--- author: - contributor_roles: [] family: Chamberlain given: Scott blog: authors: - name: The rOpenSci Team community_id: 19c501a7-647b-4a11-9f5e-cf400817cce3 created: 1780876800 current_feed_url: null description: Open Tools and R Packages for Open Science doi: https://doi.org/10.59350/ropensci favicon: https://rogue-scholar.org/api/communities/19c501a7-647b-4a11-9f5e-cf400817cce3/logo feed_format: application/feed+json feed_url: https://ropensci.org/blog/index.json filter: null generator: Other home_page_url: https://ropensci.org/blog issn: null language: eng license: https://creativecommons.org/licenses/by/4.0/legalcode prefix: '10.59350' relative_url: null secure: true slug: ropensci status: active subfield: '1710' title: rOpenSci - open tools for open science updated: 1786620569 use_api: null container: rOpenSci - open tools for open science date: '2016-11-22T00:00:00+00:00' date_updated: '2025-12-06T09:42:28+00:00' guid: https://doi.org/10.59350/k0s8h-2bn12 identifier: https://doi.org/10.59350/k0s8h-2bn12 image: https://ropensci.org/assets/blog-images/2016-11-22-geospatial-suite/unnamed-chunk-16-1.png images: - alt: plot of chunk unnamed-chunk-16 src: https://ropensci.org/assets/blog-images/2016-11-22-geospatial-suite/unnamed-chunk-16-1.png - alt: plot of chunk unnamed-chunk-39 src: https://ropensci.org/assets/blog-images/2016-11-22-geospatial-suite/unnamed-chunk-39-1.png - alt: osmplot src: https://ropensci.org/assets/blog-images/2016-11-22-geospatial-suite/osmplotr_plot.png - alt: plot of chunk unnamed-chunk-45 src: https://ropensci.org/assets/blog-images/2016-11-22-geospatial-suite/unnamed-chunk-45-1.png - alt: plot of chunk unnamed-chunk-46 src: https://ropensci.org/assets/blog-images/2016-11-22-geospatial-suite/unnamed-chunk-46-1.png issn: null keywords: - Geospatial lang: en license: https://creativecommons.org/licenses/by/4.0/legalcode rid: r16ta-t4862 rights: https://creativecommons.org/licenses/by/4.0/legalcode summary: Geospatial data - data embedded in a spatial context - is used across disciplines, whether it be history, biology, business, tech, public health, etc. Along with community contributors, we're working on a suite of tools to make working with spatial data in R as easy as possible. If you're not familiar with geospatial tools, it's helpful to see what people do with them in the real world. title: The rOpenSci geospatial suite url: https://ropensci.org/blog/2016/11/22/geospatial-suite version: v1 --- Geospatial data - data embedded in a spatial context - is used across disciplines, whether it be history, biology, business, tech, public health, etc. Along with community contributors, we\'re working on a suite of tools to make working with spatial data in R as easy as possible. If you\'re not familiar with geospatial tools, it\'s helpful to see what people do with them in the real world. **Example 1** One of our geospatial packages, [geonames](https://github.com/ropensci/geonames), is used for geocoding, the practice of either sorting out place names from geographic data, or vice versa. `geonames` interfaces with the open database of the same name: . A recent paper in PlosONE highlights a common use case. Harsch & HilleRisLambers^[^1]^ asked how plant species distributions have shifted due to climate warming. They used the `GNsrtm3()` function in `geonames`, which uses [Shuttle Radar Topography Mission](https://www.geonames.org/export/web-services.html#srtm3) elevation data, to fill in missing or incorrect elevation values in their dataset. **Example 2** Another of our packages, [geojsonio](https://github.com/ropensci/geojsonio), is used as a tool to ingest GeoJSON, or make GeoJSON from various inputs. `geojsonio` was used in Frankfurt\'s Open Data Hackathon in March 2016 in a project to present users with random Google Streetview Images of Frankfurt. Check out the repo at [safferli/opendataday2016](https://github.com/safferli/opendataday2016). ------------------------------------------------------------------------ [We covered](/blog/2016/03/17/ropensci-geospatial-stack) the state of our geospatial tools in March of this year, but a lot has changed since then so we thought it would be useful to do an overview of these tools and future work. There are [many geospatial data formats](https://en.wikipedia.org/wiki/GIS_file_formats),including [shapefiles](https://en.wikipedia.org/wiki/Shapefile),[GeoTIFF](https://en.wikipedia.org/wiki/GeoTIFF), [netCDF](https://en.wikipedia.org/wiki/NetCDF),[Well-known text/Well-known binary](https://en.wikipedia.org/wiki/Well-known_text),[GeoJSON](https://geojson.org/), and many more. Readers may be more familiar with shape filesthan WKT or GeoJSON. There are [R tools for shape files](https://cran.rstudio.com/web/views/Spatial.html),so our tools largely don\'t concern themselves with shape files and other geospatialdata formats. Two formats in particular that we create tools for are GeoJSON and WKT. ## GeoJSON With the explosion of Javascript/Node and web-first tools, and increasing dominance of JSON as a data format, [GeoJSON](https://geojson.org/) as a spatial data format has seen increasing use. GeoJSON is a lightweight format based on JSON, and has a very new standard specification: [RFC 7946](https://tools.ietf.org/html/rfc7946). Many of our geospatial tools center around GeoJSON. Our goal with GeoJSON focused tools is to create a pipeline in which users can process GeoJSON data without any headaches due to dependencies. - links: [specification](https://tools.ietf.org/html/rfc7946) - [Wikipedia entry](https://en.wikipedia.org/wiki/GeoJSON) - GeoJSON was inspired in part from Simple Features, but is not part of that specification. The most recent iteration is called [RFC 7946 GeoJSON](https://tools.ietf.org/html/rfc7946). - Features of note: - JSON character representation only (though see [geobuf](https://github.com/mapbox/geobuf) for binary GeoJSON - not part of RFC 7946) - All data is WGS84 - Often found in web applications ## WKT Well-known text is a plain text format, just like GeoJSON (WKB is a binary form of WKT). It is often used in SQL databases to store geospatial data. Many of the data sources our R packages work with, for example (see our package [rgbif](https://github.com/ropensci/rgbif)), use WKT to specify geospatial extent. Thus, `rgbif` shouldn\'t need to import an entire spatial stack that is hard for some to install only for dealing with a single spatial data format - and only some users will do geospatial queries wih WKT as you can constrain queries simply with country names, while others may not need to constrain spatially. We\'ve been working on tools to make dealing with WKT more lightweight. - links: [specification](https://d17oy1vhnax1f7.cloudfront.net/items/291d412o1c3E3s0R112A/06-103r4_Implementation_Specification_for_Geographic_Information_-_Simple_feature_access_-_Part_1_Common_Architecture_v1.2.1%20(1).pdf) - [Wikipedia entry](https://en.wikipedia.org/wiki/Well-known_text) - WKT is part of [Simple Features](https://en.wikipedia.org/wiki/Simple_Features) (see `sf` below) - Features of note: - Character and binary representations - Supports any coordinate reference system - Often used in databases to store geospatial information ------------------------------------------------------------------------ ## rOpenSci use cases The motivation for our geospatial tools is supported in part by these use cases for software we make that use WKT and GeoJSON: - Web services that some of our packages interact with accept a geospatial filter as a query component. This often means WKT. Having tools that are light weight is important here as we don\'t need a full geospatial stack when we only need to lint (i.e., validate) WKT or create it from a bounding box, for