#36 minor updates for presentability and reorder articles to match ADaM order

Author: rossfarrugia

sdtm/ae.qmd

@@ -14,11 +14,11 @@ source("functions/print_df.R")
 
 # Introduction
 
-This article describes how to create an events SDTM domain using the {sdtm.oak} package. Examples are currently presented and tested in the context of the AE domain.
+This article describes how to create an events SDTM domain using the `{sdtm.oak}` package. Examples are currently presented and tested in the context of the `AE` domain.
 
-Before reading this article, it is recommended that users review some of the articles in the package documentation of {sdtm.oak} to understand some of the key concepts:
+Before reading this article, it is recommended that users review some of the articles in the package documentation of `{sdtm.oak}` to understand some of the key concepts:
 [Algorithms & Sub-Algorithms](https://pharmaverse.github.io/sdtm.oak/articles/algorithms.html),
-[Creating an Interventions Domain](https://pharmaverse.github.io/sdtm.oak/articles/interventions_domain.html), which provides a detailed explanation of various concepts in {sdtm.oak}, such as `oak_id_vars`, `condition_add`, etc. It also offers guidance on which mapping algorithms or functions to use for different mappings and provides a more detailed explanation of how these mapping algorithms or functions work.
+[Creating an Interventions Domain](https://pharmaverse.github.io/sdtm.oak/articles/interventions_domain.html), which provides a detailed explanation of various concepts in `{sdtm.oak}`, such as `oak_id_vars`, `condition_add`, etc. It also offers guidance on which mapping algorithms or functions to use for different mappings and provides a more detailed explanation of how these mapping algorithms or functions work.
 
 In this article, we will dive directly into programming and provide further explanation only where it is required.
 
@@ -48,7 +48,7 @@ library(dplyr)
 ae_raw <- pharmaverseraw::ae_raw
 ```
 
-Adverse Events Raw dataset. 
+Adverse Events Raw dataset
 ```{r eval=TRUE, echo=FALSE, purl=FALSE}
 print_df(ae_raw, n = 5)
 ```
@@ -93,7 +93,7 @@ print_df(study_ct, n = 10)
 
 The topic variable is mapped as a first step in the mapping process. It is the primary variable in the SDTM domain. The rest of the variables add further definition to the topic variable. In this example, the topic variable is `AETERM`. It is mapped from the raw dataset column `IT.AETERM`. The mapping logic is `Map the collected value in the ae_raw dataset IT.AETERM variable to AE.AETERM`.
 
-This mapping does not involve any controlled terminology. The `assign_no_ct` function is used for mapping. Once the topic variable is mapped, the Qualifier, Identifier, and Timing variables can be mapped.
+This mapping does not involve any controlled terminology. The `sdtm.oak::assign_no_ct()` function is used for mapping. Once the topic variable is mapped, the Qualifier, Identifier, and Timing variables can be mapped.
 
 ```{r}
 ae <-
@@ -313,4 +313,4 @@ print_df(ae, n = 10)
 
 ## Add Labels and Attributes {#attributes}
 
-Yet to be developed.
+Yet to be developed. Please refer to `{metatools}` package to investigate options.

sdtm/dm.qmd

@@ -1,6 +1,6 @@
 ---
 title: "DM"
-order: 2
+order: 1
 ---
 
 ```{r setup script, include=FALSE, purl=FALSE}
@@ -14,9 +14,9 @@ source("functions/print_df.R")
 
 # Introduction
 
-This article describes how to create a demographics(DM) domain using the {sdtm.oak} package.
+This article describes how to create a demographics (`DM`) domain using the `{sdtm.oak}` package.
 
-Before reading this article, it is recommended that users review some of the articles in the package documentation of {sdtm.oak} to understand some of the key concepts:
+Before reading this article, it is recommended that users review some of the articles in the package documentation of `{sdtm.oak}` to understand some of the key concepts:
 [Algorithms & Sub-Algorithms](https://pharmaverse.github.io/sdtm.oak/articles/algorithms.html),
 [Creating an Interventions Domain](https://pharmaverse.github.io/sdtm.oak/articles/interventions_domain.html), which provides a detailed explanation of various concepts in {sdtm.oak}, such as `oak_id_vars`, `condition_add`, etc. It also offers guidance on which mapping algorithms or functions to use for different mappings and provides a more detailed explanation of how these mapping algorithms or functions work.
 
@@ -118,12 +118,13 @@ print_df(study_ct, n = 10)
 ## Create reference dates configuration file {#refdates}
 
 Create reference date configuration file, a data frame which has the details of the variables to be used for the calculation of reference dates. The data frame should have columns listed below:
+
 - **`raw_dataset_name`**: Name of the raw dataset.  
 - **`date_var`**: Date variable name from the raw dataset.  
 - **`time_var`**: Time variable name from the raw dataset.  
 - **`dformat`**: Format of the date collected in raw data.  
 - **`tformat`**: Format of the time collected in raw data.  
-- **`sdtm_var_name`**: Reference date variable name in DM domain where the raw variable is used.  
+- **`sdtm_var_name`**: Reference date variable name in `DM` domain where the raw variable is used.  
 
