R Under development (unstable) (2024-10-10 r87224 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> 
> # Load necessary libraries
> library(diceplot)
> library(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

> library(ggplot2)
> library(tidyr)
> # library(diceplot) # Ensure this library is installed or remove if not needed
> 
> # Define genes
> gene_list <- c("GeneA", "GeneB", "GeneC")
> 
> # Define cell types
> cell_types <- c("Neuron", "Astrocyte", "Microglia")
> 
> # Define Contrasts
> contrasts <- c("Clinical", "Pathological")
> 
> # Define vars for each Contrast
> vars_clinical <- c("MCI-NCI", "AD-MCI", "AD-NCI")
> vars_pathological <- c("Amyloid", "Plaq N", "Tangles", "NFT")
> 
> # Create a data frame with all combinations
> data <- expand.grid(
+   gene = gene_list,
+   Celltype = cell_types,
+   Contrast = contrasts,
+   stringsAsFactors = FALSE
+ )
> 
> # Add the appropriate vars to each Contrast
> set.seed(123) # Ensure reproducibility
> data_clinical <- data %>% 
+   filter(Contrast == "Clinical") %>% 
+   mutate(var = sample(vars_clinical, n(), replace = TRUE))
> 
> data_pathological <- data %>% 
+   filter(Contrast == "Pathological") %>% 
+   mutate(var = sample(vars_pathological, n(), replace = TRUE))
> 
> # Combine the data
> data <- bind_rows(data_clinical, data_pathological)
> 
> # Assign random values for avg_log2FC and p_val_adj
> data <- data %>%
+   mutate(
+     avg_log2FC = runif(n(), min = -2, max = 2),
+     p_val_adj = runif(n(), min = 0.0001, max = 0.05)
+   )
> 
> # Use the corrected function
> p <- domino_plot(
+   data = data,
+   gene_list = gene_list,
+   switch_axis = FALSE,
+   min_dot_size = 1,
+   max_dot_size = 5,
+   output_file = "domino_plot_example.png"
+ )
Plot saved to domino_plot_example.png (7.0 x 5.0 inches)
Warning messages:
1: Removed 90 rows containing missing values or values outside the scale range
(`geom_point()`). 
2: Removed 90 rows containing missing values or values outside the scale range
(`geom_point()`). 
> 
> # Display the plot
> print(p)
Warning messages:
1: Removed 90 rows containing missing values or values outside the scale range
(`geom_point()`). 
2: Removed 90 rows containing missing values or values outside the scale range
(`geom_point()`). 
> 
> proc.time()
   user  system elapsed 
   2.54    0.35    2.93