R Under development (unstable) (2024-10-15 r87238 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)
> 
> # Define genes
> gene_list <- c("GeneA", "GeneB", "GeneC")
> 
> # Define cell types
> cell_types <- c("Neuron", "Astrocyte", "Microglia")
> 
> # Define Contrasts
> contrasts <- c("Type1", "Type2")  # Changed for demonstration
> 
> # Define vars for each Contrast
> vars_type1 <- c("MCI-NCI", "AD-MCI", "AD-NCI")
> vars_type2 <- c("Amyloid", "Plaq N", "Tangles", "NFT")
> 
> # Create a data frame with all combinations
> data <- expand.grid(
+   gene = gene_list,
+   Cell_Type = cell_types,  # Renamed column
+   Group = contrasts,       # Renamed column
+   stringsAsFactors = FALSE
+ )
> 
> # Add the appropriate vars to each Contrast
> set.seed(123) # Ensure reproducibility
> data_type1 <- data %>% 
+   filter(Group == "Type1") %>% 
+   mutate(var = sample(vars_type1, n(), replace = TRUE))
> 
> data_type2 <- data %>% 
+   filter(Group == "Type2") %>% 
+   mutate(var = sample(vars_type2, n(), replace = TRUE))
> 
> # Combine the data
> data <- bind_rows(data_type1, data_type2)
> 
> # Assign random values for logFC and adjusted p-values
> data <- data %>%
+   mutate(
+     logFC = runif(n(), min = -2, max = 2),  # Renamed column
+     adj_p_value = runif(n(), min = 0.0001, max = 0.05)
+   )
> 
> # Use the modified function with custom parameters
> p <- domino_plot(
+   data = data,
+   gene_list = gene_list,
+   feature_col = "gene",
+   celltype_col = "Cell_Type",
+   contrast_col = "Group",
+   contrast_levels = c("Type1", "Type2"),
+   contrast_labels = c("Type 1", "Type 2"),
+   logfc_col = "logFC",
+   pval_col = "adj_p_value",
+   switch_axis = FALSE,
+   min_dot_size = 1,
+   max_dot_size = 5,
+   output_file = "domino_plot_example.png"
+ )
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 
   2.65    0.46    3.12