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server.R
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server.R
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# Shiny Caret is an interactive interface for using various machine learning methods from the caret package
# (c) Nick Ward (University of Canterbury) 2018-2020 ----
# Server Code ------------------------------------------------------
shinyServer(function(input, output, session) {
# input ServerFile ------------------------------------------------------
shinyFiles::shinyFileChoose(input, 'ServerFile', session = session, roots = roots, defaultRoot = "wd")
# reactive values ------------------------------------------------------
react <- reactiveValues(
ModelSet = list(),
Recipe = NULL,
RecipeChanged = 0,
Log = c(),
Warn = c(),
AllowEnsemble = FALSE,
Prob2Class = FALSE,
MethodSet = c(),
Help = TRUE,
HighCardinality = c(),
ROC = NULL,
File = NULL
)
# on session ended ------------------------------------------------------
onSessionEnded(fun = function() {
print("Session ended")
# rm(list = ls(all.names = TRUE))
# gc()
})
# reactive function getProjects ----------------------------------------------
getProjects <- reactive({
files <- file.info(list.files(PROJECT_FOLDER, full.names = TRUE), extra_cols = FALSE)
key <- !files$isdir & str_ends(rownames(files), ".RDA")
proj <- rownames(files)[key]
proj <- sub(pattern = paste0("^", PROJECT_FOLDER, .Platform$file.sep), replacement = "", x = proj)
sub(pattern = "\\.RDA$", replacement = "", x = proj)
})
# observe event Save ------------------------------------------------------
observeEvent(input$Save, {
req(input$Project != "")
print("Saving state")
if (!dir.exists(PROJECT_FOLDER)) {
dir.create(PROJECT_FOLDER)
}
FileName <- paste0(PROJECT_FOLDER, .Platform$file.sep, input$Project, ".RDA")
INPUT <- shiny::reactiveValuesToList(input)
REACT <- shiny::reactiveValuesToList(react)
save(list = c("INPUT", "REACT"), file = FileName)
})
# Reactive Poll loadProject ----------------------------------------------------
loadProject <- reactive({
input$Save
f <- paste0(PROJECT_FOLDER, .Platform$file.sep, isolate(input$Project), ".RDA")
cat("Project file", f, "read\n")
load(f, envir = .GlobalEnv)
}
)
# observe event LoadProject ------------------------------------------------------
observe({
input$LoadProjectReq
req(isolate(input$Project != ""))
shinyjs::disable(id = "LoadProjectReq")
loadProject()
changed <- 0
for (id in names(INPUT)) {
VAR <- isolate(input[[id]])
if (!equals(VAR, INPUT[[id]])) {
# Avoid processing buttons of all kinds (Action & bsButton)
if (id %in% c("help", "Save", "Next", "LoadProjectReq", "Evaluate", "Restart",
"RefreshModels", "Suggest", "ChooseAll", "Install", "Reset", "Train",
"Pause", "UndoModel", "AddModel", "GradTrain")) {
# Avoid processing file inputs of any kind
} else if (id %in% c("LocalFile", "ServerFile")) {
# do nothing
# Avoid processing the non-tab menus
} else if (id %in% c("Submenu", "Submenu_state", "sidebarItemExpanded")) {
# Special processing for Plotly events
} else if (grepl(pattern = "^plotly_.*", x = id)) {
# do nothing
# Special processing for DT::Table events
} else if (grepl(pattern = ".*_(rows_|cells_|search|row_last_).*", x = id)) {
table <- gsub(pattern = "_(rows_|cells_|search).*", replacement = "", x = id)
if (stringr::str_ends(string = id, pattern = "_rows_selected")) {
DTproxy <- DT::dataTableProxy(table)
print(paste("Updating", id, "from", VAR, "to", INPUT[[id]]))
DT::selectRows(proxy = DTproxy, selected = INPUT[[id]], ignore.selectable = TRUE)
#changed <- changed + 1 # As this does not reflect in Table_rows_selected it must be trusted to have occurred
} else if (stringr::str_ends(string = id, pattern = "_search")) {
DTproxy <- DT::dataTableProxy(table)
print(paste("Updating", id, "from", VAR, "to", INPUT[[id]]))
DT::updateSearch(proxy = DTproxy, keywords = list(global = INPUT[[id]], columns = NULL))
#changed <- changed + 1 # As this does not reflect in Table_search it must be trusted to have occurred
}
} else if (is.numeric(VAR) | is.numeric(INPUT[[id]])) {
print(paste("Updating", id, "from", VAR, "to", INPUT[[id]]))
updateSliderInput(session = session, inputId = id, value = INPUT[[id]])
changed <- changed + 1
} else if (is.logical(VAR) | is.logical(INPUT[[id]])) {
print(paste("Updating", id, "from", VAR, "to", INPUT[[id]]))
updateCheckboxInput(session = session, inputId = id, value = INPUT[[id]])
changed <- changed + 1
} else if (id %in% c("Sep", "Quote", "Decimal", "ProbType", "PredictorsTag", "ResultsFor", "SelectedModel")) {
print(paste("Updating", id, "from", VAR, "to", INPUT[[id]]))
updateRadioButtons(session = session, inputId = id, selected = INPUT[[id]])
