我正在对股票 yield 的多个窗口进行范围计算(即最大值和最小值)。
我在 dplyr 中有我的版本,但是很多人发布了基准测试,其中使用 data.table 的计算要快得多。我已经用 data.table 语法创建了这个版本,但是它比 dplyr 慢。
谁能帮我找到更好的方法来使用 data.table 使其更快?
非常感谢。
library(Quandl)
library(tidyr)
library(dplyr)
library(data.table)
library(microbenchmark)
tickers <- c("GOOG/NASDAQ_AAPL", "GOOG/NASDAQ_MSFT",
"GOOG/NYSE_IBM", "GOOG/NASDAQ_GOOG")
data <- Quandl(tickers,transformation = "rdiff")
returns <- gather(data, stock, value, -Date) %>%
separate(stock, c("name", "field"), " - ") %>%
filter(
field == "Close"
) %>%
select(
- field
)
returns_dt <- data.table(returns)
multi_window_range <- function(data) {
result_1y <- data %>%
filter(
Date >= Sys.Date() - 365
) %>%
group_by(name) %>%
summarise(
max_1y = max(value, na.rm = TRUE),
min_1y = min(value, na.rm = TRUE)
)
result_2y <- data %>%
filter(
Date >= Sys.Date() - 365 * 2
) %>%
group_by(name) %>%
summarise(
max_2y = max(value, na.rm = TRUE),
min_2y = min(value, na.rm = TRUE)
)
result_5y <- data %>%
filter(
Date >= Sys.Date() - 365 * 5
) %>%
group_by(name) %>%
summarise(
max_5y = max(value, na.rm = TRUE),
min_5y = min(value, na.rm = TRUE)
)
return(inner_join(inner_join(result_1y, result_2y, by = "name"), result_5y, by = "name"))
}
multi_window_range_dt <- function(data) {
setkey(data, name)
result_1y <- data[Date >= Sys.Date() - 365,
list(
max_1y = max(value, na.rm = TRUE),
min_1y = min(value, na.rm = TRUE)
), by = "name"]
result_2y <- data[Date >= Sys.Date() - 365 * 2,
list(
max_2y = max(value, na.rm = TRUE),
min_2y = min(value, na.rm = TRUE)
), by = "name"]
result_5y <- data[Date >= Sys.Date() - 365 * 5,
list(
max_5y = max(value, na.rm = TRUE),
min_5y = min(value, na.rm = TRUE)
), by = "name"]
return(result_1y[result_2y][result_5y])
}
microbenchmark(
multi_window_range(returns),
multi_window_range_dt(returns_dt)
)
Unit: milliseconds
expr min lq mean median uq max neval
multi_window_range(returns) 6.341532 6.522303 6.915266 6.692666 6.922623 10.16709 100
multi_window_range_dt(returns_dt) 7.537073 7.738516 8.066579 7.865968 8.073114 12.68021 100
最佳答案
尝试这个:
multi_window_range_dt2 <- function(data) {
data[, {
rng1 <- range(value[Date > Sys.Date() - 365], na.rm = TRUE)
rng2 <- range(value[Date > Sys.Date() - 2*365], na.rm = TRUE)
rng5 <- range(value[Date > Sys.Date() - 5*365], na.rm = TRUE)
list(max_1y = rng1[2], min_1y = rng1[1],
max_2y = rng2[2], min_2y = rng2[1],
max_5y = rng5[2], min_5y = rng5[1])
}, by = "name"]
}
library(rbenchmark)
benchmark(multi_window_range(returns), multi_window_range_dt2(returns_dt))[1:4]
这在我的笔记本电脑上给出了这个:
test replications elapsed relative
1 multi_window_range(returns) 100 2.39 1.189
2 multi_window_range_dt2(returns_dt) 100 2.01 1.000
这表明
multi_window_range
比 multi_window_range_dt2
多花 18.9% 的时间:
关于r - 多窗口范围计算 data.table 与 dplyr,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/26864090/