R/jsPropensityGadget.R
In leevenstar/jstest: 'RStudio' Addins and 'Shiny' Modules for Medical Research
Defines functions
jsPropensityExtAddin
jsPropensityAddin
jsPropensityGadget
Documented in
jsPropensityAddin
jsPropensityExtAddin
jsPropensityGadget
#' @title jsPropensityGadget: Shiny Gadget for propensity score analysis.
#' @description Shiny Gadget including original/matching/IPTW data, Label info, Table 1, Cox model, Basic/kaplan-meier plot.
#' @param data data
#' @param nfactor.limit nlevels limit for categorical variables, Default: 20
#' @return Shiny Gadget including original/matching/IPTW data, Label info, Table 1, Cox model, Basic/kaplan-meier plot.
#' @details Shiny Gadget including original/matching/IPTW data, Label info, Table 1, Cox model, Basic/kaplan-meier plot.
#' @examples
#' if(interactive()){
#' jsPropensityGadget(mtcars)
#' }
#' @seealso
#' \code{\link[data.table]{data.table}}
#' \code{\link[MatchIt]{matchit}},\code{\link[MatchIt]{match.data}}
#' \code{\link[jstable]{cox2.display}},\code{\link[jstable]{svycox.display}}
#' \code{\link[survival]{survfit}},\code{\link[survival]{coxph}},\code{\link[survival]{Surv}}
#' \code{\link[jskm]{jskm}},\code{\link[jskm]{svyjskm}}
#' \code{\link[ggplot2]{ggsave}}
#' \code{\link[survey]{svykm}}
#' @rdname jsPropensityGadget
#' @export
#' @importFrom data.table data.table
#' @importFrom MatchIt matchit match.data
#' @importFrom jstable cox2.display svycox.display
#' @importFrom survival survfit
#' @importFrom jskm jskm svyjskm
#' @importFrom ggplot2 ggsave
#' @importFrom survey svykm
#' @importFrom purrr map_lgl
#' @importFrom stats model.frame
jsPropensityGadget <- function(data, nfactor.limit = 20){
requireNamespace("survival")
requireNamespace("survC1")
## To remove NOTE.
ID.pscal2828 <- level <- val_label <- BinaryGroupRandom <- variable <- NULL
## Data label
out.old <- data.table::data.table(data)
name.old <- names(out.old)
out <- data.table::data.table(data, check.names = T)
name.new <- names(out)
ref <- list(name.old = name.old, name.new = name.new)
data_varStruct1 = list(variable = names(out))
## Vars
naCol <- names(out)[colSums(is.na(out)) > 0]
#out <- out[, .SD, .SDcols = -naCol]
factor_vars <- names(out)[out[, lapply(.SD, class) %in% c("factor", "character")]]
if (!is.null(factor_vars) & length(factor_vars) > 0){
out[, (factor_vars) := lapply(.SD, as.factor), .SDcols= factor_vars]
}
factor_original <- factor_vars
conti_original <- setdiff(names(out), factor_vars)
nclass <- unlist(out[, lapply(.SD, function(x){length(unique(x))}), .SDcols = conti_original])
factor_adds_list = mklist(data_varStruct1, names(nclass)[nclass <= nfactor.limit])
except_vars <- names(nclass)[ nclass== 1]
#except_vars <- names(nclass)[ nclass== 1 | nclass >= nfactor.limit]
factor_adds <- names(nclass)[nclass >= 1 & nclass <= 5]
ui <- navbarPage("Propensity score analysis",
tabPanel("Data", icon = icon("table"),
sidebarLayout(
sidebarPanel(
uiOutput("factor"),
uiOutput("binary_check"),
uiOutput("binary_var"),
uiOutput("binary_val"),
uiOutput("ref_check"),
uiOutput("ref_var"),
uiOutput("ref_val"),
uiOutput("subset_check"),
uiOutput("subset_var"),
uiOutput("subset_val"),
uiOutput("group_ps"),
uiOutput("indep_ps"),
uiOutput("pcut"),
uiOutput("caliperps"),
uiOutput("ratio")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Data", withLoader(DTOutput("data"), type="html", loader="loader6")),
tabPanel("Matching data", withLoader(DTOutput("matdata"), type="html", loader="loader6")),
tabPanel("Label", withLoader(DTOutput("data_label", width = "100%"), type="html", loader="loader6"))
),
htmlOutput("naomit")
)
)
),
tabPanel("Table 1", icon = icon("percentage"),
sidebarLayout(
sidebarPanel(
tb1moduleUI("tb1")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("table1_original"), type="html", loader="loader6"),
wellPanel(
h5("Normal continuous variables are summarized with Mean (SD) and t-test(2 groups) or ANOVA(> 2 groups)"),
h5("Non-normal continuous variables are summarized with median [IQR or min,max] and kruskal-wallis test"),
h5("Categorical variables are summarized with table")
)
),
tabPanel("Matching",
withLoader(DTOutput("table1_ps"), type="html", loader="loader6"),
wellPanel(
h5("Normal continuous variables are summarized with Mean (SD) and t-test(2 groups) or ANOVA(> 2 groups)"),
h5("Non-normal continuous variables are summarized with median [IQR or min,max] and kruskal-wallis test"),
h5("Categorical variables are summarized with table")
)
),
tabPanel("IPTW",
withLoader(DTOutput("table1_iptw"), type="html", loader="loader6"),
wellPanel(
h5("Normal continuous variables are summarized with Mean (SD) and complex survey regression"),
h5("Non-normal continuous variables are summarized with median [IQR or min,max] and complex sampling rank test"),
h5("Categorical variables are summarized with table")
)
)
)
)
)
),
navbarMenu("Regression", icon = icon("list-alt"),
tabPanel("Linear regression",
sidebarLayout(
sidebarPanel(
regressModuleUI("linear")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("linear_original"), type="html", loader="loader6"),
br(),
uiOutput("warning_linear_original")
),
tabPanel("Matching",
withLoader(DTOutput("linear_ps"), type="html", loader="loader6"),
br(),
uiOutput("warning_linear_ps")
),
tabPanel("IPTW",
withLoader(DTOutput("linear_iptw"), type="html", loader="loader6")
)
)
)
)
),
tabPanel("Logistic regression",
sidebarLayout(
sidebarPanel(
regressModuleUI("logistic")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("logistic_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(DTOutput("logistic_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(DTOutput("logistic_iptw"), type="html", loader="loader6")
)
)
)
)
),
tabPanel("Cox model",
sidebarLayout(
sidebarPanel(
coxUI("cox")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("cox_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(DTOutput("cox_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(DTOutput("cox_iptw"), type="html", loader="loader6")
)
)
)
)
)
),
navbarMenu("Plot", icon = icon("bar-chart-o"),
tabPanel("Scatter plot",
sidebarLayout(
sidebarPanel(
ggpairsModuleUI1("ggpairs")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("ggpairs_plot_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("ggpairs_plot_ps"), type="html", loader="loader6")
)
),
ggpairsModuleUI2("ggpairs")
)
)
),
tabPanel("Kaplan-meier plot",
sidebarLayout(
sidebarPanel(
kaplanUI("kaplan")
),
mainPanel(
optionUI("kaplan"),
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("kaplan_plot_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("kaplan_plot_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(plotOutput("kaplan_plot_iptw"), type="html", loader="loader6")
)
),
ggplotdownUI("kaplan")
)
)
)
),
navbarMenu("ROC analysis", icon = icon("check"),
tabPanel("ROC",
sidebarLayout(
sidebarPanel(
rocUI("roc")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("plot_roc_original"), type="html", loader="loader6"),
withLoader(DTOutput("table_roc_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("plot_roc_ps"), type="html", loader="loader6"),
withLoader(DTOutput("table_roc_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(plotOutput("plot_roc_iptw"), type="html", loader="loader6"),
withLoader(DTOutput("table_roc_iptw"), type="html", loader="loader6")
)
),
ggplotdownUI("roc")
)
)
),
tabPanel("Time-dependent ROC",
sidebarLayout(
sidebarPanel(
timerocUI("timeroc")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("plot_timeroc_original"), type="html", loader="loader6"),
withLoader(DTOutput("table_timeroc_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("plot_timeroc_ps"), type="html", loader="loader6"),
withLoader(DTOutput("table_timeroc_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(plotOutput("plot_timeroc_iptw"), type="html", loader="loader6"),
withLoader(DTOutput("table_timeroc_iptw"), type="html", loader="loader6")
)
),
ggplotdownUI("timeroc")
)
)
)
)
)
server <- function(input, output, session) {
output$pcut <- renderUI({
radioButtons("pcut_ps", label = "Default p-value cut for ps calculation",
choices = c("No", 0.05, 0.1, 0.2),
selected = "No", inline =T)
})
output$ratio <- renderUI({
radioButtons("ratio_ps", label = "Case:control ratio",
choices = c("1:1" = 1, "1:2" = 2, "1:3" = 3, "1:4" = 4),
selected = 1, inline =T)
})
output$factor <- renderUI({
selectInput("factor_vname", label = "Additional categorical variables",
choices = factor_adds_list, multiple = T,
selected = factor_adds)
})
observeEvent(c(factor_original, input$factor_vname), {
output$binary_check <- renderUI({
checkboxInput("check_binary", "Make binary variables")
})
output$ref_check <- renderUI({
checkboxInput("check_ref", "Change reference of categorical variables")
})
output$subset_check <- renderUI({
checkboxInput("check_subset", "Subset data")
})
})
observeEvent(input$check_binary, {
var.conti <- setdiff(names(out), c(factor_original, input$factor_vname))
output$binary_var <- renderUI({
req(input$check_binary == T)
selectInput("var_binary", "Variables to dichotomize",
choices = var.conti, multiple = T,
selected = var.conti[1])
})
output$binary_val <- renderUI({
req(input$check_binary == T)
req(length(input$var_binary) > 0)
outUI <- tagList()
for (v in seq_along(input$var_binary)){
med <- stats::quantile(out[[input$var_binary[[v]]]], c(0.05, 0.5, 0.95), na.rm = T)
outUI[[v]] <- splitLayout(cellWidths = c("25%", "75%"),
selectInput(paste0("con_binary", v), paste0("Define reference:"),
choices = c("\u2264", "\u2265", "\u003c", "\u003e"), selected = "\u2264"
),
numericInput(paste0("cut_binary", v), input$var_binary[[v]],
