One of the tasks in my Google Summer of Code 2015 was to integrate new package bdsns with existing package bdvis to identify strengths and gaps in the data. This can be achieved with few simple steps.

Begin with opening both libraries

library(devtools)
install_github("vijaybarve/bdsns")
install_github("vijaybarve/bdvis")
library(bdsns)
library(bdvis)

Get data for few species of butterflies using bdsns package from Flickr and store in sqlite database. User needs to get own API key form Flickr website from here. A file containing few scientific names of butterfly species

bflytest.txt
scname
Graphium agetes
Graphium antiphates 
Graphium aristeus
Colias nilagiriensis
Dercas verhuelli
Eurema andersoni 
Gonepteryx rhamni
Hebomoia glaucippe
Euripus nyctelius 
Hestinalis nama
Mimathyma ambica 
Ariadne merione
Byblia ilithyia
Abisara echerius
Abisara neophron 
Zemeros flegyas
Curetis thetis
Heliophorus epicles
Spalgis epeus
Hasora badra
Hasora chromus
Gangara lebadea
Gangara thyrsis

And then we are all set to run the command to download and store the data in sqlite database.

flickrtodatabase(myapikey,"bflytest.txt",
                  "scname","testdb")

Read in the sqlite database

dat=extract_flickrdb("testdb","t1.csv")

Set up the data for use in bdvis.Function format_bdvis will set the field names for scientific name, latitude, longitude and date in the bdvis format and also assigh grid cell ids. Function gettaxo will fetch and store higher taxonomy of the species.

dat=format_bdvis(dat)
dat=gettaxo(dat)

Now bdvis functions can be used for visualizations

mapgrid(dat)
tempolar(dat)
taxotree(dat)
chronohorogram(dat)
bdcalenderheat(dat)

Here is a sample of what this code will produce:

MapGrid output of Butterfly Data

Temporal output of daily butterfly data

Please note the results may not exactly match, since new photographs are being posted continuously on Flickr.

Read more about bdsns here

Barve, V. (2014). Discovering and developing primary biodiversity data from social networking sites: A novel approach. Ecological Informatics, 24, 194–199. doi:10.1016/j.ecoinf.2014.08.008