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server.R
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server.R
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{library(shinydashboard)
library(reshape2)
library(shinyjs)
library(dplyr)
library(ggplot2)
library(plotly)
library(DT)
library(chron)
library(stringr)
library(corrplot)
library(survival)
library(survminer)
library(randomForest)
library(e1071)
library(TSclust)
library(mice)
library(Amelia)
library(missForest)
library(Boruta)
library(pROC)
library(caret)
library(ROSE)
library(rattle)
library(rpart.plot)
library(RColorBrewer)
library(unbalanced)
library(varSelRF)
}
{
remove_missing_levels <- function(fit, test_data) {
# https://stackoverflow.com/a/39495480/4185785
# drop empty factor levels in test data
test_data %>%
droplevels() %>%
as.data.frame() -> test_data
# 'fit' object structure of 'lm' and 'glmmPQL' is different so we need to
# account for it
if (any(class(fit) == "glmmPQL")) {
# Obtain factor predictors in the model and their levels
factors <- (gsub("[-^0-9]|as.factor|\\(|\\)", "",
names(unlist(fit$contrasts))))
# do nothing if no factors are present
if (length(factors) == 0) {
return(test_data)
}
map(fit$contrasts, function(x) names(unmatrix(x))) %>%
unlist() -> factor_levels
factor_levels %>% str_split(":", simplify = TRUE) %>%
extract(, 1) -> factor_levels
model_factors <- as.data.frame(cbind(factors, factor_levels))
} else {
# Obtain factor predictors in the model and their levels
factors <- (gsub("[-^0-9]|as.factor|\\(|\\)", "",
names(unlist(fit$xlevels))))
# do nothing if no factors are present
if (length(factors) == 0) {
return(test_data)
}
factor_levels <- unname(unlist(fit$xlevels))
model_factors <- as.data.frame(cbind(factors, factor_levels))
}
# Select column names in test data that are factor predictors in
# trained model
predictors <- names(test_data[names(test_data) %in% factors])
# For each factor predictor in your data, if the level is not in the model,
# set the value to NA
for (i in 1:length(predictors)) {
found <- test_data[, predictors[i]] %in% model_factors[
model_factors$factors == predictors[i], ]$factor_levels
if (any(!found)) {
# track which variable
var <- predictors[i]
# set to NA
test_data[!found, predictors[i]] <- NA
# drop empty factor levels in test data
test_data %>%
droplevels() -> test_data
# issue warning to console
message(sprintf(paste0("Setting missing levels in '%s', only present",
" in test data but missing in train data,",
" to 'NA'."),
var))
}
}
return(test_data)
}
} #extra functions
{ExVec <- 1
TPname <<- "Time Point data"
TIname <<- "Time Interval data"
Basename <<- "Baseline data"
Compname <<- "Complication data"
counter <<- 0
TPsets <<- 0
TIsets <<- 0
Basesets <<- 0
Compsets <<- 0
Nevents <<- 0
windowsize <<- 10
Neventpatients <<- 0
Npatients <<- 0
Ntimepoints <<- 0
PercentageCom <<- as.integer((Neventpatients/Npatients)*100)
AverTime <<- as.integer(Ntimepoints/Npatients)
NvarTP <<- 0
Ninterval <<- 0
NvarTI <<- 0
eventyear <<- data.frame(vector = numeric(), Freq = numeric())
IDS <<- 0
namecol <<- vector()
showrep <<- "no"
if (exists("comp_data") == FALSE)
{types<- c("", "")
typeslength <<- 0
compcol1 <<- 0}
if (exists("TPtable") == FALSE)
{ TPtable <<- data.frame()
}
TPDDate <<- 0
TIDDate <<- 0
compDDate <<- 0
baseDDate <<- 0
TP_NAID <- vector()
TI_NAID <- vector()
Comp_NAID <- vector()
Base_NAID <- vector()
i <- 1
Sys.setlocale("LC_TIME","English")
if (exists("TP_data1") == FALSE)
{ TPVariables <<- c("")}
if (exists("comp_data") == TRUE)
{ if (exists("Comp_type"))
{types <<- unique(comp_data[,Comp_type])
typeslength <<- length(types)
compcol1 <<- typeslength-as.integer(typeslength/2)}
}
} #Basic Settings
function(input, output, session) {
options(shiny.maxRequestSize=400*1024^2)
output$summary <- renderPrint({
summary(cars)
})
observeEvent(input$clear, {
rm(comp_data, pos = ".GlobalEnv")
rm(TP_data1, pos = ".GlobalEnv")
rm(TI_data1, pos = ".GlobalEnv")
rm(base_data, pos = ".GlobalEnv")
counter <<- 0
TPsets <<- 0
TIsets <<- 0
Basesets <<- 0
Compsets <<- 0
Nevents <<- 0
Neventpatients <<- 0
Npatients <<- 0
Ntimepoints <<- 0
PercentageCom <<- as.integer((Neventpatients/Npatients)*100)
AverTime <<- as.integer(Ntimepoints/Npatients)
NvarTP <<- 0
Ninterval <<- 0
NvarTI <<- 0
eventyear <<- data.frame(vector = numeric(), Freq = numeric())
IDS <<- 0
types <<- c("", "")
typeslength <<- 0
session$reload();
})
observeEvent(input$loadTP, {
TPname <<- input$nameTP
if(is.null(input$TPD) == FALSE)
