289 lines
8.5 KiB
Text
289 lines
8.5 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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<!-- badges: start -->
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[](https://github.com/gnoblet/visualizeR/actions/workflows/R-CMD-check.yml)
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[](https://app.codecov.io/gh/gnoblet/visualizeR?branch=main)
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<!-- badges: end -->
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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. The package is thoroughly tested with comprehensive code coverage.
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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:
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- [ ] Full revamp of core functions (colors, pattern, incl. adding test and pre-commit structures)
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- [x] Add test coverage reporting via codecov
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- [ ] Maintain >80% test coverage across all functions
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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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- [ ] Option for tag with css code + for titles/subtitles/captions
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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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## Code Coverage
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`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.
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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", 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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```
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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, 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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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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# 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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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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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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