README.md
In annakrystalli/seabirddietDB: Seabird Diet Database
seabirddietDB
The goal of seabirddietDB is to provide access and tools to interact
with a database of seabird diets collected around the British Isles.
Installation
You can install the development version from
GitHub with:
# install.packages("remotes")
remotes::install_github("annakrystalli/seabirddietDB")
Data
For more details on the dataset, check out the full
documentation
Example
To access the data simply load the package. The data is then available
library(seabirddietDB)
seabirddiet
#> # A tibble: 2,857 x 33
#> id year startyear endyear multiyear location latitude longitude
#> <int> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
#> 1 1 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 2 2 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 3 3 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 4 4 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 5 5 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 6 6 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 7 7 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 8 8 1983 1983 1983 FALSE East An… 52.5 1
#> 9 9 1983 1983 1983 FALSE East An… 52.5 1
#> 10 10 1983 1983 1983 FALSE East An… 52.5 1
#> # … with 2,847 more rows, and 25 more variables: pred_common_name <chr>,
#> # pred_species <chr>, pred_rank <chr>, pred_aphia_id <int>,
#> # pred_valid_name <chr>, pred_valid_aphia_id <int>,
#> # pred_breeding_status <chr>, pred_age_group <chr>,
#> # prey_orig_descr <chr>, prey_taxon <chr>, prey_rank <chr>,
#> # prey_aphia_id <int>, prey_valid_name <chr>, prey_valid_aphia_id <int>,
#> # prey_size <chr>, prey_age_group <chr>, freq_occ <dbl>, freq_num <dbl>,
#> # freq_biomass <dbl>, sample_size <dbl>, sample_type <chr>, ref_n <dbl>,
#> # ref_ids <chr>, source <chr>, notes <chr>
A version with more formal data types/structures in the columns (ie
geographic information stored as sf, categorical data as factors etc)
is also available. Note that you will need package sf installed to
load this version of the data.
class(seabirddiet_)
Helpers
metadata
A number of metadata datasets are included with the package:
attributes, references and classification. Load them as you would
one of the datasets, e.g. :
references
#> # A tibble: 97 x 2
#> ref_id ref_valid
#> <chr> <chr>
#> 1 REF001 Anderson et al 2014 Ibis 156, 23-34
#> 2 REF002 Bailey et al 1991 ICES Mar. Sci. Symp., 193, 209-216
#> 3 REF003 Baker et 1999 JNCC Report 289, 1-51
#> 4 REF004 Blake BF 1984 J. Experimental. Mar.Biol. Ecol. 76, 89-103
#> 5 REF005 Blake et al 1955 Estuarine, Coastal & Shelf Science 20, 559-568
#> 6 REF006 Blake et al 1980 Unpubl NCC Report
#> 7 REF007 Blake et al 1985 Estuarine, Coastal & Shelf Science 20, 559-568
#> 8 REF008 Bull et al 2001 JNCC Report, No. 303
#> 9 REF009 Bull et al 2001 JNCC Report, No. 315
#> 10 REF010 Bull et al 2004 Ardea 92, 43-52
#> # … with 87 more rows
List
List predators
sbd_predators()
#> [1] "Alca torda" "Fratercula arctica"
#> [3] "Fulmarus glacialis" "Morus bassanus"
#> [5] "Phalacrocorax aristotelis" "Puffinus puffinus"
#> [7] "Rissa tridactyla" "Uria aalge"
List prey
sbd_prey(verbose = TRUE)
#> # A tibble: 129 x 5
#> prey_taxon prey_rank prey_aphia_id prey_valid_name prey_valid_aphi…
#> <chr> <chr> <int> <chr> <int>
#> 1 Acanthephyra genus 107018 Acanthephyra 107018
#> 2 Acanthephyra… species 107581 Acanthephyra pel… 107581
#> 3 Actinopteryg… class 10194 Actinopterygii 10194
#> 4 Agonidae family 125588 Agonidae 125588
#> 5 Agonus catap… species 127190 Agonus cataphrac… 127190
#> 6 Alloteuthis … species 153131 Alloteuthis subu… 153131