example. - Likewise, some web services only accept GeoJSON. Same argument as above applies here. - Vizualize WKT and GeoJSON: given the above, users should be able to vizualize the area that they are defining with their WKT or GeoJSON. - WKT-GeoJSON conversion: sometimes one needs to convert WKT to GeoJSON, or vice versa. Light weight tools to do that task are really useful. ------------------------------------------------------------------------ ## Tools rOpenSci has a growing suite of database tools: ### GeoJSON/WKT Tools - [geojson](https://github.com/ropensci/geojson) (geojson classes for R) (on CRAN) - [geojsonio](https://github.com/ropensci/geojsonio) (I/O for GeoJSON) (on CRAN) - [geojsonlint](https://github.com/ropenscilabs/geojsonlint) (Lint GeoJSON) (on CRAN) - [lawn](https://github.com/ropensci/lawn) (Turf.js R client) (on CRAN) - [geoaxe](https://github.com/ropenscilabs/geoaxe) (split up well known text into chunks) (on CRAN) - [wellknown](https://github.com/ropensci/wellknown) (Well-Known-Text \<--\> GeoJSON) (on CRAN) - [geoops](https://github.com/ropenscilabs/geoops) (Operations on GeoJSON, sort of like `rgeos`) ### Data/Data Services - [geoparser](https://github.com/ropenscilabs/geoparser) (Geoparser.io client for place names) (on CRAN) - [rgeospatialquality](https://github.com/ropenscilabs/rgeospatialquality) (spatial quality of biodiversity records) (on CRAN) - [getlandsat](https://github.com/ropenscilabs/getlandsat) (Landsat images) (on CRAN) - [osmplotr](https://github.com/ropenscilabs/osmplotr) (OpenStreeMap data and vizualization) (on CRAN) - [rnaturalearth](https://github.com/ropenscilabs/rnaturalearth) (Natural Earth data) - [geonames](https://github.com/ropensci/geonames) (Access Geonames.org API) (on CRAN) ------------------------------------------------------------------------ For each package below, there are 2-3 badges. One for whether the package is on CRANor not ([cran]{.label .label-warning} if on CRAN, [cran]{.label .label-default}if not), another for link to source on GitHub ([github]{.label .label-info}),and another when the package is community contributed ([community]{.label .label-success}). ## geojson [](https://cran.r-project.org/package=geojson){target="_blank"} [](https://github.com/ropensci/geojson){target="_blank"} We\'re excited to announce a new package `geojson`, which is now on CRAN. Check out the vignettes ([geojson classes](https://cran.rstudio.com/web/packages/geojson/vignettes/geojson.html), [geojson operations](https://cran.rstudio.com/web/packages/geojson/vignettes/geojson-operations.html)) to get started. You can install the package from CRAN: ::: {.highlight} ``` {.chroma tabindex="0"} install.packages("geojson") ``` ::: ::: {.highlight} ``` {.chroma tabindex="0"} library("geojson") ``` ::: The `geojson` package has functions for creating each of the GeoJSON classes from character strings of GeoJSON. - `feature()` - Feature - `featurecollection()` - FeatureCollection - `geometrycollection()` - GeometryCollection - `linestring()` - LineString - `multilinestring()` - MultiLineString - `multipoint()` - MultiPoint - `multipolygon()` - MultiPolygon - `point()` - Point - `polygon()` - Polygon Internally, we perform some basic checks that the string is proper JSON, then if you want to lintthe GeoJSON (see the `linting_opts()` function) we\'ll lint the GeoJSON as well using our`geojsonlint` package. Make a `Point` ::: {.highlight} ``` {.chroma tabindex="0"} (x <- point('{ "type": "Point", "coordinates": [100.0, 0.0] }'))#> #> coordinates: [100,0] ``` ::: In addition, you can perform some basic operations, such as adding (`properties_add()`) or getting properties (`properties_get()`), adding (`crs_add()`) or getting CRS (`crs_get()`), adding (`bbox_add()`) or getting (`bbox_get()`) a bounding box. You can calculate a bounding box on your GeoJSON with `geo_bbox()`, prettify your GeoJSON