---
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" alt="" width="120"/>

> `r desc$Title`

`visualizeR` proposes some utils to get REACH and AGORA colors, ready-to-go color palettes, and a few visualization functions (horizontal hist graph for instance).

## 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:

- [X] Add IMPACT's colors
- [X] Add all color palettes from the internal documentation
- [ ] There remains to be added more-than-7-color palettes and black color palettes
- [X] Add new types of visualization (e.g. dumbbell plot, lollipop plot, etc.)
- [X] Use examples
- [ ] Add some ease-map functions
- [ ] Add some interactive functions (maps and graphs)
- [ ] Consolidate and make errors transparent

## Request

Please, do not hesitate to pull request any new viz or colors or color palettes, or to email request any change (guillaume.noblet@reach-initiative.org or gnoblet@zaclys.net).

## Colors 

Color palettes for REACH, AGORA and IMPACT are available. Functions to access colors and palettes are `cols_initiative()` or `pal_initiative()`. For now, the initiative with the most colors and color palettes is REACH. Feel free to pull requests new AGORA and IMPACT colors.

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

# Get all saved REACH colors, named
cols_reach(unnamed = F)[1:10]

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

# Get all color palettes names
pal_reach(show_palettes = T)
```

## Charts

### Example 1: Bar chart, already REACH themed

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

# Simple bar chart by group with some alpha transparency
bar(df, island, mean_bl, species, percent = FALSE, alpha = 0.6, x_title = "Mean of bill length")

# Using another color palette through `theme_reach()` and changing scale to percent
bar(df, island, mean_bl, species, percent = TRUE, theme = theme_reach(palette = "artichoke_3"))

# Not flipped, with text added, group_title, no y-axis and no bold for legend
bar(df, island, mean_bl, species, group_title = "Species", flip = FALSE, add_text = TRUE, add_text_suffix = "%", percent = FALSE, theme = theme_reach(text_font_face = "plain", axis_y = FALSE))
```

### Example 2: Point chart, already REACH themed

At this stage, `point_reach()` only supports categorical grouping colors with the `group` arg.

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

# Point chart with grouping colors, greater dot size, some transparency, reversed color palette
point(penguins, bill_length_mm, flipper_length_mm, island, alpha = 0.6, size = 3, theme = theme_reach(reverse = TRUE))

# Using another color palettes
point(penguins, bill_length_mm, flipper_length_mm, island, size = 1.5, x_title = "Bill", y_title = "Flipper", title = "Length (mm)", theme = theme_reach(palette = "artichoke_3", text_font_face = , grid_major_x = TRUE, title_position_to_plot = FALSE))
```


### Example 3: Dumbbell plot, REACH themed

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))

# Example, adding a parameter to `theme_reach()` passed on `ggplot2::theme()` to align legend title

dumbbell(df,
  stat,
  setting,
  admin1,
  title = "% of HHs that reported open defecation as sanitation facility",
  group_y_title = "Admin 1",
  group_x_title = "Setting",
  theme = theme_reach(
    legend_position = "bottom",
    legend_direction = "horizontal",
    legend_title_font_face = "bold",
    palette = "primary",
    title_position_to_plot = FALSE,
    legend.title.align = 0.5
  )
) +
  # Change legend title position (could be included as part of the function)
  ggplot2::guides(
    color = ggplot2::guide_legend(title.position = "left"),
    fill =  ggplot2::guide_legend(title.position = "left")
  )
```

### Example 4: donut chart, REACH themed (to used once, not twice)
```{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 = cols_reach("dk_grey"),
  add_text_treshold_display = 5,
  x_title = "Displacement status",
  title = "% of HHs by displacement status",
  theme = theme_reach(legend_reverse = TRUE)
)
```


### 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, REACH themed
```{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",
  theme = theme_reach(
    axis_y = FALSE,
    legend_position = "none"
  )
)
```

### Example 7: lollipop chart
```{r example-lollipop-chart, out.width = "65%", warning = FALSE}
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))

# Make lollipop plot, REACH themed, vertical with 45 degrees angle X-labels
lollipop(df,
  admin1,
  stat,
  arrange = FALSE,
  add_text = FALSE,
  flip = FALSE,
  y_title = "% of HHs",
  x_title = "Admin 1",
  title = "% of HHs that reported having received a humanitarian assistance",
  theme = theme_reach(
    axis_text_x_angle = 45,
    grid_major_y = TRUE,
    grid_major_y_size = 0.2,
    grid_major_x = TRUE,
    grid_minor_y = TRUE
  )
)

# Horizontal, greater point size, arranged by value, no grid, and text labels added
lollipop(df,
  admin1,
  stat,
  arrange = TRUE,
  point_size = 10,
  point_color = cols_reach("main_beige"),
  segment_size = 2,
  add_text = TRUE,
  add_text_suffix = "%",
  y_title = "% of HHs",
  x_title = "Admin 1",
  title = "% of HHs that reported having received a humanitarian assistance in the 12 months prior to the assessment",
  theme = theme_reach(title_position_to_plot = FALSE)
)
```


## Maps

```{r example-map, out.width = "50%"}
# Add indicator layer
# - based on "pretty" classes and title "Proportion (%)"
# - buffer to add a 10% around the bounding box
map <- add_indicator_layer(
  indicator_admin1,
  opn_dfc,
  buffer = 0.1
) +
  # Layout - some defaults - add the map title
  add_layout("% of HH that reported open defecation as sanitation facility") +
  # Admin boundaries as list of shape files (lines) and colors, line widths and labels as vectors
  add_admin_boundaries(
    lines = list(line_admin1, border_admin0, frontier_admin0),
    colors = cols_reach("main_lt_grey", "dk_grey", "black"),
    lwds = c(0.5, 2, 3),
    labels = c("Department", "Country", "Dominican Rep. frontier"),
    title = "Administrative boundaries"
  ) +
  # Add text labels - centered on admin 1 centroids
  add_admin_labels(centroid_admin1, ADM1_FR_UPPER) +
  # Add a compass
  add_compass() +
  # Add a scale bar
  add_scale_bar() +
  # Add credits
  add_credits("Admin. boundaries. : CNIGS \nCoord. system: GCS WGS 1984")
```

```{r map-save, eval = TRUE, include = FALSE, echo = TRUE}
tmap::tmap_save(map,
  "man/figures/README-example-map.png",
  height = 4.5,
  width = 6
)
```

![Once exported with `tmap::tmap_save()`.](man/figures/README-example-map.png)
