fastplyr

fastplyr aims to provide a tidyverse frontend using a collapse backend. This means from a user’s point of view the functions behave like the tidyverse equivalents and thus require little to no changes to existing code to convert.

fastplyr is designed to handle operations that involve larger numbers of groups and generally larger data.

Installation

You can install the development version of fastplyr from GitHub with:

# install.packages("pak")
pak::pak("NicChr/fastplyr")

Load packages

library(tidyverse)
#> Warning: package 'dplyr' was built under R version 4.4.1
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.4     ✔ readr     2.1.5
#> ✔ forcats   1.0.0     ✔ stringr   1.5.1
#> ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
#> ✔ purrr     1.0.2     
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(fastplyr)
#> 
#> Attaching package: 'fastplyr'
#> 
#> The following object is masked from 'package:dplyr':
#> 
#>     desc
#> 
#> The following objects are masked from 'package:tidyr':
#> 
#>     crossing, nesting
library(nycflights13)
library(bench)

While the syntax and user-interface of fastplyr aligns very closely with dplyr most of the time, there can be a few key differences.

Differences between fastplyr and dplyr

dplyr alternatives

All tidyverse alternative functions are prefixed with ‘f_’. For example, dplyr::distinct becomes fastplyr::f_distinct.

distinct

flights |> 
  f_distinct(origin, dest)
#> # A tibble: 224 × 2
#>   origin dest 
#>   <chr>  <chr>
#> 1 EWR    IAH  
#> 2 LGA    IAH  
#> 3 JFK    MIA  
#> 4 JFK    BQN  
#> 5 LGA    ATL  
#> # ℹ 219 more rows

f_distinct has an additional sort argument which is much faster than sorting afterwards.

mark(
  fastplyr_distinct_sort = flights |> 
  f_distinct(origin, dest, tailnum, sort = TRUE),
  dplyr_distinct_sort = flights |> 
    distinct(origin, dest, tailnum) |> 
    arrange_all()
)
#> # A tibble: 2 × 6
#>   expression                  min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>             <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_distinct_sort   10.2ms   11.2ms      85.8    2.95MB     4.19
#> 2 dplyr_distinct_sort      23.8ms   25.3ms      39.2   11.38MB     7.36

group_by

f_group_by operates very similarly with an additional feature that allows you to specify whether group data should be ordered or not. This ultimately controls if the groups end up sorted in expressions like count and summarise, but also in this case f_count and f_summarise.

# Like dplyr
flights |> 
  f_group_by(month) |> 
  f_count()
#> # A tibble: 12 × 2
#> # Groups:   month [12]
#>   month     n
#>   <int> <int>
#> 1     1 27004
#> 2     2 24951
#> 3     3 28834
#> 4     4 28330
#> 5     5 28796
#> # ℹ 7 more rows

# Group data is sorted by order-of-first appearance
flights |> 
  f_group_by(month, order = FALSE) |> 
  f_count()
#> # A tibble: 12 × 2
#> # Groups:   month [12]
#>   month     n
#>   <int> <int>
#> 1     1 27004
#> 2    10 28889
#> 3    11 27268
#> 4    12 28135
#> 5     2 24951
#> # ℹ 7 more rows

Just a reminder that all fastplyr functions are interchangeable with dplyr ones both ways

### With dplyr::count

flights |> 
  f_group_by(month) |> 
  count()
#> # A tibble: 12 × 2
#> # Groups:   month [12]
#>   month     n
#>   <int> <int>
#> 1     1 27004
#> 2     2 24951
#> 3     3 28834
#> 4     4 28330
#> 5     5 28796
#> # ℹ 7 more rows

### With dplyr::group_by

flights |> 
  group_by(month) |> 
  f_count()
#> # A tibble: 12 × 2
#> # Groups:   month [12]
#>   month     n
#>   <int> <int>
#> 1     1 27004
#> 2     2 24951
#> 3     3 28834
#> 4     4 28330
#> 5     5 28796
#> # ℹ 7 more rows

summarise

f_summarise behaves like dplyr’s summarise except for two things:

