Update README with grouped lollipop chart examples and roadmap
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131
README.Rmd
131
README.Rmd
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@ -32,12 +32,21 @@ You can install the last version of visualizeR from [GitHub](https://github.com/
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```{r, eval = FALSE}
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# install.packages("devtools")
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devtools::install_github('gnoblet/visualizeR', build_vignettes = TRUE)
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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: - [ ] Full revamp \## Request
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Roadmap is as follows:
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- [ ] Full revamp of core functions (colors, pattern, incl. adding test 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 palettes, or to email request any change ([gnoblet\@zaclys.net](mailto:gnoblet@zaclys.net){.email}).
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@ -52,7 +61,7 @@ library(visualizeR)
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color(unname = F)[1:10]
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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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palette(palette = "cat_5_main", reversed = TRUE, color_ramp_palette = FALSE)
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# Get all color palettes names
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palette(show_palettes = TRUE)
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@ -83,29 +92,29 @@ df_island <- penguins |>
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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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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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# 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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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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# 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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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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# 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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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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### Example 2: Scatterplot
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```{r example-point-chart, out.width = '65%', eval = TRUE}
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# Simple scatterplot
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point(penguins, 'bill_length_mm', 'flipper_length_mm')
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point(penguins, "bill_length_mm", "flipper_length_mm")
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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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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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# 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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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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### Example 3: Dumbbell plot
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@ -116,7 +125,7 @@ Remember to ensure that your data are in the long format and you only have two g
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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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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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@ -140,7 +149,7 @@ df <- tibble::tibble(
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```{r example-donut-plot, out.width = '65%', warning = FALSE}
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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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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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@ -170,12 +179,12 @@ df <- tibble::tibble(
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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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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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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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@ -195,7 +204,7 @@ df <- tibble::tibble(
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### Example 7: Lollipop chart
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```{r example-lollipop-chart, out.width = "65%", warning = FALSE}
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```{r example-lollipop-chart, out.width = "65%", warning = FALSE, eval = TRUE}
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library(tidyr)
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# Prepare long data
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df <- tibble::tibble(
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@ -204,15 +213,79 @@ df <- tibble::tibble(
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) |>
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dplyr::mutate(stat = round(stat, 0))
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# Make lollipop plot, vertical with 45 degrees angle X-labels
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# lollipop(df,
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# admin1,
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# stat,
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# arrange = FALSE,
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# add_text = FALSE,
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# flip = FALSE,
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# y_title = "% of HHs",
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# x_title = "Admin 1",
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# title = "% of HHs that reported having received a humanitarian assistance"
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# )
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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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# 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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# 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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order = "grouped_y",
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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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# 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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## 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 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 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 distinct from dumbbell plots, which typically connect related points on the same line.
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182
README.md
182
README.md
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@ -15,12 +15,22 @@ You can install the last version of visualizeR from
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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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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: - \[ \] Full revamp \## Request
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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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@ -35,13 +45,13 @@ 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 black
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#> "#FFFFFF" "#F5F5F5" "#E3E3E3" "#464647" "#000000"
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#> cat_2_yellow_1 cat_2_yellow_2 cat_2_light_1 cat_2_light_2 cat_2_green_1
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#> "#ffc20a" "#0c7bdc" "#fefe62" "#d35fb7" "#1aff1a"
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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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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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@ -81,7 +91,7 @@ df_island <- penguins |>
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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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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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@ -89,7 +99,7 @@ bar(df, "island", "mean_bl", "species", x_title = "Mean of bill length", title =
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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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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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@ -97,15 +107,15 @@ hbar(df, "island", "mean_bl", "species", x_title = "Mean of bill length", title
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``` r
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# Facetted
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bar(df, "island", "mean_bl", "species", 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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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
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hbar(df = df_island, x = "island", y = "mean_bl", group = "island", 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)
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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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@ -114,7 +124,7 @@ hbar(df = df_island, x = "island", y = "mean_bl", group = "island", title = "Mea
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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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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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@ -122,7 +132,7 @@ point(penguins, "bill_length_mm", "flipper_length_mm")
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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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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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@ -130,7 +140,7 @@ point(penguins, "bill_length_mm", "flipper_length_mm", "island", group_title = "
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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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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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@ -145,7 +155,7 @@ values.
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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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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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@ -155,12 +165,12 @@ df <- tibble::tibble(
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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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# '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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@ -169,7 +179,7 @@ df <- tibble::tibble(
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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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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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@ -178,11 +188,11 @@ df <- tibble::tibble(
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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_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",
|
||||
# title = "% of HHs by displacement status"
|
||||
# x_title = 'Displacement status',
|
||||
# title = '% of HHs by displacement status'
|
||||
# )
|
||||
```
|
||||
|
||||
|
|
@ -190,7 +200,7 @@ df <- tibble::tibble(
|
|||
|
||||
``` r
|
||||
#
|
||||
# waffle(df, status, percentage, x_title = "A caption", title = "A title", subtitle = "A subtitle")
|
||||
# waffle(df, status, percentage, x_title = 'A caption', title = 'A title', subtitle = 'A subtitle')
|
||||
```
|
||||
|
||||
### Example 6: Alluvial chart
|
||||
|
|
@ -199,12 +209,12 @@ df <- tibble::tibble(
|
|||
# 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)
|
||||
)
|
||||
|
||||
|
|
@ -233,15 +243,99 @@ df <- tibble::tibble(
|
|||
) |>
|
||||
dplyr::mutate(stat = round(stat, 0))
|
||||
|
||||
# Make lollipop plot, vertical with 45 degrees angle X-labels
|
||||
# lollipop(df,
|
||||
# admin1,
|
||||
# stat,
|
||||
# arrange = FALSE,
|
||||
# add_text = FALSE,
|
||||
# flip = FALSE,
|
||||
# y_title = "% of HHs",
|
||||
# x_title = "Admin 1",
|
||||
# title = "% of HHs that reported having received a humanitarian assistance"
|
||||
# )
|
||||
# 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"
|
||||
)
|
||||
```
|
||||
|
||||
<img src="man/figures/README-example-lollipop-chart-1.png" width="65%" />
|
||||
|
||||
``` 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"
|
||||
)
|
||||
```
|
||||
|
||||
<img src="man/figures/README-example-lollipop-chart-2.png" width="65%" />
|
||||
|
||||
``` 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"
|
||||
)
|
||||
```
|
||||
|
||||
<img src="man/figures/README-example-lollipop-chart-3.png" width="65%" />
|
||||
|
||||
``` 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"
|
||||
)
|
||||
```
|
||||
|
||||
<img src="man/figures/README-example-lollipop-chart-4.png" width="65%" />
|
||||
|
||||
## 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.
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue