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app.R
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app.R
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# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Bland&Altman PlotteR - A Shiny app for quantitative comparison of measurements
# Created by Joachim Goedhart (@joachimgoedhart), first version 2020
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Joachim Goedhart (C) 2020
# electronic mail address: j #dot# goedhart #at# uva #dot# nl
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
boot_LoA = function(x) {
quantile(sample(x, replace = TRUE), probs = c(0.05,0.5,0.95))
}
#We define a function `sos()` to calculate the 'sum of squares' (SoS).
sos <- function(x) {
sum((mean(x)-x)^2)
}
#number of replicates for bootstrap
nrep = 500
# Allow files up to 10 Mb
options(shiny.maxRequestSize=10*1024^2)
#Load necessary packages
library(shiny)
library(ggplot2)
library(magrittr)
library(dplyr)
library(tidyr)
library(readxl)
library(DT)
library(RCurl)
library(readr)
df_example_1 <- read.csv("df_SystBloodPressure.csv", na.strings = "")
df_example_2 <- read.csv("df_PlasmaVolume.csv", na.strings = "")
df_example_3 <- read.csv("df_T4.csv", na.strings = "")
# Create a reactive object here that we can share between all the sessions.
vals <- reactiveValues(count=0)
# Define UI
ui <- fluidPage(
# Application title
titlePanel("Bland&Altman Plot"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(width=3,
conditionalPanel(
condition = "input.tabs=='Plot'",
radioButtons("plot_type", "Select Plot:", choices =
list(
"BA, y-axis: Difference [absolute]" = 1,
"BA, y-axis: Difference/Average [percentage]" = 2,
"BA, y-axis: Difference of log10 transformed data" = 3
# "Correlation (Measurement_2 vs. Measurement_1)"=5,
),
selected = 1),
sliderInput("pointSize", "Size of the datapoints", 0, 10, 4),
sliderInput("alphaInput", "Visibility of the data", 0, 1, 0.8),
h4("Statistics"),
radioButtons("LoA", "Limits of Agreement", choices =
list(
"Ordinary" = 1,
"Regression" = 2,
"Non parametric" = 3
),
selected = 1),
conditionalPanel(condition = "input.LoA!='2'",
checkboxInput(inputId = "add_CI",
label = "Show 95% confidence intervals",
value = FALSE)),
checkboxInput(inputId = "show_rugs",
label = "Show rugs",
value = FALSE),
sliderInput("alphaInput_summ", "Visibility of the statistics", 0, 1, 1),
checkboxInput(inputId = "show_labels",
label = "Label limits on secondary y-axis",
value = FALSE),
conditionalPanel(condition = "input.show_labels == true",
numericInput("digits", "Number of digits:", value = 2)),
h4("Layout"),
radioButtons("adjustcolors", "Color of datapoints", choices =
list(
"Black" = 1,
"Grey" = 3,
"User defined"=5),
selected = 1),
conditionalPanel(condition = "input.adjustcolors == 5",
textInput("user_color_list", "List of names or hexadecimal codes", value = "turquoise2")),
# checkboxInput(inputId = "rotate_plot",
# label = "Rotate plot 90 degrees",
# value = FALSE),
checkboxInput(inputId = "change_scale",
label = "Change scale",
value = FALSE),
conditionalPanel(
condition = "input.change_scale == true",
textInput("range_x", "Range x-axis (min,max)", value = "")
),
conditionalPanel(
condition = "input.change_scale == true",
textInput("range_y", "Range y-axis (min,max)", value = ""),
checkboxInput(inputId = "scale_log_10", label = "Log10 scale on y-axis", value = FALSE)
),
numericInput("plot_height", "Plot height (# pixels): ", value = 600),
numericInput("plot_width", "Plot width (# pixels):", value = 600),
h4("Labels"),
checkboxInput(inputId = "add_title",
