
DPLYR . TIDYVERSE . ORG {
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Title:
Mutating joins — mutate-joins • dplyr
Description:
Mutating joins add columns from y to x, matching observations based on the keys. There are four mutating joins: the inner join, and the three outer joins. Inner join An inner_join() only keeps observations from x that have a matching key in y. The most important property of an inner join is that unmatched rows in either input are not included in the result. This means that generally inner joins are not appropriate in most analyses, because it is too easy to lose observations. Outer joins The three outer joins keep observations that appear in at least one of the data frames: A left_join() keeps all observations in x. A right_join() keeps all observations in y. A full_join() keeps all observations in x and y.
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Keywords {🔍}
relationship, joins, chr, join, rows, null, manytomany, row, keys, unmatched, multiple, match, beatles, tibble, matches, columns, joining, guitar, copy, false, default, suffix, result, data, dataframe, variables, returns, relationships, john, paul, observations, methods, true, output, bandmembers, joinbyname, band, plays, bass, dplyr, namatches, joinby, inequality, detected, warning, leftjoindf, joinbyx, innerjoin, key, leftjoin,
Topics {✒️}
incomparables = na potentially expensive operation == artist accidentally dropping rows variable names differ na joined duplicate variables named character vector simple equality joins lazy data frames rolling joins follow preemptively set relationship matching observations based output columns include data frame extensions join specification created potentially drop rows join usage arguments matches multiple rows drops unmatched keys nan values match output columns included mutating joins frame' right_join frame' full_join data source variable names examples developed character vector column names frame' left_join matching key inequality retain inequality conditions nan values equality joins typically surprising multiple matches usage inner_join equality retain equality expression key columns key columns multiple expressions multiple handling data frames outer joins overlap joins full joins left joins
Questions {❓}
- How should unmatched keys that would result in dropped rows be handled?
- See the documentation at ?
- Should the join keys from both x and y be preserved in the output?
- Should two NA or two NaN values match?
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