From 19287398df6219352820817f037ba93e73e2adfc Mon Sep 17 00:00:00 2001 From: Jason Cory Brunson Date: Thu, 1 Feb 2024 11:39:35 -0500 Subject: [PATCH] harmonize dimension variable + debug vignette --- R/persistence.R | 8 +-- R/simplicial-complex-engines.R | 58 +++++++++--------- R/simplicial-complex.R | 47 +++++++------- docs/articles/grouped-list-data.html | 38 ++++++------ .../figure-html/unnamed-chunk-11-1.png | Bin 34450 -> 29464 bytes .../figure-html/unnamed-chunk-12-1.png | Bin 46426 -> 39863 bytes .../figure-html/unnamed-chunk-3-1.png | Bin 172903 -> 171725 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 203835 -> 196999 bytes .../figure-html/unnamed-chunk-6-1.png | Bin 197251 -> 197594 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 121536 -> 105409 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 31134 -> 28506 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 44844 -> 39664 bytes docs/articles/illustrate-constructions.html | 34 +++++----- .../figure-html/2skeleton-1.png | Bin 40543 -> 69866 bytes .../figure-html/after-1.png | Bin 19388 -> 20427 bytes .../figure-html/after-2.png | Bin 28860 -> 31252 bytes .../figure-html/after-3.png | Bin 120975 -> 144552 bytes .../figure-html/after-4.png | Bin 58805 -> 80983 bytes .../figure-html/before-1.png | Bin 19292 -> 20472 bytes .../figure-html/before-2.png | Bin 28866 -> 31424 bytes .../figure-html/before-3.png | Bin 86125 -> 100645 bytes .../figure-html/before-4.png | Bin 64149 -> 80362 bytes .../figure-html/cech-1.png | Bin 32473 -> 36243 bytes .../figure-html/cech-2.png | Bin 29036 -> 32265 bytes .../figure-html/cech-3.png | Bin 22613 -> 24967 bytes .../figure-html/clutter-1.png | Bin 102347 -> 121253 bytes .../figure-html/disk-1.png | Bin 56384 -> 71637 bytes .../figure-html/disk-1skeleton-1.png | Bin 107297 -> 110582 bytes .../figure-html/during-1.png | Bin 19284 -> 20464 bytes .../figure-html/during-2.png | Bin 28949 -> 31369 bytes .../figure-html/during-3.png | Bin 123725 -> 147373 bytes .../figure-html/during-4.png | Bin 65136 -> 91739 bytes .../figure-html/phom-1.png | Bin 19251 -> 20202 bytes .../figure-html/phom-2.png | Bin 27918 -> 30471 bytes docs/articles/visualize-persistence.html | 2 +- docs/pkgdown.yml | 2 +- docs/reference/Rplot007.png | Bin 69425 -> 69193 bytes docs/reference/Rplot008.png | Bin 77215 -> 73857 bytes docs/reference/Rplot011.png | Bin 80369 -> 80146 bytes docs/reference/persistence.html | 4 +- docs/reference/simplicial_complex-11.png | Bin 247910 -> 246579 bytes docs/reference/simplicial_complex-7.png | Bin 174845 -> 173316 bytes docs/reference/simplicial_complex-8.png | Bin 199737 -> 198724 bytes docs/reference/simplicial_complex.html | 18 +++--- inst/examples/ex-simplicial-complex.R | 6 +- man/persistence.Rd | 4 +- man/simplicial_complex.Rd | 14 ++--- tests/testthat/test-simplicial-complex.R | 8 +-- vignettes/grouped-list-data.Rmd | 42 +++++++------ vignettes/illustrate-constructions.Rmd | 32 +++++----- 50 files changed, 158 insertions(+), 159 deletions(-) diff --git a/R/persistence.R b/R/persistence.R index 87c908d..e20f1b1 100644 --- a/R/persistence.R +++ b/R/persistence.R @@ -41,9 +41,9 @@ #' stat = "persistence", #' start = "birth value of each feature (from 'dataset' aesthetic).", #' end = "death value of each feature (from 'dataset' aesthetic).", -#' dimension = "feature dimension (from 'dataset' aesthetic).", +#' dimension = "integer feature dimension (from 'dataset' aesthetic).", #' group = "interaction of existing 'group', dataset ID, and 'dimension'.", -#' id = "feature identifier (across 'group').", +#' id = "character feature identifier (across 'group').", #' part = #' "whether features belong to ordinary, relative, or extended homology.", #' persistence = @@ -130,8 +130,8 @@ StatPersistence <- ggproto( # + issue warnings when choices are incompatible params$filtration <- match.arg(params$filtration, c("Vietoris", "Rips", "alpha")) - params$engine <- - match.arg(params$engine, c("TDA", "GUDHI", "Dionysus", "ripserr")) + if (! is.null(params$engine)) params$engine <- + match.arg(params$engine, c("TDA", "GUDHI", "Dionysus", "ripserr")) params$engine <- assign_filtration_engine(params$filtration, params$engine) diff --git a/R/simplicial-complex-engines.R b/R/simplicial-complex-engines.R index 7b6ebec..a3815ce 100644 --- a/R/simplicial-complex-engines.R +++ b/R/simplicial-complex-engines.R @@ -10,7 +10,7 @@ simplicial_complex_base <- function( # df_zero_simplices <- indices_to_data(data) df_zero_simplices <- data - df_zero_simplices$dim <- 0L + df_zero_simplices$dimension <- 0L df_zero_simplices$id <- seq(nrow(data)) # Compute other data.frame objects based on complex @@ -20,7 +20,7 @@ simplicial_complex_base <- function( edges <- proximate_pairs(data, diameter) df_one_simplices <- indices_to_data(data, edges) - # Now do simplices (only of dim 1) + # Now do simplices (only of dimension 1) # (need edges as a nice data.frame again) edges <- as.data.frame(proximate_pairs(data, diameter)) @@ -54,14 +54,14 @@ simplicial_complex_base <- function( edges <- proximate_pairs(data, diameter) df_one_simplices <- indices_to_data(data, edges) - # Now do simplices (only of dim 1) + # Now do simplices (only of dimension 1) faces <- proximate_triples(data, diameter) df_high_simplices <- indices_to_data(data, faces) } # Pair down to maximal simplices if necessary - # necessary <=> only want maximal 0/1-simplices AND no higher-dim simplices + # necessary <=> only want maximal 0/1-simplices AND no higher simplices # being plotted if (one_simplices == "maximal" & dimension_max > 1L) { df_one_simplices <- @@ -105,7 +105,7 @@ simplicial_complex_RTriangle <- function( df_one_simplices <- indices_to_data(data, edges) - # Now do simplices (only of dim 2) + # Now do simplices (only of dimension 2) # Delaunay triangulation, keeping only indices of edges and of triangles dt <- RTriangle::triangulate( @@ -175,7 +175,7 @@ simplicial_complex_RTriangle <- function( } # Pair down to maximal simplices if necessary - # necessary <=> only want maximal 0/1-simplices AND no higher-dim simplices + # necessary <=> only want maximal 0/1-simplices AND no higher simplices # being plotted if (one_simplices == "maximal" & dimension_max > 1L) { df_one_simplices <- @@ -196,7 +196,7 @@ simplicial_complex_RTriangle <- function( # Converts a matrix of indices corresponding to simplices of equal # dimensions (0, 1, or 2) to correct representation for GeomSimplicialComplex # (indices arg. is missing when we want the 0-simplices) -# m = dim + 1, no. of points in each simplex +# m = dimension + 1, no. of points in each simplex indices_to_data <- function( data, indices = matrix(seq(nrow(data)), ncol = 1L), m = NULL ) { @@ -212,9 +212,9 @@ indices_to_data <- function( res$id <- rep(seq(length(indices) / m), each = m) # Always needs to be 2 -- GeomSimplicialComplex only wants different values - # if simplices of dim > 2 being plotted (i.e. engine = "simplextree") - # res$dim <- ordered(2) - res$dim <- m - 1L + # if simplices of dimension > 2 being plotted (i.e. engine = "simplextree") + # res$dimension <- ordered(2) + res$dimension <- m - 1L # Type of object for GeomSimplicialComplex to plot # res$type <- switch( @@ -228,8 +228,8 @@ indices_to_data <- function( # If res is empty, still need columns res$id <- vector("integer") - # res$dim <- ordered(x = character(), levels = 2) - res$dim <- vector("integer") + # res$dimension <- ordered(x = character(), levels = 2) + res$dimension <- vector("integer") # res$type <- vector("character") } @@ -345,7 +345,7 @@ simplicial_complex_simplextree <- function( # The entire set of 0-simplices needs to come from data df_zero_simplices <- data df_zero_simplices$id <- seq(nrow(data)) - df_zero_simplices$dim <- 0L + df_zero_simplices$dimension <- 0L # Compute simplicial complex up to `dimension_max`, encoded as a 'simplextree' # (all further computed values derive from st) @@ -387,12 +387,12 @@ simplicial_complex_simplextree <- function( # include relevant computed values df_simplices$id <- ids - df_simplices$dim <- dims + df_simplices$dimension <- dims - # reorder rows so that higher dim simplices are plotted last - # note: order among simplices of equal dim doesn't matter + # reorder rows so that higher dimension simplices are plotted last + # note: order among simplices of equal dimension doesn't matter # b/c we split() on id - df_simplices <- df_simplices[order(df_simplices$dim), , drop = FALSE] + df_simplices <- df_simplices[order(df_simplices$dimension), , drop = FALSE] # take convex hull of each simplex for polygon grob: # (df_simplices is brielfly a list of data.frames, each corresponding to a @@ -401,13 +401,13 @@ simplicial_complex_simplextree <- function( df_simplices <- lapply(df_simplices, simplex_chull) df_simplices <- do.call(rbind, df_simplices) - # Store 1- and >1-dim simplices seperately - # (1-dim need to be drawn as line segments) - df_one_simplices <- df_simplices[df_simplices$dim == 1L, , drop = FALSE] - df_high_simplices <- df_simplices[df_simplices$dim > 1L, , drop = FALSE] + # Store 1- and >1-dimension simplices seperately + # (1-dimension need to be drawn as line segments) + df_one_simplices <- df_simplices[df_simplices$dimension == 1L, , drop = FALSE] + df_high_simplices <- df_simplices[df_simplices$dimension > 1L, , drop = FALSE] # Overwrite df_one_simplices to include non-maximal edges - # if user wants all one-simplices or if higher-dim simplices aren't plotted, + # if user wants all 1-simplices or if higher simplices aren't plotted, if (one_simplices == "all" | dimension_max == 1L) { # convert matrix of edges into a list @@ -422,7 +422,7 @@ simplicial_complex_simplextree <- function( # combine ordered pairs for each edge into dataframe df_one_simplices <- data[edges, , drop = FALSE] df_one_simplices$id <- edge_id - df_one_simplices$dim <- 1L + df_one_simplices$dimension <- 1L } @@ -432,7 +432,7 @@ simplicial_complex_simplextree <- function( maximal_vertices <- setdiff(seq(nrow(data)), st$vertices) df_zero_simplices <- data[maximal_vertices, , drop = FALSE] - df_zero_simplices$dim <- 0L + df_zero_simplices$dimension <- 0L df_zero_simplices$id <- seq(nrow(data)) } @@ -447,7 +447,7 @@ data_to_simplextree <- function(df, diameter, dimension_max, complex) { if (complex %in% c("Rips", "Vietoris")) { # For the Vietoris-Rips complex, just return the flag complex: - # w/ simplices with dim up to dimension_max + # w/ simplices with dimension up to dimension_max # Find edges given diameter: edges <- t(proximate_pairs(df, diameter)) @@ -491,7 +491,7 @@ simplicial_complex_TDA <- function( # The entire set of 0-simplices needs to come from data df_zero_simplices <- data df_zero_simplices$id <- seq(nrow(data)) - df_zero_simplices$dim <- 0L + df_zero_simplices$dimension <- 0L # Compute simplicial complex up to `dimension_max`, encoded as a 'Diagram' # (all further computed values derive from `pd`) @@ -508,10 +508,10 @@ simplicial_complex_TDA <- function( df_combin <- data.frame( row = unlist(pd$cmplx), id = rep(seq_along(pd_dim), pd_dim + 1L), - dim = rep(pd_dim, pd_dim + 1L) + dimension = rep(pd_dim, pd_dim + 1L) ) df_combin <- - df_combin[order(df_combin$dim, df_combin$id), , drop = FALSE] + df_combin[order(df_combin$dimension, df_combin$id), , drop = FALSE] # merge coordinates with combinatorics & return df_simplices <- merge(df_coords, df_combin, by = "row") @@ -553,7 +553,7 @@ assign_complex_engine <- function(complex, engine, dimension_max) { if (complex %in% c("Rips", "Vietoris")) { - # Default to "base" engine if not plotting high dim simplices + # Default to "base" engine if not plotting high dimension simplices # if (dimension_max < 3L & is.null(engine)) return("base") return(complex_engine_rules("Vietoris-Rips", engine, c( diff --git a/R/simplicial-complex.R b/R/simplicial-complex.R index f70d606..4e61525 100644 --- a/R/simplicial-complex.R +++ b/R/simplicial-complex.R @@ -51,9 +51,9 @@ #' @eval rd_sec_computed_vars( #' stat = "simplicial_complex", -#' dim = "dimension of the corresponding simplex.", -#' id = "simplex identifier within each `dim`.", -#' face = "encoding of `dim` for high-dimensional simplices (`dim >= 2L`)." +#' dimension = "integer dimension of the corresponding simplex.", +#' id = "character simplex identifier within each `dimension`.", +#' face = "factor encoding of `dimension` for `>= 2L`-dimensional simplices." #' ) #' @name simplicial_complex @@ -95,7 +95,7 @@ StatSimplicialComplex <- ggproto( required_aes = c("x", "y"), - # Alternatively, could assign fill = after_stat(dim) + # Alternatively, could assign fill = after_stat(dimension) default_aes = aes(alpha = after_stat(face)), compute_group = function( @@ -126,10 +126,11 @@ StatSimplicialComplex <- ggproto( # logic to deduce reasonable values of engine # + issue warnings when choices are incompatible complex <- match.arg(complex, c("Vietoris", "Rips", "Cech", "alpha")) - engine <- match.arg( - engine, - c("base", "RTriangle", "TDA", "GUDHI", "Dionysus", "simplextree") - ) + if (! is.null(engine)) + engine <- match.arg( + engine, + c("base", "RTriangle", "TDA", "GUDHI", "Dionysus", "simplextree") + ) engine <- assign_complex_engine(complex, engine, dimension_max) res <- switch( @@ -159,27 +160,27 @@ StatSimplicialComplex <- ggproto( # TODO: # Take care of zero_ or one_simplices == "none" - # and remove simplices w/ dim > dimension_max + # and remove simplices w/ dimension > dimension_max if (dimension_max < 2L) { - res <- res[res$dim < 2L, , drop = FALSE] + res <- res[res$dimension < 2L, , drop = FALSE] } if (dimension_max < 1L | one_simplices == "none") { - res <- res[res$dim != 1L, , drop = FALSE] + res <- res[res$dimension != 1L, , drop = FALSE] } # QUESTION: Require upstream that `dimension_max >= 0`? if (dimension_max < 0L | zero_simplices == "none") { - res <- res[res$dim != 0L, , drop = FALSE] + res <- res[res$dimension != 0L, , drop = FALSE] } # make a factor variable for high-dimensional simplices - if (max(res$dim >= 2L)) { - res$face <- as.character(ifelse(res$dim < 2L, 2L, res$dim)) + if (max(res$dimension >= 2L)) { + res$face <- as.character(ifelse(res$dimension < 2L, 2L, res$dimension)) } else { res$face <- NA_character_ } res$face <- factor( res$face, - levels = as.character(seq(2L, max(c(2L, res$dim)))) + levels = as.character(seq(2L, max(c(2L, res$dimension)))) ) res @@ -262,10 +263,10 @@ GeomSimplicialComplex <- ggproto( # order munched <- munched[order(munched$id),] - zero_simplex_data <- data[data$dim == "0", , drop = FALSE] - one_simplex_data <- data[data$dim == "1", , drop = FALSE] + zero_simplex_data <- data[data$dimension == "0", , drop = FALSE] + one_simplex_data <- data[data$dimension == "1", , drop = FALSE] high_simplex_data <- - data[data$dim != "0" & data$dim != "1", , drop = FALSE] + data[data$dimension != "0" & data$dimension != "1", , drop = FALSE] } else { @@ -273,17 +274,17 @@ GeomSimplicialComplex <- ggproto( data <- data[order(data$id), , drop = FALSE] - zero_simplex_data <- data[data$dim == "0", , drop = FALSE] - one_simplex_data <- data[data$dim == "1", , drop = FALSE] + zero_simplex_data <- data[data$dimension == "0", , drop = FALSE] + one_simplex_data <- data[data$dimension == "1", , drop = FALSE] high_simplex_data <- - data[data$dim != "0" & data$dim != "1", , drop = FALSE] + data[data$dimension != "0" & data$dimension != "1", , drop = FALSE] } # List to hold various grobs (polygons, linesegments, points) grobs <- list() - # Drawing the simplices w/ dim > 1 ----- + # Drawing the simplices w/ dimension > 1 ----- if (nrow(high_simplex_data) > 0L) { # For gpar(), there is one entry per polygon (not one entry per point). @@ -373,7 +374,7 @@ GeomSimplicialComplex <- ggproto( linewidth = 0.5, linetype = 1, shape = 21L, size = 1.5, stroke = .5), - required_aes = c("x", "y", "id", "dim"), + required_aes = c("x", "y", "id", "dimension"), draw_key = draw_key_simplex, diff --git a/docs/articles/grouped-list-data.html b/docs/articles/grouped-list-data.html index bede5f1..1a03cd1 100644 --- a/docs/articles/grouped-list-data.html +++ b/docs/articles/grouped-list-data.html @@ -87,7 +87,7 @@

