# The first filter applied is the Result Date
df2 <- raw.data() %>% subset(Date >= input$daterange[1] & Date <= input$daterange[2])
# The "Select Test" and "Select Analyzer" dropdown boxes are reactive. Here the script defines what should be displayed and filtered based on the various inputs.
if(input$test == "<All>" & input$analyzer == "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Date >= input$daterange[1] & Date <= input$daterange[2])
# updateSelectInput updates the selectInput dropdown boxes in the UI above. This allows interactivity between the two lists, which is nice when you have lots of tests.
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df2$Test)))
updateSelectInput(session, "analyzer", "Select Analyzer:", choices = c("<All>", unique(df2$Analyzer)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df2$QC_Mnemonic)))
}
# Now apply various filters when the selectInput box has a chosen Test or Analyzer, and update the selections available for the Analyzer
else if(input$test != "<All>" & input$analyzer == "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Test == input$test & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "analyzer", "Select Analyzer:", choices = c("<All>", unique(df3$Analyzer)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test == "<All>" & input$analyzer != "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Analyzer == input$analyzer & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test != "<All>" & input$analyzer != "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Test == input$test & Analyzer == input$analyzer & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test != "<All>" & input$analyzer != "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(Test == input$test & Analyzer == input$analyzer & QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
}
else if(input$test == "<All>" & input$analyzer != "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(Analyzer == input$analyzer & QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
}
else if(input$test == "<All>" & input$analyzer != "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Analyzer == input$analyzer & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test == "<All>" & input$analyzer == "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
updateSelectInput(session, "analyzer", "Select Analyzer:", choices = c("<All>", unique(df3$Analyzer)))
}
return(df4)
})
###########################################################################################################################
# End of filter application. At this point, all raw data has been processed according to the filters applied
###########################################################################################################################
summary.data <- reactive({
# Now we will generate the summary data
# First, format the Day, Week, and Month columns for summary
df4 <- filter.data() %>%  mutate(
Date =  ymd_hms(Date),
Day = floor_date(Date, unit = "days"),
Week = floor_date(Date, unit = "week"),
Month = floor_date(Date, unit = "month")
)
df4 <- df4 %>% mutate(
Day = as.Date(Day),
Week = as.Date(Week),
Month = as.Date(Month)
)
if(input$timeframe == 1){
p4 <- df4 %>% group_by(Day, Test, Analyzer, QC_Mnemonic) %>% summarize(N = length(Result),
Mean = round(mean(Result), 2),
SD = round(sd(Result), 3),
"%CV" = round(100*SD/Mean, 2))
} else if(input$timeframe == 2){
p4 <- df4 %>% group_by(Week, Test, Analyzer, QC_Mnemonic) %>% summarize(N = length(Result),
Mean = round(mean(Result), 2),
SD = round(sd(Result), 3),
"%CV" = round(100*SD/Mean, 2))
} else if(input$timeframe == 3){
p4 <- df4 %>% group_by(Month, Test, Analyzer, QC_Mnemonic) %>% summarize(N = length(Result),
Mean = round(mean(Result), 2),
SD = round(sd(Result), 3),
"%CV" = round(100*SD/Mean, 2))
}
return(p4)
})
#Generates the output table wich is displayed in the Summary Tab
output$sumtable <- renderDT(
datatable(summary.data(), rownames = FALSE)
)
###########################################################################################################################
