### Install ggcorrheatmap from GitHub

Source: https://leod123.github.io/ggcorrheatmap/index.html

Install the development version of the ggcorrheatmap package from GitHub using devtools.

```r
# install.packages("devtools")
devtools::install_github("leod123/ggcorrheatmap")
```

--------------------------------

### Install ggcorrheatmap from CRAN

Source: https://leod123.github.io/ggcorrheatmap/index.html

Install the stable version of the ggcorrheatmap package from CRAN.

```r
install.packages("ggcorrheatmap")
```

--------------------------------

### Basic gghm_tidy Example

Source: https://leod123.github.io/ggcorrheatmap/reference/gghm_tidy.html

Demonstrates the fundamental usage of gghm_tidy with a data frame in long format, specifying columns for rows, columns, and values.

```R
set.seed(123)
hm_in <- data.frame(row = rep(letters[1:10], each = 5),
                    col = rep(LETTERS[1:5], 10),
                    val = rnorm(50))
gghm_tidy(hm_in, row, col, val)
```

--------------------------------

### Controlling Annotation Colors

Source: https://leod123.github.io/ggcorrheatmap/articles/annotation.html

Illustrates how to control annotation colors with multiple brewer or viridis palettes. This example uses an 8x8 matrix and 12 annotations per row/column.

```R
# Make 8x8 matrix as some colour scales have a maximum of 8 possible colours
mat_in <- matrix(1:64, nrow = 8, ncol = 8, byrow = TRUE)
# Works without rownames or '.names' column as they are by default set to the same numbers here
row_annot <- as.data.frame(replicate(12, letters[1:8]))
col_annot <- as.data.frame(replicate(12, 1:8))
colnames(row_annot) <- colnames(col_annot) <- 1:12
gghm(mat_in, annot_rows_df = row_annot, annot_cols_df = col_annot,
     # Hide the legends
     legend_order = NA)
```

--------------------------------

### ggcorrhm with Ladderize Dendrogram Ordering

Source: https://leod123.github.io/ggcorrheatmap/articles/clustering.html

Shows how to use `ladderize` for dendrogram ordering. This example highlights a potential warning when row and column orders differ, which can scramble the diagonal in square matrices.

```R
# Only the rows are reordered, causing the matrix to become asymmetric
# If the layout were triangular (or mixed), the warning would be slightly different
# and the same plot as below would be produced (forced full layout).
ggcorrhm(mtcars, cluster_rows = TRUE, cluster_cols = TRUE,
         dend_rows_extend = list(ladderize = NULL))
#> Warning: The clustering has ordered rows and columns differently. The diagonal may be
#> scrambled due to the unequal row and column orders.
```

--------------------------------

### Apply Different Binnings to Heatmap Triangles

Source: https://leod123.github.io/ggcorrheatmap/articles/legends.html

Customize binning for different heatmap triangles even when a single `col_scale` is provided. This example applies 4 bins to the top-right triangle and 8 bins to the bottom-left.

```r
# Only one scale supplied, but it is split into two by
# specifying two different binnings
gghm(plt_dat, layout = c("tr", "bl"), mode = c("hm", "hm"),
     col_scale = "G", bins = c(4L, 8L))
```

--------------------------------

### Customizing Size Scales

Source: https://leod123.github.io/ggcorrheatmap/articles/mixed.html

Applies different scales for element sizes using 'size_scale'. This example shows a binned size scale with absolute value transformation and a continuous size scale, each with specific ranges and legend configurations.

```R
ggcorrhm(mtcars, layout = c("tr", "bl"), mode = c(23, 23),
         size_scale = list(
           # Can make a binned size scale
           scale_size_binned(n.breaks = 6, range = c(1, 5),
                             # Absolute value transform but legend loses meaning
                             transform = scales::trans_new("abs", abs, abs),
                             name = "scale_size_binned", guide = guide_bins(order = 1)),
           scale_size_continuous(range = c(5, 10), name = "scale_size_continuous")
         ), border_lwd = 1, legend_order = NA)
```

--------------------------------

### Heatmap with Custom Cell Labels and Formatting

Source: https://leod123.github.io/ggcorrheatmap/articles/heatmap.html

Displays custom labels within heatmap cells, controlling their size, color, and number of decimal places. This example demonstrates fine-tuning the appearance of cell labels.

```r
plt1 <- gghm(plot_data, cell_labels = TRUE, col_scale = fill_scl,
             cell_label_size = 5, cell_label_col = "#63B8FF", cell_label_digits = 4) +
  labs(title = "size, colours, decimals")
```

