--- title: "Using association plot" author: "NEST CoreDev" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Using association plot} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- # `teal` application to use association plot with various datasets types 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 ## 1 - Load libraries ```{r library, echo=TRUE, message=FALSE, warning=FALSE, results="hide"} library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets ``` ## 2 - Create data sets Inside this app 4 datasets will be used 1. `ADSL` A wide data set with subject data 2. `ADRS` A long data set with response data for subjects at different time points of the study 3. `ADTTE` A long data set with time to event data 4. `ADLB` A long data set with lab measurements for each subject ```{r data, echo=TRUE, message=FALSE, warning=FALSE, results="hide"} data <- teal_data() data <- within(data, { ADSL <- 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 ~ "-" ))) }) join_keys(data) <- default_cdisc_join_keys[names(data)] ``` ## 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 app, echo=TRUE, message=FALSE, warning=FALSE, results="hide"} # configuration for a single wide dataset mod1 <- tm_g_association( label = "Single wide dataset", ref = data_extract_spec( dataname = "ADSL", select = select_spec( label = "Select variable:", choices = variable_choices(data[["ADSL"]]), selected = "AGE", fixed = FALSE ) ), vars = data_extract_spec( dataname = "ADSL", select = select_spec( label = "Select variables:", choices = variable_choices(data[["ADSL"]]), selected = "BMRKR1", multiple = TRUE, fixed = FALSE ) ) ) # configuration for two wide datasets mod2 <- tm_g_association( label = "Two wide datasets", ref = data_extract_spec( dataname = "ADSL", select = select_spec( label = "Select variable:", choices = variable_choices(data[["ADSL"]], c("AGE", "SEX", "STRATA1", "RACE")), selected = "STRATA1", multiple = FALSE, fixed = FALSE ) ), vars = data_extract_spec( dataname = "ADSL", select = select_spec( label = "Select variables:", choices = variable_choices(data[["ADSL"]], c("AGE", "SEX", "RACE", "COUNTRY")), selected = c("AGE", "COUNTRY", "RACE"), multiple = TRUE, fixed = FALSE ) ) ) # configuration for multiple long datasets mod3 <- tm_g_association( label = "Multiple different long datasets", ref = data_extract_spec( dataname = "ADTTE", select = select_spec( label = "Select variables:", choices = variable_choices(data[["ADTTE"]]), selected = "AVAL", multiple = FALSE, fixed = FALSE ), filter = filter_spec( label = "Select endpoint:", vars = "PARAMCD", choices = value_choices(data[["ADTTE"]], "PARAMCD", "PARAM"), selected = c("PFS", "EFS"), multiple = TRUE ) ), vars = data_extract_spec( dataname = "ADRS", reshape = TRUE, select = select_spec( label = "Select variable:", choices = variable_choices(data[["ADRS"]], c("AVALC", "BMRKR1", "BMRKR2", "ARM")), selected = "AVALC", multiple = TRUE, fixed = FALSE ), filter = list( filter_spec( label = "Select endpoints:", vars = "PARAMCD", choices = value_choices(data[["ADRS"]], "PARAMCD", "PARAM"), selected = "BESRSPI", multiple = TRUE ), filter_spec( label = "Select endpoints:", vars = "AVISIT", choices = levels(data[["ADRS"]]$AVISIT), selected = "SCREENING", multiple = TRUE ) ) ) ) # configuration for wide and long datasets mod4 <- tm_g_association( label = "Wide and long datasets", ref = data_extract_spec( dataname = "ADRS", select = select_spec( choices = variable_choices(data[["ADRS"]], c("AVAL", "AVALC")), selected = "AVALC", multiple = FALSE, fixed = FALSE, label = "Selected variable:" ), filter = list( filter_spec( vars = "PARAMCD", choices = value_choices(data[["ADRS"]], "PARAMCD", "PARAM"), selected = levels(data[["ADRS"]]$PARAMCD), multiple = TRUE, label = "Select response" ), filter_spec( vars = "AVISIT", choices = levels(data[["ADRS"]]$AVISIT), selected = levels(data[["ADRS"]]$AVISIT), multiple = TRUE, label = "Select visit:" ) ) ), vars = data_extract_spec( dataname = "ADSL", select = select_spec( choices = variable_choices(data[["ADSL"]], c("SEX", "AGE", "RACE", "COUNTRY", "BMRKR1", "STRATA1", "ARM")), selected = "AGE", multiple = TRUE, fixed = FALSE, label = "Select variable:" ) ) ) # configuration for the same long dataset (same subsets) mod5 <- tm_g_association( label = "Same long datasets (same subsets)", ref = data_extract_spec( dataname = "ADRS", select = select_spec( choices = variable_choices(data[["ADRS"]]), selected = "AVALC", multiple = FALSE, fixed = FALSE, label = "Select variable:" ) ), vars = data_extract_spec( dataname = "ADRS", select = select_spec( choices = variable_choices(data[["ADRS"]]), selected = "PARAMCD", multiple = TRUE, fixed = FALSE, label = "Select variable:" ) ) ) # configuration for the same long dataset (different subsets) mod6 <- tm_g_association( label = "Same long datasets (different subsets)", ref = data_extract_spec( dataname = "ADLB", filter = list( filter_spec( vars = "PARAMCD", choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"), selected = levels(data[["ADLB"]]$PARAMCD)[1], multiple = FALSE, label = "Select lab:" ), filter_spec( vars = "AVISIT", choices = levels(data[["ADLB"]]$AVISIT), selected = levels(data[["ADLB"]]$AVISIT)[1], multiple = FALSE, label = "Select visit:" ) ), select = select_spec( choices = variable_choices(data[["ADLB"]], c("AVAL", "CHG2", "PCHG2")), selected = "AVAL", multiple = FALSE ) ), vars = data_extract_spec( dataname = "ADLB", filter = list( filter_spec( vars = "PARAMCD", choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"), selected = levels(data[["ADLB"]]$PARAMCD)[1], multiple = FALSE, label = "Select labs:" ), filter_spec( vars = "AVISIT", choices = levels(data[["ADLB"]]$AVISIT), selected = levels(data[["ADLB"]]$AVISIT)[1], multiple = FALSE, label = "Select visit:" ) ), select = select_spec( choices = variable_choices(data[["ADLB"]]), selected = "STRATA1", multiple = TRUE ) ) ) # initialize the app app <- init( data = data, modules = modules( # tm_g_association ---- modules( label = "Association plot", mod1, mod2, mod3, mod4, mod5, mod6 ) ) ) ``` ## 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 shinyapp, echo=TRUE, results="hide", eval=base::interactive()} shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024)) ``` ## 5 - Try it out in Shinylive ```{r shinylive_url, echo = FALSE, results = 'asis', eval = requireNamespace("roxy.shinylive", quietly = TRUE)} code <- paste0(c( knitr::knit_code$get("library"), knitr::knit_code$get("data"), knitr::knit_code$get("app"), knitr::knit_code$get("shinyapp") ), collapse = "\n") url <- roxy.shinylive::create_shinylive_url(code) cat(sprintf("[Open in Shinylive](%s)\n\n", url)) ``` ```{r shinylive_iframe, echo = FALSE, out.width = '150%', out.extra = 'style = "position: relative; z-index:1"', eval = requireNamespace("roxy.shinylive", quietly = TRUE) && knitr::is_html_output() && identical(Sys.getenv("IN_PKGDOWN"), "true")} knitr::include_url(url, height = "800px") ```