• It evaluates expressions independently
• There are optimisations for common statistical functions which are very fast for many groups

grouped_flights <- flights |> 
  group_by(across(where(is.character)))

grouped_flights |> 
  f_summarise(
    n = n(), mean_dep_delay = mean(dep_delay)
  )
#> # A tibble: 52,807 × 6
#>   carrier tailnum origin dest      n mean_dep_delay
#>   <chr>   <chr>   <chr>  <chr> <int>          <dbl>
#> 1 9E      N146PQ  JFK    ATL       8           9.62
#> 2 9E      N153PQ  JFK    ATL       5          -0.4 
#> 3 9E      N161PQ  JFK    ATL       3          -2   
#> 4 9E      N162PQ  EWR    DTW       1         160   
#> 5 9E      N162PQ  JFK    ATL       1          -6   
#> # ℹ 52,802 more rows

And a benchmark

mark(
  fastplyr_summarise = grouped_flights |> 
  f_summarise(
    n = n(), mean_dep_delay = mean(dep_delay)
  ),
  dplyr_summarise = grouped_flights |> 
  summarise(
    n = n(), mean_dep_delay = mean(dep_delay, na.rm = TRUE),
    .groups = "drop"
  )
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#>   expression              min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>         <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_summarise   4.66ms   6.66ms    148.      2.09MB     1.98
#> 2 dplyr_summarise    748.56ms 748.56ms      1.34    9.57MB     9.35

Joins

Joins work much the same way as in dplyr.

left <- flights |> 
  f_select(origin, dest, time_hour)
hours <- sample(unique(left$time_hour), 5000)
right <- as.data.frame(unclass(as.POSIXlt(hours)))
right$time_hour <- hours

# Left join

left |> 
  f_left_join(right)
#> # A tibble: 336,776 × 14
#>   origin dest  time_hour             sec   min  hour  mday   mon  year  wday
#> * <chr>  <chr> <dttm>              <dbl> <int> <int> <int> <int> <int> <int>
#> 1 EWR    IAH   2013-01-01 05:00:00     0     0     5     1     0   113     2
#> 2 LGA    IAH   2013-01-01 05:00:00     0     0     5     1     0   113     2
#> 3 JFK    MIA   2013-01-01 05:00:00     0     0     5     1     0   113     2
#> 4 JFK    BQN   2013-01-01 05:00:00     0     0     5     1     0   113     2
#> 5 LGA    ATL   2013-01-01 06:00:00     0     0     6     1     0   113     2
#> # ℹ 336,771 more rows
#> # ℹ 4 more variables: yday <int>, isdst <int>, zone <chr>, gmtoff <int>

# inner join

left |> 
  f_inner_join(right)
#> # A tibble: 244,029 × 14
#>   origin dest  time_hour             sec   min  hour  mday   mon  year  wday
#>   <chr>  <chr> <dttm>              <dbl> <int> <int> <int> <int> <int> <int>
#> 1 EWR    IAH   2013-01-01 05:00:00     0     0     5     1     0   113     2
#> 2 LGA    IAH   2013-01-01 05:00:00     0     0     5     1     0   113     2
#> 3 JFK    MIA   2013-01-01 05:00:00     0     0     5     1     0   113     2
#> 4 JFK    BQN   2013-01-01 05:00:00     0     0     5     1     0   113     2
#> 5 LGA    ATL   2013-01-01 06:00:00     0     0     6     1     0   113     2
#> # ℹ 244,024 more rows
#> # ℹ 4 more variables: yday <int>, isdst <int>, zone <chr>, gmtoff <int>

# Anti join

left |> 
  f_anti_join(right)
#> # A tibble: 92,747 × 3
#>   origin dest  time_hour          
#>   <chr>  <chr> <dttm>             
#> 1 LGA    ATL   2013-01-01 14:00:00
#> 2 LGA    ATL   2013-01-01 14:00:00
#> 3 EWR    ORD   2013-01-01 14:00:00
#> 4 EWR    SEA   2013-01-01 14:00:00
#> 5 EWR    ORD   2013-01-01 14:00:00
#> # ℹ 92,742 more rows