--------------------------------

### Arrange Legends with patchwork

Source: https://leod123.github.io/ggcorrheatmap/articles/legends.html

Demonstrates arranging legends using the patchwork package. It shows how to create a custom layout by combining a plot with multiple legends, including an example of horizontal legend orientation.

```R
# Arrange in a grid (patchwork)
# lgd_grid <- wrap_plots(lgd)
# 
# # Combine with patchwork
# wrap_plots(pl, lgd_grid, widths = c(3, 1))
```

```R
# Try a different layout with patchwork. Turn the legends horizontal
lgd <- get_legend(p + theme(legend.direction = "horizontal",
                            legend.title = element_text(vjust = 0.8)))[["grobs"]][1:4]

# Append the plot to the legend list to give to patchwork::wrap_plots
wrap_plots(append(list(pl), lgd),
           design = "
           ##BBB##
           AAAAAAA
           AAAAAAA
           AAAAAAA
           AAAAAAA
           CCDDEEE
           ")
```

--------------------------------

### Customize Scale Arguments Triangle-Wise

Source: https://leod123.github.io/ggcorrheatmap/articles/mixed.html

Apply scale-related arguments like `limits`, `bins`, `high`, `mid`, `low`, and `midpoint` independently to each triangle using lists. This example demonstrates custom limits, bin counts, and color gradients for the bottom-right triangle.

```r
# Make correlation data to plot (100x100)
plt_dat <- sapply(seq(-1, 0, length.out = 100), function(x) {
  seq(x, x + 1, length.out = 100)
})

# Using high, mid, low which only work if the default ggcorrhm scale is used
ggcorrhm(plt_dat, cor_in = TRUE, show_names_diag = FALSE, border_col = 0,
         layout = c("tl", "br"), mode = c("hm", "hm"),
         # Some arguments can be NULL to use the default
         # If using NULL, use a list as it disappears in atomic vectors
         limits = list(NULL, c(-0.75, 0.75)),
         bins = list(NULL, 5L),
         high = list("pink", NULL),
         mid = list(NULL, NULL),
         low = c("cyan", "purple"),
         # midpoint cannot take NULL
         midpoint = c(-0.25, 0))
```

--------------------------------

### Split Diagonal in Heatmap Layouts

Source: https://leod123.github.io/ggcorrheatmap/articles/mixed.html

Illustrates using 'split_diag = TRUE' to divide the diagonal of heatmap triangles. This example also shows how to customize diagonal label parameters and combine plots with different layouts.

```R
plt1 <- ggcorrhm(mtcars, layout = "br", mode = "hm", split_diag = TRUE,
                 names_diag_params = list(angle = -45, hjust = 1.2))

plt2 <- ggcorrhm(mtcars, layout = c("tr", "bl"), mode = c("hm", "hm"),
                 col_scale = c("A", "G_rev"), split_diag = TRUE,
                 # Move the names for visibility
                 show_names_diag = FALSE, show_names_rows = TRUE, show_names_cols = TRUE)

plt1 + plt2
```

--------------------------------

### Displaying P-values in Specific Heatmap Triangles

Source: https://leod123.github.io/ggcorrheatmap/articles/mixed.html

Illustrates how to selectively display p-values and adjust cell label sizes for different triangles in a mixed layout. The first example applies p-values to the entire heatmap, while the second applies them only to the upper triangle.

```R
plt1 <- ggcorrhm(mtcars, layout = c("tl", "br"), p_values = TRUE,
                 cell_label_size = c(4, 2))
plt2 <- ggcorrhm(mtcars, layout = c("tl", "br"), p_values = c(FALSE, TRUE),
                 cell_label_size = 2)

plt1 + plt2
```

--------------------------------

### Faceting with Layout Options

Source: https://leod123.github.io/ggcorrheatmap/articles/facets.html

Demonstrates faceting with different layout options ('bl' for bottom-left, 'br' for bottom-right). Row counting is from the bottom when the y-axis is flipped.

```R
# Two from the top
plt1 <- ggcorrhm(mtcars, split_rows = 2, split_cols = 2,
                 layout = "bl")
# Two from the bottom
plt2 <- ggcorrhm(mtcars, split_rows = 2, split_cols = 2,
                 layout = "br")

plt1 + plt2
```

--------------------------------

### Create custom labels with adjusted p-values

Source: https://leod123.github.io/ggcorrheatmap/articles/tidy_input.html

Mutate the data to create custom labels for the heatmap. This example shows how to display adjusted p-values in the top right and unadjusted in the bottom left, only if they are less than 0.05.

```r
plt_dat %>%
  # Make custom labels with adjusted p-values in the
  # top right and unadjusted in the bottom left
  # but only display if < 0.05
  mutate(labs = case_when(layout == "tr" & p_adj < 0.05 ~ round(p_adj, 2),
                          layout == "bl" & p_val < 0.05 ~ round(p_val, 2),
                          TRUE ~ NA)) %>%
  ggcorrhm_tidy(row, col, value, labels = labs,
                cor_in = TRUE)
```

