---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  warning = FALSE,
  message = FALSE,
  dpi = 300,
  dev.args = list(type = "cairo")
)

desc <- read.dcf("DESCRIPTION")
desc <- setNames(as.list(desc), colnames(desc))
```

# `r desc$Package` <img src="man/figures/logo.png" align="right" width="120"/>

> `r desc$Title`

`visualizeR` proposes some utils to sane colors, ready-to-go color palettes, and a few visualization functions.

## Installation

You can install the last version of visualizeR from [GitHub](https://github.com/) with:

```{r, eval = FALSE}
# install.packages("devtools")
devtools::install_github("gnoblet/visualizeR", build_vignettes = TRUE)
```

## Roadmap

Roadmap is as follows:

- [ ] Full revamp of core functions (colors, pattern, incl. adding test and pre-commit structures)
- [ ] Add other types of plots:
  - [ ] Dumbell
  - [ ] Waffle
  - [ ] Donut
  - [ ] Alluvial

## Request

Please, do not hesitate to pull request any new viz or colors or color palettes, or to email request any change ([gnoblet\@zaclys.net](mailto:gnoblet@zaclys.net){.email}).

## Colors

Functions to access colors and palettes are `color()` or `palette()`. Feel free to pull request new colors.

```{r example-colors, eval = TRUE}
library(visualizeR)

# Get all saved colors, named
color(unname = F)[1:10]

# Extract a color palette as hexadecimal codes and reversed
palette(palette = "cat_5_main", reversed = TRUE, color_ramp_palette = FALSE)

# Get all color palettes names
palette(show_palettes = TRUE)
```

## Charts

### Example 1: Bar chart

```{r example-bar-chart, out.width = '65%', eval = TRUE}
library(palmerpenguins)
library(dplyr)

df <- penguins |>
  group_by(island, species) |>
  summarize(
    mean_bl = mean(bill_length_mm, na.rm = T),
    mean_fl = mean(flipper_length_mm, na.rm = T)
  ) |>
  ungroup()

df_island <- penguins |>
  group_by(island) |>
  summarize(
    mean_bl = mean(bill_length_mm, na.rm = T),
    mean_fl = mean(flipper_length_mm, na.rm = T)
  ) |>
  ungroup()

# Simple bar chart by group with some alpha transparency
bar(df, "island", "mean_bl", "species", x_title = "Mean of bill length", title = "Mean of bill length by island and species")

# Flipped / Horizontal
hbar(df, "island", "mean_bl", "species", x_title = "Mean of bill length", title = "Mean of bill length by island and species")

# Facetted
bar(df, "island", "mean_bl", facet = "species", x_title = "Mean of bill length", title = "Mean of bill length by island and species", add_color_guide = FALSE)

#  Flipped, with text, smaller width, and caption
hbar(df = df_island, x = "island", y = "mean_bl", title = "Mean of bill length by island", add_text = T, width = 0.6, add_text_suffix = "mm", add_text_expand_limit = 1.3, add_color_guide = FALSE, caption = "Data: palmerpenguins package.")
```

### Example 2: Scatterplot

```{r example-point-chart, out.width = '65%', eval = TRUE}
# Simple scatterplot
point(penguins, "bill_length_mm", "flipper_length_mm")

# Scatterplot with grouping colors, greater dot size, some transparency
point(penguins, "bill_length_mm", "flipper_length_mm", "island", group_title = "Island", alpha = 0.6, size = 3, title = "Bill vs. flipper length", , add_color_guide = FALSE)

# Facetted scatterplot by island
point(penguins, "bill_length_mm", "flipper_length_mm", "species", "island", "fixed", group_title = "Species", title = "Bill vs. flipper length by species and island", add_color_guide = FALSE)
```

### Example 3: Dumbbell plot

Remember to ensure that your data are in the long format and you only have two groups on the x-axis; for instance, IDP and returnee and no NA values.

```{r example-dumbbell-plot, out.width = '65%', eval = TRUE}
# Prepare long data
df <- tibble::tibble(
  admin1 = rep(letters[1:8], 2),
  setting = c(rep(c("Rural", "Urban"), 4), rep(c("Urban", "Rural"), 4)),
  stat = rnorm(16, mean = 50, sd = 18)
) |>
  dplyr::mutate(stat = round(stat, 0))




# dumbbell(
#   df,
#   'stat',
#   'setting',
#   'admin1',
#   title = '% of HHs that reported open defecation as sanitation facility',
#   group_y_title = 'Admin 1',
#   group_x_title = 'Setting'
# )
```

### Example 4: donut chart

```{r example-donut-plot, out.width = '65%', warning = FALSE}
# Some summarized data: % of HHs by displacement status
df <- tibble::tibble(
  status = c("Displaced", "Non displaced", "Returnee", "Don't know/Prefer not to say"),
  percentage = c(18, 65, 12, 3)
)

