DevDist.qmd

---
title: "Distribution Explorer"
format:
   dashboard:
    theme: sketchy
server: shiny
---

```{r}
#| label: Libraries
#| context: setup
library(shiny)
library(bslib)
library(DT)
library(fitdistrplus)
library(tidyverse)
```

# Welcome to the Distribution Explorer!  {orientation="columns"}

## Column  {width="65%"}

### Row {height="40%"}
<h1 style="color: #007e7e; margin-bottom: 20px;">Welcome to DevDist</h1>

DevDist is an interactive tool designed to help you explore statistical distributions that may be useful for modeling your cognitive developmental data (and beyond).

This application highlights some of the most valuable distributions commonly used in developmental research. Rather than serving as an exhaustive catalog or comprehensive statistical reference, DevDist aims to help you become familiar with key distributions, understand their parameters, and visualize how they behave under different conditions.

Consider this your starting point for exploring distributions and their characteristics. When you're ready to dive deeper, excellent resources are available online, including: [Distribution explorer](https://distribution-explorer.github.io/index.html) and [Probability Distributions Viewer](https://statdist.ksmzn.com)


### Row {height="60%"}


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## Column  {width="35%"}

### Row {height="60%"}
```{r}
#| fig-width: 6
#| fig-height: 4
#| out-width: 100%
#| out-height: 100%
#| fig-responsive: true
#| label: Cute plot of distributions
# Set parameters for each distribution
set.seed(123)
n_samples <- 200000

# Sample from all distributions
distributions_data <- data.frame(
  # Normal
  Normal = rnorm(n_samples, mean = 0, sd = 1),
  
  # Lognormal
  Lognormal = rlnorm(n_samples, meanlog = 0, sdlog = 0.5),
  
  # Exponential
  Exponential = rexp(n_samples, rate = 1),
  
  # Gamma
  Gamma = rgamma(n_samples, shape = 2, rate = 1),
  
  # Cauchy
  Cauchy = rcauchy(n_samples, location = 0, scale = 1),
  
  # Student's t
  Student_t = rt(n_samples, df = 5)

)

# Transform to long format for ggplot
distributions_long <- distributions_data %>%
  pivot_longer(everything(), names_to = "Distribution", values_to = "Value")

# Create the density plot with just lines
ggplot(distributions_long, aes(x = Value, color = Distribution)) +
  geom_density(size = 1.2, alpha = 0.8) +
  scale_color_manual(values = c("Normal" = "#1f77b4",
                               "Lognormal" = "#ff7f0e", 
                               "Exponential" = "#2ca02c",
                               "Gamma" = "#d62728",
                               "Cauchy" = "#9467bd",
                               "Student_t" = "#8c564b")) +
  theme_void(base_size = 14) +
  theme(legend.position = "none") +
  xlim(-5, 8)  # Adjust x-axis to show most distributions clearly

```

### Row {height="40%"}

::: card
# tester

DevDist is an interactive tool designed to help you explore statistical distributions that may be useful for modeling your cognitive developmental data (and beyond).

This application highlights some of the most valuable distributions commonly used in developmental research. Rather than serving as an exhaustive catalog or comprehensive statistical reference, DevDist aims to help you become familiar with key distributions, understand their parameters, and visualize how they behave under different conditions. Consider this your starting point for exploring distributions and their characteristics. When you're ready to dive deeper, excellent resources are available online, including: [Distribution explorer](https://distribution-explorer.github.io/index.html) and [Probability Distributions Viewer](https://statdist.ksmzn.com)
:::