with `geo_pretty()`, and write your GeoJSON to disk with `geo_write()`. Add and get properties ::: {.highlight} ``` {.chroma tabindex="0"} (y <- linestring('{ "type": "LineString", "coordinates": [ [100.0, 0.0], [101.0, 1.0] ]}'))#> #> coordinates: [[100,0],[101,1]](z <- y %>% feature() %>% properties_add(population = 1000))#> {#> "type": "Feature",#> "properties": {#> "population": 1000#> },#> "geometry": {#> "type": "LineString",#> "coordinates": [#> [#> 100,#> 0#> ],#> [#> 101,#> 1#> ]#> ]#> }#> }properties_get(z, property = 'population')#> 1000 ``` ::: Add bbox - without an input, we figure out the 2D bbox for you ::: {.highlight} ``` {.chroma tabindex="0"} x <- '{ "type": "Polygon","coordinates": [ [ [100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0], [100.0, 0.0] ] ]}'y <- polygon(x)y %>% feature() %>% bbox_add()#> {#> "type": "Feature",#> "properties": {#>#> },#> "geometry": {#> "type": "Polygon",#> "coordinates": [#> [#> [#> 100,#> 0#> ],#> [#> 101,#> 0#> ],#> [#> 101,#> 1#> ],#> [#> 100,#> 1#> ],#> [#> 100,#> 0#> ]#> ]#> ]#> },#> "bbox": [#> 100,#> 0,#> 101,#> 1#> ]#> } ``` ::: Get the GeoJSON type ::: {.highlight} ``` {.chroma tabindex="0"} geo_type(y)#> [1] "Polygon" ``` ::: Pretty print the GeoJSON ::: {.highlight} ``` {.chroma tabindex="0"} geo_pretty(y)#> {#> "type": "Polygon",#> "coordinates": [#> [#> [#> 100.0,#> 0.0#> ],#> [#> 101.0,#> 0.0#> ],#> [#> 101.0,#> 1.0#> ],#> [#> 100.0,#> 1.0#> ],#> [#> 100.0,#> 0.0#> ]#> ]#> ]#> }#> ``` ::: Write to disk (and read back) ::: {.highlight} ``` {.chroma tabindex="0"} f <- tempfile(fileext = ".geojson")geo_write(y, f)jsonlite::fromJSON(f, FALSE)#> $type#> [1] "Polygon"#>#> $coordinates#> $coordinates[[1]]#> $coordinates[[1]][[1]]#> $coordinates[[1]][[1]][[1]]#> [1] 100#>#> $coordinates[[1]][[1]][[2]]#> [1] 0#>#>#> $coordinates[[1]][[2]]#> $coordinates[[1]][[2]][[1]]#> [1] 101#>#> $coordinates[[1]][[2]][[2]]#> [1] 0#>#>#> $coordinates[[1]][[3]]#> $coordinates[[1]][[3]][[1]]#> [1] 101#>#> $coordinates[[1]][[3]][[2]]#> [1] 1#>#>#> $coordinates[[1]][[4]]#> $coordinates[[1]][[4]][[1]]#> [1] 100#>#> $coordinates[[1]][[4]][[2]]#> [1] 1#>#>#> $coordinates[[1]][[5]]#> $coordinates[[1]][[5]][[1]]#> [1] 100#>#> $coordinates[[1]][[5]][[2]]#> [1] 0 ``` ::: Lastly, the Mapbox folks have a compact binary encoding for geographic data ([Geobuf](https://github.com/mapbox/geobuf)) that provides lossless compression of GeoJSON data into protocol buffers. Our own [Jeroen Ooms](/about/#staff) added Geobuf serialization to his [protolite](https://github.com/jeroen/protolite) package, which we import in `geojson` to allow you to read Geobuf with `from_geobuf()` and write Geobuf with `to_geobuf()`. ::: {.highlight} ``` {.chroma tabindex="0"} file <- system.file("examples/test.pb", package = "geojson")from_geobuf(file, pretty = TRUE)#> {#> "type": "FeatureCollection",#> "features": [#> {#> "type": "Feature",#> "geometry": {#> "type": "Point",#> "coordinates": [102, 0.5]#> },#> "id": 999,... ``` ::: ::: {.highlight} ``` {.chroma tabindex="0"} to_geobuf(from_geobuf(file))#> [1] 0a 05 70 72 6f 70 30 0a 06 64 6f 75 62 6c 65 0a 0c 6e 65 67 61 74 69#> [24] 76 65 5f 69 6e 74 0a 0c 70 6f 73 69 74 69 76 65 5f 69 6e 74 0a 0f 6e#> [47] 65 67 61 74 69 76 65 5f 64 6f 75 62 6c 65 0a 0f 70 6f 73 69 74 69 76#> [70] 65 5f 64 6f 75 62 6c 65 0a 04 6e 75 6c 6c 0a 05 61 72 72 61 79 0a 06#> [93] 6f 62 6a 65 63 74 0a 06 62 6c 61 62 6c 61 0a 07 63 75 73 74 6f 6d 31... ``` ::: ## geoops [](https://github.com/ropensci/geoops){target="_blank"} [geoops](https://github.com/ropenscilabs/geoops) - `geoops` is not quite ready to use yet, butthe goal with `geoops` is to provide spatial operations on GeoJSON that work with the `geojson`package. Example operations are: - Find the set of points that are also in a polygon - Find centroid of a polygon - Calculate distance between two points - Calculate buffer of a given