Tag Archives: google maps

Package rinat use case: map of iNaturalist project

11 Mar

iNaturalist projects are collection of records posted on iNatualist. Now that we have a R package rinat from rOpenSci I thought of playing around with the data. Here is a function I wrote, to quickly map all the records of a project using ggmap package.


inatmap <- function(grpid){
  data1=get_inat_obs_project(grpid, type = "observations")
  map <-get_map(location =c(min(data1$Longitude),
                messaging = FALSE)
  p <-ggplot()
  p= ggmap(map)+geom_point(data=data1,

We can used get_inat_obs_project function from rinat package to get all the observation from the specified project. get_map function form ggmap package to download google maps base layer and ggplot function form ggplot2 package to actually plot the map with points.

Now call to the function with a group name will produce a map with all the records in the project.



We can use other ggplot options to add title, legend etc. to the map. This is just a simple example.


GSoC Proposal 2013: Biodiversity Visualizations using R

29 Apr

I am applying for Google Summer of Code 2013 with this “Biodiversity Visualizations using R” proposal. I am posting this idea to get feedback and suggestions from Biodiversity Informatics community.

[During next few days I will keep updating this to accommodate suggestions. The example visualizations here are crude examples of the ideas, and need lot of work to convert them into reusable functions.]


R is increasingly being used in Biodiversity information analysis. There are several R packages like rgbif and rvertnet in rOpenSci suite to query, download and to some extent analyse the data within R workflow. We also have packages like dismo and SDMTools for modelling the data. It will be useful to have a package to quickly visualize biodiversity data. These visualizations would be helpful to understand extent of geographical, taxonomic and temporal coverage, gaps and biases in data.

The proposal is to work on a R package to provide functionality to quickly generate the visualizations of the data set user has gathered or generated.

The functions provided would be for following tasks:

  • Data preparation – The data needs to be converted into suitable format for visualizations and analysis i.e. date format, taxonomic classification and geographical co-ordinates should be in uniform and usable formats.
  • Data summary: Function(s) to quickly summarize the data set telling user number of records, number of records with Lat Long values, Bounding box of Lat Long Values, Date range and so on.
  • Geographic coverage – functions to visualize the data points on maps, density maps at different scales like Country level, Degree grid and so on.
Density of the records worldwide

Density of the records worldwide. Darker color indicates higher density of records.

Temporal coverage of the records

Temporal coverage of the records. Each line represents number of records on that particular day.

  • Taxonomic coverage – functions to visualize the taxonomic coverage of data in Tree Map formats by Number of records per species and number of species covered.
Familywise records

Family wise records present in the data set. (White block indicates records with unassigned family)

  • Completeness analysis – functions to assess and visualize completeness of biodiversity inventory of the region or in other words a measure of how exhaustive is the sampling in the study area [Ref:http://dx.doi.org/10.1111/j.0906-7590.2007.04627.x ]

Mentor(s): Javier Otegui

Data set: The data set used for the sample visualizations here is records published by iNaturalist.org on GBIF data portal. This data set contains Research Grade records (~46K) for all the organisms posted. The details of the data set are available here. The description on GBIF dat postal says “iNaturalist.org is a website where anyone can record their observations from nature. Members record observations for numerous reasons, including participation in citizen science projects, class projects, and personal fulfillment.”


  • Chamberlain, S., & Barve, V. (2012). rvertnet: Search VertNet database from R. Retrieved from http://cran.r-project.org/package=rvertnet
  • Chamberlain, S., Boettiger, C., Ram, K., & Barve, V. (2013). rgbif: Interface to the Global Biodiversity Information Facility API methods. Retrieved from http://cran.r-project.org/package=rgbif
  • Hijmans, R. J., Phillips, S., Leathwick, J., & Elith, J. (2012). dismo: Species distribution modeling. Retrieved from http://cran.r-project.org/package=dismo
  • Otegui, J., & Ariño, A. H. (2012). BIDDSAT: visualizing the content of biodiversity data publishers in the Global Biodiversity Information Facility network. Bioinformatics (Oxford, England), 28(16), 2207–8. doi:10.1093/bioinformatics/bts359
  • Soberón, J., Jiménez, R., Golubov, J., & Koleff, P. (2007). Assessing completeness of biodiversity databases at different spatial scales. Ecography, 30(1), 152–160. doi:10.1111/j.2006.0906-7590.04627.x
  • VanDerWal, J., Falconi, L., Januchowski, S., Shoo, L., & Storlie, C. (2012). SDMTools: Species Distribution Modelling Tools: Tools for processing data associated with species distribution modelling exercises. Retrieved from http://cran.r-project.org/package=SDMTools

