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## How do joins work?
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Now that you've used joins a few times it's time to learn more about how they work, focusing on how each row in `x` matches rows in `y`.
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We'll begin by introducing a visual representation of joins, using the simple tibbles defined below and shown in @fig-join-setup. In these examples we'll use a single key called `key` and a single value column (`val_x` and `val_y`), but the ideas all generalize to multiple keys and multiple values.
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We'll begin by introducing a visual representation of joins, using the simple tibbles defined below and shown in @fig-join-setup.
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In these examples we'll use a single key called `key` and a single value column (`val_x` and `val_y`), but the ideas all generalize to multiple keys and multiple values.
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```{r}
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x <- tribble(
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If you are doing this deliberately, you can set `relationship = "many-to-many"`, as the warning suggests.
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### Filtering joins {#sec-non-equi-joins}
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The number of matches also determines the behavior of the filtering joins.
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