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SumedhSankheSumedhSankhe
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use mtcars dataset with some nice tabs
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‎app.R‎

Lines changed: 168 additions & 75 deletions
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## Example Shiny Application for Docker Optimization Demo
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## Simple proteomics QC dashboard to demonstrate containerization
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## Interactive mtcars dataset explorer to demonstrate containerization
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library(shiny)
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library(ggplot2)
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library(dplyr)
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library(DT)
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# Simulate proteomics QC data
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generate_qc_data <- function(n_samples = 50) {
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data.frame(
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sample_id = paste0("S", 1:n_samples),
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protein_count = rnorm(n_samples, mean = 5000, sd = 500),
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cv = rnorm(n_samples, mean = 15, sd = 3),
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missing_values = rbinom(n_samples, size = 100, prob = 0.05),
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stringsAsFactors = FALSE
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)
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}
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# Load mtcars dataset and add car names as a column
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cars_data <- mtcars %>%
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mutate(car_name = rownames(mtcars)) %>%
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select(car_name, everything())
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ui <- fluidPage(
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titlePanel("Proteomics QC Dashboard - Docker Demo"),
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titlePanel("Motor Trend Cars Dashboard - Docker Demo"),
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sidebarLayout(
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sidebarPanel(
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sliderInput("n_samples",
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"Number of Samples:",
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min = 10,
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max = 100,
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value = 50),
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numericInput("cv_threshold",
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"CV Threshold (%):",
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value = 20,
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min = 5,
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max = 50),
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actionButton("refresh", "Refresh Data", class = "btn-primary")
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selectInput("x_var",
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"X-axis Variable:",
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choices = c("Weight (1000 lbs)" = "wt",
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"Horsepower" = "hp",
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"Displacement" = "disp",
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"1/4 Mile Time" = "qsec"),
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selected = "wt"),
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selectInput("y_var",
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"Y-axis Variable:",
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choices = c("MPG" = "mpg",
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"Horsepower" = "hp",
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"Displacement" = "disp",
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"1/4 Mile Time" = "qsec"),
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selected = "mpg"),
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selectInput("color_var",
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"Color By:",
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choices = c("Cylinders" = "cyl",
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"Transmission" = "am",
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"Engine Type" = "vs",
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"Gears" = "gear"),
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selected = "cyl"),
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sliderInput("mpg_filter",
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"Minimum MPG:",
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min = 10,
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max = 35,
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value = 10,
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step = 1),
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hr(),
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h4("Dataset Info"),
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p("The mtcars dataset comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973-74 models)."),
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p(strong("Total cars:"), nrow(cars_data))
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),
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mainPanel(
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tabsetPanel(
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tabPanel("Summary",
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h3("Quality Control Metrics"),
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plotOutput("protein_dist"),
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plotOutput("cv_plot")
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tabPanel("Scatter Plot",
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h3("Relationship Explorer"),
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plotOutput("scatter_plot", height = "500px"),
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hr(),
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h4("Summary Statistics"),
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verbatimTextOutput("summary_stats")
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),
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tabPanel("Distribution",
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h3("MPG Distribution"),
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plotOutput("mpg_dist", height = "400px"),
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h3("Horsepower vs Cylinders"),
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plotOutput("hp_by_cyl", height = "400px")
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),
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tabPanel("Data Table",
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DTOutput("qc_table")
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h3("Full Dataset"),
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DTOutput("cars_table")
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)
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)
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)
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)
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)
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server <- function(input, output, session) {
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# Reactive data generation
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qc_data <- eventReactive(input$refresh, {
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generate_qc_data(input$n_samples)
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}, ignoreNULL = FALSE)
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# Protein count distribution
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output$protein_dist <- renderPlot({
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data <- qc_data()
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ggplot(data, aes(x = protein_count)) +
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geom_histogram(bins = 30, fill = "#2563eb", alpha = 0.7) +
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labs(title = "Protein Count Distribution",
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x = "Proteins Identified",
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y = "Frequency") +
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theme_minimal()
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# Reactive filtered data
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filtered_data <- reactive({
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cars_data %>%
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filter(mpg >= input$mpg_filter)
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})
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# Scatter plot
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output$scatter_plot <- renderPlot({
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data <- filtered_data()
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# Convert color variable to factor for better legend
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data[[input$color_var]] <- as.factor(data[[input$color_var]])
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ggplot(data, aes_string(x = input$x_var, y = input$y_var, color = input$color_var)) +
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geom_point(size = 4, alpha = 0.7) +
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geom_smooth(method = "lm", se = TRUE, alpha = 0.2) +
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scale_color_brewer(palette = "Set1") +
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labs(title = paste(input$y_var, "vs", input$x_var),
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x = names(which(c("wt" = "Weight (1000 lbs)",
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"hp" = "Horsepower",
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"disp" = "Displacement (cu.in.)",
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"qsec" = "1/4 Mile Time (sec)") == input$x_var)),
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y = names(which(c("mpg" = "Miles Per Gallon",
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"hp" = "Horsepower",
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"disp" = "Displacement (cu.in.)",
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"qsec" = "1/4 Mile Time (sec)") == input$y_var)),
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color = names(which(c("cyl" = "Cylinders",
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"am" = "Transmission",
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"vs" = "Engine",
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"gear" = "Gears") == input$color_var))) +
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theme_minimal(base_size = 14) +
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theme(legend.position = "right")
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})
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# CV plot with threshold
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output$cv_plot <- renderPlot({
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data <- qc_data()
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ggplot(data, aes(x = sample_id, y = cv)) +
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geom_point(aes(color = cv > input$cv_threshold), size = 3) +
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geom_hline(yintercept = input$cv_threshold,
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linetype = "dashed",
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color = "red") +
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scale_color_manual(values = c("TRUE" = "red", "FALSE" = "#2563eb"),
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labels = c("Pass", "Fail"),
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name = "QC Status") +
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labs(title = "Coefficient of Variation by Sample",
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x = "Sample ID",
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y = "CV (%)") +
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theme_minimal() +
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theme(axis.text.x = element_blank())
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# Summary statistics
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output$summary_stats <- renderPrint({
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data <- filtered_data()
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cat("Filtered Data Summary\n")
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cat("=====================\n\n")
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cat("Cars displayed:", nrow(data), "\n\n")
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cat("MPG Statistics:\n")
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cat(" Mean:", round(mean(data$mpg), 2), "\n")
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cat(" Median:", round(median(data$mpg), 2), "\n")
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cat(" Range:", round(min(data$mpg), 2), "-", round(max(data$mpg), 2), "\n\n")
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cat("Horsepower Statistics:\n")
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cat(" Mean:", round(mean(data$hp), 2), "\n")
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cat(" Median:", round(median(data$hp), 2), "\n")
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cat(" Range:", round(min(data$hp), 2), "-", round(max(data$hp), 2), "\n")
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})
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# MPG distribution
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output$mpg_dist <- renderPlot({
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data <- filtered_data()
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ggplot(data, aes(x = mpg)) +
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geom_histogram(bins = 15, fill = "#2563eb", alpha = 0.7, color = "white") +
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geom_vline(aes(xintercept = mean(mpg)),
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color = "red", linetype = "dashed", size = 1) +
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labs(title = "Miles Per Gallon Distribution",
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subtitle = paste("Mean:", round(mean(data$mpg), 2), "MPG"),
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x = "Miles Per Gallon",
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y = "Count") +
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theme_minimal(base_size = 14)
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})
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# HP by cylinders
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output$hp_by_cyl <- renderPlot({
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data <- filtered_data()
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ggplot(data, aes(x = as.factor(cyl), y = hp, fill = as.factor(cyl))) +
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geom_boxplot(alpha = 0.7) +
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geom_jitter(width = 0.2, alpha = 0.5, size = 2) +
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scale_fill_brewer(palette = "Set2") +
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labs(title = "Horsepower by Number of Cylinders",
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x = "Number of Cylinders",
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y = "Horsepower",
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fill = "Cylinders") +
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theme_minimal(base_size = 14)
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})
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# Data table
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output$qc_table <- renderDT({
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data <- qc_data() %>%
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mutate(qc_status = ifelse(cv > input$cv_threshold, "Fail", "Pass"))
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datatable(data,
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options = list(pageLength = 10),
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rownames = FALSE)
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output$cars_table <- renderDT({
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data <- filtered_data() %>%
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mutate(
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am = ifelse(am == 0, "Automatic", "Manual"),
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vs = ifelse(vs == 0, "V-shaped", "Straight")
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) %>%
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rename(
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Car = car_name,
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MPG = mpg,
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Cylinders = cyl,
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Displacement = disp,
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Horsepower = hp,
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`Rear Axle Ratio` = drat,
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`Weight (1000lbs)` = wt,
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`1/4 Mile Time` = qsec,
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Engine = vs,
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Transmission = am,
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Gears = gear,
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Carburetors = carb
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)
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datatable(data,
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options = list(
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pageLength = 15,
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scrollX = TRUE,
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dom = 'Bfrtip'
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),
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rownames = FALSE,
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filter = 'top')
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})
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}
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shinyApp(ui = ui, server = server)
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# Test comment 1766040179

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