Title:	Creates Generative Art Data
Version:	0.2.0
Maintainer:	Meghan Harris <meghanha01@gmail.com>
Description:	Create data that displays generative art when mapped into a 'ggplot2' plot. Functionality includes specialized data frame creation for geometric shapes, tools that define artistic color palettes, tools for geometrically transforming data, and other miscellaneous tools that are helpful when using 'ggplot2' for generative art.
License:	MIT + file LICENSE
URL:	https://meghansaha.github.io/artpack/, https://github.com/Meghansaha/artpack
BugReports:	https://github.com/Meghansaha/artpack/issues
Imports:	cli, dplyr (≥ 1.0.8), grDevices, knitr, lifecycle, purrr (≥ 0.3.4), rlang, sf, stringr (≥ 1.5.0), tibble (≥ 3.1.6)
Suggests:	covr, rmarkdown, spelling, testthat (≥ 3.0.0), ggplot2 (≥ 3.4.2), withr
Depends:	R (≥ 4.1.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
Config/testthat/parallel:	true
Encoding:	UTF-8
Language:	en-US
RoxygenNote:	7.3.3
NeedsCompilation:	no
Packaged:	2025-09-11 04:42:07 UTC; megha
Author:	Meghan Harris [aut, cre, cph]
Repository:	CRAN
Date/Publication:	2025-09-13 18:40:02 UTC

artpack: Creates Generative Art Data

Description

Create data that displays generative art when mapped into a 'ggplot2' plot. Functionality includes specialized data frame creation for geometric shapes, tools that define artistic color palettes, tools for geometrically transforming data, and other miscellaneous tools that are helpful when using 'ggplot2' for generative art.

Author(s)

Maintainer: Meghan Harris meghanha01@gmail.com (ORCID) [copyright holder]

See Also

Useful links:

• https://meghansaha.github.io/artpack/

• https://github.com/Meghansaha/artpack

• Report bugs at https://github.com/Meghansaha/artpack/issues

Custom-built artpack Color Palettes

Description

The artpack palette picker. The art_pals function consists of 18 palettes: "arctic", "beach", "bw", "brood", "cosmos", "explorer", "gemstones", "grays", "icecream", "imagination", "majestic", "nature", "neon", "ocean", "plants", "rainbow", "sunnyside", "super"

Usage

art_pals(pal = NULL, n = 5, direction = "regular", randomize = FALSE)

Arguments

Value

A Character Vector.

Examples

library(ggplot2)
dots <- data.frame(x = c(1:10), y = 2.5)
dots$fills <- art_pals("rainbow", 10)

dots |>
  ggplot(aes(x, y)) +
  theme_void() +
  geom_point(
    shape = 21,
    fill = dots$fills,
    color = "#000000",
    size = 10,
    stroke = 2
  )


dots_rev <- data.frame(x = c(1:10), y = 2.5)
dots_rev$fills <- art_pals("rainbow", 10, "reverse")

dots_rev |>
  ggplot(aes(x, y)) +
  theme_void() +
  geom_point(
    shape = 21,
    fill = dots_rev$fills,
    color = "#000000",
    size = 10,
    stroke = 2
  )

dots_random <- data.frame(x = c(1:10), y = 2.5)
dots_random$fills <- art_pals("rainbow", 10, randomize = TRUE)

dots_random |>
  ggplot(aes(x, y)) +
  theme_void() +
  geom_point(
    shape = 21,
    fill = dots_random$fills,
    color = "#000000",
    size = 10,
    stroke = 2
  )

Data Generation for Circles

Description

A tool for creating a data frame of values that creates a circle with a specified radius when plotted.

The geom_path and geom_polygon geoms are recommended with this data for use in ggplot2 for generative art.