#> 7 Ammodytes la… species 146485 Hyperoplus lance… 126756
#> 8 Ammodytes ma… species 126751 Ammodytes marinus 126751
#> 9 Ammodytes to… species 126752 Ammodytes tobian… 126752
#> 10 Ammodytidae family 125516 Ammodytidae 125516
#> # … with 119 more rows
Filter data
filter predator
sbd_filter(pred_species = "Fratercula arctica")
#> # A tibble: 615 x 33
#> id year startyear endyear multiyear location latitude longitude
#> <int> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
#> 1 1 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 2 2 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 3 3 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 4 4 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 5 5 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 6 6 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 7 7 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 8 8 1983 1983 1983 FALSE East An… 52.5 1
#> 9 9 1983 1983 1983 FALSE East An… 52.5 1
#> 10 10 1983 1983 1983 FALSE East An… 52.5 1
#> # … with 605 more rows, and 25 more variables: pred_common_name <chr>,
#> # pred_species <chr>, pred_rank <chr>, pred_aphia_id <int>,
#> # pred_valid_name <chr>, pred_valid_aphia_id <int>,
#> # pred_breeding_status <chr>, pred_age_group <chr>,
#> # prey_orig_descr <chr>, prey_taxon <chr>, prey_rank <chr>,
#> # prey_aphia_id <int>, prey_valid_name <chr>, prey_valid_aphia_id <int>,
#> # prey_size <chr>, prey_age_group <chr>, freq_occ <dbl>, freq_num <dbl>,
#> # freq_biomass <dbl>, sample_size <dbl>, sample_type <chr>, ref_n <dbl>,
#> # ref_ids <chr>, source <chr>, notes <chr>
filter metrics
sbd_filter(pred_species = "Fratercula arctica", metrics = "freq_biomass")
#> # A tibble: 219 x 31
#> id year startyear endyear multiyear location latitude longitude
#> <int> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
#> 1 291 1973 1973 1973 FALSE Isle of… 56.2 -2.56
#> 2 292 1973 1973 1973 FALSE Isle of… 56.2 -2.56
#> 3 293 1973 1973 1973 FALSE Isle of… 56.2 -2.56
#> 4 298 1973 1973 1973 FALSE Isle of… 56.2 -2.56
#> 5 299 1973 1973 1973 FALSE Isle of… 56.2 -2.56
#> 6 300 1974 1974 1974 FALSE Isle of… 56.2 -2.56
#> 7 301 1974 1974 1974 FALSE Isle of… 56.2 -2.56
#> 8 308 1974 1974 1974 FALSE Isle of… 56.2 -2.56
#> 9 309 1974 1974 1974 FALSE Isle of… 56.2 -2.56
#> 10 310 1975 1975 1975 FALSE Isle of… 56.2 -2.56
#> # … with 209 more rows, and 23 more variables: pred_common_name <chr>,
#> # pred_species <chr>, pred_rank <chr>, pred_aphia_id <int>,
#> # pred_valid_name <chr>, pred_valid_aphia_id <int>,
#> # pred_breeding_status <chr>, pred_age_group <chr>,
#> # prey_orig_descr <chr>, prey_taxon <chr>, prey_rank <chr>,
#> # prey_aphia_id <int>, prey_valid_name <chr>, prey_valid_aphia_id <int>,
#> # prey_size <chr>, prey_age_group <chr>, freq_biomass <dbl>,
#> # sample_size <dbl>, sample_type <chr>, ref_n <dbl>, ref_ids <chr>,
#> # source <chr>, notes <chr>
filter prey
sbd_filter(prey_taxon = c("Cottidae", "Actinopterygii"))
#> # A tibble: 124 x 33
#> id year startyear endyear multiyear location latitude longitude
#> <int> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
#> 1 360 1982 1982 1982 FALSE Isle of… 56.2 -2.56
#> 2 366 1983 1983 1983 FALSE Isle of… 56.2 -2.56
#> 3 374 1984 1984 1984 FALSE Isle of… 56.2 -2.56
#> 4 899 1990 1990 1990 FALSE Isle of… 56.2 -2.56
#> 5 911 1992 1992 1992 FALSE Isle of… 56.2 -2.56
#> 6 1055 2006 2006 2006 FALSE Isle of… 56.2 -2.56
#> 7 1197 2009 2006 2011 TRUE Anglesey 53.3 -4.43
#> 8 1201 2009 2006 2011 TRUE Bempton 54.1 -0.17
#> 9 1214 2009 2006 2011 TRUE Bullers… 57.4 -1.82
#> 10 1218 2009 2006 2011 TRUE Burravoe 60.6 -1.05
