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Hadley Wickham
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joins.qmd
81
joins.qmd
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@ -93,17 +93,14 @@ These relationships are summarized visually in @fig-flights-relationships.
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#| Variables making up a primary key are coloured grey, and are connected
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#| to their corresponding foreign keys with arrows.
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#| fig-alt: >
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#| Diagram showing the relationships between airports, planes, flights,
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#| weather, and airlines datasets from the nycflights13 package. The faa
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#| variable in the airports data frame is connected to the origin and dest
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#| variables in the flights data frame. The tailnum variable in the planes
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#| data frame is connected to the tailnum variable in flights. The
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#| time_hour and origin variables in the weather data frame are connected
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#| to the variables with the same name in the flights data frame. And
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#| finally the carrier variables in the airlines data frame is connected
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#| to the carrier variable in the flights data frame. There are no direct
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#| connections between airports, planes, airlines, and weather data
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#| frames.
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#| The relationships between airports, planes, flights, weather, and
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#| airlines datasets from the nycflights13 package. airports$faa
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#| connected to the flights$origin and flights$dest. planes$tailnum
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#| is connected to the flights$tailnum. weather$time_hour and
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#| weather$origin are jointly connected to flights$time_hour and
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#| flights$origin. airlines$carrier is connected to flights$carrier.
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#| There are no direct connections between airports, planes, airlines,
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#| and weather data frames.
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knitr::include_graphics("diagrams/relational.png", dpi = 270)
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```
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@ -433,9 +430,9 @@ y <- tribble(
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#| columns map background colour to key value. The grey columns represent
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#| the "value" columns that are carried along for the ride.
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#| fig-alt: >
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#| x and y are two data frames with 2 columns and 3 rows each. The first
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#| column in each is the key and the second is the value. The contents of
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#| these data frames are given in the previous code chunk.
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#| x and y are two data frames with 2 columns and 3 rows, with contents
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#| as described in the text. The values of the keys are coloured:
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#| 1 is green, 2 is purple, 3 is orange, and 4 is yellow.
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knitr::include_graphics("diagrams/join/setup.png", dpi = 270)
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```
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@ -453,8 +450,8 @@ The rows and columns in the output are primarily determined by `x`, so the `x` t
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#| fig-alt: >
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#| x and y are placed at right-angles, with horizonal lines extending
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#| from x and vertical lines extending from y. There are 3 rows in x and
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#| 3 rows in y leading to 9 intersections that represent nine potential
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#| matches.
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#| 3 rows in y, which leads to nine intersections representing nine
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#| potential matches.
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knitr::include_graphics("diagrams/join/setup2.png", dpi = 270)
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```
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@ -473,8 +470,9 @@ We'll come back to non-equi joins in @sec-non-equi-joins.
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#| An inner join matches each row in `x` to the row in `y` that has the
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#| same value of `key`. Each match becomes a row in the output.
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#| fig-alt: >
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#| Keys 1 and 2 appear in both x and y, so there values are equal and
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#| we get a match, indicated by a dot. Each dot corresponds to a row
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#| x and y are placed at right-angles with lines forming a grid of
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#| potential matches. Keys 1 and 2 appear in both x and y, so we
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#| get a match, indicated by a dot. Each dot corresponds to a row
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#| in the output, so the resulting joined data frame has two rows.
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knitr::include_graphics("diagrams/join/inner.png", dpi = 270)
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@ -496,10 +494,11 @@ There are three types of outer joins:
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#| A visual representation of the left join where every row in `x`
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#| appears in the output.
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#| fig-alt: >
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#| Compared to the inner join, the `y` table gets a new virtual row
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#| that will match any row in `x` that doesn't otherwise have a match.
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#| This means that the output now has three rows. For key = 3, which
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#| matches this virtual row, the value of val_y is NA.
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#| Compared to the previous diagram showing an inner join, the y table
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#| gets a new virtual row containin NA that will match any row in x
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#| that didn't otherwise match. This means that the output now has
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#| three rows. For key = 3, which matches this virtual row, val_y takes
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#| value NA.
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knitr::include_graphics("diagrams/join/left.png", dpi = 270)
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```
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@ -516,12 +515,9 @@ There are three types of outer joins:
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#| A visual representation of the right join where every row of `y`
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#| appears in the output.
