R Under development (unstable) (2024-10-19 r87247 ucrt) -- "Unsuffered Consequences"
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Platform: x86_64-w64-mingw32/x64

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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)
> 
> plot_path = "./"
> 
> # Define the variables and their colors for 3 variables
> pathology_variables <- c("Stroke", "Cancer", "Flu")
> cat_c_colors <- c(
+   "Stroke" = "#d5cccd",
+   "Cancer" = "#cb9992",
+   "Flu" = "#ad310f"
+ )
> 
> # Define cell types (cat_a)
> cell_types <- c("Neuron", "Astrocyte", "Microglia", "Oligodendrocyte", "Endothelial")
> 
> # Define pathways (cat_b) and groups
> pathways <- c(
+   "Apoptosis", "Inflammation", "Metabolism", "Signal Transduction", "Synaptic Transmission",
+   "Cell Cycle", "DNA Repair", "Protein Synthesis", "Lipid Metabolism", "Neurotransmitter Release"
+ )
> 
> # Assign groups to pathways
> pathway_groups <- data.frame(
+   Pathway = pathways,
+   Group = c(
+     "Linked", "UnLinked", "Other", "Linked", "UnLinked",
+     "UnLinked", "Other", "Other", "Other", "Linked"
+   ),
+   stringsAsFactors = FALSE
+ )
> 
> # Define group colors
> group_colors <- c(
+   "Linked" = "#333333",
+   "UnLinked" = "#888888",
+   "Other" = "#DDDDDD"
+ )
> 
> # Create dummy data
> set.seed(123)
> data <- expand.grid(CellType = cell_types, Pathway = pathways, stringsAsFactors = FALSE)
> 
> # Assign random pathology variables to each combination
> data <- data %>%
+   rowwise() %>%
+   mutate(
+     PathologyVariable = list(sample(pathology_variables, size = sample(1:3, 1)))
+   ) %>%
+   unnest(cols = c(PathologyVariable))
> 
> # Merge the group assignments into the data
> data <- data %>%
+   left_join(pathway_groups, by = c("Pathway" = "Pathway"))
> 
> # Use the dice_plot function
> dice_plot(data = data, 
+           cat_a = "CellType", 
+           cat_b = "Pathway", 
+           cat_c = "PathologyVariable", 
+           group = "Group",
+           plot_path = plot_path, 
+           output_str = "dice_plot_3_example", 
+           group_alpha = 0.6,
+           title = "Dice Plot with 3 Pathology Variables",
+           cat_c_colors = cat_c_colors, 
+           group_colors = group_colors, 
+           format = ".png",
+           custom_theme = theme_minimal())
> 
> 
> 
> # Define the pathology variables and their colors
> pathology_variables <- c("Stroke", "Cancer", "Flu", "ADHD")
> cat_c_colors <- c(
+   "Stroke" = "#d5cccd",
+   "Cancer" = "#cb9992",
+   "Flu" = "#ad310f",
+   "ADHD" = "#7e2a20"
+ )
> 
> # Define cell types (cat_a)
> cell_types <- c("Neuron", "Astrocyte", "Microglia", "Oligodendrocyte", "Endothelial")
> 
> # Define pathways (cat_b) and add 10 more to make a total of 15
> 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 (ensuring each pathway has only one group)
> 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
+ )
> 
> # Update group colors to shades of greys
> group_colors <- c(
+   "Linked" = "#333333",
+   "UnLinked" = "#888888",
+   "Other" = "#DDDDDD"
+ )
> 
> # Create dummy data
> set.seed(123)
> data <- expand.grid(CellType = cell_types, Pathway = pathways, stringsAsFactors = FALSE)
> 
> # Assign random pathology variables to each combination
> data <- data %>%
+   rowwise() %>%
+   mutate(
+     PathologyVariable = list(sample(pathology_variables, size = sample(1:4, 1)))
+   ) %>%
+   unnest(cols = c(PathologyVariable))
> 
> # Merge the group assignments into the data
> data <- data %>%
+   left_join(pathway_groups, by = "Pathway")
> 
> group_colors <- c(
+   "Linked" = "#333333",
+   "UnLinked" = "#888888",