label = "Add title",
value = FALSE),
conditionalPanel(
condition = "input.add_title == true",
checkboxInput("align", "Centre the title", value = FALSE),
textInput("title", "Title:", value = "")
),
checkboxInput(inputId = "label_axes",
label = "Change axis labels",
value = FALSE),
conditionalPanel(
condition = "input.label_axes == true",
textInput("lab_x", "X-axis:", value = ""),
textInput("lab_y", "Y-axis:", value = "")
),
checkboxInput(inputId = "adj_fnt_sz",
label = "Change font size",
value = FALSE),
conditionalPanel(
condition = "input.adj_fnt_sz == true",
numericInput("fnt_sz_title", "Plot title:", value = 24),
numericInput("fnt_sz_labs", "Axis titles:", value = 24),
numericInput("fnt_sz_ax", "Axis labels:", value = 18),
numericInput("fnt_sz_cand", "Labels of hits:", value = 6)
),
checkboxInput(inputId = "add_legend",
label = "Add legend",
value = FALSE),
#
# conditionalPanel(
# condition = "input.add_legend == true",
# textInput("legend_title", "Legend title:", value = "")
# ),
checkboxInput(inputId = "add_description",
label = "Add figure description",
value = FALSE),
NULL),
conditionalPanel(
condition = "input.tabs=='iPlot'",h4("iPlot")
),
conditionalPanel(
condition = "input.tabs=='Data'",
h4("Data upload"),
radioButtons(
"data_input", "",
choices =
list("Example data 1" = 1,
"Example data 2" = 2,
"Upload file (CSV, text, excel)" = 3,
"Paste data" = 4,
"URL (CSV files only)" = 5
)
,
selected = 1),
conditionalPanel(
condition = "input.data_input=='1'",p('Systolic blood pressure measurements made simultaneously by two observers (J and R) and an automatic blood pressure measuring machine (S), each making three observations in quick succession. Data retrieved from table 1 by Bland & Altman (1999): '),a('https://doi.org/10.1177/096228029900800204', href ='https://doi.org/10.1177/096228029900800204'),hr()),
# conditionalPanel(
# condition = "input.data_input=='2'",p('Measurements of plasma volume expressed as a percentage of normal in 99 subjects, using two alternative sets of normal values due to Nadler and Hurley. Data retrieved from table 2 by Bland & Altman (1999): '), a('https://doi.org/10.1177/096228029900800204', href ='https://doi.org/10.1177/096228029900800204'),hr()),
conditionalPanel(
condition = "input.data_input=='3'",
fileInput("upload", NULL, multiple = FALSE, accept = c(".xlsx", ".xls", ".txt", ".csv")),
selectInput("upload_delim", label = "Select Delimiter (for text file):", choices =list("Comma" = ",",
"Tab" = "\t",
"Semicolon" = ";",
"Space" = " ")),
selectInput("sheet", label = "Select sheet (for excel workbook):", choices = " ")
),
conditionalPanel(
condition = "input.data_input=='4'",
h5("Paste data below:"),
tags$textarea(id = "data_paste",
placeholder = "Add data here",
rows = 10,
cols = 20, ""),
actionButton("submit_data_button", "Submit data"),
radioButtons(
"text_delim", "Delimiter",
choices =
list("Tab (from Excel)" = "\t",
"Space" = " ",
"Comma" = ",",
"Semicolon" = ";"),
selected = "\t")),
### csv via URL as input
conditionalPanel(
condition = "input.data_input=='5'",
# textInput("URL", "URL", value = "https://zenodo.org/record/2545922/files/FRET-efficiency_mTq2.csv"),
textInput("URL", "URL", value = ""),
NULL
),
h4('Select measurements'),
selectInput("x_var", label = "Measurement 1", choices = "-"),
selectInput("y_var", label = "Measurement 2", choices = "-"),
textInput("unit", "Units of the variables (optional)", value = ""),
# selectInput("g_var", label = "Select column with names", choices = "-"),
hr(),
NULL
),
conditionalPanel(
