README

install.packages("powerjoin")

remotes::install_github("moodymudskipper/powerjoin")

Now let’s match penguins

library(powerjoin)
library(tidyverse)

# toy dataset built from Allison Horst's {palmerpenguins} package and 
# Hadley Wickham's {babynames}

male_penguins <- tribble(
     ~name,    ~species,     ~island, ~flipper_length_mm, ~body_mass_g,
 "Giordan",    "Gentoo",    "Biscoe",               222L,        5250L,
  "Lynden",    "Adelie", "Torgersen",               190L,        3900L,
  "Reiner",    "Adelie",     "Dream",               185L,        3650L
)

female_penguins <- tribble(
     ~name,    ~species,  ~island, ~flipper_length_mm, ~body_mass_g,
  "Alonda",    "Gentoo", "Biscoe",               211,        4500L,
     "Ola",    "Adelie",  "Dream",               190,        3600L,
"Mishayla",    "Gentoo", "Biscoe",               215,        4750L,
)

Safer joins

The check argument receives an object created by the check_specs() function, which provides ways to handle specific input properties, its arguments can be :

check_specs()
#> # powerjoin check specifications
#> ℹ implicit_keys
#> → column_conflict
#> → duplicate_keys_left
#> → duplicate_keys_right
#> → unmatched_keys_left
#> → unmatched_keys_right
#> → missing_key_combination_left
#> → missing_key_combination_right
#> → inconsistent_factor_levels
#> → inconsistent_type
#> → grouped_input
#> → na_keys

By default it works like {dplyr}, informing in case of implicit keys, and no further checks :

power_inner_join(
  male_penguins[c("species", "island")],
  female_penguins[c("species", "island")]
)
#> Joining, by = c("species", "island")
#> # A tibble: 3 × 2
#>   species island
#>   <chr>   <chr> 
#> 1 Gentoo  Biscoe
#> 2 Gentoo  Biscoe
#> 3 Adelie  Dream

We can silence the implicit key detection and check that we have unique keys in the right table

check_specs(implicit_keys = "ignore", duplicate_keys_right = "abort")
#> # powerjoin check specifications
#> → implicit_keys
#> → column_conflict
#> → duplicate_keys_left
#> x duplicate_keys_right
#> → unmatched_keys_left
#> → unmatched_keys_right
#> → missing_key_combination_left
#> → missing_key_combination_right
#> → inconsistent_factor_levels
#> → inconsistent_type
#> → grouped_input
#> → na_keys

power_inner_join(
  male_penguins[c("species", "island")],
  female_penguins[c("species", "island")],
  check = check_specs(implicit_keys = "ignore", duplicate_keys_right = "abort")
)
#> Error: Keys in the right table have duplicates:
#> # A tibble: 1 × 2
#>   species island
#>   <chr>   <chr> 
#> 1 Gentoo  Biscoe

The column_conflict argument guarantees that you won’t have columns renamed without you knowing, you might need it most of the time, we could setup some development and production specs for our most common joins:

dev_specs <- check_specs(
  column_conflict = "abort",
  inconsistent_factor_levels = "inform",
  inconsistent_type = "inform"
)

prod_specs <- check_specs(
  column_conflict = "abort",
  implicit_keys = "abort"
)

power_inner_join(
  male_penguins,
  female_penguins,
  by = c("species", "island"),
  check = dev_specs
)
#> Error: The following columns are conflicted and their conflicts are not handled: 
#> 'name', 'flipper_length_mm', 'body_mass_g'

Handle column conflict

We saw above how to fail when encountering column conflict, here we show how to handle it.