# Semi join

left |> 
  f_semi_join(right)
#> # A tibble: 244,029 × 3
#>   origin dest  time_hour          
#>   <chr>  <chr> <dttm>             
#> 1 EWR    IAH   2013-01-01 05:00:00
#> 2 LGA    IAH   2013-01-01 05:00:00
#> 3 JFK    MIA   2013-01-01 05:00:00
#> 4 JFK    BQN   2013-01-01 05:00:00
#> 5 LGA    ATL   2013-01-01 06:00:00
#> # ℹ 244,024 more rows

# full join

left |> 
  f_full_join(right)
#> # A tibble: 336,776 × 14
#>   origin dest  time_hour             sec   min  hour  mday   mon  year  wday
#> * <chr>  <chr> <dttm>              <dbl> <int> <int> <int> <int> <int> <int>
#> 1 EWR    IAH   2013-01-01 05:00:00     0     0     5     1     0   113     2
#> 2 LGA    IAH   2013-01-01 05:00:00     0     0     5     1     0   113     2
#> 3 JFK    MIA   2013-01-01 05:00:00     0     0     5     1     0   113     2
#> 4 JFK    BQN   2013-01-01 05:00:00     0     0     5     1     0   113     2
#> 5 LGA    ATL   2013-01-01 06:00:00     0     0     6     1     0   113     2
#> # ℹ 336,771 more rows
#> # ℹ 4 more variables: yday <int>, isdst <int>, zone <chr>, gmtoff <int>

And a benchmark comparing fastplyr and dplyr joins

mark(
  fastplyr_left_join = f_left_join(left, right, by = "time_hour"),
  dplyr_left_join = left_join(left, right, by = "time_hour")
)
#> # A tibble: 2 × 6
#>   expression              min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>         <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_left_join   11.6ms   13.9ms      71.2    19.3MB     35.6
#> 2 dplyr_left_join      34.9ms   37.5ms      26.7      45MB     60.1

mark(
  fastplyr_inner_join = f_inner_join(left, right, by = "time_hour"),
  dplyr_inner_join = inner_join(left, right, by = "time_hour")
)
#> # A tibble: 2 × 6
#>   expression               min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>          <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_inner_join   8.57ms   12.4ms      81.9    22.2MB     45.5
#> 2 dplyr_inner_join     34.05ms     38ms      27.0    37.9MB     48.7

mark(
  fastplyr_anti_join = f_anti_join(left, right, by = "time_hour"),
  dplyr_anti_join = anti_join(left, right, by = "time_hour")
)
#> # A tibble: 2 × 6
#>   expression              min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>         <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_anti_join   2.47ms   4.06ms     240.     3.76MB     16.2
#> 2 dplyr_anti_join     16.66ms  19.25ms      50.6    21.8MB     30.4

mark(
  fastplyr_semi_join = f_semi_join(left, right, by = "time_hour"),
  dplyr_semi_join = semi_join(left, right, by = "time_hour")
)
#> # A tibble: 2 × 6
#>   expression              min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>         <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_semi_join   3.75ms   6.15ms     155.      7.8MB     17.9
#> 2 dplyr_semi_join     15.75ms  20.31ms      48.3    21.9MB     12.7

mark(
  fastplyr_full_join = f_full_join(left, right, by = "time_hour"),
  dplyr_full_join = full_join(left, right, by = "time_hour")
)
#> # A tibble: 2 × 6
#>   expression              min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>         <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_full_join   10.7ms   14.3ms      71.4    20.6MB     45.1
#> 2 dplyr_full_join      33.6ms   34.8ms      27.0    44.6MB     99.1

slice

f_slice and other f_slice_ functions are very fast for many groups.

grouped_flights |> 
  f_slice(1)
#> # A tibble: 52,807 × 19
#> # Groups:   carrier, tailnum, origin, dest [52,807]
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     7      614            615        -1      812            855
#> 2  2013     1     8      612            615        -3      901            855
#> 3  2013     1     9      615            615         0       NA            855
#> 4  2013     1    25     1530           1250       160     1714           1449
#> 5  2013     2    24      609            615        -6      835            855
#> # ℹ 52,802 more rows
#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#> #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#> #   hour <dbl>, minute <dbl>, time_hour <dttm>

grouped_flights |>
  f_slice_head(3)
#> # A tibble: 125,770 × 19
#> # Groups:   carrier, tailnum, origin, dest [52,807]
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     7      614            615        -1      812            855
#> 2  2013     1    13      612            615        -3      853            855
#> 3  2013     2     3      617            615         2      902            855
#> 4  2013     1     8      612            615        -3      901            855
#> 5  2013     1    22      614            615        -1      857            855
#> # ℹ 125,765 more rows
#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#> #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#> #   hour <dbl>, minute <dbl>, time_hour <dttm>