# End of "Summary" tab data.
###########################################################################################################################
output$chart <- renderPlotly({
plot.data <- summary.data() %>% ungroup()
validate(
need(input$qcproduct != "<All>", "Please Select QC Product")
)
if(input$timeframe == 1){
p <- plot_ly(data = plot.data, x = ~Day, y = ~Mean, type = 'scatter', mode = 'lines+markers',
error_y = ~list(array = SD,
color = '#000000')
)
} else if (input$timeframe == 2) {
p <- plot_ly(data = plot.data, x = ~Week, y = ~Mean, type = 'scatter', mode = 'lines+markers',
error_y = ~list(array = SD,
color = '#000000')
)
} else if (input$timeframe == 3) {
p <- plot_ly(data = plot.data, x = ~Month, y = ~Mean, type = 'scatter', mode = 'lines+markers',
error_y = ~list(array = SD,
color = '#000000')
)
}
})
}
# Run the application
shinyApp(ui = ui, server = server)
server <- function(session, input, output) {
raw.data <- reactive({
# Tell user to select QC data if none selected
validate(
need(!is.null(input$datainput$datapath), "Select QC Data File")
)
#if the file import filter is not empty, the user has imported different data files, so make a new list
list <- input$datainput$datapath
# same as above, but no need to sort
raw.qcdata <- do.call("rbind", lapply(list, function(x) read.csv(x, stringsAsFactors = FALSE)))
# Format the date column, same as above
raw.qcdata$Date <- ymd_hms(raw.qcdata$Date)
# Tell the date range box in the UI what ranges it can display
updateDateRangeInput(session, "daterange", "Select Date Range:",
min = min(round_date(raw.qcdata$Date, unit = "days")),
max = max(round_date(raw.qcdata$Date, unit = "days")),
start = min(round_date(raw.qcdata$Date, unit = "days")),
end = max(round_date(raw.qcdata$Date, unit = "days"))
)
# Tells R to assign the imported data to the reactive variable raw.data
return(raw.qcdata)
})
filter.data <- reactive({
# The first filter applied is the Result Date
df2 <- raw.data() %>% subset(Date >= input$daterange[1] & Date <= input$daterange[2])
# The "Select Test" and "Select Analyzer" dropdown boxes are reactive. Here the script defines what should be displayed and filtered based on the various inputs.
if(input$test == "<All>" & input$analyzer == "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Date >= input$daterange[1] & Date <= input$daterange[2])
# updateSelectInput updates the selectInput dropdown boxes in the UI above. This allows interactivity between the two lists, which is nice when you have lots of tests.
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df2$Test)))
updateSelectInput(session, "analyzer", "Select Analyzer:", choices = c("<All>", unique(df2$Analyzer)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df2$QC_Mnemonic)))
}
# Now apply various filters when the selectInput box has a chosen Test or Analyzer, and update the selections available for the Analyzer
else if(input$test != "<All>" & input$analyzer == "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Test == input$test & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "analyzer", "Select Analyzer:", choices = c("<All>", unique(df3$Analyzer)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test == "<All>" & input$analyzer != "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Analyzer == input$analyzer & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test != "<All>" & input$analyzer != "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Test == input$test & Analyzer == input$analyzer & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test != "<All>" & input$analyzer != "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(Test == input$test & Analyzer == input$analyzer & QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
}
else if(input$test == "<All>" & input$analyzer != "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(Analyzer == input$analyzer & QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
}
else if(input$test == "<All>" & input$analyzer != "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Analyzer == input$analyzer & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test == "<All>" & input$analyzer == "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
updateSelectInput(session, "analyzer", "Select Analyzer:", choices = c("<All>", unique(df3$Analyzer)))
}
return(df3)
})
###########################################################################################################################
# End of filter application. At this point, all raw data has been processed according to the filters applied
###########################################################################################################################
summary.data <- reactive({
# Now we will generate the summary data
# First, format the Day, Week, and Month columns for summary
df4 <- filter.data() %>%  mutate(
Date =  ymd_hms(Date),
Day = floor_date(Date, unit = "days"),
Week = floor_date(Date, unit = "week"),
Month = floor_date(Date, unit = "month")
)
df4 <- df4 %>% mutate(
Day = as.Date(Day),
Week = as.Date(Week),
Month = as.Date(Month)
)
if(input$timeframe == 1){
p4 <- df4 %>% group_by(Day, Test, Analyzer, QC_Mnemonic) %>% summarize(N = length(Result),
Mean = round(mean(Result), 2),
SD = round(sd(Result), 3),
"%CV" = round(100*SD/Mean, 2))
} else if(input$timeframe == 2){
p4 <- df4 %>% group_by(Week, Test, Analyzer, QC_Mnemonic) %>% summarize(N = length(Result),
Mean = round(mean(Result), 2),
SD = round(sd(Result), 3),
"%CV" = round(100*SD/Mean, 2))
} else if(input$timeframe == 3){
p4 <- df4 %>% group_by(Month, Test, Analyzer, QC_Mnemonic) %>% summarize(N = length(Result),
Mean = round(mean(Result), 2),
SD = round(sd(Result), 3),
"%CV" = round(100*SD/Mean, 2))
}
return(p4)
})
#Generates the output table wich is displayed in the Summary Tab
output$sumtable <- renderDT(
datatable(summary.data(), rownames = FALSE)
)
###########################################################################################################################
