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modifications to ui and using DT for tables

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Andreas Gammelgaard Damsbo 2024-11-28 21:02:23 +01:00
parent c06d887c24
commit 15fe4ca188
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16 changed files with 567 additions and 128 deletions
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5
DESCRIPTION
View file

@ -26,7 +26,10 @@ Imports:
REDCapCAST,
purrr,
broom,
broom.helpers
broom.helpers,
cardx,
parameters,
DT
Suggests:
styler,
devtools,

5
NAMESPACE
View file

@ -1,5 +1,7 @@
# Generated by roxygen2: do not edit by hand
S3method(regression_table,list)
S3method(regression_table,webresearch_model)
export(argsstring2list)
export(baseline_table)
export(default_format_arguments)
@ -8,11 +10,12 @@ export(format_writer)
export(getfun)
export(index_embed)
export(modify_qmd)
export(panel_space)
export(read_input)
export(regression_model)
export(regression_model_uv)
export(regression_table)
export(shiny_webResearch)
export(specify_qmd_format)
export(tbl_merge)
export(write_quarto)
importFrom(stats,as.formula)

6
R/helpers.R
View file

@ -118,7 +118,11 @@ dummy_Imports <- function() {
MASS::as.fractions(),
broom::augment(),
broom.helpers::all_categorical(),
here::here()
here::here(),
cardx::all_of(),
parameters::ci(),
DT::addRow(),
bslib::accordion()
)
#https://github.com/hadley/r-pkgs/issues/828
}

107
R/regression_model.R
View file

@ -1,4 +1,4 @@
#' Print a flexible baseline characteristics table
#' Create a regression model programatically
#'
#' @param data data set
#' @param fun Name of function as character vector or function to use for model creation.
@ -7,6 +7,7 @@
#' @param auto.mode Make assumptions on function dependent on outcome data format. Overwrites other arguments.
#' @param formula.str Formula as string. Passed through 'glue::glue'. If given, 'outcome.str' and 'vars' are ignored. Optional.
#' @param args.list List of arguments passed to 'fun' with 'do.call'.
#' @param ... ignored for now
#'
#' @importFrom stats as.formula
#'
@ -36,7 +37,8 @@ regression_model <- function(data,
formula.str = NULL,
args.list = NULL,
fun = NULL,
vars = NULL) {
vars = NULL,
...) {
if (!is.null(formula.str)) {
if (formula.str == "") {
formula.str <- NULL
@ -61,9 +63,18 @@ regression_model <- function(data,
# Formatting character variables as factor
# Improvement should add a missing vector to format as NA
data <- data |> dplyr::mutate(dplyr::across(dplyr::where(is.character), as.factor))
data <- data |>
purrr::map(\(.x){
if (is.character(.x)) {
suppressWarnings(REDCapCAST::as_factor(.x))
} else {
.x
}
}) |>
dplyr::bind_cols()
if (is.null(fun)) auto.mode <- TRUE
# browser()
if (auto.mode) {
if (is.numeric(data[[outcome.str]])) {
fun <- "stats::lm"
@ -101,5 +112,93 @@ regression_model <- function(data,
# Recreating the call
# out$call <- match.call(definition=eval(parse(text=fun)), call(fun, data = 'data',formula = as.formula(formula.str),args.list))
class(out) <- c("webresearch_model", class(out))
return(out)
}
#' Create a regression model programatically
#'
#' @param data data set
#' @param fun Name of function as character vector or function to use for model creation.
#' @param vars character vector of variables to include
#' @param outcome.str Name of outcome variable. Character vector.
#' @param args.list List of arguments passed to 'fun' with 'do.call'.
#' @param ... ignored for now
#'
#' @importFrom stats as.formula
#'
#' @return object of standard class for fun
#' @export
#'
#' @examples
#' \dontrun{
#' gtsummary::trial |>
#' regression_model_uv(outcome.str = "age")
#' gtsummary::trial |>
#' regression_model_uv(
#' outcome.str = "age",
#' fun = "stats::lm",
#' args.list = NULL
#' )
#' gtsummary::trial |> regression_model_uv(
#' outcome.str = "trt",
#' fun = "stats::glm",
#' args.list = list(family = stats::binomial(link = "logit"))
#' )
#' }
regression_model_uv <- function(data,
outcome.str,
args.list = NULL,
fun = NULL,
vars = NULL,
...) {
if (!is.null(vars)) {
data <- data |>
dplyr::select(dplyr::all_of(
unique(c(outcome.str, vars))
))
}
if (is.null(args.list)) {
args.list <- list()
}
if (is.null(fun)) {
if (is.numeric(data[[outcome.str]])) {
fun <- "stats::lm"
} else if (is.factor(data[[outcome.str]])) {
if (length(levels(data[[outcome.str]])) == 2) {
fun <- "stats::glm"
args.list <- list(family = stats::binomial(link = "logit"))
} else if (length(levels(data[[outcome.str]])) > 2) {
fun <- "MASS::polr"
args.list <- list(
Hess = TRUE,
method = "logistic"
)
} else {
stop("The provided output variable only has one level")
}
} else {
stop("Output variable should be either numeric or factor for auto.mode")
}
}
assertthat::assert_that("character" %in% class(fun),
msg = "Please provide the function as a character vector."
)
out <- names(data)[!names(data) %in% outcome.str] |>
purrr::map(\(.var){
do.call(
regression_model,
c(
list(data = data[match(c(outcome.str,.var),names(data))]),
list(outcome.str=outcome.str),
list(args.list=args.list)
)
)
})
return(out)
}

