chore: #84 spelling updates across package

.Rbuildignore

@@ -23,4 +23,4 @@
 ^advs_Define-Excel-Spec_match_admiral\.xlsx
 ^cran-comments\.md$
 ^example_data_specs$
-^deepdive.Rmd$
+

NEWS.md

@@ -3,7 +3,7 @@
 ## New Features and Bug Fixes
 
 * Fixed an issue where `xportr_type()` would overwrite column labels, widths, and "sas.formats"
-* Fixed messaging of `xportr_order()`to give better visability of the number of variables being reordered.
+* Fixed messaging of `xportr_order()`to give better visibility of the number of variables being reordered.
 * Add new argument to `xportr_write()` to allow users to specify how xpt validation checks are handled.
 * Fixed bug where character_types were case sensitive. They are now case insensitive.
 * Updated `xportr_type()` to make type coercion more explicit. 

R/messages.R

@@ -91,7 +91,7 @@ type_log <- function(meta_ordered, type_mismatch_ind, verbose) {
 
 #' Utility for Lengths
 #'
-#' @param miss_vars Variables missing from metatdata
+#' @param miss_vars Variables missing from metadata
 #' @param verbose Provides additional messaging for user
 #'
 #' @return Output to Console

inst/WORDLIST

@@ -1,34 +1,48 @@
 ADAE
 ADSL
 ADaM
+AE
 Atorus
+BMI
 CDISC
 CDSIC
+Codelist
+Completers
+DCREASCD
 DM
 GSK
 JPT
 Lifecycle
+MMSE
 ORCID
+PHUSE
 Pharma
 Repostiory
 SASformat
 SASlength
 SAStype
+SDSP
 SDTM
+Standardisation
+TRTDUR
+Trt
 Vignesh
+Vis
 XPT
+acrf
+adrg
 bootswatch
 chr
 cli
+deliverables
 df
+iso
 magrittr
 metacore
-metatdata
 pre
-sas
+repo
+sdrg
 validator
 validators
-visability
 xportr's
-xportr’s
 xpt

vignettes/deepdive.Rmd

@@ -92,7 +92,7 @@ In this section, we are going to explore the 5 core `{xportr}` functions using:
 
 We will focus on warning and error messaging with contrived examples from these functions by manipulating either the datasets or the specification files.
 
-**NOTE:** We have made the ADSL and Spec available in this package. Users can find additionl datasets and specification files on our [repo](https://github.com/atorus-research/xportr) in the `example_data_specs` folder. This is to keep the package to a minimum size.  
+**NOTE:** We have made the ADSL and Spec available in this package. Users can find additional datasets and specification files on our [repo](https://github.com/atorus-research/xportr) in the `example_data_specs` folder. This is to keep the package to a minimum size.  
 
 
 ## Using `options()` and `xportr_metadata()` to enhance your experience.
@@ -165,7 +165,7 @@ adsl %>%
 
 For the next six sections, we are going to explore the Warnings and Errors messages generated by the `{xportr}` core functions. To better explore these, we will either manipulate the ADaM dataset or specification file to help showcase the ability of the `{xportr}` functions to detect issues.  
 
-**NOTE:** We have made the ADSL, `adsl`, and Specificaion File, `var_spec`, available in this package. Users can find additional datasets and specification files on our [repo](https://github.com/atorus-research/xportr) in the `example_data_specs` folder. We have not included these in our package to help keep the package to a minimum size.  
+**NOTE:** We have made the ADSL, `adsl`, and Specification File, `var_spec`, available in this package. Users can find additional datasets and specification files on our [repo](https://github.com/atorus-research/xportr) in the `example_data_specs` folder. We have not included these in our package to help keep the package to a minimum size.  
 
 ### Setting up our metadata object
 
@@ -269,7 +269,7 @@ As you are creating your dataset in R you will often find that R removes the lab
 
 For this example, we are going to manipulate both the metadata and the `ADSL` dataset:
 
-* The metatdata will have the variable `TRTSDT` label be greater than 40 characters. 
+* The metadata will have the variable `TRTSDT` label be greater than 40 characters. 
 * The `ADSL` dataset will have all its labels stripped from it.
 
 Remember in the length example, the labels were on the original dataset as seen in the `str()` output.
@@ -323,7 +323,7 @@ adsl_ord <- xportr_order(.df = adsl, metadata = var_spec, domain = "ADSL", verbo
 Readers are encouraged to inspect the dataset and metadata to see the past order and updated order after calling the function. Note the messaging from `xportr_order()`:
 
 * Variables not in the metadata are moved to the end
-* Variables not in order are re-ordered and a message is printed out on which ones were re-rordered.
+* Variables not in order are re-ordered and a message is printed out on which ones were re-ordered.
 
 
 ```{r, echo = TRUE, error = TRUE}
@@ -350,7 +350,7 @@ Success! We have taken the metadata formats and applied them to the dataset. Ple
 str(adsl_fmt)
 ```
 
-At the time of `{xportr} v0.3.0` we have not impleneted any warnings or error messaging for this function. However, `xportr_write()` through `xpt_validate()` will check that formats have been applied for Date-Time variables.
+At the time of `{xportr} v0.3.0` we have not implemented any warnings or error messaging for this function. However, `xportr_write()` through `xpt_validate()` will check that formats have been applied for Date-Time variables.
 
 ## `xportr_write()`
 
@@ -401,4 +401,4 @@ adsl %>%
 
 ## Future Work
 
-`{xportr}` is still undergoing development.  We hope to produce more vignettes and functions that will allow users to bulk process multiple datasets as well as have examples of piping `xpt` files and related documenation to a validation software service. As always, please let us know of any feature requests, documentation updates or bugs on [our GitHub repo](https://github.com/Atorus-Research/xportr/issues).
+`{xportr}` is still undergoing development.  We hope to produce more vignettes and functions that will allow users to bulk process multiple datasets as well as have examples of piping `xpt` files and related documentation to a validation software service. As always, please let us know of any feature requests, documentation updates or bugs on [our GitHub repo](https://github.com/Atorus-Research/xportr/issues).

vignettes/xportr.Rmd

@@ -176,7 +176,7 @@ DT::datatable(adsl_order, options = list(
 
 # xportr_format()
 
-Now we apply formats to the dataset.  These will typically be `DATE9.`, `DATETIME20` or `TIME5`, but many others can be used.  Notice that in the `ADSL` dataset there are 8 Date/Time variables and they are missing formts. Here we just take a peak at a few `TRT` variables, which have a `NULL` format.
+Now we apply formats to the dataset.  These will typically be `DATE9.`, `DATETIME20` or `TIME5`, but many others can be used.  Notice that in the `ADSL` dataset there are 8 Date/Time variables and they are missing formats. Here we just take a peak at a few `TRT` variables, which have a `NULL` format.
 
 ```{r}
 attr(adsl$TRTSDT, "format.sas")
@@ -185,7 +185,7 @@ attr(adsl$TRTSDTM, "format.sas")
 attr(adsl$TRTEDTM, "format.sas")
 ```
 
-Using our `xportr_format()` we can cpply our formats to the dataset.
+Using our `xportr_format()` we can apply our formats to the dataset.
 
 ```{r}
 adsl_fmt <- adsl %>% xportr_format(var_spec, domain = "ADSL", "message")