291 lines
8.4 KiB
Text
291 lines
8.4 KiB
Text
---
|
|
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" width="120"/>
|
|
|
|
> `r desc$Title`
|
|
|
|
`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, eval = FALSE}
|
|
# 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 ([gnoblet\@zaclys.net](mailto:gnoblet@zaclys.net){.email}).
|
|
|
|
## Colors
|
|
|
|
Functions to access colors and palettes are `color()` or `palette()`. Feel free to pull request new colors.
|
|
|
|
```{r example-colors, eval = TRUE}
|
|
library(visualizeR)
|
|
|
|
# Get all saved colors, named
|
|
color(unname = F)[1:10]
|
|
|
|
# Extract a color palette as hexadecimal codes and reversed
|
|
palette(palette = "cat_5_main", reversed = TRUE, color_ramp_palette = FALSE)
|
|
|
|
# Get all color palettes names
|
|
palette(show_palettes = TRUE)
|
|
```
|
|
|
|
## Charts
|
|
|
|
### Example 1: Bar chart
|
|
|
|
```{r example-bar-chart, out.width = '65%', eval = TRUE}
|
|
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")
|
|
|
|
# Flipped / Horizontal
|
|
hbar(df, "island", "mean_bl", "species", x_title = "Mean of bill length", title = "Mean of bill length by island and species")
|
|
|
|
# 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)
|
|
|
|
# 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 example-point-chart, out.width = '65%', eval = TRUE}
|
|
# Simple scatterplot
|
|
point(penguins, "bill_length_mm", "flipper_length_mm")
|
|
|
|
# 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)
|
|
|
|
# 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 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))
|
|
|
|
|
|
|
|
|
|
# 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 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 = color('dark_grey'),
|
|
# add_text_treshold_display = 5,
|
|
# x_title = 'Displacement status',
|
|
# title = '% of HHs by displacement status'
|
|
# )
|
|
```
|
|
|
|
### 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
|
|
|
|
```{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"
|
|
# )
|
|
```
|
|
|
|
### Example 7: Lollipop chart
|
|
|
|
```{r example-lollipop-chart, out.width = "65%", warning = FALSE, eval = TRUE}
|
|
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"
|
|
)
|
|
|
|
# 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"
|
|
)
|
|
|
|
# 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",
|
|
order = "grouped_y",
|
|
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"
|
|
)
|
|
|
|
# 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.
|