93
R/regression_table.R
View file

@ -1,13 +1,16 @@
#' Create table of regression model
#'
#' @param data regression model
#' @param x regression model
#' @param args.list list of arguments passed to 'fun'.
#' @param fun function to use for table creation. Default is "gtsummary::tbl_regression".
#' @param ... passed to methods
#'
#' @return object of standard class for fun
#' @export
#' @name regression_table
#'
#' @examples
#' \dontrun{
#' gtsummary::trial |>
#' regression_model(
#' outcome.str = "stage",
@ -29,14 +32,82 @@
#' args.list = list(family = binomial(link = "logit"))
#' ) |>
#' regression_table()
regression_table <- function(data, args.list = NULL, fun = "gtsummary::tbl_regression") {
if (any(c(length(class(data))!=1, class(data)!="lm"))){
if (!"exponentiate" %in% names(args.list)){
args.list <- c(args.list,list(exponentiate=TRUE))
}
}
out <- do.call(getfun(fun), c(list(x = data), args.list))
out |> gtsummary::add_glance_source_note()
#' gtsummary::trial |>
#' regression_model_uv(
#' outcome.str = "trt",
#' fun = "stats::glm",
#' args.list = list(family = stats::binomial(link = "logit"))
#' ) |>
#' regression_table()
#' gtsummary::trial |>
#' regression_model_uv(
#' outcome.str = "stage",
#' args.list = list(family = stats::binomial(link = "logit"))
#' ) |>
#' regression_table()
#'
#' list(
#' "Univariable" = regression_model_uv,
#' "Multivariable" = regression_model
#' ) |>
#' lapply(\(.fun){
#' do.call(
#' .fun,
#' c(
#' list(data = gtsummary::trial),
#' list(outcome.str = "stage")
#' )
#' )
#' }) |>
#' purrr::map(regression_table) |>
#' tbl_merge()
#' }
regression_table <- function(x, ...) {
UseMethod("regression_table")
}
#' @rdname regression_table
#' @export
regression_table.list <- function(x, ...) {
x |>
purrr::map(\(.m){
regression_table(x = .m, ...) |>
gtsummary::add_n()
}) |>
gtsummary::tbl_stack()
}
#' @rdname regression_table
#' @export
regression_table.webresearch_model <- function(x, ..., args.list = NULL, fun = "gtsummary::tbl_regression") {
# Stripping custom class
class(x) <- class(x)[class(x) != "webresearch_model"]
if (any(c(length(class(x)) != 1, class(x) != "lm"))) {
if (!"exponentiate" %in% names(args.list)) {
args.list <- c(args.list, list(exponentiate = TRUE))
}
}
out <- do.call(getfun(fun), c(list(x = x), args.list))
out |>
gtsummary::add_glance_source_note() # |>
# gtsummary::bold_p()
}
#' A substitue to gtsummary::tbl_merge, that will use list names for the tab
#' spanner names.
#'
#' @param data gtsummary list object
#'
#' @return gt summary list object
#' @export
#'
tbl_merge <- function(data) {
if (is.null(names(data))) {
data |> gtsummary::tbl_merge()
} else {
data |> gtsummary::tbl_merge(tab_spanner = names(data))
}
}

