamList[i,]==grossM[y,6]&grossM[y,5]=="@")(homeV2[y,1] <- grossM[y,11]) else 0
....
这显然是低效的，但直到现在我都不必担心我的写作效率，因为循环花费的时间太长了（我实际上还没有让它完成，因为它需要一个多小时）。有人可以指出我的写作方向，也许是我没有想到的某种矩阵运算吗？
提前感谢您的回复！
编辑：只是随机意识到我应该在这里以某种方式使用聚合，对吧？
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10L, 7L, 5L, 34L, 31L, 4L, 12L, 8L, 6L, 4L, 23L, 4L, 7L,
4L, 8L, 5L, 21L, 4L, 4L, 5L, 4L, 5L, 11L, 2L, 13L, 16L, 12L,
15L, 31L, 5L, 32L, 22L, 3L, 4L, 18L, 14L, 10L, 21L, 28L,
21L, 29L, 32L, 11L, 4L, 11L, 21L, 2L, 12L, 19L, 11L, 7L,
11L, 8L, 11L, 21L, 33L, 31L, 7L, 32L, 11L, 1L, 2L, 3L, 31L,
12L, 5L, 15L, 14L, 7L, 4L, 5L, 16L, 8L, 8L, 5L, 8L, 11L,
5L, 7L, 2L, 18L, 27L, 18L, 11L, 15L, 12L, 11L, 3L, 8L, 4L,
31L, 8L, 8L, 8L, 4L, 18L, 31L, 33L, 8L, 3L, 8L, 7L, 11L,
2L, 11L, 22L, 2L, 31L, 14L, 8L, 4L, 15L, 7L, 8L, 31L, 7L,
26L, 31L, 5L, 11L, 8L, 15L, 18L, 7L, 8L, 2L), .Label = c("0",
"10", "12", "13", "14", "15", "16", "17", "18", "19", "20",
"21", "22", "23", "24", "25", "26", "27", "28", "29", "3",
"30", "31", "32", "33", "34", "35", "38", "39", "49", "6",
"7", "8", "9"), class = "factor"), YdsW = structure(c(72L,
151L, 58L, 52L, 103L, 122L, 13L, 4L, 130L, 65L, 51L, 45L,
10L, 37L, 102L, 102L, 32L, 106L, 158L, 52L, 83L, 64L, 64L,
97L, 16L, 137L, 72L, 33L, 140L, 70L, 103L, 59L, 74L, 113L,
12L, 126L, 123L, 5L, 136L, 132L, 147L, 21L, 146L, 81L, 88L,
141L, 67L, 137L, 69L, 47L, 135L, 132L, 59L, 34L, 88L, 121L,
81L, 132L, 8L, 73L, 49L, 115L, 60L, 46L, 157L, 3L, 117L,
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128L, 148L, 93L, 134L, 68L, 92L, 99L, 44L, 115L, 80L, 167L,
38L, 123L, 96L, 23L, 112L, 68L, 66L, 101L, 36L, 138L, 88L,
29L, 87L, 121L, 156L, 114L, 131L, 48L, 46L, 96L, 139L, 30L,
155L, 15L, 57L, 152L, 76L, 24L, 170L, 89L, 81L, 142L, 118L,
27L, 24L, 143L, 122L, 48L, 76L, 41L, 151L, 169L, 136L, 19L,
104L, 11L, 139L, 108L, 34L, 48L, 94L, 34L, 23L, 22L, 160L,
142L, 10L, 36L, 17L, 162L, 97L, 159L, 93L, 85L, 78L, 96L,
75L, 95L, 94L, 43L, 69L, 28L, 24L, 125L, 111L, 76L, 113L,
79L, 109L, 127L, 73L, 109L, 86L, 130L, 54L, 55L, 54L, 39L,
96L, 87L, 154L, 164L, 145L, 127L, 6L, 4L, 144L, 27L, 163L,
98L, 9L, 25L, 41L, 100L, 96L, 62L, 50L, 133L, 120L, 90L,
129L, 67L, 120L, 150L, 1L, 39L, 84L, 40L, 124L, 42L, 82L,
119L, 134L, 126L, 64L, 49L, 18L, 153L, 27L, 7L, 61L, 35L,
87L, 149L, 103L, 20L, 116L, 48L, 123L, 14L, 78L, 83L, 110L,
95L, 81L, 2L, 11L, 166L, 51L, 91L, 98L, 89L, 98L, 141L, 63L,
77L, 156L, 129L, 132L, 106L, 131L, 29L, 7L, 48L, 26L, 56L,
67L, 105L, 168L, 44L, 53L, 71L, 42L, 72L, 156L, 90L, 118L,
107L), .Label = c("126", "205", "209", "219", "231", "232",
"242", "244", "248", "256", "258", "259", "263", "268", "270",
"274", "276", "277", "280", "281", "284", "287", "289", "290",
"291", "292", "293", "294", "295", "296", "297", "299", "300",
"303", "305", "308", "309", "310", "313", "317", "318", "319",
"320", "321", "322", "323", "326", "327", "328", "329", "330",
"333", "334", "336", "337", "339", "340", "342", "344", "345",
"346", "348", "349", "350", "352", "353", "354", "355", "356",
"359", "360", "361", "362", "363", "364", "365", "366", "368",
"369", "370", "371", "372", "373", "374", "375", "376", "377",
"378", "379", "380", "381", "382", "383", "385", "386", "388",
"389", "390", "391", "393", "394", "395", "396", "397", "398",
"402", "405", "406", "407", "408", "411", "412", "413", "414",
"417", "418", "419", "420", "423", "424", "425", "427", "428",
"429", "430", "431", "433", "434", "435", "436", "437", "438",
"439", "442", "445", "446", "448", "449", "451", "453", "458",
"459", "460", "463", "467", "469", "471", "474", "480", "481",
"483", "491", "493", "497", "500", "503", "505", "507", "508",
"510", "519", "521", "522", "524", "525", "537", "546", "568",
"597", "608"), class = "factor"), TOW = structure(c(1L, 4L,
1L, 2L, 1L, 2L, 2L, 2L, 4L, 4L, 1L, 1L, 2L, 2L, 2L, 3L, 2L,
1L, 3L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 3L, 1L, 2L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 3L, 3L, 3L,
1L, 3L, 3L, 1L, 1L, 3L, 2L, 1L, 2L, 4L, 1L, 2L, 1L, 1L, 2L,
4L, 1L, 1L, 2L, 3L, 2L, 2L, 3L, 3L, 1L, 4L, 1L, 1L, 4L, 2L,