condition = "input.tabs=='Data Summary'",
h4("Data summary"),
numericInput("digits_table", "Digits:", 2, min = 0),
hr(),
h4("Repeatability"),
selectizeInput(inputId = 'repeats',
label = "Select Repeats:",
choices = "-",
selected = "-",
multiple = TRUE, # allow for multiple inputs
options = list(create = TRUE)) # if TRUE, allows newly created inputs
),
conditionalPanel(
condition = "input.tabs=='About'",
#Session counter: https://gist.github.com/trestletech/9926129
h4("About"), "There are currently",
verbatimTextOutput("count"),
"session(s) connected to this app.",
hr(),
h4("Find our other dataViz apps at:"),a("https://huygens.science.uva.nl/", href = "https://huygens.science.uva.nl/")
)
), #Close sidebarPanel
# Show a plot of the generated distribution
mainPanel(
tabsetPanel(id="tabs",
tabPanel("Data", h4("Data as provided"),dataTableOutput("data_uploaded")),
tabPanel("Plot",h3("BA Plot"
),
downloadButton("downloadPlotPDF", "Download pdf-file"),
# downloadButton("downloadPlotSVG", "Download svg-file"),
downloadButton("downloadPlotPNG", "Download png-file"),
# actionButton("settings_copy", icon = icon("clone"),
# label = "Clone current setting"),
plotOutput("coolplot",
height = 'auto'),htmlOutput("LegendText", width="200px", inline =FALSE),
NULL
),
# tabPanel("iPlot", h4("iPlot"), plotlyOutput("out_plotly")),
tabPanel("Data Summary",dataTableOutput('data_summary'),htmlOutput("stats", width="200px", inline =FALSE),
h4("QQ-plot:"),
plotOutput("qqplot", height = 'auto')
),
tabPanel("About", includeHTML("about.html"))
)
) #Close mainPanel
) #Close sidebarLayout
) #Close fluidPage
server <- function(input, output, session) {
# Session variable - initialize defaults
x_var.selected <- "x"
y_var.selected <- "y"
repeats.selected <- ""
g_var.selected <- ""
sheet.selected <- " "
isolate(vals$count <- vals$count + 1)
###### DATA INPUT ###################
df_upload <- reactive({
if (input$data_input == 1) {
x_var.selected <<- "J1"
y_var.selected <<- "S1"
repeats.selected <<- c("J1","J2","J3")
g_var.selected <<- "Subject"
data <- df_example_1
} else if (input$data_input == 2) {
x_var.selected <<- "T4X"
y_var.selected <<- "T4A"
repeats.selected <<- ""
g_var.selected <<- "Subject"
data <- df_example_3
} else if (input$data_input == 3) {
file_in <- input$upload
# Avoid error message while file is not uploaded yet
if (is.null(input$upload)) {
return(data.frame(x = "Click 'Browse...' to select a datafile or drop file onto 'Browse' button"))
# } else if (input$submit_datafile_button == 0) {
# return(data.frame(x = "Press 'submit datafile' button"))
} else {
#Isolate extenstion and convert to lowercase
filename_split <- strsplit(file_in$datapath, '[.]')[[1]]
fileext <- tolower(filename_split[length(filename_split)])
if (fileext == "txt" || fileext=="csv") {
data <- read.csv(file=file_in$datapath, sep = input$upload_delim, na.strings=c("",".","NA", "NaN", "#N/A", "#VALUE!"))
updateSelectInput(session, "sheet", choices = " ", selected = " ")
} else if (fileext=="xls" || fileext=="xlsx") {
names <- excel_sheets(path = input$upload$datapath)
# updateSelectInput(session, "sheet_names", choices = names)
sheet.selected <<- input$sheet
updateSelectInput(session, "sheet", choices = names, selected = sheet.selected)
if (input$sheet %in% names)
{
n <- which(names==input$sheet)
# sheet.selected <<- input$sheet
} else {
n <- 1
#Ensures update and selection of first sheet upon loading the data
updateSelectInput(session, "sheet", choices = names)
}
data <- read_excel(file_in$datapath, sheet = n , na = c("",".","NA", "NaN", "#N/A", "#VALUE!"))