12
R/app.R → R/shiny_webResearch.R
View file

@ -14,7 +14,7 @@
shiny_webResearch <- function(data = NULL, ...) {
appDir <- system.file("apps", "data_analysis", package = "webResearch")
if (appDir == "") {
stop("Could not find example directory. Try re-installing `webResearch`.", call. = FALSE)
stop("Could not find the app directory. Try re-installing `webResearch`.", call. = FALSE)
}
G <- .GlobalEnv
@ -22,13 +22,3 @@ shiny_webResearch <- function(data = NULL, ...) {
a <- shiny::runApp(appDir = appDir, ...)
return(invisible(a))
}
#' Wrapping nav_spacer to avoid errors on dependencies when packaging
#'
#' @return bslib object
#' @export
#'
panel_space <- function() {
bslib::nav_spacer()
}

2
inst/apps/data_analysis/rsconnect/shinyapps.io/agdamsbo/webResearch.dcf
View file

@ -5,6 +5,6 @@ account: agdamsbo
server: shinyapps.io
hostUrl: https://api.shinyapps.io/v1
appId: 13276335
bundleId: 9425248
bundleId: 9426438
url: https://agdamsbo.shinyapps.io/webResearch/
version: 1

114
inst/apps/data_analysis/server.R
View file

@ -53,7 +53,7 @@ server <- function(input, output, session) {
# or all rows if selected, will be shown.
if (v$input) {
out <- webResearch_data
} else if (v$test=="test") {
} else if (v$test == "test") {
out <- gtsummary::trial
} else {
shiny::req(input$file)
@ -61,6 +61,15 @@ server <- function(input, output, session) {
}
v$ds <- "present"
if (input$factorize == "yes") {
out <- out |>
(\(.x){
suppressWarnings(
REDCapCAST::numchar2fct(.x)
)
})()
}
return(out)
})
@ -69,7 +78,7 @@ server <- function(input, output, session) {
inputId = "include_vars",
selected = NULL,
label = "Covariables to include",
choices = colnames(ds())[-match(input$outcome_var, colnames(ds()))],
choices = colnames(ds()),
multiple = TRUE
)
})
@ -89,7 +98,7 @@ server <- function(input, output, session) {
inputId = "strat_var",
selected = "none",
label = "Select variable to stratify baseline",
choices = c("none" ,colnames(ds())),
choices = c("none", colnames(ds()[base_vars()])),
multiple = FALSE
)
})
@ -99,21 +108,39 @@ server <- function(input, output, session) {
inputId = "factor_vars",
selected = colnames(ds())[sapply(ds(), is.factor)],
label = "Covariables to format as categorical",
choices = colnames(ds())[sapply(ds(), is.character)],
choices = colnames(ds()),
multiple = TRUE
)
})
output$data.input <- shiny::renderTable({
utils::head(ds(),20)
base_vars <- shiny::reactive({
if (is.null(input$include_vars)) {
out <- colnames(ds())
} else {
out <- unique(c(input$include_vars, input$outcome_var))
}
return(out)
})
output$data.classes <- shiny::renderTable({
