180 lines
5.9 KiB
Text
180 lines
5.9 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" alt="" width="120"/>
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> `r desc$Title`
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`visualizeR` proposes some utils to get REACH and AGORA colors, ready-to-go color palettes, and a few visualization functions (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, 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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- [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 color palettes
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- [ ] Add new types of visualization (e.g. dumbbell plot)
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- [ ] 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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## 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 (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 access colors and palettes are `cols_initiative()` or `pal_initiative()`. For now, the initiative with the most colors and color palettes is REACH. Feel free to pull requests new AGORA and IMPACT colors.
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```{r example-colors, eval = TRUE}
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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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# 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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# Get all color palettes names
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pal_reach(show_palettes = T)
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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 example-bar-chart, eval = TRUE}
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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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# 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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# 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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### Example 2: Point chart, already REACH themed
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At this stage, `point_reach()` only supports categorical grouping colors with the `group` arg.
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```{r example-point-chart, eval = TRUE}
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# Simple point chart
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point(penguins, bill_length_mm, flipper_length_mm)
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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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# 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_x = T, title_position_to_plot = FALSE))
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```
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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 have two groups on the x-axis; for instance, IDP and returnee and no NA values.
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```{r example-dumbbell-plot, eval = TRUE}
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# Prepare long data
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df <- tibble::tibble(
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admin1 = rep(c("A", "B", "C", "D", "E", "F", "G", "H"), 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
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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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theme = theme_reach(legend_position = "bottom",
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legend_direction = "horizontal",
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palette = "primary",
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title_position_to_plot = FALSE))
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```
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## Maps
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```{r example-map}
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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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```{r map-save, eval = TRUE, include = FALSE, echo = TRUE}
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tmap::tmap_save(map,
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"man/figures/README-example-map.png",
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height = 4.5,
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width = 6
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)
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```
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