# Distributions

## Row {height="70%"}

###  {.sidebar width="30%"}

```{r}
#| label: Selections
selectInput("dist", "Distribution:",
            choices = c("Normal", "Lognormal", "Gamma", "Exponential", 
                       "Cauchy", "Student's t", "Beta"))
```

```{r}
#| label: Normal
# Normal
conditionalPanel(
  condition = "input.dist == 'Normal'",
  sliderInput("n_mu", "Mean (μ)", value = 0, min = -5, max = 5, step = 0.1),
  helpText("Moves the center left/right."),
  sliderInput("n_sigma", "SD (σ)", value = 1, min = 0.1, max = 3, step = 0.1),
  helpText("Controls width of the distribution.")
)
```

```{r}
#| label: Lognormal
# Lognormal
conditionalPanel(
  condition = "input.dist == 'Lognormal'",
  sliderInput("ln_meanlog", "Log Mean (μ)", value = 0, min = -3, max = 3, step = 0.1),
  helpText("Mean of the underlying normal distribution."),
  sliderInput("ln_sdlog", "Log SD (σ)", value = 1, min = 0.1, max = 2, step = 0.1),
  helpText("SD of the underlying normal distribution.")
)
```

```{r}
#| label: Exponential
# Exponential
conditionalPanel(
  condition = "input.dist == 'Exponential'",
  sliderInput("exp_rate", "Rate (λ)", value = 1, min = 0.1, max = 5, step = 0.1),
  helpText("Higher rate = shorter waiting times (mean = 1/λ).")
)
```

```{r}
#| label: Cauchy
# Cauchy
conditionalPanel(
  condition = "input.dist == 'Cauchy'",
  sliderInput("cauchy_location", "Location", value = 0, min = -5, max = 5, step = 0.1),
  helpText("Shifts the center left/right."),
  sliderInput("cauchy_scale", "Scale", value = 1, min = 0.1, max = 3, step = 0.1),
  helpText("Controls the spread (no mean or variance exists).")
)
```

```{r}
#| label: Student's t
# Student's t
conditionalPanel(
  condition = "input.dist == 'Student\\'s t'",
  sliderInput("t_df", "Degrees of Freedom (ν)", value = 10, min = 1, max = 30, step = 1),
  helpText("Higher values approximate Normal (lighter tails)."),
  sliderInput("t_mu", "Location (μ)", value = 0, min = -5, max = 5, step = 0.1),
  helpText("Shifts the distribution left/right."),
  sliderInput("t_sigma", "Scale (σ)", value = 1, min = 0.1, max = 3, step = 0.1),
  helpText("Controls spread (larger = wider).")
)
```

```{r}
#| label: Gamma
# Gamma
conditionalPanel(
  condition = "input.dist == 'Gamma'",
  helpText("Gamma parameterization: shape α and rate β (mean = α/β)."),
  sliderInput("g_shape", "Shape (α > 0)", value = 2, min = 0.1, max = 10, step = 0.1),
  helpText("Higher values → more bell-shaped."),
  sliderInput("g_rate", "Rate (β > 0)", value = 1, min = 0.1, max = 5, step = 0.1),
  helpText("Higher rate shifts left and narrows.")
)
```

```{r}
#| label: Beta
# Beta
conditionalPanel(
  condition = "input.dist == 'Beta'",
  radioButtons("beta_mode", "Parameterization:",
               c("Mean μ & Precision φ" = "muphi",
                 "Alpha & Beta" = "ab"),
               inline = TRUE),

  conditionalPanel(
    condition = "input.beta_mode == 'muphi'",
    sliderInput("b_mu", "Mean (μ in (0,1))", value = 0.3, min = 0.01, max = 0.99, step = 0.01),
    helpText("Shifts the peak between 0 and 1."),
    sliderInput("b_phi", "Precision (φ > 0)", value = 20, min = 1, max = 100, step = 1),
    helpText("Higher φ = more concentrated around μ.")
  ),

  conditionalPanel(
    condition = "input.beta_mode == 'ab'",
    sliderInput("b_alpha", "Alpha (α > 0)", value = 3, min = 0.1, max = 20, step = 0.1),
    helpText("Higher α shifts toward 1."),
    sliderInput("b_beta", "Beta (β > 0)", value = 7, min = 0.1, max = 20, step = 0.1),
    helpText("Higher β shifts toward 0.")
  )
)
```