1L, 3L, 4L, 3L, 4L, 1L, 1L, 1L, 3L, 2L, 1L, 4L, 1L, 5L, 3L,
1L, 1L, 2L, 1L, 4L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 3L, 2L, 1L, 1L, 5L, 1L, 1L, 3L, 4L, 1L, 1L, 1L,
2L, 1L, 3L, 2L, 3L, 1L, 3L, 2L, 3L, 3L, 1L, 3L, 2L, 4L, 3L,
1L, 3L, 1L, 1L, 1L, 2L, 1L, 3L, 2L, 3L, 4L, 2L, 1L, 1L, 3L,
1L, 1L, 3L, 2L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
3L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 3L, 1L, 4L, 1L, 3L,
2L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 4L, 1L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 1L, 3L, 1L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, 3L,
2L, 1L, 2L, 1L, 1L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 1L,
2L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 2L, 4L, 2L,
2L, 1L, 3L, 6L, 3L, 1L, 2L, 1L, 2L, 3L, 2L, 1L, 3L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 4L, 1L), .Label = c("0",
"1", "2", "3", "4", "5"), class = "factor"), YdsL = structure(c(168L,
69L, 70L, 85L, 32L, 142L, 44L, 6L, 59L, 102L, 135L, 140L,
106L, 50L, 34L, 138L, 79L, 130L, 106L, 87L, 17L, 67L, 87L,
108L, 128L, 172L, 88L, 97L, 143L, 23L, 129L, 102L, 133L,
82L, 41L, 125L, 132L, 87L, 5L, 105L, 63L, 118L, 109L, 4L,
159L, 137L, 60L, 90L, 42L, 157L, 167L, 158L, 89L, 78L, 156L,
23L, 30L, 97L, 144L, 11L, 164L, 84L, 39L, 163L, 124L, 52L,
136L, 57L, 125L, 130L, 58L, 160L, 48L, 43L, 89L, 96L, 141L,
146L, 66L, 22L, 96L, 65L, 117L, 134L, 151L, 91L, 143L, 178L,
163L, 169L, 33L, 3L, 166L, 150L, 171L, 121L, 106L, 114L,
123L, 39L, 122L, 99L, 51L, 86L, 52L, 48L, 71L, 20L, 9L, 116L,
110L, 2L, 15L, 52L, 37L, 120L, 173L, 149L, 111L, 17L, 144L,
136L, 84L, 161L, 36L, 140L, 127L, 75L, 69L, 149L, 162L, 99L,
82L, 56L, 136L, 102L, 152L, 29L, 19L, 120L, 21L, 161L, 121L,
107L, 101L, 39L, 80L, 122L, 127L, 105L, 72L, 124L, 19L, 8L,
17L, 46L, 14L, 12L, 101L, 83L, 14L, 115L, 24L, 95L, 92L,
148L, 151L, 35L, 68L, 47L, 94L, 104L, 177L, 103L, 117L, 98L,
72L, 83L, 50L, 96L, 16L, 127L, 1L, 112L, 122L, 109L, 49L,
28L, 139L, 155L, 114L, 45L, 131L, 61L, 145L, 53L, 62L, 69L,
86L, 48L, 128L, 25L, 21L, 76L, 9L, 124L, 93L, 113L, 175L,
75L, 25L, 26L, 165L, 63L, 119L, 116L, 154L, 121L, 82L, 54L,
10L, 128L, 141L, 138L, 102L, 137L, 131L, 55L, 112L, 113L,
47L, 73L, 74L, 86L, 77L, 117L, 145L, 40L, 7L, 64L, 42L, 147L,
114L, 104L, 129L, 81L, 174L, 11L, 18L, 176L, 67L, 107L, 26L,
100L, 13L, 27L, 38L, 170L, 121L, 43L, 155L, 126L, 142L, 131L,
153L, 72L, 31L), .Label = c("125", "140", "142", "146", "156",
"173", "178", "186", "189", "190", "196", "201", "205", "210",
"211", "212", "213", "215", "216", "220", "221", "225", "226",
"227", "230", "231", "232", "240", "242", "243", "245", "246",
"247", "248", "249", "250", "254", "255", "256", "257", "258",
"263", "264", "265", "266", "267", "268", "270", "272", "273",
"274", "276", "280", "282", "284", "285", "287", "288", "289",
"290", "291", "292", "293", "294", "298", "299", "300", "301",
"302", "304", "305", "306", "307", "308", "309", "310", "312",
"313", "314", "316", "317", "318", "319", "320", "321", "322",
"323", "324", "325", "326", "328", "329", "331", "332", "333",
"334", "335", "338", "339", "341", "342", "343", "344", "345",
"347", "349", "350", "354", "355", "356", "359", "361", "363",
"364", "365", "366", "368", "369", "370", "371", "372", "375",
"376", "377", "380", "382", "383", "385", "386", "388", "389",
"391", "393", "394", "396", "397", "398", "399", "401", "402",
"406", "408", "409", "411", "412", "413", "414", "415", "416",
"417", "420", "422", "423", "424", "427", "428", "431", "432",
"433", "435", "436", "440", "444", "447", "452", "460", "461",
"464", "469", "472", "479", "493", "496", "502", "509", "512",
"538", "548"), class = "factor"), TOL = structure(c(2L, 3L,
4L, 6L, 3L, 2L, 4L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 6L,
2L, 4L, 4L, 1L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 2L, 4L, 1L, 6L,
4L, 2L, 2L, 3L, 4L, 5L, 5L, 2L, 2L, 3L, 3L, 1L, 3L, 2L, 4L,
2L, 1L, 1L, 2L, 2L, 2L, 3L, 5L, 3L, 3L, 1L, 6L, 2L, 4L, 3L,
2L, 3L, 1L, 2L, 2L, 1L, 1L, 5L, 4L, 7L, 3L, 3L, 3L, 5L, 3L,