#> # … with 114 more rows, and 25 more variables: pred_common_name <chr>,
#> # pred_species <chr>, pred_rank <chr>, pred_aphia_id <int>,
#> # pred_valid_name <chr>, pred_valid_aphia_id <int>,
#> # pred_breeding_status <chr>, pred_age_group <chr>,
#> # prey_orig_descr <chr>, prey_taxon <chr>, prey_rank <chr>,
#> # prey_aphia_id <int>, prey_valid_name <chr>, prey_valid_aphia_id <int>,
#> # prey_size <chr>, prey_age_group <chr>, freq_occ <dbl>, freq_num <dbl>,
#> # freq_biomass <dbl>, sample_size <dbl>, sample_type <chr>, ref_n <dbl>,
#> # ref_ids <chr>, source <chr>, notes <chr>
filter year
sbd_filter(year = 1973:1976)
#> # A tibble: 207 x 33
#> id year startyear endyear multiyear location latitude longitude
#> <int> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
#> 1 1 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 2 2 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 3 3 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 4 4 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 5 5 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 6 6 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 7 7 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 8 11 1974 1974 1974 FALSE Fair Is… 59.6 -1.63
#> 9 12 1974 1974 1974 FALSE Fair Is… 59.6 -1.63
#> 10 13 1974 1974 1974 FALSE Fair Is… 59.6 -1.63
#> # … with 197 more rows, and 25 more variables: pred_common_name <chr>,
#> # pred_species <chr>, pred_rank <chr>, pred_aphia_id <int>,
#> # pred_valid_name <chr>, pred_valid_aphia_id <int>,
#> # pred_breeding_status <chr>, pred_age_group <chr>,
#> # prey_orig_descr <chr>, prey_taxon <chr>, prey_rank <chr>,
#> # prey_aphia_id <int>, prey_valid_name <chr>, prey_valid_aphia_id <int>,
#> # prey_size <chr>, prey_age_group <chr>, freq_occ <dbl>, freq_num <dbl>,
#> # freq_biomass <dbl>, sample_size <dbl>, sample_type <chr>, ref_n <dbl>,
#> # ref_ids <chr>, source <chr>, notes <chr>
filter multiple
sbd_filter(year = 1973:1976, pred_species = "Uria aalge", prey_taxon = "Gadidae")
#> # A tibble: 3 x 33
#> id year startyear endyear multiyear location latitude longitude
#> <int> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
#> 1 1333 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 2 1699 1973 1973 1973 FALSE Skomer 51.6 -5.29
#> 3 1704 1975 1975 1975 FALSE Skomer 51.6 -5.29
#> # … with 25 more variables: pred_common_name <chr>, pred_species <chr>,
#> # pred_rank <chr>, pred_aphia_id <int>, pred_valid_name <chr>,
#> # pred_valid_aphia_id <int>, pred_breeding_status <chr>,
#> # pred_age_group <chr>, prey_orig_descr <chr>, prey_taxon <chr>,
#> # prey_rank <chr>, prey_aphia_id <int>, prey_valid_name <chr>,
#> # prey_valid_aphia_id <int>, prey_size <chr>, prey_age_group <chr>,
#> # freq_occ <dbl>, freq_num <dbl>, freq_biomass <dbl>, sample_size <dbl>,
#> # sample_type <chr>, ref_n <dbl>, ref_ids <chr>, source <chr>,
#> # notes <chr>
Plot data
There are additional helpers for interactive plotting of data
sbd_plot_predators(year = 1973)
To cite use
citation("seabirddietDB")
#>
#> To cite package 'seabirddietDB' in publications use:
#>
#> Anna Krystalli, Agnes Olin, James Grecian and Ruedi Nager
#> (2019). seabirddietDB: Seabird Diet Database. R package version
#> 0.0.1. https://github.com/annakrystalli/seabirddietDB
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {seabirddietDB: Seabird Diet Database},
#> author = {Anna Krystalli and Agnes Olin and James Grecian and Ruedi Nager},
#> year = {2019},
#> note = {R package version 0.0.1},
#> url = {https://github.com/annakrystalli/seabirddietDB},
#> }
annakrystalli/seabirddietDB documentation built on Nov. 2, 2019, 1:54 p.m.
