Calculator: Simple linear regression

Summary

A calculator to provide simple linear regression for your data.

Simple linear regression calculator

#| '!! shinylive warning !!': |
#|   shinylive does not work in self-contained HTML documents.
#|   Please set `embed-resources: false` in your metadata.
#| standalone: true
#| viewerHeight: 1400

library(shiny)
library(bslib)
library(ggplot2)

ui <- page_fillable(
  title = "Simple linear regression calculator",
  
  card(
    card_header("Variable names"),
    height = "175px",
    layout_columns(
      col_widths = c(6, 6),
      textInput("x_name", "X Variable Name:", value = "X Variable", placeholder = "Enter X variable name"),
      textInput("y_name", "Y Variable Name:", value = "Y Variable", placeholder = "Enter Y variable name")
    )
  ),
  
  card(
    card_header("Input data"),
    height = "375px",
    p("Enter x and y values (one pair per line, separated by space, comma, or tab):"),
    textAreaInput(
      "data_input",
      NULL,
      placeholder = "for example,\n1 2\n2 4\n3 5\n4 4\n5 5",
      height = "150px",
      width = "100%"
    ),
    actionButton("calculate", "Calculate simple linear regression", class = "btn-primary")
  ),
  
  card(
    card_header("Regression results"),
    height = "350px",
    verbatimTextOutput("regression_summary")
  ),
  
  card(
    card_header("Regression plot"),
    height = "500px",
    plotOutput("regression_plot", height = "400px")
  )
)

server <- function(input, output, session) {
  
  # Reactive expression to parse and validate data
  parsed_data <- reactive({
    req(input$calculate)
    
    # Get input text
    text <- input$data_input
    
    if (text == "" || is.null(text)) {
      return(NULL)
    }
    
    # Split by newlines
    lines <- strsplit(text, "\n")[[1]]
    lines <- lines[lines != ""]  # Remove empty lines
    
    # Parse each line (split by space, comma, or tab)
    data_list <- lapply(lines, function(line) {
      # Split by space, comma, or tab
      parts <- strsplit(line, "[,\\s\t]+")[[1]]
      parts <- parts[parts != ""]  # Remove empty strings
      
      if (length(parts) >= 2) {
        return(c(as.numeric(parts[1]), as.numeric(parts[2])))
      } else {
        return(NULL)
      }
    })
    
    # Remove NULL entries
    data_list <- data_list[!sapply(data_list, is.null)]
    
    if (length(data_list) < 2) {
      return(NULL)
    }
    
    # Convert to data frame
    df <- data.frame(
      x = sapply(data_list, `[`, 1),
      y = sapply(data_list, `[`, 2)
    )
    
    # Remove rows with NA values
    df <- na.omit(df)
    
    if (nrow(df) < 2) {
      return(NULL)
    }
    
    return(df)
  })
  
  # Reactive expression to fit linear model
  regression_model <- reactive({
    df <- parsed_data()
    req(df)
    
    lm(y ~ x, data = df)
  })
  
  # Output: Regression summary
  output$regression_summary <- renderPrint({
    model <- regression_model()
    req(model)
    
    coefs <- coef(model)
    r_squared <- summary(model)$r.squared
    x_name <- input$x_name
    y_name <- input$y_name
    
    cat("Simple linear regression results\n")
    cat("=========================\n\n")
    cat(sprintf("Equation: %s = %.4f + %.4f * %s\n\n", y_name, coefs[1], coefs[2], x_name))
    cat(sprintf("Intercept (alpha): %.4f\n", coefs[1]))
    cat(sprintf("Gradient (beta): %.4f\n", coefs[2]))
    cat(sprintf("R-squared: %.4f\n\n", r_squared))
    cat("Sample size:", nrow(parsed_data()), "\n")
  })
  
  # Output: Regression plot
  output$regression_plot <- renderPlot({
    df <- parsed_data()
    model <- regression_model()
    req(df, model)
    
    x_name <- input$x_name
    y_name <- input$y_name
    
    # Get predictions for the regression line
    df$predicted <- predict(model)
    df$se <- predict(model, se.fit = TRUE)$se.fit
    
    ggplot(df, aes(x = x, y = y)) +
      geom_point(size = 3, color = "#3f68b6") +
      geom_line(aes(y = predicted), color = "#db4315", linewidth = 1) +
      geom_ribbon(aes(ymin = predicted - 1.96 * se, ymax = predicted + 1.96 * se), 
                  fill = "#db4315", alpha = 0.2) +
      labs(
        title = "Simple linear regression",
        x = x_name,
        y = y_name
      ) +
      theme_minimal(base_size = 14) +
      theme(
        plot.title = element_text(hjust = 0.5, face = "bold"),
        panel.grid.minor = element_blank()
      )
  })
}

shinyApp(ui, server)

Version history

v1.0: created by tdhc 03/26.

This work is licensed under CC BY-NC-SA 4.0.