changed <- changed + 1
} else if (id %in% c("URL", "DateFormat")) {
print(paste("Updating", id, "from", VAR, "to", INPUT[[id]]))
updateTextInput(session = session, inputId = id, value = INPUT[[id]])
changed <- changed + 1
} else if (id %in% c("DataSource", "Navbar", "TrainTab")) {
print(paste("Updating", id, "from", VAR, "to", INPUT[[id]]))
updateTabItems(session = session, inputId = id, selected = INPUT[[id]])
changed <- changed + 1
} else if (id %in% c()) {
print(paste("Updating", id, "from", VAR, "to", INPUT[[id]]))
updateTabItems(session = session, inputId = id, selected = INPUT[[id]])
changed <- changed + 1
} else if (is.character(VAR) | is.character(INPUT[[id]])) {
print(paste("Updating", id, "from", VAR, "to", INPUT[[id]]))
updateSelectizeInput(session = session, inputId = id, selected = INPUT[[id]]) #Assume no selectInput()
changed <- changed + 1
}
}
}
for (id in names(REACT)) {
VAR <- isolate(react[[id]])
if (!equals(VAR, REACT[[id]])) {
print(paste0("Updating react$", id))
react[[id]] <- REACT[[id]]
}
}
# ResultsFor update is masked behind a button
if (length(react$ModelSet) > 0) {
updateRadioButtons(session = session, inputId = "ResultsFor", choices = names(react$ModelSet), selected = INPUT$ResultsFor)
}
if (changed > 0) {
print(paste("Changed =", changed))
invalidateLater(millis = 2000, session = session)
} else {
showNotification(ui = "Project-load completed", duration = 5, type = "message")
shinyjs::enable(id = "LoadProjectReq")
}
}, priority = -10)
# observe event Package ------------------------------------------------------
observeEvent(input$Package, {
results <- data(package = input$Package)$results
if (nrow(results) == 0 || ncol(results) != 4) {
updateSelectizeInput(session = session, inputId = "DataSet", choices = "", selected = "")
} else {
choices <- try({
results <- data.frame(results, stringsAsFactors = FALSE)
results$key <- paste0(results$Item," - ", results$Title)
results <- results[ !duplicated(results$key), ]
choices <- results$Item
names(choices) <- results$key
choices
}, silent = TRUE)
if (length(choices) == 0) {
updateSelectizeInput(session = session, inputId = "DataSet", choices = "")
} else {
updateSelectizeInput(session = session, inputId = "DataSet", choices = as.list(choices))
}
}
})
# reactive function LoadFileType ------------------------------------------------------
LoadFileType <- reactive({
d <- tryCatch({
# when reading semicolon separated files, having a comma separator causes `read.csv` to error - hence the trycatch
if (grepl("\\.csv$", react$File, ignore.case = TRUE)) {
dd <- read.csv(react$File, header = input$Header, sep = input$Sep, quote = input$Quote, na.strings = input$MissingStrings,
dec = input$Decimal, check.names = TRUE, strip.white = TRUE, stringsAsFactors = FALSE)
dd
} else if (grepl("\\.xls.{0,1}$", react$File, ignore.case = TRUE)) {
dd <- as.data.frame(readxl::read_excel(react$File, sheet = 1, na = input$MissingStrings, col_names = input$Header))
dd
} else if (grepl("\\.rdata$", react$File, ignore.case = TRUE)) {
# when reading semicolon separated files, having a comma separator causes `read.csv` to error - hence the trycatch
loaded <- new.env()
what <- load(react$File, envir = loaded)
req(len(what) > 0)
if (length(what) > 1) {
#create alert to resolve VarName
choices <- what[base::apply(X = what, FUN = is.data.frame)]
showElement(id = "VarName")
# updateSelectizeInput(session = session, inputId = "VarName", choices = choices)
get(input$VarName, envir = loaded)
} else {
hideElement(id = "VarName")
get(what, envir = loaded)
}
}
},
warn = function(e) {
shinyalert(title = paste0("File \"",react$File,"\" did not load"), text = e$message, type = "warning")
NULL},
error = function(e) {
shinyalert(title = paste0("File \"",react$File,"\" did not load"), text = e$message, type = "error")
NULL
})
names(d) <- make.names(names(d),unique = TRUE)
d
})
# reactive function loadR ------------------------------------------------------
loadR <- reactive({
d <- tryCatch({
if (input$Package == "All") {
data(list = input$DataSet)
get(input$DataSet)
} else {
data(list = input$DataSet, package = input$Package)
get(input$DataSet, asNamespace(input$Package))
}
},
warn = function(e) {
shinyalert(title = paste0("Dataset \"",input$DataSet,"\" did not load"), text = e$message, type = "warning")
NULL},
error = function(e) {
shinyalert(title = paste0("Dataset \"",input$DataSet,"\" did not load"), text = e$message, type = "error")
NULL
})
})
# reactive Continuous ####------------------------------------------------------------
Continuous <- reactive({
input$Continuous
})
getContinuousDebounced <- debounce(Continuous, millis = 1000)
observeEvent(input$ServerFile, {