}
# })
}
} else if (input$data_input == 4) {
x_var.selected <<- ""
y_var.selected <<- ""
g_var.selected <<- ""
repeats.selected <<- ""
if (input$data_paste == "") {
data <- data.frame(x = "Copy your data into the textbox,
select the appropriate delimiter, and
press 'Submit data'")
} else {
if (input$submit_data_button == 0) {
return(data.frame(x = "Press 'submit data' button"))
} else {
# isolate({
data <- read_delim(input$data_paste,
delim = input$text_delim,
col_names = TRUE)
# })
}
}
} else if (input$data_input == 5) {
#Read data from a URL
#This requires RCurl
if(input$URL == "") {
return(data.frame(x = "Enter a full HTML address, for example: https://zenodo.org/record/3713174/files/CSV_1-GFP-CSB-WT_vs_GFP-NLS.csv"))
} else if (url.exists(input$URL) == FALSE) {
return(data.frame(x = paste("Not a valid URL: ",input$URL)))
} else {data <- read.csv(input$URL)}
}
#Replace space and dot of header names by underscore
data <- data %>% select_all(~gsub("\\s+|\\.", "_", .))
#Change empty cells to NA
return(data)
})
#### DISPLAY UPLOADED DATA (as provided) ##################
output$data_uploaded <- renderDataTable(
df_upload(),
# df_transformed(),
rownames = FALSE,
options = list(pageLength = 20, autoWidth = FALSE,
lengthMenu = c(20, 100, 1000, 10000)),
editable = FALSE,selection = 'none'
)
##### Get Variables from the input ##############
observe({
df <- df_upload()
var_names <- names(df)
# Get the names of columns that are factors.
nms_fact <- names(Filter(function(x) is.factor(x) || is.integer(x) || is.logical(x) || is.character(x), df))
nms_var <- names(Filter(function(x) is.integer(x) || is.numeric(x) || is.double(x), df))
nms_fact <- c("-",nms_fact)
updateSelectInput(session, "x_var", choices = nms_var, selected = x_var.selected)
updateSelectInput(session, "y_var", choices = nms_var, selected = y_var.selected)
updateSelectInput(session, "g_var", choices = nms_fact, selected = g_var.selected)
updateSelectizeInput(session, "repeats", choices = nms_var, selected = repeats.selected)
})
########### GET INPUT VARIABLEs FROM HTML ##############
observe({
query <- parseQueryString(session$clientData$url_search)
############ ?url ################
if (!is.null(query[['url']])) {
# updateRadioButtons(session, "data_input", selected = 5)
updateTextInput(session, "URL", value= query[['url']])
observe(print((query[['url']])))
# updateTabsetPanel(session, "tabs", selected = "Plot")
}
############ ?data ################
if (!is.null(query[['data']])) {
presets_data <- query[['data']]
presets_data <- unlist(strsplit(presets_data,";"))
observe(print((presets_data)))
updateRadioButtons(session, "data_input", selected = presets_data[1])
# updateCheckboxInput(session, "tidyInput", value = presets_data[2])
# updateSelectInput(session, "x_var", selected = presets_data[3])
# updateSelectInput(session, "y_var", selected = presets_data[4])
# updateSelectInput(session, "g_var", selected = presets_data[5])
x_var.selected <<- presets_data[3]
y_var.selected <<- presets_data[4]
g_var.selected <<- presets_data[5]
if (presets_data[1] == "1" || presets_data[1] == "2") {
updateTabsetPanel(session, "tabs", selected = "Plot")
}
}
############ ?vis ################
if (!is.null(query[['vis']])) {
presets_vis <- query[['vis']]
presets_vis <- unlist(strsplit(presets_vis,";"))
observe(print((presets_vis)))
