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问题描述
这是
我有以下 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
使用 dplyr
什么是最优雅,最快捷的方法:
Using dplyr
what is the most elegant and fast way to:
- 拆分:对于每一行,定义其他具有
datetime
在过去或将来最多60秒(时间差小于60秒),并且与InstrumentSymbol
相同 - 应用:在这些接近的行中,最接近该行的
TradePrice的
:在原始TradePrice
[一世]data.frame
和<$ c $中获得索引
另一行的c> TradePrice - 合并:将结果重新合并为原始
data.table $ c $的新列c>例如作为新列
index.minpricewithin60
和minpricewithin60
- Split: For each line define the other lines that have a
datetime
at most 60 secs in the past or future (time difference less than 60secs), and have the sameInstrumentSymbol
as this line's. - Apply: among these close lines, which one has the closest
TradePrice
to this line'sTradePrice[i]
: get theindex
in the originaldata.frame
and theTradePrice
of this other row - Combine: recombine the results as new columns into the original
data.table
for example as new columnsindex.minpricewithin60
andminpricewithin60
示例结果:
> 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
我想我的问题可以被问为如何在 dplyr
中修复行,其方式与 apply(df,1,function(x)df $ column-x [ column])
我有使用 dplyr
的潜在解决方案,但是到目前为止,一切都很缓慢。
I guess my problem can be asked as "how to fix a row in dplyr
in a similar way to apply(df,1, function(x) df$column-x["column"])
I have potential solutions using dplyr
but so far all were quite slow.
推荐答案
使用 dplyr
软件包和 lapply解决方案
函数:
result_df <- do.call(rbind, lapply(1:nrow(res), function(row_id) {
temp <- res %>% filter(InstrumentSymbol == res$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
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