# End of "Summary" tab data.
###########################################################################################################################
output$chart <- renderPlotly({
plot.data <- summary.data() %>% ungroup()
validate(
need(input$qcproduct != "<All>", "Please Select QC Product")
)
if(input$timeframe == 1){
p <- plot_ly(data = plot.data, x = ~Day, y = ~Mean, type = 'scatter', mode = 'lines+markers',
error_y = ~list(array = SD,
color = '#000000')
)
} else if (input$timeframe == 2) {
p <- plot_ly(data = plot.data, x = ~Week, y = ~Mean, type = 'scatter', mode = 'lines+markers',
error_y = ~list(array = SD,
color = '#000000')
)
} else if (input$timeframe == 3) {
p <- plot_ly(data = plot.data, x = ~Month, y = ~Mean, type = 'scatter', mode = 'lines+markers',
error_y = ~list(array = SD,
color = '#000000')
)
}
})
}
# Run the application
shinyApp(ui = ui, server = server)
server <- function(session, input, output) {
raw.data <- reactive({
# Tell user to select QC data if none selected
validate(
need(!is.null(input$datainput$datapath), "Select QC Data File")
)
#if the file import filter is not empty, the user has imported different data files, so make a new list
list <- input$datainput$datapath
# same as above, but no need to sort
raw.qcdata <- do.call("rbind", lapply(list, function(x) read.csv(x, stringsAsFactors = FALSE)))
# Format the date column, same as above
raw.qcdata$Date <- ymd_hms(raw.qcdata$Date)
# Tell the date range box in the UI what ranges it can display
updateDateRangeInput(session, "daterange", "Select Date Range:",
min = min(round_date(raw.qcdata$Date, unit = "days")),
max = max(round_date(raw.qcdata$Date, unit = "days")),
start = min(round_date(raw.qcdata$Date, unit = "days")),
end = max(round_date(raw.qcdata$Date, unit = "days"))
)
# Tells R to assign the imported data to the reactive variable raw.data
return(raw.qcdata)
})
filter.data <- reactive({
# The first filter applied is the Result Date
df2 <- raw.data() %>% subset(Date >= input$daterange[1] & Date <= input$daterange[2])
# The "Select Test" and "Select Analyzer" dropdown boxes are reactive. Here the script defines what should be displayed and filtered based on the various inputs.
if(input$test == "<All>" & input$analyzer == "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Date >= input$daterange[1] & Date <= input$daterange[2])
# updateSelectInput updates the selectInput dropdown boxes in the UI above. This allows interactivity between the two lists, which is nice when you have lots of tests.
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df2$Test)))
updateSelectInput(session, "analyzer", "Select Analyzer:", choices = c("<All>", unique(df2$Analyzer)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df2$QC_Mnemonic)))
}
# Now apply various filters when the selectInput box has a chosen Test or Analyzer, and update the selections available for the Analyzer
else if(input$test != "<All>" & input$analyzer == "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Test == input$test & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "analyzer", "Select Analyzer:", choices = c("<All>", unique(df3$Analyzer)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test == "<All>" & input$analyzer != "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Analyzer == input$analyzer & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test != "<All>" & input$analyzer != "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Test == input$test & Analyzer == input$analyzer & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test != "<All>" & input$analyzer != "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(Test == input$test & Analyzer == input$analyzer & QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
}
else if(input$test == "<All>" & input$analyzer != "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(Analyzer == input$analyzer & QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
}
else if(input$test != "<All>" & input$analyzer == "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(Test == input$test & QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test == "<All>" & input$analyzer == "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
updateSelectInput(session, "analyzer", "Select Analyzer:", choices = c("<All>", unique(df3$Analyzer)))
}
return(df3)
})
###########################################################################################################################
# End of filter application. At this point, all raw data has been processed according to the filters applied
###########################################################################################################################
summary.data <- reactive({
# Now we will generate the summary data
# First, format the Day, Week, and Month columns for summary
df4 <- filter.data() %>%  mutate(
Date =  ymd_hms(Date),
Day = floor_date(Date, unit = "days"),
Week = floor_date(Date, unit = "week"),
Month = floor_date(Date, unit = "month")
)
df4 <- df4 %>% mutate(
Day = as.Date(Day),
Week = as.Date(Week),
Month = as.Date(Month)
)
if(input$timeframe == 1){
p4 <- df4 %>% group_by(Day, Test, Analyzer, QC_Mnemonic) %>% summarize(N = length(Result),
Mean = round(mean(Result), 2),
SD = round(sd(Result), 3),
"%CV" = round(100*SD/Mean, 2))
} else if(input$timeframe == 2){
p4 <- df4 %>% group_by(Week, Test, Analyzer, QC_Mnemonic) %>% summarize(N = length(Result),
Mean = round(mean(Result), 2),
SD = round(sd(Result), 3),
"%CV" = round(100*SD/Mean, 2))
} else if(input$timeframe == 3){
p4 <- df4 %>% group_by(Month, Test, Analyzer, QC_Mnemonic) %>% summarize(N = length(Result),
Mean = round(mean(Result), 2),
SD = round(sd(Result), 3),
"%CV" = round(100*SD/Mean, 2))
}
return(p4)
})
#Generates the output table wich is displayed in the Summary Tab
output$sumtable <- renderDT(
datatable(summary.data(), rownames = FALSE)
)
###########################################################################################################################
# End of "Summary" tab data.
###########################################################################################################################
output$chart <- renderPlotly({
plot.data <- summary.data() %>% ungroup()
validate(
need(input$qcproduct != "<All>", "Please Select QC Product")
)
if(input$timeframe == 1){
p <- plot_ly(data = plot.data, x = ~Day, y = ~Mean, type = 'scatter', mode = 'lines+markers',
error_y = ~list(array = SD,
color = '#000000')
)
} else if (input$timeframe == 2) {
p <- plot_ly(data = plot.data, x = ~Week, y = ~Mean, type = 'scatter', mode = 'lines+markers',
error_y = ~list(array = SD,
color = '#000000')
)
} else if (input$timeframe == 3) {
p <- plot_ly(data = plot.data, x = ~Month, y = ~Mean, type = 'scatter', mode = 'lines+markers',
error_y = ~list(array = SD,
color = '#000000')
)
}
})
}
# Run the application
shinyApp(ui = ui, server = server)
server <- function(session, input, output) {
raw.data <- reactive({
# Tell user to select QC data if none selected
validate(
need(!is.null(input$datainput$datapath), "Select QC Data File")
)
#if the file import filter is not empty, the user has imported different data files, so make a new list
list <- input$datainput$datapath
# same as above, but no need to sort
raw.qcdata <- do.call("rbind", lapply(list, function(x) read.csv(x, stringsAsFactors = FALSE)))
# Format the date column, same as above
raw.qcdata$Date <- ymd_hms(raw.qcdata$Date)
# Tell the date range box in the UI what ranges it can display
updateDateRangeInput(session, "daterange", "Select Date Range:",
min = min(round_date(raw.qcdata$Date, unit = "days")),
max = max(round_date(raw.qcdata$Date, unit = "days")),
start = min(round_date(raw.qcdata$Date, unit = "days")),
end = max(round_date(raw.qcdata$Date, unit = "days"))
)
# Tells R to assign the imported data to the reactive variable raw.data
return(raw.qcdata)
})
filter.data <- reactive({
# The first filter applied is the Result Date
df2 <- raw.data() %>% subset(Date >= input$daterange[1] & Date <= input$daterange[2])
# The "Select Test" and "Select Analyzer" dropdown boxes are reactive. Here the script defines what should be displayed and filtered based on the various inputs.
if(input$test == "<All>" & input$analyzer == "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Date >= input$daterange[1] & Date <= input$daterange[2])
# updateSelectInput updates the selectInput dropdown boxes in the UI above. This allows interactivity between the two lists, which is nice when you have lots of tests.
