R dplyr：基于行的条件拆分/应用/组合

这是 dplyr 版本这个问题

我有以下内容data.table

initial.date <- as.POSIXct('2018-10-27 10:00:00',tz='GMT')
last.date <- as.POSIXct('2018-10-28 17:00:00',tz='GMT') 
    PriorityDateTime=seq.POSIXt(from=initial.date,to = last.date,by = '30 sec')
    TradePrice=seq(from=1, to=length(PriorityDateTime),by = 1)
    ndf<- data.frame(PriorityDateTime,TradePrice)
    ndf$InstrumentSymbol <- rep_len(x = c('asset1','asset2'),length.out = length(ndf$PriorityDateTime))
    ndf$id <- seq(1:length(x = ndf$InstrumentSymbol))
    ndf$datetime <- ymd_hms(ndf$PriorityDateTime)
    res <- ndf %>% data.table()

看起来像这样：

    > res
         PriorityDateTime TradePrice InstrumentSymbol   id            datetime
   1: 2018-10-27 10:00:00          1           asset1    1 2018-10-27 10:00:00
   2: 2018-10-27 10:00:30          2           asset2    2 2018-10-27 10:00:30
   3: 2018-10-27 10:01:00          3           asset1    3 2018-10-27 10:01:00
   4: 2018-10-27 10:01:30          4           asset2    4 2018-10-27 10:01:30
   5: 2018-10-27 10:02:00          5           asset1    5 2018-10-27 10:02:00

Using dplyr最优雅、最快速的方法是什么：

1. Split: 对于每行定义其他具有datetime过去或未来最多 60 秒（时间差小于 60 秒），并且具有相同的InstrumentSymbol正如这条线的。
2. 应用：在这些接近的线中，哪一条最接近TradePrice到这条线的TradePrice[i]: 得到index在原来的data.frame和TradePrice另一行的
3. 合并：将结果作为新列重新组合到原始列中data.table例如作为新列index.minpricewithin60 and minpricewithin60

结果示例：

> res
         PriorityDateTime TradePrice InstrumentSymbol   id            datetime minpricewithin60 index.minpricewithin60
   1: 2018-10-27 10:00:00          1           asset1    1 2018-10-27 10:00:00                2                      2
   2: 2018-10-27 10:00:30          2           asset2    2 2018-10-27 10:00:30                4                      4
   3: 2018-10-27 10:01:00          3           asset1    3 2018-10-27 10:01:00                1                      1
   4: 2018-10-27 10:01:30          4           asset2    4 2018-10-27 10:01:30                2                      2
   5: 2018-10-27 10:02:00          5           asset1    5 2018-10-27 10:02:00                3                      3

我想我的问题可以问为“如何修复行dplyr以类似的方式apply(df,1, function(x) df$column-x["column"])我有潜在的解决方案dplyr但到目前为止一切都相当缓慢。

解决方案使用dplyr包装和lapply功能：

result_df <- do.call(rbind, lapply(1:nrow(res), function(row_id) {

             temp <-   res %>% filter(InstrumentSymbol == res$InstrumentSymbol[row_id]) %>% 
                       mutate(time_diff = abs(difftime(res$datetime[row_id], datetime, units = "secs")),
                              diff_price = abs(TradePrice - res$TradePrice[row_id])) %>% 
                       filter(id != res$id[row_id], time_diff <= 60) %>% 
                       filter(diff_price == min(diff_price)) %>% select(TradePrice, id) %>% 
                       rename(minpricewithin60 = TradePrice, index.minpricewithin60 = id)

             if(nrow(temp) == 0) temp[1,] <- c(NA, NA)

             return(bind_cols(res %>% slice(rep(row_id, nrow(temp))), temp))
                                                                  }))

head(result_df)

     PriorityDateTime TradePrice InstrumentSymbol id            datetime minpricewithin60 index.minpricewithin60
1 2018-10-27 10:00:00          1           asset1  1 2018-10-27 10:00:00                3                      3
2 2018-10-27 10:00:30          2           asset2  2 2018-10-27 10:00:30                4                      4
3 2018-10-27 10:01:00          3           asset1  3 2018-10-27 10:01:00                1                      1
4 2018-10-27 10:01:00          3           asset1  3 2018-10-27 10:01:00                5                      5
5 2018-10-27 10:01:30          4           asset2  4 2018-10-27 10:01:30                2                      2
6 2018-10-27 10:01:30          4           asset2  4 2018-10-27 10:01:30                6                      6