Right join

Right join of x and y

Usage

fjoin_right(
  x = NULL,
  y = NULL,
  on,
  match.na = FALSE,
  mult.x = "all",
  mult.y = "all",
  indicate = FALSE,
  order = "left",
  select = NULL,
  select.x = NULL,
  select.y = NULL,
  prefix.y = "R.",
  on.first = FALSE,
  both = FALSE,
  do = !(is.null(x) && is.null(y)),
  show = !do
)

Arguments

x, y: data.frame-like objects (plain, data.table, tibble, sf, list, etc.) or else both omitted for a mock join statement with no data. See Details.
on: A character vector of join predicates, e.g. c("id", "col_x == col_y", "date > date", "cost <= budget").
match.na: Whether to allow equality matches between NAs or NaNs. Default FALSE.
mult.x: When a row of x has multiple matching rows in y, which to accept: "all" (the default), "first", or "last".
mult.y: When a row of y has multiple matching rows in x, which to accept: "all" (the default), "first", or "last". Can be combined with mult.x.
indicate: Whether to add a column ".join" at the front of the result, with values 1L if from x only, 2L if from y only, and 3L if joined from both tables (c.f. _merge in Stata). Default FALSE.
order: Whether the row order of the result should reflect x then y ("left") or y then x ("right"). Default "left".
select, select.x, select.y: Character vectors of columns to be selected from either input if present (select) or specifically from one or other of them (e.g. select.x). NULL (the default) selects all columns. Use "" or NA to select no columns. Join columns are always selected. See Details.
prefix.y: A prefix to attach to column names in y that are the same as a column name in x. Default "R.".
on.first: Whether to place the join columns first in the join result. Default FALSE.
both: Whether to include y's equality join column(s) separately in the output, instead of combining them with x's Default FALSE. Note that non-equality join columns from x are always included separately.
do: Whether to execute the join. If FALSE, show is set to TRUE and the data.table code for the join is printed to the console instead. Default is TRUE unless x and y are both omitted/NULL, in which case a mock join statement is produced. See Details.
show: Whether to print the data.table code for the join to the console. Default is the opposite of do. If x and y are both omitted/NULL, mock join code is displayed.

Value

A data.frame, data.table, (grouped) tibble, sf, or sf-tibble, or else NULL if do is FALSE. See Details.

Details

Input and output class

Each input can be any object with class data.frame, or a plain list of same-length vectors.

The output class depends on x as follows:

a data.table if x is a pure data.table
a tibble if it is a tibble (and a grouped tibble if it has class grouped_df)
an sf if it is an sf with its active geometry selected in the output
a plain data.frame in all other cases

The following attributes are carried through and refreshed: data.table key, tibble groups, sf agr (and bbox etc. of all individual sfc-class columns regardless of output class). See below for specifics.

Using `select`, `select.x`, and `select.y`

Used on its own, select keeps the join columns plus the specified non-join columns from both inputs if present.

If select.x is provided (and similarly for select.y) then:

if select is also specified, non-join columns of x named in either select or select.x are included
if select is not specified, only non-join columns named in select.x are included from x. Thus e.g. select.x = "" excludes all of x's non-join columns.

Non-existent column names are ignored without warning.

Column order

When select is specified but select.x and select.y are not, the output consists of all join columns followed by the selected non-join columns from either input in the order given in select.

In all other cases:

columns from x come before columns from y
within each group of columns, non-join columns are in the order given by select.x/select.y, or in their original data order if no selection is provided
if on.first is TRUE, join columns from both inputs are moved to the front of the overall output.

Using `mult.x` and `mult.y`

See the Examples for an application of using mult.x and mult.y together. Note that mult.y is applied after mult.x except with order = "right".

Displaying code and 'mock joins'

The option of displaying the join code with show = TRUE or by passing null inputs is aimed at data.table users wanting to use the package as a cookbook of recipes for adaptation. If x and y are both NULL, template code is displayed based on join column names implied by on, plus sample non-join column names. select arguments are ignored in this case.

