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	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`
rowwise	`rowwise_df` accepted and everything sub-setted implictly using `[[`	`rowwise_df` not accepted, must use `f_rowwise_df` which creates a `grouped_df` with a row ID col. Implicit `[[` subsetting does not occur.

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   10.5ms   12.6ms      75.0    2.96MB     2.08
#> 2 dplyr_distinct_sort      22.2ms   23.5ms      38.6   11.38MB     7.71

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   3.29ms   4.33ms    214.      2.09MB     4.00
#> 2 dplyr_summarise    647.77ms 647.77ms      1.54    9.57MB    10.8

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   10.1ms   13.4ms      75.5    19.3MB     75.5
#> 2 dplyr_left_join      36.5ms   36.6ms      27.2      45MB     72.5

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   5.58ms   11.4ms      93.9    22.2MB     60.4
#> 2 dplyr_inner_join     25.06ms   30.1ms      32.8    37.9MB     43.7

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   2.33ms   3.45ms     299.     3.76MB     24.3
#> 2 dplyr_anti_join     14.53ms  19.39ms      53.3    21.8MB     16.0

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   3.67ms    5.8ms     177.      7.8MB     22.2
#> 2 dplyr_semi_join     14.75ms   18.5ms      55.4    21.9MB     29.3

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   9.64ms   14.1ms      73.5    20.6MB     27.6
#> 2 dplyr_full_join     28.18ms   32.4ms      31.4    44.6MB     78.5

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  24.18ms  29.85ms    26.7      21.4MB     11.5
#> 2 dplyr_slice       3.32s    3.32s     0.301    26.6MB     10.2

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   2.24ms   2.77ms    342.      1.46MB     5.97
#> 2 dplyr_group_id    284.75ms 290.54ms      3.44    3.24MB    10.3

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  20.19ms  21.81ms    42.3      8.87MB     7.69
#> 2 tidyr_expand       3.83s    3.83s     0.261   81.02MB     3.65


# 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   15.9ms   19.3ms     49.9     15.6MB     9.97
#> 2 tidyr_expand     218.2ms  222.8ms      4.39    66.7MB     4.39

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   18.7ms   25.4ms      39.6    45.1MB     30.8
#> 2 dplyr_duplicates      65.8ms   70.3ms      14.4    59.5MB     23.9

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    736ms    736ms      1.36    1.12GB     1.36
#> 2 dplyr_filter       946ms    946ms      1.06    1.68GB     2.11

# 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    172ms    176ms      5.73     191MB     0   
#> 2 dplyr_filter       520ms    520ms      1.92     763MB     1.92

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     58µs   65.9µs  14533.     48.55KB     5.31
#> 2 dplyr_bind_cols       243ms  243.1ms      4.11    1.31MB     8.23

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     59ms   59.8ms     16.3     86.6MB     0   
#> 2 dplyr_bind_rows       223ms    224ms      4.46   157.6MB     2.23

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, pivot = "wide")
#> # 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 |> 
  tidy_quantiles(dep_delay, pivot = "long",
                 .by = c(year, month, day, origin)),
  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   22.8ms   23.5ms     42.4     4.22MB     0   
#> 2 dplyr_quantiles     214.4ms  214.8ms      4.65   24.98MB     4.65

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   780.4ms 921.61ms     1.14      133MB    0.757
#> 2 tidytable_slice     6.2s    6.25s     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  722.23ms 817.01ms     1.26      191MB     0   
#> 2 tidytable_slice_head     1.6s    1.62s     0.590     175MB     1.38
#> 3 fastplyr_slice_tail   763.4ms 796.16ms     1.26      194MB     0   
#> 4 tidytable_slice_tail    3.39s    3.45s     0.281     175MB     1.69

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     275ms    286ms      3.43    57.2MB     0   
#> 2 tidytable_sumarise    242ms    244ms      4.09   305.4MB     2.05

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     348ms    360ms      2.72    72.5MB     0   
#> 2 tidytable_sumarise2    386ms    393ms      2.55   320.7MB     1.27

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  346.42ms 354.08ms     2.82      229MB    1.41 
#> 2 tidytable_count    3.39s    3.39s     0.295     496MB    0.589

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

Name		Name	Last commit message	Last commit date
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.github		.github
R		R
man		man
src		src
tests		tests
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.Rmd		README.Rmd
README.md		README.md
cran-comments.md		cran-comments.md
fastplyr.Rproj		fastplyr.Rproj

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fastplyr

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Differences between fastplyr and dplyr

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dplyr alternatives

distinct

group_by

summarise

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slice

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expand

duplicate rows

filter

bind rows and cols

Quantiles

Quantile benchmark for many groups

tidytable vs fastplyr

10 million rows

slice benchmark

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summarise benchmark

count benchmark

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fastplyr

Installation

Differences between fastplyr and dplyr

dplyr

fastplyr

dplyr alternatives

distinct

group_by

summarise

Joins

slice

Group IDs

expand

duplicate rows

filter

bind rows and cols

Quantiles

Quantile benchmark for many groups

tidytable vs fastplyr

10 million rows

slice benchmark

slice_head & slice_tail

summarise benchmark

count benchmark

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