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m_SG_p4.R
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# Plot 2 - Covariate vs accuracy plot ------------------------------------------------------------------------------------------------------------------------------------------------
# UI for plot (output the plot)
SG_plot_2_UI <- function(id) {
ns <- NS(id)
tagList(
p("NOTE: The vertical bars represent the 95% credible intervals for each subgroup from the bivariate model;
the black points represent the study-specific data points for sensitivity (left panel) and specificity (right panel)
for each subgroup"),
uiOutput(outputId = ns("my_tooltip_2")),
plotOutput(outputId = ns("plot_2"),
click = ns("plot_click_2"))
)
}
# UI for plot 2 - settings menu
SG_plot_2_settings_menu_UI <- function(id) {
ns <- NS(id)
tagList(
h4("Options"),
br(),
sliderInput(inputId = ns("plot_2_dimention_slider_height"),
label = "Change size of plot - height",
min = 1,
max = 2000,
value = 300,
ticks = FALSE),
sliderInput(inputId = ns("plot_2_dimention_slider_width"),
label = "Change size of plot - width",
min = 1,
max = 2000,
value = 500,
ticks = FALSE),
checkboxInput(inputId = ns("plot_2_study_level_data_points"),
"Observed data Points",
TRUE),
checkboxInput(inputId = ns("plot_2_study_id"),
"Study ID",
FALSE),
conditionalPanel(condition = 'input.plot_2_study_id == 1',
ns = ns,
sliderInput(ns("plot_2_study_id_nudge_x"),
"select x-axis nudge for Study ID label",
min = -50,
max = 50,
value = 20,
ticks = FALSE)),
conditionalPanel(condition = 'input.plot_2_study_id == 1',
ns = ns,
sliderInput(ns("plot_2_study_id_nudge_y"),
"select y-axis nudge for Study ID label",
min = -50,
max = 50,
value = 20,
ticks = FALSE)),
checkboxInput(inputId = ns("plot_2_prevcheck"),
"Display disease prevalence",
FALSE),
conditionalPanel(condition = 'input.plot_2_prevcheck == 1',
ns = ns,
sliderInput(ns("plot_2_prevcheck_nudge_x"),
"select x-axis nudge for prevelance label",
min = -50,
max = 50,
value = -20,
ticks = FALSE)),
conditionalPanel(condition = 'input.plot_2_prevcheck == 1',
ns = ns,
sliderInput(ns("plot_2_prevcheck_nudge_y"),
"select y-axis nudge for prevelance label",
min = -50,
max = 50,
value = -20,
ticks = FALSE)),
br(),
# Download plot options:
h5("Download plot options:"),
radioButtons(inputId = ns("plot_file_type_2"), label = h5("File type"), choices = c(".png", ".pdf")),
numericInput(inputId = ns("plot_width_2"), label=h5("Plot width"), value = 5),
numericInput(inputId = ns("plot_height_2"), label=h5("Plot height"), value = 5),
numericInput(inputId = ns("plot_dpi_2"), label=h5("Plot DPI"), value = 600),
downloadButton(outputId = ns("plot_download_2"), label = "Download Plot")
)
}
# Server function to generate and download plot
SG_plot_2_server <- function(id,
data,
draws) {
moduleServer(
id,
function(input, output, session) {
plot_object_2 <- reactive({
req(data(), draws(), input$covariate_subgroup, cancelOutput = TRUE)
mod <- draws()
X <- data()
C <- ncol(X)
Names <- colnames(X)
if (C > 8 & Names[7] != "rob_PS") { j <<- 6 } else { j <<- 13 }
cov_index <- as.integer(as.double(input$covariate_subgroup)) - 1
X <- X %>% arrange(!!as.name(colnames(X)[j + cov_index]))
N <- nrow(X)
if (cov_index != 0) {
if (str_sub(colnames(X)[j + cov_index], start = -3) != "cts") { # categorical covariate only
num_levels <- length(rstan::summary(mod,
probs = c(0.025, 0.5, 0.975),
pars = c("Se"))$summary[,5])
## medians
median_sens <- (summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary[,5])
l_sens <- (summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary[,4])
u_sens <- (summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary[,6])
median_spec <- (summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Sp"))$summary[,5])
l_spec <- (summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Sp"))$summary[,4])