--------------------------------

### Comparing Full and Mixed Layouts

Source: https://leod123.github.io/ggcorrheatmap/articles/mixed.html

Compares the default full layout ('f') with a mixed layout ('tl', 'br') that achieves a similar top-left to bottom-right diagonal. This highlights how mixed layouts can replicate or modify default arrangements.

```R
plt1 <- ggcorrhm(mtcars, layout = "f")
plt2 <- ggcorrhm(mtcars, layout = c("tl", "br"), mode = c("hm", "hm"))

plt1 + plt2
```

--------------------------------

### Load Libraries

Source: https://leod123.github.io/ggcorrheatmap/articles/facets.html

Load the necessary libraries for ggcorrheatmap, ggplot2, and patchwork.

```r
library(ggcorrheatmap)
library(ggplot2)
library(patchwork)
```

--------------------------------

### Load Libraries

Source: https://leod123.github.io/ggcorrheatmap/articles/annotation.html

Loads necessary libraries for ggcorrheatmap, dplyr, ggplot2, and patchwork.

```R
library(ggcorrheatmap)
library(dplyr)
library(ggplot2)
library(patchwork)
```

--------------------------------

### Highlighting Non-Significant P-values with ggplot2

Source: https://leod123.github.io/ggcorrheatmap/articles/correlation.html

Customize a correlation heatmap by highlighting non-significant p-values using ggplot2. This example retrieves plot data, filters for p-values above a threshold, and marks them with a specific shape and size.

```R
p <- ggcorrhm(mtcars, p_values = TRUE, p_thresholds = NULL,
              p_adjust = "bonferroni", return_data = TRUE,
              layout = "tl", include_diag = FALSE)
ggcorrhm(mtcars, layout = "tl", include_diag = FALSE) + 
  geom_point(data = p$plot_data %>%
               # Mark cells above a threshold
               filter(p_adj >= 0.05, as.character(row) != as.character(col)),
             mapping = aes(x = col, y = row),
             shape = 4, size = 8)
```

--------------------------------

### Customize Continuous and Binned Scales with Scale Objects

Source: https://leod123.github.io/ggcorrheatmap/articles/legends.html

Illustrates customizing breaks in continuous and binned scales using scale objects like scale_fill_gradient2 and scale_fill_steps2.

```R
plt_dat_cor <- sapply(seq(-1, 0, length.out = 10), function(x) {
  seq(x, x + 1, length.out = 10)
})

plt1 <- ggcorrhm(plt_dat_cor, cor_in = TRUE)

plt2 <- ggcorrhm(plt_dat_cor, cor_in = TRUE,
                 layout = c("tr", "bl"),
                 mode = c("hm", "hm"),
                 col_scale = list(
                   scale_fill_gradient2(
                     # Mimic the default correlation scale
                     high = "sienna2", mid = "white", low = "skyblue2",
                     # If not binned this only changes the legend but not the colours
                     breaks = seq(-1, 1, length.out = 11)
                   ),
                   # Binned scales can be made with e.g.
                   # scale_fill_fermenter, scale_fill_viridis_b, scale_fill_steps etc
                   scale_fill_steps2(
                     high = "sienna2", mid = "white", low = "skyblue2",
                     # Specify exact break points for bins
                     breaks = c(-1, -0.75, -0.25, 0, 0.25, 0.75, 1)
                   )
                 ))

plt1 + plt2
```

--------------------------------

### Apply Single Scale to Mixed Layout Heatmap

Source: https://leod123.github.io/ggcorrheatmap/articles/legends.html

When using a mixed layout with different modes (e.g., heatmap and text), a single value for `col_scale` applies the same scale to all heatmap components. This example uses 'A' for the scale.

```r
plt_dat <- sapply(1:10, function(x) seq(x, x + 9))

gghm(plt_dat, layout = c("tl", "br"),
     mode = c("hm", "text"), col_scale = "A")
```

--------------------------------

### Clustering in Mixed Layouts

Source: https://leod123.github.io/ggcorrheatmap/articles/mixed.html

Shows how clustering can be applied to mixed layout heatmaps. Clustering is only possible if both rows and columns are clustered, as demonstrated with the 'cluster_rows' argument.

```r
ggcorrhm(mtcars, layout = c("tr", "bl"), cluster_rows = TRUE)
#> Warning: Cannot cluster only one dimension for triangular layouts, clustering both rows
#> and columns instead.
```

--------------------------------

### Apply Different Scales to Heatmap Triangles

Source: https://leod123.github.io/ggcorrheatmap/articles/legends.html

Use a vector or list for `col_scale` to apply distinct color scales to different triangles in a mixed layout heatmap. This example applies 'magma' to the top-left and 'RdYlGn' to the bottom-right.

```r
plt_dat <- sapply(1:10, function(x) seq(x, x + 9))

gghm(plt_dat, layout = c("tl", "br"),
     mode = c("hm", "hm"),
     # For Viridis options, both the short and long versions work
     col_scale = c("magma", "RdYlGn"))
```