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#| fig-alt: >
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#| Keys 1 and 2 from x are matched to those in y, key 4 is
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#| also carried along to the joined result since it's on the right data
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#| frame, but key 3 from x is not carried along since it's on the left
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#| but not on the right. The result is a data frame with 3 rows: keys
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#| 1, 2, and 4, all values from val_y, and the corresponding values
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#| from val_x for keys 1 and 2 with an NA for key 4, val_x.
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#| Compared to the previous diagram showing an left join, the x table
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#| now gains a virtual row so that every row in y gets a match in x.
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#| val_x contains NA for the row in y that didn't match x.
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knitr::include_graphics("diagrams/join/right.png", dpi = 270)
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```
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@ -538,6 +534,7 @@ There are three types of outer joins:
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#| A visual representation of the full join where every row in `x`
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#| and `y` appears in the output.
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#| fig-alt: >
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#| Now both x and y have a virtual row that always matches.
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#| The result has 4 rows: keys 1, 2, 3, and 4 with all values
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#| from val_x and val_y, however key 2, val_y and key 4, val_x are NAs
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#| since those keys don't have a match in the other data frames.
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@ -561,10 +558,10 @@ However, this is not a great representation because while it might jog your memo
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#| and y, with x on the right and y on the left. Shading indicates the
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#| result of the join.
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#|
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#| Inner join: Only intersection is shaded.
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#| Inner join: the intersection is shaded.
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#| Full join: Everything is shaded.
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#| Left join: All of x is shaded.
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#| Right: All of y is shaded.
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#| Right join: All of y is shaded.
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knitr::include_graphics("diagrams/join/venn.png", dpi = 270)
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```
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@ -690,11 +687,9 @@ This means that filtering joins never duplicate rows like mutating joins do.
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#| In a semi-join it only matters that there is a match; otherwise
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#| values in `y` don't affect the output.
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#| fig-alt: >
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#| Diagram of a semi join. Data frame x is on the left and has two columns
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#| (key and val_x) with keys 1, 2, and 3. Diagram y is on the right and also
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#| has two columns (key and val_y) with keys 1, 2, and 4. Semi joining these
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#| two results in a data frame with two rows and two columns (key and val_x),
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#| with keys 1 and 2 (the only keys that match between the two data frames).
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#| A join diagram with old friends x and y. In a semi join, only the
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#| presence of a match matters so the output contains the same columns
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#| as x.
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knitr::include_graphics("diagrams/join/semi.png", dpi = 270)
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```
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@ -707,11 +702,8 @@ knitr::include_graphics("diagrams/join/semi.png", dpi = 270)
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#| An anti-join is the inverse of a semi-join, dropping rows from `x`
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#| that have a match in `y`.
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#| fig-alt: >
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#| Diagram of an anti join. Data frame x is on the left and has two columns
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#| (key and val_x) with keys 1, 2, and 3. Diagram y is on the right and also
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#| has two columns (key and val_y) with keys 1, 2, and 4. Anti joining these
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#| two results in a data frame with one row and two columns (key and val_x),
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#| with keys 3 only (the only key in x that is not in y).
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#| An anti-join is the inverse of a semi-join so matches are drawn with
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#| red lines indicating that they will be dropped from the output.
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knitr::include_graphics("diagrams/join/anti.png", dpi = 270)
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```
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@ -737,7 +729,7 @@ x |> left_join(y, by = "key", keep = TRUE)
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#| A join diagram showing an inner join betwen x and y. The result
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#| now includes four columns: key.x, val_x, key.y, and val_y. The
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#| values of key.x and key.y are identical, which is why we usually
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#| omit one.
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#| only show one.
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#| echo: false
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#| out-width: ~
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@ -807,7 +799,8 @@ Inequality joins use `<`, `<=`, `>=`, or `>` to restrict the set of possible mat
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#| out-width: ~
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#| fig-cap: >
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#| An inequality join where `x` is joined to `y` on rows where the key
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#| of `x` is less than the key of `y`.
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#| of `x` is less than the key of `y`. This makes a triangular
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#| shape in the top-left corner.
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knitr::include_graphics("diagrams/join/lt.png", dpi = 270)
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```
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@ -833,6 +826,10 @@ For example `join_by(closest(x <= y))` matches the smallest `y` that's greater t
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#| fig-cap: >
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#| A following join is similar to a greater-than-or-equal inequality join
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#| but only matches the first value.
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#| fig-alt: >
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#| A rolling join is a subset of an inequality join so some matches are
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#| grayed out indicating that they're not used because they're not the
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#| "closest".
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knitr::include_graphics("diagrams/join/closest.png", dpi = 270)
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```
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