{ TP_data1 <<- read.csv(input$TPD$datapath,
header = input$headerTP,
sep = input$sepTP)
namecolTP <<- names(TP_data1)
updateSelectInput(session, "ITP_ID",
label = paste("Select ID column"),
choices = namecolTP,
selected = tail(1, 1))
updateSelectInput(session, "ITP_Date",
label = paste("Select Date of Timepoint Column"),
choices = namecolTP,
selected = tail(1, 1))
updateSelectInput(session, "ITP_Time",
label = paste("Select Time of Timepoint Column (optional)"),
choices = namecolTP,
selected = tail(1, 1))
updateSelectInput(session, "ITP_Type",
label = paste("Select Type column"),
choices = namecolTP,
selected = tail(1, 1))
updateSelectInput(session, "ITP_Outcome",
label = paste("Select Outcome column"),
choices = namecolTP,
selected = tail(1, 1))
TPDDate <<- 1
TPsets <<- 1}
})
observeEvent(input$loadTI, {
TIname <<- input$nameTI
if(is.null(input$TID) == FALSE)
{TI_data1 <<- read.csv(input$TID$datapath,
header = input$headerTI,
sep = input$sepTI)
namecolTI <<- names(TI_data1)
updateSelectInput(session, "ITI_ID",
label = paste("Select ID column"),
choices = namecolTI,
selected = tail(1, 1))
updateSelectInput(session, "ITI_SD",
label = paste("Select Start date column"),
choices = namecolTI,
selected = tail(1, 1))
updateSelectInput(session, "ITI_ED",
label = paste("Select End date column"),
choices = namecolTI,
selected = tail(1, 1))
updateSelectInput(session, "ITI_EDD",
label = paste("Select End Date 2 Column (optional)"),
choices = namecolTI,
selected = tail(1, 1))
updateSelectInput(session, "ITI_Type1",
label = paste("Select Type1 column"),
choices = namecolTI,
selected = tail(1, 1))
updateSelectInput(session, "ITI_Type2",
label = paste("Select Type2 column (optional)"),
choices = namecolTI,
selected = tail(1, 1))
TIDDate <<- 1
TIsets <<- 1}
})
observeEvent(input$loadcomp, {
Compname <<- input$nameComp
if(is.null(input$COMPD) == FALSE)
{
comp_data <<- read.csv(input$COMPD$datapath,
header = input$headerComp,
sep = input$sepComp)
namecolComp <<- names(comp_data)
updateSelectInput(session, "ICOMP_ID",
label = paste("Select ID column"),
choices = namecolComp,
selected = tail(1, 1))
updateSelectInput(session, "ICOMP_Date",
label = paste("Select Date Column"),
choices = namecolComp,
selected = tail(1, 1))
updateSelectInput(session, "ICOMP_NDays",
label = paste("Select intervention days Column"),
choices = namecolComp,
selected = tail(1, 1))
updateSelectInput(session, "ICOMP_Type",
label = paste("Select Type Column"),
choices = namecolComp,
selected = tail(1, 1))
compDDate <<- 1
Compsets <<- 1
}
})
observeEvent(input$loadBase, {
Basename <<- input$nameBase
if(is.null(input$BASED) == FALSE)
{base_data <<- read.csv(input$BASED$datapath,
header = input$headerBase,
sep = input$sepBase)
namecolBase <<- names(base_data)
updateSelectInput(session, "IBASE_ID",
label = paste("Select ID column"),
choices = namecolBase,
selected = tail(1, 1))
updateSelectInput(session, "IBASE_Start",
label = paste("Select Start Column"),
choices = namecolBase,
selected = tail(1, 1))
updateSelectInput(session, "IBASE_End",
label = paste("Select End Column"),
choices = namecolBase,
selected = tail(1, 1))
updateSelectInput(session, "IBASE_Age",
label = paste("Select Age Column"),
choices = namecolBase,
selected = tail(1, 1))
updateSelectInput(session, "IBASE_Gender",
label = paste("Select Gender Column"),
choices = namecolBase,
selected = tail(1, 1))
updateSelectInput(session, "IBASE_Mort",
label = paste("Select Mortality date Column"),
choices = namecolBase,
selected = tail(1, 1))
baseDDate <<- 1
Basesets <<- 1}
})
observeEvent(input$run, {
withProgress(message = 'Preparing datasets!', value = 0, {
if (exists("TP_data1") == TRUE)
{if (TPDDate == 1)
{TP_ID <<- which(names(TP_data1) == input$ITP_ID)
TP_date <<- which(names(TP_data1)== input$ITP_Date)
TP_Type <<- which(names(TP_data1)== input$ITP_Type)
TP_Outcome <<- which(names(TP_data1)== input$ITP_Outcome)
#TP_time <<- which(names(TP_data1)== input$ITP_Time)
TP_data1[,TP_Type] <<-str_replace_all(TP_data1[,TP_Type], "[^[:alnum:]]", "_")
if (class(TP_data1[,input$ITP_Date]) != "Date")
{ if (!is.na(as.Date(TP_data1[1,TP_date], "%d%B%Y")))
TP_data1[,TP_date] <<- as.Date(TP_data1[,TP_date], "%d%B%Y")
else
TP_data1[,TP_date] <<- as.Date(TP_data1[,TP_date])}
TP_data1 <<- TP_data1[TP_data1[,TP_Outcome] != "-volgt-" ,]
TP_data1 <<- TP_data1[TP_data1[,TP_Outcome] != "NEG" ,]
TP_data1 <<- TP_data1[TP_data1[,TP_Outcome] != "POS" ,]
TP_data1[,TP_Outcome] <<- gsub("<", "", TP_data1[,TP_Outcome])
TP_data1[,TP_Outcome] <<- gsub(">", "", TP_data1[,TP_Outcome])