updateSliderInput(session, "pointSize", value = presets_vis[1])
updateSliderInput(session, "alphaInput", value = presets_vis[2])
updateSliderInput(session, "fc_cutoff", value = unlist(strsplit(presets_vis[3],",")))
updateSliderInput(session, "p_cutoff", value = presets_vis[4])
updateSelectInput(session, "direction", selected = presets_vis[5])
updateSelectInput(session, "criterion", selected = presets_vis[6])
}
############ ?can ################
if (!is.null(query[['can']])) {
presets_can <- query[['can']]
presets_can <- unlist(strsplit(presets_can,";"))
observe(print((presets_can)))
updateNumericInput(session, "top_x", value = presets_can[1])
updateCheckboxInput(session, "show_table", value = presets_can[2])
# updateCheckboxInput(session, "hide_labels", value= presets_can[3])
# updateCheckboxInput(session, "user_selected", value= presets_can[4])
# updateTextInput(session, "user_label_list", value= presets_can[4])
}
############ ?layout ################
if (!is.null(query[['layout']])) {
presets_layout <- query[['layout']]
presets_layout <- unlist(strsplit(presets_layout,";"))
# observe(print((presets_layout)))
# updateCheckboxInput(session, "rotate_plot", value = presets_layout[1])
# updateCheckboxInput(session, "no_grid", value = (presets_layout[2]))
updateCheckboxInput(session, "change_scale", value = presets_layout[3])
updateTextInput(session, "range_x", value= presets_layout[4])
updateTextInput(session, "range_y", value= presets_layout[5])
# updateCheckboxInput(session, "transform", value = presets_layout[6])
# updateRadioButtons(session, "transform_x", selected = presets_layout[7])
# updateRadioButtons(session, "transform_y", selected = presets_layout[8])
# updateCheckboxInput(session, "add_description", value = presets_layout[9])
if ((presets_layout[6])=='X') {
updateNumericInput(session, "plot_height", value= presets_layout[7])
updateNumericInput(session, "plot_width", value= presets_layout[8])
}
# updateTabsetPanel(session, "tabs", selected = "Plot")
}
############ ?color ################
if (!is.null(query[['color']])) {
presets_color <- query[['color']]
presets_color <- unlist(strsplit(presets_color,";"))
updateSelectInput(session, "adjustcolors", selected = presets_color[1])
updateTextInput(session, "user_color_list", value= presets_color[2])
}
############ ?label ################
if (!is.null(query[['label']])) {
presets_label <- query[['label']]
presets_label <- unlist(strsplit(presets_label,";"))
updateCheckboxInput(session, "add_title", value = presets_label[1])
updateTextInput(session, "title", value= presets_label[2])
updateCheckboxInput(session, "label_axes", value = presets_label[3])
updateTextInput(session, "lab_x", value= presets_label[4])
updateTextInput(session, "lab_y", value= presets_label[5])
updateCheckboxInput(session, "adj_fnt_sz", value = presets_label[6])
updateNumericInput(session, "fnt_sz_title", value= presets_label[7])
updateNumericInput(session, "fnt_sz_labs", value= presets_label[8])
updateNumericInput(session, "fnt_sz_ax", value= presets_label[9])
updateNumericInput(session, "fnt_sz_cand", value= presets_label[10])
updateCheckboxInput(session, "add_legend", value = presets_label[11])
# updateTextInput(session, "legend_title", value= presets_label[12])
}
############ ?url ################
if (!is.null(query[['url']])) {
updateRadioButtons(session, "data_input", selected = 5)
