Apply suggestions from code review from Andree

Co-authored-by: Andree Valle Campos <[email protected]>

episodes/clean-data.Rmd

@@ -314,10 +314,10 @@ data <- linelist::make_linelist(
 )
 utils::head(data, 7)
 ```
-The resulting linelist object resembles a dataframe but offers richer features 
+The resulting `linelist` object resembles a data frame but offers richer features 
 and functionalities. Packages that are linelist-aware can leverage these 
 features. For example, you can extract a dataframe of only the tagged columns 
-using the tags_df() function, as shown below:
+using the `linelist::tags_df()` function, as shown below:
 ```{r, warning=FALSE}
 head(linelist::tags_df(data), 5)
 ```
@@ -326,17 +326,18 @@ Safeguarding is implicitly built into the linelist objects. If you try to delete
 columns, you will receive an error or warning message, as shown in the example below.
 
 ```{r, warning=TRUE}
-new_df <- data |> dplyr::select(linelist::has_tag(c("id", "age")))
+new_df <- data |> 
+  dplyr::select(linelist::has_tag(c("id", "age")))
 ```
 
-The {linelist} package supplies tags for the commen epidemiological variables 
+The `{linelist}` package supplies tags for the common epidemiological variables 
 and specify them the appropriate data types. You can view this by running the 
 following command:
 ```{r, warning=FALSE}
 linelist::tags_types()
 ```
 To ensure that all tagged variables are standardized and have the correct data 
-types, use the validate_tags() and validate_types() functions, respectively, as 
+types, use the `linelist::validate_tags()` and `linelist::validate_types()` functions, respectively, as 
 shown in the example below:
 
 ```r