Merge pull request #84 from pharmaverse/17_display_dataset_extracts

Closes #17 display dataset extracts

Author: rossfarrugia

DESCRIPTION

@@ -28,6 +28,8 @@ Imports:
     metatools,
     pharmaversesdtm,
     pharmaverseadam,
+    reactable,
+    reactablefmtr,
     sparkline,
     stringr,
     shinylive,

adam/adpc.R

@@ -9,6 +9,7 @@ library(metatools)
 library(xportr)
 library(pharmaversesdtm)
 library(pharmaverseadam)
+library(reactable)
 
 ## ----r echo=TRUE--------------------------------------------------------------
 # ---- Load Specs for Metacore ----
@@ -19,11 +20,11 @@ metacore <- spec_to_metacore("./metadata/pk_spec.xlsx") %>%
 ## ----r------------------------------------------------------------------------
 # ---- Load source datasets ----
 # Load PC, EX, VS, LB and ADSL
-data("pc")
-data("ex")
-data("vs")
+ex <- pharmaversesdtm::ex
+pc <- pharmaversesdtm::pc
+vs <- pharmaversesdtm::vs
 
-data("adsl")
+adsl <- pharmaverseadam::adsl
 
 ex <- convert_blanks_to_na(ex)
 pc <- convert_blanks_to_na(pc)

adam/adpc.qmd

@@ -4,8 +4,12 @@ order: 2
 ---
 
 ```{r setup script, include=FALSE, purl=FALSE}
-invisible_hook_purl <- function(before, options, ...) {knitr::hook_purl(before, options, ...); NULL}
+invisible_hook_purl <- function(before, options, ...) {
+  knitr::hook_purl(before, options, ...)
+  NULL
+}
 knitr::knit_hooks$set(purl = invisible_hook_purl)
+source("functions/print_df.R")
 ```
 
 The Non-compartmental analysis (NCA) ADaM uses the CDISC Implementation Guide (<https://www.cdisc.org/standards/foundational/adam/adamig-non-compartmental-analysis-input-data-v1-0>). This example presented uses underlying `EX` and `PC` domains where the `EX` and `PC` domains represent data as collected and the `ADPC` ADaM is output. For more details see the `{admiral}` [vignette](https://pharmaverse.github.io/admiral/articles/pk_adnca.html){target="_blank"}.
@@ -28,6 +32,7 @@ library(metatools)
 library(xportr)
 library(pharmaversesdtm)
 library(pharmaverseadam)
+library(reactable)
 ```
 
 ## Next Load Specifications for Metacore
@@ -51,11 +56,11 @@ We will load are SDTM data from `{pharmaversesdtm}`. The main components of this
 #| label: Load Source
 # ---- Load source datasets ----
 # Load PC, EX, VS, LB and ADSL
-data("pc")
-data("ex")
-data("vs")
+ex <- pharmaversesdtm::ex
+pc <- pharmaversesdtm::pc
+vs <- pharmaversesdtm::vs
 
-data("adsl")
+adsl <- pharmaverseadam::adsl
 
 ex <- convert_blanks_to_na(ex)
 pc <- convert_blanks_to_na(pc)
@@ -100,6 +105,10 @@ pc_dates <- pc %>%
   )
 ```
 
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(pc_dates %>% select(USUBJID, PCTEST, ADTM, VISIT, PCTPT, NFRLT))
+```
+
 ### Get Dosing Information
 
 Here we also create nomimal time from first dose `NFRLT` for `EX` data based on `VISITDY`.
@@ -188,6 +197,12 @@ ex_exp <- ex_dates %>%
   derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT))
 ```
 
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(
+  ex_exp %>% select(USUBJID, DRUG, EXDOSFRQ, ASTDT, AVISIT, NFRLT)
+)
+```
+
 ### Find First Dose
 
 In this section we will find the first dose for each subject and drug.
@@ -214,6 +229,12 @@ adpc_first_dose <- pc_dates %>%
   )
 ```
 
+
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(adpc_first_dose %>% select(USUBJID, FANLDTM, NFRLT, ADTM, AVISITN, AVISIT, PCTPT))
+```
+
 ### Find Previous Dose and Next Dose
 
 Use `derive_vars_joined()` to find the previous dose and the next dose.
@@ -292,6 +313,11 @@ adpc_nom_next <- adpc_nom_prev %>%
   )
 ```
 
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(adpc_nom_prev %>% select(USUBJID, NFRLT, AVISIT, PCTPT, NFRLT_prev))
+```
+
+
 ### Combine PC and EX Data
 
 Combine `PC` and `EX` records and derive the additional relative time variables.
@@ -379,6 +405,12 @@ adpc_nrrlt <- adpc_arrlt %>%
   )
 ```
 
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(adpc_nrrlt %>% select(USUBJID, NFRLT, NRRLT, AVISIT, PCTPT))
+```
+
+
 ### Derive Analysis Variables
 
 Here we derive the analysis variables such as `AVAL` and `ATPTREF`.
@@ -479,6 +511,12 @@ dtype <- adpc_aval %>%
   derive_vars_dtm_to_tm(exprs(PCRFTDTM))
 ```
 
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(dtype %>% select(USUBJID, DTYPE, BASETYPE, ATPT, NFRLT, NRRLT, AFRLT, ARRLT))
+```
+
+
 ### Combine Original and DTYPE Copy
 
 Now the duplicated records are combined with the original records.
@@ -496,6 +534,10 @@ adpc_dtype <- bind_rows(adpc_aval, dtype) %>%
   )
 ```
 
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(adpc_dtype %>% select(USUBJID, DTYPE, BASETYPE, ATPT, NFRLT, NRRLT, AFRLT, ARRLT))
+```
+
 ### Derive BASE and CHG
 
 ```{r}
@@ -563,6 +605,10 @@ adpc_baselines <- adpc_aseq %>%
   )
 ```
 
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(adpc_baselines %>% select(USUBJID, HTBL, HTBLU, WTBL, WTBLU, BMIBL, BMIBLU, BASETYPE, ATPT, AVAL))
+```
+
 ### Combine with ADSL
 
 If needed, the other `ADSL` variables can now be added:

adam/adppk.R

@@ -19,12 +19,13 @@ metacore <- spec_to_metacore("./metadata/pk_spec.xlsx") %>%
 ## ----r------------------------------------------------------------------------
 # ---- Load source datasets ----
 # Load PC, EX, VS, LB and ADSL
-data("pc")
-data("ex")
-data("vs")
-data("lb")
 
-data("adsl")
+ex <- pharmaversesdtm::ex
+pc <- pharmaversesdtm::pc
+vs <- pharmaversesdtm::vs
+lb <- pharmaversesdtm::lb
+
+adsl <- pharmaverseadam::adsl
 
 ex <- convert_blanks_to_na(ex)
 pc <- convert_blanks_to_na(pc)

adam/adppk.qmd

@@ -4,8 +4,12 @@ order: 3
 ---
 
 ```{r setup script, include=FALSE, purl=FALSE}
-invisible_hook_purl <- function(before, options, ...) {knitr::hook_purl(before, options, ...); NULL}
+invisible_hook_purl <- function(before, options, ...) {
+  knitr::hook_purl(before, options, ...)
+  NULL
+}
 knitr::knit_hooks$set(purl = invisible_hook_purl)
+source("functions/print_df.R")
 ```
 
 The Population PK Analysis Data (ADPPK) follows the CDISC Implementation Guide (<https://www.cdisc.org/standards/foundational/adam/basic-data-structure-adam-poppk-implementation-guide-v1-0>). Population PK models generally make use of nonlinear mixed effects models that require numeric variables. The data used in the models will include both dosing and concentration records, relative time variables, and numeric covariate variables. A `DV` or dependent variable is often expected. For more details see the `{admiral}` [vignette](https://pharmaverse.github.io/admiral/articles/pk_adnca.html){target="_blank"}.
@@ -51,12 +55,13 @@ We will load are SDTM data from `{pharmaversesdtm}`. The main components of this
 #| label: Load Source
 # ---- Load source datasets ----
 # Load PC, EX, VS, LB and ADSL
-data("pc")
-data("ex")
-data("vs")
-data("lb")
 
-data("adsl")
+ex <- pharmaversesdtm::ex
+pc <- pharmaversesdtm::pc
+vs <- pharmaversesdtm::vs
+lb <- pharmaversesdtm::lb
+
+adsl <- pharmaverseadam::adsl
 
 ex <- convert_blanks_to_na(ex)
 pc <- convert_blanks_to_na(pc)
@@ -102,6 +107,11 @@ pc_dates <- pc %>%
   )
 ```
 
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(pc_dates %>% select(USUBJID, PCTEST, ADTM, VISIT, PCTPT, NFRLT))
+```
+
 ### Get Dosing Information
 
 Here we also create nominal time from first dose `NFRLT` for `EX` data based on `VISITDY`.
@@ -150,6 +160,14 @@ ex_dates <- ex %>%
   derive_vars_dtm_to_dt(exprs(AENDTM))
 ```
 
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(ex_dates %>% select(
+  USUBJID, EXTRT, EXDOSFRQ, EXSTDTC, EXENDTC, VISIT, VISITDY
+))
+```
+
+
 ### Expand Dosing Records
 
 Since there is a start date and end date for dosing records we need to expand the dosing records between the start date and end date using the function `admiral::create_single_dose_dataset()`.
@@ -190,6 +208,13 @@ ex_exp <- ex_dates %>%
   derive_vars_dtm_to_tm(exprs(AENDTM))
 ```
 
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(
+  ex_exp %>% select(USUBJID, DRUG, EXDOSFRQ, ASTDT, AVISIT, NFRLT)
+)
+```
+
 ### Find First Dose
 
 In this section we will find the first dose for each subject and drug.
@@ -218,6 +243,11 @@ adppk_first_dose <- pc_dates %>%
   )
 ```
 
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(adppk_first_dose %>% select(USUBJID, FANLDTM, NFRLT, ADTM, AVISITN, AVISIT, PCTPT))
+```
+
 ### Find Previous Dose
 
 For `ADPPK` we will find the previous dose with respect to actual time and nominal time.
@@ -244,6 +274,11 @@ adppk_prev <- adppk_first_dose %>%
   )
 ```
 
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(adppk_prev %>% select(USUBJID, VISIT, ADTM, VISIT, PCTPT, ADTM_prev, AVISIT_prev))
+```
+
 ### Find Previous Nominal Dose
 
 ```{r}
@@ -264,6 +299,12 @@ adppk_nom_prev <- adppk_prev %>%
   )
 ```
 
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(adppk_nom_prev %>% select(USUBJID, VISIT, ADTM, NFRLT, NFRLT_prev))
+```
+
+
 ### Combine PC and EX Data
 
 Here we combine `PC` and `EX` records. We will derive the relative time variables `AFRLT` (Actual Relative Time from First Dose), `APRLT` (Actual Relative Time from Previous Dose), and `NPRLT` (Nominal Relative Time from Previous Dose).
@@ -314,6 +355,14 @@ adppk_aprlt <- bind_rows(adppk_nom_prev, ex_exp) %>%
   )
 ```
 
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(adppk_aprlt %>% select(USUBJID, FANLDTM, AVISIT, PCTPT, APRLT, NPRLT))
+```
+
+
+
+
 ### Derive Analysis Variables
 
 The expected analysis variable for `ADPPK` is `DV` or dependent variable. For this example `DV` is set to the numeric concentration value `PCSTRESN`. We will also include `AVAL` equivalent to `DV` for consistency with CDISC ADaM standards. `MDV` missing dependent variable will also be included.
@@ -391,6 +440,12 @@ adppk_aval <- adppk_aprlt %>%
   )
 ```
 
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(adppk_aval %>% select(USUBJID, AVISIT, EVID, NFRLT, DV))
+```
+
+
 ### Add ASEQ
 
 ```{r}
@@ -447,6 +502,11 @@ covar <- adsl %>%
   create_var_from_codelist(metacore, input_var = SUBJTYPC, out_var = SUBJTYP)
 ```
 
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(covar %>% select(USUBJID, SEX, SEXN, RACE, RACEN))
+```
+
 ### Derive Additional Baselines
 
 Next we add additional baselines from vital signs and laboratory data.
@@ -497,6 +557,13 @@ covar_vslb <- covar %>%
   rename(TBILBL = BILIBL)
 ```
 
+
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(covar_vslb %>% select(USUBJID, WTBL, HTBL, BMIBL, CRCLBL))
+```
+
+
 ### Combine with Covariates
 
 We combine our covariates with the rest of the data

adam/adrs.R

@@ -15,12 +15,9 @@ metacore <- spec_to_metacore("./metadata/onco_spec.xlsx") %>%
   select_dataset("ADRS")
 
 ## ----r load-data--------------------------------------------------------------
-data("adsl")
-data("rs_onco_recist")
-data("tu_onco_recist")
-
-rs <- rs_onco_recist
-tu <- tu_onco_recist
+adsl <- pharmaverseadam::adsl
+rs <- pharmaversesdtm::rs_onco_recist
+tu <- pharmaversesdtm::tu_onco_recist
 
 # Convert blanks to NA
 rs <- convert_blanks_to_na(rs)