这个问题与我试图为我的体育工作优化的一段R代码有关。对于那里的所有体育迷来说，背景是篮球。我的任务是使用以下数据框的比赛和球员ID来估计谁在篮球场上任何时候（5名球员）。数据数据帧对应于一个篮球比赛。因此，我使用的数据集是来自一个篮球比赛的数据，按每个比赛类型细分。篮球队有大约10个独特的球员ID。我有一些可以使用代码的可行数据：
team1 = c(76580, 76584, 76593, 76586, 76583, 76585, 76588, 76596, 76582, 76592, 76589, 76587)
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接下来，下面是我试图填充的on_court数据帧（未填写，仅使用默认的NA初始化）。 on_court数据帧的行数与数据数据帧的行数相同。我有效地猜测谁在场上每场比赛。一旦填写，on_court数据帧的每一行将有5个player_ID。
head(on_court, n = 20)
# P1 P2 P3 P4 P5 Q1 Q2 Q3 Q4 Q5
# 1 NA NA NA NA NA NA NA NA NA NA
# 2 NA NA NA NA NA NA NA NA NA NA
# 3 NA NA NA NA NA NA NA NA NA NA
# 4 NA NA NA NA NA NA NA NA NA NA
# 5 NA NA NA NA NA NA NA NA NA NA
# 6 NA NA NA NA NA NA NA NA NA NA
# 7 NA NA NA NA NA NA NA NA NA NA
# 8 NA NA NA NA NA NA NA NA NA NA
# 9 NA NA NA NA NA NA NA NA NA NA
# 10 NA NA NA NA NA NA NA NA NA NA
# 11 NA NA NA NA NA NA NA NA NA NA
# 12 NA NA NA NA NA NA NA NA NA NA
# 13 NA NA NA NA NA NA NA NA NA NA
# 14 NA NA NA NA NA NA NA NA NA NA
# 15 NA NA NA NA NA NA NA NA NA NA
# 16 NA NA NA NA NA NA NA NA NA NA
# 17 NA NA NA NA NA NA NA NA NA NA
# 18 NA NA NA NA NA NA NA NA NA NA
# 19 NA NA NA NA NA NA NA NA NA NA
# 20 NA NA NA NA NA NA NA NA NA NA
使用两个玩家ID列，对应于每个游戏中涉及的玩家，可以使用以下策略猜测球场上的谁 - 我只是假设最近的5个玩家被标记在事件中的人都在球场上。重要的是要注意数据数据帧按顺序包含篮球比赛的所有比赛，这使得这种方法成为可能。
我的代码使用前后计数器（分别为i和j），总是试图找到最近出现在篮球比赛中的5个玩家ID，并假设这些是球场上的五名球员。这个特定的游戏有514个播放/事件/ nrow（数据）。由于每场比赛最多有2个玩家ID，这意味着我有1028个玩家ID可供使用。我当前的代码迭代了这1028个玩家ID，看起来像这样。此代码应该能够与上面的列表和数据框一起运行：
# 1 - Before the for loop
# 1.a create custom rounding function that rounds 0.5 upwards
round2 = function(x, n) {
posneg = sign(x)
z = abs(x)*10^n
z = z + 0.5
z = trunc(z)
z = z/10^n
z*posneg
}
#1.b create on_court dataframe
on_court = data.frame(matrix(ncol = 10, nrow = nrow(data)))
names(on_court) = c("P1", "P2", "P3", "P4", "P5", "Q1", "Q2", "Q3", "Q4", "Q5")
# 1.c combine the two player ID columns into one column
player_ids_unlisted = as.vector(t(data[, c("player_id", "player2_id")]))
K = 0.525
j = 1
# begin the for loop
for(i in 1:length(player_ids_unlisted)) {
# dividing the counter in half gives the play number
i_2 = round2(i/2, 0)
j_2 = round2(j/2, 0)
