Update joins.qmd (#1758)

Minor typos have been fixed.

Author: gungorMetehan

joins.qmd

@@ -114,7 +114,7 @@ This will become important when we start actually joining tables together.
 
 ### Checking primary keys
 
-Now that that we've identified the primary keys in each table, it's good practice to verify that they do indeed uniquely identify each observation.
+Now that we've identified the primary keys in each table, it's good practice to verify that they do indeed uniquely identify each observation.
 One way to do that is to `count()` the primary keys and look for entries where `n` is greater than one.
 This reveals that `planes` and `weather` both look good:
 
@@ -495,7 +495,7 @@ There are three types of outer joins:
     #|   appears in the output.
     #| fig-alt: |
     #|   Compared to the previous diagram showing an inner join, the y table
-    #|   gets a new virtual row containin NA that will match any row in x
+    #|   gets a new virtual row containing NA that will match any row in x
     #|   that didn't otherwise match. This means that the output now has
     #|   three rows. For key = 3, which matches this virtual row, val_y takes
     #|   value NA.
@@ -667,7 +667,7 @@ x |> inner_join(y, join_by(key == key), keep = TRUE)
 #| fig-cap: |
 #|   An inner join showing both `x` and `y` keys in the output.
 #| fig-alt: |
-#|   A join diagram showing an inner join betwen x and y. The result
+#|   A join diagram showing an inner join between x and y. The result
 #|   now includes four columns: key.x, val_x, key.y, and val_y. The
 #|   values of key.x and key.y are identical, which is why we usually
 #|   only show one.
@@ -895,4 +895,4 @@ Finally, you've gained a glimpse into the power of non-equi joins and seen a few
 This chapter concludes the "Transform" part of the book where the focus was on the tools you could use with individual columns and tibbles.
 You learned about dplyr and base functions for working with logical vectors, numbers, and complete tables, stringr functions for working with strings, lubridate functions for working with date-times, and forcats functions for working with factors.
 
-In the next part of the book, you'll learn more about getting various types of data into R in a tidy form.
+In the next part of the book, you'll learn more about getting various types of data into R in a tidy form.