我被要求更清楚地说明不同的问题，所以它们位于顶部：
- 如何计算团队的滚动平均值，不包括本周
- 如何为对手球队添加包含类似统计数据的列
这是原始文本：
我正在学习R对运动进行一些纸上谈兵的分析。现在，我遇到了一个问题，我有一个NFL赛季每场比赛的列表，我正试图计算这场比赛前几周的平均进攻次数。最终，我希望能够得到本赛季迄今为止的平均值，以及过去X个时期的移动平均值。
更复杂的是，我也想在本周之前为对手获得同样的信息。我找了很多类似的问题，但找不到任何解决方案。
以下是数据示例。给我的数据库有一些不幸的列名。ScoreOff实际上是指球队在TeamName字段中的总得分，无论是进攻、防守还是特殊球队的比赛*Def同样指的是反对者。代码示例是使用标记为"df2"的数据帧
dput(head(df2))
structure(list(Date = structure(c(14126, 14126, 14129, 14129,
14129, 14129), class = "Date"), TeamName = structure(c(21L, 32L,
1L, 2L, 3L, 4L), .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"),
ScoreOff = c(16L, 7L, 23L, 34L, 17L, 34L), FirstDownOff = c(21L,
11L, 18L, 23L, 21L, 13L), ThirdDownPctOff = structure(c(34L,
14L, 20L, 21L, 35L, 16L), .Label = c("0%", "10%", "11%",
"12%", "13%", "14%", "15%", "17%", "18%", "19%", "20%", "21%",
"22%", "23%", "24%", "25%", "27%", "29%", "30%", "31%", "33%",
"35%", "36%", "37%", "38%", "40%", "41%", "42%", "43%", "44%",
"45%", "46%", "47%", "50%", "53%", "54%", "55%", "56%", "57%",
"58%", "59%", "60%", "61%", "62%", "63%", "64%", "65%", "67%",
"69%", "73%", "77%", "8%", "80%", "9%", "92%"), class = "factor"),
RushAttOff = c(32L, 24L, 39L, 42L, 46L, 29L), RushYdsOff = c(154L,
84L, 109L, 318L, 229L, 106L), PassAttOff = c(35L, 27L, 30L,
13L, 29L, 31L), PassCompOff = c(19L, 15L, 19L, 9L, 15L, 20L
), PassYdsOff = c(216L, 133L, 197L, 161L, 129L, 234L), PassIntOff = c(1L,
0L, 0L, 0L, 0L, 0L), FumblesOff = c(0L, 0L, 0L, 0L, 2L, 0L
), SackYdsOff = c(16L, 8L, 21L, 5L, 0L, 2L), PenYdsOff = c(70L,
35L, 40L, 68L, 64L, 14L), TimePossOff = structure(c(348L,
52L, 368L, 175L, 354L, 239L), .Label = c("14:45", "18:15",
"18:27", "19:31", "19:56", "20:11", "20:12", "20:26", "20:48",
"21:03", "21:08", "21:16", "21:26", "21:28", "21:35", "21:44",
"21:45", "21:52", "21:54", "22:03", "22:08", "22:12", "22:16",
"22:25", "22:30", "22:31", "22:33", "22:34", "22:38", "22:39",
"22:53", "22:55", "22:59", "23:09", "23:10", "23:12", "23:15",
"23:23", "23:28", "23:30", "23:33", "23:37", "23:38", "23:42",
"23:43", "23:45", "23:48", "23:49", "23:56", "24:06", "24:13",
"24:17", "24:18", "24:21", "24:33", "24:34", "24:35", "24:41",
"24:43", "24:49", "24:50", "24:54", "24:58", "24:59", "25:01",
"25:02", "25:05", "25:11", "25:14", "25:16", "25:19", "25:25",
"25:29", "25:31", "25:32", "25:34", "25:36", "25:37", "25:38",
"25:40", "25:41", "25:46", "25:47", "25:53", "25:55", "25:57",
"25:58", "26:00", "26:04", "26:09", "26:10", "26:11", "26:12",
"26:13", "26:16", "26:20", "26:27", "26:32", "26:36", "26:37",
"26:38", "26:39", "26:40", "26:41", "26:44", "26:46", "26:49",
"26:53", "26:56", "26:59", "27:01", "27:04", "27:10", "27:12",
"27:13", "27:15", "27:18", "27:20", "27:24", "27:25", "27:26",
"27:27", "27:28", "27:30", "27:32", "27:37", "27:40", "27:44",
"27:46", "27:47", "27:48", "27:50", "27:51", "27:52", "27:53",
"27:55", "27:57", "27:58", "27:59", "28:00", "28:01", "28:03",
"28:05", "28:06", "28:07", "28:13", "28:14", "28:16", "28:17",
"28:18", "28:19", "28:21", "28:22", "28:24", "28:25", "28:28",
"28:29", "28:32", "28:38", "28:40", "28:41", "28:45", "28:47",
