我有一个数据集与赢家，输家，日期，winner_points和loser_points。 $ b
对于每一行，我需要两个新的列，一个是赢家，一个是失败者，表示他们迄今获得了多少分（赢家和输家）。
示例数据：
赢家<-c（1,2,3， 1,2,3,1,2,3） loser <-c（3,1,1,2,1,1,3,1,2） date <-c（ 2017年10月1日， 2017年10月2日， 2017年10月3日， 2017年10月4日， 2017年10月5日， 2017年10月6日，2017年-10-07，2017-10-08，2017-10-09） winner_points <-c（2,1,2,1,2,1,2,1,2） loser_points< -c（1,0,1,0,1,0,1,0,1） test_data< - data.frame（winner，loser，date = as.Date日期），winner_points，loser_points）我想输出为： pre $ winner_points_sum <-c（0,0,1,3,1,3,5,3,5） loser_points_sum <-c （0，2，1，4，5，4，7，4） test_data< - data.frame（赢家，输家，日期= as.Date（日期），赢家点，输家点，赢家点数，输家点数） $ b
到目前为止我已经解决了这个问题，就是做一个for循环，例如：
library（dplyr） test_data $ winner_points_sum_loop < - 0 test_data $ loser_points_sum_loop < - 0 for（i in row.names（test_data））{ test_data [i， ] $ winner_points_sum_loop < - （ test_data％>％ dplyr :: filter（winner == test_data [i，] $ winner&日期< test_data [i，] $ date）％>％ dplyr :: summarize（points = sum（winner_points，na.rm = TRUE）） + test_data％>％ dplyr :: filter（loser == test_data [i，] $ winner& date< test_data [i，] $ date）％>％ dplyr :: summarize（points = sum（loser_points，na .rm = TRUE）））} test_data $ winner_points_sum_loop< - unlist（test_data $ winner_points_sum_loop）有什么建议如何解决这个问题？当行数加起来时，查询需要相当长的一段时间。我已经试着用AVE函数来详细说明，我可以做一列来作为赢家，但是不能弄清楚如何将他们的积分作为失败者加入。
解决方案
code>赢者<-c（1,2,3,1,2,3,1,2,3）失败者<-c（3,1,1,2,1,1 ，3,1,2）日期％＃分隔列排列（日期）％>％＃订单日期（如果不是） group_by（玩家）％> ;每个玩家的％＃ mutate（sum_points = cumsum（points） - points）％>％＃获得积分到该日期 ungroup（）％>％＃忘记分组$ b $ （pl_pts_sumpts，player，points，sum_points）％>％＃unite columns spread（type，pl_pts_sumpts）％>％＃整形 separate（loser，c（loser，loser_points ，loser_points_sum），convert = T）％>％＃分隔列并给出适当的名字 separate（winner，c（winner，winner_poin转换= T）％>％ select（赢家，输家，日期，赢家点数，输家点数，赢家点数，输家点数）＃选择你喜欢的订单 ＃＃A tibble：9 x 7 ＃赢家失败日期winner_points loser_points winner_points_sum loser_points_sum ＃*< int> < INT> <日期> < INT> < INT> < INT> < INT> ＃1 1 3 2017-10-01 2 1 0 0 ＃2 2 1 2017-10-02 1 0 0 2 ＃3 3 1 2017-10-03 2 1 1 2 ＃4 1 2 2017-10-04 1 0 3 1 ＃5 2 1 2017-10-05 2 1 1 4 ＃6 3 1 2017-10-06 1 0 3 5 ＃7 1 3 2017-10-07 2 1 5 4 ＃8 2 1 2017-10-08 1 0 3 7 ＃9 3 2 2017-10-09 2 1 5 4
I tried asking this question before but was it was poorly stated. This is a new attempt cause I haven't solved it yet.
I have a dataset with winners, losers, date, winner_points and loser_points.
For each row, I want two new columns, one for the winner and one for the loser that shows how many points they have scored so far (as both winners and losers).