4L, 5L, 3L, 1L, 4L, 2L, 4L, 3L, 2L, 1L, 2L, 2L, 4L, 4L, 1L,
5L, 3L, 2L, 2L, 2L, 5L, 4L, 3L, 5L, 4L, 1L, 2L, 3L, 3L, 1L,
1L, 1L, 2L, 4L, 1L, 4L, 3L, 3L, 1L, 5L, 2L, 5L, 1L, 2L, 2L,
1L, 5L, 3L, 3L, 4L, 3L, 1L, 3L, 5L, 2L, 5L, 3L, 5L, 2L, 2L,
3L, 2L, 3L, 6L, 2L, 1L, 3L, 4L, 3L, 2L, 5L, 5L, 3L, 1L, 2L,
1L, 6L, 5L, 3L, 2L, 3L, 2L, 2L, 4L, 3L, 2L, 3L, 4L, 3L, 2L,
3L, 2L, 2L, 4L, 4L, 5L, 2L, 3L, 1L, 2L, 2L, 3L, 2L, 2L, 2L,
2L, 4L, 3L, 2L, 4L, 4L, 2L, 3L, 2L, 2L, 4L, 1L, 3L, 2L, 2L,
3L, 3L, 2L, 4L, 5L, 1L, 3L, 2L, 2L, 2L, 2L, 3L, 5L, 2L, 2L,
1L, 3L, 2L, 4L, 5L, 3L, 5L, 4L, 3L, 3L, 3L, 1L, 2L, 3L, 4L,
2L, 2L, 4L, 2L, 3L, 4L, 2L, 4L, 2L, 3L, 5L, 5L, 2L, 5L, 3L,
1L, 4L, 4L, 2L, 2L, 1L, 5L, 5L, 4L, 3L, 2L, 3L, 2L, 4L, 2L,
3L, 3L, 4L, 4L, 5L, 3L, 1L, 3L, 2L, 4L), .Label = c("0",
"1", "2", "3", "4", "5", "6"), class = "factor")), .Names = c("Week",
"Day", "Date", "Winner/tie", "V5", "Loser/tie", "PtsW", "PtsL",
"YdsW", "TOW", "YdsL", "TOL"), row.names = c(NA, -267L), class = "data.frame")
也许您可以尝试 foreach 循环。因此，您可以使用所有 CPU 内核的强大功能。本例使用4核，请根据您的硬件进行更改。
library(foreach)
library(doSNOW)
ncpus <- 4
cl <- makeCluster(ncpus, type = "SOCK")
registerDoSNOW(cl)
f <- foreach(i = 1:nrow(teamList), .combine = rbind, .inorder = F) %dopar% {
for (y in 1:nrow(grossM)){
homeV1 <- matrix(0,1000000,1)
awayV1 <- matrix(0,1000000,1)
homeV2 <- matrix(0,1000000,1)
awayV2 <- matrix(0,1000000,1)
if (teamList[i,]==grossM[y,4]&grossM[y,5]=="@")(awayV1[y,1] <- grossM[y,9]) else 0
if (teamList[i,]==grossM[y,4]&grossM[y,5]=="")(homeV1[y,1] <- grossM[y,9]) else 0
if (teamList[i,]==grossM[y,6]&grossM[y,5]=="")(awayV2[y,1] <- grossM[y,11]) else 0
if (teamList[i,]==grossM[y,6]&grossM[y,5]=="@")(homeV2[y,1] <- grossM[y,11]) else 0
# .... #
}
data.frame(team = teamList[i], awayV1, homeV1, awayV2, homeV2)
}
如果不清楚，请原谅我，但这是我的第一个 Stack overflow post。
我假设您是从专业足球参考资料中读到这篇文章的。我大概像你一样清理了它。
library(XML)
library(dplyr)
先读入数据
url <- "http://www.pro-football-reference.com/years/2015/games.htm"
nfl_weeks <- XML::readHTMLTable(url)[[1]]
然后清理数据
nfl_weeks <- nfl_weeks[nfl_weeks$Week != "Week",][,-7]
names(nfl_weeks)[5] <- 'vs'
nfl_weeks[,7:ncol(nfl_weeks)] <- apply(nfl_weeks[,7:ncol(nfl_weeks)],2,as.numeric)
随后是拆分-应用-组合策略
# split
s <- split(nfl_weeks, nfl_weeks$`Winner/tie`)[2:33]
YdsW <- lapply(s, function(i) mYdsW = mean(i$YdsW))
YdsW <- do.call("c", YdsW)
# apply
YdsL<- lapply(s, function(i) mYdsW = mean(i$YdsL))
# combine
YdsL <- do.call("c", YdsL)
好的，根据您输入的数据框，以下内容似乎可以为您提供所需的信息，尽管格式可能略有不同。
首先我加载 dplyr 并将 Yard 编号转换为数字格式：
library(dplyr)
df$YdsW <- as.numeric(df$YdsW)
df$YdsL <- as.numeric(df$YdsL)
比，根据我对数据集的理解：
Avg_win_yard <- df %>%
group_by(`Winner/tie`, V5) %>%
summarise(Total_yard = mean(YdsW))
查看数据框的前 4 行，它们包含：
head(Avg_win_yard, 4)
Source: local data frame [4 x 3]
Groups: Winner/tie [2]
Winner/tie V5 Total_yard
(fctr) (fctr) (dbl)
1 Arizona Cardinals 109.3333
2 Arizona Cardinals @ 108.0000
3 Atlanta Falcons 111.4000
4 Atlanta Falcons @ 93.7500
我对此的解释是，红雀队赢得了 运行 主场比赛平均 109.3 码，外场比赛 108 码。
同理，败方：
Avg_lose_yard <- df %>%
group_by(`Loser/tie`, V5) %>%
summarise(Total_yard = mean(YdsL))
head(Avg_lose_yard, 4)
Source: local data frame [4 x 3]
Groups: Loser/tie [2]
Loser/tie V5 Total_yard
(fctr) (fctr) (dbl)
1 Arizona Cardinals 169.00
2 Arizona Cardinals @ 95.50
3 Atlanta Falcons 80.75
4 Atlanta Falcons @ 133.50
我将此解释为红雀队输掉了 运行 169 场外场比赛平均码数和 95.50 码主场比赛。
一旦你掌握了信息，你就可以随心所欲地剖析它。
这是@thepule 回答的扩展的总结解决方案
其中一个主要问题是数据不是"tidy" (http://garrettgman.github.io/tidying/)。 IE。团队有一个单一的变量，结果是一个单一的变量
#wtf <- structure(.....
# in the previous structure the continous variables were encoded as factors and should be transformed to integers
for(i in 7:11) {
wtf[[i]] <- as.integer(as.character(wtf[[i]]))
}
wtf %>%
tbl_df() %>%
tidyr::gather(key = "outcome", value = "team", 4, 6) %>%
group_by(team, outcome, V5) %>%
summarise(meanYds = mean(ifelse(outcome == "Winner/tie",
(YdsW),
(YdsL))))
说明
wtf %>% tbl_df() 简单的把 data.frame 变成漂亮的 classtidyr::gather 将 "flattens" 球队和比赛结果转换成两列- 然后我们可以简单地按
team、outcome、V5 分组，这意味着对于这些变量值的每个不同组合，我们可以计算一些度量...
