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https://github.com/eliocamp/metamer

Create data sets with identical statistics.
https://github.com/eliocamp/metamer

r r-package rstats

Last synced: 11 days ago
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Create data sets with identical statistics.

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README 

        
---

output: github_document

---



```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%",

  cache = TRUE,

  cache.extra = 42

)



```

# metamer



[![DOI](https://zenodo.org/badge/159563811.svg)](https://zenodo.org/badge/latestdoi/159563811)  [![Travis build status](https://travis-ci.org/eliocamp/metamer.svg?branch=master)](https://travis-ci.org/eliocamp/metamer) [![Codecov test coverage](https://codecov.io/gh/eliocamp/metamer/branch/master/graph/badge.svg)](https://app.codecov.io/gh/eliocamp/metamer?branch=master)



Implements the algorithm proposed by [Matejka & Fitzmaurice (2017)](https://www.autodesk.com/research/publications/same-stats-different-graphs) to create metamers (datasets with identical statistical properties but very different graphs) with an annealing scheme derived from [de Vicente et al. (2003)](https://www.sciencedirect.com/science/article/abs/pii/S0375960103013653?via%3Dihub).



In colour theory, [metamers](https://en.wikipedia.org/wiki/Metamerism_(color)) are colours that have very different wavelength distribution but are perceived as equal by out visual system. This happens because out eyes essentially summarise a continuous distribution of wavelength by just 3 numbers: the amount that each type of cone cell is exited. Colour metamerism is how artists can reproduce so many colours with a few pigments, or how PC monitors use only 3 lights to show colourful pictures. 



![](man/figures/lemon.jpg)



(from the excellent [Color: From Hexcodes to Eyeballs](http://jamie-wong.com/post/color/) by [Jamie Wong](https://github.com/jlfwong))



Statistical transformations such as mean, standard deviation and correlation behave very similarly in that they summarise data with just a few numbers for the benefit of our limited cognitive capacity. Thus, statistical metamers are sets of data that share some statistical properties. 



[This article](https://eliocamp.github.io/codigo-r/en/2019/01/statistical-metamerism/) explores statistical metamerism in more detail. 



## Installation



You can install metamer with:



```r

install.packages("metamer")

```



or install the development version with:



``` r

# install.packages("devtools")

devtools::install_github("eliocamp/metamer")

```



## Example



You can construct metamers from a starting dataset and a vector of statistical properties to remain constant (by default, up to 2 significant figures). 



```{r example}

library(metamer)

# Start with the datasaurus

# install.packages("datasauRus")

dino <- subset(datasauRus::datasaurus_dozen, dataset == "dino")

dino$dataset <- NULL



# And we want to preserve means and correlation

mean_cor <- delayed_with(mean(x), mean(y), cor(x, y)) 



set.seed(42) # To make results reproducible

metamers <- metamerise(dino, preserve = mean_cor, 

                       stop_if = n_metamers(300), 

                       perturbation = 1,

                       keep = 19)

print(metamers)

```



We found `r length(metamers$metamers)` metamers. Let's see the final one, with the starting dataset as background. 



```{r}

library(ggplot2)



ggplot(tail(metamers), aes(x, y)) +

  geom_point(data = dino, color = "red", alpha = 0.5, size = 0.4) +

  geom_point()

```



We can check that the statistical properties have been preserved up to 2 significant figures:



```{r}

cbind(dino = signif(mean_cor(dino), 2),

      last = signif(mean_cor(tail(metamers)), 2))

```



However, a semi random cloud of points is not that interesting, so we can specify a minimizing function so that the result is similar to another dataset. `metamerise` will start from the last metamer of the previous run if the `data` argument is a list of metamers and append the result. 



```{r}

x_shape <- subset(datasauRus::datasaurus_dozen, dataset == "x_shape")

x_shape$dataset <- NULL

```



```{r}

metamers <- metamerise(dino, 

                       preserve = mean_cor,

                       minimize = mean_dist_to(x_shape),

                       stop_if = minimize_ratio(0.02),

                       keep = 99)

```



Now the result is a bit more impressive. 



```{r}

ggplot(tail(metamers), aes(x, y)) +

  geom_point(data = dino, color = "red", alpha = 0.5, size = 0.4) +

  geom_point()

```



We can animate the whole thing. 



```{r, gganimate = list(fps = 30)}

library(gganimate)



ggplot(metamers, aes(x, y)) +

  geom_point() +

  transition_manual(.metamer)

```



You can freehand your own starting or target data with the `draw_data()` utility, that will open a shiny interface. You might need to install `shiny` and `miniUI` with `install.packages(c("shiny", "miniUI"))`.



Metamerizing operations can be chained while changing the minimizing function. 



```{r}



star <- subset(datasauRus::datasaurus_dozen, dataset == "star")

star$dataset <- NULL

set.seed(42)

metamers <- metamerise(dino,

                       preserve = mean_cor, 

                       minimize = mean_dist_to(x_shape),

                       stop_if = minimize_ratio(0.05),

                       keep = 29) |> 

  set_minimize(mean_dist_to(star)) |> 

  metamerise(stop_if =  minimize_ratio(0.05),

             keep = 30) |> 

  set_minimize(mean_dist_to(dino)) |> 

  metamerise(stop_if =  minimize_ratio(0.05), 

             keep = 30) 

```



And the full sequence 



```{r, gganimate = list(nframes = 30*3, fps = 30)}

ggplot(metamers, aes(x, y)) +

  geom_point() +

  transition_manual(.metamer)

```