 ```{r}
 ref_date_conf_df <- tibble::tribble(
@@ -144,7 +145,7 @@ print_df(ref_date_conf_df)
 
 ## Map Topic Variable {#maptopic}
 
-In DM domain SUBJID is the topic variable and it can be mapped from PATNUM using a simple mutate statement.
+In `DM` domain, `SUBJID` is the topic variable and it can be mapped from `PATNUM` using a simple `dplyr::mutate()` statement.
 
 ```{r}
 dm <- dm_raw %>%
@@ -160,7 +161,7 @@ print_df(dm, n = 10)
 
 ## Map Rest of the Variables {#maprest}
 
-Map rest of the variables in DM domain using either `assign_no_ct` or `assign_ct` depending
+Map rest of the variables in `DM` domain using either `sdtm.oak::assign_no_ct()` or `sdtm.oak::assign_ct()` depending
 on if the variable has controlled terminologies associated.
 
 ```{r}
@@ -249,11 +250,11 @@ print_df(dm, n = 10)
 
 ## Map Reference Date Variables {#mapvars}
 
-Use `oak_cal_ref_dates` to calculate reference dates variables in ISO8601 format.
+Use `sdtm.oak::oak_cal_ref_dates()` to calculate reference dates variables in ISO 8601 format.
 The function takes the raw variable names from reference date configuration file,
 and calculated the minimum or maximum dates based upon the `min_max` parameter.
 
-Variable `RFSTDTC` is the reference Start Date/time for the subject in ISO 8601 character format. Usually equivalent to date/time when subject was first exposed to study treatment. So as specified in the reference date configuration file, we need to calculate the minumum date of  the `IT.ECSTDAT` for each subject from the `ec_raw` dataset. Therefore, in `min_max` parameter, "min" is selected for the calculation.
+Variable `RFSTDTC` is the reference Start Date/time for the subject in ISO 8601 character format. Usually equivalent to date/time when subject was first exposed to study treatment. So as specified in the reference date configuration file, we need to calculate the minimum date of  the `IT.ECSTDAT` for each subject from the `ec_raw` dataset. Therefore, in `min_max` parameter, "min" is selected for the calculation.
 
 ```{r eval=TRUE}
 dm <- dm %>%
@@ -395,4 +396,4 @@ print_df(dm, n = 10)
 
 ## Add Labels and Attributes {#attributes}
 
-Yet to be developed.
+Yet to be developed. Please refer to `{metatools}` package to investigate options.

sdtm/vs.qmd

@@ -1,6 +1,6 @@
 ---
 title: "VS"
-order: 1
+order: 2
 ---
 
 ```{r setup script, include=FALSE, purl=FALSE}
@@ -14,17 +14,17 @@ source("functions/print_df.R")
 
 # Introduction
 
-This article describes how to create a Findings SDTM domain using the {sdtm.oak} package. Examples are currently presented and tested in the context of the VS domain.
+This article describes how to create a Findings SDTM domain using the `{sdtm.oak}` package. Examples are currently presented and tested in the context of the `VS` domain.
 