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seabirddietDB
The goal of seabirddietDB is to provide access and tools to interact with a database of seabird diets collected around the British Isles.
Installation
You can install the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("annakrystalli/seabirddietDB")
Data
For more details on the dataset, check out the full documentation
Example
To access the data simply load the package. The data is then available
library(seabirddietDB)
seabirddiet
#> # A tibble: 2,857 x 33
#> id year startyear endyear multiyear location latitude longitude
#> <int> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
#> 1 1 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 2 2 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 3 3 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 4 4 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 5 5 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 6 6 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 7 7 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 8 8 1983 1983 1983 FALSE East An… 52.5 1
#> 9 9 1983 1983 1983 FALSE East An… 52.5 1
#> 10 10 1983 1983 1983 FALSE East An… 52.5 1
#> # … with 2,847 more rows, and 25 more variables: pred_common_name <chr>,
#> # pred_species <chr>, pred_rank <chr>, pred_aphia_id <int>,
#> # pred_valid_name <chr>, pred_valid_aphia_id <int>,
#> # pred_breeding_status <chr>, pred_age_group <chr>,
#> # prey_orig_descr <chr>, prey_taxon <chr>, prey_rank <chr>,
#> # prey_aphia_id <int>, prey_valid_name <chr>, prey_valid_aphia_id <int>,
#> # prey_size <chr>, prey_age_group <chr>, freq_occ <dbl>, freq_num <dbl>,
#> # freq_biomass <dbl>, sample_size <dbl>, sample_type <chr>, ref_n <dbl>,
#> # ref_ids <chr>, source <chr>, notes <chr>
A version with more formal data types/structures in the columns (ie
geographic information stored as sf, categorical data as factors etc)
is also available. Note that you will need package sf installed to
load this version of the data.
class(seabirddiet_)
Helpers
metadata
A number of metadata datasets are included with the package:
attributes, references and classification. Load them as you would
one of the datasets, e.g. :
references
#> # A tibble: 97 x 2
#> ref_id ref_valid
#> <chr> <chr>
#> 1 REF001 Anderson et al 2014 Ibis 156, 23-34
#> 2 REF002 Bailey et al 1991 ICES Mar. Sci. Symp., 193, 209-216
#> 3 REF003 Baker et 1999 JNCC Report 289, 1-51
#> 4 REF004 Blake BF 1984 J. Experimental. Mar.Biol. Ecol. 76, 89-103
#> 5 REF005 Blake et al 1955 Estuarine, Coastal & Shelf Science 20, 559-568
#> 6 REF006 Blake et al 1980 Unpubl NCC Report
#> 7 REF007 Blake et al 1985 Estuarine, Coastal & Shelf Science 20, 559-568
#> 8 REF008 Bull et al 2001 JNCC Report, No. 303
#> 9 REF009 Bull et al 2001 JNCC Report, No. 315
#> 10 REF010 Bull et al 2004 Ardea 92, 43-52
#> # … with 87 more rows
List
List predators
sbd_predators()
#> [1] "Alca torda" "Fratercula arctica"
#> [3] "Fulmarus glacialis" "Morus bassanus"
#> [5] "Phalacrocorax aristotelis" "Puffinus puffinus"
#> [7] "Rissa tridactyla" "Uria aalge"
List prey
sbd_prey(verbose = TRUE)