A quick benchmark to prove the point

mark(
    fastplyr_slice = grouped_flights |> 
    f_slice_head(n = 3),
    dplyr_slice = grouped_flights |>
        slice_head(n = 3)
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#>   expression          min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>     <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_slice  28.14ms  31.57ms    29.2      21.4MB     7.79
#> 2 dplyr_slice       3.63s    3.63s     0.276    26.6MB     9.65

Group IDs

In dplyr to work with group IDs you must use the mutate() + cur_group_id() paradigm.

In fastplyr you can just use add_group_id() which is blazing fast.

## Unique ID for each group

grouped_flights |> 
  add_group_id() |> 
  f_select(group_id)
#> Adding missing grouping variables: 'carrier', 'tailnum', 'origin', 'dest'
#> # A tibble: 336,776 × 5
#> # Groups:   carrier, tailnum, origin, dest [52,807]
#>   carrier tailnum origin dest  group_id
#>   <chr>   <chr>   <chr>  <chr>    <int>
#> 1 UA      N14228  EWR    IAH      35951
#> 2 UA      N24211  LGA    IAH      36937
#> 3 AA      N619AA  JFK    MIA       8489
#> 4 B6      N804JB  JFK    BQN      15462
#> 5 DL      N668DN  LGA    ATL      20325
#> # ℹ 336,771 more rows

Another benchmark

mark(
  fastplyr_group_id = grouped_flights |> 
  add_group_id() |> 
  f_select(all_of(group_vars(grouped_flights)), group_id),
  dplyr_group_id = grouped_flights |> 
  mutate(group_id = cur_group_id()) |> 
  select(all_of(group_vars(grouped_flights)), group_id)
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#>   expression             min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>        <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_group_id   3.06ms   4.38ms    218.      1.46MB     3.93
#> 2 dplyr_group_id     303.6ms 306.89ms      3.26    3.24MB     9.78

expand

Based closely on tidyr::expand, f_expand() can cross joins multiple vectors and data frames.

mark(
    fastplyr_expand = flights |> 
        f_group_by(origin, tailnum) |> 
        f_expand(month = 1:12),
    tidyr_expand = flights |> 
        group_by(origin, tailnum) |> 
        expand(month = 1:12),
    check = FALSE
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#>   expression           min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>      <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_expand  19.83ms  21.95ms    43.5      8.86MB     5.94
#> 2 tidyr_expand       4.03s    4.03s     0.248   81.02MB     3.72


# Using `.cols` in `f_expand()` is very fast!
mark(
    fastplyr_expand = flights |> 
        f_group_by(origin, dest) |> 
        f_expand(.cols = c("year", "month", "day")),
    tidyr_expand = flights |> 
        group_by(origin, dest) |> 
        expand(year, month, day),
    check = FALSE
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#>   expression           min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>      <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_expand   15.7ms   19.4ms     48.5     15.5MB     7.77
#> 2 tidyr_expand     233.7ms    254ms      3.94    66.7MB     3.94

duplicate rows

Finding duplicate rows is a very common dataset operation and there is a dedicated function f_duplicates() to do exactly this.

flights |> 
  f_duplicates(time_hour)
#> # A tibble: 329,840 × 1
#>   time_hour          
#>   <dttm>             
#> 1 2013-01-01 05:00:00
#> 2 2013-01-01 05:00:00
#> 3 2013-01-01 05:00:00
#> 4 2013-01-01 05:00:00
#> 5 2013-01-01 06:00:00
#> # ℹ 329,835 more rows