u_spec <- (summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Sp"))$summary[,6])
len <- length(median_sens)
medians_facet <- tibble(Measure = c(rep("Sensitivity", times = len), rep("Specificity", times = len)),
Median = c(median_sens, median_spec),
lower = c(l_sens, l_spec),
upper = c(u_sens, u_spec),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := c(factor(seq(from = 1, to = num_levels, by = 1)),
factor(seq(from = 1, to = num_levels, by = 1))))
medians_facet2 <- dplyr::mutate(medians_facet, !!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(c((levels(factor(X[, j + cov_index]))),
(levels(factor(X[, j + cov_index]))))))
# identify subgroups w/ N = 1
X_counts <- X %>%
group_by(!!as.name(colnames(X)[j + cov_index])) %>%
summarise(no_rows = length(!!as.name(colnames(X)[j + cov_index])))
X_counts_one <- filter(X_counts, no_rows==1)
X_counts_one_df <- data.frame(X_counts_one)
X_counts_one_df[,1] # vector of factor levels s.t. N = 1
# remove summary points, credible and prediction regions for any subgroups s.t. N = 1
medians_facet3 <- filter( medians_facet2, !(!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) %in% c(X_counts_one_df[,1]) ) )
# plot
g <- ggplot(data = medians_facet3, aes(y=Median, x = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5)))) +
geom_point(size=5, shape = 21) + # summary points
geom_errorbar(data = medians_facet3, aes(ymin = lower, ymax = upper), alpha = 0.3) +
theme_bw() +
scale_y_continuous(breaks = seq(0,1,0.2), limits = c(0,1)) +
theme(text = element_text(size=15)) +
ylab("Accuracy") +
facet_wrap(~Measure)
g
# observed values
ss<- tibble(
Study =as.numeric(as.factor(X$author)),
TP=X$TP, FN=X$FN, FP=X$FP, TN=X$TN,
N=(X$TP+X$FN+X$FP+X$TN) ,
Sensitivity= (X$TP/(X$TP+X$FN)) ,
Specificity= (X$TN/(X$TN+X$FP)) ,
prev = round((X$TP+X$FN)/N, 2),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(X[, j + cov_index])
)
ss_facet <- tibble(Study = rep(ss$Study, 2),
Measure = c(rep("Sensitivity", times = nrow(X)), rep("Specificity", times = nrow(X))),
Accuracy = c(ss$Sensitivity, ss$Specificity),
TP = rep(ss$TP, 2),
FN = rep(ss$FN, 2),
FP = rep(ss$FP, 2),
TN = rep(ss$TN, 2),
N = rep(ss$N, 2),
prev = rep(ss$prev, 2),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(rep((X[, j + cov_index]), 2)))
ss2_facet <- left_join(ss_facet, X)
#get ranges of the plot axes
x_range <- ggplot_build(g)$layout$panel_params[[1]]$x.range[2] - ggplot_build(g)$layout$panel_params[[1]]$x.range[1]
y_range <- ggplot_build(g)$layout$panel_params[[1]]$y.range[2] - ggplot_build(g)$layout$panel_params[[1]]$y.range[1]
# Plot observed data (study points)
if (input$plot_2_study_level_data_points == TRUE) {
g <- g + geom_point(data = ss2_facet,
inherit.aes = FALSE,
aes(y = Accuracy, x = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5)))) +
facet_wrap(~Measure)
g
}
else { g }
# display study ID # (matches tables) [default]
if (input$plot_2_study_id == TRUE) {
g <- g + geom_label_repel(data = ss2_facet,
#inherit.aes = FALSE,
aes(y = Accuracy, x = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5)),
label = Study), size = 3.5, box.padding = 0.25,
nudge_x = x_range/input$plot_2_study_id_nudge_x,
nudge_y = y_range/input$plot_2_study_id_nudge_y)
g
}
else { g }
# display disease prevalence's
if (input$plot_2_prevcheck == TRUE) {
g <- g + geom_label_repel(data = ss2_facet,
#inherit.aes = FALSE,
aes(y = Accuracy, x = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5)),
label = prev), size = 3.5, box.padding = 0.25,
nudge_x = x_range/input$plot_2_prevcheck_nudge_x,
nudge_y = y_range/input$plot_2_prevcheck_nudge_y)
g
}
else { g }
}
}
})
# Output ggplot object
observe({
output$plot_2 <- renderPlot({
plot_object_2()
}, height = as.numeric(input$plot_2_dimention_slider_height),
width = as.numeric(input$plot_2_dimention_slider_width)
)
})
# Download ggplot object
output$plot_download_2 <- downloadHandler(
