rearrr (version 0.3.4)

triangularize: Create x-coordinates so the points form a triangle

Description

lifecycle::badge("experimental")

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

The data points are stochastically distributed based on edge lengths, why it might be preferable to set a random seed.

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

Usage

triangularize(
  data,
  y_col = NULL,
  .min = NULL,
  .max = NULL,
  offset_x = 0,
  keep_original = TRUE,
  x_col_name = ".triangle_x",
  edge_col_name = ".edge",
  overwrite = FALSE
)

Value

data.frame (tibble) with the added x-coordinates and an identifier for the edge the data point is a part of.

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.

edge_col_name

Name of new column with the edge identifiers. If NULL, no column is added.

Numbering is clockwise and starts at the upper-right edge.

overwrite

Whether to allow overwriting of existing columns. (Logical)

Author

Ludvig Renbo Olsen,

See Also

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

Examples

# 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))
)

# Triangularize 'y'
df_tri <- triangularize(df, y_col = "y")
df_tri

# Plot triangle
if (has_ggplot){
  df_tri %>%
    ggplot(aes(x = .triangle_x, y = y, color = .edge)) +
    geom_point() +
    theme_minimal()
}

#
# Grouped squaring
#

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

# Plot triangles
if (has_ggplot){
  df_tri %>%
    ggplot(aes(x = .triangle_x, y = y, color = g)) +
    geom_point() +
    theme_minimal()
}

#
# Specifying minimum value
#

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

# Plot triangle
if (has_ggplot){
  df_tri %>%
    ggplot(aes(x = .triangle_x, y = y, color = .edge)) +
    geom_point() +
    theme_minimal()
}

#
# Multiple triangles by contraction
#

# \donttest{
# Start by squaring 'y'
df_tri <- triangularize(df, y_col = "y")

# Contract '.triangle_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_tri,
      cols = c(".triangle_x", "y"),
      multiplier = mult,
      origin_fn = centroid,
      overwrite = TRUE
    )
  }
)
df_expanded

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