From 2ded03567a99ed966757eecfdd023d8a0032c5e2 Mon Sep 17 00:00:00 2001 From: Peter Baumgartner Date: Mon, 17 Apr 2023 15:15:16 +0200 Subject: [PATCH] Fix typo in missing-values (#1430) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * "… when you compare them to another" instead of "… when you compare it another" * Update missing-values.qmd --------- Co-authored-by: Mine Cetinkaya-Rundel --- missing-values.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/missing-values.qmd b/missing-values.qmd index 6bebc3e..365f7cb 100644 --- a/missing-values.qmd +++ b/missing-values.qmd @@ -176,7 +176,7 @@ In that case, you can do manually what `complete()` does for you: create a data ### Joins This brings us to another important way of revealing implicitly missing observations: joins. -You'll learn more about joins in @sec-joins, but we wanted to quickly mention them to you here since you can often only know that values are missing from one dataset when you compare it another. +You'll learn more about joins in @sec-joins, but we wanted to quickly mention them to you here since you can often only know that values are missing from one dataset when you compare it to another. `dplyr::anti_join(x, y)` is a particularly useful tool here because it selects only the rows in `x` that don't have a match in `y`. For example, we can use two `anti_join()`s to reveal that we're missing information for four airports and 722 planes mentioned in `flights`: