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Add code coverage reporting and update README badges

Browse source 
- Integrate codecov for test coverage reporting - Add codecov and
R-CMD-check badges to README - Update roadmap and documentation to
reflect coverage goals - Add codecov.yml configuration file - Enable
tests in tests/testthat.R - Update example images in man/figures
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gnoblet 2025-07-02 13:20:39 +02:00
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1
DESCRIPTION
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@ -29,6 +29,7 @@ Imports:
viridisLite,
waffle
Suggests:
covr,
knitr,
rio,
rmarkdown,

13
README.Rmd
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@ -22,9 +22,14 @@ desc <- setNames(as.list(desc), colnames(desc))
# `r desc$Package` <img src="man/figures/logo.png" align="right" width="120"/>
<!-- badges: start -->
[![R-CMD-check](https://github.com/gnoblet/visualizeR/actions/workflows/R-CMD-check.yml/badge.svg)](https://github.com/gnoblet/visualizeR/actions/workflows/R-CMD-check.yml)
[![Codecov test coverage](https://codecov.io/gh/gnoblet/visualizeR/branch/main/graph/badge.svg)](https://app.codecov.io/gh/gnoblet/visualizeR?branch=main)
<!-- badges: end -->
> `r desc$Title`
`visualizeR` proposes some utils to sane colors, ready-to-go color palettes, and a few visualization functions.
`visualizeR` proposes some utils to sane colors, ready-to-go color palettes, and a few visualization functions. The package is thoroughly tested with comprehensive code coverage.
## Installation
@ -40,6 +45,8 @@ devtools::install_github("gnoblet/visualizeR", build_vignettes = TRUE)
Roadmap is as follows:
- [ ] Full revamp of core functions (colors, pattern, incl. adding test and pre-commit structures)
- [x] Add test coverage reporting via codecov
- [ ] Maintain >80% test coverage across all functions
- [ ] Add other types of plots:
- [ ] Dumbell
- [ ] Waffle
@ -50,6 +57,10 @@ Roadmap is as follows:
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}).
## Code Coverage
`visualizeR` uses [codecov](https://codecov.io/) for test coverage reporting. You can see the current coverage status by clicking on the codecov badge at the top of this README. We aim to maintain high test coverage to ensure code reliability and stability.
## Colors
Functions to access colors and palettes are `color()` or `palette()`. Feel free to pull request new colors.

146
README.md
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@ -1,29 +1,39 @@
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
# visualizeR <img src="man/figures/logo.png" align="right" width="120"/>
<!-- badges: start -->
[![R-CMD-check](https://github.com/gnoblet/visualizeR/actions/workflows/R-CMD-check.yml/badge.svg)](https://github.com/gnoblet/visualizeR/actions/workflows/R-CMD-check.yml)
[![Codecov test coverage](https://codecov.io/gh/gnoblet/visualizeR/branch/main/graph/badge.svg)](https://app.codecov.io/gh/gnoblet/visualizeR?branch=main)
<!-- badges: end -->
> What a color! What a viz!
`visualizeR` proposes some utils to sane colors, ready-to-go color
palettes, and a few visualization functions.
`visualizeR` proposes some utils to sane colors, ready-to-go color palettes, and a few visualization functions. The package is thoroughly tested with comprehensive code coverage.
## Installation
You can install the last version of visualizeR from
[GitHub](https://github.com/) with:
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)
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)
- [ ] Full revamp of core functions (colors, pattern, incl. adding test and pre-commit structures)
- [x] Add test coverage reporting via codecov
- [ ] Maintain >80% test coverage across all functions
- [ ] Add other types of plots:
- [ ] Dumbell
- [ ] Waffle
@ -32,13 +42,16 @@ Roadmap is as follows:
## 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>).
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}).
## Code Coverage
`visualizeR` uses [codecov](https://codecov.io/) for test coverage reporting. You can see the current coverage status by clicking on the codecov badge at the top of this README. We aim to maintain high test coverage to ensure code reliability and stability.
## Colors
Functions to access colors and palettes are `color()` or `palette()`.
Feel free to pull request new colors.
Functions to access colors and palettes are `color()` or `palette()`. Feel free to pull request new colors.
``` r
library(visualizeR)
@ -51,7 +64,7 @@ color(unname = F)[1:10]
#> "#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)
palette(palette = "cat_5_main", reversed = TRUE, color_ramp_palette = FALSE)
#> [1] "#083d77" "#4ecdc4" "#f4c095" "#b47eb3" "#ffd5ff"
# Get all color palettes names
@ -70,6 +83,7 @@ palette(show_palettes = TRUE)
### Example 1: Bar chart
``` r
library(palmerpenguins)
library(dplyr)
@ -91,71 +105,92 @@ df_island <- penguins |>
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')
bar(df, "island", "mean_bl", "species", x_title = "Mean of bill length", title = "Mean of bill length by island and species")
```
<img src="man/figures/README-example-bar-chart-1.png" width="65%" />
<div class="figure">
<img src="man/figures/README-example-bar-chart-1.png" alt="plot of chunk example-bar-chart" width="65%" />
<p class="caption">plot of chunk example-bar-chart</p>
</div>
``` r
# Flipped / Horizontal
hbar(df, 'island', 'mean_bl', 'species', x_title = 'Mean of bill length', title = 'Mean of bill length by island and species')
hbar(df, "island", "mean_bl", "species", x_title = "Mean of bill length", title = "Mean of bill length by island and species")
```
<img src="man/figures/README-example-bar-chart-2.png" width="65%" />
<div class="figure">
<img src="man/figures/README-example-bar-chart-2.png" alt="plot of chunk example-bar-chart" width="65%" />
<p class="caption">plot of chunk example-bar-chart</p>
</div>
``` 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)
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)
```
<img src="man/figures/README-example-bar-chart-3.png" width="65%" />
<div class="figure">
<img src="man/figures/README-example-bar-chart-3.png" alt="plot of chunk example-bar-chart" width="65%" />
<p class="caption">plot of chunk example-bar-chart</p>
</div>
``` 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.")
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.")
```
<img src="man/figures/README-example-bar-chart-4.png" width="65%" />
<div class="figure">
<img src="man/figures/README-example-bar-chart-4.png" alt="plot of chunk example-bar-chart" width="65%" />
<p class="caption">plot of chunk example-bar-chart</p>
</div>
### Example 2: Scatterplot
``` r
# Simple scatterplot
point(penguins, 'bill_length_mm', 'flipper_length_mm')
point(penguins, "bill_length_mm", "flipper_length_mm")
```
<img src="man/figures/README-example-point-chart-1.png" width="65%" />
<div class="figure">
<img src="man/figures/README-example-point-chart-1.png" alt="plot of chunk example-point-chart" width="65%" />
<p class="caption">plot of chunk example-point-chart</p>
</div>
``` 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)
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)
```
<img src="man/figures/README-example-point-chart-2.png" width="65%" />
<div class="figure">
<img src="man/figures/README-example-point-chart-2.png" alt="plot of chunk example-point-chart" width="65%" />
<p class="caption">plot of chunk example-point-chart</p>
</div>
``` 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)
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)
```
<img src="man/figures/README-example-point-chart-3.png" width="65%" />
<div class="figure">
<img src="man/figures/README-example-point-chart-3.png" alt="plot of chunk example-point-chart" width="65%" />
<p class="caption">plot of chunk example-point-chart</p>
</div>
### 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.
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)),
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))
@ -176,10 +211,11 @@ df <- tibble::tibble(
### 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'),
status = c("Displaced", "Non displaced", "Returnee", "Don't know/Prefer not to say"),
percentage = c(18, 65, 12, 3)
)
@ -198,6 +234,7 @@ df <- tibble::tibble(
### Example 5: Waffle chart
``` r
#
# waffle(df, status, percentage, x_title = 'A caption', title = 'A title', subtitle = 'A subtitle')
@ -205,16 +242,17 @@ df <- tibble::tibble(
### 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)
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'),
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)
)
@ -234,6 +272,7 @@ df <- tibble::tibble(
### Example 7: Lollipop chart
``` r
library(tidyr)
# Prepare long data
@ -256,7 +295,10 @@ lollipop(
)
```
<img src="man/figures/README-example-lollipop-chart-1.png" width="65%" />
<div class="figure">
<img src="man/figures/README-example-lollipop-chart-1.png" alt="plot of chunk example-lollipop-chart" width="65%" />
<p class="caption">plot of chunk example-lollipop-chart</p>
</div>
``` r
@ -275,7 +317,10 @@ hlollipop(
)
```
<img src="man/figures/README-example-lollipop-chart-2.png" width="65%" />
<div class="figure">
<img src="man/figures/README-example-lollipop-chart-2.png" alt="plot of chunk example-lollipop-chart" width="65%" />
<p class="caption">plot of chunk example-lollipop-chart</p>
</div>
``` r
@ -294,7 +339,8 @@ lollipop(
x = "admin1",
y = "stat",
group = "group",
dodge_width = 0.8, # Control spacing between grouped lollipops
order = "grouped_y",
dodge_width = 0.8, # Control spacing between grouped lollipops
dot_size = 3.5,
line_size = 0.8,
y_title = "Value",
@ -303,7 +349,10 @@ lollipop(
)
```
<img src="man/figures/README-example-lollipop-chart-3.png" width="65%" />
<div class="figure">
<img src="man/figures/README-example-lollipop-chart-3.png" alt="plot of chunk example-lollipop-chart" width="65%" />
<p class="caption">plot of chunk example-lollipop-chart</p>
</div>
``` r
@ -313,7 +362,7 @@ hlollipop(
x = "admin1",
y = "stat",
group = "group",
dodge_width = 0.7, # Narrower spacing for horizontal orientation
dodge_width = 0.7, # Narrower spacing for horizontal orientation
dot_size = 3.5,
line_size = 0.8,
y_title = "Category",
@ -322,20 +371,19 @@ hlollipop(
)
```
<img src="man/figures/README-example-lollipop-chart-4.png" width="65%" />
<div class="figure">
<img src="man/figures/README-example-lollipop-chart-4.png" alt="plot of chunk example-lollipop-chart" width="65%" />
<p class="caption">plot of chunk example-lollipop-chart</p>
</div>
## 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
- 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
- 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.
The side-by-side positioning for grouped lollipops makes them visually distinct from dumbbell plots, which typically connect related points on the same line.

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codecov.yml Normal file
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@ -0,0 +1,14 @@
comment: false
coverage:
status:
project:
default:
target: auto
threshold: 1%
informational: true
patch:
default:
target: auto
threshold: 1%
informational: true

4
inst/WORDLIST
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@ -1,7 +1,10 @@
aut
CMD
codecov
Codecov
coercible
Config
covr
cre
Customizable
donut
@ -27,6 +30,7 @@ knitr
LazyData
Noblet
pre
README
rio
rlang
rmarkdown

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20
tests/testthat.R
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@ -1,12 +1,12 @@
# # This file is part of the standard setup for testthat.
# # It is recommended that you do not modify it.
# #
# # Where should you do additional test configuration?
# # Learn more about the roles of various files in:
# # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
# # * https://testthat.r-lib.org/articles/special-files.html
# This file is part of the standard setup for testthat.
# It is recommended that you do not modify it.
#
# Where should you do additional test configuration?
# Learn more about the roles of various files in:
# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
# * https://testthat.r-lib.org/articles/special-files.html
# library(testthat)
# library(visualizeR)
library(testthat)
library(visualizeR)
# test_check("visualizeR")
test_check("visualizeR")