value = med[2], min = med[1], max = med[3]
)
)
}
outUI
})
})
observeEvent(input$check_ref, {
var.factor <- c(factor_original, input$factor_vname)
output$ref_var <- renderUI({
req(input$check_ref == T)
selectInput("var_ref", "Variables to change reference",
choices = var.factor, multiple = T,
selected = var.factor[1])
})
output$ref_val <- renderUI({
req(input$check_ref == T)
req(length(input$var_ref) > 0)
outUI <- tagList()
for (v in seq_along(input$var_ref)){
outUI[[v]] <- selectInput(paste0("con_ref", v), paste0("Reference: ", input$var_ref[[v]]),
choices = levels(factor(out[[input$var_ref[[v]]]])), selected = levels(factor(out[[input$var_ref[[v]]]]))[2])
}
outUI
})
})
observeEvent(input$check_subset, {
output$subset_var <- renderUI({
req(input$check_subset == T)
#factor_subset <- c(factor_original, input$factor_vname)
#validate(
# need(length(factor_subset) > 0 , "No factor variable for subsetting")
#)
tagList(
selectInput("var_subset", "Subset variables",
choices = names(out), multiple = T,
selected = names(out)[1])
)
})
output$subset_val <- renderUI({
req(input$check_subset == T)
req(input$var_subset)
var.factor <- c(factor_original, input$factor_vname)
outUI <- tagList()
for (v in seq_along(input$var_subset)){
if (input$var_subset[[v]] %in% var.factor){
varlevel <- levels(as.factor(out[[input$var_subset[[v]]]]))
outUI[[v]] <- selectInput(session$ns(paste0("val_subset", v)), paste0("Subset value: ", input$var_subset[[v]]),
choices = varlevel, multiple = T,
selected = varlevel[1])
} else{
val <- stats::quantile(out[[input$var_subset[[v]]]], na.rm = T)
outUI[[v]] <- sliderInput(session$ns(paste0("val_subset", v)), paste0("Subset range: ", input$var_subset[[v]]),
min = val[1], max = val[5],
value = c(val[2], val[4]))
}
}
outUI
})
})
data.info <- reactive({
out1 <- data.table::data.table(out)
out1[, (conti_original) := lapply(.SD, function(x){as.numeric(as.vector(x))}), .SDcols = conti_original]
if (!is.null(input$factor_vname) & length(input$factor_vname) > 0){
out1[, (input$factor_vname) := lapply(.SD, as.factor), .SDcols= input$factor_vname]
}
out.label <- mk.lev(out1)
if (!is.null(input$check_binary)){
if (input$check_binary){
validate(
need(length(input$var_binary) > 0 , "No variables to dichotomize")
)
sym.ineq <- c("\u2264", "\u2265", "\u003c", "\u003e")
names(sym.ineq) <- sym.ineq[4:1]
sym.ineq2 <- c("le", "ge", "l", "g")
names(sym.ineq2) <- sym.ineq
for (v in seq_along(input$var_binary)){
req(input[[paste0("con_binary", v)]])
req(input[[paste0("cut_binary", v)]])
if (input[[paste0("con_binary", v)]] == "\u2264"){
out1[, BinaryGroupRandom := factor(1 - as.integer(get(input$var_binary[[v]]) <= input[[paste0("cut_binary", v)]]))]
} else if (input[[paste0("con_binary", v)]] == "\u2265"){
out1[, BinaryGroupRandom := factor(1 - as.integer(get(input$var_binary[[v]]) >= input[[paste0("cut_binary", v)]]))]
} else if (input[[paste0("con_binary", v)]] == "\u003c"){
out1[, BinaryGroupRandom := factor(1 - as.integer(get(input$var_binary[[v]]) < input[[paste0("cut_binary", v)]]))]
} else{
out1[, BinaryGroupRandom := factor(1 - as.integer(get(input$var_binary[[v]]) > input[[paste0("cut_binary", v)]]))]
}
cn.new <- paste0(input$var_binary[[v]], "_group_", sym.ineq2[input[[paste0("con_binary", v)]]], input[[paste0("cut_binary", v)]])
data.table::setnames(out1, "BinaryGroupRandom", cn.new)
label.binary <- mk.lev(out1[, .SD, .SDcols = cn.new])
label.binary[, var_label := paste0(input$var_binary[[v]], " _group")]
#label.binary[, val_label := paste0(c(input[[paste0("con_binary", v)]], sym.ineq[input[[paste0("con_binary", v)]]]), " ", input[[paste0("cut_binary", v)]])]
out.label <- rbind(out.label, label.binary)
}
}
}
if (!is.null(input$check_ref)){
if (input$check_ref){
validate(
need(length(input$var_ref) > 0 , "No variables to change reference")
)
for (v in seq_along(input$var_ref)){
req(input[[paste0("con_ref", v)]])
out1[[input$var_ref[[v]]]] <- stats::relevel(out1[[input$var_ref[[v]]]], ref = input[[paste0("con_ref", v)]])
out.label[variable == input$var_ref[[v]], ':='(level = levels(out1[[input$var_ref[[v]]]]), val_label = levels(out1[[input$var_ref[[v]]]]))]
}
}
}
if (!is.null(input$check_subset)){
if (input$check_subset){
validate(
need(length(input$var_subset) > 0 , "No variable for subsetting"),
need(all(sapply(1:length(input$var_subset), function(x){length(input[[paste0("val_subset", x)]])})), "No value for subsetting")
)
var.factor <- c(factor_original, input$factor_vname)
for (v in seq_along(input$var_subset)){
if (input$var_subset[[v]] %in% var.factor){
out1 <- out1[get(input$var_subset[[v]]) %in% input[[paste0("val_subset", v)]]]
#var.factor <- c(data()$factor_original, input$factor_vname)
out1[, (var.factor) := lapply(.SD, factor), .SDcols = var.factor]
out.label2 <- mk.lev(out1)[, c("variable", "level")]
data.table::setkey(out.label, "variable", "level")
data.table::setkey(out.label2, "variable", "level")
out.label <- out.label[out.label2]
} else{
out1 <- out1[get(input$var_subset[[v]]) >= input[[paste0("val_subset", v)]][1] & get(input$var_subset[[v]]) <= input[[paste0("val_subset", v)]][2]]
#var.factor <- c(data()$factor_original, input$factor_vname)
out1[, (var.factor) := lapply(.SD, factor), .SDcols = var.factor]
out.label2 <- mk.lev(out1)[, c("variable", "level")]
data.table::setkey(out.label, "variable", "level")
data.table::setkey(out.label2, "variable", "level")
out.label <- out.label[out.label2]
}
}
}
}
for (vn in ref[["name.new"]]){
w <- which(ref[["name.new"]] == vn)
out.label[variable == vn, var_label := ref[["name.old"]][w]]
}
out.label <- rbind(out.label, data.table(variable = "pscore", class = "numeric", level = NA, var_label = "pscore", val_label = NA))
return(list(data = out1, label = out.label))
})
observeEvent(data.info(),{
output$group_ps <- renderUI({
factor_vars <- names(data.info()$data)[data.info()$data[, lapply(.SD, class) %in% c("factor", "character")]]
validate(
need(!is.null(factor_vars) & length(factor_vars) > 0, "No categorical variables in data")
)
class01_factor <- unlist(data.info()$data[, lapply(.SD, function(x){identical(levels(x), c("0", "1"))}), .SDcols = factor_vars])
#nclass_factor <- unlist(data()[, lapply(.SD, function(x){length(unique(x))}), .SDcols = factor_vars])
#factor_2vars <- names(nclass_factor)[nclass_factor == 2]
validate(
need(!is.null(class01_factor), "No categorical variables coded as 0, 1 in data")
)
factor_01vars <- factor_vars[class01_factor]
factor_01vars_case_small <- factor_01vars[unlist(sapply(factor_01vars, function(x){diff(table(data.info()$data[[x]])) <= 0}))]
validate(
need(length(factor_01vars_case_small) > 0, "No candidate group variable for PS calculation")
)
selectInput("group_pscal", label = "Group variable for PS calculation (0, 1 coding)",
choices = mklist(list(variable = names(data.info()$data)), factor_01vars_case_small), multiple = F,
selected = factor_01vars_case_small[1])
})
output$indep_ps <- renderUI({
req(!is.null(input$group_pscal))
if (is.null(input$group_pscal)){
return(NULL)
}
validate(
need(length(input$group_pscal) > 0, "No group variables in data")
)
vars <- setdiff(setdiff(names(data.info()$data), except_vars), c(input$var_subset, input$group_pscal))
varsIni <- sapply(vars,
function(v){
forms <- as.formula(paste(input$group_pscal, "~", v))
coef <- tryCatch(summary(glm(forms, data = data.info()$data, family = binomial))$coefficients, error = function(e){return(NULL)})
sigOK <- !all(coef[-1, 4] > as.numeric(input$pcut_ps))
return(sigOK)
})
tagList(
selectInput("indep_pscal", label = "Independent variables for PS calculation",
choices = mklist(list(variable = names(data.info()$data)), vars), multiple = T,
selected = vars[varsIni])
)
})
output$caliperps <- renderUI({
sliderInput("caliper", "Caliper (0: no)", value = 0, min = 0, max = 1)
})
})
mat.info <- eventReactive(c(input$indep_pscal, input$group_pscal, input$caliper, input$ratio_ps, data.info()), {
req(input$indep_pscal)
if (is.null(input$group_pscal) | is.null(input$indep_pscal)){
return(NULL)
}
data <- data.table(data.info()$data)
data$ID.pscal2828 <- 1:nrow(data)
case.naomit <- which(complete.cases(data[, .SD, .SDcols = c(input$group_pscal, input$indep_pscal)]))
data.naomit <- data[case.naomit]
data.na <- data[-case.naomit]
data.na$pscore <- NA
data.na$iptw <- NA
caliper <- NULL
if (input$caliper > 0){
caliper <- input$caliper
}
forms <- as.formula(paste(input$group_pscal, " ~ ", paste(input$indep_pscal, collapse = "+"), sep=""))
m.out <- MatchIt::matchit(forms, data = data.naomit[, .SD, .SDcols = c("ID.pscal2828", input$group_pscal, input$indep_pscal)], caliper = caliper, ratio = as.integer(input$ratio_ps))
pscore <- m.out$distance
iptw <- ifelse(m.out$treat == levels(factor(m.out$treat))[2], 1/pscore, 1/(1-pscore))
wdata <- rbind(data.na, cbind(data.naomit, pscore, iptw))
return(list(data = wdata, matdata = data[ID.pscal2828 %in% match.data(m.out)$ID.pscal2828]))
})
output$data <- renderDT({
datatable(mat.info()$data, rownames=F, editable = F, extensions= "Buttons", caption = "Data with ps, iptw",
options = c(opt.data("data"), list(scrollX = TRUE))
)
})
output$matdata <- renderDT({
datatable(mat.info()$matdata, rownames=F, editable = F, extensions= "Buttons", caption = "Matching data",
options = c(opt.data("matching_data"), list(scrollX = TRUE))
)
})
output$data_label <- renderDT({
datatable(data.info()$label, rownames=F, editable = F, extensions= "Buttons", caption = "Label of data",
options = c(opt.data("data_label"), list(scrollX = TRUE))
)
})
output$naomit <- renderText({
if (length(naCol) == 0) {
return("Data has <B>no</B> missing values.")