"28:49", "28:51", "28:53", "28:55", "28:57", "28:58", "28:59",
"29:00", "29:02", "29:04", "29:05", "29:07", "29:08", "29:11",
"29:13", "29:14", "29:18", "29:19", "29:20", "29:26", "29:27",
"29:29", "29:31", "29:32", "29:33", "29:34", "29:36", "29:37",
"29:38", "29:41", "29:42", "29:43", "29:49", "29:50", "29:55",
"29:56", "29:59", "30:01", "30:04", "30:05", "30:10", "30:11",
"30:17", "30:18", "30:19", "30:22", "30:23", "30:24", "30:26",
"30:27", "30:28", "30:29", "30:31", "30:33", "30:34", "30:40",
"30:41", "30:42", "30:46", "30:47", "30:49", "30:52", "30:53",
"30:55", "30:58", "31:00", "31:01", "31:02", "31:03", "31:05",
"31:07", "31:09", "31:11", "31:13", "31:15", "31:19", "31:20",
"31:22", "31:28", "31:31", "31:32", "31:35", "31:36", "31:38",
"31:39", "31:41", "31:42", "31:43", "31:44", "31:46", "31:47",
"31:53", "31:54", "31:55", "31:57", "31:59", "32:00", "32:01",
"32:02", "32:03", "32:05", "32:07", "32:08", "32:09", "32:10",
"32:12", "32:13", "32:14", "32:16", "32:20", "32:23", "32:28",
"32:30", "32:32", "32:33", "32:34", "32:35", "32:36", "32:40",
"32:42", "32:45", "32:47", "32:48", "32:50", "32:56", "32:59",
"33:01", "33:04", "33:07", "33:11", "33:14", "33:16", "33:19",
"33:20", "33:21", "33:22", "33:23", "33:24", "33:28", "33:33",
"33:40", "33:44", "33:47", "33:48", "33:49", "33:50", "33:51",
"33:56", "34:00", "34:02", "34:03", "34:05", "34:07", "34:13",
"34:14", "34:19", "34:20", "34:22", "34:23", "34:24", "34:26",
"34:28", "34:29", "34:31", "34:35", "34:41", "34:44", "34:46",
"34:49", "34:55", "34:58", "34:59", "35:01", "35:02", "35:06",
"35:10", "35:11", "35:17", "35:19", "35:25", "35:26", "35:27",
"35:39", "35:42", "35:43", "35:47", "35:54", "36:04", "36:11",
"36:12", "36:15", "36:17", "36:18", "36:22", "36:23", "36:27",
"36:30", "36:32", "36:37", "36:45", "36:48", "36:50", "36:51",
"37:01", "37:05", "37:07", "37:21", "37:22", "37:26", "37:27",
"37:29", "37:30", "37:35", "37:42", "37:44", "37:48", "37:52",
"37:57", "38:08", "38:15", "38:16", "38:23", "38:25", "38:32",
"38:34", "38:44", "38:52", "38:57", "39:12", "39:34", "39:48",
"39:49", "40:04", "40:29", "41:33", "41:45", "45:15"), class = "factor"),
PuntAvgOff = c(36.3, 37.9, 45, 38.3, 48.2, 46.6), Opponent = structure(c(32L,
21L, 27L, 11L, 7L, 28L), .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"),
ScoreDef = c(7L, 16L, 13L, 21L, 10L, 10L), FirstDownDef = c(11L,
21L, 13L, 21L, 8L, 16L), ThirdDownPctDef = structure(c(14L,
34L, 25L, 13L, 7L, 10L), .Label = c("0%", "10%", "11%", "12%",
"13%", "14%", "15%", "17%", "18%", "19%", "20%", "21%", "22%",
"23%", "24%", "25%", "27%", "29%", "30%", "31%", "33%", "35%",
"36%", "37%", "38%", "40%", "41%", "42%", "43%", "44%", "45%",
"46%", "47%", "50%", "53%", "54%", "55%", "56%", "57%", "58%",
"59%", "60%", "61%", "62%", "63%", "64%", "65%", "67%", "69%",
"73%", "77%", "8%", "80%", "9%", "92%"), class = "factor"),
RushAttDef = c(24L, 32L, 20L, 21L, 23L, 21L), RushYdsDef = c(84L,
154L, 108L, 62L, 65L, 85L), PassAttDef = c(27L, 35L, 20L,