Topological analysis of grouped data and list-columns

James Otto, Jason Cory Brunson

-

2024-01-31

+

2024-02-01

Source: vignettes/grouped-list-data.Rmd @@ -144,18 +144,15 @@

Simplicial complexes#> Warning: Using alpha for a discrete variable is not advised.

Notice, the object in the plot above clearly has two “holes”. This kind of insight is one of the main motivators of TDA. We’ll see more formal ways of identifying relevant features like this after we discuss persistent homology in the next section.

-

By default, the dimension of each simplex is communicated via opacity (the alpha aesthetic). In the next example, we’ll see how this is useful when plotting data with multiple groups. However, when we’re just interested in one complex, it can be helpful to instead map the computed variable face to the fill aesthetic, setting alpha \(<1\):

+

By default, the dimension of each simplex is communicated via opacity (the alpha aesthetic). In the next example, we’ll see how this is useful when plotting data with multiple groups. However, when we’re just interested in one complex, it can be helpful to instead map the computed variable face, a factor for polygonal objects representing simplices of dimension \(\geq 2\), to the fill aesthetic:

 ggplot(df, aes(x, y)) +
   geom_simplicial_complex(
-    mapping = aes(fill = after_stat(factor(face))),
-    max_dimension = 4L,
+    mapping = aes(fill = after_stat(face)),
+    dimension_max = 4L,
     alpha = .25, radius = .25
   ) +
   coord_fixed()
-#> Warning in geom_simplicial_complex(mapping = aes(fill =
-#> after_stat(factor(face))), : Ignoring unknown parameters:
-#> `max_dimension`
 #> Warning: Using alpha for a discrete variable is not advised.

@@ -184,26 +181,25 @@

Plotting multiple groups geom_point(shape = 21) + coord_fixed()

-

It is simple to plot the Rips complexes for multiple groups by mapping to the fill aesthetic:

+

It is simple to plot the Rips complexes for multiple groups by mapping the group identifier to the fill aesthetic:

 ggplot(df, aes(x, y, fill = lab)) +
   geom_simplicial_complex(radius = .3) +
   coord_fixed()
 #> Warning: Using alpha for a discrete variable is not advised.

-

It is also possible to use the fill aesthetic to visualize the simplex dimensions. This may become important as higher-dimensional simplices are computed. Typically, faceting the resulting plot is a good idea:

+

It is also possible to use the color and fill aesthetics together to color-code all simplices by dimension, including vertices and edges. This may become important as higher-dimensional simplices are computed. Typically, faceting the resulting plot is a good idea:

 ggplot(df, aes(x, y)) +
-  geom_simplicial_complex(
-    mapping = aes(fill = after_stat(face)),
-    max_dimension = 6L,
+  stat_simplicial_complex(
+    mapping = aes(fill = after_stat(factor(dimension)),
+                  color = after_stat(factor(dimension))),
+    dimension_max = 6L,# one_simplices = "all",
     alpha = .5, radius = .3
   ) +
   facet_wrap(vars(lab), ncol = 2) +
-  scale_fill_viridis_d() +
+  scale_fill_viridis_d(direction = -1, aesthetics = c("color", "fill")) +
   coord_fixed()
-#> Warning in geom_simplicial_complex(mapping = aes(fill = after_stat(face)), :
-#> Ignoring unknown parameters: `max_dimension`
 #> Warning: Using alpha for a discrete variable is not advised.


@@ -222,7 +218,8 @@

Perstistent Homologyripserr::vietoris_rips(df[, c("x", "y")]) |> ggplot() + geom_barcode(aes(start = birth, end = death, color = factor(dimension))) + - scale_color_viridis_d(end = .7) + scale_color_viridis_d(end = .7) + + labs(color = "dimension")

Notice the two long-lived features, the one dimensional holes. These correspond to the features we observed previously, with geom_simplicial_complex()!

Another way to plot persistent homology is persistence diagrams, in which topological features are plotted as points with \((x, y)\) values corresponding to their birth and death values:

@@ -231,7 +228,8 @@

Perstistent Homologyggplot() + stat_persistence(aes(start = birth, end = death, color = factor(dimension))) + geom_abline() + - scale_color_viridis_d(end = .7) + scale_color_viridis_d(end = .7) + + labs(color = "dimension")

Again, we see the single significant one-dimensional feature—the hole!

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Jason Cory Brunson, Jacob G. Scott, Raoul R. Wadhwa

-

2024-01-31

+

2024-02-01

Source:
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zlkBt=#$;F)IA2gwKE{8i&wYFS^V3lbaC{G_mn*57vwckvDHz65+9rPfoUl5?x$fYfJie^ra9$ z=6!8!_K%4$n8~(>@Bzs!%CfU=U|`?^_r~n;*2^`OU)hmTwPs# zj*CNoBQ9<@(htq#_Zw2vhuaZK#KOWNK#L+skZ;jvhYxLYf$`_4z}D87DX^TJoZ?(t z8G(<=!J(+vFV3y5=I~_2|2}IU-fA>8HTClL{+xiBP8A|Zmhc=>uFqb!>|8dbB0qof z-_^BIX2!;1@M0p1XDg5+-tS*^#tzY;3}g$5i2S&@X|rt1{4hwn-Syw)hxkjw0s{hm zSo6$&|DFimyT8vK?B4-yl3!2&v=kOLws%N~3*{>%^MwsUB#he`%f=Zm5gjmIVComputed variables

stat_persistence calculates the following variables that can be accessed with delayed evaluation.

  • after_stat(start)
    birth value of each feature (from 'dataset' aesthetic).

  • after_stat(end)
    death value of each feature (from 'dataset' aesthetic).