Benchmark against a common dplyr strategy for finding duplicates

mark(
 fastplyr_duplicates = flights |> 
   f_duplicates(time_hour, .both_ways = TRUE, .add_count = TRUE, .keep_all = TRUE),
 dplyr_duplicates = flights |> 
   add_count(time_hour) |> 
   filter(n > 1)
)
#> # A tibble: 2 × 6
#>   expression               min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>          <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_duplicates   24.8ms   26.7ms      37.2    45.1MB     28.9
#> 2 dplyr_duplicates      68.6ms   70.3ms      14.2    59.5MB     19.0

filter

In the worst-case scenarios, f_filter() is about the same speed as filter() and in the best-case is much faster and more efficient. This is especially true for large data where small subsets of the data are returned.

full <- new_tbl(x = rnorm(5e07))

# A worst case scenario

mark(
  fastplyr_filter = full |> 
    f_filter(abs(x) > 0),
  dplyr_filter = full |> 
    filter(abs(x) > 0)
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#>   expression           min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>      <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_filter    648ms    648ms      1.54    1.12GB     1.54
#> 2 dplyr_filter       916ms    916ms      1.09    1.68GB     2.18

# Best case scenario - filter results in small subset

mark(
  fastplyr_filter = full |> 
    f_filter(x > 4),
  dplyr_filter = full |> 
    filter(x > 4)
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#>   expression           min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>      <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_filter    174ms    178ms      5.64     191MB     0   
#> 2 dplyr_filter       509ms    509ms      1.96     763MB     1.96

bind rows and cols

Binding columns is particular much faster but binding rows is also sufficiently faster

mark(
  fastplyr_bind_cols = f_bind_cols(grouped_flights, grouped_flights),
  dplyr_bind_cols = suppressMessages(
    bind_cols(grouped_flights, grouped_flights)
    )
)
#> # A tibble: 2 × 6
#>   expression              min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>         <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_bind_cols   63.6µs   72.9µs  12902.     48.21KB     4.31
#> 2 dplyr_bind_cols     230.4ms  230.4ms      4.34    1.31MB     8.68

mark(
  fastplyr_bind_rows = f_bind_rows(grouped_flights, grouped_flights),
  dplyr_bind_rows = bind_rows(grouped_flights, grouped_flights)
)
#> # A tibble: 2 × 6
#>   expression              min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>         <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_bind_rows   59.2ms   60.2ms     16.4     86.6MB     0   
#> 2 dplyr_bind_rows     233.1ms  233.1ms      4.29   157.6MB     4.29

Quantiles

A typical tidy approach might use a mixture of reframe() and enframe() which is a perfectly tidy and neat solution

probs <- seq(0, 1, 0.25)

mtcars <- as_tbl(mtcars)

mtcars |> 
 group_by(cyl) |> 
 reframe(enframe(quantile(mpg, probs), "quantile", "mpg"))
#> # A tibble: 15 × 3
#>     cyl quantile   mpg
#>   <dbl> <chr>    <dbl>
#> 1     4 0%        21.4
#> 2     4 25%       22.8
#> 3     4 50%       26  
#> 4     4 75%       30.4
#> 5     4 100%      33.9
#> # ℹ 10 more rows

fastplyr though has a dedicated function for quantile calculation, tidy_quantiles() which requires less code to type

# Wide
mtcars |> 
  tidy_quantiles(mpg, .by = cyl)
#> # A tibble: 3 × 6
#>     cyl    p0   p25   p50   p75  p100
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     4  21.4  22.8  26    30.4  33.9
#> 2     6  17.8  18.6  19.7  21    21.4
#> 3     8  10.4  14.4  15.2  16.2  19.2

# Long
mtcars |> 
  tidy_quantiles(mpg, .by = cyl, pivot = "long")
#> # A tibble: 15 × 3
#>     cyl .quantile   mpg
#>   <dbl> <fct>     <dbl>
#> 1     4 p0         21.4
#> 2     4 p25        22.8
#> 3     4 p50        26  
#> 4     4 p75        30.4
#> 5     4 p100       33.9
#> # ℹ 10 more rows

Not only can you choose how to pivot as shown above, you can also calculate quantiles for multiple variables.

