From a4f398ab3da182f1d654e8cd3e26ab44c4827b49 Mon Sep 17 00:00:00 2001 From: gnoblet Date: Tue, 1 Jul 2025 19:48:13 +0200 Subject: [PATCH] add caption example --- README.Rmd | 68 +++++++++++++++++++++++++++--------------------------- 1 file changed, 34 insertions(+), 34 deletions(-) diff --git a/README.Rmd b/README.Rmd index fc9973d..7a8a6dc 100644 --- a/README.Rmd +++ b/README.Rmd @@ -32,7 +32,7 @@ You can install the last version of visualizeR from [GitHub](https://github.com/ ```{r, eval = FALSE} # install.packages("devtools") -devtools::install_github("gnoblet/visualizeR", build_vignettes = TRUE) +devtools::install_github('gnoblet/visualizeR', build_vignettes = TRUE) ``` ## Roadmap @@ -52,7 +52,7 @@ library(visualizeR) 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) +palette(palette = 'cat_5_main', reversed = TRUE, color_ramp_palette = FALSE) # Get all color palettes names palette(show_palettes = TRUE) @@ -62,7 +62,7 @@ palette(show_palettes = TRUE) ### Example 1: Bar chart -```{r example-bar-chart, out.width = "65%", eval = TRUE} +```{r example-bar-chart, out.width = '65%', eval = TRUE} library(palmerpenguins) library(dplyr) @@ -83,40 +83,40 @@ 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') # 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') # Facetted -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) +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 -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) +# 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} +```{r example-point-chart, out.width = '65%', eval = TRUE} # Simple scatterplot -point(penguins, "bill_length_mm", "flipper_length_mm") +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) +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) +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} +```{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)), + 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)) @@ -126,21 +126,21 @@ df <- tibble::tibble( # dumbbell( # df, -# "stat", -# "setting", -# "admin1", -# title = "% of HHs that reported open defecation as sanitation facility", -# group_y_title = "Admin 1", -# group_x_title = "Setting" +# '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} +```{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"), + status = c('Displaced', 'Non displaced', 'Returnee', 'Don\'t know/Prefer not to say'), percentage = c(18, 65, 12, 3) ) @@ -149,33 +149,33 @@ df <- tibble::tibble( # status, # percentage, # hole_size = 3, -# add_text_suffix = "%", -# add_text_color = color("dark_grey"), +# add_text_suffix = '%', +# add_text_color = color('dark_grey'), # add_text_treshold_display = 5, -# x_title = "Displacement status", -# title = "% of HHs by displacement status" +# x_title = 'Displacement status', +# title = '% of HHs by displacement status' # ) ``` ### Example 5: Waffle chart -```{r example-waffle-plot, out.width = "65%", warning = FALSE} +```{r example-waffle-plot, out.width = '65%', warning = FALSE} # -# 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 -```{r example-alluvial-plot, out.width = "65%", warning = FALSE} +```{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) + 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) )