One may wonder how fast is
tidyfst. Well, it depends. Generally, it is as fast as
data.table because it is backed by it, but it would spend extra
time on the generation of data.table codes. This extra time is marginal
on large (and even small) data sets.
Now let’s do a test to
compare the performance of tidyfst, data.table and
dplyr. In the vignette we’ll use a small data set. The example
was provided by the data.table package (https://h2oai.github.io/db-benchmark/) and tweaked here.
These tests are based on computation by groups.
First let’s load
the package and generate some data.
# load packages
library(tidyfst)
#> Thank you for using tidyfst!
#> To acknowledge our work, please cite the package:
#> Huang et al., (2020). tidyfst: Tidy Verbs for Fast Data Manipulation. Journal of Open Source Software, 5(52), 2388, https://doi.org/10.21105/joss.02388
library(data.table)
#>
#> Attaching package: 'data.table'
#> The following object is masked from 'package:base':
#>
#> %notin%
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:data.table':
#>
#> between, first, last
#> The following objects are masked from 'package:tidyfst':
#>
#> between, cummean, nth
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(bench)
# generate the data
# if you have a HPC and want to try larger data sets, increase N
N = 1e4
K = 1e2
set.seed(2020)
cat(sprintf("Producing data of %s rows and %s K groups factors\n", N, K))
#> Producing data of 10000 rows and 100 K groups factors
DT = data.table(
id1 = sample(sprintf("id%03d",1:K), N, TRUE), # large groups (char)
id2 = sample(sprintf("id%03d",1:K), N, TRUE), # large groups (char)
id3 = sample(sprintf("id%010d",1:(N/K)), N, TRUE), # small groups (char)
id4 = sample(K, N, TRUE), # large groups (int)
id5 = sample(K, N, TRUE), # large groups (int)
id6 = sample(N/K, N, TRUE), # small groups (int)
v1 = sample(5, N, TRUE), # int in range [1,5]
v2 = sample(5, N, TRUE), # int in range [1,5]
v3 = round(runif(N,max=100),4) # numeric e.g. 23.5749
)
object_size(DT)
#> 527.7 KbThis data is rather small, the size is around 527 Kb. However, with the bench package, we could detect the difference by increasing iteration times. In this way, examples listed here could be implemented even on relatively low performance computers.
Here, we try to get median and standard deviation by groups.After dplyr v1.0.0, the regrouping feature could be confusing sometimes (comes with warning message). If you are using it, make sure they are in the right groups before grouped computation. In tidyfst and data.table, we have “by” parameter to specify the groups. Here we would not check if the results are equal, because dplyr will return a tibble class even when we input a data.table in the first place. The iteration time is 10 for each of the test below.
bench::mark(
data.table = DT[,.(median_v3 = median(v3),
sd_v3 = sd(v3)),
by = .(id4,id5)],
tidyfst = DT %>%
summarise_dt(
by = c("id4", "id5"),
median_v3 = median(v3),
sd_v3 = sd(v3)
),
dplyr = DT %>%
group_by(id4,id5,.drop = TRUE) %>%
summarise(median_v3 = median(v3),sd_v3 = sd(v3)),
check = FALSE,iterations = 10
) -> q1
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> ℹ Summaries were computed grouped by id4 and id5.
#> ℹ Output is grouped by id4.
#> ℹ Use `summarise(.groups = "drop_last")` to silence this message.
#> ℹ Use `summarise(.by = c(id4, id5))` for per-operation grouping
#> (`?dplyr::dplyr_by`) instead.
q1
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 data.table 1.64ms 1.67ms 579. 2.41MB 0
#> 2 tidyfst 1.68ms 1.7ms 588. 656.64KB 0
#> 3 dplyr 264.43ms 279.96ms 3.53 6.43MB 13.1We could find that spent time of tidyfst and data.table are quite similar, but much less than dplyr.
This example performs quite similar to the above one. tidyfst might spend a tiny little more time and space on code translation than data.table, but still performs much better than dplyr.
bench::mark(
data.table =DT[,.(range_v1_v2 = max(v1) - min(v2)),by = id3],
tidyfst = DT %>% summarise_dt(
by = id3,
range_v1_v2 = max(v1) - min(v2)
),
dplyr = DT %>%
group_by(id3,.drop = TRUE) %>%
summarise(range_v1_v2 = max(v1) - min(v2)),
check = FALSE,iterations = 10
) -> q2
q2
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 data.table 835.81µs 866.71µs 1069. 93.9KB 0
#> 2 tidyfst 861.65µs 880.85µs 1110. 93.9KB 0
#> 3 dplyr 3.85ms 3.98ms 243. 951.2KB 27.0Here we’ll display a rather different test to show the flexibly in
tidyfst. In tidyfst, if your code writes more like
data.table, the codes could speed up. If you write it more like
dplyr, the codes might be more readable but slows down. In
tidyfst, there is in_dt function for you to write
data.table codes to gain speed when you meet a bottomneck.
