Scott Chamberlain

We have a number of packages for getting species occurrence data:
[rgbif](https://github.com/ropensci/rgbif) and
[rbison](https://github.com/ropensci/rbison). The power of R is that you
can pull down this occurrence data, manipulate the data, do some
analyses, and visualize the data - all in one open source framework.

However, when dealing with occurrence data on maps, it is often useful
to be able to interact with the visualization.
[Github](https://github.com/), a code hosting and collaboration site,
now renders a particular type of map file format as an interactive map.
This file format is called `.geojson`. Here is an example of an
interactive map hosted on Github, embedded here:

Note that you can go full screen on these maps too. Check it out
[here](https://render.github.com/view/geojson?url=https://raw.github.com/geobabbler/geodata/master/geojson/leonardtown_bldgs.geojson).

To demonstrate how easy it can be to do this on your own, the following
is a small demo of searching for occurrence data, converting to geojson,
and pushing up to Github.

## Install rbison from Github

::: {.highlight}
``` {.chroma tabindex="0"}
# install_github('rbison', 'ropensci') # uncomment if you haven't# installed itlibrary(rbison)
```
:::

## Step 1: Seach for some occurrence data

Search for data on the bison.

::: {.highlight}
``` {.chroma tabindex="0"}
dat <- bison(species = "Bison bison", count = 500)
```
:::

Take a peak at the data just to make sure we have some occurrence data

::: {.highlight}
``` {.chroma tabindex="0"}
head(bison_data(dat, "data_df"))
```
:::

``` {tabindex="0"}
         id        name longitude latitude1 686467428 Bison bison    -118.8    43.312 656975776 Bison bison    -118.8    43.313 785794942 Bison bison    -111.1    44.664 687029742 Bison bison    -110.7    44.465 687027385 Bison bison    -110.7    44.616 773434631 Bison bison    -110.6    44.83                                      provider1  University of Kansas Biodiversity Institute2 Natural History Museum of Los Angeles County3               Museum of Southwestern Biology4                              iNaturalist.org5                              iNaturalist.org6                              iNaturalist.org
```

## Step 2: Convert the data to geojson format

Before this step I made a folder called *mygeojson* to write data in to,
which will also be a Github repository.

In this example, we are using the option method='web' in the `togeojson`
function, but we could also use method='local' which wouldl use the
`rgdal` package to convert to a geojson file format locally. Specifying
method='web' uses the [Ogre web client](https://ogre.adc4gis.com/).

::: {.highlight}
``` {.chroma tabindex="0"}
dat_df <- bison_data(dat, "data_df")write.csv(dat_df, "~/github/sac/mygeojson/bison_bison.csv")file <- "~/github/sac/mygeojson/bison_bison.csv"togeojson(file, method = "web", destpath = "~/github/sac/mygeojson/", outfilename = "bison_bison")
```
:::

## Step 3: Go to your terminal or git GUI and push up the new geojson file

::: {.highlight}
``` {.chroma tabindex="0"}
cd ~/github/sac/mygeojson/git initgit add .git commit -a -m 'my first geojson file'git remote add origin https://github.com/sckott/mygeojson.gitgit push origin master
```
:::

Then go to your new repo on Github and click on the `.geojson` file you
created, and you'll get a map. You can share this with others, and even
embed the interactive map in other web sites, as you can see below. Try
clicking on the points - they give metadata associated with each point.

Check out the full screen version
[here](https://render.github.com/view/geojson?url=https://raw.github.com/sckott/mygeojson/master/bison_bison.geojson)

It is as easy as that! We'll add this functionality into our `rgbif`
package as well.

Further reading:

- <https://blog.geomusings.com/2013/06/18/geojson-on-github-now-what/>
- I wrote earlier about this topic on my own blog
  [here](https://sckott.github.io/2013/06/geojson/).
- Github help for geojson file
  <https://help.github.com/articles/mapping-geojson-files-on-github#embedding-your-map-elsewhere>


We have a number of packages for getting species occurrence data: rgbif and rbison. The power of R is that you can pull down this occurrence data, manipulate the data, do some analyses, and visualize the data - all in one open source framework. However, when dealing with occurrence data on maps, it is often useful to be able to interact with the visualization.

Messaggi di Rogue Scholar

From occurrence data to interactive maps on the web