Title:	Create Data with Identical Statistics
Version:	0.3.0
Description:	Creates data with identical statistics (metamers) using an iterative algorithm proposed by Matejka & Fitzmaurice (2017) <doi:10.1145/3025453.3025912>.
URL:	https://eliocamp.github.io/metamer/
BugReports:	https://github.com/eliocamp/metamer/issues
License:	GPL-3
Encoding:	UTF-8
ByteCompile:	yes
Language:	en-US
Depends:	R (≥ 2.10)
Imports:	FNN, progress (≥ 1.2.0), methods
Suggests:	shiny, miniUI, testthat (≥ 2.1.0), data.table, covr, sf
RoxygenNote:	7.2.0
NeedsCompilation:	no
Packaged:	2022-06-23 19:55:43 UTC; elio
Author:	Elio Campitelli [cre, aut]
Maintainer:	Elio Campitelli <elio.campitelli@cima.fcen.uba.ar>
Repository:	CRAN
Date/Publication:	2022-06-23 20:10:01 UTC

metamer: Create Data with Identical Statistics

Description

Creates data with identical statistics (metamers) using an iterative algorithm proposed by Matejka & Fitzmaurice (2017) doi:10.1145/3025453.3025912.

Overview

Create metamers with the metamerize() function.

Some helper functions included:

• draw_data() for drawing 2D datasets by hand and densify() for increasing the point density of those drawings.

• delayed_with() for defining statistics to preserve.

• moments_n() for preserving moments of order n.

• mean_dist_to() for minimizing the mean distance to a known target dataset.

The as.data.frame()/⁠[data.table::as.data.table()] methods included will turn a ⁠metamer_list' into a tidy data.frame.

Inspired by Matejka & Fitzmaurice (2017) awesome paper.

Author(s)

Maintainer: Elio Campitelli elio.campitelli@cima.fcen.uba.ar (ORCID)

References

Matejka, J., & Fitzmaurice, G. (2017). Same Stats, Different Graphs. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI ’17, 1290–1294. https://doi.org/10.1145/3025453.3025912

See Also

Useful links:

• https://eliocamp.github.io/metamer/

• Report bugs at https://github.com/eliocamp/metamer/issues

Set metamer parameters

Description

Set metamer parameters

Usage

clear_minimize(metamer_list)

clear_minimise(metamer_list)

set_minimise(metamer_list, minimize)

set_minimize(metamer_list, minimize)

get_last_metamer(metamer_list)

set_annealing(metamer_list, annealing)

set_perturbation(metamer_list, perturbation)

set_perturbation(metamer_list, perturbation)

set_start_probability(metamer_list, start_probability)

set_K(metamer_list, K)

set_change(metamer_list, change)

Arguments

Apply expressions to data.frames

Description

Creates a function that evaluates expressions in a future data.frame. Is like with(), but the data argument is passed at a later step.

Usage

delayed_with(...)

Arguments

Details

Each expression in ... must return a single numeric value. They can be named or return named vectors.

Value

A function that takes a data.frame and returns the expressions in ... evaluated in an environment constructed from it.

See Also

Other helper functions: densify(), draw_data(), mean_dist_to_sf(), mean_dist_to(), mean_self_proximity(), moments_n(), truncate_to()

Examples

some_stats <- delayed_with(mean_x = mean(x), mean(y), sd(x), coef(lm(x ~ y)))
data <- data.frame(x = rnorm(20) , y = rnorm(20))
some_stats(data)

Increase resolution of data

Description

Interpolates between the output of draw_data() and increases the point density of each stroke.Useful for avoiding sparse targets that result in clumping of points when metamerizing. It only has an effect on strokes (made by double clicking).

Usage

densify(data, res = 2)

Arguments

Value

A data.frame with the x and y values of your data and a .group column that identifies each stroke.

See Also

Other helper functions: delayed_with(), draw_data(), mean_dist_to_sf(), mean_dist_to(), mean_self_proximity(), moments_n(), truncate_to()

Freehand drawing

Description

Opens up a dialogue that lets you draw your data.

Usage

draw_data(data = NULL)

Arguments

Value

A data.frame with the x and y values of your data and a .group column that identifies each stroke.

See Also

Other helper functions: delayed_with(), densify(), mean_dist_to_sf(), mean_dist_to(), mean_self_proximity(), moments_n(), truncate_to()

Mean minimum distance

Description

Creates a function to get the mean minimum distance between two sets of points.

Usage

mean_dist_to(target, squared = TRUE)

Arguments

Value

A function that takes a data.frame with the same number of columns as target and then returns the mean minimum distance between them.