### Column {width="70%"}

```{r}
#| label: Distribution Plot
plotOutput("dist_plot", height = 360)
```

## Row {height="30%"}

### Column {width="15%"}

```{r}
#| label: Summary Stats
uiOutput("vb_moments")
```

### Column {width="15%"}

```{r}
#| label: Range & Quartiles
uiOutput("vb_quantiles")
```

### Column {width="70%"}

```{r}
#| label: Model Info
uiOutput("model_info")
```

# Check your data

## Row {height="70%"}

###  {.sidebar width="30%"}

```{r}
#| label: Data Upload Toolbar
fileInput("csv_file", "Choose CSV File:",
          accept = c(".csv"),
          buttonLabel = "Browse...",
          placeholder = "No file selected")

selectInput("csv_column", "Select Column:",
            choices = NULL,
            selected = NULL)

conditionalPanel(
  condition = "input.csv_column != null && input.csv_column != '' && input.csv_column != 'No numeric columns found'",
  hr(),
  radioButtons("plot_type", "Plot Type:",
               c("Density Plot" = "density",
                 "Distribution Characteristics" = "descdist",
                 "Fit Theoretical Distribution" = "fitdist"),
               selected = "density"),
  
  conditionalPanel(
    condition = "input.plot_type == 'descdist'",
    numericInput("boot_samples", "Bootstrap Samples:",
                 value = 100, min = 10, max = 1000, step = 10),
    helpText("More samples = smoother plot but slower computation.")
  ),
  
  conditionalPanel(
    condition = "input.plot_type == 'fitdist'",
    selectInput("fit_distribution", "Select Distribution:",
                choices = c("norm" = "norm",
                           "lnorm" = "lnorm", 
                           "gamma" = "gamma",
                           "exp" = "exp",
                           "cauchy" = "cauchy",
                           "beta" = "beta"),
                selected = "norm"),
    helpText("Distribution will be fitted to your data using maximum likelihood.")
  )
)

textOutput("data_info")
```

### Column {width="70%"}

```{r}
#| label: CSV Data Plot
conditionalPanel(
  condition = "input.plot_type == 'density' || !input.plot_type",
  plotOutput("csv_plot", height = 400)
)

conditionalPanel(
  condition = "input.plot_type == 'descdist'",
  plotOutput("descdist_plot", height = 400)
)

conditionalPanel(
  condition = "input.plot_type == 'fitdist'",
  plotOutput("fitdist_plot", height = 400)
)
```