file <- parseFilePaths(roots, input$ServerFile)[1,"datapath", drop = TRUE]
req(file)
updateTextInput(session = session, inputId = "ServerFileName", value = as.character(file))
})
observeEvent(input$LocalFile, {
file <- input$LocalFile[1,"datapath", drop = TRUE]
req(file)
updateTextInput(session = session, inputId = "LocalFileName", value = as.character(file))
})
observe({
req(input$Project == "")
updateSelectizeInput(session = session, inputId = "Project", choices = getProjects(), selected = input$Project)
})
# reactive function getRawData ------------------------------------------------------
getRawData <- reactive({
req(input$DataSource)
if (input$DataSource == "Server Data File") {
req(input$ServerFileName != "")
react$File <- input$ServerFileName
d <- LoadFileType()
name <- gsub(pattern = ".*(/|\\\\)", replacement = "", x = input$ServerFileName)
updateSelectizeInput(session = session, inputId = "Project", choices = c(name, isolate(getProjects())), selected = name)
} else if (input$DataSource == "Local Data File") {
req(input$LocalFileName != "")
react$File <- input$LocalFileName
d <- LoadFileType()
name <- gsub(pattern = ".*(/|\\\\)", replacement = "", x = input$LocalFileName)
updateSelectizeInput(session = session, inputId = "Project", choices = c(name, isolate(getProjects())), selected = name)
} else if (input$DataSource == "Remote Data Resource") {
req(input$URL)
URL <- input$URL
react$File <- URL
updateSelectizeInput(session = session, inputId = "Project", choices = c(URL, isolate(getProjects())), selected = URL)
d <- LoadFileType()
} else if (input$DataSource == "Package Dataset") {
req(input$Package != "", input$DataSet != "")
name <- paste(sep = "::", input$Package, input$DataSet)
updateSelectizeInput(session = session, inputId = "Project", choices = c(name, isolate(getProjects())), selected = name)
d <- loadR()
}
req(d, nrow(d) > 0, ncol(d) > 0)
showNotification(id = "checking", ui = "Checking column types", duration = NULL)
# converts whole-number columns to "integer"
numCols <- which(allClass(d) == "numeric")
for (col in numCols) {
if (all(is.wholenumber(d[,col]))) {
showNotification(ui = paste("Changing numeric column", colnames(d)[col], "to integer"), duration = 3)
d[,col] <- as.integer(d[,col])
}
}
# converts 100% compatible character columns back to date variables
charCols <- which(allClass(d) %in% c("character","factor"))
for (col in charCols) {
dates <- as.Date(d[,col], format = input$DateFormat)
if (all(is.na(dates) == is.na(d[,col]))) {
showNotification(ui = paste("Changing character column", colnames(d)[col], "to date"), duration = 3)
d[,col] <- dates
}
}
# converts <= 15 unique levels to factor
charCols <- which(allClass(d) == "character")
max <- nrow(d)
for (col in charCols) {
if (length(unique(d[,col])) <= getContinuousDebounced()[1]) {
showNotification(ui = paste("Changing text column", colnames(d)[col], "to factor"), duration = 3)
d[,col] <- as.factor(d[,col])
}
}
updateTextInput(session = session, inputId = "Project", value = input$CSVFile$name)
d
removeNotification(id = "checking")
choices <- formattedColNames(d)
CHOICES <- toupper(choices)
#set a default value for input$Target and appropriate choices
best <- c( choices[CHOICES == "TARGET"],
choices[CHOICES == "Y"],
choices[CHOICES == "CLASS"],
choices[CHOICES == "LABEL"],
choices[CHOICES == "CHURN"],
rev(choices)
)
guess <- best[1]
updateSelectizeInput(session = session, inputId = "Target", choices = as.list(choices), selected = as.list(guess))
updateSelectizeInput(session = session, inputId = "Weights", selected = "")
updateSelectizeInput(session = session, inputId = "PreSplit", selected = "")
updateSelectizeInput(session = session, inputId = "ID", selected = list())
updateSelectizeInput(session = session, inputId = "HideCol", selected = list())
updateSelectizeInput(session = session, inputId = "Group", selected = NULL)
updateCheckboxInput(session = session, inputId = "AddWeights", value = FALSE)
updateSelectizeInput(session = session, inputId = "BalanceFactors", selected = NULL)
d
})
# render ObservationCount ------------------------------------------------------
output$ObservationCount <- renderInfoBox({
d <- getRawData()
infoBox(title = "Observations", value = nrow(d), icon = icon("align-justify"), color = DATAColour, fill = TRUE )
})
# render VariableCount ------------------------------------------------------
output$VariableCount <- renderInfoBox({
d <- getRawData()
infoBox(title = "Variables", value = ncol(d), icon = icon("columns"), color = DATAColour, fill = TRUE)
})
# reactive GetData ------------------------------------------------------
getData <- reactive({
d <- getRawData()
req(d)
req(nrow(d) > 0, ncol(d) > 0, length(colnames(d)) > 0)