Usage

circle_data(
  x,
  y,
  radius,
  color = NULL,
  fill = NULL,
  n_points = 100,
  group_var = FALSE,
  group_prefix = "circle_"
)

Arguments

Value

A Tibble

Examples

# Creating one circle

library(ggplot2)
one_circle <- circle_data(x = 0, y = 0, radius = 5)

# Plot The Data
one_circle |>
  ggplot(aes(x, y)) +
  geom_path(color = "green") +
  coord_equal()

# To create multiple circles, use your preferred method of iteration:
# Creating two circles

library(purrr)
library(dplyr)

# Make your specs
x_vals <- c(0, 10)
y_vals <- c(0, 0)
radi <- c(1, 3)
fills <- c("purple", "yellow")
circle_n <- 1:2

# Prep for your iteration
lst_circle_specs <-
  list(
    x_vals,
    y_vals,
    radi,
    fills,
    circle_n
  )

# Use `circle_data()` in your preferred iteration methods
two_circles <- pmap(lst_circle_specs, ~ circle_data(
  x = ..1,
  y = ..2,
  radi = ..3,
  fill = ..4,
  color = "#000000",
  group_var = TRUE
) |>
  # circle_data adds a `group` variable if `group_var` = TRUE.
  # For multiple circles, a unique identifier should be added/pasted in.
  mutate(group = paste0(group, ..5))) |>
  list_rbind()

# Plot the data

two_circles |>
  ggplot(aes(x, y, group = group)) +
  theme(legend.position = "none") +
  geom_polygon(
    color = two_circles$color,
    fill = two_circles$fill
  ) +
  coord_equal() #Always use coord_equal() or coord_fixed for circles!

Data Generation for A Custom-built Square Grid

Description

Creates a dataframe of x and y points to visualize a square grid based on given x and y limits. Providing a color palette and fill style are optional.

Usage

grid_maker(
  xlim,
  ylim,
  size,
  fill_pal = NULL,
  fill_style = "range",
  color_pal = NULL,
  color_style = "range"
)

Arguments

Value

A Tibble

Examples

# Creating data for a grid:

library(ggplot2)
grid_data <- grid_maker(
  xlim = c(0, 50),
  ylim = c(0, 50),
  size = 10,
  fill_pal = c("turquoise", "black", "purple"),
  color_pal = c("black", "limegreen")
)

ggplot() +
  geom_polygon(
    data = grid_data,
    aes(x, y, group = group),
    fill = grid_data$fill,
    color = grid_data$color
  ) +
  coord_equal()

Convert Numbers into Padded Strings for Easier Group Numbering

Description

Convert Numbers into Padded Strings for Easier Group Numbering

Usage

group_numbers(numbers, prefix = NULL, suffix = NULL, sep = NULL)

Arguments

Value

A Character Vector

Examples

# Useful for easier group numbering so groups are ordered as intended
# Expects a numeric vector of numbers to convert to padded numbers
regular_numbers <- 1:19
padded_numbers <- group_numbers(regular_numbers)

# The padding matters when creating labels for groupings
# as numbers will be converted to characters if attached to strings.
# Sorts as expected:
sort(regular_numbers)

# Does not as a character:
sort(paste0("group_", regular_numbers))

# Will sort as expected when padded:
sort(paste0("group_", padded_numbers))

Sample Data Frames by a Group Variable

Description

Sample Data Frames by a Group Variable

Usage

group_sample(
  data,
  group,
  n = 1,
  prop = NULL,
  prob = NULL,
  group_output = FALSE
)

Arguments

Value

A sampled dataframe

Examples

vec_coords <- 1:10
df_data <-
 data.frame(
   "x" = vec_coords,
   "y" = vec_coords,
   "group_col" = group_numbers(1:5) |> rep(each = 2)
 )

df_sampled_data_prop <-
 df_data |>
 group_sample(group_col, prop = .2)

df_sampled_data_prop

df_sampled_data_n <-
 df_data |>
 group_sample(group_col, n = 2)

df_sampled_data_n

Subset Data Frames by a Group Variable Using Their Positions

Description

Subset Data Frames by a Group Variable Using Their Positions

Usage

group_slice(
  data,
  group,
  n = 1,
  prop = NULL,
  position = "head",
  group_output = FALSE
)

Arguments

Value

A sliced dataframe

Examples

vec_coords <- 1:10
df_data <-
 data.frame(
   "x" = vec_coords,
   "y" = vec_coords,
   "group_col" = group_numbers(1:5) |> rep(each = 2)
 )

df_sliced_data_head <-
 df_data |>
 group_slice(group_col, n = 2, position = "head")

df_sliced_data_head

df_sliced_data_tail <-
 df_data |>
 group_slice(group_col, n = 2, position = "tail")

df_sliced_data_tail

df_sliced_data_prop <-
 df_data |>
 group_slice(group_col, prop = .80)

df_sliced_data_prop

Data Generation for Circle Packing

Description

A tool for creating a data frame of values that create a circle packing design when plotted. When the default circle_type "whole" is used, the output should mapped with geom_polygon in a ggplot. When "swirl" is used, the output should be mapped with geom_path for the best results.