The code displayed is for the join operation after casting the inputs as data.tables if necessary, and before casting the result as a tibble and/or sf if applicable. Note that fjoin departs from the usual j = list() idiom in order to avoid a deep copy of the output made by as.data.table.list. (Likewise, internally it takes only shallow copies of columns when casting inputs or outputs to different classes.)

tibble `groups`

If x is a grouped tibble (class grouped_df), the output is grouped by the grouping columns that are selected in the result.

data.table `key`s

If the output is a data.table, it inherits a key as follows:

fjoin_inner or fjoin_left with order = "left" (default): x's key if present
fjoin_inner or fjoin_right with order = "right": y's key if present

If not all of the key columns are selected in the result, the leading subset is used.

sf objects and `sfc`-class columns

Joins between two sf objects are supported. The active geometry and relation-to-geometry attribute agr are determined by x. All sfc-class columns in the output are refreshed after joining (using sf::st_sfc() with recompute_bbox = TRUE); this is true regardless of whether or not the inputs and output are sfs.

Examples

# ---------------------------------------------------------------------------
# True joins (inner/left/right/full): basic usage
# ---------------------------------------------------------------------------

# data frames
x <- data.table::fread(data.table = FALSE, input = "
  country  pop_m
Australia   27.2
   Brazil  212.0
     Chad    3.0
")

y <- data.table::fread(data.table = FALSE, input = "
  country forest_pc
   Brazil      59.1
     Chad       3.2
  Denmark      15.8
")

NULL # section break
#> NULL

fjoin_full(x, y, on = "country", indicate = TRUE)
#>   .join   country pop_m forest_pc
#> 1     1 Australia  27.2        NA
#> 2     3    Brazil 212.0      59.1
#> 3     3      Chad   3.0       3.2
#> 4     2   Denmark    NA      15.8
fjoin_left(x, y, on = "country", indicate = TRUE)
#>   .join   country pop_m forest_pc
#> 1     1 Australia  27.2        NA
#> 2     3    Brazil 212.0      59.1
#> 3     3      Chad   3.0       3.2
fjoin_right(x, y, on = "country", indicate = TRUE)
#>   .join country pop_m forest_pc
#> 1     3  Brazil   212      59.1
#> 2     3    Chad     3       3.2
#> 3     2 Denmark    NA      15.8
fjoin_inner(x, y, on = "country", indicate = TRUE)
#>   .join country pop_m forest_pc
#> 1     3  Brazil   212      59.1
#> 2     3    Chad     3       3.2

# ---------------------------------------------------------------------------
# Core options and arguments (in a 1:1 equality join with fjoin_full())
# ---------------------------------------------------------------------------

# data frames
dfQ <- data.table::fread(data.table = FALSE, quote ="'", input = "
id quantity                   notes other_cols
 2        5                      ''        ...
 1        6                      ''        ...
 3        7                      ''        ...
NA        8  'oranges (not listed)'        ...
")

dfP <- data.table::fread(data.table = FALSE, input = "
id     item price other_cols
NA   apples    10        ...
 3  bananas    20        ...
 2 cherries    30        ...
 1    dates    40        ...
 ")

NULL # section break
#> NULL

# (1) basic syntax
# cf. dplyr: full_join(dfQ, dfP, join_by(id), na.matches = "never")
fjoin_full(dfQ, dfP, on = "id")
#>   id quantity                notes other_cols     item price R.other_cols
#> 1  2        5                             ... cherries    30          ...
#> 2  1        6                             ...    dates    40          ...
#> 3  3        7                             ...  bananas    20          ...
#> 4 NA        8 oranges (not listed)        ...     <NA>    NA         <NA>
#> 5 NA       NA                 <NA>       <NA>   apples    10          ...

# (2) join-select in one line
fjoin_full(dfQ, dfP, on = "id", select = c("item", "price", "quantity"))
#>   id     item price quantity
#> 1  2 cherries    30        5
#> 2  1    dates    40        6
#> 3  3  bananas    20        7
#> 4 NA     <NA>    NA        8
#> 5 NA   apples    10       NA

# equivalent operation in dplyr
# x <- dfQ |> select(id, quantity)
# y <- dfP |> select(id, item, price)
# full_join(x, y, join_by(id), na.matches = "never") |>
#   select(id, item, price, quantity)
NULL # section break
#> NULL

# (an aside) equality matches on NA if you insist
fjoin_full(dfQ, dfP, on = "id", select = c("item", "price", "quantity", "notes"), match.na = TRUE)
#>   id     item price quantity                notes
#> 1  2 cherries    30        5                     
#> 2  1    dates    40        6                     
#> 3  3  bananas    20        7                     
#> 4 NA   apples    10        8 oranges (not listed)

