remove backticks around CDISC (#1055)

As a response to @lcd2yyz comments

insightsengineering/coredev-tasks#492 (comment)

Signed-off-by: Marcin <[email protected]>
Co-authored-by: Marcin <[email protected]>

NEWS.md

@@ -249,7 +249,7 @@ function call.
 
 ### Enhancements
 * Released `snowflake` connection and connectors.
-* Changed ordering of datasets to be more intuitive (topologically first for `CDISC` datasets only and then according to input datasets order).
+* Changed ordering of datasets to be more intuitive (topologically first for CDISC datasets only and then according to input datasets order).
 * When closing a `teal` app (ending a user `shiny` session), all `DataConnection`s will now try to close their connections.
 * Added `ADHY` keys to configuration file.
 * Extended the `filter_spec` function: the parameter `choices` is no longer mandatory (the function will take all possible choices by default) and the `vars` parameter additionally accepts the `choices_selected` and allows to change the variables for filtering using the UI elements in the encoding panel.

README.md

@@ -28,14 +28,14 @@
 
 <!-- markdownlint-disable MD007 MD030 -->
 -   Data, which can be:
-    -    `CDISC` data, commonly used for clinical trial reporting
+    -    CDISC data, commonly used for clinical trial reporting
     -    Independent datasets, for example from a `data.frame`
     -    Related datasets, for example a set of `data.frames` with key columns to enable data joins
     -    `MultiAssayExperiment` objects which are `R` data structures for representing and analyzing multi-omics experiments
 -   `teal` modules:
-    -   `teal` modules are `shiny` modules built within the `teal` framework that specify analysis to be performed. For example, it can be a module for exploring outliers in the data, or a module for visualizing the data in line plots. Although these can be created from scratch, many `teal` modules have been released and we recommend starting with modules found in the following packages:
-        -   [`teal.modules.general`](https://insightsengineering.github.io/teal.modules.general/latest-tag/): general modules for exploring relational/independent/`CDISC` data
-        -   [`teal.modules.clinical`](https://insightsengineering.github.io/teal.modules.clinical/latest-tag/): modules specific to `CDISC` data and clinical trial reporting
+    -   `teal modules` are `shiny` modules built within the `teal` framework that specify analysis to be performed. For example, it can be a module for exploring outliers in the data, or a module for visualizing the data in line plots. Although these can be created from scratch, many `teal` modules have been released and we recommend starting with modules found in the following packages:
+        -   [`teal.modules.general`](https://insightsengineering.github.io/teal.modules.general/latest-tag/): general modules for exploring relational/independent/CDISC data
+        -   [`teal.modules.clinical`](https://insightsengineering.github.io/teal.modules.clinical/latest-tag/): modules specific to CDISC data and clinical trial reporting
         -   [`teal.modules.hermes`](https://insightsengineering.github.io/teal.modules.hermes/latest-tag/): modules for analyzing `MultiAssayExperiment` objects
 
 <!-- markdownlint-enable MD007 MD030 -->

inst/WORDLIST

@@ -1,3 +1,5 @@
+ADaM
+CDISC
 Biomarker
 Forkers
 Hoffmann

vignettes/blueprint/intro.Rmd

@@ -8,7 +8,7 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-`teal` is an interactive data exploration framework based on Shiny, designed primarily to analyze `CDISC` clinical trial data.
+`teal` is an interactive data exploration framework based on Shiny, designed primarily to analyze CDISC clinical trial data.
 
 A Shiny Application created with `teal` offers users the ability to:
 

vignettes/getting-started-with-teal.Rmd

@@ -10,7 +10,7 @@ vignette: >
 
 ## Introduction
 
-`teal` is a shiny-based interactive exploration framework for analyzing data, with particular emphasis on `CDISC` clinical trial data. 
+`teal` is a shiny-based interactive exploration framework for analyzing data, with particular emphasis on CDISC clinical trial data. 
 `teal` applications allow their users to:
 
 * "Pull" in data from external data sources
@@ -89,7 +89,7 @@ It is also possible to specify relationships between the datasets using the `joi
 If data is not available and has to be pulled from a remote source, `init` must receive a `teal_data_module` that specifies how to obtain the desired datasets and put them into a `teal_data` object.
 See [this vignette](data-as-shiny-module.html) for details.
 
-In order to use `CDISC` clinical trial data in a `teal` application the `cdisc_data` function is used instead. If you would like to use custom `SDTM` standards, you can use the `teal_data` along with `join_keys` to specify the relationships between the datasets.
+In order to use CDISC clinical trial data in a `teal` application the `cdisc_data` function is used instead. If you would like to use custom `SDTM` standards, you can use the `teal_data` along with `join_keys` to specify the relationships between the datasets.
 
 For further details, we recommend exploring the [`teal.data`](https://insightsengineering.github.io/teal.data/) package documentation.
 

vignettes/including-data-in-teal-applications.Rmd

@@ -127,7 +127,7 @@ Currently `teal` supports two specialized data formats.
 
 ### `ADaM` data
 
-The `ADaM` data model, defined in `CDISC` standards, specifies relationships between the subject-level parent dataset and observation-level child datasets.
+The `ADaM` data model, defined in CDISC standards, specifies relationships between the subject-level parent dataset and observation-level child datasets.
 The `cdisc_data` function takes advantage of that fact to automatically set default joining keys (see [`join_keys` property](#join_keys)).
 In the example below, two standard `ADaM` datasets (`ADSL` and `ADTTE`) are passed to `cdisc_data`.