## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## ----install, eval=FALSE------------------------------------------------------
# # install.packages("devtools")
# devtools::install_github("xability/r-maidr-prototype")

## ----ggplot2-example----------------------------------------------------------
# library(maidr)
# library(ggplot2)
# 
# # Create sample data
# sales_data <- data.frame(
#   Product = c("A", "B", "C", "D"),
#   Sales = c(150, 230, 180, 290)
# )
# 
# # Create a bar chart
# p <- ggplot(sales_data, aes(x = Product, y = Sales)) +
#   geom_bar(stat = "identity", fill = "steelblue") +
#   labs(
#     title = "Product Sales by Category",
#     x = "Product",
#     y = "Sales Amount"
#   ) +
#   theme_minimal()
# 
# # Display interactively
# show(p)
# 
# # Or save as HTML file
# save_html(p, "sales_chart.html")

## ----base-r-example-----------------------------------------------------------
# library(maidr)
# 
# # Create a simple barplot
# categories <- c("A", "B", "C", "D")
# values <- c(150, 230, 180, 290)
# 
# barplot(
#   values,
#   names.arg = categories,
#   col = "steelblue",
#   main = "Product Sales by Category",
#   xlab = "Product",
#   ylab = "Sales Amount"
# )
# 
# # Note: For Base R plots, call show() with NO arguments
# # after creating the plot
# show()

## ----histogram-example--------------------------------------------------------
# library(maidr)
# library(ggplot2)
# 
# # Normal distribution
# hist_data <- data.frame(values = rnorm(1000, mean = 100, sd = 15))
# 
# p <- ggplot(hist_data, aes(x = values)) +
#   geom_histogram(bins = 30, fill = "skyblue", color = "black") +
#   labs(
#     title = "Distribution of Test Scores",
#     x = "Score",
#     y = "Frequency"
#   ) +
#   theme_minimal()
# 
# show(p)

## ----scatter-example----------------------------------------------------------
# library(maidr)
# library(ggplot2)
# 
# # Create sample data
# scatter_data <- data.frame(
#   height = rnorm(50, 170, 10),
#   weight = rnorm(50, 70, 8),
#   gender = sample(c("Male", "Female"), 50, replace = TRUE)
# )
# 
# p <- ggplot(scatter_data, aes(x = height, y = weight, color = gender)) +
#   geom_point(size = 3, alpha = 0.7) +
#   labs(
#     title = "Height vs Weight",
#     x = "Height (cm)",
#     y = "Weight (kg)"
#   ) +
#   theme_minimal()
# 
# show(p)

## ----line-example-------------------------------------------------------------
# library(maidr)
# library(ggplot2)
# 
# # Time series data
# months <- month.abb[1:12]
# temperature <- c(5, 7, 12, 18, 22, 26, 28, 27, 23, 17, 11, 6)
# 
# temp_data <- data.frame(
#   Month = factor(months, levels = months),
#   Temperature = temperature
# )
# 
# p <- ggplot(temp_data, aes(x = Month, y = Temperature, group = 1)) +
#   geom_line(color = "red", linewidth = 1.5) +
#   geom_point(color = "darkred", size = 3) +
#   labs(
#     title = "Average Monthly Temperature",
#     x = "Month",
#     y = "Temperature (°C)"
#   ) +
#   theme_minimal()
# 
# show(p)