TP_data1[,TP_Outcome] <<- as.numeric(as.character(TP_data1[,TP_Outcome]))
TP_data1 <<- TP_data1[!is.na(TP_data1[,TP_Outcome]),]}
TP_Nvariables <<- as.data.frame(table(TP_data1[,TP_Type]))
TP_Nvariables <<- TP_Nvariables[order(-TP_Nvariables$Freq),]
TPtable <<- TP_Nvariables[1:100,]
NVar <- length(unique(TP_Nvariables$Var1))
if (NVar > 20) {NVar <- 20}
TPVariables <<- TP_Nvariables[1:NVar,1]
Npatients <<- length(unique(TP_data1[,TP_ID]))
Ntimepoints <<- nrow(TP_data1)
NvarTP <<- length(unique(TP_data1[,TP_Type]))
incProgress((5/100), detail = paste("Preparing Environment", "5% done"))
patientID <- unique(TP_data1[,TP_ID])
vec <- vector()
for (i in 1:length(patientID))
{subdf <- TP_data1[TP_data1[,TP_ID] == patientID[i],]
subdf <- subdf[subdf[,TP_Type] == TPVariables[1], TP_date]
subdf <- subdf[order(subdf)]
subdf <- subdf[!duplicated(subdf)]
for (j in 1:(length(subdf))-1)
{ x <- subdf[j+1]-subdf[j]
vec <- c(vec, x)
}
}
meanTP <<- mean(vec)
}
if (exists("comp_data") == TRUE)
{ if(compDDate == 1)
{Comp_ID <<- which(names(comp_data) == input$ICOMP_ID)
Comp_date <<- which(names(comp_data) == input$ICOMP_Date)
Comp_type <<- which(names(comp_data) ==input$ICOMP_Type)
Comp_NDays <<- which(names(comp_data) ==input$ICOMP_NDays)
if (class(comp_data[,Comp_date]) != "Date")
{if (!is.na (as.Date(comp_data[1,Comp_date], "%d-%m-%Y")))
{comp_data[,Comp_date] <<-as.Date(comp_data[,Comp_date], "%d-%m-%Y")
vector <- format(as.Date(comp_data[,Comp_date], format="%d-%m-%Y"),"%Y")}
else
{comp_data[,Comp_date] <<-as.Date(comp_data[,Comp_date], "%Y-%m-%d")
vector <- format(as.Date(comp_data[,Comp_date], format="%Y-%m-%d"),"%Y")
}
}}
Nevents <<- nrow(comp_data)
Neventpatients <<- length(unique(comp_data[,Comp_ID]))
#vector <- format(as.Date(comp_data[,Comp_date], format="%d-%m-%Y"),"%Y")
eventyear <<- as.data.frame(table(vector))
types <<- unique(comp_data[,Comp_type])
typeslength <<- length(types)
compcol1 <<- typeslength-as.integer(typeslength/2)
incProgress((5/100), detail = paste("Preparing Environment", "10% done"))
}
if (exists("base_data") == TRUE)
{ if( baseDDate == 1)
{
Base_ID <<- which(names(base_data)== input$IBASE_ID)
Base_Start <<- which(names(base_data)== input$IBASE_Start)
Base_End <<- which(names(base_data)== input$IBASE_End)
Base_Age <<- which(names(base_data)== input$IBASE_Age)
Base_Gender <<- which(names(base_data)== input$IBASE_Gender)
Base_Mort <<- which(names(base_data)== input$IBASE_Mort)
if (!is.na(as.Date(base_data[1,Base_Start], "%d-%m-%Y")))
{base_data[,Base_Start] <<- as.Date(base_data[,Base_Start], "%d-%m-%Y")
base_data[,Base_End] <<- as.Date(base_data[,Base_End], "%d-%m-%Y")}
else
{base_data[,Base_Start] <<- as.Date(base_data[,Base_Start], "%Y-%m-%d")
base_data[,Base_End] <<- as.Date(base_data[,Base_End], "%Y-%m-%d")}
base_data$stat <<-0
base_data[is.na(base_data[,Base_Mort]),ncol(base_data)] <<- 1
Base_stat <<- ncol(base_data)
base_data$stat1 <<-1
# base_data[is.na(base_data[,Base_Mort]),ncol(base_data)] <<- 0
# base_data[(base_data[,Base_Mort]== "") & base_data[,Base_End]< as.Date("1-1-2018" ,"%d-%m-%Y"),ncol(base_data)] <<- 0
Base_stat1 <<- ncol(base_data)
base_data$NdayPat <<- base_data[,Base_End]-base_data[,Base_Start]
Base_nday <<- ncol(base_data)
base_data <<- base_data[,c(Base_ID, Base_Start, Base_End, Base_Age, Base_Gender, Base_stat, Base_nday, Base_stat1)]
base_data <<- base_data[!duplicated(base_data),]
Base_ID <<- 1
Base_Start <<- 2
Base_End <<- 3
Base_Age <<- 4
Base_Gender <<- 5
Base_stat <<- 6
Base_nday <<- 7
Base_stat1 <<- 8
base_data[,Base_Age] <<- as.numeric(gsub(",", ".", base_data[,Base_Age]))
doubleID <<- which(duplicated(base_data[,Base_ID])== TRUE)
IDS <<- base_data[,Base_ID]
incProgress((5/100), detail = paste("Preparing Environment", "15% done"))
}}
if (exists("TI_data1") == TRUE)
{ if (TIDDate == 1)
{
TI_ID <<- which(names(TI_data1)== input$ITI_ID)
TI_start <<- which(names(TI_data1)== input$ITI_SD)
TI_end <<- which(names(TI_data1)== input$ITI_ED)
TI_Type1 <<- which(names(TI_data1)== input$ITI_Type1)
TI_Type2 <<- which(names(TI_data1)== input$ITI_Type2)
TI_endd <<-which(names(TI_data1)== input$ITI_EDD)
if (class(TI_data1[,input$ITI_SD]) != "Date")
#{if (!is.na(as.Date(TI_data1[1,TI_start], "%d%B%Y")))
TI_data1[,TI_start] <<- as.Date(TI_data1[,TI_start], "%d%B%Y")
#else
# TI_data1[,TI_start] <<- as.Date(TI_data1[,TI_start], "%Y-%m-%d")}
if (class(TI_data1[,input$ITI_ED]) != "Date")
# {if (!is.na(as.Date(TI_data1[1,TI_end], "%d%B%Y")))
TI_data1[,TI_end] <<- as.Date(TI_data1[,TI_end], "%d%B%Y")