To resolve conflicts between identically named join columns, set the conflict argument to a 2 argument function (or formula) that will take as arguments the 2 conflicting joined columns after the join.

df1 <- tibble(id = 1:3, value = c(10, NA, 30))
df2 <- tibble(id = 2:4, value = c(22, 32, 42))

power_left_join(df1, df2, by = "id", conflict = `+`)
#> # A tibble: 3 × 2
#>      id value
#>   <int> <dbl>
#> 1     1    NA
#> 2     2    NA
#> 3     3    62

Coalescing is the most common use case and we provide the functions coalesce_xy() and coalesce_yx() to ease this task (both wrapped around dplyr::coalesce()).

power_left_join(df1, df2, by = "id", conflict = coalesce_xy)
#> # A tibble: 3 × 2
#>      id value
#>   <int> <dbl>
#> 1     1    10
#> 2     2    22
#> 3     3    30

power_left_join(df1, df2, by = "id", conflict = coalesce_yx)
#> # A tibble: 3 × 2
#>      id value
#>   <int> <dbl>
#> 1     1    10
#> 2     2    22
#> 3     3    32

Note that the function is operating on vectors by default, not rowwise, however we can make it work rowwise by using rw in the lhs of the formula.

power_left_join(df1, df2, by = "id", conflict = ~ sum(.x, .y, na.rm = TRUE))
#> # A tibble: 3 × 2
#>      id value
#>   <int> <dbl>
#> 1     1    94
#> 2     2    94
#> 3     3    94

power_left_join(df1, df2, by = "id", conflict = rw ~ sum(.x, .y, na.rm = TRUE))
#> # A tibble: 3 × 2
#>      id value
#>   <int> <dbl>
#> 1     1    10
#> 2     2    22
#> 3     3    62

If you need finer control, conflict can also be a named list of such functions, formulas or special values, each to be applied on the relevant pair of conflicted columns.

Preprocess inputs

Traditionally key columns need to be repeated when preprocessing inputs before a join, which is an annoyance and an opportunity for mistakes. With {powerjoin} we can do :

power_inner_join(
  male_penguins %>% select_keys_and(name),
  female_penguins %>% select_keys_and(female_name = name),
  by = c("species", "island")
)
#> # A tibble: 3 × 4
#>   species island name    female_name
#>   <chr>   <chr>  <chr>   <chr>      
#> 1 Gentoo  Biscoe Giordan Alonda     
#> 2 Gentoo  Biscoe Giordan Mishayla   
#> 3 Adelie  Dream  Reiner  Ola

power_inner_join(
  male_penguins,
  female_penguins %>% select_keys_and(),
  by = c("species", "island")
)
#> # A tibble: 3 × 5
#>   name    species island flipper_length_mm body_mass_g
#>   <chr>   <chr>   <chr>              <int>       <int>
#> 1 Giordan Gentoo  Biscoe               222        5250
#> 2 Giordan Gentoo  Biscoe               222        5250
#> 3 Reiner  Adelie  Dream                185        3650

We could also aggregate on keys before the join, without the need for any group_by()/ungroup() gymnastics :

power_left_join(
  male_penguins %>% summarize_by_keys(male_weight = mean(body_mass_g)),
  female_penguins %>% summarize_by_keys(female_weight = mean(body_mass_g)),
  by = c("species", "island")
)
#> # A tibble: 3 × 4
#>   species island    male_weight female_weight
#>   <chr>   <chr>           <dbl>         <dbl>
#> 1 Adelie  Dream            3650          3600
#> 2 Adelie  Torgersen        3900            NA
#> 3 Gentoo  Biscoe           5250          4625

pack_along_keys() packs given columns, or all non key columns by default, into a data frame column named by the name argument, it’s useful to namespace the data and avoid conflicts

power_left_join(
  male_penguins %>% pack_along_keys(name = "m"),
  female_penguins %>% pack_along_keys(name = "f"),
  by = c("species", "island")
)
#> # A tibble: 4 × 4
#>   species island    m$name $flipper_length… $body_mass_g f$name $flipper_length…
#>   <chr>   <chr>     <chr>             <int>        <int> <chr>             <dbl>
#> 1 Gentoo  Biscoe    Giord…              222         5250 Alonda              211
#> 2 Gentoo  Biscoe    Giord…              222         5250 Misha…              215
#> 3 Adelie  Torgersen Lynden              190         3900 <NA>                 NA
#> 4 Adelie  Dream     Reiner              185         3650 Ola                 190

These functions do not modify the data but add an attribute that will be processed by the join function later on, so no function should be used on top of them.