Map biodiversity records with rgbif and ggmap packages in R

23 Jul

When I attended usrR! 2012 last month, there was an interesting presentation by Dr. David Kahle about the package ggmap. It is a package built over ggmap2 and helps us map spatial data over online maps like Google maps or Open Street Maps. I decided to give ggmap package a try with biodiversity data.

So first let us create a map for the Plain Tiger or the African Monarch Butterfly (Danaus chrysippus). We use occurrencelist from rgbif package again like previous post.

We use qmap function from ggmap package to quickly pull up the base map from Google Maps. So in essence the qmap function eliminates two step process of getting map data using map_data function and then setting up map display using ggplot function into one step. We use geom_jitter function to plot the occurrence points in the specified size(size = 4) and color(color = “red”).

Dan_chr=occurrencelist(sciname = 'Danaus chrysippus',
                       coordinatestatus = TRUE,
                       maxresults = 1000,
                       latlongdf = TRUE, removeZeros = TRUE)
wmap1 = qmap('India',zoom=2)
wmap1 +
      geom_jitter(data = Dan_chr,
                  aes(decimalLongitude, decimalLatitude),
                  alpha=0.6, size = 4, color = "red") +
                    opts(title = "Danaus chrysippus")

Here is the opuput map of the code snippet:

Though in earlier code we have used geom_jitter, high density of the points in some regions are not clearly seen. If we want to get better idea about the number of points we can try two dimensional density maps using the stat_density2d function. It just adds density lines on the map showing higher density with darker circles.

Dan_chr=occurrencelist(sciname = 'Danaus chrysippus',
                       coordinatestatus = TRUE,
                       maxresults = 1000,
                       latlongdf = TRUE, removeZeros = TRUE)
wmap1 = qmap('India',zoom=2)
wmap1 +
  stat_density2d(aes(x = decimalLongitude, y = decimalLatitude,
                     fill = ..level.., alpha = ..level..),
                 size = 4, bins = 6,
                 data = Dan_chr, geom = 'line') +
      geom_jitter(data = Dan_chr,
                  aes(decimalLongitude, decimalLatitude),
                  alpha=0.6, size = 4, color = "red") +
                    opts(title = "Danaus chrysippus :: Density Plot")

Map biodiversity records with rgbif and dismo packages in R

16 Jul

In the earlier post we generated maps from GBIF biodiversity records using maps and ggplot2 packages. We used world map with country borders for that. Now we will generate maps with google maps as base layer using dismo package.

Like earlier we download data for Danaus chrysippus from GBIF using occurrencelist function into a data frame Dan_chr.

Then use dismo package which has function gmap to quickly download base layer maps form google and display it using plot function. We can specify the extent of map range we need to download using extent function and specifying Latitude and Longitude range. We plot the points first by converting them into Mercator system using points.

Dan_chr=occurrencelist(sciname = 'Danaus chrysippus',
                       coordinatestatus = TRUE,
                       maxresults = 1000,
                       latlongdf = TRUE, removeZeros = TRUE)
e = extent( -179 , 179 , -80 , 80 )
r = gmap(e)
plot(r, interpolate=TRUE, main="Map")
points(Mercator(xy1) , col='blue', pch=20)
text(160,0, "\n\n\nDanaus \nchrysippus", adj = c(0,1),

The output of the code snippet is as follows:

Map of Danaus chrysippus