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teal
application to use association plot with various
datasets typesThis 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()
:
app
variableInside 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_association()
using different combinations of data sets.
# 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
)
)
)
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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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