Data. Table Fcase Versus Dplyr Case_When
The Following Code Throws an Error When Evaluating the Line with Fcase. Library(Data. Table) Library(Dplyr) Tbl. Test # a Tibble: 2 X 6 #> Expression Min...
The following code throws an error when evaluating the line with fcase.
library(data.table)
library(dplyr)
tbl.test <- data.table(x = 0.5*(1:2))
tbl.test[, .(fcase(x < 1, x+1,
default = x))]
tbl.test[, .(case_when(x < 1 ~ x+1,
TRUE ~ x)
)]
As I use case_when in this way (for most complex stuff ..), I wanted to switch to fcase, hoping a real gain in performance ..
Does anyone see where the devil hide with this usage of fcase ? The point is that I can't use a column of my table as default value with fcase ..
1 Answer
You don't explicitly state a question, but I assume you'd like to know if there's a way to use a vectorized default value with fcase(). One way to do that would be to construct a vector of TRUEs of equal length to your other conditions as the last element, similarly to how case_when() works:
library(data.table)
library(dplyr)
set.seed(123)
tbl.test <- data.table(x = rnorm(1e6))
bench::mark(
fcase = tbl.test[, .(fcase(x < 1, x + 1, rep_len(TRUE, length(x)), x))],
case_when = tbl.test[, .(case_when(x < 1 ~ x + 1, TRUE ~ x))]
)
#> Warning: Some expressions had a GC in every iteration; so filtering is disabled.
#> # A tibble: 2 x 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 fcase 16.5ms 24.3ms 33.3 36.1MB 43.1
#> 2 case_when 146.4ms 147.9ms 6.60 129.9MB 28.1
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