radius around a point - Combine one or more polygons together Another feature of `geoops` we\'re excited about is slicing up GeoJSON easily by using ourpackage [jqr](https://github.com/ropensci/jqr). It\'s similar in concept to using `dplyr` for drilling down intoa data.frame, but instead we can do that with GeoJSON. > note: this package used to be called `siftgeojson` ## geojsonio [](https://cran.r-project.org/package=geojsonio){target="_blank"} [](https://github.com/ropensci/geojsonio){target="_blank"} [geojsonio](https://github.com/ropensci/geojsonio) - `geojsonio` is a client for making iteasy to convert lots of different things to GeoJSON, and for reading/writing GeoJSON. We had a new version (`v0.2`) come out in July this year, with major performance improvements to`geojson_json()` - and we\'ve deprecated GeoJSON linting functionality and now point peopleto our package `geojsonlint` for all GeoJSON linting tasks. ### Example A quick example of the power of `geojsonio` ::: {.highlight} ``` {.chroma tabindex="0"} install.packages("geojsonio") ``` ::: ::: {.highlight} ``` {.chroma tabindex="0"} library("geojsonio") ``` ::: Convert a numeric vector to a GeoJSON `Point`: ::: {.highlight} ``` {.chroma tabindex="0"} geojson_json(c(32.45, -99.74))#> {"type":"FeatureCollection","features":[{"type":"Feature","geometry":{"type":"Point","coordinates":[32.45,-99.74]},"properties":{}}]} ``` ::: Read GeoJSON from a file with one simple command and plot it: ::: {.highlight} ``` {.chroma tabindex="0"} file <- system.file("examples", "california.geojson", package = "geojsonio")out <- geojson_read(file, what = "sp")library('sp')plot(out) ``` ::: ![plot of chunk unnamed-chunk-16](/assets/blog-images/2016-11-22-geospatial-suite/unnamed-chunk-16-1.png) ## geojsonlint [](https://cran.r-project.org/package=geojsonlint){target="_blank"} [](https://github.com/ropensci/geojsonlint){target="_blank"} [geojsonlint](https://github.com/ropensci/geojsonlint) - `geojsonlint` is a client for lintingGeoJSON. It provides three different ways to lint GeoJSON, using: the API at \,the JS library `geojsonhint`, or the JS library `is-my-json-valid`. The package provides a consistentinterface to the three different linters, always returning a boolean, and toggles provided forverbose output and whether to stop when invalid GeoJSON is found. We released a new version (`v0.2`) this month that uses the newer version of the JSlibrary `geojsonhint`, affecting the `geojson_hint()` function. Note that the dev version of`geojsonlint` has an even newer version of the JS `geojsonhint` library, so you may want toupgrade if you\'re using that linter: `devtools::install_github("ropensci/geojsonlint")`. ### Example A quick example of the power of `geojsonlint` ::: {.highlight} ``` {.chroma tabindex="0"} install.packages("geojsonlint") ``` ::: ::: {.highlight} ``` {.chroma tabindex="0"} library("geojsonlint") ``` ::: Good GeoJSON ::: {.highlight} ``` {.chroma tabindex="0"} geojson_hint(x = '{"type": "Point", "coordinates": [-100, 80]}')#> [1] TRUE ``` ::: Bad GeoJSON ::: {.highlight} ``` {.chroma tabindex="0"} geojson_hint('{ "type": "FeatureCollection" }')#> [1] FALSEgeojson_hint('{ "type": "FeatureCollection" }', verbose = TRUE)#> [1] FALSE#> attr(,"errors")#> line message#> 1 1 "features" member requiredgeojson_hint('{ "type": "FeatureCollection" }', error = TRUE)#> Error: Line 1#> - "features" member required ``` ::: ## lawn [](https://cran.r-project.org/package=lawn){target="_blank"} [](https://github.com/ropensci/lawn){target="_blank"} [lawn](https://github.com/ropensci/lawn) - `lawn` is an R client wrapping [Turf.js](https://turfjs.org/)from Mapbox. Turf is a JS library for doing advanced geospatial analysis. Using the great [V8](https://github.com/jeroen/v8)R client from [Jeroen Ooms](/about/#staff) we can wrap Turf.jsin R. We released a new version (`v0.3`) late last