updateTextInput(session, "URL", value= query[['url']])
# observe(print((query[['url']])))
updateTabsetPanel(session, "tabs", selected = "Plot")
}
})
########### RENDER URL ##############
output$HTMLpreset <- renderText({
url()
})
######### GENERATE URL with the settings #########
url <- reactive({
base_URL <- paste(sep = "", session$clientData$url_protocol, "//",session$clientData$url_hostname, ":",session$clientData$url_port, session$clientData$url_pathname)
data <- c(input$data_input, "", input$x_var, input$y_var, input$g_var)
# vis <- c(input$pointSize, input$alphaInput, input$fc_cutoff, input$p_cutoff, input$direction, input$criterion)
#Convert upper/lower boundary to comma seperated values
fc <- input$fc_cutoff
fc <- paste(fc, collapse=",")
vis <- c(input$pointSize, input$alphaInput, fc, input$p_cutoff, input$direction, input$criterion)
#Convert the list of genes to a comma-seperated string
a <- input$user_label_list
a <- paste(a, collapse=",")
#as.character is necessary; if omitted TRUE is converted to 0 and FALSE to 1 which is undesired
can <- c(input$top_x, as.character(input$show_table), a)
layout <- c("", "", input$change_scale, input$range_x, input$range_y, "X", input$plot_height, input$plot_width)
#Hide the standard list of colors if it is'nt used
if (input$adjustcolors != "5") {
color <- c(input$adjustcolors, "none")
} else if (input$adjustcolors == "5") {
color <- c(input$adjustcolors, input$user_color_list)
}
############ COLOR ##########
label <- c(input$add_title, input$title, input$label_axes, input$lab_x, input$lab_y, input$adj_fnt_sz, input$fnt_sz_title, input$fnt_sz_labs, input$fnt_sz_ax, input$fnt_sz_cand, input$add_legend, input$legend_title, input$hide_labels_y)
#replace FALSE by "" and convert to string with ; as seperator
data <- sub("FALSE", "", data)
data <- paste(data, collapse=";")
data <- paste0("data=", data)
vis <- sub("FALSE", "", vis)
vis <- paste(vis, collapse=";")
vis <- paste0("vis=", vis)
can <- sub("FALSE", "", can)
can <- paste(can, collapse=";")
can <- paste0("can=", can)
#
layout <- sub("FALSE", "", layout)
layout <- paste(layout, collapse=";")
layout <- paste0("layout=", layout)
#
color <- sub("FALSE", "", color)
color <- paste(color, collapse=";")
color <- paste0("color=", color)
label <- sub("FALSE", "", label)
label <- paste(label, collapse=";")
label <- paste0("label=", label)
# stim <- sub("FALSE", "", stim)
# stim <- paste(stim, collapse=";")
# stim <- paste0("stim=", stim)
if (input$data_input == "5") {url <- paste("url=",input$URL,sep="")} else {url <- NULL}
# parameters <- paste(data, vis,layout,color,label,stim,url, sep="&")
parameters <- paste(data,vis,can,layout,color,label,url, sep="&")
preset_URL <- paste(base_URL, parameters, sep="?")
# observe(print(parameters))
return(preset_URL)
})
############# Pop-up that displays the URL to 'clone' the current settings ################
observeEvent(input$settings_copy , {
showModal(urlModal(url=url(), title = "Use the URL to launch VolcaNoseR with the current setting"))
})
################ SELECT COLUMNS AND ANNOTATE CHANGES #########
df_filtered <- reactive({
df <- df_upload()
observe({print('input')})
observe({print(head(df))})
x_choice <- input$x_var
y_choice <- input$y_var
koos <- df %>% select(`Measurement_1` = !!x_choice , `Measurement_2` = !!y_choice)