--------------------------------

### Basic Mixed Layouts

Source: https://leod123.github.io/ggcorrheatmap/articles/mixed.html

Creates basic mixed layouts by specifying different modes for the upper and lower triangles of the heatmap. The 'layout' argument takes a vector of two strings, and 'mode' can specify different display types for each triangle.

```r
plt1 <- ggcorrhm(mtcars, layout = c("tl", "br"))
plt2 <- ggcorrhm(mtcars, layout = c("tl", "br"), mode = c("21", "23"))

plt1 + plt2
```

--------------------------------

### Custom P-value Marking with ggplot2

Source: https://leod123.github.io/ggcorrheatmap/articles/correlation.html

Generate custom p-value markings by returning plot data from ggcorrhm and manipulating it with ggplot2 functions. This example customizes border line types, widths, and uses geom_point for significant p-values.

```R
p <- ggcorrhm(mtcars, p_values = TRUE, p_thresholds = NULL,
              p_adjust = "bonferroni", return_data = TRUE,
              layout = "br", include_diag = FALSE)
p$plot_data <- mutate(p$plot_data,
                      p_lty = case_when(p_adj < 0.05 ~ 1, TRUE ~ 3),
                      p_lwd = case_when(p_adj < 0.05 ~ 1, TRUE ~ 0.5),
                      p_size = -log10(p_adj))
ggcorrhm(mtcars, border_lty = p$plot_data$p_lty, border_lwd = p$plot_data$p_lwd,
         layout = "br", include_diag = FALSE) + 
  geom_point(aes(x = col, y = row, size = p_size), data = p$plot_data %>%
               filter(p_adj < 0.05, as.character(row) != as.character(col)),
             shape = "*", 
             size = 8) + 
  scale_size(range = c(4, 8)) + 
  labs(size = "-log10(p_adj)")
```

--------------------------------

### Load Required Libraries

Source: https://leod123.github.io/ggcorrheatmap/articles/correlation.html

Load the necessary libraries for creating correlation heatmaps and data manipulation.

```R
library(ggcorrheatmap)
library(ggplot2)
library(dplyr)
library(patchwork)
```

--------------------------------

### Correlate two matrices using cor_long

Source: https://leod123.github.io/ggcorrheatmap/reference/cor_long.html

This example illustrates how to use cor_long to calculate correlations between two different data frames. Both data frames must be provided along with their respective row, column, and value specifications.

```R
# Correlation between two matrices
cor_in2 <- data.frame(rows = rep(letters[1:10], each = 10),
                      cols = rep(letters[1:10], 10),
                      values = rnorm(100))
corr2 <- cor_long(cor_in, row, col, val,
                  cor_in2, rows, cols, values)
```

--------------------------------

### Customizing Column Annotations

Source: https://leod123.github.io/ggcorrheatmap/articles/annotation.html

Demonstrates how to apply custom color scales and themes to column annotations based on data types. Use `annot_cols_df` for annotation data and `annot_cols_col` for styling.

```r
col_annot <- as.data.frame(replicate(8, 1:8)) %>%
  rename_with(function(x) {paste(c("custom", "default"), rep(1:4, each = 2), sep = "")}) %>%
  mutate(across(1:4, as.character))
col_annot$.names <- colnames(mtcars)[1:8]

col_annot_fill <- list(custom1 = "Oranges",
                       custom2 = scale_fill_hue(guide = "none"),
                       custom3 = "mako",
                       custom4 = scale_fill_gradient(guide = "none"))
ggcorrhm(mtcars[, 1:8], annot_cols_df = col_annot,
         annot_cols_col = col_annot_fill, legend_order = NA)
```

--------------------------------

### Correlate long format data to long format matrix

Source: https://leod123.github.io/ggcorrheatmap/reference/cor_long.html

This example shows how to obtain a long-format output from the cor_long function by specifying `out_format = "long"`. This is useful for subsequent plotting or processing in a tidy format.

```R
# Long format output
corr_long <- cor_long(cor_in, row, col, val,
                      out_format = "long")
```

--------------------------------

### Load necessary libraries

Source: https://leod123.github.io/ggcorrheatmap/articles/clustering.html

Load the required libraries for ggcorrheatmap, data manipulation, dendrograms, and plot arrangement.

```r
library(ggcorrheatmap)
library(dplyr)
library(dendextend)
library(patchwork)
```

--------------------------------

### Using NULL for Default Colour Scale

Source: https://leod123.github.io/ggcorrheatmap/articles/mixed.html

Demonstrates using NULL for one of the colour scales, which reverts to the default scale customizable with 'high', 'mid', and 'low' arguments. The other scale is a custom distiller scale.

```R
# NULL uses the default that changes with high, mid, low etc
ggcorrhm(mtcars, layout = c("tl", "br"), mode = c("hm", "hm"),
         col_scale = list(
           NULL,
           # Scale object will not be affected by the default scale arguments
           scale_fill_distiller(palette = "PRGn", limits = c(-1, 1))
         ), bins = 5L, high = "pink", mid = "beige", low = "lightblue")
```