# (3) indicator column (in Stata since 1984)
fjoin_full(
  dfQ,
  dfP,
  on = "id",
  select = c("item", "price", "quantity"),
  indicate = TRUE
)
#>   .join id     item price quantity
#> 1     3  2 cherries    30        5
#> 2     3  1    dates    40        6
#> 3     3  3  bananas    20        7
#> 4     1 NA     <NA>    NA        8
#> 5     2 NA   apples    10       NA

# (4) order rows by y then x
fjoin_full(
  dfQ,
  dfP,
  on = "id",
  select = c("item", "price", "quantity"),
  indicate = TRUE,
  order = "right"
)
#>   .join id     item price quantity
#> 1     2 NA   apples    10       NA
#> 2     3  3  bananas    20        7
#> 3     3  2 cherries    30        5
#> 4     3  1    dates    40        6
#> 5     1 NA     <NA>    NA        8

# (5) display code instead
fjoin_full(
  dfQ,
  dfP,
  on = "id",
  select = c("item", "price", "quantity"),
  indicate = TRUE,
  order = "right",
  do = FALSE
)
#> .DT : x = dfQ (cast as data.table)
#> .i  : y = dfP (cast as data.table)
#> Join: setDF(with(list(fjoin.temp = setDT(.DT[, fjoin.which.DT := .I][, na.omit(.SD, cols = "id"), .SDcols = c("id", "quantity", "fjoin.which.DT")][, fjoin.ind.DT := TRUE][.i, on = "id", data.frame(.join = fifelse(is.na(fjoin.ind.DT), 2L, 3L), id, item, price, quantity, fjoin.which.DT), allow.cartesian = TRUE])), rbind(fjoin.temp, setDT(.DT[!fjoin.temp$fjoin.which.DT, data.frame(id, quantity, fjoin.which.DT, .join = rep(1L, .N))]), fill = TRUE))[, fjoin.which.DT := NULL])[]
#> 

# ---------------------------------------------------------------------------
# M:M inequality join reduced to 1:1 using `mult.x` and `mult.y`
# ---------------------------------------------------------------------------

# data.table (`mult`) and dplyr (`multiple`) have options for reducing the
# cardinality on one side of the join from many ("all") to one ("first" or
# "last"). fjoin (`mult.x`, `mult.y`) permits this on either side of the
# join, or on both sides at once.

# This example (using `fjoin_left()`) shows an application to temporally
# ordered data frames of "events" and "reactions".

# data frames
events <- data.table::fread(data.table = FALSE, input = "
event_id event_ts
       1       10
       2       20
       3       40
")

reactions <- data.table::fread(data.table = FALSE, input = "
reaction_id reaction_ts
          1          30
          2          50
          3          60
")
NULL # section break
#> NULL

# (1) for each event, all subsequent reactions (M:M)
fjoin_left(
  events,
  reactions,
  on = c("event_ts < reaction_ts"),
)
#>   event_id event_ts reaction_id reaction_ts
#> 1        1       10           1          30
#> 2        1       10           2          50
#> 3        1       10           3          60
#> 4        2       20           1          30
#> 5        2       20           2          50
#> 6        2       20           3          60
#> 7        3       40           2          50
#> 8        3       40           3          60

# (2) for each event, the next reaction (1:M)
fjoin_left(
  events,
  reactions,
  on = c("event_ts < reaction_ts"),
  mult.x = "first"
)
#>   event_id event_ts reaction_id reaction_ts
#> 1        1       10           1          30
#> 2        2       20           1          30
#> 3        3       40           2          50

# (3) for each event, the next reaction, provided there was no intervening event (1:1)
fjoin_left(
  events,
  reactions,
  on = c("event_ts < reaction_ts"),
  mult.x = "first",
  mult.y = "last"
)
#>   event_id event_ts reaction_id reaction_ts
#> 1        1       10          NA          NA
#> 2        2       20           1          30
#> 3        3       40           2          50

Usage

Arguments

Value

Details

Input and output class

Using select, select.x, and select.y

Column order

Using mult.x and mult.y

Displaying code and 'mock joins'

tibble groups

data.table keys