} else{
txt_miss <- paste(naCol, collapse = ", ")
return(paste("Column <B>", txt_miss, "</B> contain missing values.", sep = ""))
}
})
## tb1
data <- reactive({
mat.info()$data[, .SD, .SDcols = -c("iptw")]
})
matdata <- reactive(data.table::data.table(mat.info()$matdata))
data.label <- reactive(data.info()$label)
#data_varStruct <- reactive(list(variable = names(mat.info()$matdata)))
design.survey <- reactive(survey::svydesign(ids = ~ 1, data = mat.info()$data[!is.na(iptw), ], weights = ~ iptw))
tb1_original <- callModule(tb1module2, "tb1", data = data, data_label = data.label, data_varStruct = NULL, design.survey = NULL, nfactor.limit = nfactor.limit)
tb1_ps <- callModule(tb1module2, "tb1", data = matdata, data_label = data.label, data_varStruct = NULL, design.survey = NULL, nfactor.limit = nfactor.limit)
tb1_iptw <- callModule(tb1module2, "tb1", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$table1_original <- renderDT({
tb <- tb1_original()$table
cap <- tb1_original()$caption
out.tb1 <- datatable(tb, rownames = T, extensions= "Buttons", caption = cap,
options = c(opt.tb1("tb1"),
list(columnDefs = list(list(visible=FALSE, targets= which(colnames(tb) %in% c("test","sig"))))
),
list(scrollX = TRUE)
)
)
if ("sig" %in% colnames(tb)){
out.tb1 <- out.tb1 %>% formatStyle("sig", target = 'row' ,backgroundColor = styleEqual("**", 'yellow'))
}
return(out.tb1)
})
output$table1_ps <- renderDT({
tb <- tb1_ps()$table
cap <- tb1_ps()$caption
out.tb1 <- datatable(tb, rownames = T, extensions= "Buttons", caption = cap,
options = c(opt.tb1("tb1"),
list(columnDefs = list(list(visible=FALSE, targets= which(colnames(tb) %in% c("test","sig"))))
),
list(scrollX = TRUE)
)
)
if ("sig" %in% colnames(tb)){
out.tb1 <- out.tb1 %>% formatStyle("sig", target = 'row' ,backgroundColor = styleEqual("**", 'yellow'))
}
return(out.tb1)
})
output$table1_iptw <- renderDT({
tb <- tb1_iptw()$table
cap <- tb1_iptw()$caption
out.tb1 <- datatable(tb, rownames = T, extensions= "Buttons", caption = cap,
options = c(opt.tb1("tb1"),
list(columnDefs = list(list(visible=FALSE, targets= which(colnames(tb) %in% c("test","sig"))))
),
list(scrollX = TRUE)
)
)
if ("sig" %in% colnames(tb)){
out.tb1 <- out.tb1 %>% formatStyle("sig", target = 'row' ,backgroundColor = styleEqual("**", 'yellow'))
}
return(out.tb1)
})
## Regression
out_linear_original <- callModule(regressModule2, "linear", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_linear_ps <- callModule(regressModule2, "linear", data = matdata, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_linear_iptw <- callModule(regressModule2, "linear", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$linear_original <- renderDT({
hide <- which(colnames(out_linear_original()$table) == "sig")
datatable(out_linear_original()$table, rownames=T, extensions= "Buttons", caption = out_linear_original()$caption,
options = c(opt.tbreg(out_linear_original()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$warning_linear_original <- renderText({
paste("<b>", out_linear_original()$warning, "</b>")
})
output$linear_ps <- renderDT({
hide <- which(colnames(out_linear_ps()$table) == "sig")
datatable(out_linear_ps()$table, rownames=T, extensions= "Buttons", caption = out_linear_ps()$caption,
options = c(opt.tbreg(out_linear_ps()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$warning_linear_ps <- renderText({
paste("<b>", out_linear_ps()$warning, "</b>")
})
output$linear_iptw <- renderDT({
hide <- which(colnames(out_linear_iptw()$table) == "sig")
datatable(out_linear_iptw()$table, rownames=T, extensions= "Buttons", caption = out_linear_iptw()$caption,
options = c(opt.tbreg(out_linear_iptw()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
## Logistic
out_logistic_original <- callModule(logisticModule2, "logistic", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_logistic_ps <- callModule(logisticModule2, "logistic", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_logistic_iptw <- callModule(logisticModule2, "logistic", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$logistic_original <- renderDT({
hide = which(colnames(out_logistic_original()$table) == "sig")
datatable(out_logistic_original()$table, rownames=T, extensions= "Buttons", caption = out_logistic_original()$caption,
options = c(opt.tbreg(out_logistic_original()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$logistic_ps <- renderDT({
hide = which(colnames(out_logistic_ps()$table) == "sig")
datatable(out_logistic_ps()$table, rownames=T, extensions= "Buttons", caption = out_logistic_ps()$caption,
options = c(opt.tbreg(out_logistic_ps()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$logistic_iptw <- renderDT({
hide = which(colnames(out_logistic_iptw()$table) == "sig")
datatable(out_logistic_iptw()$table, rownames=T, extensions= "Buttons", caption = out_logistic_iptw()$caption,
options = c(opt.tbreg(out_logistic_iptw()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
## Cox
out_cox_original <- callModule(coxModule, "cox", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_cox_ps <- callModule(coxModule, "cox", data = matdata, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_cox_iptw <- callModule(coxModule, "cox", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$cox_original <- renderDT({
hide = which(colnames(out_cox_original()$table) == c("sig"))
datatable(out_cox_original()$table, rownames=T, extensions= "Buttons", caption = out_cox_original()$caption,
options = c(opt.tbreg(out_cox_original()$caption),
list(columnDefs = list(list(visible=FALSE, targets= hide))
)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$cox_ps <- renderDT({
hide = which(colnames(out_cox_ps()$table) == c("sig"))
datatable(out_cox_ps()$table, rownames=T, extensions= "Buttons", caption = out_cox_ps()$caption,
options = c(opt.tbreg(out_cox_ps()$caption),
list(columnDefs = list(list(visible=FALSE, targets= hide))
)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$cox_iptw <- renderDT({
hide = which(colnames(out_cox_iptw()$table) == c("sig"))
datatable(out_cox_iptw()$table, rownames=T, extensions= "Buttons", caption = out_cox_iptw()$caption,
options = c(opt.tbreg(out_cox_iptw()$caption),
list(columnDefs = list(list(visible=FALSE, targets= hide))
)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
## ggpairs
out_ggpairs_original <- callModule(ggpairsModule2, "ggpairs", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_ggpairs_ps <- callModule(ggpairsModule2, "ggpairs", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
output$ggpairs_plot_original <- renderPlot({
print(out_ggpairs_original())
})
output$ggpairs_plot_ps <- renderPlot({
print(out_ggpairs_ps())
})
## Kaplan
out_kaplan_original <- callModule(kaplanModule, "kaplan", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_kaplan_ps <- callModule(kaplanModule, "kaplan", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_kaplan_iptw <- callModule(kaplanModule, "kaplan", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$kaplan_plot_original <- renderPlot({
print(out_kaplan_original())
})
output$kaplan_plot_ps <- renderPlot({
print(out_kaplan_ps())
})
output$kaplan_plot_iptw <- renderPlot({
print(out_kaplan_iptw())
})
## ROC
out_roc_original <- callModule(rocModule2, "roc", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_roc_ps <- callModule(rocModule2, "roc", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_roc_iptw <- callModule(rocModule2, "roc", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$plot_roc_original <- renderPlot({
print(out_roc_original()$plot)
})
output$table_roc_original <- renderDT({
datatable(out_roc_original()$tb, rownames=F, editable = F, extensions= "Buttons",
caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_roc_ps <- renderPlot({
print(out_roc_ps()$plot)
})
output$table_roc_ps <- renderDT({
datatable(out_roc_ps()$tb, rownames=F, editable = F, extensions= "Buttons",
caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_roc_iptw <- renderPlot({
print(out_roc_iptw()$plot)
})
output$table_roc_iptw <- renderDT({
datatable(out_roc_iptw()$tb, rownames=F, editable = F, extensions= "Buttons",
caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
## Time-ROC
out_timeroc_original <- callModule(timerocModule2, "timeroc", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_timeroc_ps <- callModule(timerocModule2, "timeroc", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_timeroc_iptw <- callModule(timerocModule2, "timeroc", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$plot_timeroc_original <- renderPlot({
print(out_timeroc_original()$plot)
})
output$table_timeroc_original <- renderDT({
datatable(out_timeroc_original()$tb, rownames=F, editable = F, extensions= "Buttons", caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_timeroc_ps <- renderPlot({
print(out_timeroc_ps()$plot)
})
output$table_timeroc_ps <- renderDT({
datatable(out_timeroc_ps()$tb, rownames=F, editable = F, extensions= "Buttons", caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_timeroc_iptw <- renderPlot({
print(out_timeroc_iptw()$plot)
})
output$table_timeroc_iptw <- renderDT({
datatable(out_timeroc_iptw()$tb, rownames=F, editable = F, extensions= "Buttons", caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
session$onSessionEnded(function() {
stopApp()
})
}
#viewer <- dialogViewer("Descriptive statistics", width = 1100, height = 850)
viewer <- browserViewer(browser = getOption("browser"))
runGadget(ui, server, viewer = viewer)
}
#' @title jsPropensityAddin: Rstudio addin of jsPropensityGadget
#' @description Rstudio addin of jsPropensityGadget
#' @return Rstudio addin of jsPropensityGadget
#' @details Rstudio addin of jsPropensityGadget
#' @examples
#' if(interactive()){
#' jsPropensityAddin()
#' }
#' @seealso
#' \code{\link[rstudioapi]{rstudio-editors}}
#' @rdname jsPropensityAddin
#' @export
#' @importFrom rstudioapi getActiveDocumentContext
jsPropensityAddin <- function(){
context <- rstudioapi::getActiveDocumentContext()
# Set the default data to use based on the selection.
dataString <- context$selection[[1]]$text
data <- get(dataString, envir = .GlobalEnv)
#viewer <- dialogViewer("Subset", width = 1000, height = 800)
jsPropensityGadget(data)
}
#' @title jsPropensityExtAddin: RStudio Addin for propensity score analysis with external data.
#' @description RStudio Addin for propensity score analysis with external csv/xlsx/sas7bdat/sav/dta file.
#' @param nfactor.limit nlevels limit for categorical variables, Default: 20
#' @param max.filesize Maximum file size to upload (MB), Default: 2048 (2 GB)
#' @return RStudio Addin for propensity score analysis with external data.
#' @details RStudio Addin for propensity score analysis with external csv/xlsx/sas7bdat/sav/dta file.
#' @examples
#' if(interactive()){
#' jsPropensityExtAddin()
#' }
#' @seealso
#' \code{\link[survival]{pbc}}
#' \code{\link[data.table]{fwrite}},\code{\link[data.table]{data.table}}
#' \code{\link[survey]{svydesign}}
#' \code{\link[jstable]{opt.tbreg}}
#' @rdname jsPropensityExtAddin
#' @export
#' @importFrom data.table fwrite data.table
#' @importFrom survey svydesign
#' @importFrom jstable opt.tbreg
#' @importFrom DT datatable %>% formatStyle styleEqual renderDT DTOutput
#' @importFrom shinycustomloader withLoader
#' @import shiny
jsPropensityExtAddin <- function(nfactor.limit = 20, max.filesize = 2048){
iptw <- NULL
options(shiny.maxRequestSize = max.filesize * 1024^2)
ui <- navbarPage("Propensity score analysis",
tabPanel("Data", icon = icon("table"),
sidebarLayout(
sidebarPanel(
uiOutput("import"),
downloadButton("downloadData", "Example data")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Data", withLoader(DTOutput("data"), type="html", loader="loader6")),
tabPanel("Matching data", withLoader(DTOutput("matdata"), type="html", loader="loader6")),
tabPanel("Label", withLoader(DTOutput("data_label", width = "100%"), type="html", loader="loader6"))
),
htmlOutput("naomit")
)
)
),
tabPanel("Table 1", icon = icon("percentage"),
sidebarLayout(
sidebarPanel(
tb1moduleUI("tb1")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("table1_original"), type="html", loader="loader6"),
wellPanel(
h5("Normal continuous variables are summarized with Mean (SD) and t-test(2 groups) or ANOVA(> 2 groups)"),
h5("Non-normal continuous variables are summarized with median [IQR or min,max] and kruskal-wallis test"),
h5("Categorical variables are summarized with table")
)
),
tabPanel("Matching",
withLoader(DTOutput("table1_ps"), type="html", loader="loader6"),
wellPanel(
h5("Normal continuous variables are summarized with Mean (SD) and t-test(2 groups) or ANOVA(> 2 groups)"),
h5("Non-normal continuous variables are summarized with median [IQR or min,max] and kruskal-wallis test"),
h5("Categorical variables are summarized with table")
)
),
tabPanel("IPTW",
withLoader(DTOutput("table1_iptw"), type="html", loader="loader6"),
wellPanel(
h5("Normal continuous variables are summarized with Mean (SD) and complex survey regression"),
h5("Non-normal continuous variables are summarized with median [IQR or min,max] and complex sampling rank test"),
h5("Categorical variables are summarized with table")
)
)
)
)
)
),
navbarMenu("Regression", icon = icon("list-alt"),
tabPanel("Linear regression",
sidebarLayout(
sidebarPanel(
regressModuleUI("linear")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("linear_original"), type="html", loader="loader6"),
br(),
uiOutput("warning_linear_original")
),
tabPanel("Matching",
withLoader(DTOutput("linear_ps"), type="html", loader="loader6"),
br(),
uiOutput("warning_linear_ps")
),
tabPanel("IPTW",
withLoader(DTOutput("linear_iptw"), type="html", loader="loader6")