- ... 在我们的例子中是平均值。但是我们需要根据比赛结果的状态来选择要计算的码数（同样，这里的数据并不整洁，但在这里我忽略了这个事实！）。
ifelse 将 return vector 年的输赢，我们只计算平均值。
这应该导致 table 包含所有球队、结果和主场优势的平均码数。
我正在从收集 NFL 数据的特定网站上抓取和清理数据，所以我最终得到了不同长度的数据框，如下所示：
现在我想以特定方式总结数据，从球队客场和主场平均数开始。所以我使用 NFL 团队向量并循环这些行，如下所示：
teamList <- as.matrix(c("New England Patriots", "Dallas Cowboys", "Denver Broncos",
"Pittsburgh Steelers", "Seattle Seahawks","Oakland Raiders", "Philadelphia Eagles", "Green Bay Packers"
,"San Francisco 49ers", "New York Giants","Chicago Bears", "Minnesota Vikings", "Washington Redskins",
"Carolina Panthers", "Carolina Panthers", "New Orleans Saints", "St. Louis Rams", "New York Jets",
"Baltimore Ravens", "San Diego Chargers", "Indianapolis Colts", "Houston Texans", "Arizona Cardinals",
"Detroit Lions", "Cleveland Browns", "Atlanta Falcons", "Buffalo Bills", "Jacksonville Jaguars", "Cincinnati Bengals",
"Kansas City Chiefs", "Tampa Bay Buccaneers", "Tennesee Titans", "Miami Dolphins"))
#Calcuating average yards per game vs any opponent for each team (home and away)
for (i in 1:nrow(teamList)){
for (y in 1:nrow(grossM)){
homeV1 <- matrix(0,1000000,1)
awayV1 <- matrix(0,1000000,1)
homeV2 <- matrix(0,1000000,1)
awayV2 <- matrix(0,1000000,1)
if (teamList[i,]==grossM[y,4]&grossM[y,5]=="@")(awayV1[y,1] <- grossM[y,9]) else 0
if (teamList[i,]==grossM[y,4]&grossM[y,5]=="")(homeV1[y,1] <- grossM[y,9]) else 0
if (teamList[i,]==grossM[y,6]&grossM[y,5]=="")(awayV2[y,1] <- grossM[y,11]) else 0
if (teamList[i,]==grossM[y,6]&grossM[y,5]=="@")(homeV2[y,1] <- grossM[y,11]) else 0
....
这显然是低效的，但直到现在我都不必担心我的写作效率，因为循环花费的时间太长了（我实际上还没有让它完成，因为它需要一个多小时）。有人可以指出我的写作方向，也许是我没有想到的某种矩阵运算吗？
提前感谢您的回复！
编辑：只是随机意识到我应该在这里以某种方式使用聚合，对吧？
structure(list(Week = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), .Label = c(" 1", " 2", " 3", " 4", " 5", " 6", " 7",
" 8", " 9", "10", "11", "12", "13", "14", "15", "16", "17"), class = "factor"),
Day = structure(c(4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 4L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 4L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L), .Label = c("Mon", "Sat", "Sun", "Thu"), class = "factor"),
Date = structure(c(43L, 44L, 44L, 44L, 44L, 44L, 44L, 44L,
44L, 44L, 44L, 44L, 44L, 44L, 45L, 45L, 46L, 47L, 47L, 47L,
47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 48L,
49L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 51L, 30L, 40L, 40L, 40L, 40L, 40L, 40L, 40L,
40L, 40L, 40L, 40L, 40L, 40L, 41L, 42L, 31L, 31L, 31L, 31L,
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 32L, 33L, 34L, 34L,
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 35L, 36L,
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L,
38L, 39L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 21L, 27L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 29L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 19L, 20L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 24L, 24L, 24L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 26L,
12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 14L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 4L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 7L, 8L, 9L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 52L, 53L, 53L, 53L, 53L, 53L, 53L, 53L, 53L, 53L, 53L
), .Label = c("December 10", "December 13", "December 14",
"December 17", "December 19", "December 20", "December 21",
"December 24", "December 26", "December 27", "December 28",
"December 3", "December 6", "December 7", "January 3", "November 1",
"November 12", "November 15", "November 16", "November 19",
"November 2", "November 22", "November 23", "November 26",
"November 29", "November 30", "November 5", "November 8",
"November 9", "October 1", "October 11", "October 12", "October 15",
"October 18", "October 19", "October 22", "October 25", "October 26",
"October 29", "October 4", "October 5", "October 8", "September 10",
"September 13", "September 14", "September 17", "September 20",
"September 21", "September 24", "September 27", "September 28",
"September 4", "September 7"), class = "factor"), `Winner/tie` = structure(c(19L,
26L, 4L, 22L, 7L, 1L, 5L, 10L, 9L, 29L, 16L, 12L, 17L, 31L,
27L, 2L, 10L, 2L, 19L, 30L, 32L, 5L, 18L, 7L, 8L, 23L, 12L,
1L, 25L, 9L, 15L, 22L, 21L, 13L, 25L, 5L, 4L, 24L, 1L, 2L,
19L, 18L, 23L, 28L, 14L, 7L, 10L, 12L, 3L, 14L, 7L, 26L,
10L, 21L, 2L, 22L, 6L, 20L, 5L, 12L, 29L, 32L, 28L, 14L,