Usage

packer(
  n,
  min_x = 0,
  max_x = 100,
  min_y = 0,
  max_y = 100,
  big_r = 5,
  med_r = 3,
  small_r = 1,
  color_pal = NULL,
  color_type = "regular",
  circle_type = "whole"
)

Arguments

Value

A Tibble

Examples

library(ggplot2)
set.seed(0310)
packed_circles <- packer(
  n = 50, big_r = 5, med_r = 3, small_r = 1,
  min_x = 0, max_x = 100, min_y = 0, max_y = 100
)
packed_circles

packed_circles |>
  ggplot(aes(x, y, group = group)) +
  theme_void() +
  theme(plot.background = element_rect(fill = "black")) +
  geom_polygon(fill = "white", color = "red") +
  coord_equal()

Point in Polygon Test for Generative Art

Description

Tests whether points are inside a polygon using Cartesian coordinates. Distinguishes between points that are inside, outside, or on the polygon boundary.

Usage

point_in_polygon(point_x = NULL, point_y = NULL, poly_x = NULL, poly_y = NULL)

Arguments

Details

The function uses the sf package's geometry operations with Cartesian coordinates. If the provided polygon's first and last vertices are different, it is not considered to be a closed polygon. If this occurs, it will be automatically closed. All input vectors must be numeric, and point_x/point_y must have matching lengths, as must poly_x/poly_y.

Value

Integer vector: 0 = outside polygon, 1 = inside polygon -OR- on polygon boundary

Examples

# point_in_polygon can be used with basic vectors#
# X points we want to check the position of relevant to the polygon
point_x <- c(0.5, 1.5, 2.5, 1.0, 3.0)
# Y points we want to check the position of relevant to the polygon
point_y <- c(0.5, 1.5, 2.5, 0.0, 1.0)
# X points of the polygon we want to test against
poly_x <- c(0, 2, 2, 0, 0)
# Y points of the polygon we want to test against
poly_y <- c(0, 0, 2, 2, 0)

# 0 = outside polygon, 1 = inside polygon or on boundary
point_in_polygon(point_x, point_y, poly_x, poly_y)

# point_in_polygon can also be used within a data frame#
library(dplyr)

test_data <- data.frame(px = c(0.5, 1.5, 2.5), py = c(0.5, 1.5, 2.5))
polygon_x <- c(0, 2, 2, 0, 0)
polygon_y <- c(0, 0, 2, 2, 0)

test_data |>
  mutate(logic = point_in_polygon(px, py, polygon_x, polygon_y))

# You can also see the results of the function visualized on a ggplot#
library(ggplot2)

# Create test points and polygon for visualization
df_points_prep <-
  tibble(
    x = c(0.5, 1.5, 2.5, 1.0, 0.0, 3.0),
    y = c(0.5, 1.5, 2.5, 0.0, 1.0, 1.0)
  )
df_polygon <-
  tibble(
    x = c(0, 2, 2, 0, 0),
    y = c(0, 0, 2, 2, 0)
  )

# Test the points and add labels for the plot
df_points <-
  df_points_prep |>
  mutate(
    position = point_in_polygon(x, y, df_polygon$x, df_polygon$y),
    position_string = case_match(position,
                                 0 ~ "Outside",
                                 1 ~ "Inside/On Boundary"
                                 ) |> factor(),
    color = case_match(position,
                       0 ~ "#e31a1c",
                       1 ~ "#33a02c"
                       )
  )

# Pull out the colors for the plot points
unique_df <- unique(df_points[c("position_string", "color")])
vec_colors <- setNames(unique_df$color, unique_df$position_string)

# Plot it
ggplot() +
  geom_polygon(data = df_polygon, aes(x = x, y = y),
               fill = "#104d70", alpha = 0.3, color = "black", linewidth = 1) +
  geom_point(data = df_points, aes(x = x, y = y, color = position_string), size = 4) +
  scale_color_manual(values = vec_colors) +
  labs(color = "Position:") +
  coord_equal()