)
)
)
)
),
tabPanel("Logistic regression",
sidebarLayout(
sidebarPanel(
regressModuleUI("logistic")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("logistic_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(DTOutput("logistic_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(DTOutput("logistic_iptw"), type="html", loader="loader6")
)
)
)
)
),
tabPanel("Cox model",
sidebarLayout(
sidebarPanel(
coxUI("cox")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("cox_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(DTOutput("cox_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(DTOutput("cox_iptw"), type="html", loader="loader6")
)
)
)
)
)
),
navbarMenu("Plot", icon = icon("bar-chart-o"),
tabPanel("Scatter plot",
sidebarLayout(
sidebarPanel(
ggpairsModuleUI1("ggpairs")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("ggpairs_plot_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("ggpairs_plot_ps"), type="html", loader="loader6")
)
),
ggpairsModuleUI2("ggpairs")
)
)
),
tabPanel("Kaplan-meier plot",
sidebarLayout(
sidebarPanel(
kaplanUI("kaplan")
),
mainPanel(
optionUI("kaplan"),
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("kaplan_plot_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("kaplan_plot_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(plotOutput("kaplan_plot_iptw"), type="html", loader="loader6")
)
),
ggplotdownUI("kaplan")
)
)
)
),
navbarMenu("ROC analysis", icon = icon("check"),
tabPanel("ROC",
sidebarLayout(
sidebarPanel(
rocUI("roc")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("plot_roc_original"), type="html", loader="loader6"),
withLoader(DTOutput("table_roc_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("plot_roc_ps"), type="html", loader="loader6"),
withLoader(DTOutput("table_roc_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(plotOutput("plot_roc_iptw"), type="html", loader="loader6"),
withLoader(DTOutput("table_roc_iptw"), type="html", loader="loader6")
)
),
ggplotdownUI("roc")
)
)
),
tabPanel("Time-dependent ROC",
sidebarLayout(
sidebarPanel(
timerocUI("timeroc")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("plot_timeroc_original"), type="html", loader="loader6"),
withLoader(DTOutput("table_timeroc_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("plot_timeroc_ps"), type="html", loader="loader6"),
withLoader(DTOutput("table_timeroc_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(plotOutput("plot_timeroc_iptw"), type="html", loader="loader6"),
withLoader(DTOutput("table_timeroc_iptw"), type="html", loader="loader6")
)
),
ggplotdownUI("timeroc")
)
)
)
)
)
server <- function(input, output, session) {
output$downloadData <- downloadHandler(
filename = function() {
paste("example_ps", ".csv", sep = "")
},
content = function(file) {
out <- survival::pbc
out$status <- as.integer(out$status == 2)
data.table::fwrite(na.omit(out)[, -1], file)
}
)
output$import <- renderUI({
FilePsInput("datafile")
})
mat.info <- callModule(FilePs, "datafile", nfactor.limit = nfactor.limit)
output$data <- renderDT({
datatable(mat.info()$data, rownames=F, editable = F, extensions= "Buttons", caption = "Data with ps, iptw",
options = c(opt.data("data"), list(scrollX = TRUE))
)
})
output$matdata <- renderDT({
datatable(mat.info()$matdata, rownames=F, editable = F, extensions= "Buttons", caption = "Matching data",
options = c(opt.data("matching_data"), list(scrollX = TRUE))
)
})
output$data_label <- renderDT({
datatable(mat.info()$data.label, rownames=F, editable = F, extensions= "Buttons", caption = "Label of data",
options = c(opt.data("data_label"), list(scrollX = TRUE))
)
})
output$naomit <- renderText({
paste("<font size = 5 ><b>", "The variables below contain missing values.</b></font><br>", '<font size = 4 color=\"#FF0000\"><b>', mat.info()$naomit, "</b></font>")
#mat.info()$naomit
})
## tb1
data <- reactive({
mat.info()$data[, .SD, .SDcols = -c("iptw")]
})
matdata <- reactive(data.table::data.table(mat.info()$matdata))
data.label <- reactive(mat.info()$data.label)
#data_varStruct <- reactive(list(variable = names(mat.info()$matdata)))
design.survey <- reactive(survey::svydesign(ids = ~ 1, data = mat.info()$data[!is.na(iptw), ], weights = ~ iptw))
tb1_original <- callModule(tb1module2, "tb1", data = data, data_label = data.label, data_varStruct = NULL, design.survey = NULL, nfactor.limit = nfactor.limit)
tb1_ps <- callModule(tb1module2, "tb1", data = matdata, data_label = data.label, data_varStruct = NULL, design.survey = NULL, nfactor.limit = nfactor.limit)
tb1_iptw <- callModule(tb1module2, "tb1", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$table1_original <- renderDT({
tb <- tb1_original()$table
cap <- tb1_original()$caption
out.tb1 <- datatable(tb, rownames = T, extensions= "Buttons", caption = cap,
options = c(opt.tb1("tb1"),
list(columnDefs = list(list(visible=FALSE, targets= which(colnames(tb) %in% c("test","sig"))))
),
list(scrollX = TRUE)
)
)
if ("sig" %in% colnames(tb)){
out.tb1 <- out.tb1 %>% formatStyle("sig", target = 'row' ,backgroundColor = styleEqual("**", 'yellow'))
}
return(out.tb1)
})
output$table1_ps <- renderDT({
tb <- tb1_ps()$table
cap <- tb1_ps()$caption
out.tb1 <- datatable(tb, rownames = T, extensions= "Buttons", caption = cap,
options = c(opt.tb1("tb1"),
list(columnDefs = list(list(visible=FALSE, targets= which(colnames(tb) %in% c("test","sig"))))
),
list(scrollX = TRUE)
)
)
if ("sig" %in% colnames(tb)){
out.tb1 <- out.tb1 %>% formatStyle("sig", target = 'row' ,backgroundColor = styleEqual("**", 'yellow'))
}
return(out.tb1)
})
output$table1_iptw <- renderDT({
tb <- tb1_iptw()$table
cap <- tb1_iptw()$caption
out.tb1 <- datatable(tb, rownames = T, extensions= "Buttons", caption = cap,
options = c(opt.tb1("tb1"),
list(columnDefs = list(list(visible=FALSE, targets= which(colnames(tb) %in% c("test","sig"))))
),
list(scrollX = TRUE)
)
)
if ("sig" %in% colnames(tb)){
out.tb1 <- out.tb1 %>% formatStyle("sig", target = 'row' ,backgroundColor = styleEqual("**", 'yellow'))
}
return(out.tb1)
})
## Regression
out_linear_original <- callModule(regressModule2, "linear", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_linear_ps <- callModule(regressModule2, "linear", data = matdata, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_linear_iptw <- callModule(regressModule2, "linear", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$linear_original <- renderDT({
hide <- which(colnames(out_linear_original()$table) == "sig")
datatable(out_linear_original()$table, rownames=T, extensions= "Buttons", caption = out_linear_original()$caption,
options = c(opt.tbreg(out_linear_original()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$warning_linear_original <- renderText({
paste("<b>", out_linear_original()$warning, "</b>")
})
output$linear_ps <- renderDT({
hide <- which(colnames(out_linear_ps()$table) == "sig")
datatable(out_linear_ps()$table, rownames=T, extensions= "Buttons", caption = out_linear_ps()$caption,
options = c(opt.tbreg(out_linear_ps()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$warning_linear_ps <- renderText({
paste("<b>", out_linear_ps()$warning, "</b>")
})
output$linear_iptw <- renderDT({
hide <- which(colnames(out_linear_iptw()$table) == "sig")
datatable(out_linear_iptw()$table, rownames=T, extensions= "Buttons", caption = out_linear_iptw()$caption,
options = c(opt.tbreg(out_linear_iptw()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
## Logistic
out_logistic_original <- callModule(logisticModule2, "logistic", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_logistic_ps <- callModule(logisticModule2, "logistic", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_logistic_iptw <- callModule(logisticModule2, "logistic", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$logistic_original <- renderDT({
hide = which(colnames(out_logistic_original()$table) == "sig")
datatable(out_logistic_original()$table, rownames=T, extensions= "Buttons", caption = out_logistic_original()$caption,
options = c(opt.tbreg(out_logistic_original()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$logistic_ps <- renderDT({
hide = which(colnames(out_logistic_ps()$table) == "sig")
datatable(out_logistic_ps()$table, rownames=T, extensions= "Buttons", caption = out_logistic_ps()$caption,
options = c(opt.tbreg(out_logistic_ps()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$logistic_iptw <- renderDT({
hide = which(colnames(out_logistic_iptw()$table) == "sig")
datatable(out_logistic_iptw()$table, rownames=T, extensions= "Buttons", caption = out_logistic_iptw()$caption,
options = c(opt.tbreg(out_logistic_iptw()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
## Cox
out_cox_original <- callModule(coxModule, "cox", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_cox_ps <- callModule(coxModule, "cox", data = matdata, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_cox_iptw <- callModule(coxModule, "cox", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$cox_original <- renderDT({
hide = which(colnames(out_cox_original()$table) == c("sig"))
datatable(out_cox_original()$table, rownames=T, extensions= "Buttons", caption = out_cox_original()$caption,
options = c(opt.tbreg(out_cox_original()$caption),
list(columnDefs = list(list(visible=FALSE, targets= hide))
)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$cox_ps <- renderDT({
hide = which(colnames(out_cox_ps()$table) == c("sig"))
datatable(out_cox_ps()$table, rownames=T, extensions= "Buttons", caption = out_cox_ps()$caption,
options = c(opt.tbreg(out_cox_ps()$caption),
list(columnDefs = list(list(visible=FALSE, targets= hide))
)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$cox_iptw <- renderDT({
hide = which(colnames(out_cox_iptw()$table) == c("sig"))
datatable(out_cox_iptw()$table, rownames=T, extensions= "Buttons", caption = out_cox_iptw()$caption,
options = c(opt.tbreg(out_cox_iptw()$caption),
list(columnDefs = list(list(visible=FALSE, targets= hide))
)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
## ggpairs
out_ggpairs_original <- callModule(ggpairsModule2, "ggpairs", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_ggpairs_ps <- callModule(ggpairsModule2, "ggpairs", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
output$ggpairs_plot_original <- renderPlot({
print(out_ggpairs_original())
})
output$ggpairs_plot_ps <- renderPlot({
print(out_ggpairs_ps())
})
## Kaplan
out_kaplan_original <- callModule(kaplanModule, "kaplan", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_kaplan_ps <- callModule(kaplanModule, "kaplan", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_kaplan_iptw <- callModule(kaplanModule, "kaplan", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$kaplan_plot_original <- renderPlot({
print(out_kaplan_original())
})
output$kaplan_plot_ps <- renderPlot({
print(out_kaplan_ps())
})
output$kaplan_plot_iptw <- renderPlot({
print(out_kaplan_iptw())
})
## ROC
out_roc_original <- callModule(rocModule2, "roc", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_roc_ps <- callModule(rocModule2, "roc", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_roc_iptw <- callModule(rocModule2, "roc", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$plot_roc_original <- renderPlot({
print(out_roc_original()$plot)
})
output$table_roc_original <- renderDT({
datatable(out_roc_original()$tb, rownames=F, editable = F, extensions= "Buttons",
caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_roc_ps <- renderPlot({
print(out_roc_ps()$plot)
})
output$table_roc_ps <- renderDT({
datatable(out_roc_ps()$tb, rownames=F, editable = F, extensions= "Buttons",
caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_roc_iptw <- renderPlot({
print(out_roc_iptw()$plot)
})
output$table_roc_iptw <- renderDT({
datatable(out_roc_iptw()$tb, rownames=F, editable = F, extensions= "Buttons",
caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
## Time-ROC
out_timeroc_original <- callModule(timerocModule2, "timeroc", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_timeroc_ps <- callModule(timerocModule2, "timeroc", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_timeroc_iptw <- callModule(timerocModule2, "timeroc", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$plot_timeroc_original <- renderPlot({
print(out_timeroc_original()$plot)
})
output$table_timeroc_original <- renderDT({
datatable(out_timeroc_original()$tb, rownames=F, editable = F, extensions= "Buttons", caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_timeroc_ps <- renderPlot({
print(out_timeroc_ps()$plot)
})
output$table_timeroc_ps <- renderDT({
datatable(out_timeroc_ps()$tb, rownames=F, editable = F, extensions= "Buttons", caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_timeroc_iptw <- renderPlot({
print(out_timeroc_iptw()$plot)
})
output$table_timeroc_iptw <- renderDT({
datatable(out_timeroc_iptw()$tb, rownames=F, editable = F, extensions= "Buttons", caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
session$onSessionEnded(function() {
stopApp()
})
}
#viewer <- dialogViewer("Descriptive statistics", width = 1100, height = 850)
viewer <- browserViewer(browser = getOption("browser"))
runGadget(ui, server, viewer = viewer)
}
leevenstar/jstest documentation built on Dec. 23, 2021, 12:16 a.m.
R Package Documentation
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Defines functions jsPropensityExtAddin jsPropensityAddin jsPropensityGadget
Documented in jsPropensityAddin jsPropensityExtAddin jsPropensityGadget
#' @title jsPropensityGadget: Shiny Gadget for propensity score analysis.
#' @description Shiny Gadget including original/matching/IPTW data, Label info, Table 1, Cox model, Basic/kaplan-meier plot.
