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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
library(sf, quietly = TRUE)
set.seed(2023)
```
# ggmapinset
<!-- badges: start -->
[](https://cidm-ph.r-universe.dev)
[](https://CRAN.R-project.org/package=ggmapinset)
[](https://github.com/cidm-ph/ggmapinset/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->
Add zoomed inset panels to your ggplot maps.
## Installation
You can install ggmapinset like so:
``` r
# CRAN release
install.packages('ggmapinset')
# development version
install.packages('ggmapinset', repos = c('https://cidm-ph.r-universe.dev', 'https://cloud.r-project.org'))
```
## Replacing 'ggplot2' sf layers
`{ggmapinset}` provides drop-in replacements for each of the `{sf}`-related layers
from `{ggplot2}`:
| ‘ggplot2’ function | ‘ggmapinset’ replacement |
|:------------------------|:------------------------------|
| `geom_sf()` | `geom_sf_inset()` |
| `geom_sf_text()` | `geom_sf_text_inset()` |
| `geom_sf_label()` | `geom_sf_label_inset()` |
| `stat_sf()` | `stat_sf_inset()` |
| `stat_sf_coordinates()` | `stat_sf_coordinates_inset()` |
| `coord_sf()` | `coord_sf_inset()` |
The replacements work the same as their normal versions but copy, zoom, and clip
the layers to make the inset work. The stats can be used to add inset support to
geoms from third-party packages. For extension developers, tools are provided to
make `{sf}`-based layers inset-aware (see `{ggautomap}` for examples).
## Example
This example adds an inset to the first example from `ggplot2::geom_sf()`.
The inset area is defined as a circle centred on the named county, with radius
50 miles. The inset is enlarged by a factor of 2 and shifted to an empty part
of the map.
```{r example, fig.width=7, fig.height=4}
library(ggmapinset)
library(ggplot2)
# load the North Carolina map example shipped with sf
nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)
# find the centroid of the specified county
inset_centre <- sf::st_centroid(sf::st_geometry(nc)[nc$NAME == "Yancey"])
# pick some counties to label
labelled_counties <- sample(nc$NAME, 10)
# the basic ggplot example:
base_plot <- ggplot(nc) +
geom_sf(aes(fill = AREA)) +
geom_sf_label(aes(label = NAME), data = ~dplyr::filter(.x, NAME %in% labelled_counties)) +
coord_sf()
# the same plot with an inset zooming in on one area:
ggplot(nc) +
# replace sf layers with their `_inset` versions
geom_sf_inset(aes(fill = AREA)) +
# add the inset frame (the two circles with the connecting lines)
geom_inset_frame() +
geom_sf_label_inset(aes(label = NAME), data = ~dplyr::filter(.x, NAME %in% labelled_counties)) +
# configure the inset in the coordinate system so that all layers can see it
coord_sf_inset(inset = configure_inset(centre = inset_centre, scale = 2, units = "mi",
translation = c(70, -180), radius = 50))
```
For more information, see the [online documentation](https://cidm-ph.github.io/ggmapinset/) and `vignette("ggmapinset")`.
## Limitations
The package implements insets by duplicating and transforming spatial data within a single
coordinate system. That means that you don't get separate grid lines for the inset panel
and, more significantly, the inset can be distorted by the projection of the base map if
you move it too far. This tends not to be a problem in practice if you choose a
coordinate system that isn't too distorted over the area of the base map.
## Alternatives
Other packages implement different approaches:
* [`facet_zoom()`](https://ggforce.data-imaginist.com/reference/facet_zoom.html)
from [ggforce](https://cran.r-project.org/package=ggforce)
* [`geom_magnify()`](https://hughjonesd.github.io/ggmagnify/reference/geom_magnify.html)
from [ggmagnify](https://github.com/hughjonesd/ggmagnify/)
* [`mf_inset_on()`](https://riatelab.github.io/mapsf/reference/mf_inset_on.html)
from [mapsf](https://cran.r-project.org/package=mapsf)
There are also several articles describing more manual ways to achieve different insets:
* https://www.datawim.com/post/inset-map-in-r/
* https://dieghernan.github.io/202203_insetmaps/
* https://upgo.lab.mcgill.ca/2019/12/13/making-beautiful-maps/