218 lines
6.4 KiB
Text
218 lines
6.4 KiB
Text
---
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output: github_document
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---
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<!-- README.md is generated from README.Rmd. Please edit that file -->
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```{r, include = FALSE}
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knitr::opts_chunk$set(
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collapse = TRUE,
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comment = "#>",
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fig.path = "man/figures/README-",
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out.width = "100%",
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warning = FALSE,
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message = FALSE,
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dpi = 300,
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dev.args = list(type = "cairo")
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)
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desc <- read.dcf("DESCRIPTION")
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desc <- setNames(as.list(desc), colnames(desc))
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```
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# `r desc$Package` <img src="man/figures/logo.png" align="right" width="120"/>
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> `r desc$Title`
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`visualizeR` proposes some utils to sane colors, ready-to-go color palettes, and a few visualization functions.
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## Installation
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You can install the last version of visualizeR from [GitHub](https://github.com/) with:
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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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```
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## Roadmap
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Roadmap is as follows: - [ ] Full revamp \## 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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## Colors
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Functions to access colors and palettes are `color()` or `palette()`. Feel free to pull request new colors.
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```{r example-colors, eval = TRUE}
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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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# 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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# Get all color palettes names
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palette(show_palettes = TRUE)
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```
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## Charts
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### Example 1: Bar chart
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```{r example-bar-chart, out.width = "65%", eval = TRUE}
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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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# 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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# 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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# 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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```
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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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# 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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# 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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### Example 3: Dumbbell plot
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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.
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```{r example-dumbbell-plot, out.width = "65%", eval = TRUE}
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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 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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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 example-waffle-plot, out.width = "65%", warning = FALSE}
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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 example-alluvial-plot, out.width = "65%", warning = FALSE}
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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 example-lollipop-chart, out.width = "65%", warning = FALSE}
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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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# 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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```
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