## Row {height="30%"}

### Column {width="50%"}

```{r}
#| label: CSV Summary Stats
uiOutput("csv_stats")
```

### Column {width="50%"}

```{r}
#| label: CSV Data Table
DT::dataTableOutput("csv_table")
```

```{r}
#| label: Server logic
#| context: server

# Custom function to replace brms::rstudent_t
rstudent_t_custom <- function(n, df, mu = 0, sigma = 1) {
  mu + sigma * rt(n, df)
}

# ---- Reactive sampler ----
samples <- reactive({
  n_req <- 3000L

  if (input$dist == "Normal") {
    rnorm(n_req, mean = input$n_mu, sd = input$n_sigma)

  } else if (input$dist == "Lognormal") {
    rlnorm(n_req, meanlog = input$ln_meanlog, sdlog = input$ln_sdlog)

  } else if (input$dist == "Exponential") {
    rexp(n_req, rate = input$exp_rate)

  } else if (input$dist == "Cauchy") {
    rcauchy(n_req, location = input$cauchy_location, scale = input$cauchy_scale)

  } else if (input$dist == "Student's t") {
    rstudent_t_custom(n_req, df = input$t_df, mu = input$t_mu, sigma = input$t_sigma)

  } else if (input$dist == "Gamma") {
    rgamma(n_req, shape = input$g_shape, rate = input$g_rate)

  } else if (input$dist == "Beta") {
    if (input$beta_mode == "ab") {
      alpha <- input$b_alpha
      beta  <- input$b_beta
    } else {
      mu  <- input$b_mu
      phi <- input$b_phi
      alpha <- mu * phi
      beta  <- (1 - mu) * phi
    }
    rbeta(n_req, shape1 = alpha, shape2 = beta)

  } else {
    validate(need(FALSE, "Unknown distribution."))
  }
})

# ---- Plot ----
output$dist_plot <- renderPlot({
  s <- samples()
  df <- data.frame(x = s)
  ggplot(df, aes(x)) +
    geom_histogram(aes(y = after_stat(density)), bins = 50, alpha = 0.7, fill = "steelblue") +
    geom_density(linewidth = 1.2, color = "darkred") +
    geom_segment(inherit.aes = F, aes(x = 0, xend = 0, y = -Inf, yend = +Inf), color = "black") +
    geom_segment(inherit.aes = F ,aes(x = -Inf, xend = +Inf, y = 0, yend = 0), color = "black") +
    labs(title = paste("Sampled", input$dist, "Distribution"),
         x = "Value", y = "Density") +
    theme_minimal(base_size = 20)
})

# ---- Summary stats (computed once) ----
stats <- reactive({
  s <- samples()
  n <- length(s)
  q <- quantile(s, c(0.25, 0.75), names = FALSE)
  list(
    mean   = mean(s),
    median = median(s),
    sd     = sd(s),
    se     = sd(s) / sqrt(n),
    min    = min(s),
    q1     = q[1],
    q3     = q[2],
    max    = max(s)
  )
})

# ---- Value box 1: Mean / Median / SD / SE ----
output$vb_moments <- renderUI({
  st <- stats()
  tagList(
    tags$h4("Center & Spread", style = "margin-bottom: 10px;"),
    tags$div(style = "font-size:0.9rem; line-height:1.2; margin-bottom: 5px;",
             sprintf("Mean: %.3f | Median: %.3f", st$mean, st$median)),
    tags$div(style = "font-size:0.9rem; line-height:1.2;",
             sprintf("SD: %.3f | SE: %.3f", st$sd, st$se))
  )
})

# ---- Value box 2: Min / Max / Q1 / Q3 ----
output$vb_quantiles <- renderUI({
  st <- stats()
  tagList(
    tags$h4("Range & Quartiles", style = "margin-bottom: 10px;"),
    tags$div(style = "font-size:0.9rem; line-height:1.2; margin-bottom: 5px;",
             sprintf("Min: %.3f | Max: %.3f", st$min, st$max)),
    tags$div(style = "font-size:0.9rem; line-height:1.2;",
             sprintf("Q1: %.3f | Q3: %.3f", st$q1, st$q3))
  )
})

# ---- Model info cards ----
output$model_info <- renderUI({
  if (input$dist == "Normal") {
    tagList(
      h4("Model: Normal (Gaussian)"),
      p("Symmetric, light-tailed; mean & SD."),
      tags$ul(
        tags$li("Params: μ, σ"),
        tags$li("CLT often justifies Normality."),
        tags$li("Support: (-∞, +∞)")))
  } else if (input$dist == "Lognormal") {
    tagList(
      h4("Model: Lognormal"),
      p("Right-skewed, multiplicative processes."),
      tags$ul(
        tags$li("Params: μ (meanlog), σ (sdlog)"),
        tags$li("X ~ Lognormal ⟺ log(X) ~ Normal"),
        tags$li("Support: (0, +∞)")))
  } else if (input$dist == "Exponential") {
    tagList(
      h4("Model: Exponential"),
      p("Memoryless waiting times, survival analysis."),
      tags$ul(
        tags$li("Params: λ (rate); mean = 1/λ"),
        tags$li("Constant hazard rate"),