33L, 25L, 41L), PassCompDef = c(15L, 19L, 14L, 24L, 10L,
17L), PassYdsDef = c(133L, 216L, 195L, 262L, 99L, 190L),
PassIntDef = c(0L, 1L, 1L, 1L, 1L, 1L), FumblesDef = c(0L,
0L, 4L, 0L, 1L, 1L), SackYdsDef = c(8L, 16L, 12L, 16L, 10L,
23L), PenYdsDef = c(35L, 70L, 20L, 30L, 40L, 30L), TimePossDef = structure(c(52L,
348L, 32L, 225L, 46L, 161L), .Label = c("14:45", "18:15",
"18:27", "19:31", "19:56", "20:11", "20:12", "20:26", "20:48",
"21:03", "21:08", "21:16", "21:26", "21:28", "21:35", "21:44",
"21:45", "21:52", "21:54", "22:03", "22:08", "22:12", "22:16",
"22:25", "22:30", "22:31", "22:33", "22:34", "22:38", "22:39",
"22:53", "22:55", "22:59", "23:09", "23:10", "23:12", "23:15",
"23:23", "23:28", "23:30", "23:33", "23:37", "23:38", "23:42",
"23:43", "23:45", "23:48", "23:49", "23:56", "24:06", "24:13",
"24:17", "24:18", "24:21", "24:33", "24:34", "24:35", "24:41",
"24:43", "24:49", "24:50", "24:54", "24:58", "24:59", "25:01",
"25:02", "25:05", "25:11", "25:14", "25:16", "25:19", "25:25",
"25:29", "25:31", "25:32", "25:34", "25:36", "25:37", "25:38",
"25:40", "25:41", "25:46", "25:47", "25:53", "25:55", "25:57",
"25:58", "26:00", "26:04", "26:09", "26:10", "26:11", "26:12",
"26:13", "26:16", "26:20", "26:27", "26:32", "26:36", "26:37",
"26:38", "26:39", "26:40", "26:41", "26:44", "26:46", "26:49",
"26:53", "26:56", "26:59", "27:01", "27:04", "27:10", "27:12",
"27:13", "27:15", "27:18", "27:20", "27:24", "27:25", "27:26",
"27:27", "27:28", "27:30", "27:32", "27:37", "27:40", "27:44",
"27:46", "27:47", "27:48", "27:50", "27:51", "27:52", "27:53",
"27:55", "27:57", "27:58", "27:59", "28:00", "28:01", "28:03",
"28:05", "28:06", "28:07", "28:13", "28:14", "28:16", "28:17",
"28:18", "28:19", "28:21", "28:22", "28:24", "28:25", "28:28",
"28:29", "28:32", "28:38", "28:40", "28:41", "28:45", "28:47",
"28:49", "28:51", "28:53", "28:55", "28:57", "28:58", "28:59",
"29:00", "29:02", "29:05", "29:07", "29:08", "29:11", "29:13",
"29:14", "29:18", "29:19", "29:20", "29:26", "29:27", "29:29",
"29:31", "29:32", "29:33", "29:34", "29:36", "29:37", "29:38",
"29:41", "29:42", "29:43", "29:49", "29:50", "29:55", "29:56",
"29:59", "30:01", "30:04", "30:05", "30:10", "30:11", "30:17",
"30:18", "30:19", "30:22", "30:23", "30:24", "30:26", "30:27",
"30:28", "30:29", "30:31", "30:33", "30:34", "30:40", "30:41",
"30:42", "30:46", "30:47", "30:49", "30:52", "30:53", "30:55",
"30:56", "30:58", "31:00", "31:01", "31:02", "31:03", "31:05",
"31:07", "31:09", "31:11", "31:13", "31:15", "31:19", "31:20",
"31:22", "31:28", "31:31", "31:32", "31:35", "31:36", "31:38",
"31:39", "31:41", "31:42", "31:43", "31:44", "31:46", "31:47",
"31:53", "31:54", "31:55", "31:57", "31:59", "32:00", "32:01",
"32:02", "32:03", "32:05", "32:07", "32:08", "32:09", "32:10",
"32:12", "32:13", "32:14", "32:16", "32:20", "32:23", "32:28",
"32:30", "32:32", "32:33", "32:34", "32:35", "32:36", "32:40",
"32:42", "32:45", "32:47", "32:48", "32:50", "32:56", "32:59",
"33:01", "33:04", "33:07", "33:11", "33:14", "33:16", "33:19",
"33:20", "33:21", "33:22", "33:23", "33:24", "33:28", "33:33",