8L, 4L, 7L, 6L, 21L, 24L, 10L, 1L, 2L, 19L, 30L, 12L, 25L,
20L, 17L, 22L, 5L, 10L, 7L, 13L, 27L, 18L, 19L, 12L, 11L,
25L, 24L, 28L, 21L, 23L, 32L, 19L, 5L, 29L, 20L, 2L, 15L,
16L, 18L, 17L, 1L, 19L, 18L, 28L, 29L, 23L, 7L, 10L, 13L,
16L, 1L, 3L, 30L, 20L, 5L, 7L, 24L, 4L, 18L, 22L, 19L, 5L,
21L, 14L, 27L, 31L, 25L, 6L, 4L, 6L, 15L, 1L, 19L, 5L, 30L,
18L, 16L, 17L, 11L, 32L, 25L, 13L, 15L, 14L, 30L, 10L, 28L,
1L, 11L, 5L, 3L, 13L, 9L, 16L, 12L, 19L, 5L, 6L, 11L, 22L,
18L, 16L, 26L, 23L, 10L, 13L, 32L, 7L, 28L, 14L, 1L, 3L,
12L, 30L, 7L, 5L, 1L, 31L, 28L, 16L, 17L, 22L, 10L, 25L,
4L, 24L, 27L, 9L, 1L, 20L, 24L, 16L, 22L, 28L, 15L, 12L,
25L, 5L, 8L, 23L, 19L, 32L, 29L, 21L, 29L, 22L, 28L, 26L,
32L, 18L, 2L, 16L, 1L, 12L, 7L, 19L, 13L, 25L, 5L, 11L, 23L,
32L, 6L, 22L, 14L, 18L, 2L, 4L, 3L, 11L, 29L, 16L, 20L, 13L,
1L, 10L, 25L, 20L, 27L, 11L, 5L, 10L, 24L, 17L, 13L, 32L,
4L, 18L, 14L, 7L, 16L, 28L, 28L, 2L, 13L, 5L, 4L, 27L, 10L,
25L, 7L, 24L, 31L), .Label = c("Arizona Cardinals", "Atlanta Falcons",
"Baltimore Ravens", "Buffalo Bills", "Carolina Panthers",
"Chicago Bears", "Cincinnati Bengals", "Cleveland Browns",
"Dallas Cowboys", "Denver Broncos", "Detroit Lions", "Green Bay Packers",
"Houston Texans", "Indianapolis Colts", "Jacksonville Jaguars",
"Kansas City Chiefs", "Miami Dolphins", "Minnesota Vikings",
"New England Patriots", "New Orleans Saints", "New York Giants",
"New York Jets", "Oakland Raiders", "Philadelphia Eagles",
"Pittsburgh Steelers", "San Diego Chargers", "San Francisco 49ers",
"Seattle Seahawks", "St. Louis Rams", "Tampa Bay Buccaneers",
"Tennessee Titans", "Washington Redskins"), class = "factor"),
V5 = structure(c(1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L,
1L, 2L), .Label = c("", "@"), class = "factor"), `Loser/tie` = structure(c(25L,
11L, 14L, 8L, 23L, 20L, 15L, 3L, 21L, 28L, 13L, 6L, 32L,
30L, 18L, 24L, 16L, 21L, 4L, 20L, 29L, 13L, 11L, 26L, 31L,
3L, 28L, 6L, 27L, 24L, 17L, 14L, 32L, 30L, 29L, 20L, 17L,
22L, 27L, 9L, 15L, 26L, 8L, 6L, 31L, 3L, 11L, 16L, 25L, 15L,
16L, 8L, 18L, 4L, 13L, 17L, 23L, 9L, 30L, 27L, 1L, 24L, 11L,
13L, 3L, 31L, 28L, 16L, 27L, 20L, 23L, 11L, 32L, 9L, 15L,
29L, 26L, 2L, 31L, 32L, 28L, 8L, 4L, 15L, 3L, 16L, 14L, 26L,
6L, 1L, 21L, 27L, 9L, 26L, 30L, 22L, 24L, 8L, 14L, 31L, 4L,
25L, 11L, 13L, 3L, 17L, 6L, 9L, 27L, 22L, 25L, 12L, 31L,
11L, 8L, 26L, 2L, 21L, 14L, 8L, 9L, 17L, 29L, 15L, 32L, 12L,
30L, 10L, 2L, 20L, 23L, 26L, 22L, 29L, 3L, 28L, 21L, 31L,
9L, 23L, 10L, 24L, 12L, 20L, 8L, 7L, 31L, 2L, 24L, 6L, 27L,
7L, 23L, 32L, 29L, 22L, 17L, 26L, 18L, 4L, 9L, 12L, 24L,
17L, 2L, 4L, 15L, 31L, 19L, 20L, 21L, 29L, 25L, 30L, 27L,
8L, 11L, 2L, 8L, 20L, 29L, 15L, 18L, 23L, 3L, 21L, 26L, 14L,
13L, 19L, 6L, 32L, 18L, 30L, 4L, 26L, 31L, 3L, 14L, 9L, 7L,
2L, 27L, 10L, 13L, 6L, 11L, 17L, 30L, 9L, 8L, 17L, 4L, 6L,
15L, 3L, 24L, 23L, 27L, 31L, 14L, 10L, 21L, 20L, 26L, 24L,
30L, 19L, 17L, 21L, 5L, 9L, 25L, 27L, 28L, 8L, 15L, 31L,
12L, 7L, 8L, 2L, 29L, 6L, 30L, 26L, 21L, 19L, 15L, 9L, 22L,
12L, 31L, 3L, 23L, 1L, 12L, 20L, 32L, 30L, 6L, 9L, 14L, 8L,
3L, 15L, 16L), .Label = c("Arizona Cardinals", "Atlanta Falcons",
"Baltimore Ravens", "Buffalo Bills", "Carolina Panthers",
"Chicago Bears", "Cincinnati Bengals", "Cleveland Browns",
"Dallas Cowboys", "Denver Broncos", "Detroit Lions", "Green Bay Packers",
"Houston Texans", "Indianapolis Colts", "Jacksonville Jaguars",
"Kansas City Chiefs", "Miami Dolphins", "Minnesota Vikings",
"New England Patriots", "New Orleans Saints", "New York Giants",
"New York Jets", "Oakland Raiders", "Philadelphia Eagles",
"Pittsburgh Steelers", "San Diego Chargers", "San Francisco 49ers",
"Seattle Seahawks", "St. Louis Rams", "Tampa Bay Buccaneers",
"Tennessee Titans", "Washington Redskins"), class = "factor"),
PtsW = structure(c(18L, 23L, 17L, 21L, 23L, 21L, 10L, 9L,
17L, 24L, 17L, 21L, 7L, 32L, 10L, 16L, 21L, 14L, 30L, 16L,
14L, 14L, 16L, 14L, 18L, 27L, 17L, 37L, 33L, 10L, 13L, 10L,
22L, 9L, 2L, 17L, 31L, 14L, 36L, 29L, 39L, 21L, 17L, 16L,
25L, 18L, 14L, 28L, 13L, 6L, 26L, 20L, 13L, 14L, 37L, 17L,
12L, 16L, 27L, 7L, 14L, 13L, 3L, 17L, 23L, 4L, 17L, 8L, 20L,
29L, 6L, 32L, 15L, 20L, 28L, 14L, 14L, 21L, 28L, 24L, 17L,
16L, 24L, 21L, 15L, 6L, 24L, 17L, 27L, 15L, 17L, 10L, 17L,
27L, 21L, 20L, 17L, 14L, 17L, 1L, 24L, 13L, 18L, 34L, 16L,
26L, 13L, 3L, 17L, 24L, 6L, 19L, 10L, 35L, 24L, 19L, 13L,
40L, 19L, 21L, 23L, 23L, 11L, 18L, 17L, 27L, 22L, 17L, 7L,
