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29 add non-cdisc examples to the modules #651

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Feb 22, 2024
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29 add non-cdisc examples to the modules #651

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fa98bb9
add non-CDISC examples
Feb 20, 2024
985fe74
rebuild documentation
Feb 20, 2024
499a21b
fix check note on line lengths
Feb 20, 2024
3d701d7
Merge 499a21b2a65f95cf10f4e0d761e7e532bb593766 into 244d2fa4cb4f39f54…
chlebowa Feb 20, 2024
a26c1a7
[skip actions] Restyle files
github-actions[bot] Feb 20, 2024
3a5e9ec
remove fluff
Feb 20, 2024
20ef672
change example dataset
Feb 21, 2024
3f40e1b
missing comment sign
Feb 21, 2024
a381a01
robust example for scatterplotmatrix
Feb 22, 2024
1fed0ce
format docs
Feb 22, 2024
64a32e4
improve example
Feb 22, 2024
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65 changes: 52 additions & 13 deletions R/tm_a_pca.R
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Expand Up @@ -18,45 +18,84 @@
#' @templateVar ggnames "Elbow plot", "Circle plot", "Biplot", "Eigenvector plot"
#' @template ggplot2_args_multi
#'
#' @export
#'
#' @examples
#' # general data example
#' library(teal.widgets)
#'
#' # ADSL example
#' data <- teal_data()
#' data <- within(data, {
#' library(nestcolor)
#' USArrests <- USArrests
#' })
#' datanames(data) <- "USArrests"
#'
#' app <- init(
#' data = data,
#' modules = modules(
#' tm_a_pca(
#' "PCA",
#' dat = data_extract_spec(
#' dataname = "USArrests",
#' select = select_spec(
#' choices = variable_choices(
#' data = data[["USArrests"]], c("Murder", "Assault", "UrbanPop", "Rape")
#' ),
#' selected = c("Murder", "Assault"),
#' multiple = TRUE
#' ),
#' filter = NULL
#' ),
#' ggplot2_args = ggplot2_args(
#' labs = list(subtitle = "Plot generated by PCA Module")
#' )
#' )
#' )
#' )
#'
#' if (interactive()) {
#' shinyApp(app$ui, app$server)
#' }
#'
#' # CDISC data example
#' library(teal.widgets)
#'
#' data <- teal_data()
#' data <- within(data, {
#' library(nestcolor)
#' ADSL <- teal.modules.general::rADSL
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#' ADSL <- rADSL
#' })
#' datanames <- c("ADSL")
#' datanames(data) <- datanames
#' join_keys(data) <- default_cdisc_join_keys[datanames]
#' app <- teal::init(
#' datanames(data) <- "ADSL"
#' join_keys(data) <- default_cdisc_join_keys[datanames(data)]
#'
#' app <- init(
#' data = data,
#' modules = teal::modules(
#' modules = modules(
#' teal.modules.general::tm_a_pca(
#' "PCA",
#' dat = teal.transform::data_extract_spec(
#' dat = data_extract_spec(
#' dataname = "ADSL",
#' select = teal.transform::select_spec(
#' choices = teal.transform::variable_choices(
#' select = select_spec(
#' choices = variable_choices(
#' data = data[["ADSL"]], c("BMRKR1", "AGE", "EOSDY")
#' ),
#' selected = c("BMRKR1", "AGE"),
#' multiple = TRUE
#' ),
#' filter = NULL
#' ),
#' ggplot2_args = teal.widgets::ggplot2_args(
#' ggplot2_args = ggplot2_args(
#' labs = list(subtitle = "Plot generated by PCA Module")
#' )
#' )
#' )
#' )
#'
#' if (interactive()) {
#' shinyApp(app$ui, app$server)
#' }
#'
#' @export
#'
tm_a_pca <- function(label = "Principal Component Analysis",
dat,
plot_height = c(600, 200, 2000),
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78 changes: 62 additions & 16 deletions R/tm_a_regression.R
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Expand Up @@ -28,47 +28,90 @@
#'
#' @note For more examples, please see the vignette "Using regression plots" via
#' `vignette("using-regression-plots", package = "teal.modules.general")`.
