factor-xmaps

Last updated: 2023-01-18

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Knit directory: viz-panel-maps/

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Background

Factors are the base R S3 class for categorical variables
Factors are integer vectors with a levels attribute.
forcats provides convenience and safe-guarded functions for working with factors

Visualisation Idea

Take levels before and after transformation, generate and visualise the implicit xmap.

fct <- factor(c("a", "b", "b", "c", "d"))

after_other <- fct |>
  forcats::fct_other(keep = c("a", "b"), other_level = "other")
stopifnot(identical(after_other, factor(c("a", "b", "b", "other", "other"))))

For reference this is the mapping:

xmap_ref <-
  tibble::tribble(~key1, ~key2, ~weight,
                  "a", "a", 1,
                  "b", "b", 1,
                  "c", "other", 1,
                  "d", "other", 1)

We can probably only extract the mapping if there is at most one target node involved in a many-to-one mapping.

collapse_xmap <- tibble::tibble(
  key1 = setdiff(fct, after_other),
  key2= setdiff(after_other, fct)
  )

preserve_xmap <- tibble::tibble(
  key1 = intersect(fct, after_other),
  key2 = key1
)

xmap_set <- dplyr::bind_rows(preserve_xmap, collapse_xmap) |>
  dplyr::mutate(weight = 1)

identical(xmap_set, xmap_ref)

[1] TRUE

Visualising it

require(dplyr)

Loading required package: dplyr


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

require(ggplot2)

Loading required package: ggplot2

require(ggbump)

Loading required package: ggbump

plt_pm_df <- function(pm, from, to, weights){

  edges <- pm |>
    transmute(from = {{from}}, to = {{to}}, weighted = {{weights}})

  ## calculate positions for nodes
  from_nodes <- distinct(edges, from) |> mutate(from_y = row_number())
  to_nodes <- distinct(edges, to) |> mutate(to_y = row_number() - 1 + 0.5)

  ## generate df for ggplot
  df <- edges |>
    ## generate mapping type/case variables
    group_by(from) |>
    mutate(n_dest = n()) |>
    ungroup() |>
    group_by(to) |>
    mutate(n_origin = n(),
           min_weight = min(weighted)) |>
    ungroup() |>
    mutate(value_case = case_when(n_dest == 1 ~ "one-to-one",
                                  n_dest > 1 ~ "one-to-many")) |>
    left_join(tribble(~value_case, ~line_type, ~font_type,
                      "one-to-one", "solid", "bold",
                      "one-to-many", "dashed", "italic"),
              by = "value_case") |>
    mutate(map_case = case_when(n_origin > 1 & n_dest > 1 ~ "many-from-many",
                                n_origin == 1 & n_dest > 1 ~ "many-from-one",
                                n_origin > 1 & n_dest == 1 ~ "one-from-many",
                                n_origin == 1 & n_dest == 1 ~ "one-from-one")) |>
    mutate(from_case = case_when(n_origin == 1 ~ "one-from-one",
                                 n_origin > 1 ~ "one-from-many",
                                 n_origin < 1 ~ "ERROR! origin codes < 1"),
           dest_case = case_when(min_weight < 1 ~ "contains split",
                                 min_weight == 1 ~ "aggregation only",
                                 min_weight > 1 ~ "ERROR! weight > 1")
    ) |>
    ## add y-coordinates
    left_join(from_nodes, by = "from") |>
    left_join(to_nodes, by = "to") |>
    ## add x-coordinates
    mutate(from_x = 0,
           to_x = 5) |>
    ## give each from-out instruction a unique id
    mutate(idx = row_number())

  plt <- df |>
    ggplot(aes(x = from_x, xend = to_x, y = from_y, yend = to_y, group = idx)) +
    ## edges as sigmoid curves with line type
    geom_sigmoid(aes(linetype = I(line_type))) +
    # to/from nodes
    scale_y_reverse() +
    geom_text(aes(x = from_x - 0.5, label=from, fontface=I(font_type) )) +
    geom_label(aes(x = to_x + 0.5, y = to_y, label=to, fill = dest_case)) +
    # edge labels
    geom_label(data = filter(df, value_case == "one-to-many"),
               aes(x = (((from_x + to_x) / 2) + to_x) / 2,
                   y = to_y,
                   label = scales::percent(weighted,5),
                   alpha = weighted),
               fill = "gray") +
    geom_label(data = filter(df, value_case == "one-to-one"),
               aes(x = (from_x + to_x) / 4,
                   y = from_y,
                   label = weighted)) +
    # theme
    scale_fill_manual(values = wesanderson::wes_palette(n = 4, name = "GrandBudapest2")) +
    scale_color_manual(values = wesanderson::wes_palette(n = 4, name = "GrandBudapest2")) +
    cowplot::theme_minimal_grid(font_size = 14, line_size = 0) +
    theme(legend.position = "bottom",
          panel.grid.major = element_blank(),
          axis.text.y = element_blank(),
          axis.text.x = element_blank(),
          plot.background = element_rect(fill = "white")) +
    labs(x = NULL, y = NULL, fill = "Target", fontface="Source")

  return(plt)
}

plt_pm_df(xmap_set, from = key1, to = key2, weights = weight)

sessionInfo()

R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
[1] ggbump_0.1.0    ggplot2_3.3.6   dplyr_1.0.10    workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0  xfun_0.31         bslib_0.3.1       colorspace_2.0-3 
 [5] vctrs_0.4.2       generics_0.1.3    htmltools_0.5.2   yaml_2.3.5       
 [9] utf8_1.2.2        rlang_1.0.6       jquerylib_0.1.4   later_1.3.0      
[13] pillar_1.8.1      glue_1.6.2        withr_2.5.0       DBI_1.1.3        
[17] lifecycle_1.0.3   stringr_1.4.1     munsell_0.5.0     gtable_0.3.1     
[21] evaluate_0.16     labeling_0.4.2    knitr_1.39        forcats_0.5.1    
[25] callr_3.7.0       fastmap_1.1.0     httpuv_1.6.5      ps_1.7.1         
[29] fansi_1.0.3       highr_0.9         Rcpp_1.0.9        renv_0.15.5      
[33] promises_1.2.0.1  scales_1.2.1      jsonlite_1.8.2    farver_2.1.1     
[37] fs_1.5.2          digest_0.6.30     stringi_1.7.8     processx_3.6.1   
[41] getPass_0.2-2     cowplot_1.1.1     rprojroot_2.0.3   grid_4.2.1       
[45] cli_3.4.1         tools_4.2.1       magrittr_2.0.3    sass_0.4.2.9000  
[49] tibble_3.1.8      wesanderson_0.3.6 whisker_0.4       pkgconfig_2.0.3  
[53] assertthat_0.2.1  rmarkdown_2.14    httr_1.4.3        rstudioapi_0.13  
[57] R6_2.5.1          git2r_0.30.1      compiler_4.2.1

factor-xmaps

cynthiahqy

2023-01-18

Background

Visualisation Idea