shiny::req(input$file)
data.frame(matrix(sapply(ds(),\(.x){class(.x)[1]}),nrow=1)) |>
stats::setNames(names(ds()))
# output$data.input <- shiny::renderTable({
# utils::head(ds(), 20)
# })
output$data.input <- DT::renderDT({
ds()[base_vars()]
})
output$data.classes <- gt::render_gt({
shiny::req(input$file)
data.frame(matrix(sapply(ds(), \(.x){
class(.x)[1]
}), nrow = 1)) |>
stats::setNames(names(ds())) |>
gt::gt()
})
shiny::observeEvent(
{
input$load
@ -127,28 +154,45 @@ server <- function(input, output, session) {
data <- data |> factorize(vars = input$factor_vars)
if (is.factor(data[[input$strat_var]])) {
by.var <- input$strat_var
} else {
# if (is.factor(data[[input$strat_var]])) {
# by.var <- input$strat_var
# } else {
# by.var <- NULL
# }
if (input$strat_var == "none") {
by.var <- NULL
}
if (is.null(input$include_vars)) {
base_vars <- colnames(data)
} else {
base_vars <- c(input$include_vars, input$outcome_var)
by.var <- input$strat_var
}
data <- dplyr::select(data, dplyr::all_of(base_vars))
data <- data[base_vars()]
model <- data |>
regression_model(
outcome.str = input$outcome_var,
auto.mode = input$regression_auto == 1,
formula.str = input$regression_formula,
fun = input$regression_fun,
args.list = eval(parse(text = paste0("list(", input$regression_args, ")")))
# model <- data |>
# regression_model(
# outcome.str = input$outcome_var,
# auto.mode = input$regression_auto == 1,
# formula.str = input$regression_formula,
# fun = input$regression_fun,
# args.list = eval(parse(text = paste0("list(", input$regression_args, ")")))
# )
models <- list(
"Univariable" = regression_model_uv,
"Multivariable" = regression_model
) |>
lapply(\(.fun){
do.call(
.fun,
c(
list(data = data),
list(outcome.str = input$outcome_var),
list(formula.str = input$regression_formula),
list(fun = input$regression_fun),
list(args.list = eval(parse(text = paste0("list(", input$regression_args, ")"))))
)
)
})
v$list <- list(
@ -161,14 +205,24 @@ server <- function(input, output, session) {
)
) |>
(\(.x){
if (!is.null(by.var)){
if (!is.null(by.var)) {
.x |> gtsummary::add_overall()
} else {
.x
}
})() |>
(\(.x){
if (input$add_p == "yes") {
.x |>
gtsummary::add_p() |>
gtsummary::bold_p()
} else {
.x
}
})(),
table2 = model |>
regression_table()
table2 = models |>
purrr::map(regression_table) |>
tbl_merge()
)
output$table1 <- gt::render_gt(
@ -180,7 +234,6 @@ server <- function(input, output, session) {
v$list$table2 |>
gtsummary::as_gt()
)
}
)
@ -229,5 +282,4 @@ server <- function(input, output, session) {
print(paste(.x, "deleted"))
})
})
}