# ensure target is factor for Classification
if (isRoleValid(input$Target, d) && is.binary(d[, input$Target, drop = TRUE])) {
tar <- d[, input$Target]
d[, input$Target] <- as.factor(tar)
showNotification(ui = "Converting binary outcome variable to nominal", type = "warning")
}
#clean up invalid factor level names
for (col in 1:ncol(d)) {
var <- d[,col, drop = TRUE]
if (is(var, "factor")) {
newNames <- make.names(levels(var))
if (any(newNames != levels(var))) {
levels(d[,col]) <- newNames
#print(levels(d[,col]))
}
}
}
if (input$AddWeights && length(input$BalanceFactors) > 0) {
d <- observationWeights(factors = input$BalanceFactors, data = d)
}
if (input$AddIds) {
if (!any(is.na(as.numeric(rownames(d))))) {
d$RowName <- as.numeric(rownames(d))
} else {
d$RowName <- rownames(d)
}
}
d
})
# render Data ------------------------------------------------------
output$Data <- DT::renderDataTable({
d <- getData()
dt <- DT::datatable(data = d, rownames = TRUE, selection = "none",
extensions = c('Scroller','FixedHeader'),
options = list(
scrollX = TRUE,
deferRender = TRUE,
scrollY = 540,
scroller = TRUE
))
numericCols <- colnames(d)[unlist(lapply(d, is.numeric))]
integerCols <- colnames(d)[unlist(lapply(d, is.wholenumber))]
numericCols <- setdiff(numericCols, integerCols)
if (length(numericCols) > 0) {
dt <- formatRound(table = dt, columns = numericCols, digits = 2)
}
if (length(integerCols) > 0) {
dt <- formatRound(table = dt, columns = integerCols, digits = 0)
}
dt
})
# reactive function getSomeRawData ------------------------------------------------------
getSomeRawData <- reactive({
# grab a representative subsample of the data
d <- getRawData()
req(is(d,"data.frame"))
mrow <- getMaxRowsDebounced()
if (nrow(d) <= mrow) {
return(d)
} else {
rows <- sample(nrow(d), mrow)
#preserve order
rows <- sort(rows, decreasing = FALSE)
return(d[rows,])
}
})
# reactive getMaxRows ------------------------------------------------------
MaxRows <- reactive({
input$MaxRows
})
getMaxRowsDebounced <- debounce(MaxRows, millis = 1000)
# reactive getSomeData ------------------------------------------------------
getSomeData <- reactive({
# grab a representative subsample of the data
d <- getData()
req(is(d,"data.frame"))
if (input$ID != "" & input$ID %in% colnames(d)) {
rownames(d) <- d[, input$ID]
}
mrow <- getMaxRowsDebounced()
if (nrow(d) <= mrow) {
return(d)
} else {
rows <- sample(nrow(d), mrow)
#preserve order
rows <- sort(rows, decreasing = FALSE)
return(d[rows,])
}
})
# reactive getSomePredictorData ------------------------------------------------------
# predictors plus outcome - actually
getSomePredictorData <- reactive({
d <- getSomeData()
leaveOut <- getNonPredictors()
d[, !colnames(d) %in% leaveOut]
})
# reactive getPredictorData ------------------------------------------------------
# predictors plus outcome - actually
getPredictorData <- reactive({
d <- getData()
leaveOut <- getNonPredictors()
d[, !colnames(d) %in% leaveOut]
})
# reactive getRawDataSummary ------------------------------------------------------
getRawDataSummary <- reactive({
d <- getSomeRawData()
DataSummary(d)
})
Multiplier <- reactive({
input$Multiplier
})
getMultiplier <- debounce(Multiplier, millis = 1000)
# reactive getDataSummary ------------------------------------------------------
getDataSummary <- reactive({
d <- getSomeData()
DataSummary(d, getMultiplier(), input$UseYJ)
})
# observe ------------------------------------------------------
observe({
d <- getRawData()
req(d)
req(ncol(d) > 0)
ds <- getRawDataSummary()
choices <- colnames(d)
names(choices) <- paste0(choices, " [", ds$type, "]")
#set appropriate choices
updateSelectizeInput(session = session, inputId = "HideCol", choices = as.list(choices))
})
# reactive getDataSummary ------------------------------------------------------
getIDChoices <- reactive({
ds <- getDataSummary()
choicesID <- rownames(ds)
names(choicesID) <- paste0(choicesID, " [",ds$type,"]")
index <- ds$uniqueRatio == 1 & (ds$wholeNumb | ds$text | ds$date)
if (any(index)) {
choicesID <- choicesID[index]
} else {
choicesID <- c()
}
as.list(choicesID)
})
# observe event AddIds ------------------------------------------------------
# observeEvent(
# input$AddIds,
# {
# #set a default value for input$ID and appropriate choices
# if (input$AddIds) {
# updateSelectizeInput(session = session, inputId = "ID", choices = c("RowName"), selected = "RowName")
# shinyjs::disable(id = "ID")
# } else {
# existID <- input$ID
# choicesID <- getIDChoices()
# if (!all(existID %in% choicesID)) {
# existID <- NULL
# }
# updateSelectizeInput(session = session, inputId = "ID", choices = choicesID, selected = existID)
# shinyjs::enable(id = "ID")
# }
# }
# )
# observe ------------------------------------------------------
observe({
