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README.Rmd
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README.Rmd
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---
title: "rinat: Access iNaturalist data with R"
author: Edmund Hart, Stéphane Guillou
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE)
```
[](https://travis-ci.org/ropensci/rinat)
[](https://ci.appveyor.com/project/sckott/rinat/branch/master)
[](https://codecov.io/github/ropensci/rinat?branch=master)
[](https://CRAN.R-project.org/package=rinat)
R wrapper for iNaturalist APIs for accessing the observations. The detailed documentation of the API is available on the [iNaturalist website](https://www.inaturalist.org/pages/api+reference) and is part of our larger species occurrence searching packages [SPOCC](https://github.com/ropensci/spocc).
## Installation
You can install the latest version available on CRAN with:
```{r eval=FALSE}
install.packages("rinat")
```
Alternatively, you can install the development version from Github with:
```{r eval=FALSE}
remotes::install_github("ropensci/rinat")
```
## Usage
### Get observations
`get_inat_obs()` is the primary function that retrieves observations from iNaturalist. The text or taxon search can be refined by observation date, record quality and location.
> It is recommended to set the `quality` argument to `"research"` in order to get more reliable data that has been validated by several contributors.
#### Taxon search
To return only records of a specific species or taxonomic group, use the `taxon_name` argument. For example, to return observations of anything from the Nymphalidae family, and restricting the search to the year 2015:
```{r}
library(rinat)
nymphalidae <- get_inat_obs(taxon_name = "Nymphalidae", year = 2015)
# how many unique taxa?
length(unique(nymphalidae$scientific_name))
```
> Note that `get_inat_obs()` will return 100 observations by default. This can be controlled with the `maxresults` argument.
#### Text search
You can also search observations with any string. It will search the entire iNaturalist database, so the search below will return all entries that _mention_ Monarch butterflies, not just Monarch observations.
```{r}
monarchs <- get_inat_obs(query = "Monarch Butterfly", year = 2021)
# which taxa were returned?
unique(monarchs$scientific_name)
```
You can combine the fuzzy search with the precise taxon search. For example, to get Monarch butterfly observations that also mention the term "chrysalis":
```{r}
monarch_chrysalis <- get_inat_obs(taxon_name = "Danaus plexippus", query = "chrysalis")
```
#### Bounding box search
You can also search within a bounding box by giving a simple set of coordinates.
```{r}
## Search by area
bounds <- c(38.44047, -125, 40.86652, -121.837)
deer <- get_inat_obs(query = "Mule Deer", bounds = bounds)
plot(deer$longitude, deer$latitude)
```
### Other functions
More functions are available, notably to access:
* observations in a project with `get_inat_obs_project()`
* details of a single observation with `get_inat_obs_id()`
* observations from a single user with `get_inat_obs_user()`
* taxa statistics with `get_inat_taxon_stats()`
* user statistics with `get_inat_user_stats()`
More detailed examples are included in the vignette:
```{r eval=FALSE}
vignette("rinat-intro", package = "rinat")
```
#### Mapping
Basic maps can be created with the `inat_map()` function to quickly visualize search results. The `plot = FALSE` option can be used to avoid displaying the initial plot when further customising it with ggplot2 functions.
```{r fig.width=7, fig.height=4, fig.retina=3}
library(ggplot2)
## Map 100 spotted salamanders
a_mac <- get_inat_obs(taxon_name = "Ambystoma maculatum", year = 2021)
salamander_map <- inat_map(a_mac, plot = FALSE)
### Further customise the returned ggplot object
salamander_map + borders("state") + theme_bw()
```
`inat_map()` is useful for quickly mapping single-species data obtained with rinat. However, more complicated plots are best made from scratch. Here is an example of customised map that does not make use of it. (Note the use of `quality = "research"` to restrict the search to the more reliable observations.)
```{r fig.width=7, fig.height=7, fig.retina=3}
## A more elaborate map of Colibri sp.
colibri <- get_inat_obs(taxon_name = "Colibri",
quality = "research",
maxresults = 500)
ggplot(data = colibri, aes(x = longitude,
y = latitude,
colour = scientific_name)) +
geom_polygon(data = map_data("world"),
aes(x = long, y = lat, group = group),
fill = "grey95",
color = "gray40",
size = 0.1) +
geom_point(size = 0.7, alpha = 0.5) +
coord_fixed(xlim = range(colibri$longitude, na.rm = TRUE),
ylim = range(colibri$latitude, na.rm = TRUE)) +
theme_bw()
```
---
[](https://ropensci.org/)