# if the playtype is 'new period', bring the back counter j to match the front counter i.
# at the start of a new period of the game, this resets who we think is on the court
if (data$indicator_newperiod[i_2] == 1) {
j = i
j_2 = i_2
}
# unique player_IDs in range of events / counters,
# then remove NA player IDs,
# then filter the list by players only on team 1
uq_players = unique(player_ids_unlisted[j:i])
uq_players = uq_players[!is.na(uq_players)]
uq_players = uq_players[uq_players %in% team1]
# length of unique player_ID list
uq_span = length(uq_players)
# early in the game, if 5 unique player_IDs have yet to appear, then pass
if(uq_span < 5) {
next
}
# if we have exactly 5 players identified, set them as the players on court
else if(uq_span == 5) {
on_court[i_2, 1:5] = uq_players
}
else {
# as the i counter increments, eventually the list of unique players will reach 6 players.
# when that is the case, I increment the back counter j until we get back down to 5 players
# (effectively removing the player who hasn't been tagged in an event for the most time from the list of
# players on court). Then I update whose on court again.
while (uq_span > 5) {
j = j + 1
# again, find IDs, remove NAs, filter by team 1
uq_players = unique(player_ids_unlisted[j:i])
uq_players = uq_players[!is.na(uq_players)]
uq_players = uq_players[uq_players %in% team1]
uq_span = length(uq_players)
}
# this j_2 is the play index that the player we're subbing out last was tagged in an event
# mid_idx is the index between when the j_2 player_ID was last in an event and the new i_2 player_ID was just in an event
j_2 = round2(j/2, 0)
mid_idx = round2((i_2*K + j_2*(1-K)), 0)
# update with this lineup going all the way back to the mid_idx play index
uq_players_df = as.data.frame(t(uq_players))
count = i_2 - mid_idx + 1
# and finally update the on court dataframe
uq_players_df = uq_players_df[rep(row.names(uq_players_df), count), ]
on_court[mid_idx:i_2, 1:5] = uq_players_df
}
# for the earliest plays of each half, before 5 player IDs were identified
# we left NAs in the on_court dataframe for whose on court.
# once we have 5 player IDs, drag them up to fill in the earlier NAs
this_row_NA = ifelse(is.na(on_court$P1[i_2]), 1, 0)
last_row_NA = ifelse(is.na(on_court$P1[(i_2-1)]), 1, 0)
if (last_row_NA > this_row_NA) {
NA_plays = which(is.na(on_court$P1))
start = min(NA_plays)
on_court[start:(i_2-1), 1:5] = on_court[i_2, 1:5]
}
}
那是我的代码。这有点复杂，因为即使只有514次播放，计数器i和j也会计数到1028，因此在索引播放次数时我必须使用i_2和j_2计数器。我引入mid_idx计数器进一步复杂化，我将其用于将玩家分组到游戏中，然后才能在游戏中进行标记。但是，我正在寻找帮助的主要问题是，这是完全在一个大的for循环中完成，有几个if情况，并且目前需要太多时间来运行（每场游戏约0.50秒）。我需要这个代码来运行成千上万的篮球游戏，并希望这个代码能够在1/10到1/100之间运行（可能无法实现）。矢量化代码，或使用应用函数，而不是大型for循环可能会使代码运行得更快，但我一直在努力思考它是如何实现的。今年夏天我一直在努力提高代码性能，并且努力改进这样的代码既困难又有用。如何加快速度，我们将不胜感激。
非常感谢！
答案 0 :(得分：0)
不幸的是，我认为你必须遍历数据帧的每一行。但是，我认为你在法庭上确定5名球员的算法可以更轻松。在我的解决方案中，我只迭代数据的行数，并使用长度为5的向量来控制场上的玩家：
for(i in 1:nrow(data)) {
# reset players on court
if(i == 1 | data$indicator_newperiod[i] == 1) poc = numeric(5)
# Add new Player1 (if any)
if(!is.na(data$player_id[i]) && !(data$player_id[i] %in% poc)) {
poc[1:4] = poc[2:5]
poc[5] = data$player_id[i]
}
# Add new Player2 (if any)
if(!is.na(data$player2_id[i]) && !(data$player2_id[i] %in% poc)) {
poc[1:4] = poc[2:5]
poc[5] = data$player2_id[i]
}
# fill result dataframe
on_court[i, 1:5] = poc
}
用于填写休息后的句点我向后迭代：
# Filling up players on court, after break
for(i in nrow(on_court):2) {
if(on_court$P1[i-1] == 0) on_court[i-1, 1:5] = on_court[i, 1:5]
}