Multi windows range calculations data.table vs dplyr - r

Question

I'm doing range calculations (i.e. max and min) over multiple windows on stocks returns.
I have my version in dplyr, but many people publishing benchmarking where calculations with data.table are much faster. I've created the version with data.table syntax, however it's slower than dplyr one.
Could anyone help me to find better way to use data.table to make it faster?
Many thanks.
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

Answer 1

Try this:
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]
which gives this on my laptop:
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
This indicates that multi_window_range takes 18.9% more time than multi_window_range_dt2: