Vignette changes (#687)

Closes #640 --------- Signed-off-by: Vedha Viyash <[email protected]>
insightsengineering · Feb 27, 2024 · 1fae848 · 1fae848
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diff --git a/.lintr b/.lintr
@@ -1,5 +1,6 @@
 linters: linters_with_defaults(
   line_length_linter = line_length_linter(120),
   cyclocomp_linter = NULL,
-  object_usage_linter = NULL
+  object_usage_linter = NULL,
+  object_name_linter = object_name_linter(styles = c("snake_case", "symbols"), regexes = c(ANL = "^ANL_?[0-9]*$", ADaM = "^r?AD[A-Z]{2,3}_?[0-9]*$"))
   )
diff --git a/R/tm_g_response.R b/R/tm_g_response.R
@@ -413,7 +413,6 @@ srv_g_response <- function(id,
           env = list(resp_var = resp_var)
         )
       ) %>%
-        # nolint start
         # rowf and colf will be a NULL if not set by a user
         teal.code::eval_code(
           substitute(
@@ -433,7 +432,6 @@ srv_g_response <- function(id,
             env = list(x_cl = x_cl, rowf = rowf, colf = colf)
           )
         )
-      # nolint end
 
       plot_call <- substitute(
         expr = ggplot(ANL2, aes(x = x_cl, y = ns)) +

diff --git a/vignettes/images/app-teal-modules-general.png b/vignettes/images/app-teal-modules-general.png
diff --git a/vignettes/images/app-using-association-plot.png b/vignettes/images/app-using-association-plot.png
diff --git a/vignettes/images/app-using-bivariate-plot.png b/vignettes/images/app-using-bivariate-plot.png
diff --git a/vignettes/images/app-using-cross-table.png b/vignettes/images/app-using-cross-table.png
diff --git a/vignettes/images/app-using-data-table.png b/vignettes/images/app-using-data-table.png
diff --git a/vignettes/images/app-using-outliers-module.png b/vignettes/images/app-using-outliers-module.png
diff --git a/vignettes/images/app-using-regression-plots.png b/vignettes/images/app-using-regression-plots.png
diff --git a/vignettes/images/app-using-response-plot.png b/vignettes/images/app-using-response-plot.png
diff --git a/vignettes/images/app-using-scatterplot-matrix.png b/vignettes/images/app-using-scatterplot-matrix.png
diff --git a/vignettes/images/app-using-scatterplot.png b/vignettes/images/app-using-scatterplot.png
diff --git a/vignettes/teal-modules-general.Rmd b/vignettes/teal-modules-general.Rmd
@@ -22,7 +22,7 @@ The concepts presented here require knowledge about the core features of `teal`,
 application and how to pass data into it. Therefore, it is highly recommended to refer to the [`README`](https://insightsengineering.github.io/teal/index.html) file and 
 the introductory [vignette](https://insightsengineering.github.io/teal/latest-tag/articles/teal.html) of the `teal` package.
 
-See also `teal.modules.clinical's` [`README`](https://insightsengineering.github.io/teal.modules.clinical/latest-tag/index.html).
+See also `teal.modules.clinical`'s [`README`](https://insightsengineering.github.io/teal.modules.clinical/latest-tag/index.html).
 
 ### Main features
 
@@ -38,12 +38,11 @@ See [package functions / modules](https://insightsengineering.github.io/teal.mod
 
 ### Example application
 
-A simple application including a `tm_variable_browser` module could look like this:
+A simple application including a `tm_variable_browser()` module could look like this:
 ```{r, message = FALSE, results = "hide"}
 library(teal.modules.general)
 library(teal.widgets)
 
-# nolint start
 data <- teal_data()
 data <- within(data, {
   ADSL <- teal.modules.general::rADSL
@@ -52,7 +51,6 @@ data <- within(data, {
 datanames <- c("ADSL", "ADTTE")
 datanames(data) <- datanames
 join_keys(data) <- default_cdisc_join_keys[datanames]
-# nolint end
 
 app <- init(
   data = data,
@@ -71,9 +69,12 @@ app <- init(
 shinyApp(app$ui, app$server)
 ```
 
+<img src="images/app-teal-modules-general.png" style = "display: block; margin: 0px; width: 100%"/>
+
 Let's break the above app into pieces:
 ```r
 library(teal.modules.general)
+library(teal.widgets)
 ```
 The line mentioned above imports the library required for this example and loads data from within that library.
 
@@ -85,7 +86,6 @@ so both the code and data are stored together.
 Following this, we set the `datanames` and `join_keys`.
 
 ```r
-# nolint start
 data <- teal_data()
 data <- within(data, {
   ADSL <- teal.modules.general::rADSL
@@ -94,7 +94,6 @@ data <- within(data, {
 datanames <- c("ADSL", "ADTTE")
 datanames(data) <- datanames
 join_keys(data) <- default_cdisc_join_keys[datanames]
-# nolint end
 ```
 
 There is no need to load `teal` as `teal.modules.general` already depends on it.

diff --git a/vignettes/using-association-plot.Rmd b/vignettes/using-association-plot.Rmd
@@ -11,22 +11,22 @@ vignette: >
 
 # `teal` application to use association plot with various datasets types
 
-This vignette will guide you through 4 parts to create a teal application using
-various types of datasets inside an association plot module:
+This vignette will guide you through the four parts to create a teal application using
+various types of datasets using the association plot module `tm_g_association()`:
 
 1. Load Libraries
 2. Create data sets
 3. Create an `app` variable
 4. Run the App
 
-## Loading libraries
+## 1 - Loading libraries
 
 ```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide"}
 library(teal.modules.general) # used to create the app
 library(dplyr) # used to modify data sets
 ```
 
-## Create data sets
+## 2 - Create data sets
 
 Inside this app 5 datasets will be used
 
@@ -37,30 +37,6 @@ Inside this app 5 datasets will be used
 5. `ADLB` A long data set with lab measurements for each subject
 
 ```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide"}
-# nolint start
-ADSL <- teal.modules.general::rADSL
-ADSL2 <- teal.modules.general::rADSL %>%
-  mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1))
-ADRS <- teal.modules.general::rADRS
-ADTTE <- teal.modules.general::rADTTE
-ADLB <- teal.modules.general::rADLB %>%
-  mutate(CHGC = as.factor(case_when(
-    CHG < 1 ~ "N",
-    CHG > 1 ~ "P",
-    TRUE ~ "-"
-  )))
-# nolint end
-```
-
-## Initialize an app
-
-This is the most important section. We will use the [`teal::init`](https://insightsengineering.github.io/teal/latest-tag/reference/init) function to
-create an app. The data will be handed over using [`teal.data::cdisc_data`](https://insightsengineering.github.io/teal.data/latest-tag/reference/cdisc_data). The app
-itself will be constructed by multiple calls of `tm_g_association` using different
-combinations of data sets.
-
-```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide"}
-# nolint start
 data <- teal_data()
 data <- within(data, {
   ADSL <- teal.modules.general::rADSL
@@ -82,9 +58,17 @@ jk_adsl2 <- jk
 names(jk_adsl2)[names(jk_adsl2) == "ADSL"] <- "ADSL2"
 jk <- c(jk, jk_adsl2)
 jk["ADSL2", "ADSL"] <- c("USUBJID", "STUDYID")
+join_keys(data) <- jk
+```
 
-# nolint end
+## 3 - Create an `app` variable
 
+This is the most important section. We will use the `teal::init` function to
+create an app. The data will be handed over using `teal.data::teal_data`. The app
+itself will be constructed by multiple calls of `tm_g_association()` using different
+combinations of data sets.
+
+```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide"}
 app <- init(
   data = data,
   modules = modules(
@@ -301,12 +285,13 @@ app <- init(
 )
 ```
 
-## Run the app
+## 4 - Run the app
 
 A simple `shiny::shinyApp` call will let you run the app. 
-Note that app is only displayed when running this code inside an R session.  
+Note that app is only displayed when running this code inside an R session.
 
-```{r echo=TRUE}
+```{r, echo=TRUE, results="hide"}
 shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))
 ```
 
+<img src="images/app-teal-modules-general.png" style = "display: block; margin: 0px; width: 100%"/>
diff --git a/vignettes/using-bivariate-plot.Rmd b/vignettes/using-bivariate-plot.Rmd
@@ -11,25 +11,23 @@ vignette: >
 
 # `teal` application to use bivariate plot with various datasets types
 
-This vignette will guide you through 4 parts to create a teal application using
-various types of datasets inside an bivariate plot module:
+This vignette will guide you through the four parts to create a teal application using
+various types of datasets using the bivariate plot module `tm_g_bivariate()`:
 
 1. Load Libraries
 2. Create data sets
 3. Create an `app` variable
 4. Run the App
 
-
-
-## Loading libraries
+## 1 - Loading libraries
 
 ```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide"}
 library(teal.modules.general) # used to create the app
 library(dplyr) # used to modify data sets
 ```
 
 
-## Create data sets
+## 2 - Create data sets
 
 Inside this app 5 datasets will be used
 
@@ -40,7 +38,6 @@ Inside this app 5 datasets will be used
 5. `ADLB` A long data set with lab measurements for each subject
 
 ```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide"}
-# nolint start
 data <- teal_data()
 data <- within(data, {
   ADSL <- teal.modules.general::rADSL
@@ -62,14 +59,14 @@ jk_adsl2 <- jk
 names(jk_adsl2)[names(jk_adsl2) == "ADSL"] <- "ADSL2"
 jk <- c(jk, jk_adsl2)
 jk["ADSL2", "ADSL"] <- c("USUBJID", "STUDYID")
-# nolint end
+join_keys(data) <- jk
 ```
 
-## Create an `app` variable
+## 3 - Create an `app` variable
 
-This is the most important section. We will use the [`teal::init`](https://insightsengineering.github.io/teal/latest-tag/reference/init) function to
-create an app. The data will be handed over using [`teal.data::cdisc_data`](https://insightsengineering.github.io/teal.data/latest-tag/reference/cdisc_data). The app
-itself will be constructed by multiple calls of `tm_g_bivariate` using different
+This is the most important section. We will use the `teal::init` function to
+create an app. The data will be handed over using `teal.data::teal_data`. The app
+itself will be constructed by multiple calls of `tm_g_bivariate()` using different
 combinations of data sets.
 
 ```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide"}
@@ -606,11 +603,13 @@ app <- init(
 ```
 
 
-## Run the app
+## 4 - Run the app
 
 A simple `shiny::shinyApp` call will let you run the app.
 Note that app is only displayed when running this code inside an R session.
 
-```{r echo=TRUE}
+```{r, echo=TRUE, results="hide"}
 shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))
 ```
+
+<img src="images/app-using-bivariate-plot.png" style = "display: block; margin: 0px; width: 100%"/>
diff --git a/vignettes/using-cross-table.Rmd b/vignettes/using-cross-table.Rmd
@@ -11,30 +11,29 @@ vignette: >
 
 # `teal` application to use cross table with various datasets types
 
-This vignette will guide you through 4 parts to create a teal application using
-various types of datasets inside an cross table module:
+This vignette will guide you through the four parts to create a teal application using
+various types of datasets using the cross table module `tm_t_crosstable()`:
 
 1. Load Libraries
 2. Create data sets
 3. Create an `app` variable
 4. Run the App
 
-## Loading libraries
+## 1 - Loading libraries
 
 ```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide"}
 library(teal.modules.general) # used to create the app
 library(dplyr) # used to modify data sets
 ```
 