ds <- getDataSummary()
req(ds)
choices <- as.list(rownames(ds))
names(choices) <- paste0(choices, " [",ds$type,"]")
imbalanced <- choices[ds$imbalanceRatio > 1.1]
imbalanced[is.na(imbalanced)] <- FALSE
if (input$AddWeights && length(input$BalanceFactors) > 0) {
updateSelectizeInput(session = session, inputId = "Weights", choices = list("Weighting"), selected = "Weighting")
shinyjs::disable(id = "Weights")
} else {
wChoices <- choices[ds$numeric & !ds$binary]
wChoices[["none"]] <- ""
updateSelectizeInput(session = session, inputId = "Weights", choices = wChoices, selected = isolate(input$Weights))
shinyjs::enable(id = "Weights")
}
if (input$PreSplit != "") {
updateSelectizeInput(session = session, inputId = "Groups", choices = list("none" = ""), selected = NULL)
shinyjs::disable(id = "Groups")
} else {
gChoices <- choices[(ds$factor | ds$wholeNum) & ds$uniqueness > 5]
gChoices[["none"]] <- ""
updateSelectizeInput(session = session, inputId = "Groups", choices = gChoices, selected = isolate(input$Groups))
shinyjs::enable(id = "Groups")
}
lowCard <- setdiff(choices[ds$uniqueness <= input$Continuous[1] & ds$uniqueness > 1], c(input$PreSplit, input$Groups))
updateSelectizeInput(session = session, inputId = "BalanceFactors", choices = lowCard, selected = isolate(input$BalanceFactors))
shinyjs::toggle(id = "BalanceFactors", condition = input$AddWeights)
pChoices <- choices[ds$binary | (ds$factor & ds$uniqueness == 2)]
pChoices[["none"]] <- ""
updateSelectizeInput(session = session, inputId = "PreSplit", choices = pChoices, selected = isolate(input$PreSplit))
ids <- getIDChoices()
if (length(ids) == 0) {
# updateCheckboxInput(session = session, inputId = "AddIds", value = TRUE) #triggers before length(ids) has settled down
} else if (length(ids) == 1) {
updateSelectizeInput(session = session, inputId = "ID", choices = ids, selected = ids[1])
} else {
updateSelectizeInput(session = session, inputId = "ID", choices = ids, selected = isolate(input$ID), )
}
})
# observe event Target ------------------------------------------------------
observeEvent(input$Target, {
ds <- getDataSummary()
ds <- ds[rownames(ds) == input$Target, ]
if (nrow(ds) == 0) {
ptype <- NA
} else if (ds$binary | ds$factor | (ds$wholeNumb & ds$uniqueness < getContinuousDebounced()[1]) ) {
ptype <- "Classification"
} else if (ds$numeric & ds$uniqueRatio > 0.5) { #arbitrary 50% unique
ptype <- "Regression"
} else {
ptype <- NA
}
if (is.na(ptype)) {
updateRadioButtons(session = session, inputId = "ProbType", selected = "Any")
} else {
updateRadioButtons(session = session, inputId = "ProbType", selected = ptype)
}
})
# render plot ClassPieChart ------------------------------------------------------
output$ClassPieChart <- renderPlot({
d <- getSomeData()
req(isRoleValid(input$Target, d))
target <- d[,input$Target, drop = TRUE]
req(is.factor(target) || is.binary(target))
if(isRoleValid(input$Weights, d)) {
df <- data.frame(var = target, wt = d[,input$Weights])
t <- df %>% dplyr::count(var, wt = wt)
} else {
df <- data.frame(var = target)
t <- df %>% dplyr::count(var)
}
colnames(t)[1] <- "Level"
ggplot(data = t, mapping = aes(x = 0, y = n, fill = Level)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y") +
labs(title = NULL, x = NULL, y = NULL) +
theme(axis.text.x = element_blank(),
legend.position = "none",
axis.ticks = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.text = element_blank(),
axis.ticks.length = unit(0, "mm"),
plot.margin = unit(c(0,0,0,0), "mm"),
plot.background = element_blank(),
legend.spacing = unit(c(0,0,0,0), "mm"),
axis.title = element_blank()
)
}, bg = "transparent")
# render plot GroupPieChart ------------------------------------------------------
output$GroupPieChart <- renderPlot({
req(input$Groups != "")
d <- getSomeData()
column <- d[,input$Groups, drop = TRUE]
Level <- as.factor(column)
t <- table(Level, useNA = "no")
df <- as.data.frame(t)
ggplot(data = df, mapping = aes(x = 0, y = Freq, fill = Level)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y") +
labs(title = NULL, x = NULL, y = NULL) +
theme(axis.text.x = element_blank(),
legend.position = "none",
axis.ticks = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.text = element_blank(),
axis.ticks.length = unit(0, "mm"),
plot.margin = unit(c(0,0,0,0), "mm"),
plot.background = element_blank(),
legend.spacing = unit(c(0,0,0,0), "mm"),
axis.title = element_blank()
)
}, bg = "transparent")
# render plot SplitPieChart ------------------------------------------------------
output$SplitPieChart <- renderPlot({
d <- getSomeData()
req(isRoleValid(input$PreSplit, d))
df <- data.frame(var = d[,input$PreSplit])