Transforms and Scales Numeric Points in a Data Frame by a Provided Factor and Direction

Description

Transforms and Scales Numeric Points in a Data Frame by a Provided Factor and Direction

Usage

resizer(
  data = NULL,
  x,
  y,
  x_anchor = NULL,
  y_anchor = NULL,
  factor = NULL,
  direction = "up",
  drop = FALSE,
  ...
)

Arguments

Value

A data frame

Examples

library(ggplot2)

# Resize a simple square "up" by a factor of 6
# Start with data that makes a shape#
df_square <-
  data.frame(
    x = c(0,1,1,0,0),
    y = c(0,0,1,1,0)
  )

# Resize the shape#
df_square_resized <-
  df_square |>
  resizer(x, y, factor = 6)

# Plot them
df_square |>
  ggplot(aes(x,y)) +
  # resized square - red dashed line
  geom_path(data = df_square_resized, color = "#a83246", linewidth = 2, linetype = 2) +
  # original square - black solid line
  geom_path(color = "#000000", linewidth = .8) +
  coord_equal()

# Resize a circle "down" by a factor of 3
df_circle <-
  circle_data(x = 5, y = 5, radius = 5, group_var = TRUE)

# Set then anchor point as the middle of the circle c(5,5)
# Although the point 5,5 is in the circle's bounds
# it's not actually a row in `df_circle`
# A message will display in cases like these and is "fine" to ignore.

df_circle_resized <-
  df_circle |>
  resizer(x,y, x_anchor = 5, y_anchor = 5, direction = "down", factor = 3)

# Plot them
df_circle |>
  ggplot(aes(x,y)) +
  # resized square - red dashed line
  geom_path(data = df_circle_resized, color = "#a83246", linewidth = 2, linetype = 2) +
  # original square - black solid line
  geom_path(color = "#000000", linewidth = .8) +
  coord_equal()

Rotate Points in a Data Frame Based on an Anchor Point

Description

Rotates the x and y points in a given data frame by a given angle based on a designated anchor point.

Usage

rotator(data, x, y, angle = 5, anchor = "center", drop = FALSE)

Arguments

Value

A data frame

Examples

library(ggplot2)
original_square <- data.frame(
  x = c(0, 3, 3, 0, 0),
  y = c(0, 0, 3, 3, 0)
)
rotated_square <- rotator(data = original_square,
                          x = x,
                          y = y,
                          angle = 45,
                          anchor = "center")

ggplot()+
  geom_path(data = original_square,
                     aes(x,y),
                     color = "red")+
  geom_polygon(data = rotated_square,
                        aes(x,y),
                        fill = "purple")+
  coord_equal()

Bouncing Sequence Generation

Description

Generate a regular sequence that 'bounces' between the provided start_n and end_n values in increments by the by value for the length of the length value provided.

Usage

seq_bounce(start_n = NULL, end_n = NULL, length = NULL, by = 1)

Arguments

Value

A numeric vector

Examples

#By default, seq_bounce creates sequences by increments of 1
#The length argument accepts any positive integer
seq_bounce(start_n = 1, end_n = 5, length = 15)

#The by argument accepts any positive numeric
seq_bounce(start_n = 0, end_n = 10, length = 30, by = .247)

#The end_n value must be greater than the start_n value
#This will give you an error
try(seq_bounce(start_n = 0, end_n = -10, length = 15))

#Instead, reverse the values
seq_bounce(start_n = -10, end_n = 0, length = 15)

Change the brightness of a hexadecimal color value

Description

Usage

set_brightness(color = NULL, percentage = NULL)

Arguments

Value

A character string (hexadecimal color)

Examples

# Load in ggplot2 so we can see the colors
library(ggplot2)

# Create color values
original_color <- "#7755aa" #(original brightness == .5)
darker_color <- set_brightness(original_color, .3) #(brightness == %30)
lighter_color <- set_brightness(original_color, .7) #(brightness == %70)

# Make a data frame with the color values
df_colors <-
  data.frame(
    x = 0:2,
    y = 1,
    color = c(darker_color, original_color, lighter_color)
  )

