linting

vignettes/Database-creation.Rmd

@@ -25,7 +25,8 @@ THe first iteration of a dataset to data dictionary function is the `ds2dd()`, w
 ```{r eval=FALSE}
 mtcars |>
   dplyr::mutate(record_id = seq_len(dplyr::n())) |>
-  ds2dd() |> str()
+  ds2dd() |>
+  str()
 ```
 
 The more advanced `ds2dd_detailed()` is a natural development. It will try to apply the most common data classes for data validation and will assume that the first column is the id number. It outputs a list with the dataset with modified variable names to comply with REDCap naming conventions and a data dictionary. 
@@ -37,7 +38,8 @@ dd_ls <- mtcars |>
   dplyr::mutate(record_id = seq_len(dplyr::n())) |>
   dplyr::select(record_id, dplyr::everything()) |>
   ds2dd_detailed()
-dd_ls |> str()
+dd_ls |> 
+  str()
 ```
 
 Additional specifications to the DataDictionary can be made manually, or it can be uploaded and modified manually in the graphical user interface on the web page.

vignettes/Introduction.Rmd

@@ -33,17 +33,23 @@ redcapcast_meta |> gt::gt()
 ```
 ```{r}
 list <-
-  REDCap_split(records = redcapcast_data,
-                           metadata = redcapcast_meta,
-                           forms = "repeating")|> sanitize_split()
+  REDCap_split(
+    records = redcapcast_data,
+    metadata = redcapcast_meta,
+    forms = "repeating"
+  ) |> 
+  sanitize_split()
 str(list)
 ```
 
 ```{r}
 list <-
-  REDCap_split(records = redcapcast_data,
-                           metadata = redcapcast_meta,
-                           forms = "all") |> sanitize_split()
+  REDCap_split(
+    records = redcapcast_data,
+    metadata = redcapcast_meta,
+    forms = "all"
+  ) |> 
+  sanitize_split()
 str(list)
 ```
 
@@ -62,5 +68,3 @@ The function works very similar to the `REDCapR::redcap_read()` in allowing to s
 ```{r}
 redcap_wider(list) |> str()
 ```
-
-