R Under development (unstable) (2024-10-26 r87273 ucrt) -- "Unsuffered Consequences"
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> # Load necessary libraries
> library(diceplot)
> library(tidyr)
> library(data.table)
> library(ggplot2)
> library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:data.table':

    between, first, last

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

    filter, lag

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

    intersect, setdiff, setequal, union

> library(tibble)
> library(grid)
> library(cowplot)
> library(RColorBrewer)
> 
> # Define common variables
> cell_types <- c("Neuron", "Astrocyte", "Microglia", "Oligodendrocyte", "Endothelial")
> pathways <- c(
+   "Apoptosis", "Inflammation", "Metabolism", "Signal Transduction", "Synaptic Transmission",
+   "Cell Cycle", "DNA Repair", "Protein Synthesis", "Lipid Metabolism", "Neurotransmitter Release",
+   "Oxidative Stress", "Energy Production", "Calcium Signaling", "Synaptic Plasticity", "Immune Response"
+ )
> 
> # Assign groups to pathways
> pathway_groups <- data.frame(
+   Pathway = pathways,
+   Group = c(
+     "Linked", "UnLinked", "Other", "Linked", "UnLinked",
+     "UnLinked", "Other", "Other", "Other", "Linked",
+     "Other", "Other", "Linked", "UnLinked", "Other"
+   ),
+   stringsAsFactors = FALSE
+ )
> 
> pathology_variables <- c("Alzheimer's disease", "Cancer", "Flu", "ADHD", "Age", "Weight")
> 
> n_colors <- length(pathology_variables)
> colors <- brewer.pal(n = n_colors, name = "Set1")
> cat_c_colors <- setNames(colors, pathology_variables)
> 
> 
> # Function to create and plot dice plots
> create_and_plot_dice <- function(pathology_variables, cat_c_colors, title, cell_types, pathways, pathway_groups, min_dot_size=3, max_dot_size=6) {
+   # Create dummy data
+   set.seed(123)
+   data <- expand.grid(CellType = cell_types, Pathway = pathways, stringsAsFactors = FALSE)
+ 
+   data <- data %>%
+     rowwise() %>%
+     mutate(
+       PathologyVariable = list(sample(pathology_variables, size = sample(1:length(pathology_variables), 1)))
+     ) %>%
+     unnest(cols = c(PathologyVariable))
+   
+   # Merge the group assignments into the data
+   data <- data %>%
+     left_join(pathway_groups, by = "Pathway")
+   
+   # Use the dice_plot function
+   dice_plot(data = data, 
+             cat_a = "CellType", 
+             cat_b = "Pathway", 
+             cat_c = "PathologyVariable", 
+             group = "Group",
+             group_alpha = 0.6,
+             title = title,
+             cat_c_colors = cat_c_colors, 
+             custom_theme = theme_minimal(),
+             min_dot_size = min_dot_size,
+             max_dot_size = max_dot_size
+             )
+ }
> 
> 
> # First plot with 3 pathology variables
> pathology_variables_3 <- c("Stroke", "Cancer", "Flu")
> create_and_plot_dice(
+   pathology_variables = pathology_variables_3,
+   cat_c_colors = cat_c_colors[pathology_variables_3],
+   title = "Dice Plot with 3 Pathology Variables",
+   cell_types = cell_types,
+   pathways = pathways,
+   pathway_groups = pathway_groups
+ )
Warning message:
Removed 1 row containing missing values or values outside the scale range
(`geom_text()`). 
> 
> # Second plot with 4 pathology variables
> pathology_variables_4 <- c("Stroke", "Cancer", "Flu", "ADHD")
> create_and_plot_dice(
+   pathology_variables = pathology_variables_4,
+   cat_c_colors = cat_c_colors[pathology_variables_4],
+   title = "Dice Plot with 4 Pathology Variables",
+   cell_types = cell_types,
+   pathways = pathways,
+   pathway_groups = pathway_groups
+ )
Warning message:
Removed 1 row containing missing values or values outside the scale range
(`geom_text()`). 
> 
> # Third plot with 5 pathology variables
> pathology_variables_5 <- c("Stroke", "Cancer", "Flu", "ADHD", "Lymphom")
> create_and_plot_dice(
+   pathology_variables = pathology_variables_5,
+   cat_c_colors = cat_c_colors[pathology_variables_5],
+   title = "Dice Plot with 5 Pathology Variables",
+   cell_types = cell_types,
+   pathways = pathways,
+   pathway_groups = pathway_groups
+ )
Warning message:
Removed 2 rows containing missing values or values outside the scale range
(`geom_text()`). 
> 
> # Fourth plot with 6 pathology variables
> pathology_variables_6 <- c("Alzheimer's disease", "Cancer", "Flu", "ADHD", "Age", "Weight")
> create_and_plot_dice(
+   pathology_variables = pathology_variables_6,
+   cat_c_colors = cat_c_colors[pathology_variables_6],
+   title = "Dice Plot with 6 Pathology Variables",
+   cell_types = cell_types,
+   pathways = pathways,
+   pathway_groups = pathway_groups
+ )
> 
> # Example of a large dice plot with adjusted dot sizes
> # Define more cell types and pathways for the large plot
> cell_types_large <- c(
+   "Neuron", "Astrocyte", "Microglia", "Oligodendrocyte", "Endothelial",
+   "Ependymal", "Pericyte", "Satellite Cell", "Schwann Cell", "Macrophage"
+ )
> 
> pathways_large <- c(
+   "Apoptosis", "Inflammation", "Metabolism", "Signal Transduction", "Synaptic Transmission",
+   "Cell Cycle", "DNA Repair", "Protein Synthesis", "Lipid Metabolism", "Neurotransmitter Release",
+   "Oxidative Stress", "Energy Production", "Calcium Signaling", "Synaptic Plasticity", "Immune Response",
+   "Gene Expression", "Membrane Transport", "Cell Migration", "Cell Adhesion", "Cell Differentiation",
+   "Angiogenesis", "Neurogenesis", "Protein Folding", "Autophagy", "Endocytosis"
+ )
> 
> # Assign groups to the larger set of pathways
> set.seed(456)
> pathway_groups_large <- data.frame(
+   Pathway = pathways_large,
+   Group = sample(c("Linked", "UnLinked", "Other"), size = length(pathways_large), replace = TRUE),
+   stringsAsFactors = FALSE
+ )
> 
> # Large dice plot with adjusted dot sizes
> create_and_plot_dice(
+   pathology_variables = pathology_variables_6,
+   cat_c_colors = cat_c_colors[pathology_variables_6],
+   title = "Large Dice Plot with Adjusted Dot Sizes",
+   cell_types = cell_types_large,
+   pathways = pathways_large,
+   pathway_groups = pathway_groups_large,
+   min_dot_size = 1,
+   max_dot_size = 3
+ )
> 
> proc.time()
   user  system elapsed 
   3.25    0.29    3.51