# else
# TI_data1[,TI_start] <<- as.Date(TI_data1[,TI_end], "%Y-%m-%d")}
}
TI_data1 <<-TI_data1[complete.cases(TI_data1[,TI_start]),]
# if no end-date, new end-date
if (exists("TI_endd"))
{if (!is.na(as.Date(TI_data1[1,TI_endd], "%d-%m-%Y")))
{TI_data1[,TI_endd] <<- as.Date(TI_data1[,TI_endd], "%d-%m-%Y")}
else
{TI_data1[,TI_endd] <<- as.Date(TI_data1[,TI_endd], "%Y-%m-%d")}
for (i in 1:nrow(TI_data1))
{ if(is.na(TI_data1[i,TI_end]) == TRUE)
{TI_data1[i,TI_end] <<- TI_data1[TI_data1[,TI_ID] == TI_data1[i,TI_ID],TI_endd][1]
}}}
TI_data1 <<- TI_data1[TI_data1[,TI_end] < Sys.Date(),]
TI_data1$ID <<- seq(1:nrow(TI_data1))
TItable <<- as.data.frame(table(TI_data1[,TI_Type1], TI_data1[,TI_Type2]))
TItable <<- TItable[order(-TItable$Freq),]
TItable <<- TItable[1:50,]
incProgress((5/100), detail = paste("Preparing Environment", "20% done"))
Ninterval <<- nrow(TI_data1)
NvarTI <<- length(unique(TI_data1[,TI_Type1]))}
Nsets <<- c("TP_data1", "TI_data1", "comp_data", "base_data")
counter <<- 0
for (i in 1:length(Nsets))
{if (exists(Nsets[i]))
counter <<- counter+1}
PercentageCom <<- as.integer((Neventpatients/Npatients)*100)
AverTime <<- as.integer(Ntimepoints/Npatients)
compDDate <<-0
baseDDate <<- 0
TPDDate <<- 0
TIDDate <<- 0
MisdfNames <<- "All patients"
incProgress((10/100), detail = paste("Preparing Environment", "30% done"))
IDS <<- vector()
if (exists("TP_data1"))
{TP_unID <<- unique(TP_data1[,TP_ID])
IDS <<- c(IDS, TP_unID)
MisdfNames <<- c(MisdfNames,paste("Patients in", TPname))}
if (exists("TI_data1"))
{TI_unID <<- unique(TI_data1[,TI_ID])
IDS <<- c(IDS, TI_unID)
MisdfNames <<- c(MisdfNames,paste("Patients in", TIname))}
if(exists("base_data"))
{Base_unID <<- unique(base_data[,Base_ID])
IDS <<- c(IDS, Base_unID)
MisdfNames <<- c(MisdfNames,paste("Patients in", Basename))}
if(exists("comp_data"))
{Comp_unID <<- unique(comp_data[,Comp_ID])
IDS <<- c(IDS, Comp_unID)
MisdfNames <<- c(MisdfNames,paste("Patients in", Compname))}
IDS <<- unique(IDS)
MisDF <<- data.frame()
incProgress((5/100), detail = paste("Preparing Environment", "35% done"))
for(i in 1:length(IDS))
{ TPX <- NA
TIX <- NA
COMPX <- NA
BASEX <- NA
misrow <- 1
incProgress( 0.65/length(IDS), detail = paste("Preparing Environment", (0.65/length(IDS)*i*100)+65, "% Done"))
if(exists("TP_data1"))
{ if(IDS[i] %in% TP_unID)
TPX <- 1
misrow <- c(misrow, TPX)}
if(exists("TI_data1"))
{ if(IDS[i] %in% TI_unID)
TIX <- 1
misrow <- c(misrow, TIX)}
if(exists("base_data"))
{if(IDS[i] %in% Base_unID)
BASEX <- 1
misrow <- c(misrow, BASEX)}
if(exists("comp_data"))
{ if(IDS[i] %in% Comp_unID)
COMPX <- 1
misrow <- c(misrow, COMPX)}
MisDF <<- rbind(MisDF, misrow)}
names(MisDF)<<- MisdfNames})
# All code for MISDF
session$reload();
})
observe({
if (typeslength > 1)
{updateCheckboxGroupInput(session, "comptypes1",
label = "please select your class label",
choices = types[1:compcol1])
updateCheckboxGroupInput(session, "comptypes2",
label = NULL,
choices = types[(compcol1+1):typeslength])}
})
observeEvent(input$DQRep,{
showrep <<- "yes"
if(exists("TP_data1"))
{TP_missing <<- sum(is.na(MisDF$`Patients in TP`))}
if(exists("TI_data1"))
TI_missing <<- sum(is.na(MisDF$`Patients in TI`))
if(exists("comp_data"))
Comp_missing <<- sum(is.na(MisDF$`Patients in Comp`))
if(exists("base_data"))
Base_missing <<- sum(is.na(MisDF$`Patients in Base`))
})
observeEvent(input$preparecomp, {
classlabels1 <<- input$comptypes1
classlabels2 <<- input$comptypes2
classlabels <<- c(classlabels1, classlabels2)
comp_data <<- comp_data[comp_data[,Comp_type] %in% classlabels,]
comp_data <<- comp_data[comp_data[,Comp_NDays] > 30,]
Neventpatients <<- length(unique(comp_data[,Comp_ID]))
vector <- format(as.Date(comp_data[,Comp_date], format="%d-%m-%Y"),"%Y")
eventyear <<- as.data.frame(table(vector))
Nevents <<- nrow(comp_data)
PercentageCom <<- as.integer((Neventpatients/Npatients)*100)
output$preparecompbut <- renderText({ "Ready!" })
})
output$showrepp <- renderText({showrep})
output$datadesc <- renderText({
paste("In total we are working with", counter, "datasets,", TPsets, "time points dataset(s),", TIsets, "time interval dataset(s)", Compsets, "event dataset(s) and ", Basesets, " baseline dataset(s)")
})
output$classdesc <- renderText({
paste("Let's start with the", input$nameComp, ". In total we have ", Nevents, "medical events. These events occured at ", Neventpatients, "patients. In your datasets you have ", Npatients , "unique patients, which thus means that ", PercentageCom, "of the patient was targeted by that event.")