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df2$Test)))
updateSelectInput(session, "analyzer", "Select Analyzer:", choices = c("<All>", unique(df2$Analyzer)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df2$QC_Mnemonic)))
}
# Now apply various filters when the selectInput box has a chosen Test or Analyzer, and update the selections available for the Analyzer
else if(input$test != "<All>" & input$analyzer == "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Test == input$test & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "analyzer", "Select Analyzer:", choices = c("<All>", unique(df3$Analyzer)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test == "<All>" & input$analyzer != "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Analyzer == input$analyzer & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test != "<All>" & input$analyzer != "<All>" & input$qcproduct == "<All>"){
df3 <- df2 %>% subset(Test == input$test & Analyzer == input$analyzer & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "qcproduct", "Select QC Product:", choices = c("<All>", unique(df3$QC_Mnemonic)))
}
else if(input$test != "<All>" & input$analyzer != "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(Test == input$test & Analyzer == input$analyzer & QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
}
else if(input$test == "<All>" & input$analyzer != "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(Analyzer == input$analyzer & QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
}
else if(input$test != "<All>" & input$analyzer == "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(Test == input$test & QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "analyzer", "Select Analyzer:", choices = c("<All>", unique(df3$Analyzer)))
}
else if(input$test == "<All>" & input$analyzer == "<All>" & input$qcproduct != "<All>"){
df3 <- df2 %>% subset(QC_Mnemonic == input$qcproduct & Date >= input$daterange[1] & Date <= input$daterange[2])
updateSelectInput(session, "test", "Select Test:", choices = c("<All>", unique(df3$Test)))
updateSelectInput(session, "analyzer", "Select Analyzer:", choices = c("<All>", unique(df3$Analyzer)))
}
return(df3)
})
###########################################################################################################################
# End of filter application. At this point, all raw data has been processed according to the filters applied
###########################################################################################################################
summary.data <- reactive({
# Now we will generate the summary data
# First, format the Day, Week, and Month columns for summary
df4 <- filter.data() %>%  mutate(
Date =  ymd_hms(Date),
Day = floor_date(Date, unit = "days"),
Week = floor_date(Date, unit = "week"),
Month = floor_date(Date, unit = "month")
)
df4 <- df4 %>% mutate(
Day = as.Date(Day),
Week = as.Date(Week),
Month = as.Date(Month)
)
if(input$timeframe == 1){
p4 <- df4 %>% group_by(Day, Test, Analyzer, QC_Mnemonic) %>% summarize(N = length(Result),
Mean = round(mean(Result), 2),
SD = round(sd(Result), 3),
"%CV" = round(100*SD/Mean, 2))
} else if(input$timeframe == 2){
p4 <- df4 %>% group_by(Week, Test, Analyzer, QC_Mnemonic) %>% summarize(N = length(Result),
Mean = round(mean(Result), 2),
SD = round(sd(Result), 3),
"%CV" = round(100*SD/Mean, 2))
} else if(input$timeframe == 3){
p4 <- df4 %>% group_by(Month, Test, Analyzer, QC_Mnemonic) %>% summarize(N = length(Result),
Mean = round(mean(Result), 2),
SD = round(sd(Result), 3),
"%CV" = round(100*SD/Mean, 2))
}
return(p4)
})
#Generates the output table wich is displayed in the Summary Tab
output$sumtable <- renderDT(
datatable(summary.data(), rownames = FALSE)
)
###########################################################################################################################
# End of "Summary" tab data.
###########################################################################################################################
output$chart <- renderPlotly({
plot.data <- summary.data() %>% ungroup()
validate(
need(input$qcproduct != "<All>", "Please Select QC Product")
)
if(input$timeframe == 1){
p <- plot_ly(data = plot.data, x = ~Day, y = ~Mean, type = 'scatter', mode = 'lines+markers',
error_y = ~list(array = SD,
color = '#000000')
)
} else if (input$timeframe == 2) {
p <- plot_ly(data = plot.data, x = ~Week, y = ~Mean, type = 'scatter', mode = 'lines+markers',
error_y = ~list(array = SD,
color = '#000000')
)
} else if (input$timeframe == 3) {
p <- plot_ly(data = plot.data, x = ~Month, y = ~Mean, type = 'scatter', mode = 'lines+markers',
error_y = ~list(array = SD,
color = '#000000')
)
}
})
}
# Run the application
shinyApp(ui = ui, server = server)