Rループに時間がかかりすぎるのですが、どうやってこれにアプローチできますか？

Question

私はこのようになり、データフレームの異なる長さで終わるように、NFLのデータを収集し、特定のサイトからデータを掻き落とし、清掃しています：今、私はしたいつもり

チーム・アウェイとホームヤードの平均から始まる特定の方法でデータを要約します。以下に示すようにだから私はNFLのチームのベクトルとループこれらの行を使用します。

明らかに非効率的であるが、ループは完全に時間がかかりすぎるので、私は今まで私の文章の効率化を心配する必要はありませんでした

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 
    .... 

（私は実際にそれが1時間以上のどこかにかかるので、それを完了させていない）。誰かが書き込みの方向、おそらく私が考えていないいくつかのタイプの行列演算を指してくれますか？

お返事ありがとうございます！

編集：ランダムに実現私はここで何らかの方法で集約を使用する必要がありますか？

structure(list(Week = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
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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", 
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"Day", "Date", "Winner/tie", "V5", "Loser/tie", "PtsW", "PtsL", 
"YdsW", "TOW", "YdsL", "TOL"), row.names = c(NA, -267L), class = "data.frame") 

Answer 1

これがはっきりしない場合は私に許してください。これは私の最初のスタックオーバーフローポストです。

あなたはこれをプロフットボールのリファレンスから読んでいると仮定します。おそらくあなたがしたように私はそれをきれいにした。データで

library(XML) 
library(dplyr) 

まず読み取り

url <- "http://www.pro-football-reference.com/years/2015/games.htm" 
nfl_weeks <- XML::readHTMLTable(url)[[1]] 

は、次に続くデータ

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) 

きれいに分割し、適用コンバイン戦略

# 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) 

Answer 2

おそらく、foreachループを試してみてください。したがって、すべてのCPUカーネルのパワーを使用することができます。この例では4つのコアを使用していますが、ハードウェアに応じて変更する必要があります。

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) 
    } 

Answer 3

さてさて、に基づいて、あなたがそれを言いましたデータフレーム次のように、必要な情報を提供する必要があります。

まずI負荷dplyrと数値形式でヤード数を変換します。最初の4行を見ると

Avg_win_yard <- df %>% 
     group_by(`Winner/tie`, V5) %>% 
     summarise(Total_yard = mean(YdsW)) 

：データセットの私の理解に基づいて、より

library(dplyr) 
df$YdsW <- as.numeric(df$YdsW) 
df$YdsL <- as.numeric(df$YdsL) 

、データフレームには、次のものが含まれています：

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 

私がこれを解釈する方法はCardinalsですnは平均109.3ヤード、ホームでは108プレイしています。負けたチームのために同様に

、：

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 

この枢機卿は、家の外で遊ぶと95.50が家遊ん169平均ヤードを実行している失われたとして、私は解釈しています。 情報を取得したら、必要に応じて解読できます。ここで

Answer 4

は、主な問題の@thepule解答の延長

一つとしてまとめたソリューションです。データは（http://garrettgman.github.io/tidying/）「整頓」ではないということです。私。チームは単一の変数でおり、結果は単一の変数

#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)))) 

説明

• wtf %>% tbl_df()シンプルで素敵なクラスにdata.frameを入れている
• tidyr::gatherはチームを「平坦化」に変換します2つの列にゲームのアウトコム
• それから我々は単にグループ化することができますteam、outcome、V5これらの変数の値の個々の組み合わせごとに、ある測定値を計算することができることを意味します。
• ...私たちの場合は平均です。しかし、ゲームの結果の状態に基づいてどのヤードを計算するかを選択する必要があります（ここでのデータはきちんと整理されていませんが、ここでは無視します）。 ifelseは勝ち負けのヤードのvectorを返し、我々は単に平均を計算します。

結果として、すべてのチーム、結果、およびホームアドバンテージには、ゲーム全体の平均ヤードが含まれます。