341 lines
9.3 KiB
Markdown
341 lines
9.3 KiB
Markdown
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<!-- README.md is generated from README.Rmd. Please edit that file -->
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# visualizeR <img src="man/figures/logo.png" align="right" width="120"/>
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> What a color! What a viz!
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`visualizeR` proposes some utils to sane colors, ready-to-go color
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palettes, and a few visualization functions.
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## Installation
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You can install the last version of visualizeR from
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[GitHub](https://github.com/) with:
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``` r
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# install.packages("devtools")
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devtools::install_github('gnoblet/visualizeR', build_vignettes = TRUE)
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```
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## Roadmap
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Roadmap is as follows:
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- [ ] Full revamp of core functions (colors, pattern, incl. adding test
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and pre-commit structures)
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- [ ] Add other types of plots:
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- [ ] Dumbell
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- [ ] Waffle
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- [ ] Donut
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- [ ] Alluvial
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## Request
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Please, do not hesitate to pull request any new viz or colors or color
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palettes, or to email request any change (<gnoblet@zaclys.net>).
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## Colors
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Functions to access colors and palettes are `color()` or `palette()`.
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Feel free to pull request new colors.
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``` r
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library(visualizeR)
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# Get all saved colors, named
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color(unname = F)[1:10]
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#> white lighter_grey light_grey dark_grey light_blue_grey
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#> "#FFFFFF" "#F5F5F5" "#E3E3E3" "#464647" "#B3C6D1"
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#> grey black cat_2_yellow_1 cat_2_yellow_2 cat_2_light_1
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#> "#71716F" "#000000" "#ffc20a" "#0c7bdc" "#fefe62"
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# Extract a color palette as hexadecimal codes and reversed
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palette(palette = 'cat_5_main', reversed = TRUE, color_ramp_palette = FALSE)
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#> [1] "#083d77" "#4ecdc4" "#f4c095" "#b47eb3" "#ffd5ff"
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# Get all color palettes names
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palette(show_palettes = TRUE)
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#> [1] "cat_2_yellow" "cat_2_light"
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#> [3] "cat_2_green" "cat_2_blue"
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#> [5] "cat_5_main" "cat_5_ibm"
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#> [7] "cat_3_aquamarine" "cat_3_tol_high_contrast"
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#> [9] "cat_8_tol_adapted" "cat_3_custom_1"
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#> [11] "cat_4_custom_1" "cat_5_custom_1"
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#> [13] "cat_6_custom_1" "div_5_orange_blue"
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#> [15] "div_5_green_purple"
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```
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## Charts
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### Example 1: Bar chart
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``` r
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library(palmerpenguins)
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library(dplyr)
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df <- penguins |>
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group_by(island, species) |>
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summarize(
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mean_bl = mean(bill_length_mm, na.rm = T),
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mean_fl = mean(flipper_length_mm, na.rm = T)
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) |>
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ungroup()
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df_island <- penguins |>
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group_by(island) |>
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summarize(
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mean_bl = mean(bill_length_mm, na.rm = T),
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mean_fl = mean(flipper_length_mm, na.rm = T)
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) |>
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ungroup()
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# Simple bar chart by group with some alpha transparency
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bar(df, 'island', 'mean_bl', 'species', x_title = 'Mean of bill length', title = 'Mean of bill length by island and species')
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```
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<img src="man/figures/README-example-bar-chart-1.png" width="65%" />
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``` r
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# Flipped / Horizontal
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hbar(df, 'island', 'mean_bl', 'species', x_title = 'Mean of bill length', title = 'Mean of bill length by island and species')
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```
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<img src="man/figures/README-example-bar-chart-2.png" width="65%" />
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``` r
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# Facetted
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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)
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```
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<img src="man/figures/README-example-bar-chart-3.png" width="65%" />
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``` r
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# Flipped, with text, smaller width, and caption
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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.")
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```
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<img src="man/figures/README-example-bar-chart-4.png" width="65%" />
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### Example 2: Scatterplot
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``` r
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# Simple scatterplot
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point(penguins, 'bill_length_mm', 'flipper_length_mm')
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```
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<img src="man/figures/README-example-point-chart-1.png" width="65%" />
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``` r
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# Scatterplot with grouping colors, greater dot size, some transparency
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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)
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```
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<img src="man/figures/README-example-point-chart-2.png" width="65%" />
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``` r
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# Facetted scatterplot by island
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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)
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```
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<img src="man/figures/README-example-point-chart-3.png" width="65%" />
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### Example 3: Dumbbell plot
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Remember to ensure that your data are in the long format and you only
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have two groups on the x-axis; for instance, IDP and returnee and no NA
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values.