t <- df %>% dplyr::count(var)
colnames(t)[1] <- "Level"
ggplot(data = t, mapping = aes(x = 0, y = n, fill = Level)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y") +
labs(title = NULL, x = NULL, y = NULL) +
theme(axis.text.x = element_blank(),
legend.position = "none",
axis.ticks = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.text = element_blank(),
axis.ticks.length = unit(0, "mm"),
plot.margin = unit(c(0,0,0,0), "mm"),
plot.background = element_blank(),
legend.spacing = unit(c(0,0,0,0), "mm"),
axis.title = element_blank()
)
}, bg = "transparent")
# reactive getNonPredictors ------------------------------------------------------
getNonPredictors <- reactive({
ignore <- c(input$ID, input$Weights, input$PreSplit, input$HideCol, input$Groups)
ignore[ignore != ""]
})
# observe ------------------------------------------------------
observe({
req(input$Navbar == "DataColumns")
d <- getSomeData()
req(d)
req(isRoleValid(input$Target, d))
col <- d[,input$Target, drop = TRUE]
type <- class(col)
if (isRoleValid(input$Weights, d)) {
Xweighted <- "weighted "
} else {
Xweighted <- ""
}
uniqCnt <- length(unique(na.omit(col)))
if (uniqCnt == 1) {
text <- paste("Error: The outcome has constant data.\nIt is not a legitimate outcome variable.")
} else if ("Date" %in% type) {
text <- paste("Warning: This outcome is a date/time variablea.\nIt is not a legitimate outcome variable.")
} else if (is.numeric(col)) {
isInt <- is.wholenumber(col)
if (isInt & uniqCnt < input$Continuous[1]) {
text <- paste("Warning: There are", uniqCnt, "unique integer outcome values.\nTo make a multi-class classification problem you should explicitly change the outcome to a factor")
} else if (!isInt & uniqCnt < input$Continuous[2]) {
text <- paste("Warning: The outcome has only", uniqCnt, "unique numeric values.\nIt is a dubious outcome variable for regression")
} else {
text <- paste0("This is a ", Xweighted ," regression problem.")
}
} else if ("character" %in% type) {
text <- paste("Warning: The outcome is textual data and is not a legitimate outcome variable")
} else if ("factor" %in% type) {
if (uniqCnt == 2 && nrow(d) > 2) {
text <- paste0("This is a ", Xweighted ,"binary classification problem.")
} else if (uniqCnt > input$Continuous[2]) {
text <- paste("Error: The outcome has", uniqCnt, "factor levels.\nIt is not a legitimate outcome variable for classification.")
} else if (uniqCnt > input$Continuous[1]) {
text <- paste("Warning: The outcome has", uniqCnt, "factor levels.\nIt is a dubious outcome variable for classification.")
} else {
text <- paste0("This is a ", Xweighted ,"multinomial classification problem with ",uniqCnt," levels.")
}
} else {
text <- paste0("Error: The outcome type is '", type,"'.\nIt is not a legitimate outcome variable.")
}
targetMessage <- text
output$TargetCheck <- renderInfoBox({
infoBox(title = "Outcome", value = targetMessage, icon = icon("bullseye"), color = DATAColour, fill = TRUE)
})
predCount <- length(setdiff(colnames(d), c(input$Target, getNonPredictors())))
output$PredictorCount <- renderInfoBox({
infoBox(title = "Predictors", value = predCount, icon = icon("columns"), color = DATAColour, fill = TRUE)
})
ds <- getDataSummary()
ds <- ds[!rownames(ds) %in% getNonPredictors(),]
cont <- getContinuousDebounced()
possCard <- sum(ds$uniqueness > cont[1] & ds$uniqueness <= cont[2] & (ds$text | ds$factor))
highCard <- sum(ds$uniqueness > cont[2] & (ds$text | ds$factor))
output$StringCount <- renderInfoBox({
infoBox(title = "High Cardinality", value = paste0("Certain ", highCard, ", Possible ", possCard), icon = icon("layer-group"), color = DATAColour, fill = TRUE)
})
countnzv <- sum(ds$nzv & !rownames(ds) %in% getNonPredictors())
output$NZVCount <- renderInfoBox({
infoBox(title = "Near-Zero Var.", value = countnzv, icon = icon("adjust"), color = DATAColour, fill = TRUE)
})
ds <- ds[(ds$numeric | ds$date),]
possCont <- sum(ds$uniqueness > cont[1] & ds$uniqueness <= cont[2])
certainCont <- sum(ds$uniqueness > cont[2])
output$ContCount <- renderInfoBox({
infoBox(title = "Continuous", value = paste0("Certain ", certainCont, ", Possible ", possCont), icon = icon("sort-amount-down"), color = DATAColour, fill = TRUE)
})
})
###########################################################################
output$TargetCheck <- renderInfoBox({
d <- getSomeData()
req(isRoleValid(input$Target, d))
col <- d[,input$Target, drop = TRUE]
type <- class(col)
uniqCnt <- length(unique(na.omit(col)))
if (is.numeric(col)) {
isInt <- is.wholenumber(col)
if (isInt & uniqCnt == 2 & nrow(d) > uniqCnt) {
text <- paste("Since there are only", uniqCnt, "unique integer Y values, this is a binary classification problem.")
