Using scatterplot matrix
NEST CoreDev
Source:vignettes/using-scatterplot-matrix.Rmd
using-scatterplot-matrix.Rmd
teal
application to use scatter plot matrix 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 matrix module tm_g_scatterplotmatrix()
:
- Load libraries
- Create data sets
- Create an
app
variable - Run the app
2 - Create data sets
Inside this app 4 datasets will be used
-
ADSL
A wide data set with subject data -
ADRS
A long data set with response data for subjects at different time points of the study -
ADTTE
A long data set with time to event data -
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_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
)
)
)
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))