})
output$tpdesc <- renderText({
paste("Now let's take a look at your",input$nameTP, ", in total you have ", Ntimepoints, "datapoints. This means that the average amount of time points per patient is ", AverTime, ". Obviously this is not from one variable, in total we have ", NvarTP, "variables. The average time between the time points is", as.integer(meanTP), " days.")
})
output$tidesc <- renderText({
paste("Your", input$nameTI, "has ", Ninterval, "intervals. These intervals are distributed over ", NvarTI, "variables.")
})
output$doubleBase <- renderText({if(exists("doubleID"))
{paste(c("It seems that you have patients appearing multiple times in your in your", input$nameBase ,"please check if this is correct. I found in total", as.character(length(double)), "patient(s)."), sep = " ")
}
})
output$doubleBase1 <- renderText({ if(length(doubleID)< 5) paste(c("The following patientID's appeared multiple times:", IDS[doubleID]), sep = ":" )
})
output$datasimu <- renderUI({
str1 <- paste("")
strTP <- paste("")
strTI <- paste("")
strCOMP <- paste("")
strBASE <- paste("")
if(counter > 0)
str1 <- paste("Before we can integrate the datasets into one dataset, we need check wether all patients are present in all datasets. In figure 1 we first plotted the ", Npatients, "patients in the first column and is followed by the other ", counter, "datasets. These patients are chronologically ordered (patient 1 is the first patients, patient ", Npatients, "the most recent) So we can conclude that")
if(exists("TP_data1"))
strTP <- paste(" the time point dataset has", TP_missing, "missing patients" )
if(exists("TI_data1"))
strTI <- paste(", the time interval dataset has", TI_missing, "missing patients" )
if(exists("comp_data"))
strCOMP <- paste(", the complication dataset has", Comp_missing, "missing patients" )
if(exists("base_data"))
strBASE <- paste(", the baseline dataset has", Base_missing, "missing patients" )
HTML(paste(str1, strTP, strTI, strCOMP, strBASE, sep = ''))
})
output$TP_name <- renderText({TPname})
output$TI_name <- renderText({TIname})
output$COmp_name <- renderText({Compname})
output$Base_name <- renderText({Basename})
output$counterr <- renderText({counter})
output$Neventss <- renderText({Nevents})
output$Neventpatientss <- renderText({Neventpatients})
output$Npatientss <- renderText({Npatients})
output$PercentageComm <- renderText({PercentageCom})
output$Ntimepointss <- renderText({Ntimepoints})
output$AverTimee <- renderText({AverTime})
output$NvarTPp <- renderText({NvarTP})
output$Nintervall <- renderText({Ninterval})
output$NvarTIi <- renderText({NvarTI})
output$histoplot <- renderPlot({barplot(eventyear$Freq, names.arg = eventyear$vector)})
output$missplot <- renderPlot({MisDF %>% is.na %>% melt %>%
ggplot(data = .,aes(x = Var2,y = Var1)) +
geom_raster(aes(fill = value)) +
scale_fill_grey(name = "", labels = c("Observed","Missing")) +
theme_minimal() +
theme(axis.text.x = element_text(angle=45, vjust=0.5, size=10)) +
labs(x = "Datasets",y = "Patients")})
output$labpatientplot <- renderPlot({
if(exists("TP_data1"))
{TP_Npatients <- as.data.frame(table(TP_data1[,TP_ID], TP_data1[,TP_Type]))
TP_Npatients <- TP_Npatients[TP_Npatients$Freq > 0,]
TP_Nvariables <- as.data.frame(table(TP_Npatients$Var2))
TP_Nvariables <- TP_Nvariables[order(-TP_Nvariables$Freq),]
TP_Nvariables$ID <- seq.int(nrow(TP_Nvariables))
TP_Nvariables <- TP_Nvariables[order(TP_Nvariables$Var1),]
ggplot(TP_Nvariables, aes(x= ID, y=Freq))+ ggtitle ("Amount Variables vs amount patients") + geom_line() + xlab("N Variables") + ylab("N Patients")
}})
output$Dplot <- renderPlotly({
if (input$DDDplot == FALSE) return()
if (input$demo == TRUE)
{yyy <- seq(1:length(Demo_Patients))
xxx <- Demo_variables
zzz <- as.matrix(Demo_TP3D)}
else
{
TP_Npatients <- as.data.frame(table(TP_data1[,TP_ID], TP_data1[,TP_Type]))
TP_Npatients <- TP_Npatients[TP_Npatients$Freq > 0,]
TP_Nvariables <- as.data.frame(table(TP_Npatients$Var2))
TP_Nvariables <- TP_Nvariables[order(-TP_Nvariables$Freq),]
TP_Nvariables$ID <- seq.int(nrow(TP_Nvariables))
TP_Nvariables <- TP_Nvariables[order(TP_Nvariables$Var1),]
TP_Nvariables1 <- as.data.frame(table(TP_data1[,TP_Type]))
TP_Nvariables1 <- TP_Nvariables1[order(TP_Nvariables1$Var1),]
TP_Nvariables$TPs <- TP_Nvariables1$Freq/TP_Nvariables$Freq
TP_Nvariables1 <- TP_Nvariables1[order(-TP_Nvariables1$Freq),]
variables <- as.character(TP_Nvariables1$Var1)
if (nrow(TP_Nvariables1) > 150)
{variables <- as.character(TP_Nvariables1$Var1[1:150])}
for (i in 1:length(variables))
{variables[i] <- paste(as.character(i), variables[i])}
TP_patientTimepoints <- as.data.frame(table(TP_data1[,TP_ID]))
TP_patientTimepoints <- TP_patientTimepoints[order(-TP_patientTimepoints$Freq),]
