R/fragmentum.R

In nrennie/aRt: Generative Art with R

#' Fragmentum
#'
#' This function generates a coloured generative art ggplot object using randomly tessellated polygons.
#'
#' @param n_x Number of polygons per row. Default 10.
#' @param n_y Number of polygons per column. Default 10.
#' @param deg_jitter Numeric between 0 and 0.5 specifying the degree of jitter. Default 0.1.
#' @param linewidth Width of lines between polygons. Default 0.5.
#' @param line_col Colour of lines between polygons. Default `"black"`.
#' @param bg_col Background colour. Default `"black"`.
#' @param col_palette Vector of colours. Can be any length. Default `PrettyCols::prettycols("Lively")`.
#' @param s Seed value. Default 1234.
#' @return A ggplot object.
#' @examples
#' fragmentum()
#' @export

fragmentum <- function(n_x = 10,
                       n_y = 10,
                       deg_jitter = 0.1,
                       linewidth = 0.5,
                       line_col = "black",
                       bg_col = "black",
                       col_palette = PrettyCols::prettycols("Lively"),
                       s = 1234) {
  if (n_x < 1 || n_y < 1) {
    stop("Number of rows and columns must be at least 1")
  }
  if (deg_jitter < 0 || deg_jitter > 0.5) {
    stop("deg_jitter must be between 0 and 0.5")
  }
  plot_data <- withr::with_seed(
    seed = s,
    code = {
      x <- rep(1:(n_x + 1), times = n_y + 1) + stats::runif((n_x + 1) * (n_y + 1), 0, deg_jitter)
      y <- rep(1:(n_y + 1), each = n_x + 1) + stats::runif((n_x + 1) * (n_y + 1), 0, deg_jitter)

      poly_data <- tibble::tibble(x = x, y = y) |>
        dplyr::mutate(
          id = dplyr::row_number(),
          x_id = rep(1:(n_x + 1), times = n_y + 1),
          y_id = rep(1:(n_y + 1), each = n_x + 1)
        )

      x1 <- matrix(NA, ncol = n_x, nrow = n_y)
      x2 <- matrix(NA, ncol = n_x, nrow = n_y)
      x3 <- matrix(NA, ncol = n_x, nrow = n_y)
      x4 <- matrix(NA, ncol = n_x, nrow = n_y)

      y1 <- matrix(NA, ncol = n_x, nrow = n_y)
      y2 <- matrix(NA, ncol = n_x, nrow = n_y)
      y3 <- matrix(NA, ncol = n_x, nrow = n_y)
      y4 <- matrix(NA, ncol = n_x, nrow = n_y)

      group <- matrix(NA_character_, ncol = n_x, nrow = n_y)

      for (i in 1:n_x) {
        for (j in 1:n_y) {
          x1[j, i] <- dplyr::filter(
            poly_data, .data$x_id == i, .data$y_id == j
          ) |> dplyr::pull(x)
          x2[j, i] <- dplyr::filter(
            poly_data, .data$x_id == i + 1, .data$y_id == j
          ) |> dplyr::pull(x)
          x3[j, i] <- dplyr::filter(
            poly_data, .data$x_id == i + 1, .data$y_id == j + 1
          ) |> dplyr::pull(x)
          x4[j, i] <- dplyr::filter(
            poly_data, .data$x_id == i, .data$y_id == j + 1
          ) |> dplyr::pull(x)

          y1[j, i] <- dplyr::filter(
            poly_data, .data$x_id == i, .data$y_id == j
          ) |> dplyr::pull(y)
          y2[j, i] <- dplyr::filter(
            poly_data, .data$x_id == i + 1, .data$y_id == j
          ) |> dplyr::pull(y)
          y3[j, i] <- dplyr::filter(
            poly_data, .data$x_id == i + 1, .data$y_id == j + 1
          ) |> dplyr::pull(y)
          y4[j, i] <- dplyr::filter(
            poly_data, .data$x_id == i, .data$y_id == j + 1
          ) |> dplyr::pull(y)

          i_val <- ifelse(i <= 9, paste0(i), paste0(0, i))
          j_val <- ifelse(j <= 9, paste0(j), paste0(0, j))
          group[j, i] <- paste0(i_val, j_val)
        }
      }

      plot_data <- tibble::tibble(
        x = c(as.vector(x1), as.vector(x2), as.vector(x3), as.vector(x4)),
        y = c(as.vector(y1), as.vector(y2), as.vector(y3), as.vector(y4)),
        group = c(as.vector(group), as.vector(group), as.vector(group), as.vector(group)),
        fill = rep(sample(col_palette, size = n_x * n_y, replace = TRUE), times = 4)
      )
      plot_data
    }
  )

  p <- ggplot2::ggplot(
    data = plot_data,
    mapping = ggplot2::aes(
      x = .data$x,
      y = .data$y,
      group = .data$group,
      fill = .data$fill
    )
  ) +
    ggplot2::geom_polygon(colour = line_col, linewidth = linewidth) +
    ggplot2::scale_fill_identity() +
    theme_aRt(bg_col)
  return(p)
}