Welcome to ClientVPS Mirrors

Using scatterplot

Using scatterplot

NEST CoreDev

teal application to use scatter 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 scatter plot module tm_g_scatterplot():

  1. Load libraries
  2. Create data sets
  3. Create an app variable
  4. Run the app

1 - Load libraries

library(teal.modules.general) # used to create the app
library(dplyr) # used to modify data sets
library(ggpmisc)
library(ggExtra)
library(colourpicker)

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
data <- 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)]

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_scatterplot() using different combinations of data sets.

# configuration for the single wide datasets
mod1 <- tm_g_scatterplot(
  label = "Single wide dataset",
  x = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADSL"]], c("AGE", "BMRKR1", "BMRKR2")),
      selected = "AGE",
      multiple = FALSE,
      fixed = FALSE
    )
  ),
  y = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADSL"]], c("AGE", "BMRKR1", "BMRKR2")),
      selected = "BMRKR1",
      multiple = FALSE,
      fixed = FALSE
    )
  ),
  color_by = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variables:",
      choices = variable_choices(data[["ADSL"]], c("RACE", "SEX")),
      selected = NULL,
      multiple = TRUE,
      fixed = FALSE
    )
  )
)

# configuration for the two wide datasets
mod2 <- tm_g_scatterplot(
  label = "Two wide datasets",
  x = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADSL"]], c("BMRKR1", "BMRKR2")),
      selected = "BMRKR1",
      multiple = FALSE,
      fixed = FALSE
    )
  ),
  y = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADSL"]], c("AGE", "SEX")),
      selected = "AGE",
      multiple = FALSE,
      fixed = FALSE
    )
  ),
  color_by = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADSL"]], c("COUNTRY", "AGE", "RACE")),
      selected = "COUNTRY",
      multiple = FALSE,
      fixed = FALSE
    )
  )
)

# configuration for the different long datasets
mod3 <- tm_g_scatterplot(
  label = "Different long datasets",
  x = data_extract_spec(
    dataname = "ADRS",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADRS"]]),
      selected = "AVAL",
      multiple = FALSE,
      fixed = FALSE
    ),
    filter = filter_spec(
      label = "Select endpoint:",
      vars = c("PARAMCD", "AVISIT"),
      choices = value_choices(data[["ADRS"]], c("PARAMCD", "AVISIT"), c("PARAM", "AVISIT")),
      selected = "OVRINV - SCREENING",
      multiple = FALSE
    )
  ),
  y = data_extract_spec(
    dataname = "ADTTE",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADTTE"]]),
      selected = "AVAL",
      multiple = FALSE,
      fixed = FALSE
    ),
    filter = filter_spec(
      label = "Select parameters:",
      vars = c("PARAMCD"),
      choices = value_choices(data[["ADTTE"]], "PARAMCD", "PARAM"),
      selected = "OS",
      multiple = TRUE
    )
  ),
  color_by = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADSL"]], c("AGE", "SEX")),
      selected = "AGE",
      multiple = FALSE,
      fixed = FALSE
    )
  )
)

# configuration for the wide and long datasets
mod4 <- tm_g_scatterplot(
  label = "Wide and long datasets",
  x = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADSL"]], c("SEX", "AGE", "BMRKR1", "COUNTRY")),
      selected = "AGE",
      multiple = FALSE,
      fixed = FALSE
    )
  ),
  y = data_extract_spec(
    dataname = "ADLB",
    filter = list(
      filter_spec(
        vars = "PARAMCD",
        choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
        selected = levels(data[["ADLB"]]$PARAMCD)[1],
        multiple = FALSE,
        label = "Select measurement:"
      ),
      filter_spec(
        vars = "AVISIT",
        choices = levels(data[["ADLB"]]$AVISIT),
        selected = levels(data[["ADLB"]]$AVISIT)[1],
        multiple = FALSE,
        label = "Select visit:"
      )
    ),
    select = select_spec(
      label = "Selected variable:",
      choices = "AVAL",
      selected = "AVAL",
      multiple = FALSE,
      fixed = TRUE
    )
  ),
  color_by = data_extract_spec(
    dataname = "ADSL",
    select = select_spec(
      label = "Select variable:",
      choices = variable_choices(data[["ADSL"]], c("SEX", "AGE", "RACE", "COUNTRY")),
      selected = NULL,
      multiple = FALSE,
      fixed = FALSE
    )
  )
)

# configuration for the same long datasets (same subsets)
mod5 <- tm_g_scatterplot(
  label = "Same long datasets (same subsets)",
  x = data_extract_spec(
    dataname = "ADRS",
    select = select_spec(
      choices = variable_choices(data[["ADRS"]], c("AVAL", "BMRKR1", "BMRKR2")),
      selected = "AVAL",
      multiple = FALSE,
      fixed = FALSE,
      label = "Select variable:"
    )
  ),
  y = data_extract_spec(
    dataname = "ADRS",
    select = select_spec(
      choices = variable_choices(data[["ADRS"]], c("AVAL", "BMRKR1", "BMRKR2")),
      selected = "BMRKR1",
      multiple = FALSE,
      fixed = FALSE,
      label = "Select variable:"
    )
  ),
  color_by = data_extract_spec(
    dataname = "ADRS",
    select = select_spec(
      choices = variable_choices(data[["ADRS"]], c("AGE", "SEX", "RACE")),
      selected = NULL,
      multiple = FALSE,
      fixed = FALSE,
      label = "Select variable:"
    )
  )
)

# configuration for the same long datasets (different subsets)
mod6 <- tm_g_scatterplot(
  label = "Same long datasets (different subsets)",
  x = 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"]]$AVISIT)[1],
        multiple = FALSE,
        label = "Select visit:"
      )
    ),
    select = select_spec(
      choices = "AVAL",
      selected = "AVAL",
      multiple = FALSE,
      fixed = TRUE
    )
  ),
  y = data_extract_spec(
    dataname = "ADLB",
    filter = list(
      filter_spec(
        vars = "PARAMCD",
        choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
        selected = levels(data[["ADLB"]]$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 = "AVAL",
      selected = "AVAL",
      multiple = FALSE,
      fixed = TRUE
    )
  ),
  color_by = 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("RACE", "SEX")),
      selected = "SEX",
      multiple = FALSE,
      fixed = FALSE,
      label = "Select variable:"
    )
  )
)

# initialize the app
app <- init(
  data = data,
  modules = modules(
    modules(
      label = "Scatterplot",
      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.

shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))

5 - Try it out in Shinylive

Open in Shinylive

Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.

This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.