fastplyr

R-CMD-check

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

fastplyr

.by Groups are sorted by order of first appearance always when using .by Groups are always sorted by default, even when using .by. One can use the other sorting through f_group_by(order = F)
Many groups Generally slow for data with many groups. Designed to be fast for data with many groups.
Handling of dots (…) dplyr almost always executes … expressions in a way that latter expressions depend on previous ones Some functions like f_summarise and f_expand execute the expressions in … independently.
Duplicate rows No dedicated function for this, solution using group_by |> filter(n() > 1) are generally slow for larger data. Dedicated function f_duplicates can do this very fast and with fine control.
Unique group IDs Achieved through mutate(cur_group_id()) Dedicated fast function add_group_id()
Row slicing slice() supports data-masked expressions supplied to … Data-masked expressions not supported in f_slice_ functions. Use f_filter() for this behaviour.
Memory usage High memory usage Lower usage compared to dplyr
joins Accepts different types of joins, e.g. rolling and equality joins. Accepts only equality joins of the form x == y

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   11.2ms   12.9ms      76.9    2.95MB     2.08
#> 2 dplyr_distinct_sort      23.8ms   25.4ms      38.7   11.38MB     7.26

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:

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   4.41ms   5.02ms    179.      2.09MB     3.97
#> 2 dplyr_summarise    661.11ms 661.11ms      1.51    9.57MB     9.08

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    8.5ms   12.3ms      85.1    19.3MB     63.8
#> 2 dplyr_left_join      29.9ms   30.1ms      33.2      45MB    199.
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      6ms   9.88ms     106.     22.2MB     56.9
#> 2 dplyr_inner_join      24.3ms  26.92ms      37.6    37.9MB     53.8
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   2.27ms    3.2ms     320.     3.76MB     26.9
#> 2 dplyr_anti_join     15.72ms   18.9ms      54.3    21.8MB     20.0
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   3.49ms   5.54ms     188.      7.8MB     25.3
#> 2 dplyr_semi_join     16.43ms  19.09ms      53.0    21.9MB     24.9
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   10.6ms     13ms      77.3    20.6MB     26.8
#> 2 dplyr_full_join      31.1ms   32.4ms      30.9    44.6MB    113.

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  24.98ms   34.5ms    24.4      21.4MB     15.0
#> 2 dplyr_slice       3.18s    3.18s     0.314    26.6MB     10.4

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      3ms    3.5ms    266.      1.46MB     3.98
#> 2 dplyr_group_id       307ms  307.3ms      3.25    3.24MB     9.76

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  20.79ms   23.5ms    41.1      8.85MB     7.83
#> 2 tidyr_expand       3.87s    3.87s     0.258   81.02MB     3.87


# 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   18.6ms     49.0     15.5MB     9.79
#> 2 tidyr_expand     206.9ms  212.6ms      4.63    66.7MB     4.63

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   19.4ms   22.2ms      44.3    45.1MB     29.6
#> 2 dplyr_duplicates      70.4ms     72ms      13.9    59.5MB     34.7

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    1.71s    1.71s     0.586    1.12GB    0.586
#> 2 dplyr_filter       2.35s    2.35s     0.426    1.68GB    0.852

# 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 363.43ms 387.22ms     2.58      191MB    0    
#> 2 dplyr_filter       1.01s    1.01s     0.987     763MB    0.987

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   54.1µs   62.4µs  14563.     40.43KB     4.45
#> 2 dplyr_bind_cols     241.4ms  241.4ms      4.14    1.31MB     8.28

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     50ms    142ms      8.84    86.6MB     2.95
#> 2 dplyr_bind_rows       351ms    360ms      2.78   157.6MB     0

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
)
#> # 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     25ms   27.4ms     34.1     7.17MB     2.13
#> 2 dplyr_quantiles       199ms  199.5ms      5.01   24.98MB    10.0

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  835.38ms  960.6ms     0.991     133MB    0.330
#> 2 tidytable_slice    6.19s     6.3s     0.159     176MB    1.80

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  972.63ms    1.05s     0.955     191MB    0    
#> 2 tidytable_slice_head    1.57s    1.65s     0.604     175MB    1.41 
#> 3 fastplyr_slice_tail  985.17ms    1.01s     0.993     194MB    0.331
#> 4 tidytable_slice_tail    3.53s     3.6s     0.276     175MB    1.84

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     342ms    393ms      2.59    57.2MB     0   
#> 2 tidytable_sumarise    258ms    302ms      3.31   305.4MB     1.66

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     449ms    461ms      2.17    72.5MB     1.08
#> 2 tidytable_sumarise2    465ms    481ms      2.08   320.7MB     1.04

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  337.14ms 352.53ms     2.84      229MB    1.42 
#> 2 tidytable_count    3.52s    3.54s     0.282     496MB    0.141

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