--------------------------------

### Custom Scales with ggplot2 Objects

Source: https://leod123.github.io/ggcorrheatmap/articles/legends.html

Applies custom ggplot2 scale objects to specific legends within `gghm`, overriding default behavior and `legend_order`. This example customizes color scales for triangles and annotations, and hides one annotation legend.

```R
gghm(cor(mtcars), layout = c("tl", "br"), mode = c(19, 21),
     col_name = c("1", "3"), size_name = "2",
     col_scale = list(
       # Keep default scale for first triangle
       NULL,
       # Pass a scale object for second triangle
       scale_fill_viridis_c(name = "scale object 1", option = "G",
                            guide = guide_colourbar(order = 1))
     ),
     annot_rows_df = annot_rows,
     annot_cols_df = annot_cols,
     annot_rows_col = list(
       # Scale object for second row annotation
       "5" = scale_fill_distiller(name = "scale object 2",
                                  palette = "Purples")
     ),
     annot_cols_col = list(
       # Scale object for first column annotation
       "6" = scale_fill_brewer(name = "scale object 3",
                               palette = "RdBu",
                               # Hide the legend
                               guide = "none")
     ),
     # (Try to) Remove all legends
     legend_order = NA) +
  theme(legend.direction = "horizontal")
```

--------------------------------

### Faceting with Tidy Data in gghm_tidy

Source: https://leod123.github.io/ggcorrheatmap/articles/tidy_input.html

Demonstrates using facet_rows and facet_cols arguments in gghm_tidy to create faceted heatmaps based on annotation columns. It also shows an alternative using split_rows and split_cols.

```r
plt1 <- mtcars_long %>%
  # Use the annotations as facets
  left_join(row_key, by = "cars") %>%
  left_join(col_key, by = "vars") %>%
  # Change facet order for rows
  mutate(b = factor(b, levels = letters[1:3])) %>%
  gghm_tidy(cars, vars, vals,
            facet_rows = b, facet_cols = d)

plt2 <- gghm_tidy(mtcars_long,
                  cars, vars, vals,
                  split_rows = 16,
                  split_cols = 6)

plt1 + plt2
```

--------------------------------

### ggcorrhm with Row and Column Clustering

Source: https://leod123.github.io/ggcorrheatmap/reference/ggcorrhm.html

Demonstrates enabling clustering for both rows and columns in the heatmap.

```R
ggcorrhm(mtcars, layout = "tl", cluster_rows = TRUE, cluster_cols = TRUE)
```

--------------------------------

### Load Required Libraries

Source: https://leod123.github.io/ggcorrheatmap/articles/tidy_input.html

Load the necessary libraries for data manipulation and ggcorrheatmap functionality.

```R
library(ggcorrheatmap)
library(dplyr)
library(tidyr)    # For converting between long and wide formats
library(tibble)
library(patchwork)
```

--------------------------------

### Correlate long format data to wide format matrix

Source: https://leod123.github.io/ggcorrheatmap/reference/cor_long.html

This example demonstrates the default usage of cor_long to create a wide-format correlation matrix from a long-format data frame. Ensure the input data frame is properly structured with columns for rows, columns, and values.

```R
set.seed(123)
cor_in <- data.frame(row = rep(letters[1:10], each = 5),
                     col = rep(LETTERS[1:5], 10),
                     val = rnorm(50))
# Wide format output (default)
corr_wide <- cor_long(cor_in, row, col, val)
```

--------------------------------

### Customizing Heatmap Triangles with Vectors and Lists

Source: https://leod123.github.io/ggcorrheatmap/articles/mixed.html

Demonstrates using vectors and lists to control border linewidth, line style, and colors for different triangles in a mixed layout heatmap. It also shows how to conditionally display and color cell labels based on triangle.

```R
colr <- c("#70369D", "#59369D", "#4A44AB", "#486ED8", "#5B9892", "#79C314", "#C6DB28", "#FBDD2B", "#FEB30A", "#F56B08", "#E81416")

ggcorrhm(mtcars, layout = c("bl", "tr"), mode = c("none", "hm"),
         # One value for the whole heatmap (border linewidth)
         border_lwd = 1.5,
         # Length two vector, each value being used for the whole corresponding triangle
         border_lty = c(1, 3),
         # A list with values for each triangle
         border_col = list(
           # The first triangle is 66 values (if the diagonal is drawn), second 55
           # If not known, can return the plotting data to see how many cells are in each triangle
           # Since the matrix must be square the numbers can also be calculated
           # Total: ncol*ncol, triangle without diagonal: (total - ncol) / 2, with diagonal: without + ncol
           # First triangle, provide all values, moving down each column from the left
           rev(unlist(sapply(seq_along(colr), function(i) rep(colr[i], i)))),
           # Second triangle, recycle one value
           "white"
         ),
         # Also draw cell labels on the first triangle and set the same colours as the cells
         cell_labels = c(TRUE, FALSE),
         cell_label_col = list(
           # Provide the same values as for the cell border colours, but minus one as the diagonal
           # names are written instead of cell labels and are controlled differently
           rev(unlist(sapply(seq_along(colr), function(i) rep(colr[i], i - 1)))),
           1 # Anything as there are no labels
         ),
         names_diag_param = list(colour = rev(colr)))
```