#' @export
#'
#' @examples
#' # Regression graphs from selected response variable (BMRKR1) and
#' # selected regressors (AGE)
#' # general data example
#' library(teal.widgets)
#'
#' data <- teal_data()
#' data <- within(data, {
#' library(nestcolor)
#' ADSL <- teal.modules.general::rADSL
#' CO2 <- CO2
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#' })
#' datanames <- c("ADSL")
#' datanames(data) <- datanames
#' join_keys(data) <- default_cdisc_join_keys[datanames]
#' datanames(data) <- c("CO2")
#'
#' app <- teal::init(
#' app <- init(
#' data = data,
#' modules = teal::modules(
#' teal.modules.general::tm_a_regression(
#' modules = modules(
#' tm_a_regression(
#' label = "Regression",
#' response = teal.transform::data_extract_spec(
#' response = data_extract_spec(
#' dataname = "CO2",
#' select = select_spec(
#' label = "Select variable:",
#' choices = "uptake",
#' selected = "uptake",
#' multiple = FALSE,
#' fixed = TRUE
#' )
#' ),
#' regressor = data_extract_spec(
#' dataname = "CO2",
#' select = select_spec(
#' label = "Select variables:",
#' choices = variable_choices(data[["CO2"]], c("conc", "Treatment")),
#' selected = "conc",
#' multiple = TRUE,
#' fixed = FALSE
#' )
#' ),
#' ggplot2_args = ggplot2_args(
#' labs = list(subtitle = "Plot generated by Regression Module")
#' )
#' )
#' )
#' )
#' if (interactive()) {
#' shinyApp(app$ui, app$server)
#' }
#'
#' # CDISC data example
#' library(teal.widgets)
#'
#' data <- teal_data()
#' data <- within(data, {
#' library(nestcolor)
#' ADSL <- rADSL
#' })
#' datanames(data) <- "ADSL"
#' join_keys(data) <- default_cdisc_join_keys[datanames(data)]
#'
#' app <- init(
#' data = data,
#' modules = modules(
#' tm_a_regression(
#' label = "Regression",
#' response = data_extract_spec(
#' dataname = "ADSL",
#' select = teal.transform::select_spec(
#' select = select_spec(
#' label = "Select variable:",
#' choices = "BMRKR1",
#' selected = "BMRKR1",
#' multiple = FALSE,
#' fixed = TRUE
#' )
#' ),
#' regressor = teal.transform::data_extract_spec(
#' regressor = data_extract_spec(
#' dataname = "ADSL",
#' select = teal.transform::select_spec(
#' select = select_spec(
#' label = "Select variables:",
#' choices = teal.transform::variable_choices(data[["ADSL"]], c("AGE", "SEX", "RACE")),
#' choices = variable_choices(data[["ADSL"]], c("AGE", "SEX", "RACE")),
#' selected = "AGE",
#' multiple = TRUE,
#' fixed = FALSE
#' )
#' ),
#' ggplot2_args = teal.widgets::ggplot2_args(
#' ggplot2_args = ggplot2_args(
#' labs = list(subtitle = "Plot generated by Regression Module")
#' )
#' )
Expand All @@ -77,6 +120,9 @@
#' if (interactive()) {
#' shinyApp(app$ui, app$server)
#' }
#'
#' @export
#'
tm_a_regression <- function(label = "Regression Analysis",
regressor,
response,
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42 changes: 34 additions & 8 deletions R/tm_data_table.R
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Expand Up @@ -25,22 +25,45 @@
#' The `DT` package has an option `DT.TOJSON_ARGS` to show `Inf` and `NA` in data tables. If this is something
#' you require then set `options(DT.TOJSON_ARGS = list(na = "string"))` before running the module.
#' Note though that sorting of numeric columns with `NA`/`Inf` will be lexicographic not numerical.
#' @export
#'
#' @examples
#' # general data example
#'
#' data <- teal_data()
#' data <- within(data, {
#' library(nestcolor)
#' ADSL <- teal.modules.general::rADSL
#' iris <- iris
#' })
#' datanames <- c("ADSL")
#' datanames(data) <- datanames
#' join_keys(data) <- default_cdisc_join_keys[datanames]
#' datanames(data) <- c("iris")
#'
#' app <- teal::init(
#' app <- init(
#' data = data,
#' modules = teal::modules(
#' teal.modules.general::tm_data_table(
#' modules = modules(
#' tm_data_table(
#' variables_selected = list(
#' iris = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species")
#' ),
#' dt_args = list(caption = "ADSL Table Caption")
#' )
#' )
#' )
#' if (interactive()) {
#' shinyApp(app$ui, app$server)
#' }
#'
#' # CDISC data example
#' data <- teal_data()
#' data <- within(data, {
#' library(nestcolor)
#' ADSL <- rADSL
#' })
#' datanames(data) <- "ADSL"
#' join_keys(data) <- default_cdisc_join_keys[datanames(data)]
#'
#' app <- init(
#' data = data,
#' modules = modules(
#' tm_data_table(
#' variables_selected = list(ADSL = c("STUDYID", "USUBJID", "SUBJID", "SITEID", "AGE", "SEX")),
#' dt_args = list(caption = "ADSL Table Caption")
#' )
Expand All @@ -49,6 +72,9 @@
#' if (interactive()) {
#' shinyApp(app$ui, app$server)
#' }
#'
#' @export
#'
tm_data_table <- function(label = "Data Table",
variables_selected = list(),
datasets_selected = character(0),
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82 changes: 65 additions & 17 deletions R/tm_g_association.R
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Expand Up @@ -17,40 +17,85 @@
#'
#' @note For more examples, please see the vignette "Using association plot" via
#' \code{vignette("using-association-plot", package = "teal.modules.general")}.
