计算足球队的积分 [英] Computing points for a football team

问题描述

每个人.我正在尝试计算足球队的积分.我有我的数据框df.work"行名=团队名: rownames(df.work)<-c(as.vector(teams $ HomeTeam))

everyone. I am trying to calculate football team points. I have my dataframe "df.work" with rownames = teamnames: rownames(df.work) <- c(as.vector(teams$HomeTeam))

我已从另一个数据帧导入了所有已进行的比赛.对于每个团队，我都尝试根据团队匹配项的子集执行if循环.首先，我过滤所有主场比赛.所以我的代码是

I have imported all played matches from another dataframe. And for every team I try to execute if loop based on subset of team matches. First I filter all home matches. So my code is

for (Team1 in row.names.data.frame(df.work)){
a1 <- filter(df.clean, HomeTeam == Team1)
  if (a1$home.goals > a1$away.goals) {
    df.work[Team1,"Points"] <- df.work[Team1,"Points"] + 3
  } else  if(a1$home.goals == a1$away.goals) {
    df.work[Team1,"Points"] <- df.work[Team1,"Points"] + 1
  }

但是我很努力，为什么它没有对a1中的每一行进行循环并求和呢?

But I struggle why it did not loop for every row in a1 and sum the points?

这是头(df.clean):

this is head(df.clean):

        HomeTeam         AwayTeam home.goals away.goals
1      Liverpool          Norwich          4          1
2       West Ham         Man City          0          5
3    Bournemouth Sheffield United          1          1
4        Burnley      Southampton          3          0
5 Crystal Palace          Everton          0          0
6        Watford         Brighton          0          3

头(df.work)是

and head(df.work) is

               Games Goals.Scored Goals.Conceded Points 
Liverpool         32           70             25      3 
West Ham          32           38             56      0
Bournemouth       32           30             54      1
Burnley           32           36             45      3
Crystal Palace    32           28             37      1
Watford           32           29             49      0 

推荐答案

我相信以下是问题的要求.

I believe the following is what the question asks for.

goals2points <- function(x, y){
  pnts <- sign(x - y) + 1
  pnts[pnts == 2] <- 3
  sum(pnts)
}

hg <- aggregate(home.goals ~ HomeTeam, df.clean, sum)
ag <- aggregate(away.goals ~ AwayTeam, df.clean, sum)
Goals.Scored <- hg[[2]] + ag[[2]]
hg <- aggregate(home.goals ~ AwayTeam, df.clean, sum)
ag <- aggregate(away.goals ~ HomeTeam, df.clean, sum)
Goals.Conceded <- hg[[2]] + ag[[2]]

sp1 <- split(df.clean, df.clean$HomeTeam)
home <- sapply(sp1, function(DF){
  goals2points(DF[['home.goals']], DF[['away.goals']])
})

sp2 <- split(df.clean, df.clean$AwayTeam)
away <- sapply(sp2, function(DF){
  goals2points(DF[['away.goals']], DF[['home.goals']])
})

Points <- rowSums(cbind(home, away))
result <- data.frame(Games = lengths(sp1) + lengths(sp2), 
                     Goals.Scored, 
                     Goals.Conceded, 
                     Points)
result
#  Games Goals.Scored Goals.Conceded Points
#A     8           24             22     12
#B     8           26             21     15
#C     8           23             21     12
#D     8           17             24      7
#E     8           23             25     13

测试数据

set.seed(2020)
df.clean <- expand.grid(HomeTeam = LETTERS[1:5], AwayTeam = LETTERS[1:5])
df.clean <- df.clean[apply(df.clean, 1, function(x) x[1] != x[2]), ]
df.clean$home.goals <- sample(0:5, nrow(df.clean), TRUE)
df.clean$away.goals <- sample(0:5, nrow(df.clean), TRUE)

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