} else if (isInt & uniqCnt < 11 & nrow(d) > uniqCnt) { #arbitrary 11 used here
text <- paste("Warning: Since there are", uniqCnt, "unique integer Y values, this might be a multi-class classification problem.\nTo make this explicit you may choose to make this column a factor")
} else if (uniqCnt == 1) {
text <- paste("Error: This column has constant data and should be removed. It is not a legitimate target variable.")
} else if (!isInt & uniqCnt/nrow(d) < 0.5) { #arbitrary 0.5 used here
text <- paste("Warning: A column with only", paste0(ceiling(uniqCnt/nrow(d)*100),"%"), "unique numeric values is a suspicious outcome variable for regression")
} else {
text <- paste("This is a regression problem.")
}
} else if ("character" %in% type) {
if (uniqCnt == nrow(d)) {
text <- paste("Warning: This column effectively has text data and should be encoded or used as a row identifier")
} else {
text <- paste("Warning: This column has text data and should be removed or used as a partial row identifier")
}
} else if ("factor" %in% type) {
if (uniqCnt == 2 & nrow(d) > uniqCnt) {
text <- paste("This is a binary classification problem.")
} else if (uniqCnt == nrow(d) ) {
text <- paste("Error: This column effectively has unique text in each row and should be removed or used as a row identifier. \nIt is not a legitimate target variable.")
} else if (uniqCnt/nrow(d) > 0.3) { #arbitrary 0.3 used here
text <- paste("Warning: There are", uniqCnt, "unique Y levels, that is", paste0(ceiling(uniqCnt/nrow(d)*100),"%"),"of the rows. \nThis is a suspicious outcome variable for classification")
} else if (uniqCnt == 1) {
text <- paste("Error: This column has constant data and should be removed. It is not a legitimate target variable.")
} else {
text <- paste0("This is a multinomial (",uniqCnt," levels) classification problem.")
}
} else if ("Date" %in% type) {
text <- paste("Warning: This column has date/time data and should be removed or used as a row identifier. \nIt is not a legitimate outcome variable.")
} else {
text <- paste0("Error: The type is '", type,"'. It is not a legitimate outcome variable.")
}
infoBox(title = "Outcome", value = text, icon = icon("bullseye"), color = DATAColour, fill = TRUE)
})
###########################################################################
output$RatioObs <- renderInfoBox({
d <- getSomePredictorData()
count <- length(colnames(d)) - ifelse(isRoleValid(input$Target, d), 1, 0)
rows <- nrow(getData())
text <- paste0("Observations ", rows,", Predictors ",count, ", Ratio of Obs/Pred ", round(rows/count,1))
infoBox(title = "Ratio", value = text, icon = icon("balance-scale-right"), color = DATAColour, fill = TRUE)
})
###########################################################################
output$StringCount <- renderInfoBox({
ds <- getDataSummary()
ds <- ds[!rownames(ds) %in% getNonPredictors(),]
cont <- getContinuousDebounced()
poss <- sum(ds$uniqueness > cont[1] & ds$uniqueness <= cont[2] & (ds$text | ds$factor))
high <- sum(ds$uniqueness > cont[2] & (ds$text | ds$factor))
infoBox(title = "High Cardinality", value = paste0("Certain ", high, ", Possible ", poss), icon = icon("layer-group"), color = DATAColour, fill = TRUE)
})
###########################################################################
output$NZVCount <- renderInfoBox({
ds <- getDataSummary()
count <- sum(ds$nzv & !rownames(ds) %in% getNonPredictors())
infoBox(title = "Near-Zero Var.", value = count, icon = icon("adjust"), color = DATAColour, fill = TRUE)
})
###########################################################################
output$ContCount <- renderInfoBox({
ds <- getDataSummary()
ds <- ds[!rownames(ds) %in% getNonPredictors() & (ds$numeric | ds$date),]
cont <- getContinuousDebounced()
poss <- sum(ds$uniqueness > cont[1] & ds$uniqueness <= cont[2])
certain <- sum(ds$uniqueness > cont[2])
infoBox(title = "Continuous", value = paste0("Certain ", certain, ", Possible ", poss), icon = icon("sort-amount-down"), color = DATAColour, fill = TRUE)
})
###########################################################################
output$MissObs <- renderInfoBox({
d <- getSomePredictorData() # avoid the hidden vars
req(d)
m <- sum(base::apply(X = d, MARGIN = 1, FUN = function(x) any(is.na(x))))
some <- round(m / dim(d)[1] * 100, 1)
rows <- base::apply(X = d, MARGIN = 1, FUN = function(x) sum(is.na(x))) / ncol(d)