See Also

Other helper functions: delayed_with(), densify(), draw_data(), mean_dist_to_sf(), mean_self_proximity(), moments_n(), truncate_to()

Examples

target <- data.frame(x = rnorm(100), y = rnorm(100))
data <- data.frame(x = rnorm(100), y = rnorm(100))
distance <- mean_dist_to(target)
distance(data)

Mean distance to an sf object

Description

Mean distance to an sf object

Usage

mean_dist_to_sf(target, coords = c("x", "y"), buffer = 0, squared = TRUE)

Arguments

See Also

Other helper functions: delayed_with(), densify(), draw_data(), mean_dist_to(), mean_self_proximity(), moments_n(), truncate_to()

Inverse of the mean self distance

Description

Returns the inverse of the mean minimum distance between different pairs of points. It's intended to be used as a minimizing function to, then, maximize the distance between points.

Usage

mean_self_proximity(data)

Arguments

See Also

Other helper functions: delayed_with(), densify(), draw_data(), mean_dist_to_sf(), mean_dist_to(), moments_n(), truncate_to()

Create metamers

Description

Produces very dissimilar datasets with the same statistical properties.

Usage

metamerise(
  data,
  preserve,
  minimize = NULL,
  change = colnames(data),
  round = truncate_to(2),
  stop_if = n_tries(100),
  keep = NULL,
  annealing = TRUE,
  K = 0.02,
  start_probability = 0.5,
  perturbation = 0.08,
  name = "",
  verbose = interactive()
)

metamerize(
  data,
  preserve,
  minimize = NULL,
  change = colnames(data),
  round = truncate_to(2),
  stop_if = n_tries(100),
  keep = NULL,
  annealing = TRUE,
  K = 0.02,
  start_probability = 0.5,
  perturbation = 0.08,
  name = "",
  verbose = interactive()
)

new_metamer(data, preserve, round = truncate_to(2))

Arguments

Details

It follows Matejka & Fitzmaurice (2017) method of constructing metamers. Beginning from a starting dataset, it iteratively adds a small perturbation, checks if preserve returns the same value (up to signif significant digits) and if minimize has been lowered, and accepts the solution for the next round. If annealing is TRUE, it also accepts solutions with bigger minimize with an ever decreasing probability to help the algorithm avoid local minimums.

The annealing scheme is adapted from de Vicente et al. (2003).

If data is a metamer_list, the function will start the algorithm from the last metamer of the list. Furthermore, if preserve and/or minimize are missing, the previous functions will be carried over from the previous call.

minimize can be also a vector of functions. In that case, the process minimizes the product of the functions applied to the data.

Value

A metamer_list object (a list of data.frames).

References

Matejka, J., & Fitzmaurice, G. (2017). Same Stats, Different Graphs. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI ’17, 1290–1294. https://doi.org/10.1145/3025453.3025912 de Vicente, Juan, Juan Lanchares, and Román Hermida. (2003). ‘Placement by Thermodynamic Simulated Annealing’. Physics Letters A 317(5): 415–23.

See Also

delayed_with() for a convenient way of making functions suitable for preserve, mean_dist_to() for a convenient way of minimizing the distance to a known target in minimize, mean_self_proximity() for maximizing the "self distance" to prevent data clumping.

Examples

data(cars)
# Metamers of `cars` with the same mean speed and dist, and correlation
# between the two.
means_and_cor <- delayed_with(mean_speed = mean(speed),
                              mean_dist = mean(dist),
                              cor = cor(speed, dist))
set.seed(42)  # for reproducibility.
metamers <- metamerize(cars,
                       preserve = means_and_cor,
                       round = truncate_to(2),
                       stop_if = n_tries(1000))
print(metamers)

last <- tail(metamers)

# Confirm that the statistics are the same
cbind(original = means_and_cor(cars),
      metamer = means_and_cor(last))

# Visualize
plot(tail(metamers))
points(cars, col = "red")

Compute moments

Description

Returns a function that will return uncentered moments

Usage

moments_n(orders, cols = NULL)

Arguments

Value

A function that takes a data.frame and return a named numeric vector of the uncentered moments of the columns.

See Also

Other helper functions: delayed_with(), densify(), draw_data(), mean_dist_to_sf(), mean_dist_to(), mean_self_proximity(), truncate_to()

Examples

data <- data.frame(x = rnorm(100), y = rnorm(100))
moments_3 <- moments_n(1:3)

moments_3(data)

moments_3 <- moments_n(1:3, "x")
moments_3(data)

Stop conditions

Description

Stop conditions

Usage

n_tries(n)

n_metamers(n)

minimize_ratio(r)

Arguments

Rounding functions

Description

Rounding functions

Usage

truncate_to(digits)

round_to(digits)

Arguments

See Also

Other helper functions: delayed_with(), densify(), draw_data(), mean_dist_to_sf(), mean_dist_to(), mean_self_proximity(), moments_n()

Other helper functions: delayed_with(), densify(), draw_data(), mean_dist_to_sf(), mean_dist_to(), mean_self_proximity(), moments_n()