#Filter empty rows
koos <- koos %>% na.omit()
koos <- koos %>% mutate(Difference = Measurement_1-Measurement_2, Average = 0.5*Measurement_1+0.5*Measurement_2, Ratio=Measurement_1/Measurement_2, Percentage=100*Difference/(Average))
if (input$plot_type==1) {
koos$y <- koos$Difference
} else if (input$plot_type==2) {
koos$y <- koos$Percentage
} else if (input$plot_type==3) {
koos$y <- log10(koos$Ratio)
} else if (input$plot_type==5) {
koos$y <- koos$Measurement_2
}
observe({(print(head(koos)))})
########## SOME STATISTICAL EVALUATION OF THE DATA ##########
# Test the difference for normality
test_result <- shapiro.test(koos$y)
observe({print(test_result$p.value)})
return(koos)
})
df_stats <- reactive({
df <- df_filtered()
n <- length(df$y)
if (input$LoA != '3') {
diff <- mean(df$y)
stdev <- sd(df$y)
LoA_hi <- diff + 1.96* stdev
LoA_lo <- diff - 1.96* stdev
LoA_d <- LoA_hi - LoA_lo
# Approximate standard error of the standard deviation; source: Bland&Altman (1999)
SE_d <- stdev / sqrt(n - 1)
SE_stdev <- 1.71*SE_d
mean_CI_lo = diff + qt((1-0.95)/2, n - 1) * SE_d
mean_CI_hi = diff - qt((1-0.95)/2, n - 1) * SE_d
LoA_hi_CI_lo = LoA_hi + qt((1-0.95)/2, n - 1)*SE_stdev
LoA_hi_CI_hi = LoA_hi - qt((1-0.95)/2, n - 1)*SE_stdev
LoA_lo_CI_lo = LoA_lo + qt((1-0.95)/2, n - 1)*SE_stdev
LoA_lo_CI_hi = LoA_lo - qt((1-0.95)/2, n - 1)*SE_stdev
LoA_widest <- LoA_hi_CI_hi - LoA_lo_CI_lo
df_difference <- data.frame(parameter=c('difference', 'upper Limit of Agreement', 'lower Limit of Agreement', 'Difference between LoA'),Value=c(diff,LoA_hi, LoA_lo, LoA_d), lo=c(mean_CI_lo,LoA_hi_CI_lo,LoA_lo_CI_lo, NA),hi=c(mean_CI_hi,LoA_hi_CI_hi,LoA_lo_CI_hi, NA))
}
else if (input$LoA == '3') {
diff <- median(df$y)
LoA_hi <- quantile(df$y, probs=0.95)
LoA_lo <- quantile(df$y, probs=0.05)
# observe({print(LoA_hi, LoA_lo)})
LoA_d <- LoA_hi - LoA_lo
# Bootstrap to determine median, and the 95% coverage that reflects the LoA
df_boot <- as.data.frame(t(replicate(nrep, boot_LoA(df$y))))
# 95%CI of the median
median_CI <- quantile(df_boot$`50%`, probs=c(0.025,0.975))
LoA_lo_CI <- quantile(df_boot$`5%`, probs=c(0.025,0.975))
LoA_hi_CI <- quantile(df_boot$`95%`, probs=c(0.025,0.975))
LoA_widest <- LoA_hi_CI[2] - LoA_lo_CI[1]
###### NEED TO ADD 95%CI for Median and LoA #######
# Draw median 1000x from bootstrapping, calculate percentiles as well: boot_value <- quantile(new_sample, probs=c(0.05,0.5,0.95))
df_difference <- data.frame(parameter=c('difference', 'upper Limit of Agreement', 'lower Limit of Agreement', 'Difference between LoA'),Value=c(diff,LoA_hi, LoA_lo, LoA_d), lo=c(median_CI[1],LoA_hi_CI[1],LoA_lo_CI[1], NA), hi=c(median_CI[2],LoA_hi_CI[2],LoA_lo_CI[2], NA))
}
# rownames(df_difference) <- NULL
observe({print(df_difference)})
linearMod <- lm(y ~ Average, data=df)
x <- confint(linearMod, level=0.95)
intercept_lo <- x[1,1]
intercept_hi<- x[1,2]
slope_lo <- x[2,1]
slope_hi <- x[2,2]
intercept <- linearMod$coefficients[1]
slope <- linearMod$coefficients[2]
df_slopes <- data.frame(parameter=c('intercept', 'slope', 'widest possible LoA'),Value=c(intercept, slope, LoA_widest), lo=c(intercept_lo, slope_lo, NA), hi=c(intercept_hi,slope_hi, NA))
# Calculate the CI of intercept and slope
# df_x <- (data.frame(lo=x[,1], hi=x[,2]))
# df_coef <- bind_cols(df_coef,df_x)
df_difference <- bind_rows(df_difference,df_slopes)