In
the following example, we use the exact same syntax of
data.table in tidyfst::in_dt.
bench::mark(
data.table =DT[order(-v3),.(largest2_v3 = head(v3,2L)),by = id6],
tidyfst = DT %>%
in_dt(order(-v3),.(largest2_v3 = head(v3,2L)),by = id6),
dplyr = DT %>%
select(id6,largest2_v3 = v3) %>%
group_by(id6) %>%
slice_max(largest2_v3,n = 2,with_ties = FALSE),
check = FALSE,iterations = 10
) -> q3
q3
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 data.table 1.81ms 1.89ms 506. 619.91KB 0
#> 2 tidyfst 2.39ms 2.45ms 406. 1.03MB 0
#> 3 dplyr 14.34ms 15.95ms 63.1 2.27MB 7.01To summarise multiple columns by group, tidyfst has designed
a function named summarise_vars, which is even more
convenient than the across function in dplyr. It
first choose the columns, then tell it what to do, and you can provide
the “by” parameter to operate by groups (optional).
bench::mark(
data.table =DT[,lapply(.SD,mean),by = id4,.SDcols = v1:v3],
tidyfst = DT %>%
summarise_vars(
v1:v3,
mean,
by = id4
),
dplyr = DT %>%
group_by(id4) %>%
summarise(across(v1:v3,mean)),
check = FALSE,iterations = 10
) -> q4
q4
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 data.table 1.04ms 1.09ms 874. 491.4KB 0
#> 2 tidyfst 3.77ms 4.09ms 242. 356.51KB 26.9
#> 3 dplyr 6.51ms 7.19ms 140. 1.04MB 0Take a look at the performance, tidyfst still lies between data.table and dplyr.
Now let’s try more groups, here we use all the id (id1~id6) as group,
and get the sum and count. Note that tidyfst is written in
data.table, so it do not use n() in dplyr
but .N in data.table to get counts by group.
bench::mark(
data.table =DT[,.(v3 = sum(v3),count = .N),by = id1:id6],
tidyfst = DT %>%
summarise_dt(
by = id1:id6,
v3 = sum(v3),
count = .N
),
dplyr = DT %>%
group_by(id1,id2,id3,id4,id5,id6) %>%
summarise(v3 = sum(v3),count = n()),
check = FALSE,iterations = 10
) -> q5
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> `summarise()` has regrouped the output.
#> ℹ Summaries were computed grouped by id1, id2, id3, id4, id5, and id6.
#> ℹ Output is grouped by id1, id2, id3, id4, and id5.
#> ℹ Use `summarise(.groups = "drop_last")` to silence this message.
#> ℹ Use `summarise(.by = c(id1, id2, id3, id4, id5, id6))` for per-operation
#> grouping (`?dplyr::dplyr_by`) instead.
q5
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 data.table 2.2ms 2.35ms 408. 1.02MB 0
#> 2 tidyfst 2.64ms 2.75ms 365. 1.02MB 0
#> 3 dplyr 151.04ms 168.18ms 5.62 2.77MB 13.5While in a data set of ~0.5 Mb we find that the performance of tidyfst lies between data.table and dplyr, we could discover that the speed is much closer to data.table. In fact, if you try a much larger data set in a computer with large RAM and multiple cores, you’ll find that the performance of tidyfst sticks close to data.table. If you are interested and has a high-performance computer, try to generate a larger data set and test out. Moreover, while the dplyr user might find these data manipulation verbs friendly, the innate syntax of tidyfst is more like data.table, and could be a good companion of data.table for some frequently used complex tasks.
sessionInfo()
#> R version 4.6.0 (2026-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] bench_1.1.4 dplyr_1.2.1 data.table_1.18.4 tidyfst_1.8.4
#> [5] rmarkdown_2.31
#>
#> loaded via a namespace (and not attached):
#> [1] jsonlite_2.0.0 compiler_4.6.0 tidyselect_1.2.1 Rcpp_1.1.1-1.1
#> [5] stringr_1.6.0 parallel_4.6.0 jquerylib_0.1.4 yaml_2.3.12
#> [9] fastmap_1.2.0 R6_2.6.1 pak_0.9.5 generics_0.1.4
#> [13] knitr_1.51 tibble_3.3.1 maketools_1.3.2 bslib_0.11.0
#> [17] pillar_1.11.1 rlang_1.2.0 utf8_1.2.6 cachem_1.1.0
#> [21] stringi_1.8.7 xfun_0.57 sass_0.4.10 sys_3.4.3
#> [25] cli_3.6.6 withr_3.0.2 magrittr_2.0.5 digest_0.6.39
#> [29] fst_0.9.8 lifecycle_1.0.5 vctrs_0.7.3 evaluate_1.0.5
#> [33] glue_1.8.1 buildtools_1.0.0 profmem_0.7.0 fstcore_0.10.0
#> [37] tools_4.6.0 pkgconfig_2.0.3 htmltools_0.5.9