# What a color! What a viz! • visualizeR
It basically provides colors as hex codes, color palettes, and some viz functions (graphs and maps).

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# visualizeR

> What a color! What a viz!

`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 with:

    # install.packages("devtools")
    devtools::install_github("gnoblet/visualizeR", build_vignettes = TRUE)

## Roadmap

Roadmap is as follows:

- Add IMPACT’s colors
- Add all color palettes from the internal documentation
- There remains to be added more-than-7-color palettes and black color palettes
- Add new types of visualization (e.g. dumbbell plot)
- Use examples
- Add some ease-map functions
- Add some interactive functions (maps and graphs)

## 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.

    library(visualizeR)

    # Get all saved REACH colors, named
    cols_reach(unnamed = F)[1:10]
    #>        white        black    main_grey     main_red main_lt_grey   main_beige
    #>    "#FFFFFF"    "#000000"    "#58585A"    "#EE5859"    "#C7C8CA"    "#D2CBB8"
    #>     iroise_1     iroise_2     iroise_3     iroise_4
    #>    "#DFECEF"    "#B1D7E0"    "#699DA3"    "#236A7A"

    # Extract a color palette as hexadecimal codes and reversed
    pal_reach(palette = "main", reversed = TRUE, color_ramp_palette = FALSE)
    #> [1] "#58585A" "#EE5859" "#C7C8CA" "#D2CBB8"

    # Get all color palettes names
    pal_reach(show_palettes = T)
    #>  [1] "main"            "primary"         "secondary"       "two_dots"
    #>  [5] "two_dots_flashy" "red_main"        "red_main_5"      "red_alt"
    #>  [9] "red_alt_5"       "iroise"          "iroise_5"        "discrete_6"
    #> [13] "red_2"           "red_3"           "red_4"           "red_5"
    #> [17] "red_6"           "red_7"           "green_2"         "green_3"
    #> [21] "green_4"         "green_5"         "green_6"         "green_7"
    #> [25] "artichoke_2"     "artichoke_3"     "artichoke_4"     "artichoke_5"
    #> [29] "artichoke_6"     "artichoke_7"     "blue_2"          "blue_3"
    #> [33] "blue_4"          "blue_5"          "blue_6"          "blue_7"

## Charts

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

    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.

    # 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_x = T))

## Maps

    # 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")

Once exported with `tmap::tmap_save()`.

## Links

- Browse source code

## License

- Full license
- GPL (>= 3)

## Citation

- Citing visualizeR

## Developers

- Noblet Guillaume
  Author, maintainer