        tags$li("Support: [0, +∞)")))
  } else if (input$dist == "Cauchy") {
    tagList(
      h4("Model: Cauchy"),
      p("Heavy-tailed, no mean or variance."),
      tags$ul(
        tags$li("Params: location, scale"),
        tags$li("Median = location parameter"),
        tags$li("Support: (-∞, +∞)")))
  } else if (input$dist == "Student's t") {
    tagList(
      h4("Model: Student's t"),
      p("Heavy-tailed, robust alternative to Normal."),
      tags$ul(
        tags$li("Params: ν (df), μ (location), σ (scale)"),
        tags$li("As ν → ∞, approaches Normal."),
        tags$li("Use when outliers are expected.")))
  } else if (input$dist == "Gamma") {
    tagList(
      h4("Model: Gamma"),
      p("Positive, right-skewed (e.g., waiting times)."),
      tags$ul(
        tags$li("Params: α (shape), β (rate); mean = α/β"),
        tags$li("Support: (0, +∞)")))
  } else if (input$dist == "Beta") {
    tagList(
      h4("Model: Beta"),
      p("Proportions on (0,1)."),
      tags$ul(
        tags$li("Params: α, β or (μ, φ)"),
        tags$li("Flexible shapes (U, unimodal, etc.)"),
        tags$li("Support: (0, 1)")))
  }
})

# ---- CSV Data Handling ----

# Reactive to read CSV file
csv_data <- reactive({
  req(input$csv_file)
  
  tryCatch({
    df <- read_csv(input$csv_file$datapath)
    return(df)
  }, error = function(e) {
    return(NULL)
  })
})

# Update column choices when new file is loaded
observe({
  df <- csv_data()
  
  if (!is.null(df)) {
    # Get only numeric columns
    numeric_cols <- names(df)[sapply(df, is.numeric)]
    
    if (length(numeric_cols) > 0) {
      updateSelectInput(session, "csv_column",
                        choices = numeric_cols,
                        selected = numeric_cols[1])
    } else {
      updateSelectInput(session, "csv_column",
                        choices = "No numeric columns found",
                        selected = NULL)
    }
  } else {
    updateSelectInput(session, "csv_column",
                      choices = NULL,
                      selected = NULL)
  }
})

# Data info output
output$data_info <- renderText({
  df <- csv_data()
  
  if (!is.null(df)) {
    paste0("Rows: ", nrow(df), " | Cols: ", ncol(df))
  } else if (!is.null(input$csv_file)) {
    "Error loading file"
  } else {
    "No file uploaded"
  }
})

# ---- Best fit analysis (computed once when fitdist mode is selected) ----
best_fit_analysis <- reactive({
  req(input$csv_file, input$csv_column, input$plot_type == 'fitdist')
  
  df <- csv_data()
  
  if (!is.null(df) && input$csv_column %in% names(df)) {
    column_data <- df[[input$csv_column]]
    column_data <- column_data[!is.na(column_data)]
    
    if (length(column_data) > 10) {
      distributions_to_test <- c("norm", "lnorm", "gamma", "exp")
      
      # Add cauchy and beta only if data meets requirements
      if (all(is.finite(column_data))) {
        distributions_to_test <- c(distributions_to_test, "cauchy")
      }
      
      if (all(column_data > 0 & column_data < 1)) {
        distributions_to_test <- c(distributions_to_test, "beta")
      }
      
      results <- list()
      aic_values <- c()
      
      for (dist in distributions_to_test) {
        tryCatch({
          if (dist == "gamma" && any(column_data <= 0)) {
            next  # Skip gamma if data contains non-positive values
          }
          
          if (dist == "lnorm" && any(column_data <= 0)) {
            next  # Skip lognormal if data contains non-positive values
          }
          
          if (dist == "exp" && any(column_data < 0)) {
            next  # Skip exponential if data contains negative values
          }
          
          fit <- fitdist(column_data, dist)
          results[[dist]] <- fit
          aic_values[dist] <- AIC(fit)
          
        }, error = function(e) {
          # Skip distributions that fail to fit
        })
      }
      
      if (length(aic_values) > 0) {
        best_dist <- names(which.min(aic_values))
        return(list(
          results = results,
          aic_values = aic_values,
          best_distribution = best_dist,
          best_aic = min(aic_values)
        ))
      }
    }
