rearrr (version 0.3.5)

circularize: Create x-coordinates so the points form a circle

Description

lifecycle::badge("experimental")

Create the x-coordinates for a vector of y-coordinates such that they form a circle.

This will likely look most like a circle when the y-coordinates are somewhat equally distributed, e.g. a uniform distribution.

Usage

circularize(
  data,
  y_col = NULL,
  .min = NULL,
  .max = NULL,
  offset_x = 0,
  keep_original = TRUE,
  x_col_name = ".circle_x",
  degrees_col_name = ".degrees",
  origin_col_name = ".origin",
  overwrite = FALSE
)

Value

data.frame (tibble) with the added x-coordinates and the angle in degrees.

Arguments

data

data.frame or vector.

y_col

Name of column in `data` with y-coordinates to create x-coordinates for.

.min

Minimum y-coordinate. If NULL, it is inferred by the given y-coordinates.

.max

Maximum y-coordinate. If NULL, it is inferred by the given y-coordinates.

offset_x

Value to offset the x-coordinates by.

keep_original

Whether to keep the original columns. (Logical)

Some columns may have been overwritten, in which case only the newest versions are returned.

x_col_name

Name of new column with the x-coordinates.

degrees_col_name

Name of new column with the angles in degrees. If NULL, no column is added.

Angling is counterclockwise around (0, 0) and starts at (max(x), 0).

origin_col_name

Name of new column with the origin coordinates (center of circle). If NULL, no column is added.

overwrite

Whether to allow overwriting of existing columns. (Logical)

Author

Ludvig Renbo Olsen,

See Also

Other forming functions: hexagonalize(), square(), triangularize()

Examples

# \donttest{
# Attach packages
library(rearrr)
library(dplyr)
library(purrr)
has_ggplot <- require(ggplot2)  # Attach if installed

# Set seed
set.seed(1)

# Create a data frame
df <- data.frame(
  "y" = runif(200),
  "g" = factor(rep(1:5, each = 40))
)

# Circularize 'y'
df_circ <- circularize(df, y_col = "y")
df_circ

# Plot circle
if (has_ggplot){
  df_circ %>%
    ggplot(aes(x = .circle_x, y = y, color = .degrees)) +
    geom_point() +
    theme_minimal()
}

#
# Grouped circularization
#

# Circularize 'y' for each group
# First cluster the groups a bit to move the
# circles away from each other
df_circ <- df %>%
  cluster_groups(
    cols = "y",
    group_cols = "g",
    suffix = "",
    overwrite = TRUE
  ) %>%
  dplyr::group_by(g) %>%
  circularize(
    y_col = "y",
    overwrite = TRUE
  )

# Plot circles
if (has_ggplot){
  df_circ %>%
    ggplot(aes(x = .circle_x, y = y, color = g)) +
    geom_point() +
    theme_minimal()
}

#
# Specifying minimum value
#

# Specify minimum value manually
df_circ <- circularize(df, y_col = "y", .min = -2)
df_circ

# Plot circle
if (has_ggplot){
  df_circ %>%
    ggplot(aes(x = .circle_x, y = y, color = .degrees)) +
    geom_point() +
    theme_minimal()
}

#
# Multiple circles by contraction
#

# Start by circularizing 'y'
df_circ <- circularize(df, y_col = "y")

# Contract '.circle_x' and 'y' towards the centroid
# To contract with multiple multipliers at once,
# we wrap the call in purrr::map_dfr
df_expanded <- purrr::map_dfr(
  .x = 1:10 / 10,
  .f = function(mult) {
    expand_distances(
      data = df_circ,
      cols = c(".circle_x", "y"),
      multiplier = mult,
      origin_fn = centroid,
      overwrite = TRUE
    )
  }
)
df_expanded

if (has_ggplot){
  df_expanded %>%
    ggplot(aes(
      x = .circle_x_expanded, y = y_expanded,
      color = .degrees, alpha = .multiplier
    )) +
    geom_point() +
    theme_minimal()
}
# }