Created
November 25, 2019 12:34
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test of dtplyr speed
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library(dplyr) | |
library(data.table) | |
library(dtplyr) | |
df <- data.frame(customer = sample(100000, size = 5000000, replace = TRUE), | |
value = runif(5000000)) | |
# dplyr | |
t0 <- Sys.time() | |
res_dp <- df %>% | |
group_by(customer) %>% | |
summarise(value = sum(value)) | |
Sys.time() - t0 | |
# data.table | |
t0 <- Sys.time() | |
dt <- as.data.table(df) | |
res_dt <- dt[, .(value = sum(value)), keyby = .(customer)] | |
Sys.time() - t0 | |
# dtplyr | |
t0 <- Sys.time() | |
res_dtp <- df %>% | |
lazy_dt() %>% | |
group_by(customer) %>% | |
summarise(value = sum(value)) %>% | |
as_tibble() | |
Sys.time() - t0 | |
# dtplyr is /lazy/ | |
# dtplyr shows how R can manipulate language | |
# | |
# why use dtplyr if you know DT? | |
# - consistency of style | |
# - verbosity is good for larger code bases | |
# - immutability is good for production code | |
# - cooperating with people who don't use DT | |
# | |
# what if you must use DT snippets but don't want to import it all? | |
# withr::with_package() |
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