cnt <- sum(rows > input$MissObsThreshold / 100)
heavy <- round(cnt / nrow(d) * 100, 1)
infoBox(title = "Missing Observations", value = paste0("Heavy ", heavy, "%, Some ", some, "%"), icon = icon("align-justify"), color = DATAColour, fill = TRUE)
})
###########################################################################
output$MissVar <- renderInfoBox({
d <- getSomePredictorData()
req(d)
some <- sum(base::apply(X = d, MARGIN = 2, FUN = function(x) any(is.na(x))))
ds <- getDataSummary()
cols <- ds$missingRate > input$MissVarThreshold / 100
heavy <- sum(cols)
infoBox(title = "Missing Predictor Variables", value = paste0("Heavy ", heavy, ", Some ", some), icon = icon("columns"), color = DATAColour, fill = TRUE)
})
###########################################################################
output$MissTarget <- renderInfoBox({
d <- getSomeData()
req(d)
req(isRoleValid(input$Target, d))
some <- round(sum(is.na(d[,input$Target])) / nrow(d) * 100, 1)
infoBox(title = "Missing Outcome Variable", value = paste0(some,"%"), icon = icon("exclamation-triangle"), color = DATAColour, fill = TRUE)
})
###########################################################################
output$DataSummary <- renderUI({
Data <- getSomeData()
summary <- summarytools::dfSummary(Data, graph.magnif = 1.5)
summarytools::view(summary,
method = 'render',
report.title = NA,
headings = FALSE,
bootstrap.css = FALSE,
footnote = NA,
max.tbl.height = 600,
collapse = TRUE,
silent = TRUE
)
})
###########################################################################
output$MissingChart1 <- renderPlotly({
data <- getSomeData()
req(ncol(data) > 0)
sort = input$SortOrder
if (length(sort) > 0 && all(sort %in% colnames(data))) {
data <- data[order(data[,sort]), ]
ylab = paste("Observations in", paste0(sort, collapse = ","), "order")
} else {
ylab = "Observations in natural order"
}
if (input$HideIrrelevant) {
colCounts <- base::colSums(is.na(data))
if (any(colCounts > 0)) {
data <- data[, colCounts > 0, drop = FALSE]
}
}
plot <- visdat::vis_miss(data, cluster = FALSE, sort_miss = FALSE, show_perc = TRUE) +
labs(x = "Variables", title = "Missing values throughout the data", y = ylab) +
coord_flip()
plotly(plot, tooltip = c("variable", "value"))
})
###########################################################################
output$MissingChart2 <- renderPlot({
d <- getSomeData()
req(ncol(d) > 0)
missingCounts(d)
}, bg = "transparent")
###########################################################################
output$MissObservations <- renderText({
d <- getData()
# remove unwanted columns
m <- sum(base::apply(X = d, MARGIN = 1, FUN = function(x) any(is.na(x))))
paste0(m, " (", round(m / dim(d)[1] * 100), "%)")
})
###########################################################################
output$MissVariables <- renderText({
d <- getData()
# remove unwanted columns
m <- sum(base::apply(X = d, MARGIN = 2, FUN = function(x) any(is.na(x))))
paste0(m, " (", round(m / dim(d)[2] * 100), "%)")
})
###########################################################################
output$MissValues <- renderText({
d <- getData()
# remove unwanted columns
m <- sum(is.na(d))
paste0(m, " (", round(m / dim(d)[1] / dim(d)[2] * 100), "%)")
})
# reactive function getMissObsRatios ####
getMissObsRatios <- reactive({
d <- getData()
base::apply(X = d, MARGIN = 1, FUN = function(x) sum(is.na(x))) / ncol(d)
})
###########################################################################
output$HeavyMissObs <- renderText({
cnt <- sum(getMissObsRatios() > input$MissObsThreshold / 100)
prop <- cnt / nrow(getData()) * 100
paste0(cnt, " (", round(prop,1), "%)")
})
###########################################################################
output$HeavyMissVar <- renderText({
d <- getData()
ds <- getDataSummary()
cols <- ds$missingRate > input$MissVarThreshold / 100
cnt <- sum(cols)
prop <- cnt / ncol(d) * 100
paste0(cnt, " (",round(prop,1), "%)")
})
###########################################################################
getTrainedModels <- reactive({
req(length(react$ModelSet) >= 1)
bad <- unlist(lapply(react$ModelSet, FUN = inherits, what = c("character")))
req(sum(!bad) > 0)
react$ModelSet[!bad]
})
###########################################################################
getTimings <- reactive({
d <- getTrainData()
thousand <- sample(nrow(d), size = 1000, replace = TRUE)
d <- d[thousand, ]
timing <- c()