# Donut
# donut(df,
#   status,
#   percentage,
#   hole_size = 3,
#   add_text_suffix = '%',
#   add_text_color = color('dark_grey'),
#   add_text_treshold_display = 5,
#   x_title = 'Displacement status',
#   title = '% of HHs by displacement status'
# )
```

### Example 5: Waffle chart

```{r example-waffle-plot, out.width = '65%', warning = FALSE}
#
# waffle(df, status, percentage, x_title = 'A caption', title = 'A title', subtitle = 'A subtitle')
```

### Example 6: Alluvial chart

```{r example-alluvial-plot, out.width = '65%', warning = FALSE}
# Some summarized data: % of HHs by self-reported status of displacement in 2021 and in 2022
df <- tibble::tibble(
  status_from = c(
    rep("Displaced", 4),
    rep("Non displaced", 4),
    rep("Returnee", 4),
    rep("Dnk/Pnts", 4)
  ),
  status_to = c("Displaced", "Non displaced", "Returnee", "Dnk/Pnts", "Displaced", "Non displaced", "Returnee", "Dnk/Pnts", "Displaced", "Non displaced", "Returnee", "Dnk/Pnts", "Displaced", "Non displaced", "Returnee", "Dnk/Pnts"),
  percentage = c(20, 8, 18, 1, 12, 21, 0, 2, 0, 3, 12, 1, 0, 0, 1, 1)
)

# Alluvial, here the group is the status for 2021

# alluvial(df,
#   status_from,
#   status_to,
#   percentage,
#   status_from,
#   from_levels = c("Displaced", "Non displaced", "Returnee", "Dnk/Pnts"),
#   alpha = 0.8,
#   group_title = "Status for 2021",
#   title = "% of HHs by self-reported status from 2021 to 2022"
# )
```

### Example 7: Lollipop chart

```{r example-lollipop-chart, out.width = "65%", warning = FALSE, eval = TRUE}
library(tidyr)
# Prepare long data
df <- tibble::tibble(
  admin1 = replicate(15, sample(letters, 8)) |> t() |> as.data.frame() |> unite("admin1", sep = "") |> dplyr::pull(admin1),
  stat = rnorm(15, mean = 50, sd = 15)
) |>
  dplyr::mutate(stat = round(stat, 0))

# Simple vertical lollipop chart
lollipop(
  df = df,
  x = "admin1",
  y = "stat",
  flip = FALSE,
  dot_size = 3,
  y_title = "% of HHs",
  x_title = "Admin 1",
  title = "% of HHs that received humanitarian assistance"
)

# Horizontal lollipop chart with custom colors
hlollipop(
  df = df,
  x = "admin1",
  y = "stat",
  dot_size = 4,
  line_size = 1,
  add_color = color("cat_5_main_2"),
  line_color = color("cat_5_main_4"),
  y_title = "% of HHs",
  x_title = "Admin 1",
  title = "% of HHs that received humanitarian assistance"
)

# Create data for grouped lollipop - using set.seed for reproducibility
set.seed(123)
df_grouped <- tibble::tibble(
  admin1 = rep(c("A", "B", "C", "D", "E", "F"), 2),
  group = rep(c("Group A", "Group B"), each = 6),
  stat = c(rnorm(6, mean = 40, sd = 10), rnorm(6, mean = 60, sd = 10))
) |>
  dplyr::mutate(stat = round(stat, 0))

# Grouped lollipop chart with proper side-by-side positioning
lollipop(
  df = df_grouped,
  x = "admin1",
  y = "stat",
  group = "group",
  order = "grouped_y",
  dodge_width = 0.8, # Control spacing between grouped lollipops
  dot_size = 3.5,
  line_size = 0.8,
  y_title = "Value",
  x_title = "Category",
  title = "True side-by-side grouped lollipop chart"
)

# Horizontal grouped lollipop chart
hlollipop(
  df = df_grouped,
  x = "admin1",
  y = "stat",
  group = "group",
  dodge_width = 0.7, # Narrower spacing for horizontal orientation
  dot_size = 3.5,
  line_size = 0.8,
  y_title = "Category",
  x_title = "Value",
  title = "Horizontal side-by-side grouped lollipop chart"
)
```

## Lollipop Chart Features

Lollipop charts offer several advantages:

- Clean visualization of point data with connecting lines to a baseline
- True side-by-side grouped display for easy comparison between categories
- Each lollipop maintains its position from dot to baseline
- Customizable appearance with parameters for dot size, line width, and colors
- The `dodge_width` parameter controls spacing between grouped lollipops

The side-by-side positioning for grouped lollipops makes them visually distinct from dumbbell plots, which typically connect related points on the same line.