# visualizeR 
> What a color! What a viz!
`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
# 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 ().
## Colors
Functions to access colors and palettes are `color()` or `palette()`.
Feel free to pull request new colors.
``` r
library(visualizeR)
# Get all saved colors, named
color(unname = F)[1:10]
#> white lighter_grey light_grey dark_grey light_blue_grey
#> "#FFFFFF" "#F5F5F5" "#E3E3E3" "#464647" "#B3C6D1"
#> grey black cat_2_yellow_1 cat_2_yellow_2 cat_2_light_1
#> "#71716F" "#000000" "#ffc20a" "#0c7bdc" "#fefe62"
# Extract a color palette as hexadecimal codes and reversed
palette(palette = 'cat_5_main', reversed = TRUE, color_ramp_palette = FALSE)
#> [1] "#083d77" "#4ecdc4" "#f4c095" "#b47eb3" "#ffd5ff"
# Get all color palettes names
palette(show_palettes = TRUE)
#> [1] "cat_2_yellow" "cat_2_light"
#> [3] "cat_2_green" "cat_2_blue"
#> [5] "cat_5_main" "cat_5_ibm"
#> [7] "cat_3_aquamarine" "cat_3_tol_high_contrast"
#> [9] "cat_8_tol_adapted" "cat_3_custom_1"
#> [11] "cat_4_custom_1" "cat_5_custom_1"
#> [13] "cat_6_custom_1" "div_5_orange_blue"
#> [15] "div_5_green_purple"
```
## Charts
### Example 1: Bar chart
``` r
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')
```
``` r
# Flipped / Horizontal
hbar(df, 'island', 'mean_bl', 'species', x_title = 'Mean of bill length', title = 'Mean of bill length by island and species')
```
``` r
# 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)
```
``` r
# 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
# Simple scatterplot
point(penguins, 'bill_length_mm', 'flipper_length_mm')
```
``` r
# 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)
```
``` r
# 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
# 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
# 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
#
# waffle(df, status, percentage, x_title = 'A caption', title = 'A title', subtitle = 'A subtitle')
```
### Example 6: Alluvial chart
``` r
# 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
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"
)
```
``` r
# 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"
)
```
``` r
# 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",
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"
)
```
``` r
# 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.