powerjoin (version 0.1.0)

power_left_join: Power joins

Description

Power joins

Usage

power_left_join(
  x,
  y = NULL,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  keep = NULL,
  na_matches = c("na", "never"),
  check = check_specs(),
  conflict = NULL,
  fill = NULL
)
power_right_join(
  x,
  y = NULL,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  keep = NULL,
  na_matches = c("na", "never"),
  check = check_specs(),
  conflict = NULL,
  fill = NULL
)
power_inner_join(
  x,
  y = NULL,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  keep = NULL,
  na_matches = c("na", "never"),
  check = check_specs(),
  conflict = NULL,
  fill = NULL
)
power_full_join(
  x,
  y = NULL,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  keep = NULL,
  na_matches = c("na", "never"),
  check = check_specs(),
  conflict = NULL,
  fill = NULL
)

Value

A data frame

Arguments

x, y

A pair of data frames, data frame extensions (e.g. a tibble), or lazy data frames (e.g. from dbplyr or dtplyr). See Methods, below, for more details.

by

As in dplyr, but extended so user can supply a formula or a list of character and formulas. Formulas are used for fuzzy joins and

copy

If x and y are not from the same data source, and copy is TRUE, then y will be copied into the same src as x. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.

suffix

If there are non-joined duplicate variables in x and y, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.

keep

A boolean for compatibility with dplyr, or a value among "left", "right", "both", "none" or "default". See details.

The vales of the keep parameter work as follow :

• NULL (default) : merge keys and name them as the left table's keys, and keep columns used for fuzzy joins from both tables

• left : keep only key columns for left table

• right: keep only key columns for right table

• both or TRUE: keep key columns from both tables, adding suffix if relevant

• none : drop all key columns from the output

• FALSE : merge keys and name them as the left table's keys, maps to none for fuzzy joins

na_matches

Should NA and NaN values match one another?

The default, "na", treats two NA or NaN values as equal, like %in%, match(), merge().

Use "never" to always treat two NA or NaN values as different, like joins for database sources, similarly to merge(incomparables = FALSE).

check

A list created with check_specs()

conflict

A function, formula, the special value amongst "patch", or a named list of such items. If the LHS of the formula is rw the rhs will be applied rowwise. Note that the columns will be subsetted with [ so for list columns .x or .y will refer to length 1 lists and you might sometimes need .x[[1]] or .y[[1]].

fill

Values used to replace missing values originating in unmatched keys, or a named list of such items.

Examples

# See README for a more verbose version
library(tibble)
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,
)

# apply different checks
power_inner_join(
  male_penguins[c("species", "island")],
  female_penguins[c("species", "island")],
  check = check_specs(implicit_keys = "ignore", duplicate_keys_right = "inform")
)

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

# handle conflicted columns when joining
power_left_join(df1, df2, by = "id", conflict = `+`)

# the most frequent use case is to coalesce
power_left_join(df1, df2, by = "id", conflict = coalesce_xy)
power_left_join(df1, df2, by = "id", conflict = coalesce_yx)

# the conflict function is applied colwise by default!
power_left_join(df1, df2, by = "id", conflict = ~ sum(.x, .y, na.rm = TRUE))

# apply conflict function rowwise
power_left_join(df1, df2, by = "id", conflict = rw ~ sum(.x, .y, na.rm = TRUE))

# subset columns without repeating keys
power_inner_join(
  male_penguins %>% select_keys_and(name),
  female_penguins %>% select_keys_and(female_name = name),
  by = c("species", "island")
)

# semi join
power_inner_join(
  male_penguins,
  female_penguins %>% select_keys_and(),
  by = c("species", "island")
)

# agregate without repeating keys
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")
)

# pack auxiliary colums without repeating keys
power_left_join(
  male_penguins %>% pack_along_keys(name = "m"),
  female_penguins %>% pack_along_keys(name = "f"),
  by = c("species", "island")
)

# fuzzy join
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)
)

# fuzzy + equi join
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)
)

# define new column without repeating computation
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
)
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,
  keep = "none"
)

# fill unmatched values
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)
power_left_join(df1, df2, by = "id", fill = list(value2 = 0))

# 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")
power_left_join(df1, list(df2, df3), by = "id")