93
inst/apps/data_analysis/ui.R
View file

@ -1,39 +1,16 @@
library(shiny)
library(bslib)
requireNamespace("gt")
# require(ggplot2)
# source("https://raw.githubusercontent.com/agdamsbo/cognitive.index.lookup/main/R/index_from_raw.R")
# source("https://raw.githubusercontent.com/agdamsbo/cognitive.index.lookup/main/R/plot_index.R")
# source(here::here("R/index_from_raw.R"))
# source(here::here("R/plot_index.R"))
# ui <- fluidPage(
cards <- list(
bslib::card(
max_height = "200px",
full_screen = TRUE,
bslib::card_header("Data overview"),
shiny::uiOutput("data.input")
),
bslib::card(
# max_height = "200px",
full_screen = TRUE,
bslib::card_header("Baseline characteristics"),
gt::gt_output(outputId = "table1")
),
bslib::card(
full_screen = TRUE,
bslib::card_header("Multivariable regression table"),
gt::gt_output(outputId = "table2")
)
)
panels <- list(
bslib::nav_panel(
title = "Data overview",
shiny::uiOutput("data.classes"),
shiny::uiOutput("data.input")
# shiny::uiOutput("data.classes"),
# shiny::uiOutput("data.input"),
shiny::p("Classes of uploaded data"),
gt::gt_output("data.classes"),
shiny::p("Subset data"),
DT::DTOutput("data.input")
),
bslib::nav_panel(
title = "Baseline characteristics",
@ -47,25 +24,15 @@ panels <- list(
ui <- bslib::page(
theme = bslib::bs_theme(bootswatch = "minty",
theme = bslib::bs_theme(
bootswatch = "minty",
base_font = font_google("Inter"),
code_font = font_google("JetBrains Mono")
),
# theme = bslib::bs_theme(
# bg = "#101010",
# fg = "#FFF",
# primary = "#E69F00",
# secondary = "#0072B2",
# success = "#009E73",
# base_font = font_google("Inter"),
# code_font = font_google("JetBrains Mono")
# ),
title = "webResearcher for easy data analysis",
bslib::page_navbar(
title = "webResearcher",
header = h6("Welcome to the webResearcher tool. This is an early alpha version to act as a proof-of-concept and in no way intended for wider public use."),
# sidebarPanel(
sidebar = bslib::sidebar(
width = 300,
open = "open",
@ -98,6 +65,16 @@ ui <- bslib::page(
shiny::conditionalPanel(
condition = "output.uploaded=='yes'",
shiny::h4("Parameter specifications"),
shiny::radioButtons(
inputId = "factorize",
label = "Factorize variables with few levels?",
selected = "yes",
inline = TRUE,
choices = list(
"Yes" = "yes",
"No" = "no"
)
),
shiny::radioButtons(
inputId = "regression_auto",
label = "Automatically choose function",
@ -126,9 +103,23 @@ ui <- bslib::page(
value = ""
)
),
shiny::helpText(em("Please specify relevant columns from your data, and press 'Load data'")),
shiny::helpText(em("Please specify relevant settings for your data, and press 'Analyse'")),
shiny::uiOutput("outcome_var"),
shiny::uiOutput("strat_var"),
shiny::conditionalPanel(
condition = "input.strat_var!='none'",
shiny::radioButtons(
inputId = "add_p",
label = "Compare strata?",
selected = "no",
inline = TRUE,
choices = list(
"No" = "no",
"Yes" = "yes"
)
),
shiny::helpText("Option to perform statistical comparisons between strata in baseline table.")
),
shiny::radioButtons(
inputId = "all",
label = "Specify covariables",
@ -149,15 +140,27 @@ ui <- bslib::page(
selected = "no",
inline = TRUE,
choices = list(
"No" = "no",
"Yes" = "yes"
"Yes" = "yes",
"No" = "no"
)
),
shiny::conditionalPanel(
condition = "input.specify_factors=='yes'",
shiny::uiOutput("factor_vars")
),
shiny::actionButton("load", "Analyse", class = "btn-primary"),
bslib::input_task_button(
id = "load",
label = "Analyse",
icon = shiny::icon("pencil", lib = "glyphicon"),
label_busy = "Working...",
icon_busy = fontawesome::fa_i("arrows-rotate",
class = "fa-spin",
"aria-hidden" = "true"),
type = "primary",
auto_reset = TRUE
),
shiny::helpText("If you change the parameters, press 'Analyse' again to update the tables"),
# shiny::actionButton("load", "Analyse", class = "btn-primary"),
#
# # Horizontal line ----
tags$hr(),