# df_coef <- data.frame(df_coef, row.names = 1)
rownames(df_difference) <- NULL
observe({print(df_difference)})
return(df_difference)
})
output$data_summary <- renderDataTable(
datatable(
df_filtered_stats(),
colnames = c("Parameter","Value","95%CI lower limit","95%CI upper limit"),
selection = 'none',
extensions = c('Buttons', 'ColReorder'),
options = list(dom = 'Bfrtip', pageLength = 100,
buttons = c('copy', 'csv','excel', 'pdf'),
editable=FALSE, colReorder = list(realtime = FALSE), columnDefs = list(list(className = 'dt-center', targets = 2:4))
)
)
# %>% formatRound(columns = names(df_filtered_stats()), digits=input$digits_table)
)
##### Render the plot ############
##### Set width and height of the plot area
width <- reactive ({ input$plot_width })
height <- reactive ({ input$plot_height })
output$coolplot <- renderPlot(width = width, height = height, {
plot(plotdata())
}
)
output$qqplot <- renderPlot(width = 400, height = 400, {
plot(plotqq())
}
)
df_filtered_stats <- reactive({
df <- df_stats()
df <- df %>% mutate_at(c(2:4), round, input$digits_table)
if (input$LoA=='1') {
df <- df %>% filter(grepl("difference|upper Limit of Agreement|lower Limit of Agreement|Difference between LoA|intercept|widest possible LoA|slope", parameter))
} else if (input$LoA=='2') {
df <- df %>% filter(grepl("difference|intercept|slope", parameter))
} else if (input$LoA=='3') {
df <- df %>% filter(grepl("difference|upper Limit of Agreement|lower Limit of Agreement|Difference between LoA|intercept|widest possible LoA|slope", parameter))
}
# df <- df[-c(2,3,4),]
return(df)
})
plotqq <- reactive({
df <- as.data.frame(df_filtered())
p <- ggplot(data = df)
p <- p + aes(sample=`y`) + stat_qq() + stat_qq_line()
p <- p + theme_light(base_size = 16)
#remove gridlines (if selected
# theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
})
plotdata <- reactive({
df <- as.data.frame(df_filtered())
df_stats <- as.data.frame(df_stats())
slope_CI_lo = round(df_stats[6,3],2)
slope_CI_hi = round(df_stats[6,4],2)
#95CI of Slope != 0
if ((slope_CI_lo > 0 && slope_CI_hi > 0) || (slope_CI_lo < 0 && slope_CI_hi < 0)) {
showNotification(paste("The slope of the difference has a 95%CI [",slope_CI_lo,",",slope_CI_hi,"] that does not include zero suggesting proportional bias: using a regression-based LoA is recommended"), duration = 100, type = "warning")
}
########## Determine color use #############
newColors <- c("black", "","grey")
if (input$adjustcolors < 5) {
dot_color <- newColors[as.numeric(input$adjustcolors)]
observe({print(dot_color)})
} else if (input$adjustcolors == 5) {
dot_color <- input$user_color_list
}
############## Adjust X-scaling if necessary ##########
#Adjust scale if range for x (min,max) is specified
if (input$range_x != "" && input$change_scale == TRUE) {
rng_x <- as.numeric(strsplit(input$range_x,",")[[1]])
observe({ print(rng_x) })
} else if (input$range_x == "" || input$change_scale == FALSE) {
rng_x <- c(NULL,NULL)
}
############## Adjust Y-scaling if necessary ##########
#Adjust scale if range for y (min,max) is specified
if (input$range_y != "" && input$change_scale == TRUE) {
rng_y <- as.numeric(strsplit(input$range_y,",")[[1]])
} else if (input$range_y == "" || input$change_scale == FALSE) {
rng_y <- c(NULL,NULL)
}
avg <- df_stats[1,2]
# Calculate Stats for y-variable
# avg <- mean(df$y)
# stdev <- sd(df$y)