24L, 28L, 12L, 12L, 27L, 12L, 29L, 17L, 17L, 1L, 20L, 19L,
10L, 8L, 36L, 20L, 1L, 9L, 14L, 35L, 7L, 19L, 24L, 8L, 34L,
6L, 14L, 14L, 23L, 20L, 10L, 23L, 7L, 35L, 28L, 10L, 20L,
21L, 14L, 20L, 14L, 10L, 21L, 29L, 15L, 9L, 23L, 17L, 13L,
27L, 31L, 17L, 32L, 28L, 24L, 5L, 13L, 7L, 35L, 20L, 25L,
16L, 9L, 13L, 14L, 13L, 1L, 20L, 25L, 39L, 18L, 23L, 28L,
14L, 5L, 17L, 14L, 11L, 21L, 21L, 9L, 20L, 20L, 25L, 28L,
13L, 24L, 30L, 20L, 14L, 23L, 6L, 24L, 28L, 25L, 13L, 28L,
16L, 16L, 8L, 38L, 10L, 6L, 10L, 22L, 13L, 7L, 28L, 24L,
28L, 10L, 18L, 10L, 9L, 14L, 28L, 17L, 25L, 10L, 20L, 24L,
12L, 10L, 20L, 14L, 13L, 26L, 26L, 27L, 7L, 10L, 13L, 18L,
21L, 20L, 13L, 24L, 16L), .Label = c("10", "12", "13", "14",
"15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34",
"35", "36", "37", "38", "39", "40", "41", "42", "43", "44",
"45", "47", "48", "49", "51", "52"), class = "factor"), PtsL = structure(c(12L,
19L, 5L, 2L, 4L, 10L, 34L, 4L, 17L, 23L, 11L, 14L, 2L, 5L,
21L, 15L, 15L, 11L, 24L, 10L, 2L, 8L, 7L, 10L, 5L, 25L, 8L,
14L, 9L, 2L, 11L, 32L, 12L, 34L, 31L, 13L, 5L, 8L, 32L, 19L,
8L, 5L, 11L, 1L, 25L, 15L, 3L, 19L, 11L, 4L, 12L, 18L, 11L,
2L, 12L, 5L, 11L, 11L, 14L, 21L, 13L, 11L, 2L, 11L, 22L,
4L, 15L, 8L, 18L, 8L, 2L, 8L, 10L, 31L, 23L, 2L, 11L, 12L,
2L, 11L, 14L, 14L, 12L, 11L, 11L, 2L, 18L, 11L, 26L, 4L,
32L, 21L, 11L, 20L, 22L, 14L, 7L, 31L, 12L, 32L, 23L, 4L,
10L, 17L, 9L, 32L, 11L, 3L, 31L, 11L, 2L, 2L, 31L, 2L, 11L,
17L, 11L, 30L, 17L, 2L, 18L, 8L, 9L, 14L, 2L, 20L, 9L, 15L,
7L, 19L, 27L, 10L, 8L, 4L, 11L, 24L, 17L, 2L, 31L, 5L, 4L,
10L, 7L, 5L, 34L, 31L, 4L, 12L, 8L, 6L, 4L, 23L, 4L, 7L,
4L, 8L, 5L, 21L, 4L, 4L, 5L, 4L, 5L, 11L, 2L, 13L, 16L, 12L,
15L, 31L, 5L, 32L, 22L, 3L, 4L, 18L, 14L, 10L, 21L, 28L,
21L, 29L, 32L, 11L, 4L, 11L, 21L, 2L, 12L, 19L, 11L, 7L,
11L, 8L, 11L, 21L, 33L, 31L, 7L, 32L, 11L, 1L, 2L, 3L, 31L,
12L, 5L, 15L, 14L, 7L, 4L, 5L, 16L, 8L, 8L, 5L, 8L, 11L,
5L, 7L, 2L, 18L, 27L, 18L, 11L, 15L, 12L, 11L, 3L, 8L, 4L,
31L, 8L, 8L, 8L, 4L, 18L, 31L, 33L, 8L, 3L, 8L, 7L, 11L,
2L, 11L, 22L, 2L, 31L, 14L, 8L, 4L, 15L, 7L, 8L, 31L, 7L,
26L, 31L, 5L, 11L, 8L, 15L, 18L, 7L, 8L, 2L), .Label = c("0",
"10", "12", "13", "14", "15", "16", "17", "18", "19", "20",
"21", "22", "23", "24", "25", "26", "27", "28", "29", "3",
"30", "31", "32", "33", "34", "35", "38", "39", "49", "6",
"7", "8", "9"), class = "factor"), YdsW = structure(c(72L,
151L, 58L, 52L, 103L, 122L, 13L, 4L, 130L, 65L, 51L, 45L,
10L, 37L, 102L, 102L, 32L, 106L, 158L, 52L, 83L, 64L, 64L,
97L, 16L, 137L, 72L, 33L, 140L, 70L, 103L, 59L, 74L, 113L,
12L, 126L, 123L, 5L, 136L, 132L, 147L, 21L, 146L, 81L, 88L,
141L, 67L, 137L, 69L, 47L, 135L, 132L, 59L, 34L, 88L, 121L,
81L, 132L, 8L, 73L, 49L, 115L, 60L, 46L, 157L, 3L, 117L,
49L, 165L, 161L, 31L, 60L, 116L, 69L, 79L, 45L, 63L, 94L,
128L, 148L, 93L, 134L, 68L, 92L, 99L, 44L, 115L, 80L, 167L,
38L, 123L, 96L, 23L, 112L, 68L, 66L, 101L, 36L, 138L, 88L,
29L, 87L, 121L, 156L, 114L, 131L, 48L, 46L, 96L, 139L, 30L,
155L, 15L, 57L, 152L, 76L, 24L, 170L, 89L, 81L, 142L, 118L,
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75L, 95L, 94L, 43L, 69L, 28L, 24L, 125L, 111L, 76L, 113L,
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96L, 87L, 154L, 164L, 145L, 127L, 6L, 4L, 144L, 27L, 163L,
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129L, 67L, 120L, 150L, 1L, 39L, 84L, 40L, 124L, 42L, 82L,
119L, 134L, 126L, 64L, 49L, 18L, 153L, 27L, 7L, 61L, 35L,
87L, 149L, 103L, 20L, 116L, 48L, 123L, 14L, 78L, 83L, 110L,
95L, 81L, 2L, 11L, 166L, 51L, 91L, 98L, 89L, 98L, 141L, 63L,
77L, 156L, 129L, 132L, 106L, 131L, 29L, 7L, 48L, 26L, 56L,
67L, 105L, 168L, 44L, 53L, 71L, 42L, 72L, 156L, 90L, 118L,
107L), .Label = c("126", "205", "209", "219", "231", "232",
"242", "244", "248", "256", "258", "259", "263", "268", "270",
"274", "276", "277", "280", "281", "284", "287", "289", "290",
"291", "292", "293", "294", "295", "296", "297", "299", "300",
"303", "305", "308", "309", "310", "313", "317", "318", "319",
"320", "321", "322", "323", "326", "327", "328", "329", "330",
"333", "334", "336", "337", "339", "340", "342", "344", "345",
"346", "348", "349", "350", "352", "353", "354", "355", "356",
"359", "360", "361", "362", "363", "364", "365", "366", "368",
"369", "370", "371", "372", "373", "374", "375", "376", "377",
"378", "379", "380", "381", "382", "383", "385", "386", "388",
"389", "390", "391", "393", "394", "395", "396", "397", "398",
