impactR.viz proposes ready-to-go colors and color palettes for IMPACT,
and a few functions for plats and maps.
You can install the development version of impactR.viz from GitHub like so:
# devtools::install_github("impact-initiatives-hppu/impactR.viz")
install.packages("impactR.viz", repos = c('https://impact-initiatives-hppu.r-universe.dev', 'https://cloud.r-project.org'))
Please, do not hesitate to pull request any new viz, colors or color palettes, or to email request any change (gnoblet@zaclys.net).
Branding color palettes for REACH, AGORA and IMPACT are available.
Functions to access colors and palettes are color() or palette().
library(impactR.viz)
#>
#> Attaching package: 'impactR.viz'
#> The following object is masked from 'package:grDevices':
#>
#> palette
# Get all saved REACH colors, named
color(unname = FALSE)[1:10]
#> white light_grey
#> "#FFFFFF" "#E3E3E3"
#> dark_grey black
#> "#464647" "#000000"
#> branding_reach_grey branding_reach_red
#> "#58585A" "#EE5859"
#> branding_reach_light_grey branding_reach_beige
#> "#C7C8CA" "#D2CBB8"
#> branding_agora_bordeaux branding_agora_light_beige
#> "#581522" "#DDD8C4"
# Extract a color palette as hexadecimal codes and reversed
palette(palette = "cat_5_ibm")
#> [1] "#648fff" "#785ef0" "#dc267f" "#fe6100" "#ffb000"
# Get all color palettes names
palette(show_palettes = T)[1:10]
#> [1] "branding_reach" "branding_impact"
#> [3] "branding_agora" "cat_2"
#> [5] "cat_2_light" "cat_2_green"
#> [7] "cat_2_blue" "cat_5_ibm"
#> [9] "cat_3" "cat_3_tol_high_contrast"
library(impactR.viz)
library(palmerpenguins)
#> Warning: package 'palmerpenguins' was built under R version 4.4.1
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
# install.packages("gghighlight")
# library(gghighlight)
df <- penguins |>
count(island) |>
mutate(freq = prop.table(n)*100) |>
arrange(freq)
# Simple bar chart
bar(df, "island", "freq", width = 0.5)
# Vertical one, need some theme tweaking
bar(df, "island", "freq", width = 0.6, add_color = color("branding_agora_bordeaux"), flip = F, theme = theme_reach(axis_y = F, grid_major_x = F, axis_ticks_x = F))
df <- penguins |>
count(island, species, name = "count") |>
mutate(freq = count/sum(count)*100, .by = "island")
# Simple bar chart by group with some alpha transparency
bar(df, "island", "freq", "species", title = "% of penguins by island and species", add_text_expand_limit = 1.2, group_title = "Species")
# Change of palette, change of theming to adapt
bar(df, "island", "freq", "species", group_title = "Species", alpha = 0.6, flip = FALSE, title = "% of penguins by island and species", palette = "cat_3_tol_high_contrast", theme = theme_reach(axis_y = F, grid_major_x = F, axis_ticks_x = F))
# No palette = default to magma viridis
bar(df, "island", "freq", "species", group_title = "Species", width = 0.8, add_text_expand_limit = 1.2, title = "% of penguins by island and species", palette = NULL)
At this stage, [point()] only supports categorical grouping colors
with the group arg.
# Simple point chart
point(penguins, "bill_length_mm", "flipper_length_mm", title = "What a good test title!")
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).
# Point chart with grouping colors, different palette, greater dot size, some transparency, reversed color palette
point(penguins, "bill_length_mm", "flipper_length_mm", "island", palette= "cat_8_wong", group_title = "Islands", alpha = 0.7, size = 3)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).