--------------------------------

### gghm_tidy with Annotation and Clustering

Source: https://leod123.github.io/ggcorrheatmap/reference/gghm_tidy.html

Shows how to add row and column annotations and enable clustering for both rows and columns using gghm_tidy. Column annotations can be specified using tidy selection.

```R
set.seed(123)
hm_in <- data.frame(row = rep(letters[1:10], each = 5),
                    col = rep(LETTERS[1:5], 10),
                    val = rnorm(50))
# Add annotation by giving names of columns in the data
hm_in$row_annot1 <- rep(1:10, each = 5)
hm_in$row_annot2 <- rep(10:1, each = 5)
hm_in$col_annot <- rep(letters[1:5], 10)
# Columns are given using 'tidy' selection
# so they can be unquoted, quoted, from variables (with !! notation) or indices
gghm_tidy(hm_in, row, col, val,
          annot_rows = c(row_annot1, row_annot2),
          annot_cols = col_annot,
          cluster_rows = TRUE,
          cluster_cols = TRUE)
```

--------------------------------

### Basic Row and Column Annotation

Source: https://leod123.github.io/ggcorrheatmap/articles/annotation.html

Demonstrates adding row and column annotations using data frames. Ensure row names or a '.names' column match the heatmap data.

```R
row_annot <- data.frame(.names = colnames(mtcars), a = rnorm(ncol(mtcars)))
col_annot <- data.frame(b = sample(letters[1:3], ncol(mtcars), TRUE))
rownames(col_annot) <- colnames(mtcars)

ggcorrhm(mtcars, annot_rows_df = row_annot, annot_cols_df = col_annot)
```

--------------------------------

### Create Mixed Layouts and Scales with ggcorrhm

Source: https://leod123.github.io/ggcorrheatmap/articles/legends.html

Generates a simple mixed layout with mixed scales and a more complex version with various scale arguments like bins and limits.

```R
# Make plot data between -1 and 1 (like correlation data)
plt_dat_cor <- sapply(seq(-1, 0, length.out = 100), function(x) {
  seq(x, x + 1, length.out = 100)
})

# Simple mixed layout with mixed scales
plt1 <- ggcorrhm(plt_dat_cor, cor_in = TRUE, show_names_diag = FALSE,
                 border_col = 0, layout = c("tl", "br"), mode = c("hm", "hm"),
                 col_scale = c("Spectral", "H"))

# With many scale arguments applied
plt2 <- ggcorrhm(plt_dat_cor, cor_in = TRUE, show_names_diag = FALSE,
                 border_col = 0, layout = c("tl", "br"), mode = c("hm", "hm"),
                 col_scale = c("Spectral", "H"),
                 # Provide a list as a mix of integer and double would be coerced if atomic vector
                 # Integer vs double, results in 11 and 10 bins
                 bins = list(11L, 11),
                 # NULL to use default limits (-1 to 1 in ggcorrhm)
                 limits = list(c(-.5, .5), NULL))

plt1 + plt2
```

--------------------------------

### ggcorrhm with Mixed Layouts

Source: https://leod123.github.io/ggcorrheatmap/reference/ggcorrhm.html

Demonstrates using a mixed layout for different parts of the heatmap, specified as a character vector.

```R
ggcorrhm(mtcars, layout = c("tl", "br"))
```

--------------------------------

### ggcorrheatmap with Dendrogram Extension Arguments

Source: https://leod123.github.io/ggcorrheatmap/reference/gghm.html

Shows how to use `dend_*_extend` arguments to extend dendrograms, including setting branch line types and highlighting branches.

```r
# Using the dend_*_extend arguments
gghm(scale(hm_in), cluster_rows = TRUE, dend_rows_extend =
  list("set" = list("branches_lty", c(1, 2, 3)),
       # Empty list element (or NULL) if no arguments to be given
       "highlight_branches_col" = list()))
```

--------------------------------

### Basic ggcorrheatmap Usage

Source: https://leod123.github.io/ggcorrheatmap/reference/gghm.html

Demonstrates the most basic usage of the ggcorrheatmap function with a subset of the mtcars dataset.

```r
library(ggplot2)

# Use part of the mtcars data (for visibility)
hm_in <- mtcars[1:15, ]

# Basic usage
gghm(hm_in)
```