+   "Other" = "#DDDDDD"
+ )
> # Use the modified dice_plot function
> dice_plot(data = data, 
+           cat_a = "CellType", 
+           cat_b = "Pathway", 
+           cat_c = "PathologyVariable", 
+           group = "Group",
+           plot_path = plot_path, 
+           output_str = "dice_plot_4_example", 
+           group_alpha = 0.6,
+           title = "Dummy Dice Plot with Pathology Variables",
+           cat_c_colors = cat_c_colors, 
+           group_colors = group_colors, 
+           format = ".png",
+           custom_theme = theme_minimal())
> 
> 
> # Define the variables and their colors for 5 variables
> pathology_variables <- c("Stroke", "Cancer", "Flu", "ADHD", "Lymphom")
> cat_c_colors <- c(
+   "Stroke" = "#d5cccd",
+   "Cancer" = "#cb9992",
+   "Flu" = "#ad310f",
+   "ADHD" = "#7e2a20",
+   "Lymphom" = "#FFD700"  # Gold color for Lymphom
+ )
> 
> # Create dummy data
> set.seed(123)
> data <- expand.grid(CellType = cell_types, Pathway = pathways, stringsAsFactors = FALSE)
> 
> # Assign random pathology variables to each combination
> data <- data %>%
+   rowwise() %>%
+   mutate(
+     PathologyVariable = list(sample(pathology_variables, size = sample(1:5, 1)))
+   ) %>%
+   unnest(cols = c(PathologyVariable))
> 
> # Merge the group assignments into the data
> data <- data %>%
+   left_join(pathway_groups, by = c("Pathway" = "Pathway"))
> 
> # Use the dice_plot function
> dice_plot(data = data, 
+           cat_a = "CellType", 
+           cat_b = "Pathway", 
+           cat_c = "PathologyVariable", 
+           group = "Group",
+           plot_path = plot_path, 
+           output_str = "dice_plot_5_example", 
+           group_alpha = 0.6,
+           title = "Dice Plot with 5 Pathology Variables",
+           cat_c_colors = cat_c_colors, 
+           group_colors = group_colors, 
+           format = ".png",
+           custom_theme = theme_minimal())
> 
> 
> # Define the variables and their colors for 6 variables
> pathology_variables <- c("Alzheimer's disease", "Cancer", "Flu", "ADHD", "Age", "Weight")
> cat_c_colors <- c(
+   "Stroke" = "#d5cccd",
+   "Cancer" = "#cb9992",
+   "Flu" = "#ad310f",
+   "ADHD" = "#7e2a20",
+   "Age" = "#FFD700",  # Gold color for Lymphom
+   "Weight" = "#FF6622"  # Cyan color for Var6
+ )
> 
> # Create dummy data
> set.seed(123)
> data <- expand.grid(CellType = cell_types, Pathway = pathways, stringsAsFactors = FALSE)
> 
> # Assign random pathology variables to each combination
> data <- data %>%
+   rowwise() %>%
+   mutate(
+     PathologyVariable = list(sample(pathology_variables, size = sample(1:6, 1)))
+   ) %>%
+   unnest(cols = c(PathologyVariable))
> 
> # Merge the group assignments into the data
> data <- data %>%
+   left_join(pathway_groups, by = c("Pathway" = "Pathway"))
> 
> # Use the dice_plot function
> dice_plot(data = data, 
+           cat_a = "CellType", 
+           cat_b = "Pathway", 
+           cat_c = "PathologyVariable", 
+           group = "Group",
+           plot_path = plot_path, 
+           output_str = "dice_plot_6_example", 
+           group_alpha = 0.6,
+           title = "Dice Plot with 6 Pathology Variables",
+           cat_c_colors = cat_c_colors, 
+           group_colors = group_colors, 
+           format = ".png",
+           custom_theme = theme_minimal())
Warning messages:
1: Removed 44 rows containing missing values or values outside the scale range
(`geom_point()`). 
2: Removed 44 rows containing missing values or values outside the scale range
(`geom_point()`). 
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
   3.87    0.43    4.34