multiple_quantiles <- mtcars |> 
  tidy_quantiles(across(where(is.numeric)), pivot = "long")
multiple_quantiles
#> # A tibble: 5 × 12
#>   .quantile   mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>   <fct>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 p0         10.4     4  71.1  52    2.76  1.51  14.5     0     0     3     1
#> 2 p25        15.4     4 121.   96.5  3.08  2.58  16.9     0     0     3     2
#> 3 p50        19.2     6 196.  123    3.70  3.32  17.7     0     0     4     2
#> 4 p75        22.8     8 326   180    3.92  3.61  18.9     1     1     4     4
#> 5 p100       33.9     8 472   335    4.93  5.42  22.9     1     1     5     8

# Quantile names is a convenient factor
multiple_quantiles$.quantile
#> [1] p0   p25  p50  p75  p100
#> Levels: p0 p25 p50 p75 p100

Quantile benchmark for many groups

tidy_quantiles() of course is fast when many groups are involved.

mark(
  fastplyr_quantiles = flights |> 
    f_group_by(year, month, day, origin) |> 
  tidy_quantiles(dep_delay, pivot = "long"),
  dplyr_quantiles = flights |> 
     group_by(year, month, day, origin) |> 
    reframe(enframe(quantile(dep_delay, seq(0, 1, 0.25), na.rm = TRUE))),
  check = FALSE
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#>   expression              min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>         <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_quantiles   26.1ms   26.5ms     37.1     7.17MB     0   
#> 2 dplyr_quantiles     214.3ms  218.7ms      4.56   24.98MB     4.56

tidytable vs fastplyr

Let’s run some more benchmarks for fun, this time including tidytable which fastplyr is very similar to as it also uses a tidy frontend but a data.table backend

10 million rows

n_rows <- 10^7
n_groups <- 10^6

tbl <- new_tbl(x = rnorm(n_rows))
tbl <- tbl |> 
    mutate(y = as.character(round(x, 6)),
           g = sample.int(n_groups, n_rows, TRUE))
tbl
#> # A tibble: 10,000,000 × 3
#>        x y              g
#>    <dbl> <chr>      <int>
#> 1  1.29  1.285351  433366
#> 2 -1.61  -1.613842 887462
#> 3 -0.787 -0.787209 550879
#> 4 -0.490 -0.489809 875660
#> 5  0.393 0.393453  550619
#> # ℹ 9,999,995 more rows

slice benchmark

For this we will be using the .by argument from each package. Because fastplyr still sorts the groups by default here we will set an internal option to use the alternative grouping algorithm that sorts groups by order of first appearance. This will likely be revisited at some point.

To read about the differences, see ?collapse::GRP.

library(tidytable)
#> Warning: tidytable was loaded after dplyr.
#> This can lead to most dplyr functions being overwritten by tidytable functions.
#> Warning: tidytable was loaded after tidyr.
#> This can lead to most tidyr functions being overwritten by tidytable functions.
#> 
#> Attaching package: 'tidytable'
#> The following objects are masked from 'package:fastplyr':
#> 
#>     crossing, desc, nesting
#> The following objects are masked from 'package:dplyr':
#> 
#>     across, add_count, add_tally, anti_join, arrange, between,
#>     bind_cols, bind_rows, c_across, case_match, case_when, coalesce,
#>     consecutive_id, count, cross_join, cume_dist, cur_column, cur_data,
#>     cur_group_id, cur_group_rows, dense_rank, desc, distinct, filter,
#>     first, full_join, group_by, group_cols, group_split, group_vars,
#>     if_all, if_any, if_else, inner_join, is_grouped_df, lag, last,
#>     lead, left_join, min_rank, mutate, n, n_distinct, na_if, nest_by,
#>     nest_join, nth, percent_rank, pick, pull, recode, reframe,
#>     relocate, rename, rename_with, right_join, row_number, rowwise,
#>     select, semi_join, slice, slice_head, slice_max, slice_min,
#>     slice_sample, slice_tail, summarise, summarize, tally, top_n,
#>     transmute, tribble, ungroup
#> The following objects are masked from 'package:purrr':
#> 
#>     map, map_chr, map_dbl, map_df, map_dfc, map_dfr, map_int, map_lgl,
#>     map_vec, map2, map2_chr, map2_dbl, map2_df, map2_dfc, map2_dfr,
#>     map2_int, map2_lgl, map2_vec, pmap, pmap_chr, pmap_dbl, pmap_df,
#>     pmap_dfc, pmap_dfr, pmap_int, pmap_lgl, pmap_vec, walk
#> The following objects are masked from 'package:tidyr':
#> 
#>     complete, crossing, drop_na, expand, expand_grid, extract, fill,
#>     nest, nesting, pivot_longer, pivot_wider, replace_na, separate,
#>     separate_longer_delim, separate_rows, separate_wider_delim,
#>     separate_wider_regex, tribble, uncount, unite, unnest,
#>     unnest_longer, unnest_wider
#> The following objects are masked from 'package:tibble':
#> 
#>     enframe, tribble
#> The following objects are masked from 'package:stats':
#> 
#>     dt, filter, lag
#> The following object is masked from 'package:base':
#> 
#>     %in%