-Before reading this article, it is recommended that users review some of the articles in the package documentation of {sdtm.oak} to understand some of the key concepts:
+Before reading this article, it is recommended that users review some of the articles in the package documentation of `{sdtm.oak}` to understand some of the key concepts:
 [Algorithms & Sub-Algorithms](https://pharmaverse.github.io/sdtm.oak/articles/algorithms.html),
-[Creating an Interventions Domain](https://pharmaverse.github.io/sdtm.oak/articles/interventions_domain.html), which provides a detailed explanation of various concepts in {sdtm.oak}, such as `oak_id_vars`, `condition_add`, etc. It also offers guidance on which mapping algorithms or functions to use for different mappings and provides a more detailed explanation of how these mapping algorithms or functions work.
+[Creating an Interventions Domain](https://pharmaverse.github.io/sdtm.oak/articles/interventions_domain.html), which provides a detailed explanation of various concepts in `{sdtm.oak}`, such as `oak_id_vars`, `condition_add`, etc. It also offers guidance on which mapping algorithms or functions to use for different mappings and provides a more detailed explanation of how these mapping algorithms or functions work.
 
 In this article, we will dive directly into programming and provide further explanation only where it is required.
 
 # Programming workflow
 
-In {sdtm.oak} we process one raw dataset at a time. Similar raw datasets (example Vital Signs - Screening (vs_raw), Vital Signs - Treatment (vs_t_raw)) can be stacked together before processing or can be processed separately.
+In `{sdtm.oak}` we process one raw dataset at a time. Similar raw datasets (example Vital Signs - Screening (`vs_raw`), Vital Signs - Treatment (`vs_t_raw`)) can be stacked together before processing or can be processed separately.
 
 -   [Read in data](#readdata)
 -   [Create oak_id_vars](#oakidvars)
@@ -52,13 +52,13 @@ vs_raw <- pharmaverseraw::vs_raw
 dm <- pharmaversesdtm::dm
 ```
 
-Vital Signs Raw dataset.
+Vital Signs Raw dataset
 ```{r eval=TRUE, echo=FALSE, purl=FALSE}
 print_df(vs_raw, n = 5)
 ```
 
 ### SDTM aCRF
-SDTM annotated CRF for the vs_raw can be viewed [here](https://github.com/pharmaverse/pharmaverseraw/blob/main/vignettes/articles/aCRFs/VitalSigns_aCRF.pdf):
+SDTM annotated CRF for the `vs_raw` can be viewed [here](https://github.com/pharmaverse/pharmaverseraw/blob/main/vignettes/articles/aCRFs/VitalSigns_aCRF.pdf):
 
 ## Create oak_id_vars {#oakidvars}
 
@@ -84,7 +84,7 @@ print_df(study_ct, n = 10)
 
 ## Map Topic Variable {#maptopic}
 
-This raw dataset has multiple topic variables. Lets start with SYSBP. Map topic variable SYSBP from the raw variable SYS_BP.
+This raw dataset has multiple topic variables. Lets start with `SYSBP`. Map topic variable `SYSBP` from the raw variable `SYS_BP`.
 
 ```{r}
 # Map topic variable SYSBP
@@ -108,7 +108,7 @@ print_df(vs_sysbp, n = 10)
 
 ## Map Rest of the Variables {#maprest}
 
-Map rest of the variables applicable to the topic variable SYSBP. This can include qualifiers, identifier and timing variables.
+Map rest of the variables applicable to the topic variable `SYSBP`. This can include qualifiers, identifier and timing variables.
 
 ```{r}
 ## Map Rest of the Variables
@@ -157,7 +157,7 @@ print_df(vs_sysbp, n = 10)
 
 ## Repeat Map Topic and Map Rest {#repeatsteps}
 
-This raw data source has other topic variables DIABP, PULSE, HEIGHT, WEIGHT, TEMP and its corresponding qualifiers. Repeat mapping topic and qualifiers for each topic variable.
+This raw data source has other topic variables `DIABP`, `PULSE`, `HEIGHT`, `WEIGHT`, `TEMP` and its corresponding qualifiers. Repeat mapping topic and qualifiers for each topic variable.
 
 ```{r}
 # Repeat Map Topic and Map Rest
@@ -519,4 +519,4 @@ print_df(vs)
 
 ## Add Labels and Attributes {#attributes}
 
-Yet to be developed.
+Yet to be developed. Please refer to `{metatools}` package to investigate options.