  • -
  • after_stat(dimension)
    feature dimension (from 'dataset' aesthetic).

  • +
  • after_stat(dimension)
    integer feature dimension (from 'dataset' aesthetic).

  • after_stat(group)
    interaction of existing 'group', dataset ID, and 'dimension'.

  • -
  • after_stat(id)
    feature identifier (across 'group').

  • +
  • after_stat(id)
    character feature identifier (across 'group').

  • after_stat(part)
    whether features belong to ordinary, relative, or extended homology.

  • after_stat(persistence)
    differences between birth and death values of features.

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geom_simplicial_complex() understands the following aesthetics (required aesthetics are in bold):

  • x

  • y

  • id

  • -
  • dim

  • +
  • dimension

  • alpha

  • colour

  • fill

  • @@ -273,9 +273,9 @@

    Aesthetics

    Computed variables

    -

    stat_simplicial_complex calculates the following variables that can be accessed with delayed evaluation.

    • after_stat(dim)
      dimension of the corresponding simplex.

    • -
    • after_stat(id)
      simplex identifier within each dim.

    • -
    • after_stat(face)
      encoding of dim for high-dimensional simplices (dim >= 2L).

    • +

      stat_simplicial_complex calculates the following variables that can be accessed with delayed evaluation.

      • after_stat(dimension)
        integer dimension of the corresponding simplex.

      • +
      • after_stat(id)
        character simplex identifier within each dimension.

      • +
      • after_stat(face)
        factor encoding of dimension for >= 2L-dimensional simplices.

      See also

      @@ -321,15 +321,11 @@

      Examples

      ggplot(equilateral_triangle, aes(x, y)) + coord_fixed() + geom_simplicial_complex(complex = "alpha", diameter = sqrt(3) - eps) -#> Warning: The base engine cannot construct alpha complexes; -#> switching to `engine = 'TDA'`. #> Warning: Using alpha for a discrete variable is not advised. ggplot(equilateral_triangle, aes(x, y)) + coord_fixed() + geom_simplicial_complex(complex = "alpha", diameter = sqrt(3) + eps) -#> Warning: The base engine cannot construct alpha complexes; -#> switching to `engine = 'TDA'`. #> Warning: Using alpha for a discrete variable is not advised. @@ -340,17 +336,17 @@