patients <- TP_patientTimepoints[,1]
TP3D <- data.frame()
withProgress(message = 'Making plot', value = 0, {
for(i in 1:length(patients))
{ patient_subset <- TP_data1[TP_data1[, TP_ID] == patients[i],]
rowdata <- vector()
incProgress(100/length(patients), detail = paste("Doing part", i))
for(j in 1:length(variables))
{x <- sum(patient_subset[,TP_Type] == variables[j])
rowdata <- c(rowdata, x)}
TP3D <- rbind(TP3D, rowdata)}
yyy <- seq(1:length(patients))
xxx <- variables
zzz <- as.matrix(TP3D)})}
plot_ly(x = xxx, y = yyy, z = zzz, colors = c("green", "blue"), type = "surface", width = 1200, height = 500) %>% layout(title = "3D Plot", scene = list( xaxis = list(title = "variables"), yaxis = list(title = "patients"), zaxis = list(title = "timepoints")))
})
output$TP_table <- renderDataTable({
if(exists("TPtable"))datatable(TPtable,rownames= FALSE)
})
output$DPTPsel <- renderDataTable({
if(exists("TPtable"))datatable(TPtable,rownames= FALSE, selection = list(target = 'row', selected = c(seq(1:1))) )
})
output$medtable <- renderDataTable({
if(exists("TItable"))datatable(TItable, rownames = FALSE)
})
output$DPTIsel <- renderDataTable({
if(exists("TItable"))datatable(TItable,rownames= FALSE, selection = list(target = 'row', selected = c(seq(1:5))) )
})
output$DPTIsell <- renderDataTable({
if(exists("TItable")){datatable(TItable[input$DPTIsel_rows_selected,])
} })
output$DPTIselll <- renderDataTable({
if(exists("TItable")){datatable(TItable[input$DPTIsel_rows_selected,])
} })
output$DPTIsellll <- renderDataTable({
if(exists("TItable")){datatable(TItable[input$DPTIsel_rows_selected,])
} })
outVar = reactive({
mydata = (input$DPTPsel_rows_selected)
})
observe({
updateSelectInput(session, "classBaseWindow",
label = paste("Select Time point variable for structuring rolling window"),
choices = TPtable[outVar(),1],
selected = TPtable[5,1])})
output$testrows <- renderText({input$medtable_rows_selected})
output$medplot <- renderPlotly({
if (input$Cmedplot == FALSE) return()
if (input$demo == TRUE)
{
medresult <- Demo_medresult
medtext <- Demo_medtext
datess <- Demo_datess}
else
{
selectedrows <<- input$medtable_rows_selected
if (length (selectedrows) == 0) return()
selectedmeds <<- TItable[selectedrows,1]
totstart <-as.numeric(min(base_data[,Base_Start]))-10
totend <- as.numeric( max(base_data[,Base_End]))
dates <- vector()
medresult <- data.frame()
medtext <- data.frame()
medication <<- TI_data1[TI_data1[,TI_Type1] %in% selectedmeds,]
ID <- unique(base_data[,Base_ID])
withProgress(message = 'Making plot', value = 0, {
n <- length(ID)
for (i in 1:length(ID))
{
selectmed <- medication[grep(ID[i], medication[,TI_ID]),]
incProgress(1/length(ID), detail = paste("Doing part", i))
# selectlab <- df5[grep(ID[i], df5$PseudoID_voorkeur),]
# selectlab <- selectlab[order(selectlab$AfnameDatum_LAB),]
# start <- selectlab[1,82]
#start <- selectmed[1,2]
selectlab <- base_data[grep(ID[i], base_data[,Base_ID]),]
start <- selectlab[,Base_Start]
start <- as.numeric(start)
end <- selectlab[,Base_End]
selectmed$Einddatum[is.na(selectmed[,TI_end])] <- end
end <- as.numeric(end)
framerange <- 7
dates <- vector()
complication <- comp_data[grep(ID[i], comp_data[,Comp_ID]),]
compdates <- complication[,Comp_date]
compdates <- as.numeric(compdates)
for (j in 1:611)
{date <- (framerange*j)+totstart
medresult [i,j] <- -1
medtext [i, j] <- paste(as.Date(date, origin = "1970-01-01"),"Patient:", ID[i], "Status: in cohort, not within selected time interval")
dates <- c(dates, date)
if (nrow(selectmed) != 0){
for (k in 1:nrow(selectmed)){
if (date > selectmed[k,TI_start] & date < selectmed[k, TI_end])
{ medresult[i,j] <- 0
medtext [i, j] <- paste(as.Date(date, origin = "1970-01-01"),"Patient:", ID[i], "Status: in cohort, within selected time interval")
}} #gebruik Med
}
if (date < start)
{
medresult[i,j] <- 1 # datum voor LVAD patient
medtext [i, j] <- paste(as.Date(date, origin = "1970-01-01"),"Patient:", ID[i], "Status: Not in cohort")
}
if (date > end)
{
medresult[i,j] <- 1 # datum na LVAD patient
medtext [i, j] <- paste(as.Date(date, origin = "1970-01-01"),"Patient:", ID[i], "Status: Not in cohort")
}
if (length(compdates) != 0 )
{for (l in 1:length(compdates))
{if( compdates[l] >= date & compdates[l] <= (date+6))
{medresult [i,j] <- 2
medtext [i, j] <- paste("Date", as.Date(compdates[l], origin = "1970-01-01"),"Patient:", ID[i], "Status: event present")
}}}
}}
})
medresult <- as.matrix(medresult)
medtext <- as.matrix(medtext)
framerange <- 7
dates <- vector()
for (j in 1:611)
{date <- (framerange*j)+totstart
dates <- c(dates,date)
}
datess <-as.Date(dates, origin = "1970-01-01")
datess <- as.data.frame(datess)}
plot_ly(x = datess[,1], y = rownames(medresult), z = medresult, type = "heatmap", height = 900, hoverinfo='text', text = medtext, showscale=FALSE)})
