teal
application to use scatter plot matrix with various datasets typesThis vignette will guide you through the four parts to create a teal
application using
various types of datasets using the scatter plot matrix module tm_g_scatterplotmatrix()
:
app
variablelibrary(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets library(lattice)
Inside this app 4 datasets will be used
ADSL
A wide data set with subject dataADRS
A long data set with response data for subjects at different time points of the studyADTTE
A long data set with time to event dataADLB
A long data set with lab measurements for each subjectdata <- teal_data() data <- within(data, { ADSL <- teal.data::rADSL %>% mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1)) ADRS <- teal.data::rADRS ADTTE <- teal.data::rADTTE ADLB <- teal.data::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ "N", CHG > 1 ~ "P", TRUE ~ "-" ))) }) join_keys(data) <- default_cdisc_join_keys[names(data)]
app
variableThis 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_scatterplotmatrix()
using different
combinations of data sets.
# configuration for the single wide dataset mod1 <- tm_g_scatterplotmatrix( label = "Single wide dataset", variables = data_extract_spec( dataname = "ADSL", select = select_spec( label = "Select variables:", choices = variable_choices(data[["ADSL"]]), selected = c("AGE", "RACE", "SEX", "BMRKR1", "BMRKR2"), multiple = TRUE, fixed = FALSE, ordered = TRUE ) ) ) # configuration for the one long datasets mod2 <- tm_g_scatterplotmatrix( "One long dataset", variables = data_extract_spec( dataname = "ADTTE", select = select_spec( choices = variable_choices(data[["ADTTE"]], c("AVAL", "BMRKR1", "BMRKR2")), selected = c("AVAL", "BMRKR1", "BMRKR2"), multiple = TRUE, fixed = FALSE, ordered = TRUE, label = "Select variables:" ) ) ) # configuration for the two long datasets mod3 <- tm_g_scatterplotmatrix( label = "Two long datasets", variables = list( data_extract_spec( dataname = "ADRS", select = select_spec( label = "Select variables:", choices = variable_choices(data[["ADRS"]]), selected = c("AVAL", "AVALC"), multiple = TRUE, fixed = FALSE, ordered = TRUE, ), filter = filter_spec( label = "Select endpoints:", vars = c("PARAMCD", "AVISIT"), choices = value_choices(data[["ADRS"]], c("PARAMCD", "AVISIT"), c("PARAM", "AVISIT")), selected = "OVRINV - SCREENING", multiple = FALSE ) ), data_extract_spec( dataname = "ADTTE", select = select_spec( label = "Select variables:", choices = variable_choices(data[["ADTTE"]]), selected = c("AVAL", "CNSR"), multiple = TRUE, fixed = FALSE, ordered = TRUE ), filter = filter_spec( label = "Select parameters:", vars = "PARAMCD", choices = value_choices(data[["ADTTE"]], "PARAMCD", "PARAM"), selected = "OS", multiple = TRUE ) ) ) ) # initialize the app app <- init( data = data, modules = modules( modules( label = "Scatterplot matrix", mod1, mod2, mod3 ) ) )
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.
shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))
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))
knitr::include_url(url, height = "800px")
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