# Add a label for clarity
df_colors$label <- paste(c("Darker", "Original", "Lighter"), ":", df_colors$color)

# Plot to see the brightness changes
df_colors |>
  ggplot(aes(x,y)) +
  geom_label(aes(x = 0:2), y = 2, label = df_colors$label) +
  geom_point(color = df_colors$color, shape = 15, size = 50) +
  coord_cartesian(xlim = c(-1,3), ylim = c(0,3)) +
  theme_void()

Change the saturation of a hexadecimal color value

Description

Usage

set_saturation(color = NULL, percentage = NULL)

Arguments

Value

A character string (hexadecimal color)

Examples

# Load in ggplot2 so we can see the colors
library(ggplot2)

# Create color values
original_color <- "#7340bf" #(original saturation == .5)
desaturated_color <- set_saturation(original_color, .2) #(saturation == %20)
saturated_color <- set_saturation(original_color, .9) #(saturation == %90)

# Make a data frame with the color values
df_colors <-
  data.frame(
    x = 0:2,
    y = 1,
    color = c(desaturated_color, original_color, saturated_color)
  )

# Add a label for clarity
df_colors$label <- paste(c("Desaturated", "Original", "Saturated"), ":", df_colors$color)

# Plot to see the saturation changes
df_colors |>
  ggplot(aes(x,y)) +
  geom_label(aes(x = 0:2), y = 2, label = df_colors$label) +
  geom_point(color = df_colors$color, shape = 15, size = 50) +
  coord_cartesian(xlim = c(-1,3), ylim = c(0,3)) +
  theme_void()

Data Generation for Squares

Description

A tool for creating a data frame of values that create a square with a specified size when plotted.

The geom_path and geom_polygon geoms are recommended with this data for use in ggplot2 for generative art.

Usage

square_data(
  x,
  y,
  size,
  color = NULL,
  fill = NULL,
  n_points = 100,
  group_var = FALSE,
  group_prefix = "square_"
)

Arguments

Value

A Tibble

Examples

# Creating one square
library(ggplot2)
one_square <- square_data(x = 0, y = 0, size = 5)

# Plot The Data
one_square |>
  ggplot(aes(x,y))+
  geom_path(color = "green")+
  coord_equal()

# To create multiple squares, use your preferred method of iteration:
# Creating two squares

library(purrr)
library(dplyr)

# Make your specs
x_vals <- c(0,4)
y_vals <- c(0,0)
sizes <- c(1,3)
fills <- c("purple", "yellow")
square_n <- 1:2

# Prep for your iteration
lst_square_specs <-
  list(
    x_vals,
    y_vals,
    sizes,
    fills,
    square_n
  )

# Use `square_data()` in your preferred iteration methods
two_squares <- pmap(lst_square_specs, ~square_data(
  x = ..1,
  y = ..2,
  size = ..3,
  fill = ..4,
  color = "#000000",
  group_var = TRUE
) |>
  # square_data adds a `group` variable if `group_var` = TRUE.
  # For multiple squares, a unique identifier should be added/pasted in.
  mutate(group = paste0(group,..5))
) |>
  list_rbind()

# Plot the data

two_squares |>
  ggplot(aes(x, y, group = group))+
  theme(legend.position = "none")+
  geom_polygon(color = two_squares$color,
               fill = two_squares$fill) +
  coord_equal()

Data Generation for 2D Sine and Cosine Waves

Description

A tool for making data frames filled with data that displays sine or cosine waves when graphed.

The geom_path and geom_polygon geoms are recommended with this data for use in ggplot2 for generative art.

Usage

wave_data(
  start,
  end,
  size = 1,
  type = "sin",
  orientation = "horizontal",
  freq = 3,
  n_points = 500,
  color = NULL,
  fill = NULL,
  group_var = FALSE,
  dampen = NULL,
  amplify = NULL
)

Arguments

Value

A Tibble

Examples

library(ggplot2)
wave_df <- wave_data(
  start = 0, end = 10,
  fill = "purple",
  color = "green"
)

wave_df |>
  ggplot(aes(x, y)) +
  theme_void() +
  geom_polygon(
    fill = wave_df$fill,
    color = wave_df$color,
    linewidth = 3
  ) +
  coord_equal()