  }
  
  return(NULL)
})

# CSV density plot
output$csv_plot <- renderPlot({
  req(input$csv_file, input$csv_column)
  
  df <- csv_data()
  
  if (!is.null(df) && input$csv_column %in% names(df)) {
    column_data <- df[[input$csv_column]]
    
    # Remove NA values
    column_data <- column_data[!is.na(column_data)]
    
    if (length(column_data) > 0) {
      plot_df <- data.frame(x = column_data)
      
      ggplot(plot_df, aes(x)) +
        geom_histogram(aes(y = after_stat(density)), 
                       bins = min(50, max(10, length(column_data)/20)), 
                       alpha = 0.7, fill = "steelblue") +
        geom_density(linewidth = 1.2, color = "darkred") +
        labs(title = paste("Density of", input$csv_column),
             subtitle = paste("n =", length(column_data), "observations"),
             x = input$csv_column, 
             y = "Density") +
        theme_minimal(base_size = 18)
    } else {
      # Empty plot with message
      ggplot() + 
        annotate("text", x = 0.5, y = 0.5, 
                 label = "No valid data in selected column", 
                 size = 6) +
        theme_void()
    }
  } else {
    # Default empty plot
    ggplot() + 
      annotate("text", x = 0.5, y = 0.5, 
               label = "Upload a CSV file and select a column", 
               size = 6) +
      theme_void()
  }
})

# New descdist plot
output$descdist_plot <- renderPlot({
  req(input$csv_file, input$csv_column, input$boot_samples)
  
  df <- csv_data()
  
  if (!is.null(df) && input$csv_column %in% names(df)) {
    column_data <- df[[input$csv_column]]
    
    # Remove NA values
    column_data <- column_data[!is.na(column_data)]
    
    if (length(column_data) > 5) {  # Need at least 6 observations for descdist
      tryCatch({
        # Create the descdist plot
        descdist(column_data, boot = input$boot_samples)
        title(main = paste("Distribution Characteristics:", input$csv_column),
              sub = paste("Based on", length(column_data), "observations with", 
                         input$boot_samples, "bootstrap samples"))
      }, error = function(e) {
        # Fallback plot if descdist fails
        plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
        text(1, 1, paste("Error creating descdist plot:\n", e$message), 
             cex = 1.2, col = "red")
        title("Distribution Characteristics - Error")
      })
    } else {
      # Not enough data
      plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
      text(1, 1, "Need at least 6 observations\nfor distribution characteristics plot", 
           cex = 1.5, col = "darkred")
      title("Distribution Characteristics")
    }
  } else {
    # Default empty plot
    plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
    text(1, 1, "Upload a CSV file and select a column", cex = 1.5)
    title("Distribution Characteristics")
  }
})

# New fitdist plot
output$fitdist_plot <- renderPlot({
  req(input$csv_file, input$csv_column, input$fit_distribution)
  
  df <- csv_data()
  
  if (!is.null(df) && input$csv_column %in% names(df)) {
    column_data <- df[[input$csv_column]]
    column_data <- column_data[!is.na(column_data)]
    
    if (length(column_data) > 5) {
      tryCatch({
        # Check if the selected distribution is appropriate for the data
        if (input$fit_distribution == "gamma" && any(column_data <= 0)) {
          plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
          text(1, 1, "Gamma distribution requires positive values", 
               cex = 1.2, col = "red")
          title("Distribution Fit - Invalid Data")
          return()
        }
        
        if (input$fit_distribution == "lnorm" && any(column_data <= 0)) {
          plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
          text(1, 1, "Lognormal distribution requires positive values", 
               cex = 1.2, col = "red")
          title("Distribution Fit - Invalid Data")
          return()
        }
        
        if (input$fit_distribution == "exp" && any(column_data < 0)) {
          plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
