Getting started with teal.goshawk
NEST CoreDev Team
10.11.2022
Source:vignettes/teal-goshawk.Rmd
teal-goshawk.Rmd
Introduction
teal.goshawk
is a package implementing a number of
teal
modules helpful for exploring clinical trials data,
specifically targeted at data following the ADaM
standards. teal.goshawk
modules can be used with data other
than ADaM
standard clinical data but some features of the
package will likely not be applicable.
The concepts presented here require knowledge about the core features
of teal
, specifically on how to launch a teal
application and how to pass data into it. Therefore, it is highly
recommended to refer to the README
file and the introductory vignette
of the teal
package.
Main features
The package provides ready-to-use teal
modules you can
embed in your teal
application. The modules generate highly
customizable plots and outputs often used in exploratory data analysis,
e.g.:
- box plots -
tm_g_gh_boxplot()
- correlation and scatter plots -
tm_g_gh_correlationplot()
andtm_g_gh_scatterplot()
- density distribution plots -
tm_g_gh_density_distribution_plot()
- line plots -
tm_g_gh_lineplot()
- spaghetti plots -
tm_g_spaghettiplot()
A simple application
A teal.goshawk
module needs to be embedded inside a
shiny
/ teal
application to interact with
it.
There is no need to load teal
as
teal.goshawk
already depends on it. nestcolor
is an optional package that can be loaded in to apply the standardized
NEST color palette to all module plots.
A simple application including the box plot module could look like this:
library(teal.goshawk)
library(nestcolor)
data <- teal_data()
data <- within(data, {
ADSL <- goshawk::rADSL %>%
mutate(TRTORD = case_when(
TRT01P == "A: Drug X" ~ 1,
TRT01P == "C: Combination" ~ 2,
TRT01P == "B: Placebo" ~ 3,
TRUE ~ as.numeric(NA)
)
)
ADLB <- goshawk::rADLB %>%
mutate(AVISITCD = AVISIT,
TRTORD = case_when(
TRT01P == "A: Drug X" ~ 1,
TRT01P == "C: Combination" ~ 2,
TRT01P == "B: Placebo" ~ 3,
TRUE ~ as.numeric(NA)
)
)
})
datanames <- c("ADSL", "ADLB")
datanames(data)<- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]
app <- teal::init(
data = data,
modules = list(
tm_g_gh_boxplot(
label = "Longitudinal Analysis",
dataname = "ADLB",
param_var = "PARAMCD",
param = teal.transform::choices_selected(
choices = c("ALT", "CRP", "IGA"),
selected = c("ALT")
),
trt_group = teal.transform::choices_selected(
choices = c("TRT01P", "TRT01A"),
selected = c("TRT01P")
),
facet_var = teal.transform::choices_selected(
choices = c("TRT01P", "TRT01A"),
selected = c("TRT01P")
),
rotate_xlab = TRUE
)
)
)
if (interactive()) shiny::shinyApp(app$ui, app$server)
Refer to the Get
Started section of the teal.modules.clinical package that provides
additional detail on teal
concepts as applied in another
simple app example.
Please see additional information under Articles for data expectations, requirements and pre/post-processing rationale