"402", "405", "406", "407", "408", "411", "412", "413", "414",
"417", "418", "419", "420", "423", "424", "425", "427", "428",
"429", "430", "431", "433", "434", "435", "436", "437", "438",
"439", "442", "445", "446", "448", "449", "451", "453", "458",
"459", "460", "463", "467", "469", "471", "474", "480", "481",
"483", "491", "493", "497", "500", "503", "505", "507", "508",
"510", "519", "521", "522", "524", "525", "537", "546", "568",
"597", "608"), class = "factor"), TOW = structure(c(1L, 4L,
1L, 2L, 1L, 2L, 2L, 2L, 4L, 4L, 1L, 1L, 2L, 2L, 2L, 3L, 2L,
1L, 3L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 1L, 3L, 1L, 2L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 3L, 3L, 3L,
1L, 3L, 3L, 1L, 1L, 3L, 2L, 1L, 2L, 4L, 1L, 2L, 1L, 1L, 2L,
4L, 1L, 1L, 2L, 3L, 2L, 2L, 3L, 3L, 1L, 4L, 1L, 1L, 4L, 2L,
1L, 3L, 4L, 3L, 4L, 1L, 1L, 1L, 3L, 2L, 1L, 4L, 1L, 5L, 3L,
1L, 1L, 2L, 1L, 4L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 3L, 2L, 1L, 1L, 5L, 1L, 1L, 3L, 4L, 1L, 1L, 1L,
2L, 1L, 3L, 2L, 3L, 1L, 3L, 2L, 3L, 3L, 1L, 3L, 2L, 4L, 3L,
1L, 3L, 1L, 1L, 1L, 2L, 1L, 3L, 2L, 3L, 4L, 2L, 1L, 1L, 3L,
1L, 1L, 3L, 2L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
3L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 3L, 1L, 4L, 1L, 3L,
2L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 4L, 1L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 1L, 3L, 1L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, 3L,
2L, 1L, 2L, 1L, 1L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 1L,
2L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 2L, 4L, 2L,
2L, 1L, 3L, 6L, 3L, 1L, 2L, 1L, 2L, 3L, 2L, 1L, 3L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 4L, 1L), .Label = c("0",
"1", "2", "3", "4", "5"), class = "factor"), YdsL = structure(c(168L,
69L, 70L, 85L, 32L, 142L, 44L, 6L, 59L, 102L, 135L, 140L,
106L, 50L, 34L, 138L, 79L, 130L, 106L, 87L, 17L, 67L, 87L,
108L, 128L, 172L, 88L, 97L, 143L, 23L, 129L, 102L, 133L,
82L, 41L, 125L, 132L, 87L, 5L, 105L, 63L, 118L, 109L, 4L,
159L, 137L, 60L, 90L, 42L, 157L, 167L, 158L, 89L, 78L, 156L,
23L, 30L, 97L, 144L, 11L, 164L, 84L, 39L, 163L, 124L, 52L,
136L, 57L, 125L, 130L, 58L, 160L, 48L, 43L, 89L, 96L, 141L,
146L, 66L, 22L, 96L, 65L, 117L, 134L, 151L, 91L, 143L, 178L,
163L, 169L, 33L, 3L, 166L, 150L, 171L, 121L, 106L, 114L,
123L, 39L, 122L, 99L, 51L, 86L, 52L, 48L, 71L, 20L, 9L, 116L,
110L, 2L, 15L, 52L, 37L, 120L, 173L, 149L, 111L, 17L, 144L,
136L, 84L, 161L, 36L, 140L, 127L, 75L, 69L, 149L, 162L, 99L,
82L, 56L, 136L, 102L, 152L, 29L, 19L, 120L, 21L, 161L, 121L,
107L, 101L, 39L, 80L, 122L, 127L, 105L, 72L, 124L, 19L, 8L,
17L, 46L, 14L, 12L, 101L, 83L, 14L, 115L, 24L, 95L, 92L,
148L, 151L, 35L, 68L, 47L, 94L, 104L, 177L, 103L, 117L, 98L,
72L, 83L, 50L, 96L, 16L, 127L, 1L, 112L, 122L, 109L, 49L,
28L, 139L, 155L, 114L, 45L, 131L, 61L, 145L, 53L, 62L, 69L,
86L, 48L, 128L, 25L, 21L, 76L, 9L, 124L, 93L, 113L, 175L,
75L, 25L, 26L, 165L, 63L, 119L, 116L, 154L, 121L, 82L, 54L,
10L, 128L, 141L, 138L, 102L, 137L, 131L, 55L, 112L, 113L,
47L, 73L, 74L, 86L, 77L, 117L, 145L, 40L, 7L, 64L, 42L, 147L,
114L, 104L, 129L, 81L, 174L, 11L, 18L, 176L, 67L, 107L, 26L,
100L, 13L, 27L, 38L, 170L, 121L, 43L, 155L, 126L, 142L, 131L,
153L, 72L, 31L), .Label = c("125", "140", "142", "146", "156",
"173", "178", "186", "189", "190", "196", "201", "205", "210",
"211", "212", "213", "215", "216", "220", "221", "225", "226",
"227", "230", "231", "232", "240", "242", "243", "245", "246",
"247", "248", "249", "250", "254", "255", "256", "257", "258",
"263", "264", "265", "266", "267", "268", "270", "272", "273",
"274", "276", "280", "282", "284", "285", "287", "288", "289",
"290", "291", "292", "293", "294", "298", "299", "300", "301",
"302", "304", "305", "306", "307", "308", "309", "310", "312",
"313", "314", "316", "317", "318", "319", "320", "321", "322",
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"376", "377", "380", "382", "383", "385", "386", "388", "389",
"391", "393", "394", "396", "397", "398", "399", "401", "402",
"406", "408", "409", "411", "412", "413", "414", "415", "416",
"417", "420", "422", "423", "424", "427", "428", "431", "432",
"433", "435", "436", "440", "444", "447", "452", "460", "461",
"464", "469", "472", "479", "493", "496", "502", "509", "512",
"538", "548"), class = "factor"), TOL = structure(c(2L, 3L,
4L, 6L, 3L, 2L, 4L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 6L,
