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Brief Examples

artpack can be used to create specified dataframes that will map art when fed into ggplot2 functions:

For example, circle_data() creates a data frame that maps a circle on to a ggplot:


#| fig.alt: >
#|   ggplot plot showing a black outlined circle with
#|   irregular, slightly wavy edges centered at the origin
#|   of a coordinate grid, with x and y axes ranging from
#|   approximately -5 to 5, set against a light gray background,
#|   the default ggplot2 plot theme.

# Load your libraries#
library(ggplot2)
library(artpack)

# Use the function to create a data frame#
df_circle <-
  circle_data(
    x = 0,
    y = 0,
    radius = 5,
    color = "black",
    fill = "white"
  )

# Feed it into a ggplot#
df_circle |>
  ggplot(aes(x = x, y = y)) +
  geom_polygon(
    fill = df_circle$fill,
    color = df_circle$color,
    linewidth = 1,
    position = position_jitter(width = .1, height = .2)
  ) +
  coord_equal()



rotator will mathematically “rotate” existing data points in a data frame:


#| fig.alt: >
#|   ggplot plot showing a bright green square rotated
#|   approximately 120 degrees, positioned over a red
#|   outlined square on a coordinate grid with x and y axes
#|   labeled, set against a light gray background, the
#|   default ggplot2 plot theme.



# Load in your libraries#
library(ggplot2)
library(artpack)

# Make a square yourself if you want#
original_square <-
  data.frame(
  x = c(0, 3, 3, 0, 0),
  y = c(0, 0, 3, 3, 0)
)

# Rotate your data points by 120° and...
# ...anchor the rotation around the center of the square#
rotated_square <-
  rotator(
  data = original_square,
  x = x,
  y = y,
  angle = 120,
  anchor = "center"
)

# Plot the original and rotated squares to see the difference#
ggplot() +
  geom_path(
    data = original_square,
    aes(x, y),
    color = "red"
  ) +
  geom_polygon(
    data = rotated_square,
    aes(x, y),
    fill = "green"
  ) +
  coord_equal()



artpack functions are designed to be used in any part of your workflow. Experiment for some cool results:

# Load in your libraries#
library(ggplot2)
library(purrr)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(tibble)
library(artpack)

# Create a base square with artpack if you want#
square <- square_data(x = 0, y = 0, size = .1, group_var = TRUE)

# Create square specs to be iterated on#
n_square <- 500
scaler <- seq(1, 100, length = n_square)
fills <- art_pals("imagination", n = n_square)
angles <- seq(0, 360, length = n_square)
group_n <- group_numbers(1:n_square)

# Add a random transformation for a little razzle dazzle ✨
theta <- seq(0, 30, length = 250)

# Create your list of specs to be iterated on#
list_opts <-
  list(
    scaler,
    fills,
    angles,
    group_n
  )

# Create the final data frame#
df <-
  pmap(list_opts, ~ rotator(
    square |>
      mutate(
        x = (x + ..1),
        y = (y * ..1),
        fill = ..2,
        group = paste0(group, ..4)
      ),
    x = x, y = y, angle = ..3
  )
  ) |>
  list_rbind() |>
  mutate(
    x = x + cos(theta),
    y = y + sin(theta)
  )

# Plot the final image#
df |>
  ggplot(aes(x = x, y = y, group = group)) +
  theme_void() +
  theme(plot.background = element_rect(fill = "#000000")) +
  geom_path(
    color = df$fill,
    alpha = .2
  ) 

Example ggplot of art created with the artpack package.  Abstract flowing wave form with rainbow gradient colors  transitioning from pink and magenta on the left, through yellow and green in the center, to blue and purple on the right, with fine vertical line texture throughout, set against a black background.