Merge pull request #98 from GlenHertz/main

Fix docs for `@slices_sample(5)`

Author: kdpsingh

docs/examples/UserGuide/conditionals.jl

@@ -34,7 +34,7 @@ end
 
 # Although `if_else()` is convenient when evaluating a single condition, it can be cumbersome when evaluating multiple conditions because subsequent conditions need to be nested within the `no` condition for the preceding argument. For situations where multiple conditions need to be evaluated, `case_when()` is more convenient.
 
-# Let's first consider a similar example from above and recreate it using `case_when()`. The following code creates a column `b` that assigns a value if 3 if `a >= 3` and otherwise leaves the value unchanged.
+# Let's first consider a similar example from above and recreate it using `case_when()`. The following code creates a column `b` that assigns a value of 3 if `a >= 3` and otherwise leaves the value unchanged.
 
 @chain df begin
   @mutate(b = case_when(a >= 3  =>  3,
@@ -72,4 +72,4 @@ end
 
 # ## Do these functions work outside of TidierData.jl?
 
-# Yes, both `if_else()` and `case_when()` work outside of TidierData.jl. However, you'll need to remember that if working with vectors, both the functions and conditions will need to be vectorized, and in the case of `case_when()`, the `=>` will need to be written as `.=>`. The reason this is not needed when using these functions inside of TidierData.jl is because they are auto-vectorized.
+# Yes, both `if_else()` and `case_when()` work outside of TidierData.jl. However, you'll need to remember that if working with vectors, both the functions and conditions will need to be vectorized, and in the case of `case_when()`, the `=>` will need to be written as `.=>`. The reason this is not needed when using these functions inside of TidierData.jl is because they are auto-vectorized.

docs/examples/UserGuide/slice.jl

@@ -99,4 +99,4 @@ end
 
 @chain df begin 
   @slice_head(n = 3)
-end
+end

src/docstrings.jl

@@ -1320,14 +1320,15 @@ julia> @semi_join(df1, df2, "a" = "a")
 
 const docstring_pivot_wider =
 """
-   @pivot_wider(df, names_from, values_from)
+   @pivot_wider(df, names_from, values_from[, values_fill])
 
 Reshapes the DataFrame to make it wider, increasing the number of columns and reducing the number of rows.
 
 # Arguments
 - `df`: A DataFrame.
 - `names_from`: The name of the column to get the name of the output columns from.
 - `values_from`: The name of the column to get the cell values from.
+- `values_fill`: The value to replace a missing name/value combination (default is `missing`)
 
 # Examples
 ```jldoctest
@@ -3409,4 +3410,4 @@ julia> @relocate(df, B:C) # bring columns to the front
    4 │     9  D           4  B         4  D
    5 │    10  E           5  C         5  E
 ```
-"""
+"""