# 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](https://github.com/) with: ``` r # 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 - [ ] 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 ( or ). ## Example 1: extracting 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 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" ``` ## Example 2: Bar chart, already REACH themed ``` r 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 bar_reach(df, mean_bl, island, species, percent = FALSE, x_title = "Mean of bill length") ``` ``` r # Using another color palette bar_reach(df, mean_bl, island, species, percent = FALSE, palette = "artichoke_3", legend_rev = TRUE) ``` ## Example 3: Point chart, already REACH themed At this stage, `point_reach()` only supports categorical grouping colors with the `group` arg. ``` r # Simple point chart point_reach(penguins, bill_length_mm, flipper_length_mm) ``` ``` r # Point chart with grouping colors, greater dot size, some transparency, reversed color palette point_reach(penguins, bill_length_mm, flipper_length_mm, island, alpha = 0.6, size = 3, reverse = TRUE) ``` ``` r # Using another color palettes point_reach(penguins, bill_length_mm, flipper_length_mm, island, palette = "artichoke_3") ```