This module produces ggplot2::ggplot()
type individual patient plots that display trends in parameter
values over time for each patient, using data with ADaM structure.
Usage
tm_g_ipp(
label,
dataname,
parentname = ifelse(inherits(arm_var, "data_extract_spec"),
teal.transform::datanames_input(arm_var), "ADSL"),
arm_var,
paramcd,
id_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
"USUBJID"), "USUBJID", fixed = TRUE),
visit_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
"AVISIT"), "AVISIT", fixed = TRUE),
aval_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
"AVAL"), "AVAL", fixed = TRUE),
avalu_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
"AVALU"), "AVALU", fixed = TRUE),
base_var = lifecycle::deprecated(),
baseline_var =
teal.transform::choices_selected(teal.transform::variable_choices(dataname, "BASE"),
"BASE", fixed = TRUE),
add_baseline_hline = FALSE,
separate_by_obs = FALSE,
suppress_legend = FALSE,
add_avalu = TRUE,
plot_height = c(1200L, 400L, 5000L),
plot_width = NULL,
pre_output = NULL,
post_output = NULL,
ggplot2_args = teal.widgets::ggplot2_args()
)
Arguments
- label
(
character
)
menu item label of the module in the teal app.- dataname
(
character
)
analysis data used in teal module.- parentname
(
character
)
parent analysis data used in teal module, usually this refers toADSL
.- arm_var
(
teal.transform::choices_selected()
)
object with all available choices and preselected option for variable values that can be used as arm variable.- paramcd
(
teal.transform::choices_selected()
)
object with all available choices and preselected option for the parameter code variable fromdataname
.- id_var
(
teal.transform::choices_selected()
)
object specifying the variable name for subject id.- visit_var
(
teal.transform::choices_selected()
)
object with all available choices and preselected option for variable names that can be used asvisit
variable. Must be a factor indataname
.- aval_var
(
teal.transform::choices_selected()
)
object with all available choices and pre-selected option for the analysis variable.- avalu_var
(
teal.transform::choices_selected()
)
object with all available choices and preselected option for the analysis unit variable.- base_var
- baseline_var
(
teal.transform::choices_selected()
)
object with all available choices and preselected option for variable values that can be used asbaseline_var
.- add_baseline_hline
(
logical
)
whether a horizontal line should be added to the plot at baseline y-value.- separate_by_obs
(
logical
)
whether to create multi-panel plots.- suppress_legend
(
logical
)
whether to suppress the plot legend.- add_avalu
(
logical
)
whetheravalu_first
text should be appended to the plot title and y-axis label.- plot_height
(
numeric
) optional
vector of length three withc(value, min, max)
. Specifies the height of the main plot and renders a slider on the plot to interactively adjust the plot height.- plot_width
(
numeric
) optional
vector of length three withc(value, min, max)
. Specifies the width of the main plot and renders a slider on the plot to interactively adjust the plot width.- pre_output
(
shiny.tag
) optional,
with text placed before the output to put the output into context. For example a title.- post_output
(
shiny.tag
) optional,
with text placed after the output to put the output into context. For example theshiny::helpText()
elements are useful.- ggplot2_args
-
(
ggplot2_args
) optional
object created byteal.widgets::ggplot2_args()
with settings for the module plot. For this module, this argument will only acceptggplot2_args
object withlabs
list of the following child elements:title
,subtitle
,x
,y
. No other elements are taken into account. The argument is merged with optionteal.ggplot2_args
and with default module arguments (hard coded in the module body).For more details, see the vignette:
vignette("custom-ggplot2-arguments", package = "teal.widgets")
.
See also
The TLG Catalog where additional example apps implementing this module can be found.
Examples
library(nestcolor)
library(dplyr)
data <- teal_data()
data <- within(data, {
ADSL <- tmc_ex_adsl %>%
slice(1:20) %>%
df_explicit_na()
ADLB <- tmc_ex_adlb %>%
filter(USUBJID %in% ADSL$USUBJID) %>%
df_explicit_na() %>%
filter(AVISIT != "SCREENING")
})
join_keys(data) <- default_cdisc_join_keys[names(data)]
ADSL <- data[["ADSL"]]
ADLB <- data[["ADLB"]]
app <- init(
data = data,
modules = modules(
tm_g_ipp(
label = "Individual Patient Plot",
dataname = "ADLB",
arm_var = choices_selected(
value_choices(ADLB, "ARMCD"),
"ARM A"
),
paramcd = choices_selected(
value_choices(ADLB, "PARAMCD"),
"ALT"
),
aval_var = choices_selected(
variable_choices(ADLB, c("AVAL", "CHG")),
"AVAL"
),
avalu_var = choices_selected(
variable_choices(ADLB, c("AVALU")),
"AVALU",
fixed = TRUE
),
id_var = choices_selected(
variable_choices(ADLB, c("USUBJID")),
"USUBJID",
fixed = TRUE
),
visit_var = choices_selected(
variable_choices(ADLB, c("AVISIT")),
"AVISIT"
),
baseline_var = choices_selected(
variable_choices(ADLB, c("BASE")),
"BASE",
fixed = TRUE
),
add_baseline_hline = FALSE,
separate_by_obs = FALSE
)
)
)
#> Initializing tm_g_ipp
#> Initializing reporter_previewer_module
if (interactive()) {
shinyApp(app$ui, app$server)
}