output$eventplot <- renderPlot({
x <- Comp_NDays
for(i in 1:ncol(comp_data))
{if ( is.factor(comp_data[,i]))
{if (length (levels(comp_data[,i])) > 1 & length(levels(comp_data[,i])) < 6)
x <- i}
}
ggplot(data=comp_data, aes(comp_data[,Comp_NDays]))+ geom_histogram( col = "black", aes(fill = comp_data[,x]), alpha=.2, binwidth = 100)+ labs(title="Histogram: days of event after intervention", x="Days", y="Count", fill = names(comp_data)[x])
})
output$cohortdays <- renderPlot({
base_data$NdayPat <- base_data[,Base_End] - base_data[,Base_Start]
base_data$NdayPat <- as.numeric(base_data[,ncol(base_data)])
yearspatient <- data.frame(numeric(), numeric())
yearspatient<- rbind(yearspatient, c(0, 274))
years <- as.integer(max(base_data[,ncol(base_data)]) / 356)+1
for(i in 1:(years*2))
{ x <- sum(base_data$NdayPat > ((i/2) * 356))
yearspatient <- rbind(yearspatient, c((i/2), x))
}
names(yearspatient) <- c("years", "patients")
ggplot(data=yearspatient, aes(x = yearspatient$years , y =yearspatient$patients)) + geom_bar(stat = "identity", col = "black", fill = "blue", alpha=.2)+ labs(title="Patients in cohort", x="Years", y="Patients")
})
observe({
if(input$selectall == 0) return(NULL)
else if (input$selectall%%2 == 0)
{
updateCheckboxGroupInput(session,"TPselected","please select the TP variables",choices=TPVariables, inline = TRUE)
}
else
{
updateCheckboxGroupInput(session,"TPselected","please select the TP variables",choices=TPVariables,selected=TPVariables, inline = TRUE)
}
})
output$kaplan <- renderPlot({
base_data$SurvObj <- with(base_data, Surv(NdayPat, stat ==0))
km.as.one <- survfit(SurvObj ~ 1, data = base_data, conf.type = "log-log")
ggsurvplot(km.as.one, data = base_data, risk.table = TRUE,ggtheme = theme_light())
})
output$kaplann <- renderPlot({
base_data$stat1 <- 1
base_data$NdayPat1 <- base_data$NdayPat
for (i in 1:nrow(base_data))
{ if(base_data[i,Base_ID] %in% comp_data[,Comp_ID])
base_data$stat1[i] <- 0
subdf <- comp_data[comp_data[,Comp_ID] == base_data[i,Base_ID],]
subdf <- subdf[order(subdf[,Comp_date]),]
Nday <- as.numeric(subdf[i,Comp_date]- base_data[i,Base_Start])
}
base_data$SurvObjj <- with(base_data, Surv(NdayPat1, stat1 ==0))
km.as.one <- survfit(SurvObjj ~ 1, data = base_data, conf.type = "log-log")
ggsurvplot(km.as.one, data = base_data, risk.table = TRUE,ggtheme = theme_light())
})
output$MeanComPlot <- renderPlot({
if(input$demo == TRUE)
{
patientPLN <- Demo_patientPLN
}
else
{
patientPLN <- globals$mydf}
if(nrow(patientPLN) > 3)
{
patientPLN <- patientPLN[patientPLN[,3] == input$TPselection ,]
# patientPLN <- patientPLN[patientPLN[,3] == "Glucose" ,]
patientPLN <- patientPLN[patientPLN[,5] > -input$Bdaycompp,]
patientPLN <- patientPLN[patientPLN[,5] < input$Adaycompp,]
x <- mean(patientPLN$size)/mean(patientPLN$tpoutcome)
if (input$devType == "Standard Error")
{if (all(input$sellines == c("Mean by day","Rolling Mean", "Amount Patients")))
p <- ggplot(data=patientPLN,aes(x=patientPLN$dte, y= patientPLN$tpoutcome, colour = "Mean by day")) + stat_summary(fun.data = "mean_se",geom = "smooth")+ geom_vline(xintercept=0) + geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$rollmean ,colour = "Rolling mean"), size =1)+ geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$size/x ,colour = "N Patients"), size =1)+ scale_color_manual(values=c("indianred2", "lightsteelblue", "deepskyblue3"))+ scale_y_continuous(sec.axis = sec_axis(~.*x, name = "N Patients"))
else if (all(input$sellines == c("Rolling Mean", "Amount Patients")))
p <- ggplot(data=patientPLN)+ geom_vline(xintercept=0) + geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$rollmean ,colour = "Rolling mean"), size =1)+ geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$size/x ,colour = "N Patients"), size =1)+ scale_color_manual(values=c("lightsteelblue", "deepskyblue3"))+ scale_y_continuous(sec.axis = sec_axis(~.*x, name = "N Patients"))
else if (all(input$sellines == c("Mean by day", "Amount Patients")))
p <- ggplot(data=patientPLN,aes(x=patientPLN$dte, y= patientPLN$tpoutcome, colour = "Mean by day")) + stat_summary(fun.data = "mean_se",geom = "smooth")+ geom_vline(xintercept=0) + geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$size/x ,colour = "N Patients"), size =1)+ scale_color_manual(values=c("indianred2", "lightsteelblue"))+ scale_y_continuous(sec.axis = sec_axis(~.*x, name = "N Patients"))
else if (all(input$sellines == c("Mean by day", "Rolling Mean")))
p <- ggplot(data=patientPLN,aes(x=patientPLN$dte, y= patientPLN$tpoutcome, colour = "Mean by day")) + stat_summary(fun.data = "mean_se",geom = "smooth")+ geom_vline(xintercept=0) + geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$rollmean ,colour = "Rolling mean"), size =1)+ scale_color_manual(values=c("indianred2", "deepskyblue3"))