          text(1, 1, "Exponential distribution requires non-negative values", 
               cex = 1.2, col = "red")
          title("Distribution Fit - Invalid Data")
          return()
        }
        
        if (input$fit_distribution == "beta" && (!all(column_data > 0 & column_data < 1))) {
          plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
          text(1, 1, "Beta distribution requires values between 0 and 1", 
               cex = 1.2, col = "red")
          title("Distribution Fit - Invalid Data")
          return()
        }
        
        # Fit the selected distribution
        fit <- fitdist(column_data, input$fit_distribution)
        plot(fit)
        title(main = paste("Fitted", toupper(input$fit_distribution), "Distribution"),
              sub = paste("AIC:", round(AIC(fit), 2)))
        
      }, error = function(e) {
        plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
        text(1, 1, paste("Error fitting distribution:\n", e$message), 
             cex = 1.2, col = "red")
        title("Distribution Fit - Error")
      })
    } else {
      plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
      text(1, 1, "Need at least 6 observations\nfor distribution fitting", 
           cex = 1.5, col = "darkred")
      title("Distribution Fit")
    }
  } else {
    plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
    text(1, 1, "Upload a CSV file and select a column", cex = 1.5)
    title("Distribution Fit")
  }
})

# CSV Summary Statistics - NO BOXES
output$csv_stats <- renderUI({
  req(input$csv_file, input$csv_column)
  
  df <- csv_data()
  
  if (!is.null(df) && input$csv_column %in% names(df)) {
    column_data <- df[[input$csv_column]]
    column_data <- column_data[!is.na(column_data)]
    
    if (length(column_data) > 0) {
      
      # Check if we're in fitdist mode and show best fit info
      if (input$plot_type == 'fitdist') {
        best_fit <- best_fit_analysis()
        
        if (!is.null(best_fit)) {
          tagList(
            tags$h4("Best Fitting Distribution", style = "margin-bottom: 10px;"),
            tags$div(style = "font-size:1.15rem; font-weight:600; color: #2E8B57; margin-bottom: 5px;",
                     paste("Best Fit:", toupper(best_fit$best_distribution))),
            tags$div(style = "margin-bottom: 10px;",
                     sprintf("AIC: %.2f", best_fit$best_aic)),
            tags$div(style = "font-weight: 600; margin-bottom: 5px;", "Comparison (AIC):"),
            tags$div(style = "font-size:0.9rem;",
                     paste(names(best_fit$aic_values), 
                           sprintf("%.1f", best_fit$aic_values), 
                           sep = ": ", collapse = " | "))
          )
        } else {
          tagList(
            tags$h4("Best Fitting Distribution"),
            tags$p("Unable to fit distributions to this data")
          )
        }
      } else {
        # Regular summary statistics - NO CARD/BOX
        tagList(
          tags$h4(paste("Summary:", input$csv_column), style = "margin-bottom: 10px;"),
          tags$div(style = "font-size:1.15rem; font-weight:600; margin-bottom: 5px;",
                   sprintf("Mean: %.4f", mean(column_data))),
          tags$div(style = "margin-bottom: 5px;",
                   sprintf("Median: %.4f", median(column_data))),
          tags$div(style = "margin-bottom: 5px;",
                   sprintf("SD: %.4f", sd(column_data))),
          tags$div(style = "font-size:0.9rem;",
                   sprintf("Min: %.4f | Max: %.4f", min(column_data), max(column_data)))
        )
      }
    } else {
      tagList(
        tags$h4("Summary"),
        tags$p("No valid numeric data")
      )
    }
  } else {
    tagList(
      tags$h4("Summary"),
      tags$p("Select a column to view statistics")
    )
  }
})

# CSV Data Table
output$csv_table <- DT::renderDataTable({
  df <- csv_data()
  
  if (!is.null(df)) {
    DT::datatable(df, 
                  options = list(
                    pageLength = 10,
                    scrollX = TRUE,
                    dom = 'tip'
                  ),
                  class = 'cell-border stripe')
  } else {
    NULL
  }
})
```