filename = function(){
paste0("accuracy_vs_covariate_subgroup", input$plot_file_type_2)
},
content = function(file) {
ggsave(file,
plot_object_2(),
width = input$plot_width_2,
height = input$plot_height_2,
dpi = input$plot_dpi_2,
units = "in")
}
)
# tooltips
click <- reactive({
input$plot_click_2
})
# values to display on plot
output$vals_2 <- renderPrint({
req(data(), draws(), cancelOutput = TRUE)
X <- data()
cov_index <- as.integer(as.double(input$covariate_subgroup)) - 1
C <- ncol(X)
Names <- colnames(X)
if (C > 8 & Names[7] != "rob_PS") {
j <<- 6
no_covariates <<- length(colnames(X[,7:C]))
} else {
j <<- 13
no_covariates <<- length(colnames(X[,14:C]))
}
X <- X %>%
dplyr::arrange(!!as.name(colnames(X)[j + cov_index]))
N <- nrow(X)
# observed values
ss<- tibble(
Study =as.numeric(as.factor(X$author)),
TP=X$TP, FN=X$FN, FP=X$FP, TN=X$TN,
N=(X$TP+X$FN+X$FP+X$TN) ,
Sensitivity= (TP/(TP+FN)) ,
Specificity= (TN/(TN+FP)) ,
prev = round((TP+FN)/N, 2),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(X[, j + cov_index])
)
ss_facet <- tibble(Study = rep(ss$Study, 2),
Measure = c(rep("Sensitivity", times = N), rep("Specificity", times = N)),
Accuracy = c(ss$Sensitivity, ss$Specificity),
TP = rep(ss$TP, 2),
FN = rep(ss$FN, 2),
FP = rep(ss$FP, 2),
TN = rep(ss$TN, 2),
N = rep(ss$N, 2),
prev = rep(ss$prev, 2),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5))
:= factor(rep((X[, j + cov_index]), 2)))
ss2_facet <- left_join(ss_facet, X)
# make cols without .cts and .cat endings for display
for (i in 1:no_covariates) {
ss2_facet <- dplyr::mutate(ss2_facet,
!!as.name( str_sub(colnames(X)[j + i], end = -5) ) := !!as.name( colnames(X)[j + i]) )
}
# select columns to display
cols <- c("author", "year",
str_sub( colnames(X)[c( (j+1):(j - 1 + no_covariates) )] , end = -5),
"Accuracy", "Measure")
data <- dplyr::select(ss2_facet, cols)
data[] <- lapply(data, function(x) if (is.numeric(x) ) round(x, 3) else x) # round all numeric cols to 3 s.f.
data <- data.frame(data)
y <- nearPoints(df = data, click(), yvar ="Accuracy")
req(nrow(y) != 0)
y
})
# tooltip
output$my_tooltip_2 <- renderUI({
req(data(), draws(), cancelOutput = TRUE)
ns <- session$ns
X <- data()
cov_index <- as.integer(as.double(input$covariate_subgroup)) - 1
C <- ncol(X)
Names <- colnames(X)
if (C > 8 & Names[7] != "rob_PS") {
j <<- 6
no_covariates <<- length(colnames(X[,7:C]))
} else {
j <<- 13
no_covariates <<- length(colnames(X[,14:C]))
}
X <- X %>% dplyr::arrange(!!as.name(colnames(X)[j + cov_index]))
N <- nrow(X)
# observed values
ss<- tibble(
Study =as.numeric(as.factor(X$author)),
TP=X$TP, FN=X$FN, FP=X$FP, TN=X$TN,
N=(X$TP+X$FN+X$FP+X$TN) ,
Sensitivity= (TP/(TP+FN)) ,
Specificity= (TN/(TN+FP)) ,
prev = round((TP+FN)/N, 2),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(X[, j + cov_index])
)
ss_facet <- tibble(Study = rep(ss$Study, 2),
Measure = c(rep("Sensitivity", times = N), rep("Specificity", times = N)),
Accuracy = c(ss$Sensitivity, ss$Specificity),
TP = rep(ss$TP, 2),
FN = rep(ss$FN, 2),
FP = rep(ss$FP, 2),
TN = rep(ss$TN, 2),
N = rep(ss$N, 2),
prev = rep(ss$prev, 2),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(rep((X[, j + cov_index]), 2)))
ss2_facet <- left_join(ss_facet, X)
# make cols without .cts and .cat endings for display
for (i in 1:no_covariates) {
ss2_facet <- dplyr::mutate(ss2_facet,
!!as.name( str_sub(colnames(X)[j + i], end = -5) ) := !!as.name( colnames(X)[j + i]) )
}
# select columns to display
cols <- c("author", "year",
str_sub( colnames(X)[c( (j+1):(j - 1 + no_covariates) )] , end = -5),
"Accuracy", "Measure")
data <- dplyr::select(ss2_facet, cols)
data[] <- lapply(data, function(x) if (is.numeric(x) ) round(x, 3) else x) # round all numeric cols to 3 s.f.
data <- data.frame(data)
y <- nearPoints(df = data, click() , yvar ="Accuracy")
req(nrow(y) != 0)
verbatimTextOutput(ns("vals_2"))
})
}
)
}