311 lines
11 KiB
Markdown
311 lines
11 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" alt="" width="120"/>
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> What a color! What a viz!
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`visualizeR` proposes some utils to get REACH and AGORA colors,
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ready-to-go color palettes, and a few visualization functions
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(horizontal hist graph for instance).
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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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- [x] Add IMPACT’s colors
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- [x] Add all color palettes from the internal documentation
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- [ ] There remains to be added more-than-7-color palettes and black
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color palettes
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- [x] Add new types of visualization (e.g. dumbbell plot, lollipop
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plot, etc.)
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- [x] Use examples
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- [ ] Add some ease-map functions
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- [ ] Add some interactive functions (maps and graphs)
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- [ ] Consolidate and make errors transparent
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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
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(<guillaume.noblet@reach-initiative.org> or <gnoblet@zaclys.net>).
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## Colors
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Color palettes for REACH, AGORA and IMPACT are available. Functions to
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access colors and palettes are `cols_initiative()` or
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`pal_initiative()`. For now, the initiative with the most colors and
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color palettes is REACH. Feel free to pull requests new AGORA and IMPACT
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colors.
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``` r
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library(visualizeR)
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# Get all saved REACH colors, named
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cols_reach(unnamed = F)[1:10]
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#> white black main_grey main_red main_lt_grey main_beige
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#> "#FFFFFF" "#000000" "#58585A" "#EE5859" "#C7C8CA" "#D2CBB8"
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#> iroise_1 iroise_2 iroise_3 iroise_4
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#> "#DFECEF" "#B1D7E0" "#699DA3" "#236A7A"
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# Extract a color palette as hexadecimal codes and reversed
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pal_reach(palette = "main", reversed = TRUE, color_ramp_palette = FALSE)
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#> [1] "#58585A" "#EE5859" "#C7C8CA" "#D2CBB8"
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# Get all color palettes names
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pal_reach(show_palettes = T)
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#> [1] "main" "primary" "secondary" "two_dots"
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#> [5] "two_dots_flashy" "red_main" "red_main_5" "red_alt"
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#> [9] "red_alt_5" "iroise" "iroise_5" "discrete_6"
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#> [13] "red_2" "red_3" "red_4" "red_5"
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#> [17] "red_6" "red_7" "green_2" "green_3"
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#> [21] "green_4" "green_5" "green_6" "green_7"
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#> [25] "artichoke_2" "artichoke_3" "artichoke_4" "artichoke_5"
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#> [29] "artichoke_6" "artichoke_7" "blue_2" "blue_3"
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#> [33] "blue_4" "blue_5" "blue_6" "blue_7"
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```
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## Charts
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### Example 1: Bar chart, already REACH themed
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``` r
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library(visualizeR)
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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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ungroup()
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# Simple bar chart by group with some alpha transparency
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bar(df, island, mean_bl, species, percent = FALSE, alpha = 0.6, x_title = "Mean of bill length")
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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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# Using another color palette through `theme_reach()` and changing scale to percent
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bar(df, island,mean_bl, species, percent = TRUE, theme = theme_reach(palette = "artichoke_3"))
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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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# Not flipped, with text added, group_title, no y-axis and no bold for legend
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bar(df, island, mean_bl, species, group_title = "Species", flip = FALSE, add_text = TRUE, add_text_suffix = "%", percent = FALSE, theme = theme_reach(text_font_face = "plain", axis_y = 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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### Example 2: Point chart, already REACH themed
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At this stage, `point_reach()` only supports categorical grouping colors
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with the `group` arg.
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``` r
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# Simple point chart
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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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# Point chart with grouping colors, greater dot size, some transparency, reversed color palette
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point(penguins, bill_length_mm, flipper_length_mm, island, alpha = 0.6, size = 3, theme = theme_reach(reverse = TRUE))
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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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# Using another color palettes
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point(penguins, bill_length_mm, flipper_length_mm, island, size = 1.5, x_title = "Bill", y_title = "Flipper", title = "Length (mm)", theme = theme_reach(palette = "artichoke_3", text_font_face = , grid_major_x = TRUE, title_position_to_plot = 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, REACH themed
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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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# Example, adding a parameter to `theme_reach()` passed on `ggplot2::theme()` to align legend title
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dumbbell(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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theme = theme_reach(legend_position = "bottom",
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legend_direction = "horizontal",
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legend_title_font_face = "bold",
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palette = "primary",
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title_position_to_plot = FALSE,
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legend.title.align = 0.5)) +
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# Change legend title position (could be included as part of the function)
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ggplot2::guides(
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color = ggplot2::guide_legend(title.position = "left"),
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fill = ggplot2::guide_legend(title.position = "left")
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)
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```
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<img src="man/figures/README-example-dumbbell-plot-1.png" width="65%" />
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### Example 4: donut chart, REACH themed (to used moderately)
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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 = cols_reach("dk_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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theme = theme_reach(legend_reverse = TRUE))
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```
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<img src="man/figures/README-example-donut-plot-1.png" width="65%" />
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### Example 5: alluvial chart, REACH themed
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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(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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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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theme = theme_reach(
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axis_y = FALSE,
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legend_position = "none"))
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```
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<img src="man/figures/README-example-alluvial-plot-1.png" width="65%" />
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### Example 6: 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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dplyr::mutate(stat = round(stat, 0))
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# Make lollipop plot, REACH themed, 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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theme = theme_reach(axis_text_x_angle = 45,
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grid_major_y = TRUE,
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grid_major_y_size = 0.2,
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grid_major_x = TRUE,
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grid_minor_y = TRUE))
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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, greater point size, arranged by value, no grid, and text labels added
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lollipop(df,
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admin1,
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stat,
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arrange = TRUE,
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point_size = 10,
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point_color = cols_reach("main_beige"),
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segment_size = 2,
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add_text = TRUE,
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add_text_suffix = "%",
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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 in the 12 months prior to the assessment",
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theme = theme_reach(title_position_to_plot = FALSE))
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```
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<img src="man/figures/README-example-lollipop-chart-2.png" width="65%" />
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## Maps
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``` r
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# Add indicator layer
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# - based on "pretty" classes and title "Proportion (%)"
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# - buffer to add a 10% around the bounding box
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map <- add_indicator_layer(
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indicator_admin1,
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opn_dfc,
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buffer = 0.1) +
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# Layout - some defaults - add the map title
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add_layout("% of HH that reported open defecation as sanitation facility") +
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# Admin boundaries as list of shape files (lines) and colors, line widths and labels as vectors
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add_admin_boundaries(
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lines = list(line_admin1, border_admin0, frontier_admin0),
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colors = cols_reach("main_lt_grey", "dk_grey", "black"),
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lwds = c(0.5, 2, 3),
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labels = c("Department", "Country", "Dominican Rep. frontier"),
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title = "Administrative boundaries") +
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# Add text labels - centered on admin 1 centroids
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add_admin_labels(centroid_admin1, ADM1_FR_UPPER) +
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# Add a compass
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add_compass() +
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# Add a scale bar
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add_scale_bar() +
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# Add credits
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add_credits("Admin. boundaries. : CNIGS \nCoord. system: GCS WGS 1984")
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```
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