#' @export
#'
#' @examples
#' # Association plot of selected reference variable (SEX)
#' # against other selected variables (BMRKR1)
#' # general data exapmle
#' library(teal.widgets)
#'
#' data <- teal_data()
#' data <- within(data, {
#' library(nestcolor)
#' ADSL <- teal.modules.general::rADSL
#' CO2 <- CO2
#' factors <- names(Filter(isTRUE, vapply(CO2, is.factor, logical(1L))))
#' CO2[factors] <- lapply(CO2[factors], as.character)
#' })
#' datanames <- c("ADSL")
#' datanames(data) <- datanames
#' join_keys(data) <- default_cdisc_join_keys[datanames]
#' datanames(data) <- c("CO2")
#'
#' app <- teal::init(
#' app <- init(
#' data = data,
#' modules = teal::modules(
#' teal.modules.general::tm_g_association(
#' ref = teal.transform::data_extract_spec(
#' modules = modules(
#' tm_g_association(
#' ref = data_extract_spec(
#' dataname = "CO2",
#' select = select_spec(
#' label = "Select variable:",
#' choices = variable_choices(data[["CO2"]], c("Plant", "Type", "Treatment")),
#' selected = "Plant",
#' fixed = FALSE
#' )
#' ),
#' vars = data_extract_spec(
#' dataname = "CO2",
#' select = select_spec(
#' label = "Select variables:",
#' choices = variable_choices(data[["CO2"]], c("Plant", "Type", "Treatment")),
#' selected = "Treatment",
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#' multiple = TRUE,
#' fixed = FALSE
#' )
#' ),
#' ggplot2_args = ggplot2_args(
#' labs = list(subtitle = "Plot generated by Association Module")
#' )
#' )
#' )
#' )
#' if (interactive()) {
#' shinyApp(app$ui, app$server)
#' }
#'
#' # CDISC data example
#' library(teal.widgets)
#'
#' data <- teal_data()
#' data <- within(data, {
#' library(nestcolor)
#' ADSL <- rADSL
#' })
#' datanames(data) <- "ADSL"
#' join_keys(data) <- default_cdisc_join_keys[datanames(data)]
#'
#' app <- init(
#' data = data,
#' modules = modules(
#' tm_g_association(
#' ref = data_extract_spec(
#' dataname = "ADSL",
#' select = teal.transform::select_spec(
#' select = select_spec(
#' label = "Select variable:",
#' choices = teal.transform::variable_choices(
#' choices = variable_choices(
#' data[["ADSL"]],
#' c("SEX", "RACE", "COUNTRY", "ARM", "STRATA1", "STRATA2", "ITTFL", "BMRKR2")
#' ),
#' selected = "RACE",
#' fixed = FALSE
#' )
#' ),
#' vars = teal.transform::data_extract_spec(
#' vars = data_extract_spec(
#' dataname = "ADSL",
#' select = teal.transform::select_spec(
#' select = select_spec(
#' label = "Select variables:",
#' choices = teal.transform::variable_choices(
#' choices = variable_choices(
#' data[["ADSL"]],
#' c("SEX", "RACE", "COUNTRY", "ARM", "STRATA1", "STRATA2", "ITTFL", "BMRKR2")
#' ),
Expand All @@ -59,7 +104,7 @@
#' fixed = FALSE
#' )
#' ),
#' ggplot2_args = teal.widgets::ggplot2_args(
#' ggplot2_args = ggplot2_args(
#' labs = list(subtitle = "Plot generated by Association Module")
#' )
#' )
Expand All @@ -68,6 +113,9 @@
#' if (interactive()) {
#' shinyApp(app$ui, app$server)
#' }
#'
#' @export
#'
tm_g_association <- function(label = "Association",
ref,
vars,
Expand Down
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