### Installing the waffle R Package

Source: https://github.com/hrbrmstr/waffle/blob/master/README.md

This snippet demonstrates two methods for installing the 'waffle' R package: from CRAN using `install.packages()` or directly from GitHub using `remotes::install_github()`. The GitHub option requires the 'remotes' package to be installed.

```R
install.packages("waffle") # NOTE: CRAN version is 0.7.0
# or
remotes::install_github("hrbrmstr/waffle")
```

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

### Loading Libraries and Checking waffle Version

Source: https://github.com/hrbrmstr/waffle/blob/master/README.md

This R snippet loads necessary packages for using 'waffle' charts, including 'waffle' itself, 'magrittr', 'hrbrthemes', 'ggplot2', and 'dplyr'. It then checks and prints the currently installed version of the 'waffle' package.

```R
library(waffle)
library(magrittr)
library(hrbrthemes)
library(ggplot2)
library(dplyr)
library(waffle)

# current verison
packageVersion("waffle")
## [1] '1.0.2'
```

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

### Preparing Data for Waffle and Pictogram Charts

Source: https://github.com/hrbrmstr/waffle/blob/master/README.md

This R code creates a data frame named `xdf` which will be used as input for subsequent waffle and pictogram chart examples. It contains columns for categorical parts, numerical values, colors, and a grouping factor, demonstrating how to structure data for these visualizations.

```R
data.frame(
  parts = factor(rep(month.abb[1:3], 3), levels=month.abb[1:3]),
  vals = c(10, 20, 30, 6, 14, 40, 30, 20, 10),
  col = rep(c("navy", "black", "maroon"), 3),
  fct = c(
    rep("Thing 1", 3),
    rep("Thing 2", 3),
    rep("Thing 3", 3)
  )
) -> xdf
```

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

### Creating a Pictogram Chart with geom_pictogram

Source: https://github.com/hrbrmstr/waffle/blob/master/README.md

This R code creates a pictogram chart using `geom_pictogram`. It maps `parts` to labels and `n` to values, using Font Awesome glyphs ('apple-alt', 'bread-slice', 'pizza-slice') for visualization. The chart is configured with 10 rows, flipped orientation, proportional scaling, custom colors, and enhanced with specific theme adjustments for the legend.

```R
xdf %>%
  count(parts, wt = vals) %>%
  ggplot(
    aes(label = parts, values = n)
  ) +
  geom_pictogram(
    n_rows = 10,
    aes(colour = parts),
    flip = TRUE,
    make_proportional = TRUE
  ) +
  scale_color_manual(
    name = NULL,
    values = c("#a40000", "#c68958", "#ae6056"),
    labels = c("Fruit", "Sammich", "Pizza")
  ) +
  scale_label_pictogram(
    name = NULL,
    values = c("apple-alt", "bread-slice", "pizza-slice"),
    labels = c("Fruit", "Sammich", "Pizza")
  ) +
  coord_equal() +
  theme_ipsum_rc(grid="") +
  theme_enhance_waffle() +
  theme(
    legend.key.height = unit(2.25, "line"),
    legend.text = element_text(size = 10, hjust = 0, vjust = 0.75)
  )
```

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

### Generating Package Code Metrics with cloc in R

Source: https://github.com/hrbrmstr/waffle/blob/master/README.md

This R code snippet utilizes the `cloc` package to generate and display markdown-formatted code metrics for the current R package. It's commonly used for project documentation to quantify lines of code, blank lines, and file counts across different programming languages within the package.

```R
cloc::cloc_pkg_md()
```

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

### Advanced Pictogram Chart with Custom Font Awesome Brands

Source: https://github.com/hrbrmstr/waffle/blob/master/README.md

This R snippet demonstrates an advanced pictogram chart using `geom_pictogram` with custom Font Awesome 5 Brand glyphs. It visualizes data using 'bitbucket', 'github', and 'git-alt' icons, applying specific colors and labels. The chart is set up with 20 rows, a larger glyph size, flipped orientation, and custom legend text alignment.

```R
xdf %>%
  count(parts, wt = vals) %>%
  ggplot(
    aes(label = parts, values = n)
  ) +
  geom_pictogram(
    n_rows = 20,
    size = 6,
    aes(colour = parts),
    flip = TRUE,
    family = "FontAwesome5Brands-Regular"
  ) +
  scale_color_manual(
    name = NULL,
    values = c("#073f9c", "black", "#f34323"),
    labels = c("BitBucket", "GitHub", "Other")
  ) +
  scale_label_pictogram(
    name = NULL,
    values = c("bitbucket", "github", "git-alt"),
    labels = c("BitBucket", "GitHub", "Other")
  ) +
  coord_equal() +
  theme_ipsum_rc(grid="") +
  theme_enhance_waffle() +
  theme(
    legend.text = element_text(hjust = 0, vjust = 1)
  )
```

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

### Creating a Basic Waffle Chart with geom_waffle

Source: https://github.com/hrbrmstr/waffle/blob/master/README.md

This R snippet generates a waffle chart using `geom_waffle` from the 'waffle' package. It processes the `xdf` data, maps `parts` to fill color and `n` (counted values) to the waffle squares, and applies custom colors, a white border, and a flipped orientation. The plot is enhanced with a custom theme and equal coordinates.

```R
xdf %>%
  count(parts, wt = vals) %>%
  ggplot(
    aes(fill = parts, values = n)
  ) +
  geom_waffle(
    n_rows = 20,
    size = 0.33,
    colour = "white",
    flip = TRUE
  ) +
  scale_fill_manual(
    name = NULL,
    values = c("#a40000", "#c68958", "#ae6056"),
    labels = c("Fruit", "Sammich", "Pizza")
  ) +
  coord_equal() +
  theme_ipsum_rc(grid="") +
  theme_enhance_waffle()
```

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

### Creating Faceted Waffle Charts

Source: https://github.com/hrbrmstr/waffle/blob/master/README.md

This R code generates multiple waffle charts arranged in facets, demonstrating the use of `facet_wrap` with `geom_waffle`. It creates a new tibble `xdf` with a grouping factor `fct`, then plots waffle charts for each facet, applying custom colors, white borders, and specific axis expansions for a clean layout.

```R
library(hrbrthemes)
library(waffle)
library(tidyverse)

tibble(
  parts = factor(rep(month.abb[1:3], 3), levels=month.abb[1:3]),
  values = c(10, 20, 30, 6, 14, 40, 30, 20, 10),
  fct = c(rep("Thing 1", 3), rep("Thing 2", 3), rep("Thing 3", 3))
) -> xdf

ggplot(
  data = xdf,
  aes(fill=parts, values=values)
) +
  geom_waffle(
    color = "white",
    size = 1.125,
    n_rows = 6
  ) +
  facet_wrap(~fct, ncol=1) +
  scale_x_discrete(
    expand = c(0,0,0,0)
  ) +
  scale_
```

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