#' @param data data
#' @param nfactor.limit nlevels limit for categorical variables, Default: 20
#' @return Shiny Gadget including original/matching/IPTW data, Label info, Table 1, Cox model, Basic/kaplan-meier plot.
#' @details Shiny Gadget including original/matching/IPTW data, Label info, Table 1, Cox model, Basic/kaplan-meier plot.
#' @examples
#' if(interactive()){
#' jsPropensityGadget(mtcars)
#' }
#' @seealso
#' \code{\link[data.table]{data.table}}
#' \code{\link[MatchIt]{matchit}},\code{\link[MatchIt]{match.data}}
#' \code{\link[jstable]{cox2.display}},\code{\link[jstable]{svycox.display}}
#' \code{\link[survival]{survfit}},\code{\link[survival]{coxph}},\code{\link[survival]{Surv}}
#' \code{\link[jskm]{jskm}},\code{\link[jskm]{svyjskm}}
#' \code{\link[ggplot2]{ggsave}}
#' \code{\link[survey]{svykm}}
#' @rdname jsPropensityGadget
#' @export
#' @importFrom data.table data.table
#' @importFrom MatchIt matchit match.data
#' @importFrom jstable cox2.display svycox.display
#' @importFrom survival survfit
#' @importFrom jskm jskm svyjskm
#' @importFrom ggplot2 ggsave
#' @importFrom survey svykm
#' @importFrom purrr map_lgl
#' @importFrom stats model.frame
jsPropensityGadget <- function(data, nfactor.limit = 20){
requireNamespace("survival")
requireNamespace("survC1")
## To remove NOTE.
ID.pscal2828 <- level <- val_label <- BinaryGroupRandom <- variable <- NULL
## Data label
out.old <- data.table::data.table(data)
name.old <- names(out.old)
out <- data.table::data.table(data, check.names = T)
name.new <- names(out)
ref <- list(name.old = name.old, name.new = name.new)
data_varStruct1 = list(variable = names(out))
## Vars
naCol <- names(out)[colSums(is.na(out)) > 0]
#out <- out[, .SD, .SDcols = -naCol]
factor_vars <- names(out)[out[, lapply(.SD, class) %in% c("factor", "character")]]
if (!is.null(factor_vars) & length(factor_vars) > 0){
out[, (factor_vars) := lapply(.SD, as.factor), .SDcols= factor_vars]
}
factor_original <- factor_vars
conti_original <- setdiff(names(out), factor_vars)
nclass <- unlist(out[, lapply(.SD, function(x){length(unique(x))}), .SDcols = conti_original])
factor_adds_list = mklist(data_varStruct1, names(nclass)[nclass <= nfactor.limit])
except_vars <- names(nclass)[ nclass== 1]
#except_vars <- names(nclass)[ nclass== 1 | nclass >= nfactor.limit]
factor_adds <- names(nclass)[nclass >= 1 & nclass <= 5]
ui <- navbarPage("Propensity score analysis",
tabPanel("Data", icon = icon("table"),
sidebarLayout(
sidebarPanel(
uiOutput("factor"),
uiOutput("binary_check"),
uiOutput("binary_var"),
uiOutput("binary_val"),
uiOutput("ref_check"),
uiOutput("ref_var"),
uiOutput("ref_val"),
uiOutput("subset_check"),
uiOutput("subset_var"),
uiOutput("subset_val"),
uiOutput("group_ps"),
uiOutput("indep_ps"),
uiOutput("pcut"),
uiOutput("caliperps"),
uiOutput("ratio")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Data", withLoader(DTOutput("data"), type="html", loader="loader6")),
tabPanel("Matching data", withLoader(DTOutput("matdata"), type="html", loader="loader6")),
tabPanel("Label", withLoader(DTOutput("data_label", width = "100%"), type="html", loader="loader6"))
),
htmlOutput("naomit")
)
)
),
tabPanel("Table 1", icon = icon("percentage"),
sidebarLayout(
sidebarPanel(
tb1moduleUI("tb1")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("table1_original"), type="html", loader="loader6"),
wellPanel(
h5("Normal continuous variables are summarized with Mean (SD) and t-test(2 groups) or ANOVA(> 2 groups)"),
h5("Non-normal continuous variables are summarized with median [IQR or min,max] and kruskal-wallis test"),
h5("Categorical variables are summarized with table")
)
),
tabPanel("Matching",
withLoader(DTOutput("table1_ps"), type="html", loader="loader6"),
wellPanel(
h5("Normal continuous variables are summarized with Mean (SD) and t-test(2 groups) or ANOVA(> 2 groups)"),
h5("Non-normal continuous variables are summarized with median [IQR or min,max] and kruskal-wallis test"),
h5("Categorical variables are summarized with table")
)
),
tabPanel("IPTW",
withLoader(DTOutput("table1_iptw"), type="html", loader="loader6"),
wellPanel(
h5("Normal continuous variables are summarized with Mean (SD) and complex survey regression"),
h5("Non-normal continuous variables are summarized with median [IQR or min,max] and complex sampling rank test"),
h5("Categorical variables are summarized with table")
)
)
)
)
)
),
navbarMenu("Regression", icon = icon("list-alt"),
tabPanel("Linear regression",
sidebarLayout(
sidebarPanel(
regressModuleUI("linear")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("linear_original"), type="html", loader="loader6"),
br(),
uiOutput("warning_linear_original")
),
tabPanel("Matching",
withLoader(DTOutput("linear_ps"), type="html", loader="loader6"),
br(),
uiOutput("warning_linear_ps")
),
tabPanel("IPTW",
withLoader(DTOutput("linear_iptw"), type="html", loader="loader6")
)
)
)
)
),
tabPanel("Logistic regression",
sidebarLayout(
sidebarPanel(
regressModuleUI("logistic")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("logistic_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(DTOutput("logistic_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(DTOutput("logistic_iptw"), type="html", loader="loader6")
)
)
)
)
),
tabPanel("Cox model",
sidebarLayout(
sidebarPanel(
coxUI("cox")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("cox_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(DTOutput("cox_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(DTOutput("cox_iptw"), type="html", loader="loader6")
)
)
)
)
)
),
navbarMenu("Plot", icon = icon("bar-chart-o"),
tabPanel("Scatter plot",
sidebarLayout(
sidebarPanel(
ggpairsModuleUI1("ggpairs")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("ggpairs_plot_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("ggpairs_plot_ps"), type="html", loader="loader6")
)
),
ggpairsModuleUI2("ggpairs")
)
)
),
tabPanel("Kaplan-meier plot",
sidebarLayout(
sidebarPanel(
kaplanUI("kaplan")
),
mainPanel(
optionUI("kaplan"),
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("kaplan_plot_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("kaplan_plot_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(plotOutput("kaplan_plot_iptw"), type="html", loader="loader6")
)
),
ggplotdownUI("kaplan")
)
)
)
),
navbarMenu("ROC analysis", icon = icon("check"),
tabPanel("ROC",
sidebarLayout(
sidebarPanel(
rocUI("roc")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("plot_roc_original"), type="html", loader="loader6"),
withLoader(DTOutput("table_roc_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("plot_roc_ps"), type="html", loader="loader6"),
withLoader(DTOutput("table_roc_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(plotOutput("plot_roc_iptw"), type="html", loader="loader6"),
withLoader(DTOutput("table_roc_iptw"), type="html", loader="loader6")
)
),
ggplotdownUI("roc")
)
)
),
tabPanel("Time-dependent ROC",
sidebarLayout(
sidebarPanel(
timerocUI("timeroc")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("plot_timeroc_original"), type="html", loader="loader6"),
withLoader(DTOutput("table_timeroc_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("plot_timeroc_ps"), type="html", loader="loader6"),
withLoader(DTOutput("table_timeroc_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(plotOutput("plot_timeroc_iptw"), type="html", loader="loader6"),
withLoader(DTOutput("table_timeroc_iptw"), type="html", loader="loader6")
)
),
ggplotdownUI("timeroc")
)
)
)
)
)
server <- function(input, output, session) {
output$pcut <- renderUI({
radioButtons("pcut_ps", label = "Default p-value cut for ps calculation",
choices = c("No", 0.05, 0.1, 0.2),
selected = "No", inline =T)
})
output$ratio <- renderUI({
radioButtons("ratio_ps", label = "Case:control ratio",
choices = c("1:1" = 1, "1:2" = 2, "1:3" = 3, "1:4" = 4),
selected = 1, inline =T)
})
output$factor <- renderUI({
selectInput("factor_vname", label = "Additional categorical variables",
choices = factor_adds_list, multiple = T,
selected = factor_adds)
})
observeEvent(c(factor_original, input$factor_vname), {
output$binary_check <- renderUI({
checkboxInput("check_binary", "Make binary variables")
})
output$ref_check <- renderUI({
checkboxInput("check_ref", "Change reference of categorical variables")
})
output$subset_check <- renderUI({
checkboxInput("check_subset", "Subset data")
})
})
observeEvent(input$check_binary, {
var.conti <- setdiff(names(out), c(factor_original, input$factor_vname))
output$binary_var <- renderUI({
req(input$check_binary == T)
selectInput("var_binary", "Variables to dichotomize",
choices = var.conti, multiple = T,
selected = var.conti[1])
})
output$binary_val <- renderUI({
req(input$check_binary == T)
req(length(input$var_binary) > 0)
outUI <- tagList()
for (v in seq_along(input$var_binary)){
med <- stats::quantile(out[[input$var_binary[[v]]]], c(0.05, 0.5, 0.95), na.rm = T)
outUI[[v]] <- splitLayout(cellWidths = c("25%", "75%"),
selectInput(paste0("con_binary", v), paste0("Define reference:"),
choices = c("\u2264", "\u2265", "\u003c", "\u003e"), selected = "\u2264"
),
numericInput(paste0("cut_binary", v), input$var_binary[[v]],
value = med[2], min = med[1], max = med[3]
)
)
}
outUI
})
})
observeEvent(input$check_ref, {
var.factor <- c(factor_original, input$factor_vname)
output$ref_var <- renderUI({
req(input$check_ref == T)
