R循环需要太长时间，我该如何以另一种方式处理？答案

【问题标题】：R loop takes too long, how can I approach this another way?R循环需要太长时间，我该如何以另一种方式处理？
【发布时间】：2016-11-25 14:10:08
【问题描述】：

我正在从收集 NFL 数据的特定网站上抓取和清理数据，因此我最终得到了一个不同长度的数据框，如下所示：

现在我想以特定方式总结数据，从球队客场和主场的平均数据开始。所以我使用了一个 NFL 球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，但直到现在我才不必担心我的写作效率，因为循环花费的时间太长（我实际上并没有让它完成，因为它需要一个多小时）。有人可以指出我的写方向，也许是我没有想到的某种类型的矩阵运算？

提前感谢您的回复！

编辑：只是随机地意识到我应该在这里以某种方式使用聚合，对吗？

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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, 
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    95L, 81L, 2L, 11L, 166L, 51L, 91L, 98L, 89L, 98L, 141L, 63L, 
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    67L, 105L, 168L, 44L, 53L, 71L, 42L, 72L, 156L, 90L, 118L, 
    107L), .Label = c("126", "205", "209", "219", "231", "232", 
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    "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, 
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    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", 
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    "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, 
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    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")

【问题讨论】：

查看lapply，以及其他应用函数。它们以与循环相同的方式运行（至少在我看来是这样），但可以非常快地完成很多事情！
这似乎适用于dplyr 相关功能。 V5代表什么？ YdsW 每场比赛的码数是多少？（抱歉，我所知道的唯一足球就是踢球：P）
如果你能提供一个工作样本，就有可能让它工作。 dplyrftw @thepule
@thepule V5 指示谁是主场还是客场，因此如果出现该符号，则比赛在第 6 列的主场进行。然后第 4 列和第 6 列分别是该周的赢家和输家，因此指定 ydsW 和 ydsL。
@Badger 我已经阅读了应用函数并且想不出我可以应用的函数可以帮助我在这里，现在我正在尝试使用聚合但得到错误：参数“ x" 丢失，没有默认值，运行时：aggregate(grossM,by= "Winner/tie",FUN = mean())

标签： r loops web apply scrape

【解决方案1】：

如果不清楚，请原谅我，但这是我的第一个 Stack 溢出帖子。

我假设您是从 pro-football-reference 中阅读的。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)

其次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)

【讨论】：

您可以在Hadley's paper阅读有关拆分应用组合策略的信息
您可以接受这个答案/赞成票吗？不确定限制。
我真的不确定。但只是一个关于清理数据的快速问题：由于某种原因，我的数据框没有通过你的方式或我的方式将我的值更改为数字，你遇到过问题吗？我运行了你的 apply 函数，它顺利通过，但由于某种原因最终成为了一个因素。
也许你可以提供一个可重现的例子。想帮忙，但我需要你用来解决错误的代码。此外，您可以通过单击旁边的复选标记来接受答案。
另外，很确定上面提供的 dplyr 解决方案是错误的（但接近正确）。 100 码/游戏是无意义的值。在几乎所有 NFL 比赛中，它们应该在 250-450 范围内。我不允许评论他的回答，因为我在 Stack Overflow 中没有必要的声誉。

【解决方案2】：

这是作为@thepule 答案扩展的总结解决方案

主要问题之一是数据不“整齐”（http://garrettgman.github.io/tidying/）。 IE。团队拥有是一个变量，结果是一个变量

#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() simple 将 data.frame 放入一个不错的类中
tidyr::gather 将“扁平化”的球队和比赛结果转换为两列
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的值的每个不同组合，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，这里的数据不是很整齐，但这里我忽略了这个事实！）。 ifelse 将返回 vector 的输赢年份，我们只计算平均值。

这会导致表格包含所有球队、结果和主场优势的平均码数。

【讨论】：

非常好。在最终数据集中，@ 代表输家的主场比赛和赢家的客场比赛，对吧？
我真的不知道；也许 OP 可以切碎一些光

【解决方案3】：

也许你试试foreach 循环。因此，您可以使用所有 CPU 内核的强大功能。此示例使用 4 核，您应该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)
  }

【讨论】：

你应该知道，foreach 需要一个由.组合功能（见参数）。所以foreach循环中最后一个Statement应该是homeV2左右。

【解决方案4】：

好的，根据您输入的数据框，以下内容似乎应该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 并将码数转换为数字格式：

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在主场平均跑出 109.3 码，在主场外跑出 108 码。

同样，对于失败的球队：

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

我将这解释为红雀队在主场外打球时平均输掉了 169 码，在家打球时输掉了 95.50 码。获得信息后，您可以根据需要对其进行剖析。

【讨论】：