14
man/panel_space.Rd
View file

@ -1,14 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/app.R
\name{panel_space}
\alias{panel_space}
\title{Wrapping nav_spacer to avoid errors on dependencies when packaging}
\usage{
panel_space()
}
\value{
bslib object
}
\description{
Wrapping nav_spacer to avoid errors on dependencies when packaging
}

9
man/regression_model.Rd
View file

@ -2,7 +2,7 @@
% Please edit documentation in R/regression_model.R
\name{regression_model}
\alias{regression_model}
\title{Print a flexible baseline characteristics table}
\title{Create a regression model programatically}
\usage{
regression_model(
data,
@ -11,7 +11,8 @@ regression_model(
formula.str = NULL,
args.list = NULL,
fun = NULL,
vars = NULL
vars = NULL,
...
)
}
\arguments{
@ -28,12 +29,14 @@ regression_model(
\item{fun}{Name of function as character vector or function to use for model creation.}
\item{vars}{character vector of variables to include}
\item{...}{ignored for now}
}
\value{
object of standard class for fun
}
\description{
Print a flexible baseline characteristics table
Create a regression model programatically
}
\examples{
gtsummary::trial |>

51
man/regression_model_uv.Rd Normal file
View file

@ -0,0 +1,51 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/regression_model.R
\name{regression_model_uv}
\alias{regression_model_uv}
\title{Create a regression model programatically}
\usage{
regression_model_uv(
data,
outcome.str,
args.list = NULL,
fun = NULL,
vars = NULL,
...
)
}
\arguments{
\item{data}{data set}
\item{outcome.str}{Name of outcome variable. Character vector.}
\item{args.list}{List of arguments passed to 'fun' with 'do.call'.}
\item{fun}{Name of function as character vector or function to use for model creation.}
\item{vars}{character vector of variables to include}
\item{...}{ignored for now}
}
\value{
object of standard class for fun
}
\description{
Create a regression model programatically
}
\examples{
\dontrun{
gtsummary::trial |>
regression_model_uv(outcome.str = "age")
gtsummary::trial |>
regression_model_uv(
outcome.str = "age",
fun = "stats::lm",
args.list = NULL
)
gtsummary::trial |> regression_model_uv(
outcome.str = "trt",
fun = "stats::glm",
args.list = list(family = stats::binomial(link = "logit"))
)
}
}

43
man/regression_table.Rd
View file

@ -2,12 +2,20 @@
% Please edit documentation in R/regression_table.R
\name{regression_table}
\alias{regression_table}
\alias{regression_table.list}
\alias{regression_table.webresearch_model}
\title{Create table of regression model}
\usage{
regression_table(data, args.list = NULL, fun = "gtsummary::tbl_regression")
regression_table(x, ...)
\method{regression_table}{list}(x, ...)
\method{regression_table}{webresearch_model}(x, ..., args.list = NULL, fun = "gtsummary::tbl_regression")
}
\arguments{
\item{data}{regression model}
\item{x}{regression model}
\item{...}{passed to methods}
\item{args.list}{list of arguments passed to 'fun'.}
@ -20,6 +28,7 @@ object of standard class for fun
Create table of regression model
}
\examples{
\dontrun{
gtsummary::trial |>
regression_model(
outcome.str = "stage",
@ -41,4 +50,34 @@ gtsummary::trial |>
args.list = list(family = binomial(link = "logit"))
) |>
regression_table()
gtsummary::trial |>
regression_model_uv(
outcome.str = "trt",
fun = "stats::glm",
args.list = list(family = stats::binomial(link = "logit"))
) |>
regression_table()
gtsummary::trial |>
regression_model_uv(
outcome.str = "stage",
args.list = list(family = stats::binomial(link = "logit"))
) |>
regression_table()
list(
"Univariable" = regression_model_uv,
"Multivariable" = regression_model
) |>
lapply(\(.fun){
do.call(
.fun,
c(
list(data = gtsummary::trial),
list(outcome.str = "stage")
)
)
}) |>
purrr::map(regression_table) |>
tbl_merge()
}
}