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``` r
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# Prepare long data
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df <- tibble::tibble(
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admin1 = rep(letters[1:8], 2),
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setting = c(rep(c('Rural', 'Urban'), 4), rep(c('Urban', 'Rural'), 4)),
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stat = rnorm(16, mean = 50, sd = 18)
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) |>
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dplyr::mutate(stat = round(stat, 0))
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# dumbbell(
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# df,
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# 'stat',
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# 'setting',
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# 'admin1',
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# title = '% of HHs that reported open defecation as sanitation facility',
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# group_y_title = 'Admin 1',
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# group_x_title = 'Setting'
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# )
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```
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### Example 4: donut chart
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``` r
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# Some summarized data: % of HHs by displacement status
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df <- tibble::tibble(
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status = c('Displaced', 'Non displaced', 'Returnee', 'Don\'t know/Prefer not to say'),
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percentage = c(18, 65, 12, 3)
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)
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# Donut
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# donut(df,
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# status,
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# percentage,
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# hole_size = 3,
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# add_text_suffix = '%',
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# add_text_color = color('dark_grey'),
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# add_text_treshold_display = 5,
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# x_title = 'Displacement status',
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# title = '% of HHs by displacement status'
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# )
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```
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### Example 5: Waffle chart
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``` r
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#
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# waffle(df, status, percentage, x_title = 'A caption', title = 'A title', subtitle = 'A subtitle')
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```
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### Example 6: Alluvial chart
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``` r
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# Some summarized data: % of HHs by self-reported status of displacement in 2021 and in 2022
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df <- tibble::tibble(
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status_from = c(
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rep('Displaced', 4),
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rep('Non displaced', 4),
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rep('Returnee', 4),
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rep('Dnk/Pnts', 4)
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),
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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'),
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percentage = c(20, 8, 18, 1, 12, 21, 0, 2, 0, 3, 12, 1, 0, 0, 1, 1)
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)
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# Alluvial, here the group is the status for 2021
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# alluvial(df,
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# status_from,
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# status_to,
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# percentage,
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# status_from,
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# from_levels = c("Displaced", "Non displaced", "Returnee", "Dnk/Pnts"),
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# alpha = 0.8,
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# group_title = "Status for 2021",
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# title = "% of HHs by self-reported status from 2021 to 2022"
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# )
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```
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### Example 7: Lollipop chart
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``` r
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library(tidyr)
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# Prepare long data
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df <- tibble::tibble(
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admin1 = replicate(15, sample(letters, 8)) |> t() |> as.data.frame() |> unite("admin1", sep = "") |> dplyr::pull(admin1),
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stat = rnorm(15, mean = 50, sd = 15)
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) |>
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dplyr::mutate(stat = round(stat, 0))
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# Simple vertical lollipop chart
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lollipop(
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df = df,
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x = "admin1",
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y = "stat",
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flip = FALSE,
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dot_size = 3,
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y_title = "% of HHs",
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x_title = "Admin 1",
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title = "% of HHs that received humanitarian assistance"
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)
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```
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<img src="man/figures/README-example-lollipop-chart-1.png" width="65%" />
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``` r
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# Horizontal lollipop chart with custom colors
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hlollipop(
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df = df,
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x = "admin1",
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y = "stat",
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dot_size = 4,
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line_size = 1,
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add_color = color("cat_5_main_2"),
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line_color = color("cat_5_main_4"),
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y_title = "% of HHs",
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x_title = "Admin 1",
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title = "% of HHs that received humanitarian assistance"
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)
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```
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<img src="man/figures/README-example-lollipop-chart-2.png" width="65%" />
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``` r
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# Create data for grouped lollipop - using set.seed for reproducibility
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set.seed(123)
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df_grouped <- tibble::tibble(
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admin1 = rep(c("A", "B", "C", "D", "E", "F"), 2),
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group = rep(c("Group A", "Group B"), each = 6),
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stat = c(rnorm(6, mean = 40, sd = 10), rnorm(6, mean = 60, sd = 10))
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) |>
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dplyr::mutate(stat = round(stat, 0))
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# Grouped lollipop chart with proper side-by-side positioning
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lollipop(
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df = df_grouped,
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x = "admin1",
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y = "stat",
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group = "group",
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dodge_width = 0.8, # Control spacing between grouped lollipops
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dot_size = 3.5,
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line_size = 0.8,
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y_title = "Value",
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x_title = "Category",
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title = "True side-by-side grouped lollipop chart"
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)
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```
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<img src="man/figures/README-example-lollipop-chart-3.png" width="65%" />
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``` r
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# Horizontal grouped lollipop chart
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hlollipop(
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df = df_grouped,
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x = "admin1",
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y = "stat",
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group = "group",
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dodge_width = 0.7, # Narrower spacing for horizontal orientation
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dot_size = 3.5,
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line_size = 0.8,
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y_title = "Category",
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x_title = "Value",
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title = "Horizontal side-by-side grouped lollipop chart"
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)
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```
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<img src="man/figures/README-example-lollipop-chart-4.png" width="65%" />
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## Lollipop Chart Features
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Lollipop charts offer several advantages:
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- Clean visualization of point data with connecting lines to a baseline
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- True side-by-side grouped display for easy comparison between
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categories
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- Each lollipop maintains its position from dot to baseline
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- Customizable appearance with parameters for dot size, line width, and
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colors
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- The `dodge_width` parameter controls spacing between grouped lollipops
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The side-by-side positioning for grouped lollipops makes them visually
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distinct from dumbbell plots, which typically connect related points on
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the same line.
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