open science

Open science as a lego set

Open science as a lego set

open science may be hard to do

but - you can work on different components

and - individual components are worth learning

Open Data

make your data open

funders/journals often requiring this anyway

future self will thank you

Versioning: code/data/text

Versioning: code/data/text

failure proofs your work

experiment freely!

Do all work programatically

Do all work programatically

Key to reproduciblity

Most important person that wants to reproduce your work is you!

Do all work programatically

you and yourself

- one week from now

- two months from now

- & so on

An example to shoot for

R language

• used widely in biology, psychology, medicine, etc.

• rapidly growing user base, companies surrounding it

• includes all tools for open science workflow

• though work to be done ...

Open science ecosytsem

rOpenSci does:

rOpenSci Staff

• 4 full time

• now including a community manager!

• leadership team

• advisory board

Community stats

• ~ 250 code contributors

• large no. bug reports/feature requests

• ~ 364 Github repositories

• ~ 30,000 commits

• ~ 123 published R packages

What data do you use in your research?

the research workflow

the research workflow

the research workflow

the research workflow

the research workflow

rOpenSci Tools

use case 1

use case 2

use case 3

Taxonomy

• taxize - Taxonomic toolbelt

• taxizesoap - Taxonomic toolbelt (SOAP)

• ritis - ITIS client (avail. in taxize)

• taxizedb - Access to SQL dumps

• wikitaxa - Taxonomy from Wiki-pedia/-species/-commons

• worrms - WORMS client (avail. in taxize)

• natserv - Natureserve client (avail. in taxize)

• taxa - Taxonomic classes to be used by other pkgs (coming soon)

Taxonomic data from 20 sources - taxize

Taxonomic hierarchies from NCBI/ITIS/COL/etc

library('taxize')
classification("Chironomus riparius", db = "gbif")

#> $`Chironomus riparius`
#>                  name    rank      id
#> 1            Animalia kingdom       1
#> 2          Arthropoda  phylum      54
#> 3             Insecta   class     216
#> 4             Diptera   order     811
#> 5        Chironomidae  family    3343
#> 6          Chironomus   genus 1448033
#> 7 Chironomus riparius species 1448237

Taxonomic IDs

always try to move from:

• taxonomic name -- to

• taxonomic ID -- to

• whatever other data

taxa classes - taxa

library('taxa')
taxon_name("Mammalia")
taxon_rank("class")
taxon_id(9681)

mammalia <- taxon(taxon_name("Mammalia"), taxon_rank("class"), taxon_id(9681))
#> <Taxon>
#>   name: Mammalia
#>   rank: class
#>   id: 9681
#>   authority: none

felidae <- taxon(taxon_name("Felidae"), taxon_rank("family"), taxon_id(9681))
panthera <- taxon(taxon_name("Panthera"), taxon_rank("genus"), taxon_id(146712))
tigris <- taxon(taxon_name("tigris"), taxon_rank("species"), taxon_id(9696))

tiger <- hierarchy(mammalia, felidae, panthera, tigris)
#> <Hierarchy>
#>   no. taxon's:  4
#>   Mammalia / class / 9681
#>   Felidae / family / 9681
#>   Panthera / genus / 146712
#>   tigris / species / 9696

Species occurrence data

• spocc - One client to rule them all

• rgbif - GBIF data (avail. in spocc)

• AntWeb - AntWeb ant data (avail. in spocc)

• ecoengine - Berekeley Ecoengine client (avail. in spocc)

• rinat - iNaturalist client (avail. in spocc)

• rbison - USGS BISON client (avail. in spocc)

• rebird - eBird data (avail. in spocc)

• rvertnet - VertNet data (avail. in spocc)

• rfishbase - Fishbase.org data

• rAvis - PreyectoAvis data

Species occurrence data: other

• ALA4R - Atlas of Living Australia R client

• rseaaroundus - SeaAroundUs

Mapping biodiversity data - rgbif

library(rgbif)
key <- name_backbone(name='Danaus plexippus', kingdom='animals')$speciesKey
out <- occ_search(taxonKey=key, limit=300, return='data')
gbifmap(out)

Unified species occurrence data - spocc

library(spocc)
spnames <- c('Accipiter striatus', 'Setophaga caerulescens', 'Spinus tristis')
(out <- occ(query = spnames, from = c('gbif', 'ebird')))

#> Summary of results - occurrences found for:
#>  gbif  : 75 records across 3 species
#>  bison :  0 records across 3 species
#>  inat  :  0 records across 3 species
#>  ebird :  75 records across 3 species
#>  ecoengine :  0 records across 3 species
#>  antweb :  0 records across 3 species
#>  idigbio :  0 records across 3 species
#>  obis :  0 records across 3 species
#>  ala :  0 records across 3 species

Biodiveristy related data

• finch - Parse Darwin Core archives

• rredlist - IUCN Red List

• Reol - EOL client

• mregions - Marineregions.org data

• natserv - NatureServe data

• neotoma - Neotoma data

• pangaear - Pangaea www.pangaea.de

• natserv - NatureServe data

Occurrence data cleaning

• scrubr - general purpose cleaner

• rgeospatialquality - API wrapper for checking occ. data

Species occurrence data cleaning - scrubr

library(scrubr)
NROW(sample_data_1)
#> [1] 1500

sample_data_1 %>%
  coord_impossible() %>%
  coord_incomplete() %>%
  coord_unlikely()

#> # A tibble: 1,294 × 5
#>                name  longitude latitude                date        key
#> *             <chr>      <dbl>    <dbl>              <dttm>      <int>
#> 1  Ursus americanus  -79.68283 38.36662 2015-01-14 16:36:45 1065590124
#> 2  Ursus americanus  -82.42028 35.73304 2015-01-13 00:25:39 1065588899
#> 3  Ursus americanus  -99.09625 23.66893 2015-02-20 23:00:00 1098894889
#> 4  Ursus americanus  -72.77432 43.94883 2015-02-13 16:16:41 1065611122
#> 5  Ursus americanus  -72.34617 43.86464 2015-03-01 20:20:45 1088908315
#> 6  Ursus americanus -108.53674 32.65219 2015-03-29 17:06:54 1088932238
#> 7  Ursus americanus -108.53691 32.65237 2015-03-29 17:12:50 1088932273
#> 8  Ursus americanus -123.82900 40.13240 2015-03-28 23:00:00 1132403409
#> 9  Ursus americanus  -78.25027 36.93018 2015-03-20 21:11:24 1088923534
#> 10 Ursus americanus  -76.78671 35.53079 2015-04-05 23:00:00 1088954559
#> # ... with 1,284 more rows

how do you clean your data?

Geospatial

• geojson - GeoJSON classes

• geojsonio - GeoJSON/TopoJSON input/output

• geonames - Geonames API client

• wicket Well-Known Text tools

• wellknown - WKT from R objects - and convert to GeoJSON

• rnaturalearth - NaturalEarth data

• osmplotr - Open Street Maps tools

• opencage - OpenCage geocoding API

• lawn - Turf.js javascript geo client

• geojsonlint - lint GeoJSON

Geospatial: Geonames data - geonames

http://www.geonames.org/

library(geonames)

Find a contry code

GNcountryCode(lat = 47.03, lng = 10.2)

Search for nearby streets

GNfindNearbyStreets(lat = 37.45, lng = -122.18)

Search by place name

GNsearch(q = "london", maxRows = 10)

Postal code search

GNpostalCodeSearch(postalcode = 90210, country = "FI")

Geospatial: conversion between data/spatial data formats - geojsonio

• geojson_list - convert to GeoJSON as R list

• geojson_json - convert to GeoJSON as JSON

• geojson_read/geojson_write - read/write GeoJSON

from most R object types + many spatial data formats

Geospatial: Spatial ops. w/ GeoJSON & w/o heavy dependencies - lawn

library(lawn)
bbox <- c(-118.521, 33.715, -118.145, 34.179)
lawn_hex_grid(bbox, 10, 'miles') %>%
  as_feature(hex_grid) %>%
  purrr::map(lawn_centroid) %>%
  purrr::map(lawn_circle, radius = 5) %>%
  view

Climate data

• rnoaa - Lots of NOAA data

• ccafs - Climate Change, Agriculture, and Food Security (CCAFS) General Circulation Models (GCM) data

• camsRad - CAMS Radiation service

• clifro - New Zealand's National Climate Database CliFlo

• GSODR - NOAA Global Summary Daily Weather Data

• riem - ASOS data via the Iowa Environment Mesonet

• ropenaq - OpenAQ API client

• rWBclimate - World Bank climate data

NOAA data - rnoaa

library(rnoaa)
sta <- 'GHCND:USW00014895'
out1 <- ncdc('GHCND', stationid=sta, datatypeid='PRCP',
             startdate = '2010-03-01', enddate = '2010-05-31', limit=500)
out2 <- ncdc('GHCND', stationid=sta, datatypeid='PRCP',
             startdate = '2010-09-01', enddate = '2010-10-31', limit=500)
ncdc_plot(out1, out2, breaks="1 month", dateformat="%m/%d")

Phylogenies

• brranching - Phylomatic

• phylocomr - Phylocom client :)

• rotl - Open Tree of Life

• treebase - TreeBase

Phylocom - phylocomr

Cross Platform Phylocom Client!!

library(phylocomr)
taxa_file <- system.file("examples/taxa", package = "phylocomr")
(taxa_str <- readLines(taxa_file))
[1] "campanulaceae/lobelia/lobelia_conferta"           "cyperaceae/mapania/mapania_africana"
[3] "amaryllidaceae/narcissus/narcissus_cuatrecasasii"

phylo_file <- system.file("examples/phylo", package = "phylocomr")
(phylo_str <- readLines(phylo_file))
[1] "(((((eliea_articulata,homalanthus_populneus)malpighiales,rosa_willmottiae),((macrocentrum_neblinae,qualea_clavata),hibiscus_pohlii)malvids),(((lobelia_conferta,((millotia_depauperata,(layia_chrysanthemoides,layia_pentachaeta)layia),senecio_flanaganii)asteraceae)asterales,schwenkia_americana),tapinanthus_buntingii)),(narcissus_cuatrecasasii,mapania_africana))poales_to_asterales;"

(tree <- ph_phylomatic(taxa = taxa_str, phylo = phylo_str))
[1] "(lobelia_conferta:5.000000,(mapania_africana:1.000000,narcissus_cuatrecasasii:1.000000):1.000000)poales_to_asterales;"

library(phytools)
plot(read.newick(text = tree))

What's too hard?

scotttalks.info/ossps2

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