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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 setup, include = FALSE}
library(ggplot2)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
#,out.width = "400px"
)
```
# graphhopper-R
<!-- badges: start -->
[](https://CRAN.R-project.org/package=graphhopper)
[](https://github.com/crazycapivara/graphhopper-r/releases/latest)
[](https://travis-ci.org/crazycapivara/graphhopper-r)
[](https://www.repostatus.org/#active)
[](https://github.com/crazycapivara/graphhopper-r/actions)
<!-- badges: end -->
An R Interface to the [GraphHopper](https://www.graphhopper.com/) Directions API
The purpose of {graphhopper} is to provide a quick and easy access to the GraphHopper Directions API. Responses can be converted into simple feature (sf) objects in a convenient way. The package is not a complete wrapper of the API. Currently it mainly supports the API also included in [GraphHopper's Open Source Routing Engine](https://github.com/graphhopper/graphhopper). New features will be added continuously.
Dive into the [documentation here](https://crazycapivara.github.io/graphhopper-r/).
## Installation
Install the release version from [CRAN](https://cran.r-project.org/) with:
```r
install.packages("graphhopper")
```
Install the development version from [GitHub](https://github.com/) with:
```r
# install.packages("remotes")
remotes::install_github("crazycapivara/graphhopper-r")
```
## Get started
Run your own GraphHopper instance (with data of Berlin):
```bash
docker run --name gh --rm -p 8989:8989 -d graphhopper/graphhopper:2.0
```
### Setup
```{r example}
library(graphhopper)
API_URL <- "http://localhost:8989"
# API_URL <- "https://graphhopper.com/api/1/"
gh_set_api_url(API_URL)
info <- gh_get_info()
info$version
info$data_date
gh_bbox(info)
```
If you use the [GraphHopper Web Service](https://graphhopper.com/api/1/) instead of a local instance it is recommended that you store your API key in an environment variable called `GH_API_KEY`. Alternatively, you can pass the key as parameter to the `gh_get_*` functions.
### Route
Get a route in Berlin:
```{r route-example}
start_point <- c(52.592204, 13.414307)
end_point <- c(52.539614, 13.364868)
(route <- gh_get_route(list(start_point, end_point)) %>%
gh_as_sf())
ggplot(data = route) +
geom_sf() +
theme(axis.text.x = element_text(angle = 45))
route$time
via_point <- c(52.545461, 13.435249)
route2 <- gh_get_route(list(start_point, via_point, end_point))
gh_time_distance(route2)
ggplot(data = gh_as_sf(route2)) +
geom_sf() +
theme(axis.text.x = element_text(angle = 45))
gh_points(route2) %>%
head()
gh_instructions(route2)[, c("lon", "lat", "gh_id", "gh_end_id", "text", "distance")] %>%
head()
```
### Shortest path tree
```{r spt-example}
start_point <- c(52.53961, 13.36487)
points_sf <- gh_get_spt(start_point, time_limit = 180) %>%
gh_as_sf() %>%
dplyr::mutate(time = (time / 1000 / 60))
ggplot() +
geom_sf(data = points_sf, aes(colour = time), size = 0.5) +
theme(axis.text.x = element_text(angle = 45))
```
Also query previous nodes to plot the network:
```{r spt-example-lines}
(columns <- gh_spt_columns(
prev_longitude = TRUE,
prev_latitude = TRUE,
prev_time = TRUE
))
lines_sf <- gh_get_spt(end_point, time_limit = 240, columns = columns) %>%
dplyr::mutate(mean_time = ((time + prev_time) / 2) / 1000 / 60) %>%
gh_spt_as_linestrings_sf()
ggplot() +
geom_sf(data = lines_sf, aes(color = mean_time), size = 1) +
theme(axis.text.x = element_text(angle = 45))
```
### Isochrone
```{r isochrone-example}
start_point <- c(52.53961, 13.36487)
isochrone_sf <- gh_get_isochrone(start_point, time_limit = 180) %>%
gh_as_sf()
ggplot() +
geom_sf(data = isochrone_sf, fill = "yellow") +
geom_sf(data = points_sf, aes(colour = time), size = 0.5) +
theme(axis.text.x = element_text(angle = 45))
```