tidy_tbl <- as_tidytable(tbl)

# Setting an internal option to set all grouping to use the non-sorted type
options(.fastplyr.order.groups = FALSE)

mark(
  fastplyr_slice = tbl |> 
  f_slice(3:5, .by = g),
  tidytable_slice = tidy_tbl |> 
    slice(3:5, .by = g),
  check = FALSE,
  min_iterations = 3
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#>   expression           min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>      <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_slice  816.62ms 849.03ms     1.15      133MB    0.383
#> 2 tidytable_slice    6.39s    6.56s     0.153     176MB    1.74

slice_head & slice_tail

mark(
  fastplyr_slice_head = tbl |> 
  f_slice_head(n = 3, .by = g),
  tidytable_slice_head = tidy_tbl |> 
    slice_head(n = 3, .by = g),
  fastplyr_slice_tail = tbl |> 
  f_slice_tail(n = 3, .by = g),
  tidytable_slice_tail = tidy_tbl |> 
    slice_tail(n = 3, .by = g),
  check = FALSE,
  min_iterations = 3
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 4 × 6
#>   expression                min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>           <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_slice_head  776.69ms 782.24ms     1.26      191MB    0    
#> 2 tidytable_slice_head    1.59s    1.61s     0.609     175MB    1.42 
#> 3 fastplyr_slice_tail  732.95ms 784.14ms     1.25      194MB    0.415
#> 4 tidytable_slice_tail    3.39s     3.5s     0.277     175MB    1.57

summarise benchmark

Here we’ll calculate the mean of x by each group of g

Both tidytable and fastplyr have optimisations for mean() when it involves groups. tidytable internally uses data.table’s ‘gforce’ mean function. This is basically a dedicated C function to calculate means for many groups.

mark(
  fastplyr_sumarise = tbl |> 
  f_summarise(mean = mean(x), .by = g),
  tidytable_sumarise = tidy_tbl |> 
  summarise(mean = mean(x), .by = g, .sort = FALSE),
  check = FALSE,
  min_iterations = 3
)
#> # A tibble: 2 × 6
#>   expression              min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>         <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_sumarise     290ms    322ms      3.18    57.2MB     0   
#> 2 tidytable_sumarise    265ms    270ms      3.71   305.4MB     1.85

Benchmarking more statistical functions

mark(
  fastplyr_sumarise2 = tbl |> 
  f_summarise(n = n(), mean = mean(x), min = min(x), max = max(x), .by = g),
  tidytable_sumarise2 = tidy_tbl |> 
  summarise(n = n(), mean = mean(x), min = min(x), max = max(x), 
            .by = g, .sort = FALSE),
  check = FALSE,
  min_iterations = 3
)
#> # A tibble: 2 × 6
#>   expression               min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>          <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_sumarise2     483ms    520ms      1.93    72.5MB     0   
#> 2 tidytable_sumarise2    401ms    435ms      2.30   320.7MB     1.15

count benchmark

mark(
  fastplyr_count = tbl |> 
    f_count(y, g),
  tidytable_count = tidy_tbl |> 
    count(y, g),
  check = FALSE,
  min_iterations = 3
)
#> # A tibble: 2 × 6
#>   expression           min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>      <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 fastplyr_count  348.29ms 348.31ms     2.87      229MB    1.44 
#> 2 tidytable_count    3.77s    3.77s     0.265     496MB    0.530

It’s clear both fastplyr and tidytable are fast and each have their strengths and weaknesses.