--------------------------------

### Creating a Tidy Correlation Heatmap with Annotations

Source: https://leod123.github.io/ggcorrheatmap/articles/tidy_input.html

This snippet shows how to prepare data with row and column annotations and then generate a heatmap using gghm_tidy. Ensure annotations are unique to avoid conflicts.

```r
set.seed(123)
row_key <- data.frame(cars = rownames(mtcars),
                      a = 1:nrow(mtcars),
                      b = sample(letters[1:3], nrow(mtcars), TRUE))
col_key <- data.frame(vars = colnames(mtcars),
                      c = 1:ncol(mtcars),
                      d = sample(LETTERS[1:2], ncol(mtcars), TRUE))

mtcars_long %>%
  # Add the row and column annotations
  left_join(row_key, by = "cars") %>%
  left_join(col_key, by = "vars") %>%
  # Add labels
  mutate(labs = 1:nrow(.)) %>%
  gghm_tidy(cars, vars, vals, labels = labs, 
            annot_rows = c(a, b), annot_col = c(c, d),
            # gghm arguments
            legend_order = 1, cell_label_col = "white",
            cell_label_digits = 0, col_scale = "A",
            scale_data = "col", cell_label_size = 2,
            cluster_rows = TRUE, cluster_cols = TRUE)
```

--------------------------------

### Reverse Color Scales for Heatmaps and Annotations

Source: https://leod123.github.io/ggcorrheatmap/articles/legends.html

Demonstrates reversing the color scale for both the main heatmap and row annotations using 'rev_' prefix for the heatmap and '_rev' suffix for the annotation scale character. Annotation size and distance are adjusted for better visibility.

```r
row_annot <- data.frame("Default" = 1:nrow(volcano),
                        "Reversed" = 1:nrow(volcano))
row_annot_colr <- list("Default" = "A", "Reversed" = "A_rev")

gghm(volcano, show_names_rows = FALSE, show_names_cols = FALSE, border_col = NA,
     col_scale = "rev_A", col_name = "Reversed",
     annot_rows_df = row_annot,
     annot_rows_col = row_annot_colr,
     # Make the annotation larger for visibility
     annot_size = 3, annot_dist = 1.5) +
```

--------------------------------

### Creating Facet Membership Vectors

Source: https://leod123.github.io/ggcorrheatmap/articles/facets.html

Generates ordered and unordered facet membership vectors for rows and columns using `sample()`. This is useful for creating custom faceting based on arbitrary groupings.

```R
set.seed(123)
row_facets <- sample(c("AAA", "BB", "C"), 32, TRUE)
col_facets <- sample(c(123, 456), 11, TRUE)

plt1 <- gghm(mtcars, legend_order = NA,
             split_rows = row_facets,
             split_cols = col_facets)
```

--------------------------------

### Return Data with Layout Information

Source: https://leod123.github.io/ggcorrheatmap/articles/mixed.html

Illustrates how to retrieve the underlying data used for plotting, including a 'layout' column that indicates which triangle each cell belongs to when using mixed layouts. This is enabled by setting 'return_data = TRUE'.

```r
head(ggcorrhm(mtcars, layout = c("tl", "br"), return_data = TRUE)$plot_data)
#>    row col      value layout
#> 1  mpg mpg  1.0000000     tl
#> 2  cyl mpg -0.8521620     tl
#> 3 disp mpg -0.8475514     tl
#> 4   hp mpg -0.7761684     tl
#> 5 drat mpg  0.6811719     tl
#> 6   wt mpg -0.8676594     tl
```

--------------------------------

### ggcorrheatmap with Symmetric Matrix Layout

Source: https://leod123.github.io/ggcorrheatmap/reference/gghm.html

Shows how to use ggcorrheatmap with a symmetric correlation matrix and specifies a 'tl' (top-left) layout.

```r
# Different layout (using a symmetric matrix)
gghm(cor(mtcars), layout = "tl")
```

--------------------------------

### Mixed Layouts for Annotations and Dendrograms

Source: https://leod123.github.io/ggcorrheatmap/articles/mixed.html

Demonstrates using mixed layouts ('hm', 'none') to place annotations and dendrograms on the empty sides of triangular layouts. Cell borders can be selectively hidden in specific triangles.

```R
annot <- data.frame(.names = colnames(mtcars), a = 1:11)
ggcorrhm(mtcars, layout = c("tl", "br"), mode = c("hm", "none"),
         annot_rows_df = annot, annot_cols_df = annot,
         cluster_rows = TRUE, cluster_cols = TRUE,
         # Hide cell borders only in the 'none' triangle
         border_lwd = c(0.1, 0))
```