selectInput("var_ref", "Variables to change reference",
choices = var.factor, multiple = T,
selected = var.factor[1])
})
output$ref_val <- renderUI({
req(input$check_ref == T)
req(length(input$var_ref) > 0)
outUI <- tagList()
for (v in seq_along(input$var_ref)){
outUI[[v]] <- selectInput(paste0("con_ref", v), paste0("Reference: ", input$var_ref[[v]]),
choices = levels(factor(out[[input$var_ref[[v]]]])), selected = levels(factor(out[[input$var_ref[[v]]]]))[2])
}
outUI
})
})
observeEvent(input$check_subset, {
output$subset_var <- renderUI({
req(input$check_subset == T)
#factor_subset <- c(factor_original, input$factor_vname)
#validate(
# need(length(factor_subset) > 0 , "No factor variable for subsetting")
#)
tagList(
selectInput("var_subset", "Subset variables",
choices = names(out), multiple = T,
selected = names(out)[1])
)
})
output$subset_val <- renderUI({
req(input$check_subset == T)
req(input$var_subset)
var.factor <- c(factor_original, input$factor_vname)
outUI <- tagList()
for (v in seq_along(input$var_subset)){
if (input$var_subset[[v]] %in% var.factor){
varlevel <- levels(as.factor(out[[input$var_subset[[v]]]]))
outUI[[v]] <- selectInput(session$ns(paste0("val_subset", v)), paste0("Subset value: ", input$var_subset[[v]]),
choices = varlevel, multiple = T,
selected = varlevel[1])
} else{
val <- stats::quantile(out[[input$var_subset[[v]]]], na.rm = T)
outUI[[v]] <- sliderInput(session$ns(paste0("val_subset", v)), paste0("Subset range: ", input$var_subset[[v]]),
min = val[1], max = val[5],
value = c(val[2], val[4]))
}
}
outUI
})
})
data.info <- reactive({
out1 <- data.table::data.table(out)
out1[, (conti_original) := lapply(.SD, function(x){as.numeric(as.vector(x))}), .SDcols = conti_original]
if (!is.null(input$factor_vname) & length(input$factor_vname) > 0){
out1[, (input$factor_vname) := lapply(.SD, as.factor), .SDcols= input$factor_vname]
}
out.label <- mk.lev(out1)
if (!is.null(input$check_binary)){
if (input$check_binary){
validate(
need(length(input$var_binary) > 0 , "No variables to dichotomize")
)
sym.ineq <- c("\u2264", "\u2265", "\u003c", "\u003e")
names(sym.ineq) <- sym.ineq[4:1]
sym.ineq2 <- c("le", "ge", "l", "g")
names(sym.ineq2) <- sym.ineq
for (v in seq_along(input$var_binary)){
req(input[[paste0("con_binary", v)]])
req(input[[paste0("cut_binary", v)]])
if (input[[paste0("con_binary", v)]] == "\u2264"){
out1[, BinaryGroupRandom := factor(1 - as.integer(get(input$var_binary[[v]]) <= input[[paste0("cut_binary", v)]]))]
} else if (input[[paste0("con_binary", v)]] == "\u2265"){
out1[, BinaryGroupRandom := factor(1 - as.integer(get(input$var_binary[[v]]) >= input[[paste0("cut_binary", v)]]))]
} else if (input[[paste0("con_binary", v)]] == "\u003c"){
out1[, BinaryGroupRandom := factor(1 - as.integer(get(input$var_binary[[v]]) < input[[paste0("cut_binary", v)]]))]
} else{
out1[, BinaryGroupRandom := factor(1 - as.integer(get(input$var_binary[[v]]) > input[[paste0("cut_binary", v)]]))]
}
cn.new <- paste0(input$var_binary[[v]], "_group_", sym.ineq2[input[[paste0("con_binary", v)]]], input[[paste0("cut_binary", v)]])
data.table::setnames(out1, "BinaryGroupRandom", cn.new)
label.binary <- mk.lev(out1[, .SD, .SDcols = cn.new])
label.binary[, var_label := paste0(input$var_binary[[v]], " _group")]
#label.binary[, val_label := paste0(c(input[[paste0("con_binary", v)]], sym.ineq[input[[paste0("con_binary", v)]]]), " ", input[[paste0("cut_binary", v)]])]
out.label <- rbind(out.label, label.binary)
}
}
}
if (!is.null(input$check_ref)){
if (input$check_ref){
validate(
need(length(input$var_ref) > 0 , "No variables to change reference")
)
for (v in seq_along(input$var_ref)){
req(input[[paste0("con_ref", v)]])
out1[[input$var_ref[[v]]]] <- stats::relevel(out1[[input$var_ref[[v]]]], ref = input[[paste0("con_ref", v)]])
out.label[variable == input$var_ref[[v]], ':='(level = levels(out1[[input$var_ref[[v]]]]), val_label = levels(out1[[input$var_ref[[v]]]]))]
}
}
}
if (!is.null(input$check_subset)){
if (input$check_subset){
validate(
need(length(input$var_subset) > 0 , "No variable for subsetting"),
need(all(sapply(1:length(input$var_subset), function(x){length(input[[paste0("val_subset", x)]])})), "No value for subsetting")
)
var.factor <- c(factor_original, input$factor_vname)
for (v in seq_along(input$var_subset)){
if (input$var_subset[[v]] %in% var.factor){
out1 <- out1[get(input$var_subset[[v]]) %in% input[[paste0("val_subset", v)]]]
#var.factor <- c(data()$factor_original, input$factor_vname)
out1[, (var.factor) := lapply(.SD, factor), .SDcols = var.factor]
out.label2 <- mk.lev(out1)[, c("variable", "level")]
data.table::setkey(out.label, "variable", "level")
data.table::setkey(out.label2, "variable", "level")
out.label <- out.label[out.label2]
} else{
out1 <- out1[get(input$var_subset[[v]]) >= input[[paste0("val_subset", v)]][1] & get(input$var_subset[[v]]) <= input[[paste0("val_subset", v)]][2]]
#var.factor <- c(data()$factor_original, input$factor_vname)
out1[, (var.factor) := lapply(.SD, factor), .SDcols = var.factor]
out.label2 <- mk.lev(out1)[, c("variable", "level")]
data.table::setkey(out.label, "variable", "level")
data.table::setkey(out.label2, "variable", "level")
out.label <- out.label[out.label2]
}
}
}
}
for (vn in ref[["name.new"]]){
w <- which(ref[["name.new"]] == vn)
out.label[variable == vn, var_label := ref[["name.old"]][w]]
}
out.label <- rbind(out.label, data.table(variable = "pscore", class = "numeric", level = NA, var_label = "pscore", val_label = NA))
return(list(data = out1, label = out.label))
})
observeEvent(data.info(),{
output$group_ps <- renderUI({
factor_vars <- names(data.info()$data)[data.info()$data[, lapply(.SD, class) %in% c("factor", "character")]]
validate(
need(!is.null(factor_vars) & length(factor_vars) > 0, "No categorical variables in data")
)
class01_factor <- unlist(data.info()$data[, lapply(.SD, function(x){identical(levels(x), c("0", "1"))}), .SDcols = factor_vars])
#nclass_factor <- unlist(data()[, lapply(.SD, function(x){length(unique(x))}), .SDcols = factor_vars])
#factor_2vars <- names(nclass_factor)[nclass_factor == 2]
validate(
need(!is.null(class01_factor), "No categorical variables coded as 0, 1 in data")
)
factor_01vars <- factor_vars[class01_factor]
factor_01vars_case_small <- factor_01vars[unlist(sapply(factor_01vars, function(x){diff(table(data.info()$data[[x]])) <= 0}))]
validate(
need(length(factor_01vars_case_small) > 0, "No candidate group variable for PS calculation")
)
selectInput("group_pscal", label = "Group variable for PS calculation (0, 1 coding)",
choices = mklist(list(variable = names(data.info()$data)), factor_01vars_case_small), multiple = F,
selected = factor_01vars_case_small[1])
})
output$indep_ps <- renderUI({
req(!is.null(input$group_pscal))
if (is.null(input$group_pscal)){
return(NULL)
}
validate(
need(length(input$group_pscal) > 0, "No group variables in data")
)
vars <- setdiff(setdiff(names(data.info()$data), except_vars), c(input$var_subset, input$group_pscal))
varsIni <- sapply(vars,
function(v){
forms <- as.formula(paste(input$group_pscal, "~", v))
coef <- tryCatch(summary(glm(forms, data = data.info()$data, family = binomial))$coefficients, error = function(e){return(NULL)})
sigOK <- !all(coef[-1, 4] > as.numeric(input$pcut_ps))
return(sigOK)
})
tagList(
selectInput("indep_pscal", label = "Independent variables for PS calculation",
choices = mklist(list(variable = names(data.info()$data)), vars), multiple = T,
selected = vars[varsIni])
)
})
output$caliperps <- renderUI({
sliderInput("caliper", "Caliper (0: no)", value = 0, min = 0, max = 1)
})
})
mat.info <- eventReactive(c(input$indep_pscal, input$group_pscal, input$caliper, input$ratio_ps, data.info()), {
req(input$indep_pscal)
if (is.null(input$group_pscal) | is.null(input$indep_pscal)){
return(NULL)
}
data <- data.table(data.info()$data)
data$ID.pscal2828 <- 1:nrow(data)
case.naomit <- which(complete.cases(data[, .SD, .SDcols = c(input$group_pscal, input$indep_pscal)]))
data.naomit <- data[case.naomit]
data.na <- data[-case.naomit]
data.na$pscore <- NA
data.na$iptw <- NA
caliper <- NULL
if (input$caliper > 0){
caliper <- input$caliper
}
forms <- as.formula(paste(input$group_pscal, " ~ ", paste(input$indep_pscal, collapse = "+"), sep=""))
m.out <- MatchIt::matchit(forms, data = data.naomit[, .SD, .SDcols = c("ID.pscal2828", input$group_pscal, input$indep_pscal)], caliper = caliper, ratio = as.integer(input$ratio_ps))
pscore <- m.out$distance
iptw <- ifelse(m.out$treat == levels(factor(m.out$treat))[2], 1/pscore, 1/(1-pscore))
wdata <- rbind(data.na, cbind(data.naomit, pscore, iptw))
return(list(data = wdata, matdata = data[ID.pscal2828 %in% match.data(m.out)$ID.pscal2828]))
})
output$data <- renderDT({
datatable(mat.info()$data, rownames=F, editable = F, extensions= "Buttons", caption = "Data with ps, iptw",
options = c(opt.data("data"), list(scrollX = TRUE))
)
})
output$matdata <- renderDT({
datatable(mat.info()$matdata, rownames=F, editable = F, extensions= "Buttons", caption = "Matching data",
options = c(opt.data("matching_data"), list(scrollX = TRUE))
)
})
output$data_label <- renderDT({
datatable(data.info()$label, rownames=F, editable = F, extensions= "Buttons", caption = "Label of data",
options = c(opt.data("data_label"), list(scrollX = TRUE))
)
})
output$naomit <- renderText({
if (length(naCol) == 0) {
return("Data has <B>no</B> missing values.")