Example data:
winner <- c(1,2,3,1,2,3,1,2,3) loser <- c(3,1,1,2,1,1,3,1,2) date <- c("2017-10-01","2017-10-02","2017-10-03","2017-10-04","2017-10-05","2017-10-06","2017-10-07","2017-10-08","2017-10-09") winner_points <- c(2,1,2,1,2,1,2,1,2) loser_points <- c(1,0,1,0,1,0,1,0,1) test_data <- data.frame(winner, loser, date = as.Date(date), winner_points, loser_points)I want the output to be:
winner_points_sum <- c(0, 0, 1, 3, 1, 3, 5, 3, 5) loser_points_sum <- c(0, 2, 2, 1, 4, 5, 4, 7, 4) test_data <- data.frame(winner, loser, date = as.Date(date), winner_points, loser_points, winner_points_sum, loser_points_sum)How I've solved it thus far is to do a for loop such as:
library(dplyr) test_data$winner_points_sum_loop <- 0 test_data$loser_points_sum_loop <- 0 for(i in row.names(test_data)) { test_data[i,]$winner_points_sum_loop <- ( test_data %>% dplyr::filter(winner == test_data[i,]$winner & date < test_data[i,]$date) %>% dplyr::summarise(points = sum(winner_points, na.rm = TRUE)) + test_data %>% dplyr::filter(loser == test_data[i,]$winner & date < test_data[i,]$date) %>% dplyr::summarise(points = sum(loser_points, na.rm = TRUE)) ) } test_data$winner_points_sum_loop <- unlist(test_data$winner_points_sum_loop)Any suggestions how to tackle this problem? The queries take quite some time when the row numbers add up. I've tried elaborating with the AVE function, I can do it for one column to sum a players point as winner but can't figure out how to add their points as loser.
解决方案 winner <- c(1,2,3,1,2,3,1,2,3) loser <- c(3,1,1,2,1,1,3,1,2) date <- c("2017-10-01","2017-10-02","2017-10-03","2017-10-04","2017-10-05","2017-10-06","2017-10-07","2017-10-08","2017-10-09") winner_points <- c(2,1,2,1,2,1,2,1,2) loser_points <- c(1,0,1,0,1,0,1,0,1) test_data <- data.frame(winner, loser, date = as.Date(date), winner_points, loser_points) library(dplyr) library(tidyr) test_data %>% unite(winner, winner, winner_points) %>% # unite winner columns unite(loser, loser, loser_points) %>% # unite loser columns gather(type, pl_pts, winner, loser, -date) %>% # reshape separate(pl_pts, c("player","points"), convert = T) %>% # separate columns arrange(date) %>% # order dates (in case it's not) group_by(player) %>% # for each player mutate(sum_points = cumsum(points) - points) %>% # get points up to that date ungroup() %>% # forget the grouping unite(pl_pts_sumpts, player, points, sum_points) %>% # unite columns spread(type, pl_pts_sumpts) %>% # reshape separate(loser, c("loser", "loser_points", "loser_points_sum"), convert = T) %>% # separate columns and give appropriate names separate(winner, c("winner", "winner_points", "winner_points_sum"), convert = T) %>% select(winner, loser, date, winner_points, loser_points, winner_points_sum, loser_points_sum) # select the order you prefer # # A tibble: 9 x 7 # winner loser date winner_points loser_points winner_points_sum loser_points_sum # * <int> <int> <date> <int> <int> <int> <int> # 1 1 3 2017-10-01 2 1 0 0 # 2 2 1 2017-10-02 1 0 0 2 # 3 3 1 2017-10-03 2 1 1 2 # 4 1 2 2017-10-04 1 0 3 1 # 5 2 1 2017-10-05 2 1 1 4 # 6 3 1 2017-10-06 1 0 3 5 # 7 1 3 2017-10-07 2 1 5 4 # 8 2 1 2017-10-08 1 0 3 7 # 9 3 2 2017-10-09 2 1 5 4
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