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title Joining / Merging Data

Relational Data

Relational Data

Visualizing Relational Data

  • Primary key: uniquely identifies an observation in its own table. For example, planes$tailnum is a primary key because it uniquely identifies each plane in the planes table.
  • Foreign key: uniquely identifies an observation in another table. For example, the flights$tailnum is a foreign key because it appears in the flights table where it matches each flight to a unique plane.

3 families of verbs designed to work with relational data

  • Mutating joins: add new variables to one data frame from matching observations in another
  • Filtering joins: filter observations from one data frame based on whether or not they match an observation in the other table.
  • Set operations: treat observations as if they were set elements

Inner Join

Matches pairs of observations whenever their keys are equal:

Outer Joins

  • left_join keeps all observations in x
  • right_join keeps all observations in y
  • full_join keeps all observations in x and y

Outer Joins

Outer Joins: another visualization

Potential Problems

Duplicate Keys

One table w/ duplicates

Duplicate Keys

Both tables w/ duplicates

Missing Keys

semi_join(x, y) keeps all observations in x that have a match in y.

anti_join(x, y) drops all observations in x that have a match in y.

Anti-joins are useful for diagnosing join mismatches.

Compare with other functions

merge()

base::merge() can perform all four types of joins:

dplyr merge
inner_join(x, y) merge(x, y)
left_join(x, y) merge(x, y, all.x = TRUE)
right_join(x, y) merge(x, y, all.y = TRUE)
full_join(x, y) merge(x, y, all.x = TRUE, all.y = TRUE)
  • specific dplyr verbs more clearly convey the intent of your code: they are concealed in the arguments of merge().
  • dplyr's joins are considerably faster and don't mess with the order of the rows.

SQL

SQL is the inspiration for dplyr's conventions, so the translation is straightforward:

dplyr SQL
inner_join(x, y, by = "z") SELECT * FROM x INNER JOIN y USING (z)
left_join(x, y, by = "z") SELECT * FROM x LEFT OUTER JOIN y USING (z)
right_join(x, y, by = "z") SELECT * FROM x RIGHT OUTER JOIN y USING (z)
full_join(x, y, by = "z") SELECT * FROM x FULL OUTER JOIN y USING (z)
  • Note that "INNER" and "OUTER" are optional, and often omitted.
  • SQL supports a wider range of join types than dplyr

Set Operations

  • intersect(x, y): return only observations in both x and y.
  • union(x, y): return unique observations in x and y.
  • setdiff(x, y): return observations in x, but not in y.