} else{
txt_miss <- paste(naCol, collapse = ", ")
return(paste("Column <B>", txt_miss, "</B> contain missing values.", sep = ""))
}
})
## tb1
data <- reactive({
mat.info()$data[, .SD, .SDcols = -c("iptw")]
})
matdata <- reactive(data.table::data.table(mat.info()$matdata))
data.label <- reactive(data.info()$label)
#data_varStruct <- reactive(list(variable = names(mat.info()$matdata)))
design.survey <- reactive(survey::svydesign(ids = ~ 1, data = mat.info()$data[!is.na(iptw), ], weights = ~ iptw))
tb1_original <- callModule(tb1module2, "tb1", data = data, data_label = data.label, data_varStruct = NULL, design.survey = NULL, nfactor.limit = nfactor.limit)
tb1_ps <- callModule(tb1module2, "tb1", data = matdata, data_label = data.label, data_varStruct = NULL, design.survey = NULL, nfactor.limit = nfactor.limit)
tb1_iptw <- callModule(tb1module2, "tb1", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$table1_original <- renderDT({
tb <- tb1_original()$table
cap <- tb1_original()$caption
out.tb1 <- datatable(tb, rownames = T, extensions= "Buttons", caption = cap,
options = c(opt.tb1("tb1"),
list(columnDefs = list(list(visible=FALSE, targets= which(colnames(tb) %in% c("test","sig"))))
),
list(scrollX = TRUE)
)
)
if ("sig" %in% colnames(tb)){
out.tb1 <- out.tb1 %>% formatStyle("sig", target = 'row' ,backgroundColor = styleEqual("**", 'yellow'))
}
return(out.tb1)
})
output$table1_ps <- renderDT({
tb <- tb1_ps()$table
cap <- tb1_ps()$caption
out.tb1 <- datatable(tb, rownames = T, extensions= "Buttons", caption = cap,
options = c(opt.tb1("tb1"),
list(columnDefs = list(list(visible=FALSE, targets= which(colnames(tb) %in% c("test","sig"))))
),
list(scrollX = TRUE)
)
)
if ("sig" %in% colnames(tb)){
out.tb1 <- out.tb1 %>% formatStyle("sig", target = 'row' ,backgroundColor = styleEqual("**", 'yellow'))
}
return(out.tb1)
})
output$table1_iptw <- renderDT({
tb <- tb1_iptw()$table
cap <- tb1_iptw()$caption
out.tb1 <- datatable(tb, rownames = T, extensions= "Buttons", caption = cap,
options = c(opt.tb1("tb1"),
list(columnDefs = list(list(visible=FALSE, targets= which(colnames(tb) %in% c("test","sig"))))
),
list(scrollX = TRUE)
)
)
if ("sig" %in% colnames(tb)){
out.tb1 <- out.tb1 %>% formatStyle("sig", target = 'row' ,backgroundColor = styleEqual("**", 'yellow'))
}
return(out.tb1)
})
## Regression
out_linear_original <- callModule(regressModule2, "linear", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_linear_ps <- callModule(regressModule2, "linear", data = matdata, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_linear_iptw <- callModule(regressModule2, "linear", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$linear_original <- renderDT({
hide <- which(colnames(out_linear_original()$table) == "sig")
datatable(out_linear_original()$table, rownames=T, extensions= "Buttons", caption = out_linear_original()$caption,
options = c(opt.tbreg(out_linear_original()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$warning_linear_original <- renderText({
paste("<b>", out_linear_original()$warning, "</b>")
})
output$linear_ps <- renderDT({
hide <- which(colnames(out_linear_ps()$table) == "sig")
datatable(out_linear_ps()$table, rownames=T, extensions= "Buttons", caption = out_linear_ps()$caption,
options = c(opt.tbreg(out_linear_ps()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$warning_linear_ps <- renderText({
paste("<b>", out_linear_ps()$warning, "</b>")
})
output$linear_iptw <- renderDT({
hide <- which(colnames(out_linear_iptw()$table) == "sig")
datatable(out_linear_iptw()$table, rownames=T, extensions= "Buttons", caption = out_linear_iptw()$caption,
options = c(opt.tbreg(out_linear_iptw()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
## Logistic
out_logistic_original <- callModule(logisticModule2, "logistic", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_logistic_ps <- callModule(logisticModule2, "logistic", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_logistic_iptw <- callModule(logisticModule2, "logistic", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$logistic_original <- renderDT({
hide = which(colnames(out_logistic_original()$table) == "sig")
datatable(out_logistic_original()$table, rownames=T, extensions= "Buttons", caption = out_logistic_original()$caption,
options = c(opt.tbreg(out_logistic_original()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$logistic_ps <- renderDT({
hide = which(colnames(out_logistic_ps()$table) == "sig")
datatable(out_logistic_ps()$table, rownames=T, extensions= "Buttons", caption = out_logistic_ps()$caption,
options = c(opt.tbreg(out_logistic_ps()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$logistic_iptw <- renderDT({
hide = which(colnames(out_logistic_iptw()$table) == "sig")
datatable(out_logistic_iptw()$table, rownames=T, extensions= "Buttons", caption = out_logistic_iptw()$caption,
options = c(opt.tbreg(out_logistic_iptw()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
## Cox
out_cox_original <- callModule(coxModule, "cox", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_cox_ps <- callModule(coxModule, "cox", data = matdata, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_cox_iptw <- callModule(coxModule, "cox", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$cox_original <- renderDT({
hide = which(colnames(out_cox_original()$table) == c("sig"))
datatable(out_cox_original()$table, rownames=T, extensions= "Buttons", caption = out_cox_original()$caption,
options = c(opt.tbreg(out_cox_original()$caption),
list(columnDefs = list(list(visible=FALSE, targets= hide))
)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$cox_ps <- renderDT({
hide = which(colnames(out_cox_ps()$table) == c("sig"))
datatable(out_cox_ps()$table, rownames=T, extensions= "Buttons", caption = out_cox_ps()$caption,
options = c(opt.tbreg(out_cox_ps()$caption),
list(columnDefs = list(list(visible=FALSE, targets= hide))
)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$cox_iptw <- renderDT({
hide = which(colnames(out_cox_iptw()$table) == c("sig"))
datatable(out_cox_iptw()$table, rownames=T, extensions= "Buttons", caption = out_cox_iptw()$caption,
options = c(opt.tbreg(out_cox_iptw()$caption),
list(columnDefs = list(list(visible=FALSE, targets= hide))
)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
## ggpairs
out_ggpairs_original <- callModule(ggpairsModule2, "ggpairs", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_ggpairs_ps <- callModule(ggpairsModule2, "ggpairs", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
output$ggpairs_plot_original <- renderPlot({
print(out_ggpairs_original())
})
output$ggpairs_plot_ps <- renderPlot({
print(out_ggpairs_ps())
})
## Kaplan
out_kaplan_original <- callModule(kaplanModule, "kaplan", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_kaplan_ps <- callModule(kaplanModule, "kaplan", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_kaplan_iptw <- callModule(kaplanModule, "kaplan", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$kaplan_plot_original <- renderPlot({
print(out_kaplan_original())
})
output$kaplan_plot_ps <- renderPlot({
print(out_kaplan_ps())
})
output$kaplan_plot_iptw <- renderPlot({
print(out_kaplan_iptw())
})
## ROC
out_roc_original <- callModule(rocModule2, "roc", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_roc_ps <- callModule(rocModule2, "roc", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_roc_iptw <- callModule(rocModule2, "roc", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$plot_roc_original <- renderPlot({
print(out_roc_original()$plot)
})
output$table_roc_original <- renderDT({
datatable(out_roc_original()$tb, rownames=F, editable = F, extensions= "Buttons",
caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_roc_ps <- renderPlot({
print(out_roc_ps()$plot)
})
output$table_roc_ps <- renderDT({
datatable(out_roc_ps()$tb, rownames=F, editable = F, extensions= "Buttons",
caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_roc_iptw <- renderPlot({
print(out_roc_iptw()$plot)
})
output$table_roc_iptw <- renderDT({
datatable(out_roc_iptw()$tb, rownames=F, editable = F, extensions= "Buttons",
caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
## Time-ROC
out_timeroc_original <- callModule(timerocModule2, "timeroc", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_timeroc_ps <- callModule(timerocModule2, "timeroc", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_timeroc_iptw <- callModule(timerocModule2, "timeroc", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$plot_timeroc_original <- renderPlot({
print(out_timeroc_original()$plot)
})
output$table_timeroc_original <- renderDT({
datatable(out_timeroc_original()$tb, rownames=F, editable = F, extensions= "Buttons", caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_timeroc_ps <- renderPlot({
print(out_timeroc_ps()$plot)
})
output$table_timeroc_ps <- renderDT({
datatable(out_timeroc_ps()$tb, rownames=F, editable = F, extensions= "Buttons", caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_timeroc_iptw <- renderPlot({
print(out_timeroc_iptw()$plot)
})
output$table_timeroc_iptw <- renderDT({
datatable(out_timeroc_iptw()$tb, rownames=F, editable = F, extensions= "Buttons", caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
session$onSessionEnded(function() {
stopApp()
})
}
#viewer <- dialogViewer("Descriptive statistics", width = 1100, height = 850)
viewer <- browserViewer(browser = getOption("browser"))
runGadget(ui, server, viewer = viewer)
}
#' @title jsPropensityAddin: Rstudio addin of jsPropensityGadget
#' @description Rstudio addin of jsPropensityGadget
#' @return Rstudio addin of jsPropensityGadget
#' @details Rstudio addin of jsPropensityGadget
#' @examples
#' if(interactive()){
#' jsPropensityAddin()
#' }
#' @seealso
#' \code{\link[rstudioapi]{rstudio-editors}}
#' @rdname jsPropensityAddin
#' @export
#' @importFrom rstudioapi getActiveDocumentContext
jsPropensityAddin <- function(){
context <- rstudioapi::getActiveDocumentContext()
# Set the default data to use based on the selection.
dataString <- context$selection[[1]]$text
data <- get(dataString, envir = .GlobalEnv)
#viewer <- dialogViewer("Subset", width = 1000, height = 800)
jsPropensityGadget(data)
}
#' @title jsPropensityExtAddin: RStudio Addin for propensity score analysis with external data.
#' @description RStudio Addin for propensity score analysis with external csv/xlsx/sas7bdat/sav/dta file.
#' @param nfactor.limit nlevels limit for categorical variables, Default: 20
#' @param max.filesize Maximum file size to upload (MB), Default: 2048 (2 GB)
#' @return RStudio Addin for propensity score analysis with external data.
#' @details RStudio Addin for propensity score analysis with external csv/xlsx/sas7bdat/sav/dta file.
#' @examples
#' if(interactive()){
#' jsPropensityExtAddin()
#' }
#' @seealso
#' \code{\link[survival]{pbc}}
#' \code{\link[data.table]{fwrite}},\code{\link[data.table]{data.table}}
#' \code{\link[survey]{svydesign}}
#' \code{\link[jstable]{opt.tbreg}}
#' @rdname jsPropensityExtAddin
#' @export
#' @importFrom data.table fwrite data.table
#' @importFrom survey svydesign
#' @importFrom jstable opt.tbreg
#' @importFrom DT datatable %>% formatStyle styleEqual renderDT DTOutput
#' @importFrom shinycustomloader withLoader
#' @import shiny
jsPropensityExtAddin <- function(nfactor.limit = 20, max.filesize = 2048){
iptw <- NULL
options(shiny.maxRequestSize = max.filesize * 1024^2)
ui <- navbarPage("Propensity score analysis",
tabPanel("Data", icon = icon("table"),
sidebarLayout(
sidebarPanel(
uiOutput("import"),
downloadButton("downloadData", "Example data")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Data", withLoader(DTOutput("data"), type="html", loader="loader6")),
tabPanel("Matching data", withLoader(DTOutput("matdata"), type="html", loader="loader6")),
tabPanel("Label", withLoader(DTOutput("data_label", width = "100%"), type="html", loader="loader6"))
),
htmlOutput("naomit")
)
)
),
tabPanel("Table 1", icon = icon("percentage"),
sidebarLayout(
sidebarPanel(
tb1moduleUI("tb1")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("table1_original"), type="html", loader="loader6"),
wellPanel(
h5("Normal continuous variables are summarized with Mean (SD) and t-test(2 groups) or ANOVA(> 2 groups)"),
h5("Non-normal continuous variables are summarized with median [IQR or min,max] and kruskal-wallis test"),
h5("Categorical variables are summarized with table")
)
),
tabPanel("Matching",
withLoader(DTOutput("table1_ps"), type="html", loader="loader6"),
wellPanel(
h5("Normal continuous variables are summarized with Mean (SD) and t-test(2 groups) or ANOVA(> 2 groups)"),
h5("Non-normal continuous variables are summarized with median [IQR or min,max] and kruskal-wallis test"),
h5("Categorical variables are summarized with table")
)
),
tabPanel("IPTW",
withLoader(DTOutput("table1_iptw"), type="html", loader="loader6"),
wellPanel(
h5("Normal continuous variables are summarized with Mean (SD) and complex survey regression"),
h5("Non-normal continuous variables are summarized with median [IQR or min,max] and complex sampling rank test"),
h5("Categorical variables are summarized with table")
)
)
)
)
)
),
navbarMenu("Regression", icon = icon("list-alt"),
tabPanel("Linear regression",
sidebarLayout(
sidebarPanel(
regressModuleUI("linear")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("linear_original"), type="html", loader="loader6"),
br(),
uiOutput("warning_linear_original")
),
tabPanel("Matching",
withLoader(DTOutput("linear_ps"), type="html", loader="loader6"),
br(),
uiOutput("warning_linear_ps")
),
tabPanel("IPTW",
withLoader(DTOutput("linear_iptw"), type="html", loader="loader6")
)
)
)
)
),
tabPanel("Logistic regression",
sidebarLayout(