Fuzzy joins

To do fuzzy joins we use formulas in the by argument, in this formula we use, .x and .y to describe the left and right tables. This is very flexible but can be costly since a cartesian product is computed.

power_inner_join(
    male_penguins %>% select_keys_and(male_name = name),
    female_penguins %>% select_keys_and(female_name = name),
    by = c(~.x$flipper_length_mm < .y$flipper_length_mm, ~.x$body_mass_g > .y$body_mass_g)
)
#> # A tibble: 1 × 6
#>   flipper_length_mm.x body_mass_g.x male_name flipper_length_mm.y body_mass_g.y
#>                 <int>         <int> <chr>                   <dbl>         <int>
#> 1                 185          3650 Reiner                    190          3600
#> # … with 1 more variable: female_name <chr>

power_inner_join(
    male_penguins %>% select_keys_and(male_name = name),
    female_penguins %>% select_keys_and(female_name = name),
    by = c("island", ~.x$flipper_length_mm > .y$flipper_length_mm)
)
#> # A tibble: 2 × 5
#>   island flipper_length_mm.x male_name flipper_length_mm.y female_name
#>   <chr>                <int> <chr>                   <dbl> <chr>      
#> 1 Biscoe                 222 Giordan                   211 Alonda     
#> 2 Biscoe                 222 Giordan                   215 Mishayla

Finally we might want to create a column with a value used in the comparison, in that case we will use <- in the formula (several times if needed)`:

power_inner_join(
    male_penguins %>% select_keys_and(male_name = name),
    female_penguins %>% select_keys_and(female_name = name),
    by = ~ (mass_ratio <- .y$body_mass_g / .x$body_mass_g) > 1.2
)
#> # A tibble: 3 × 5
#>   body_mass_g.x male_name body_mass_g.y female_name mass_ratio
#>           <int> <chr>             <int> <chr>            <dbl>
#> 1          3900 Lynden             4750 Mishayla          1.22
#> 2          3650 Reiner             4500 Alonda            1.23
#> 3          3650 Reiner             4750 Mishayla          1.30

Fill unmatched values

The fill argument is used to specify what to fill unmatched values with, note that missing values resulting from matches are not replaced.

df1 <- tibble(id = 1:3)
df2 <- tibble(id = 1:2, value2 = c(2, NA), value3 = c(NA, 3))

power_left_join(df1, df2, by = "id", fill = 0)
#> # A tibble: 3 × 3
#>      id value2 value3
#>   <int>  <dbl>  <dbl>
#> 1     1      2     NA
#> 2     2     NA      3
#> 3     3      0      0

power_left_join(df1, df2, by = "id", fill = list(value2 = 0))
#> # A tibble: 3 × 3
#>      id value2 value3
#>   <int>  <dbl>  <dbl>
#> 1     1      2     NA
#> 2     2     NA      3
#> 3     3      0     NA

Join recursively

df1 <- tibble(id = 1, a = "foo")
df2 <- tibble(id = 1, b = "bar")
df3 <- tibble(id = 1, c = "baz")

power_left_join(list(df1, df2, df3), by = "id")
#> # A tibble: 1 × 4
#>      id a     b     c    
#>   <dbl> <chr> <chr> <chr>
#> 1     1 foo   bar   baz

power_left_join(df1, list(df2, df3), by = "id")
#> # A tibble: 1 × 4
#>      id a     b     c    
#>   <dbl> <chr> <chr> <chr>
#> 1     1 foo   bar   baz

Enhanced keep argument

By default, as in {dplyr}, key columns are merged and given names from the left table. In case of a fuzzy join columns that participate in a fuzzy join are kept from both sides.

We provide additional values "left", "right", "both" and "none" to choose which keys to keep or drop.

Notes

This package supersedes the {safejoin} package which had an unfortunate homonym on CRAN and had a suboptimal interface and implementation.

Hadley Wickham, Romain François and David Robinson are credited for their work in {dplyr} and {fuzzyjoin} since this package contains some code copied from these packages.

powerjoin

Installation

Now let’s match penguins

Safer joins

Handle column conflict

Preprocess inputs

Fuzzy joins

Fill unmatched values

Join recursively

Enhanced `keep` argument

Notes

powerjoin

Installation

Now let’s match penguins

Safer joins

Handle column conflict

Preprocess inputs

Fuzzy joins

Fill unmatched values

Join recursively

Enhanced keep argument

Notes

Enhanced `keep` argument