month that is a big change from the previousversion as we now wrap the newest version of Turf `v3.5.2` that dropped a number of methods,and introduced new ones. ### Example A quick example of the power of `lawn` ::: {.highlight} ``` {.chroma tabindex="0"} install.packages("lawn") ``` ::: ::: {.highlight} ``` {.chroma tabindex="0"} library("lawn") ``` ::: Calcuate distance (default: km) between two points ::: {.highlight} ``` {.chroma tabindex="0"} from <- '{ "type": "Feature", "properties": {}, "geometry": { "type": "Point", "coordinates": [-75.343, 39.984] }}'to <- '{ "type": "Feature", "properties": {}, "geometry": { "type": "Point", "coordinates": [-75.534, 39.123] }}'lawn_distance(from, to)#> [1] 97.15958 ``` ::: Buffer a point (with distance of 5 km) ::: {.highlight} ``` {.chroma tabindex="0"} pt <- '{ "type": "Feature", "properties": {}, "geometry": { "type": "Point", "coordinates": [-90.548630, 14.616599] }}'lawn_buffer(pt, dist = 5)#> #> Type: Polygon#> Bounding box: -90.6 14.6 -90.5 14.7#> No. points: 66#> Properties: NULL ``` ::: ## geonames [](https://cran.r-project.org/package=geonames){target="_blank"} [](https://github.com/ropensci/geonames){target="_blank"} [geonames](https://github.com/ropensci/geonames) - `geonames` is an R client for the\ web service, that allows you to query for global geographic data suchas administrative areas, populated places, and more. ### Example A quick example of the power of `geonames` ::: {.highlight} ``` {.chroma tabindex="0"} install.packages("geonames") ``` ::: ::: {.highlight} ``` {.chroma tabindex="0"} library("geonames") ``` ::: Search for place names with by place name: ::: {.highlight} ``` {.chroma tabindex="0"} GNsearch(q = 'london', maxRows = 10)#> adminCode1 lng geonameId toponymName countryId fcl#> 1 ENG -0.12574 2643743 London 2635167 P#> 2 08 -81.23304 6058560 London 6251999 P#> 3 ENG -0.09184 2643741 City of London 2635167 P#> 4 05 27.91162 1006984 East London 953987 P... ``` ::: Find the ISO country code for a given lat/long: ::: {.highlight} ``` {.chroma tabindex="0"} GNcountryCode(lat = 47.03, lng = 10.2)#> $languages#> [1] "de-AT,hr,hu,sl"#>#> $distance#> [1] "0"#>#> $countryCode#> [1] "AT"#>#> $countryName#> [1] "Republic of Austria" ``` ::: ## geoparser [](https://cran.r-project.org/package=geoparser){target="_blank"} [](https://github.com/ropensci/geoparser){target="_blank"} [geoparser](https://github.com/ropenscilabs/geoparser) - `geoparser` is an interfaceto the Geoparser.io API for Identifying and Disambiguating Places Mentioned in Text ### Example A quick example of the power of `geoparser` ::: {.highlight} ``` {.chroma tabindex="0"} install.packages("geoparser") ``` ::: ::: {.highlight} ``` {.chroma tabindex="0"} library("geoparser") ``` ::: In a very simple example, send a small string to get geoparsed: ::: {.highlight} ``` {.chroma tabindex="0"} output <- geoparser_q("I was born in Vannes and I live in Barcelona")output$results#> # A tibble: 2 × 11#> country confidence name admin1 type geometry.type longitude latitude reference1 reference2#> * #> 1 FR 1 Vannes A2 seat of a second-order administrative division Point -2.75000 47.66667 14 20#> 2 ES 1 Barcelona 56 seat of a first-order administrative division Point 2.15899 41.38879 35 44#> # ... with 1 more variables: text_md5 output$properties#> # A tibble: 1 × 4#> apiVersion source id text_md5#> * #> 1 0.4.1 geoparser.io 7Mp287nh6XbbH0QMojB6L 51e05aeb3366e55795a9729dd74ae901 ``` ::: The `properties` data.frame gives some metadata for your request, while the`results` data.frame gives results of the geoparsing, including thename found, the type of geospatial thing, geometry type, and coordinates. ## rgeospatialquality [](https://cran.r-project.org/package=rgeospatialquality){target="_blank"} [](https://github.com/ropenscilabs/rgeospatialquality){target="_blank"} [rgeospatialquality](https://github.com/ropenscilabs/rgeospatialquality) - `rgeospatialquality`is