-## Create data sets
+## 2 - Create data sets
 
 Inside this app 2 datasets will be used
 
 1. `ADSL` A wide data set with subject data
 2. `ADLB` A long data set with lab measurements for each subject
 
 ```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide", echo=2:6}
-# nolint start
 data <- teal_data()
 data <- within(data, {
   ADSL <- teal.modules.general::rADSL
@@ -48,14 +47,13 @@ data <- within(data, {
 datanames <- c("ADSL", "ADLB")
 datanames(data) <- datanames
 join_keys(data) <- default_cdisc_join_keys[datanames]
-# nolint end
 ```
 
-## Create an `app` variable
+## 3 - Create an `app` variable
 
-This is the most important section. We will use the [`teal::init`](https://insightsengineering.github.io/teal/latest-tag/reference/init) function to
-create an app. The data will be handed over using [`teal.data::cdisc_data`](https://insightsengineering.github.io/teal.data/latest-tag/reference/cdisc_data). The app
-itself will be constructed by multiple calls of `tm_t_crosstable` using different
+This is the most important section. We will use the `teal::init` function to
+create an app. The data will be handed over using `teal.data::teal_data`. The app
+itself will be constructed by multiple calls of `tm_t_crosstable()` using different
 combinations of data sets.
 
 ```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide"}
@@ -129,11 +127,13 @@ app <- init(
 )
 ```
 
-## Run the app
+## 4 - Run the app
 
 A simple `shiny::shinyApp` call will let you run the app.
 Note that app is only displayed when running this code inside an R session.
 
-```{r echo=TRUE}
+```{r, echo=TRUE, results="hide"}
 shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))
 ```
+
+<img src="images/app-using-cross-table.png" style = "display: block; margin: 0px; width: 100%"/>
diff --git a/vignettes/using-data-table.Rmd b/vignettes/using-data-table.Rmd
@@ -9,23 +9,23 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-# `teal` application to use association plot with various datasets types
+# `teal` application to display data table with various datasets types
 
-This vignette will guide you through 4 parts to create a teal application using
-various types of datasets inside a data table module:
+This vignette will guide you through the four parts to create a teal application using
+various types of datasets using the data table module `tm_data_table()`:
 
 1. Load Libraries
 2. Create data sets
 3. Create an `app` variable
 4. Run the App
 
-## Loading libraries
+## 1 - Loading libraries
 
 ```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide"}
 library(teal.modules.general) # used to create the app
 ```
 
-## Create data sets
+## 2 - Create data sets
 
 Inside this app 3 datasets will be used
 
@@ -34,7 +34,6 @@ Inside this app 3 datasets will be used
 3. `ADLB` A long data set with lab measurements for each subject
 
 ```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide"}
-# nolint start
 data <- teal_data()
 data <- within(data, {
   ADSL <- teal.modules.general::rADSL
@@ -44,13 +43,13 @@ data <- within(data, {
 datanames <- c("ADSL", "ADTTE", "ADLB")
 datanames(data) <- datanames
 join_keys(data) <- default_cdisc_join_keys[datanames]
-# nolint end
 ```
 
-## Create an `app` variable
+## 3 - Create an `app` variable
 
-This is the most important section. We will use the [`teal::init`](https://insightsengineering.github.io/teal/latest-tag/reference/init) function to
-create an app. The data will be handed over using [`teal.data::cdisc_data`](https://insightsengineering.github.io/teal.data/latest-tag/reference/cdisc_data). The app itself will be constructed by multiple calls of `tm_data_table` using different
+This is the most important section. We will use the `teal::init` function to
+create an app. The data will be handed over using `teal.data::teal_data`.
+The app itself will be constructed by multiple calls of `tm_data_table()` using different
 combinations of data sets.
 
 ```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide"}
@@ -99,12 +98,13 @@ app <- init(
 )
 ```
 
-## Run the app
+## 4 - Run the app
 
 A simple `shiny::shinyApp` call will let you run the app. 
 Note that app is only displayed when running this code inside an R session.  
 
-```{r echo=TRUE}
+```{r, echo=TRUE, results="hide"}
 shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))
 ```
 
+<img src="images/app-using-data-table.png" style = "display: block; margin: 0px; width: 100%"/>