else if (input$sellines == ("Amount Patients"))
p <- ggplot(data=patientPLN)+ geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$size ,colour = "N Patients"), size =1)+ scale_color_manual(values=c("lightsteelblue"))+ geom_vline(xintercept=0)
else if (input$sellines == ("Rolling Mean"))
p <- ggplot(data=patientPLN)+ geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$rollmean ,colour = "Rolling mean"), size =1)+ scale_color_manual(values=c("deepskyblue3"))+ geom_vline(xintercept=0)
else if (input$sellines == ("Mean by day"))
p <- ggplot(data=patientPLN,aes(x=patientPLN$dte, y= patientPLN$tpoutcome, colour = "Mean by day")) + stat_summary(fun.data = "mean_se",geom = "smooth")+ geom_vline(xintercept=0) + scale_color_manual(values=c("indianred2"))
}
if (input$devType == "Standard Deviation")
{if (all(input$sellines == c("Mean by day","Rolling Mean", "Amount Patients")))
p <- ggplot(data=patientPLN,aes(x=patientPLN$dte, y= patientPLN$tpoutcome, colour = "Mean by day")) + stat_summary(fun.data = "mean_sd",geom = "smooth")+ geom_vline(xintercept=0) + geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$rollmean ,colour = "Rolling mean"), size =1)+ geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$size/x ,colour = "N Patients"), size =1)+ scale_color_manual(values=c("indianred2", "lightsteelblue", "deepskyblue3"))+ scale_y_continuous(sec.axis = sec_axis(~.*x, name = "N Patients"))
else if (all(input$sellines == c("Rolling Mean", "Amount Patients")))
p <- ggplot(data=patientPLN)+ geom_vline(xintercept=0) + geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$rollmean ,colour = "Rolling mean"), size =1)+ geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$size/x ,colour = "N Patients"), size =1)+ scale_color_manual(values=c("lightsteelblue", "deepskyblue3"))+ scale_y_continuous(sec.axis = sec_axis(~.*x, name = "N Patients"))
else if (all(input$sellines == c("Mean by day", "Amount Patients")))
p <- ggplot(data=patientPLN,aes(x=patientPLN$dte, y= patientPLN$tpoutcome, colour = "Mean by day")) + stat_summary(fun.data = "mean_sd",geom = "smooth")+ geom_vline(xintercept=0) + geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$size/x ,colour = "N Patients"), size =1)+ scale_color_manual(values=c("indianred2", "lightsteelblue"))+ scale_y_continuous(sec.axis = sec_axis(~.*x, name = "N Patients"))
else if (all(input$sellines == c("Mean by day", "Rolling Mean")))
p <- ggplot(data=patientPLN,aes(x=patientPLN$dte, y= patientPLN$tpoutcome, colour = "Mean by day")) + stat_summary(fun.data = "mean_sd",geom = "smooth")+ geom_vline(xintercept=0) + geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$rollmean ,colour = "Rolling mean"), size =1)+ scale_color_manual(values=c("indianred2", "deepskyblue3"))
else if (input$sellines == ("Amount Patients"))
p <- ggplot(data=patientPLN)+ geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$size ,colour = "N Patients"), size =1)+ scale_color_manual(values=c("lightsteelblue"))+ geom_vline(xintercept=0)
else if (input$sellines == ("Rolling Mean"))
p <- ggplot(data=patientPLN)+ geom_line(data = patientPLN, aes(x=patientPLN$dte, y = patientPLN$rollmean ,colour = "Rolling mean"), size =1)+ scale_color_manual(values=c("deepskyblue3")) + geom_vline(xintercept=0)
else if (input$sellines == ("Mean by day"))
p <- ggplot(data=patientPLN,aes(x=patientPLN$dte, y= patientPLN$tpoutcome, colour = "Mean by day")) + stat_summary(fun.data = "mean_sd",geom = "smooth")+ geom_vline(xintercept=0) + scale_color_manual(values=c("indianred2")) }
p + labs(x = "days", y =input$TPselection)
}
})
globals <- reactiveValues(mydf = data.frame())
observeEvent (input$CMeanComPlot, {
if (input$demo == FALSE)
{patientPLN <- data.frame()
withProgress(message = 'Calculating mean TP values before complication', value = 0, {
for(i in 1:nrow(comp_data))
{print(i)
incProgress((1/nrow(comp_data))/2, detail = paste((((i/nrow(comp_data))/2)*100),"% Completed"))
patientPL <- TP_data1[grep(comp_data[i,Comp_ID], TP_data1[,TP_ID]),]
patientPL <- patientPL[patientPL[,TP_Type] %in% TPVariables,]
names(patientPL)[TP_date] <- "tpdate"
names(patientPL)[TP_ID] <- "tpid"
names(patientPL)[TP_Outcome] <- "tpoutcome"
names(patientPL)[TP_Type] <- "tptype"
patientPL$tpoutcome <- as.numeric(as.character(patientPL$tpoutcome))
patientPL <- patientPL[complete.cases(patientPL),]
patientPL <- patientPL[,c(TP_date, TP_ID, TP_Type, TP_Outcome)]
if(nrow(patientPL) == 0) next
patientPL <- aggregate(tpoutcome ~ tpdate + tpid +tptype, patientPL, mean)
ddate <- comp_data[i,Comp_date]
patientPL$dte <- patientPL$tpdate - ddate
start_ddate <- ddate - input$Bdaycompp
end_ddate <- ddate + input$Adaycompp