"33:40", "33:44", "33:47", "33:48", "33:49", "33:50", "33:51",
"33:56", "34:00", "34:02", "34:03", "34:05", "34:07", "34:13",
"34:14", "34:19", "34:20", "34:22", "34:23", "34:24", "34:26",
"34:28", "34:29", "34:31", "34:35", "34:41", "34:44", "34:46",
"34:49", "34:55", "34:58", "34:59", "35:01", "35:02", "35:06",
"35:10", "35:11", "35:17", "35:19", "35:25", "35:26", "35:27",
"35:39", "35:42", "35:43", "35:47", "35:54", "36:04", "36:11",
"36:12", "36:15", "36:17", "36:18", "36:22", "36:23", "36:27",
"36:30", "36:32", "36:37", "36:45", "36:48", "36:50", "36:51",
"37:01", "37:05", "37:07", "37:21", "37:22", "37:26", "37:27",
"37:29", "37:30", "37:35", "37:42", "37:44", "37:48", "37:52",
"37:57", "38:08", "38:15", "38:16", "38:23", "38:25", "38:32",
"38:34", "38:44", "38:52", "38:57", "39:12", "39:34", "39:48",
"39:49", "40:04", "40:29", "41:33", "41:45", "45:15"), class = "factor"),
Site = structure(c(1L, 3L, 3L, 1L, 1L, 1L), .Label = c("H",
"N", "V"), class = "factor"), Line = c(4.5, -4.5, 2.5, -3,
-2, 1), Totalline = c(41.5, 41.5, 42, 41, 37.5, 38.5), TotalYdsOff = c(370L,
217L, 306L, 479L, 358L, 340L), TotalYdsDef = c(217L, 370L,
303L, 324L, 164L, 275L), ActualLine = c(-9L, 9L, -10L, -13L,
-7L, -24L)), .Names = c("Date", "TeamName", "ScoreOff", "FirstDownOff",
"ThirdDownPctOff", "RushAttOff", "RushYdsOff", "PassAttOff",
"PassCompOff", "PassYdsOff", "PassIntOff", "FumblesOff", "SackYdsOff",
"PenYdsOff", "TimePossOff", "PuntAvgOff", "Opponent", "ScoreDef",
"FirstDownDef", "ThirdDownPctDef", "RushAttDef", "RushYdsDef",
"PassAttDef", "PassCompDef", "PassYdsDef", "PassIntDef", "FumblesDef",
"SackYdsDef", "PenYdsDef", "TimePossDef", "Site", "Line", "Totalline",
"TotalYdsOff", "TotalYdsDef", "ActualLine"), row.names = c(NA,
6L), class = "data.frame")
我添加了TotalYds[Off|Def]列，因为这很简单。最接近正确计算移动平均值的事情是用zoo和plur库以及以下命令完成的：
ddply(df2, .(TeamName), summarise, rollmean(TotalYdsOff, k=4, fill=0, align="right"))
哪个几乎符合我的要求，只是它将平均使用当前周的信息。
至于获得对手的匹配信息，我想有一种方法可以从"TeamName"one_answers"Date"都与当前行的"opponent"one_answers"Date"匹配的行中提取相同的数据。这是因为数据库在给定的比赛中有两个条目，一个用于主队，另一个用于客场(*Off和*Def交换)。看看示例数据中的第1行和第2行，特别是Date、TeamName和Opponent，你就会明白我想说什么了。
这里有什么指导吗？我想，对于那些在R中花了几天时间进行修补的人来说，这是相对微不足道的，他们可能知道一些函数或库可以做到这一点。I、 然而，我才进去几天，因此遇到了一些麻烦。
解决问题1的一个简单方法是使用您描述的ddply调用，但在删除本周所有游戏的情况下向其传递一个数据帧：
require(plyr)
dfRedacted <- ddply(df2, .(TeamName), function(x) subset(x, Date!=max(Date)))
meanStats <- ddply(dfRedacted, .(TeamName), summarise, rollmean(TotalYdsOff, k=4, fill=0, align="right"))
目前，我最终创建了一个函数来计算某场比赛(但不包括)的赛季平均值，并将结果放在一个单独的向量中，然后只使用cbind()将其添加到数据帧中：
foo <- vector()
for(each in levels(df$TeamName)) {
foo <- c(foo, calc_avg_yds(df, each))
}
df<-cbind(df[订单(df$TeamName)，]，AvgTotalYdsOff=foo)
正如你所看到的，我按teamname重新排序了df(第二个是date，它已经按它排序了)，以确保它们匹配。
为了从相应的行(游戏中另一支球队的行)获得信息，我做了一个循环，把所有东西都放在一个向量中，然后是另一个cbind()：
for(i in nrow(df)) {
foo <- c(foo, subset(df, TeamName==df[i,]$Opponent & Date==df[i,]$Date)$AvgTotalYdsOff)
}
df <- cbind(df, AvgTotalYdsDef = foo)
最后，我选择了简单粗暴的路线，因为我不知道有更好的选择。希望这能帮助将来遇到类似问题的人。