joyn (version 0.2.4)

left_join: Left join two data frames

Description

This is a joyn wrapper that works in a similar fashion to dplyr::left_join

Usage

left_join(
  x,
  y,
  by = intersect(names(x), names(y)),
  copy = FALSE,
  suffix = c(".x", ".y"),
  keep = NULL,
  na_matches = c("na", "never"),
  multiple = "all",
  unmatched = "drop",
  relationship = NULL,
  y_vars_to_keep = TRUE,
  update_values = FALSE,
  update_NAs = update_values,
  reportvar = getOption("joyn.reportvar"),
  reporttype = c("factor", "character", "numeric"),
  roll = NULL,
  keep_common_vars = FALSE,
  sort = TRUE,
  verbose = getOption("joyn.verbose"),
  ...
)

Value

An data frame of the same class as x. The properties of the output are as close as possible to the ones returned by the dplyr alternative.

Arguments

x

data frame: referred to as left in R terminology, or master in Stata terminology.

y

data frame: referred to as right in R terminology, or using in Stata terminology.

by

a character vector of variables to join by. If NULL, the default, joyn will do a natural join, using all variables with common names across the two tables. A message lists the variables so that you can check they're correct (to suppress the message, simply explicitly list the variables that you want to join). To join by different variables on x and y use a vector of expressions. For example, by = c("a = b", "z") will use "a" in x, "b" in y, and "z" in both tables.

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

Should the join keys from both x and y be preserved in the output?

• If NULL, the default, joins on equality retain only the keys from x, while joins on inequality retain the keys from both inputs.

• If TRUE, all keys from both inputs are retained.

• If FALSE, only keys from x are retained. For right and full joins, the data in key columns corresponding to rows that only exist in y are merged into the key columns from x. Can't be used when joining on inequality conditions.

na_matches

Should two NA or two NaN values match?

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

• "never" treats two NA or two NaN values as different, and will never match them together or to any other values. This is similar to joins for database sources and to base::merge(incomparables = NA).

multiple

Handling of rows in x with multiple matches in y. For each row of x:

• "all", the default, returns every match detected in y. This is the same behavior as SQL.

• "any" returns one match detected in y, with no guarantees on which match will be returned. It is often faster than "first" and "last" if you just need to detect if there is at least one match.

• "first" returns the first match detected in y.

• "last" returns the last match detected in y.

unmatched

How should unmatched keys that would result in dropped rows be handled?

• "drop" drops unmatched keys from the result.

• "error" throws an error if unmatched keys are detected.

unmatched is intended to protect you from accidentally dropping rows during a join. It only checks for unmatched keys in the input that could potentially drop rows.

• For left joins, it checks y.

• For right joins, it checks x.

• For inner joins, it checks both x and y. In this case, unmatched is also allowed to be a character vector of length 2 to specify the behavior for x and y independently.

relationship

Handling of the expected relationship between the keys of x and y. If the expectations chosen from the list below are invalidated, an error is thrown.

• NULL, the default, doesn't expect there to be any relationship between x and y. However, for equality joins it will check for a many-to-many relationship (which is typically unexpected) and will warn if one occurs, encouraging you to either take a closer look at your inputs or make this relationship explicit by specifying "many-to-many".

  See the Many-to-many relationships section for more details.

• "one-to-one" expects:

  • Each row in x matches at most 1 row in y.

  • Each row in y matches at most 1 row in x.

• "one-to-many" expects:

  • Each row in y matches at most 1 row in x.

• "many-to-one" expects:

  • Each row in x matches at most 1 row in y.

• "many-to-many" doesn't perform any relationship checks, but is provided to allow you to be explicit about this relationship if you know it exists.

relationship doesn't handle cases where there are zero matches. For that, see unmatched.

y_vars_to_keep

character: Vector of variable names in y that will be kept after the merge. If TRUE (the default), it keeps all the brings all the variables in y into x. If FALSE or NULL, it does not bring any variable into x, but a report will be generated.

update_values

logical: If TRUE, it will update all values of variables in x with the actual of variables in y with the same name as the ones in x. NAs from y won't be used to update actual values in x. Yet, by default, NAs in x will be updated with values in y. To avoid this, make sure to set update_NAs = FALSE

update_NAs

logical: If TRUE, it will update NA values of all variables in x with actual values of variables in y that have the same name as the ones in x. If FALSE, NA values won't be updated, even if update_values is TRUE

reportvar

character: Name of reporting variable. Default is ".joyn". This is the same as variable "_merge" in Stata after performing a merge. If FALSE or NULL, the reporting variable will be excluded from the final table, though a summary of the join will be display after concluding.

reporttype

character: One of "character" or "numeric". Default is "character". If "numeric", the reporting variable will contain numeric codes of the source and the contents of each observation in the joined table. See below for more information.

roll

double: to be implemented

keep_common_vars

logical: If TRUE, it will keep the original variable from y when both tables have common variable names. Thus, the prefix "y." will be added to the original name to distinguish from the resulting variable in the joined table.

sort

logical: If TRUE, sort by key variables in by. Default is FALSE.

verbose

logical: if FALSE, it won't display any message (programmer's option). Default is TRUE.

...

Arguments passed on to joyn

match_type

character: one of "m:m", "m:1", "1:m", "1:1". Default is "1:1" since this the most restrictive. However, following Stata's recommendation, it is better to be explicit and use any of the other three match types (See details in match types sections).

allow.cartesian

logical: Check documentation in official web site. Default is NULL, which implies that if the join is "1:1" it will be FALSE, but if the join has any "m" on it, it will be converted to TRUE. By specifying TRUE of FALSE you force the behavior of the join.

suffixes

A character(2) specifying the suffixes to be used for making non-by column names unique. The suffix behaviour works in a similar fashion as the base::merge method does.

yvars

: use now y_vars_to_keep

keep_y_in_x

: use now keep_common_vars

msg_type

character: type of messages to display by default

na.last

logical. If TRUE, missing values in the data are placed last; if FALSE, they are placed first; if NA they are removed. na.last=NA is valid only for x[order(., na.last)] and its default is TRUE. setorder and setorderv only accept TRUE/FALSE with default FALSE.

See Also

Other dplyr alternatives: anti_join(), full_join(), inner_join(), right_join()

Examples

# Simple left join
library(data.table)

x1 = data.table(id = c(1L, 1L, 2L, 3L, NA_integer_),
                t  = c(1L, 2L, 1L, 2L, NA_integer_),
                x  = 11:15)
y1 = data.table(id = c(1,2, 4),
                y  = c(11L, 15L, 16))
left_join(x1, y1, relationship = "many-to-one")