an R client for the Geospatial Data Quality APIthat detects geospatial quality issues with geostpaial biodiversity occurrence data. ### Example A quick example of the power of `rgeospatialquality` ::: {.highlight} ``` {.chroma tabindex="0"} install.packages("rgeospatialquality") ``` ::: ::: {.highlight} ``` {.chroma tabindex="0"} library("rgeospatialquality") ``` ::: Make a simple occurrence record: ::: {.highlight} ``` {.chroma tabindex="0"} rec <- list( decimalLatitude = 42.1833, decimalLongitude = -1.8332, countryCode = "ES", scientificName = "Puma concolor") ``` ::: Pass the record to the API: ::: {.highlight} ``` {.chroma tabindex="0"} parse_record(record = rec)#> $hasCoordinates#> [1] TRUE#>#> $validCountry#> [1] TRUE#>#> $validCoordinates#> [1] TRUE#>#> $hasCountry#> [1] TRUE#>#> $coordinatesInsideCountry#> [1] TRUE#>#> $hasScientificName#> [1] TRUE#>#> $highPrecisionCoordinates#> [1] TRUE#>#> $coordinatesInsideRangeMap#> [1] FALSE#>#> $nonZeroCoordinates#> [1] TRUE#>#> $distanceToRangeMapInKm#> [1] 6874.023 ``` ::: The results is a named list, with results for various aspects of geospatial quality, includingwhether the record has coordinates, whether the country is valid, whether the coordinatesare valid, and more. ## getlandsat [](https://cran.r-project.org/package=getlandsat){target="_blank"} [](https://github.com/ropensci/getlandsat){target="_blank"} [getlandsat](https://github.com/ropenscilabs/getlandsat) - `getlandsat` providesaccess to Landsat 8 metadata and images hosted onAWS S3 at . The package onlyfetches data. It does not attempt to aid users in downstream usage. ### Example A quick example of the power of `getlandsat` ::: {.highlight} ``` {.chroma tabindex="0"} install.packages("getlandsat") ``` ::: ::: {.highlight} ``` {.chroma tabindex="0"} library("getlandsat") ``` ::: Get an image (see `lsat_list()`, `lsat_scenes()`, `lsat_scene_files()` to find/searchfor images): ::: {.highlight} ``` {.chroma tabindex="0"} lsat_image("LC80101172015002LGN00_B5.TIF")#> [1] "/Users/sacmac/Library/Caches/landsat-pds/L8/010/117/LC80101172015002LGN00/LC80101172015002LGN00_B5.TIF" ``` ::: Make a plot ::: {.highlight} ``` {.chroma tabindex="0"} library("raster")x <- lsat_cache_details()[[1]]img <- raster(x$file)plot(img) ``` ::: ![plot of chunk unnamed-chunk-39](/assets/blog-images/2016-11-22-geospatial-suite/unnamed-chunk-39-1.png) ## osmplotr [](https://cran.r-project.org/package=osmplotr){target="_blank"} [](https://github.com/ropensci/osmplotr){target="_blank"} [osmplotr](https://github.com/ropenscilabs/osmplotr) - `osmplotr` produces customisableimages of [OpenStreetMap (OSM)](https://www.openstreetmap.org/) data and enables datavisualisation using OSM objects. ### Example A quick example of the power of `osmplotr` ::: {.highlight} ``` {.chroma tabindex="0"} install.packages("osmplotr") ``` ::: ::: {.highlight} ``` {.chroma tabindex="0"} library("osmplotr")library("maptools") ``` ::: Make a basic map: ::: {.highlight} ``` {.chroma tabindex="0"} bbox <- get_bbox(latlon = c(-0.13,51.50,-0.11,51.52))dat_B <- extract_osm_objects(key = 'building', bbox = bbox)map <- osm_basemap(bbox = bbox, bg = 'gray20')map <- add_osm_objects(map, dat_B, col = 'gray40')print_osm_map(map) ``` ::: ![osmplot](/assets/blog-images/2016-11-22-geospatial-suite/osmplotr_plot.png) ## rnaturalearth [](https://cran.r-project.org/package=rnaturalearth){target="_blank"} [](https://github.com/ropensci/rnaturalearth){target="_blank"} [rnaturalearth](https://github.com/ropenscilabs/rnaturalearth) - `rnaturalearth`facilitates world mapping by making Natural Earth ()map data available in R. ### Example A quick example of the power of `rnaturalearth` ::: {.highlight} ``` {.chroma tabindex="0"} devtools::install_github("ropenscilabs/rnaturalearth") ``` ::: ::: {.highlight} ``` {.chroma tabindex="0"} library("rnaturalearth") ``` ::: Some data comes installed with `rnaturalearth` ::: {.highlight} ``` {.chroma tabindex="0"} sp::plot(ne_countries()) ``` ::: ![plot of chunk unnamed-chunk-45](/assets/blog-images/2016-11-22-geospatial-suite/unnamed-chunk-45-1.png) You can also download specific data: ::: {.highlight} ``` {.chroma tabindex="0"} rivers50 <- ne_download(scale = 50, type = 'rivers_lake_centerlines', category = 'physical')#> OGR data source with driver: ESRI Shapefile#> Source: "/var/folders/gs/4khph0xs0436gmd2gdnwsg080000gn/T//Rtmp8yl7Kj", layer: "ne_50m_rivers_lake_centerlines"#> with 460 features#> It has 4 fieldssp::plot(rivers50) ``` ::: ![plot of chunk unnamed-chunk-46](/assets/blog-images/2016-11-22-geospatial-suite/unnamed-chunk-46-1.png) ------------------------------------------------------------------------ ## Future/Ongoing work We still have a lot that we\'d like to do. Here\'s a run down of some of the items on our list: - `geoops` first version: We need to get `geoops` to a first stable version onCRAN. It will likely be a few months before that happens, as we\'re experimentingwith how to achieve the best performance, whether that be via `jqr` or droppingdown to C/C++. - `geojson` - We just released the first version - We\'ll be integrating `geojson`into some of our other packages that deal with GeoJSON, and may hit uponsome improvements we can make to `geojson`. - `wellknown` fixes: We\'re working on our next version of this package, see[milestone v0.2](https://github.com/ropensci/wellknown/milestone/2), which includesmaking sure we account for 3D/4D WKT ([issue \#18](https://github.com/ropensci/wellknown/issues/18)),among other things, and there\'s a possibility of changing the package interface to be slightlymore intuitive ([issue \#17](https://github.com/ropensci/wellknown/issues/17)). - `geojsonio`: [milestone v0.3](https://github.com/ropensci/geojsonio/milestone/3)has a number of bug fixes, and includes methods integrating the new [sf](https://cran.rstudio.com/web/packages/sf/) package. - `geojsonrewind`: The new [GeoJSON specification](https://tools.ietf.org/html/rfc7946) now has a rule aboutpolygons following a [right-hand rule](https://tools.ietf.org/html/rfc7946#section-3.1.6).We\'re porting some of Mapbox\'s JS stuff to R in [geojsonrewind](https://github.com/ropenscilabs/geojsonrewind) packageto be help users fix winding order. ------------------------------------------------------------------------ ## Takeaway and Feedback Our goal with our geospatial suite is to make your work -- whether it be seriousreproducible science, analysis of your company\'s data, or just fooling around withsome data -- as easy as possible with as few installation headaches as possible. How are you using our geospatial tools? We\'d love to hear about how you\'re usingour packages, whether it be in blog posts, scholarly papers, shiny apps, businessuse cases, etc. Let us know if you have any feedback on these packages, and/or if you thinkthere\'s anything else we should be thinking about making in this space. ------------------------------------------------------------------------ ## Community contributors We\'re so so grateful to our community for their hard work on these packages: - [Maëlle Salmon](https://github.com/maelle) - `geoparser` - [Andy Teucher](https://github.com/ateucher) - `geojsonio`, `geojsonlint` - [Jeff Hollister](https://github.com/jhollist) - `lawn` - [Mark Padgham](https://github.com/mpadge) - `osmplotr` - [Andy South](https://github.com/AndySouth) - `rnaturalearth` - [Javier Otegui](https://github.com/jotegui) - `rgeospatialquality` - [Barry Rowlingson](https://github.com/barryrowlingson) - `geonames` [^1]: Harsch, M. A., & HilleRisLambers, J. (2016). Climate Warming and Seasonal Precipitation Change Interact to Limit Species Distribution Shifts across Western North America. PLoS ONE, 11(7), e0159184.