2L, 4L, 4L, 1L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 2L, 4L, 1L, 6L,
4L, 2L, 2L, 3L, 4L, 5L, 5L, 2L, 2L, 3L, 3L, 1L, 3L, 2L, 4L,
2L, 1L, 1L, 2L, 2L, 2L, 3L, 5L, 3L, 3L, 1L, 6L, 2L, 4L, 3L,
2L, 3L, 1L, 2L, 2L, 1L, 1L, 5L, 4L, 7L, 3L, 3L, 3L, 5L, 3L,
4L, 5L, 3L, 1L, 4L, 2L, 4L, 3L, 2L, 1L, 2L, 2L, 4L, 4L, 1L,
5L, 3L, 2L, 2L, 2L, 5L, 4L, 3L, 5L, 4L, 1L, 2L, 3L, 3L, 1L,
1L, 1L, 2L, 4L, 1L, 4L, 3L, 3L, 1L, 5L, 2L, 5L, 1L, 2L, 2L,
1L, 5L, 3L, 3L, 4L, 3L, 1L, 3L, 5L, 2L, 5L, 3L, 5L, 2L, 2L,
3L, 2L, 3L, 6L, 2L, 1L, 3L, 4L, 3L, 2L, 5L, 5L, 3L, 1L, 2L,
1L, 6L, 5L, 3L, 2L, 3L, 2L, 2L, 4L, 3L, 2L, 3L, 4L, 3L, 2L,
3L, 2L, 2L, 4L, 4L, 5L, 2L, 3L, 1L, 2L, 2L, 3L, 2L, 2L, 2L,
2L, 4L, 3L, 2L, 4L, 4L, 2L, 3L, 2L, 2L, 4L, 1L, 3L, 2L, 2L,
3L, 3L, 2L, 4L, 5L, 1L, 3L, 2L, 2L, 2L, 2L, 3L, 5L, 2L, 2L,
1L, 3L, 2L, 4L, 5L, 3L, 5L, 4L, 3L, 3L, 3L, 1L, 2L, 3L, 4L,
2L, 2L, 4L, 2L, 3L, 4L, 2L, 4L, 2L, 3L, 5L, 5L, 2L, 5L, 3L,
1L, 4L, 4L, 2L, 2L, 1L, 5L, 5L, 4L, 3L, 2L, 3L, 2L, 4L, 2L,
3L, 3L, 4L, 4L, 5L, 3L, 1L, 3L, 2L, 4L), .Label = c("0",
"1", "2", "3", "4", "5", "6"), class = "factor")), .Names = c("Week",
"Day", "Date", "Winner/tie", "V5", "Loser/tie", "PtsW", "PtsL",
"YdsW", "TOW", "YdsL", "TOL"), row.names = c(NA, -267L), class = "data.frame")
也许您可以尝试 foreach 循环。因此，您可以使用所有 CPU 内核的强大功能。本例使用4核，请根据您的硬件进行更改。
library(foreach)
library(doSNOW)
ncpus <- 4
cl <- makeCluster(ncpus, type = "SOCK")
registerDoSNOW(cl)
f <- foreach(i = 1:nrow(teamList), .combine = rbind, .inorder = F) %dopar% {
for (y in 1:nrow(grossM)){
homeV1 <- matrix(0,1000000,1)
awayV1 <- matrix(0,1000000,1)
homeV2 <- matrix(0,1000000,1)
awayV2 <- matrix(0,1000000,1)
if (teamList[i,]==grossM[y,4]&grossM[y,5]=="@")(awayV1[y,1] <- grossM[y,9]) else 0
if (teamList[i,]==grossM[y,4]&grossM[y,5]=="")(homeV1[y,1] <- grossM[y,9]) else 0
if (teamList[i,]==grossM[y,6]&grossM[y,5]=="")(awayV2[y,1] <- grossM[y,11]) else 0
if (teamList[i,]==grossM[y,6]&grossM[y,5]=="@")(homeV2[y,1] <- grossM[y,11]) else 0
# .... #
}
data.frame(team = teamList[i], awayV1, homeV1, awayV2, homeV2)
}
如果不清楚，请原谅我，但这是我的第一个 Stack overflow post。
我假设您是从专业足球参考资料中读到这篇文章的。我大概像你一样清理了它。
library(XML)
library(dplyr)
先读入数据
url <- "http://www.pro-football-reference.com/years/2015/games.htm"
nfl_weeks <- XML::readHTMLTable(url)[[1]]
然后清理数据
nfl_weeks <- nfl_weeks[nfl_weeks$Week != "Week",][,-7]
names(nfl_weeks)[5] <- 'vs'
nfl_weeks[,7:ncol(nfl_weeks)] <- apply(nfl_weeks[,7:ncol(nfl_weeks)],2,as.numeric)
随后是拆分-应用-组合策略
# split
s <- split(nfl_weeks, nfl_weeks$`Winner/tie`)[2:33]
YdsW <- lapply(s, function(i) mYdsW = mean(i$YdsW))
YdsW <- do.call("c", YdsW)
# apply
YdsL<- lapply(s, function(i) mYdsW = mean(i$YdsL))
# combine
YdsL <- do.call("c", YdsL)
好的，根据您输入的数据框，以下内容似乎可以为您提供所需的信息，尽管格式可能略有不同。
首先我加载 dplyr 并将 Yard 编号转换为数字格式：
library(dplyr)
df$YdsW <- as.numeric(df$YdsW)
df$YdsL <- as.numeric(df$YdsL)
比，根据我对数据集的理解：
Avg_win_yard <- df %>%
group_by(`Winner/tie`, V5) %>%
summarise(Total_yard = mean(YdsW))
查看数据框的前 4 行，它们包含：
head(Avg_win_yard, 4)
Source: local data frame [4 x 3]
Groups: Winner/tie [2]
Winner/tie V5 Total_yard
(fctr) (fctr) (dbl)
1 Arizona Cardinals 109.3333
2 Arizona Cardinals @ 108.0000
3 Atlanta Falcons 111.4000
4 Atlanta Falcons @ 93.7500
我对此的解释是，红雀队赢得了 运行 主场比赛平均 109.3 码，外场比赛 108 码。
同理，败方：
Avg_lose_yard <- df %>%
group_by(`Loser/tie`, V5) %>%
summarise(Total_yard = mean(YdsL))
head(Avg_lose_yard, 4)
Source: local data frame [4 x 3]
Groups: Loser/tie [2]
Loser/tie V5 Total_yard
(fctr) (fctr) (dbl)
1 Arizona Cardinals 169.00
2 Arizona Cardinals @ 95.50
3 Atlanta Falcons 80.75
4 Atlanta Falcons @ 133.50
我将此解释为红雀队输掉了 运行 169 场外场比赛平均码数和 95.50 码主场比赛。 一旦你掌握了信息，你就可以随心所欲地剖析它。
这是@thepule 回答的扩展的总结解决方案
其中一个主要问题是数据不是"tidy" (http://garrettgman.github.io/tidying/)。 IE。团队有一个单一的变量，结果是一个单一的变量
#wtf <- structure(.....
# in the previous structure the continous variables were encoded as factors and should be transformed to integers
for(i in 7:11) {
wtf[[i]] <- as.integer(as.character(wtf[[i]]))
}
wtf %>%
tbl_df() %>%
tidyr::gather(key = "outcome", value = "team", 4, 6) %>%
group_by(team, outcome, V5) %>%
summarise(meanYds = mean(ifelse(outcome == "Winner/tie",
(YdsW),
(YdsL))))
说明
wtf %>% tbl_df()简单的把data.frame变成漂亮的 classtidyr::gather将 "flattens" 球队和比赛结果转换成两列- 然后我们可以简单地按
team、outcome、V5分组，这意味着对于这些变量值的每个不同组合，我们可以计算一些度量... - ... 在我们的例子中是平均值。但是我们需要根据比赛结果的状态来选择要计算的码数（同样，这里的数据并不整洁，但在这里我忽略了这个事实！）。
ifelse将 returnvector年的输赢，我们只计算平均值。
这应该导致 table 包含所有球队、结果和主场优势的平均码数。