sidebarPanel(
regressModuleUI("logistic")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("logistic_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(DTOutput("logistic_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(DTOutput("logistic_iptw"), type="html", loader="loader6")
)
)
)
)
),
tabPanel("Cox model",
sidebarLayout(
sidebarPanel(
coxUI("cox")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(DTOutput("cox_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(DTOutput("cox_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(DTOutput("cox_iptw"), type="html", loader="loader6")
)
)
)
)
)
),
navbarMenu("Plot", icon = icon("bar-chart-o"),
tabPanel("Scatter plot",
sidebarLayout(
sidebarPanel(
ggpairsModuleUI1("ggpairs")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("ggpairs_plot_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("ggpairs_plot_ps"), type="html", loader="loader6")
)
),
ggpairsModuleUI2("ggpairs")
)
)
),
tabPanel("Kaplan-meier plot",
sidebarLayout(
sidebarPanel(
kaplanUI("kaplan")
),
mainPanel(
optionUI("kaplan"),
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("kaplan_plot_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("kaplan_plot_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(plotOutput("kaplan_plot_iptw"), type="html", loader="loader6")
)
),
ggplotdownUI("kaplan")
)
)
)
),
navbarMenu("ROC analysis", icon = icon("check"),
tabPanel("ROC",
sidebarLayout(
sidebarPanel(
rocUI("roc")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("plot_roc_original"), type="html", loader="loader6"),
withLoader(DTOutput("table_roc_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("plot_roc_ps"), type="html", loader="loader6"),
withLoader(DTOutput("table_roc_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(plotOutput("plot_roc_iptw"), type="html", loader="loader6"),
withLoader(DTOutput("table_roc_iptw"), type="html", loader="loader6")
)
),
ggplotdownUI("roc")
)
)
),
tabPanel("Time-dependent ROC",
sidebarLayout(
sidebarPanel(
timerocUI("timeroc")
),
mainPanel(
tabsetPanel(type = "pills",
tabPanel("Original",
withLoader(plotOutput("plot_timeroc_original"), type="html", loader="loader6"),
withLoader(DTOutput("table_timeroc_original"), type="html", loader="loader6")
),
tabPanel("Matching",
withLoader(plotOutput("plot_timeroc_ps"), type="html", loader="loader6"),
withLoader(DTOutput("table_timeroc_ps"), type="html", loader="loader6")
),
tabPanel("IPTW",
withLoader(plotOutput("plot_timeroc_iptw"), type="html", loader="loader6"),
withLoader(DTOutput("table_timeroc_iptw"), type="html", loader="loader6")
)
),
ggplotdownUI("timeroc")
)
)
)
)
)
server <- function(input, output, session) {
output$downloadData <- downloadHandler(
filename = function() {
paste("example_ps", ".csv", sep = "")
},
content = function(file) {
out <- survival::pbc
out$status <- as.integer(out$status == 2)
data.table::fwrite(na.omit(out)[, -1], file)
}
)
output$import <- renderUI({
FilePsInput("datafile")
})
mat.info <- callModule(FilePs, "datafile", nfactor.limit = nfactor.limit)
output$data <- renderDT({
datatable(mat.info()$data, rownames=F, editable = F, extensions= "Buttons", caption = "Data with ps, iptw",
options = c(opt.data("data"), list(scrollX = TRUE))
)
})
output$matdata <- renderDT({
datatable(mat.info()$matdata, rownames=F, editable = F, extensions= "Buttons", caption = "Matching data",
options = c(opt.data("matching_data"), list(scrollX = TRUE))
)
})
output$data_label <- renderDT({
datatable(mat.info()$data.label, rownames=F, editable = F, extensions= "Buttons", caption = "Label of data",
options = c(opt.data("data_label"), list(scrollX = TRUE))
)
})
output$naomit <- renderText({
paste("<font size = 5 ><b>", "The variables below contain missing values.</b></font><br>", '<font size = 4 color=\"#FF0000\"><b>', mat.info()$naomit, "</b></font>")
#mat.info()$naomit
})
## tb1
data <- reactive({
mat.info()$data[, .SD, .SDcols = -c("iptw")]
})
matdata <- reactive(data.table::data.table(mat.info()$matdata))
data.label <- reactive(mat.info()$data.label)
#data_varStruct <- reactive(list(variable = names(mat.info()$matdata)))
design.survey <- reactive(survey::svydesign(ids = ~ 1, data = mat.info()$data[!is.na(iptw), ], weights = ~ iptw))
tb1_original <- callModule(tb1module2, "tb1", data = data, data_label = data.label, data_varStruct = NULL, design.survey = NULL, nfactor.limit = nfactor.limit)
tb1_ps <- callModule(tb1module2, "tb1", data = matdata, data_label = data.label, data_varStruct = NULL, design.survey = NULL, nfactor.limit = nfactor.limit)
tb1_iptw <- callModule(tb1module2, "tb1", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$table1_original <- renderDT({
tb <- tb1_original()$table
cap <- tb1_original()$caption
out.tb1 <- datatable(tb, rownames = T, extensions= "Buttons", caption = cap,
options = c(opt.tb1("tb1"),
list(columnDefs = list(list(visible=FALSE, targets= which(colnames(tb) %in% c("test","sig"))))
),
list(scrollX = TRUE)
)
)
if ("sig" %in% colnames(tb)){
out.tb1 <- out.tb1 %>% formatStyle("sig", target = 'row' ,backgroundColor = styleEqual("**", 'yellow'))
}
return(out.tb1)
})
output$table1_ps <- renderDT({
tb <- tb1_ps()$table
cap <- tb1_ps()$caption
out.tb1 <- datatable(tb, rownames = T, extensions= "Buttons", caption = cap,
options = c(opt.tb1("tb1"),
list(columnDefs = list(list(visible=FALSE, targets= which(colnames(tb) %in% c("test","sig"))))
),
list(scrollX = TRUE)
)
)
if ("sig" %in% colnames(tb)){
out.tb1 <- out.tb1 %>% formatStyle("sig", target = 'row' ,backgroundColor = styleEqual("**", 'yellow'))
}
return(out.tb1)
})
output$table1_iptw <- renderDT({
tb <- tb1_iptw()$table
cap <- tb1_iptw()$caption
out.tb1 <- datatable(tb, rownames = T, extensions= "Buttons", caption = cap,
options = c(opt.tb1("tb1"),
list(columnDefs = list(list(visible=FALSE, targets= which(colnames(tb) %in% c("test","sig"))))
),
list(scrollX = TRUE)
)
)
if ("sig" %in% colnames(tb)){
out.tb1 <- out.tb1 %>% formatStyle("sig", target = 'row' ,backgroundColor = styleEqual("**", 'yellow'))
}
return(out.tb1)
})
## Regression
out_linear_original <- callModule(regressModule2, "linear", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_linear_ps <- callModule(regressModule2, "linear", data = matdata, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_linear_iptw <- callModule(regressModule2, "linear", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$linear_original <- renderDT({
hide <- which(colnames(out_linear_original()$table) == "sig")
datatable(out_linear_original()$table, rownames=T, extensions= "Buttons", caption = out_linear_original()$caption,
options = c(opt.tbreg(out_linear_original()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$warning_linear_original <- renderText({
paste("<b>", out_linear_original()$warning, "</b>")
})
output$linear_ps <- renderDT({
hide <- which(colnames(out_linear_ps()$table) == "sig")
datatable(out_linear_ps()$table, rownames=T, extensions= "Buttons", caption = out_linear_ps()$caption,
options = c(opt.tbreg(out_linear_ps()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$warning_linear_ps <- renderText({
paste("<b>", out_linear_ps()$warning, "</b>")
})
output$linear_iptw <- renderDT({
hide <- which(colnames(out_linear_iptw()$table) == "sig")
datatable(out_linear_iptw()$table, rownames=T, extensions= "Buttons", caption = out_linear_iptw()$caption,
options = c(opt.tbreg(out_linear_iptw()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
## Logistic
out_logistic_original <- callModule(logisticModule2, "logistic", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_logistic_ps <- callModule(logisticModule2, "logistic", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_logistic_iptw <- callModule(logisticModule2, "logistic", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$logistic_original <- renderDT({
hide = which(colnames(out_logistic_original()$table) == "sig")
datatable(out_logistic_original()$table, rownames=T, extensions= "Buttons", caption = out_logistic_original()$caption,
options = c(opt.tbreg(out_logistic_original()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$logistic_ps <- renderDT({
hide = which(colnames(out_logistic_ps()$table) == "sig")
datatable(out_logistic_ps()$table, rownames=T, extensions= "Buttons", caption = out_logistic_ps()$caption,
options = c(opt.tbreg(out_logistic_ps()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$logistic_iptw <- renderDT({
hide = which(colnames(out_logistic_iptw()$table) == "sig")
datatable(out_logistic_iptw()$table, rownames=T, extensions= "Buttons", caption = out_logistic_iptw()$caption,
options = c(opt.tbreg(out_logistic_iptw()$caption),
list(columnDefs = list(list(visible=FALSE, targets =hide))
),
list(scrollX = TRUE)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
## Cox
out_cox_original <- callModule(coxModule, "cox", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_cox_ps <- callModule(coxModule, "cox", data = matdata, data_label = data.label, data_varStruct = NULL, default.unires = F, nfactor.limit = nfactor.limit)
out_cox_iptw <- callModule(coxModule, "cox", data = data, data_label = data.label, data_varStruct = NULL, default.unires = F, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$cox_original <- renderDT({
hide = which(colnames(out_cox_original()$table) == c("sig"))
datatable(out_cox_original()$table, rownames=T, extensions= "Buttons", caption = out_cox_original()$caption,
options = c(opt.tbreg(out_cox_original()$caption),
list(columnDefs = list(list(visible=FALSE, targets= hide))
)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$cox_ps <- renderDT({
hide = which(colnames(out_cox_ps()$table) == c("sig"))
datatable(out_cox_ps()$table, rownames=T, extensions= "Buttons", caption = out_cox_ps()$caption,
options = c(opt.tbreg(out_cox_ps()$caption),
list(columnDefs = list(list(visible=FALSE, targets= hide))
)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
output$cox_iptw <- renderDT({
hide = which(colnames(out_cox_iptw()$table) == c("sig"))
datatable(out_cox_iptw()$table, rownames=T, extensions= "Buttons", caption = out_cox_iptw()$caption,
options = c(opt.tbreg(out_cox_iptw()$caption),
list(columnDefs = list(list(visible=FALSE, targets= hide))
)
)
) %>% formatStyle("sig", target = 'row',backgroundColor = styleEqual("**", 'yellow'))
})
## ggpairs
out_ggpairs_original <- callModule(ggpairsModule2, "ggpairs", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_ggpairs_ps <- callModule(ggpairsModule2, "ggpairs", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
output$ggpairs_plot_original <- renderPlot({
print(out_ggpairs_original())
})
output$ggpairs_plot_ps <- renderPlot({
print(out_ggpairs_ps())
})
## Kaplan
out_kaplan_original <- callModule(kaplanModule, "kaplan", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_kaplan_ps <- callModule(kaplanModule, "kaplan", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_kaplan_iptw <- callModule(kaplanModule, "kaplan", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$kaplan_plot_original <- renderPlot({
print(out_kaplan_original())
})
output$kaplan_plot_ps <- renderPlot({
print(out_kaplan_ps())
})
output$kaplan_plot_iptw <- renderPlot({
print(out_kaplan_iptw())
})
## ROC
out_roc_original <- callModule(rocModule2, "roc", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_roc_ps <- callModule(rocModule2, "roc", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_roc_iptw <- callModule(rocModule2, "roc", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$plot_roc_original <- renderPlot({
print(out_roc_original()$plot)
})
output$table_roc_original <- renderDT({
datatable(out_roc_original()$tb, rownames=F, editable = F, extensions= "Buttons",
caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_roc_ps <- renderPlot({
print(out_roc_ps()$plot)
})
output$table_roc_ps <- renderDT({
datatable(out_roc_ps()$tb, rownames=F, editable = F, extensions= "Buttons",
caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_roc_iptw <- renderPlot({
print(out_roc_iptw()$plot)
})
output$table_roc_iptw <- renderDT({
datatable(out_roc_iptw()$tb, rownames=F, editable = F, extensions= "Buttons",
caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
## Time-ROC
out_timeroc_original <- callModule(timerocModule2, "timeroc", data = data, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_timeroc_ps <- callModule(timerocModule2, "timeroc", data = matdata, data_label = data.label, data_varStruct = NULL, nfactor.limit = nfactor.limit)
out_timeroc_iptw <- callModule(timerocModule2, "timeroc", data = data, data_label = data.label, data_varStruct = NULL, design.survey = design.survey, nfactor.limit = nfactor.limit)
output$plot_timeroc_original <- renderPlot({
print(out_timeroc_original()$plot)
})
output$table_timeroc_original <- renderDT({
datatable(out_timeroc_original()$tb, rownames=F, editable = F, extensions= "Buttons", caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_timeroc_ps <- renderPlot({
print(out_timeroc_ps()$plot)
})
output$table_timeroc_ps <- renderDT({
datatable(out_timeroc_ps()$tb, rownames=F, editable = F, extensions= "Buttons", caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
output$plot_timeroc_iptw <- renderPlot({
print(out_timeroc_iptw()$plot)
})
output$table_timeroc_iptw <- renderDT({
datatable(out_timeroc_iptw()$tb, rownames=F, editable = F, extensions= "Buttons", caption = "ROC results",
options = c(jstable::opt.tbreg("roctable"), list(scrollX = TRUE)))
})
session$onSessionEnded(function() {
stopApp()
})
}
#viewer <- dialogViewer("Descriptive statistics", width = 1100, height = 850)
viewer <- browserViewer(browser = getOption("browser"))
runGadget(ui, server, viewer = viewer)
}
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