      Examples

      y = sin(s) + rnorm(40, 0, .1) ) -# default, visualizing dim w/ alpha: +# default, visualizing dimension w/ alpha: ggplot(df, aes(x, y)) + coord_fixed() + geom_simplicial_complex(radius = .4) #> Warning: Using alpha for a discrete variable is not advised. -# visualizing dim w/ fill: +# visualizing dimension w/ fill: ggplot(df, aes(x, y)) + coord_fixed() + geom_simplicial_complex( - mapping = aes(fill = after_stat(dim)), + mapping = aes(fill = after_stat(dimension)), alpha = .5, radius = .4 ) #> Warning: Using alpha for a discrete variable is not advised. diff --git a/inst/examples/ex-simplicial-complex.R b/inst/examples/ex-simplicial-complex.R index 3879268..079161d 100644 --- a/inst/examples/ex-simplicial-complex.R +++ b/inst/examples/ex-simplicial-complex.R @@ -6,15 +6,15 @@ df <- data.frame( y = sin(s) + rnorm(40, 0, .1) ) -# default, visualizing dim w/ alpha: +# default, visualizing dimension w/ alpha: ggplot(df, aes(x, y)) + coord_fixed() + geom_simplicial_complex(radius = .4) -# visualizing dim w/ fill: +# visualizing dimension w/ fill: ggplot(df, aes(x, y)) + coord_fixed() + geom_simplicial_complex( - mapping = aes(fill = after_stat(dim)), + mapping = aes(fill = after_stat(dimension)), alpha = .5, radius = .4 ) diff --git a/man/persistence.Rd b/man/persistence.Rd index 26826fb..a7d3dfc 100644 --- a/man/persistence.Rd +++ b/man/persistence.Rd @@ -180,9 +180,9 @@ Learn more about setting these aesthetics in \code{vignette("ggplot2-specs", pac \itemize{ \item \code{after_stat(start)}\cr birth value of each feature (from 'dataset' aesthetic). \item \code{after_stat(end)}\cr death value of each feature (from 'dataset' aesthetic). -\item \code{after_stat(dimension)}\cr feature dimension (from 'dataset' aesthetic). +\item \code{after_stat(dimension)}\cr integer feature dimension (from 'dataset' aesthetic). \item \code{after_stat(group)}\cr interaction of existing 'group', dataset ID, and 'dimension'. -\item \code{after_stat(id)}\cr feature identifier (across 'group'). +\item \code{after_stat(id)}\cr character feature identifier (across 'group'). \item \code{after_stat(part)}\cr whether features belong to ordinary, relative, or extended homology. \item \code{after_stat(persistence)}\cr differences between birth and death values of features. } diff --git a/man/simplicial_complex.Rd b/man/simplicial_complex.Rd index 5b9bdbb..70b659f 100644 --- a/man/simplicial_complex.Rd +++ b/man/simplicial_complex.Rd @@ -167,7 +167,7 @@ are implemented in {\strong{simplextree}}, they will be made available here. \item \strong{\code{x}} \item \strong{\code{y}} \item \strong{\code{id}} -\item \strong{\code{dim}} +\item \strong{\code{dimension}} \item \code{alpha} \item \code{colour} \item \code{fill} @@ -185,9 +185,9 @@ Learn more about setting these aesthetics in \code{vignette("ggplot2-specs", pac \code{stat_simplicial_complex} calculates the following variables that can be accessed with \link[ggplot2:aes_eval]{delayed evaluation}. \itemize{ -\item \code{after_stat(dim)}\cr dimension of the corresponding simplex. -\item \code{after_stat(id)}\cr simplex identifier within each \code{dim}. -\item \code{after_stat(face)}\cr encoding of \code{dim} for high-dimensional simplices (\code{dim >= 2L}). +\item \code{after_stat(dimension)}\cr integer dimension of the corresponding simplex. +\item \code{after_stat(id)}\cr character simplex identifier within each \code{dimension}. +\item \code{after_stat(face)}\cr factor encoding of \code{dimension} for \verb{>= 2L}-dimensional simplices. } } @@ -230,15 +230,15 @@ df <- data.frame( y = sin(s) + rnorm(40, 0, .1) ) -# default, visualizing dim w/ alpha: +# default, visualizing dimension w/ alpha: ggplot(df, aes(x, y)) + coord_fixed() + geom_simplicial_complex(radius = .4) -# visualizing dim w/ fill: +# visualizing dimension w/ fill: ggplot(df, aes(x, y)) + coord_fixed() + geom_simplicial_complex( - mapping = aes(fill = after_stat(dim)), + mapping = aes(fill = after_stat(dimension)), alpha = .5, radius = .4 ) diff --git a/tests/testthat/test-simplicial-complex.R b/tests/testthat/test-simplicial-complex.R index 2ccbdcd..74caade 100644 --- a/tests/testthat/test-simplicial-complex.R +++ b/tests/testthat/test-simplicial-complex.R @@ -64,7 +64,7 @@ test_that("Cech layers work as expected", { expect_is(p1$layer[[1]], "ggproto") expect_equal(c(p1$labels$x, p1$labels$y), c("x", "y")) layer_p1 <- layer_data(p1) - expect_equal(nrow(layer_p1[layer_p1$dim == 0L, ]), nrow(d)) + expect_equal(nrow(layer_p1[layer_p1$dimension == 0L, ]), nrow(d)) # segment data requires two rows per simplex expect_equal(nrow(layer_p1), nrow(d) + 1090L * 2L) @@ -75,7 +75,7 @@ test_that("Cech layers work as expected", { expect_is(p2$layer[[1]], "ggproto") expect_equal(c(p2$labels$x, p2$labels$y), c("x", "y")) layer_p2 <- layer_data(p2) - expect_equal(nrow(layer_p2[layer_p2$dim == 0L, ]), nrow(d)) + expect_equal(nrow(layer_p2[layer_p2$dimension == 0L, ]), nrow(d)) # face data requires three rows per simplex expect_equal(nrow(layer_p2), nrow(d) + 5735L * 3L) @@ -108,7 +108,7 @@ test_that("Vietoris layers work as expected", { expect_is(p1$layer[[1]], "ggproto") expect_equal(c(p1$labels$x, p1$labels$y), c("x", "y")) layer_p1 <- layer_data(p1) - expect_equal(nrow(layer_p1[layer_p1$dim == 0L, ]), nrow(d)) + expect_equal(nrow(layer_p1[layer_p1$dimension == 0L, ]), nrow(d)) # segment data requires two rows per simplex expect_equal(nrow(layer_p1), nrow(d) + 1090L * 2L) @@ -120,7 +120,7 @@ test_that("Vietoris layers work as expected", { expect_is(p2$layer[[1]], "ggproto") expect_equal(c(p2$labels$x, p2$labels$y), c("x", "y")) layer_p2 <- layer_data(p2) - expect_equal(nrow(layer_p2[layer_p2$dim == 0L, ]), nrow(d)) + expect_equal(nrow(layer_p2[layer_p2$dimension == 0L, ]), nrow(d)) # face data requires three rows per simplex expect_equal(nrow(layer_p2), nrow(d) + 5738L * 3L) diff --git a/vignettes/grouped-list-data.Rmd b/vignettes/grouped-list-data.Rmd index 94387e6..5ab5ca7 100644 --- a/vignettes/grouped-list-data.Rmd +++ b/vignettes/grouped-list-data.Rmd @@ -21,8 +21,6 @@ knitr::opts_chunk$set( **ggtda** borrows some exciting features from **TDAvis**, which was developed independently with similar objectives. Key among these is the `dataset` aesthetic accepted by the persistent homology layers, which must be passed a list-column of data sets. These are then pre-processed into persistence data by the same engines that power the simplicial complex constructions. - - ```{r setup, message = FALSE} library("ggtda") ``` @@ -75,14 +73,13 @@ after we discuss persistent homology in the next section. By default, the dimension of each simplex is communicated via opacity (the `alpha` aesthetic). In the next example, we'll see how this is useful when plotting data with multiple groups. -However, when we're just interested in one complex, -it can be helpful to instead map the computed variable `face` to the `fill` aesthetic, setting `alpha` $<1$: +However, when we're just interested in one complex, it can be helpful to instead map the computed variable `face`, a factor for polygonal objects representing simplices of dimension $\geq 2$, to the `fill` aesthetic: ```{r} ggplot(df, aes(x, y)) + geom_simplicial_complex( - mapping = aes(fill = after_stat(factor(face))), - max_dimension = 4L, + mapping = aes(fill = after_stat(face)), + dimension_max = 4L, alpha = .25, radius = .25 ) + coord_fixed() @@ -101,7 +98,7 @@ ggplot(df, aes(x, y, fill = lab)) + coord_fixed() ``` -It is simple to plot the Rips complexes for multiple groups by mapping to the `fill` aesthetic: +It is simple to plot the Rips complexes for multiple groups by mapping the group identifier to the `fill` aesthetic: ```{r} ggplot(df, aes(x, y, fill = lab)) + @@ -109,19 +106,20 @@ ggplot(df, aes(x, y, fill = lab)) + coord_fixed() ``` -It is also possible to use the `fill` aesthetic to visualize the simplex dimensions. +It is also possible to use the `color` and `fill` aesthetics together to color-code all simplices by dimension, including vertices and edges. This may become important as higher-dimensional simplices are computed. Typically, faceting the resulting plot is a good idea: ```{r, fig.height=4} ggplot(df, aes(x, y)) + - geom_simplicial_complex( - mapping = aes(fill = after_stat(face)), - max_dimension = 6L, + stat_simplicial_complex( + mapping = aes(fill = after_stat(factor(dimension)), + color = after_stat(factor(dimension))), + dimension_max = 6L,# one_simplices = "all", alpha = .5, radius = .3 ) + facet_wrap(vars(lab), ncol = 2) + - scale_fill_viridis_d() + + scale_fill_viridis_d(direction = -1, aesthetics = c("color", "fill")) + coord_fixed() ``` @@ -149,11 +147,12 @@ Each feature is plotted as a horizontal line, marking its birth and death in ter Below, we have computed the persistent homology of `df` using **ripserr** and plotted its barcode chart using `geom_barcode()`. -```{r} +```{r, fig.height=4} ripserr::vietoris_rips(df[, c("x", "y")]) |> ggplot() + geom_barcode(aes(start = birth, end = death, color = factor(dimension))) + - scale_color_viridis_d(end = .7) + scale_color_viridis_d(end = .7) + + labs(color = "dimension") ``` Notice the two long-lived features, the one dimensional holes. @@ -162,12 +161,13 @@ These correspond to the features we observed previously, with `geom_simplicial_c Another way to plot persistent homology is persistence diagrams, in which topological features are plotted as points with $(x, y)$ values corresponding to their birth and death values: -```{r} +```{r, fig.height=4} ripserr::vietoris_rips(df[, c("x", "y")]) |> ggplot() + stat_persistence(aes(start = birth, end = death, color = factor(dimension))) + geom_abline() + - scale_color_viridis_d(end = .7) + scale_color_viridis_d(end = .7) + + labs(color = "dimension") ``` Again, we see the single significant one-dimensional feature---the hole! @@ -200,22 +200,24 @@ df_nested This approach allows for easy faceting. See below how we plot barcode charts for each group in `df`: -```{r} +```{r, fig.height=4} ggplot(df_nested) + geom_barcode(stat = "persistence", aes(dataset = data, color = factor(after_stat(dimension)))) + facet_wrap(vars(lab), ncol = 2) + - scale_color_viridis_d(end = .7) + scale_color_viridis_d(end = .7) + + labs(color = "dimension") ``` Similarly, we can plot multiple persistence diagrams: -```{r} +```{r, fig.height=4} ggplot(df_nested) + stat_persistence(aes(dataset = data, color = factor(after_stat(dimension)))) + geom_abline() + facet_wrap(vars(lab), ncol = 2) + - scale_color_viridis_d(end = .7) + scale_color_viridis_d(end = .7) + + labs(color = "dimension") ```

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