2
man/shiny_webResearch.Rd
View file

@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/app.R
% Please edit documentation in R/shiny_webResearch.R
\name{shiny_webResearch}
\alias{shiny_webResearch}
\title{Test version of the shiny_cast function to launch the app with a data set in

19
man/tbl_merge.Rd Normal file
View file

@ -0,0 +1,19 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/regression_table.R
\name{tbl_merge}
\alias{tbl_merge}
\title{A substitue to gtsummary::tbl_merge, that will use list names for the tab
spanner names.}
\usage{
tbl_merge(data)
}
\arguments{
\item{data}{gtsummary list object}
}
\value{
gt summary list object
}
\description{
A substitue to gtsummary::tbl_merge, that will use list names for the tab
spanner names.
}

116
renv.lock
View file

@ -9,6 +9,23 @@
]
},
"Packages": {
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"Source": "Repository",
"Repository": "CRAN",
"Requirements": [
"crosstalk",
"htmltools",
"htmlwidgets",
"httpuv",
"jquerylib",
"jsonlite",
"magrittr",
"promises"
],
"Hash": "64ff3427f559ce3f2597a4fe13255cb6"
},
"MASS": {
"Package": "MASS",
"Version": "7.3-61",
@ -169,6 +186,22 @@
],
"Hash": "543776ae6848fde2f48ff3816d0628bc"
},
"bayestestR": {
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"Version": "0.15.0",
"Source": "Repository",
"Repository": "CRAN",
"Requirements": [
"R",
"datawizard",
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"insight",
"methods",
"stats",
"utils"
],
"Hash": "d7c05ccb9d60d87dbbe8b4042c385f01"
},
"bigD": {
"Package": "bigD",
"Version": "0.3.0",
@ -301,6 +334,23 @@
],
"Hash": "2147e8448f4eb87197520fc1710855c7"
},
"cardx": {
"Package": "cardx",
"Version": "0.2.1",
"Source": "Repository",
"Repository": "CRAN",
"Requirements": [
"R",
"cards",
"cli",
"dplyr",
"glue",
"lifecycle",
"rlang",
"tidyr"
],
"Hash": "e5458dd65b0602136b16aed802d3bd50"
},
"cellranger": {
"Package": "cellranger",
"Version": "1.1.0",
@ -389,6 +439,19 @@
],
"Hash": "859d96e65ef198fd43e82b9628d593ef"
},
"crosstalk": {
"Package": "crosstalk",
"Version": "1.2.1",
"Source": "Repository",
"Repository": "CRAN",
"Requirements": [
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},
"curl": {
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"Version": "6.0.1",
@ -399,6 +462,19 @@
],
"Hash": "e8ba62486230951fcd2b881c5be23f96"
},
"datawizard": {
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"stats",
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],
"Hash": "303aeace6f3554ce2d62e5d1df6fcd6e"
},
"digest": {
"Package": "digest",
"Version": "0.6.37",
@ -706,6 +782,19 @@
],
"Hash": "ac107251d9d9fd72f0ca8049988f1d7f"
},
"insight": {
"Package": "insight",
"Version": "1.0.0",
"Source": "Repository",
"Repository": "CRAN",
"Requirements": [
"R",
"methods",
"stats",
"utils"
],
"Hash": "bee0c3abb5eea6c4170ea2a7c334893f"
},
"jquerylib": {
"Package": "jquerylib",
"Version": "0.1.4",
@ -812,6 +901,16 @@
],
"Hash": "a3e051d405326b8b0012377434c62b37"
},
"lazyeval": {
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},
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"Version": "1.0.4",
@ -931,6 +1030,23 @@
],
"Hash": "55ddd2d4a1959535f18393478b0c14a6"
},
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"Source": "Repository",
"Repository": "CRAN",
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"utils"
],
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"Version": "1.9.0",