--------------------------------

### Heatmap with Clustering, Scaling, and Annotations

Source: https://leod123.github.io/ggcorrheatmap/articles/heatmap.html

Creates a heatmap with row clustering enabled, column data scaled, and row annotations displayed on the top. This combines several customization options for a more informative visualization.

```r
gghm(mtcars[1:15, ], cluster_rows = TRUE, scale_data = "col",
     annot_rows_df = row_annot, annot_rows_names_side = "top")
```

--------------------------------

### ggcorrhm with Row and Column Annotations

Source: https://leod123.github.io/ggcorrheatmap/reference/ggcorrhm.html

Combines row and column annotations with clustering and a full layout. Note how specific annotation columns can be selected.

```R
ggcorrhm(mtcars, layout = "full", cluster_rows = TRUE, cluster_cols = TRUE,
         annot_rows_df = annot[, -3], annot_cols_df = annot[, -2])
```

--------------------------------

### Create Basic Heatmap with Row Annotations

Source: https://leod123.github.io/ggcorrheatmap/articles/heatmap.html

Generates a basic heatmap using the first 15 rows of the mtcars dataset and adds row annotations. The annotation labels are placed on the top.

```r
row_annot <- data.frame(.names = rownames(mtcars)[1:15],
                        a = sample(letters[1:4], 15, TRUE))

gghm(mtcars[1:15, ], annot_rows_df = row_annot, annot_rows_names_side = "top")
```

--------------------------------

### ggcorrhm with Different Layout

Source: https://leod123.github.io/ggcorrheatmap/reference/ggcorrhm.html

Illustrates changing the layout of the correlation heatmap using the 'layout' argument.

```R
ggcorrhm(mtcars, layout = "br")
```

--------------------------------

### Basic row and column clustering

Source: https://leod123.github.io/ggcorrheatmap/articles/clustering.html

Enable clustering for rows and columns by setting `cluster_rows` and `cluster_cols` to TRUE. Distance measure and clustering method can be specified.

```r
ggcorrhm(mtcars, cluster_rows = TRUE, cluster_cols = TRUE)
```

--------------------------------

### ggcorrheatmap with Gaps for Splitting

Source: https://leod123.github.io/ggcorrheatmap/reference/gghm.html

Illustrates adding gaps to split rows and columns in the heatmap, useful for visualizing distinct groups.

```r
# With gaps
gghm(mtcars, split_rows = c(10, 20), split_cols = 6)
```

--------------------------------

### ggcorrhm with Two Matrices

Source: https://leod123.github.io/ggcorrheatmap/reference/ggcorrhm.html

Shows how to use ggcorrhm with two different data matrices.

```R
ggcorrhm(iris[1:32, -5], mtcars)
```

--------------------------------

### Generate General Heatmap

Source: https://leod123.github.io/ggcorrheatmap/index.html

Create a general heatmap with clustered rows and columns. Data is scaled by column.

```r
gghm(mtcars, scale_data = "col", cluster_rows = TRUE, cluster_cols = TRUE)
```

--------------------------------

### Handle Out-of-Bounds Values with Color Scales

Source: https://leod123.github.io/ggcorrheatmap/articles/legends.html

Demonstrates using scale_fill_distiller with limits and na.value to handle out-of-bounds values, and the 'squish' strategy for out-of-bounds values in binned scales.

```R
plt2 <- gghm(plt_dat, layout = c("tr", "bl"),
     mode = c("hm", "hm"),
     col_scale = list(
       # scale_fill_distiller to use Brewer palettes with continuous data
       # The default direction in distiller is -1, opposite of gghm
       scale_fill_distiller(
         palette = "RdYlGn",
         # Values outside the limits are replaced with NAs
         limits = c(7, 13),
         # Can then change the colour using the na.value argument
         # (corresponding to na_col in gghm)
         na.value = "beige"
       ),
       
       scale_fill_distiller(
         palette = "RdYlGn",
         limits = c(7, 13),
         # Change oob strategy 'oob' argument
         # scales::squish assigns the extreme colours to values outside the range
         oob = scales::squish
       )
     ), cell_labels = TRUE)

plt1 + plt2
```

--------------------------------

### Generate Mixed Layout Correlation Heatmap

Source: https://leod123.github.io/ggcorrheatmap/index.html

Create a mixed layout correlation heatmap displaying correlation values and p-values in different triangles. Uses a simplified layout specification.

```r
# With correlation values and p-values
ggcorrhm(mtcars, layout = c("topright", "bottomleft"),
         cell_labels = c(FALSE, TRUE), p_values = c(FALSE, TRUE))
```

--------------------------------

### Apply Triangular Layouts to Square Heatmaps

Source: https://leod123.github.io/ggcorrheatmap/articles/correlation.html

Generate triangular correlation heatmaps for square matrices using the 'layout' argument. Options include 'topleft', 'topright', 'bottomleft', and 'bottomright'.

```R
plt1 <- ggcorrhm(mtcars, layout = "topleft")
```