#> # A tibble: 129 x 5
#> prey_taxon prey_rank prey_aphia_id prey_valid_name prey_valid_aphi…
#> <chr> <chr> <int> <chr> <int>
#> 1 Acanthephyra genus 107018 Acanthephyra 107018
#> 2 Acanthephyra… species 107581 Acanthephyra pel… 107581
#> 3 Actinopteryg… class 10194 Actinopterygii 10194
#> 4 Agonidae family 125588 Agonidae 125588
#> 5 Agonus catap… species 127190 Agonus cataphrac… 127190
#> 6 Alloteuthis … species 153131 Alloteuthis subu… 153131
#> 7 Ammodytes la… species 146485 Hyperoplus lance… 126756
#> 8 Ammodytes ma… species 126751 Ammodytes marinus 126751
#> 9 Ammodytes to… species 126752 Ammodytes tobian… 126752
#> 10 Ammodytidae family 125516 Ammodytidae 125516
#> # … with 119 more rows
Filter data
filter predator
sbd_filter(pred_species = "Fratercula arctica")
#> # A tibble: 615 x 33
#> id year startyear endyear multiyear location latitude longitude
#> <int> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
#> 1 1 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 2 2 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 3 3 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 4 4 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 5 5 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 6 6 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 7 7 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 8 8 1983 1983 1983 FALSE East An… 52.5 1
#> 9 9 1983 1983 1983 FALSE East An… 52.5 1
#> 10 10 1983 1983 1983 FALSE East An… 52.5 1
#> # … with 605 more rows, and 25 more variables: pred_common_name <chr>,
#> # pred_species <chr>, pred_rank <chr>, pred_aphia_id <int>,
#> # pred_valid_name <chr>, pred_valid_aphia_id <int>,
#> # pred_breeding_status <chr>, pred_age_group <chr>,
#> # prey_orig_descr <chr>, prey_taxon <chr>, prey_rank <chr>,
#> # prey_aphia_id <int>, prey_valid_name <chr>, prey_valid_aphia_id <int>,
#> # prey_size <chr>, prey_age_group <chr>, freq_occ <dbl>, freq_num <dbl>,
#> # freq_biomass <dbl>, sample_size <dbl>, sample_type <chr>, ref_n <dbl>,
#> # ref_ids <chr>, source <chr>, notes <chr>
filter metrics
sbd_filter(pred_species = "Fratercula arctica", metrics = "freq_biomass")
#> # A tibble: 219 x 31
#> id year startyear endyear multiyear location latitude longitude
#> <int> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
#> 1 291 1973 1973 1973 FALSE Isle of… 56.2 -2.56
#> 2 292 1973 1973 1973 FALSE Isle of… 56.2 -2.56
#> 3 293 1973 1973 1973 FALSE Isle of… 56.2 -2.56
#> 4 298 1973 1973 1973 FALSE Isle of… 56.2 -2.56
#> 5 299 1973 1973 1973 FALSE Isle of… 56.2 -2.56
#> 6 300 1974 1974 1974 FALSE Isle of… 56.2 -2.56
#> 7 301 1974 1974 1974 FALSE Isle of… 56.2 -2.56
#> 8 308 1974 1974 1974 FALSE Isle of… 56.2 -2.56
#> 9 309 1974 1974 1974 FALSE Isle of… 56.2 -2.56
#> 10 310 1975 1975 1975 FALSE Isle of… 56.2 -2.56
#> # … with 209 more rows, and 23 more variables: pred_common_name <chr>,
#> # pred_species <chr>, pred_rank <chr>, pred_aphia_id <int>,
#> # pred_valid_name <chr>, pred_valid_aphia_id <int>,
#> # pred_breeding_status <chr>, pred_age_group <chr>,
#> # prey_orig_descr <chr>, prey_taxon <chr>, prey_rank <chr>,
#> # prey_aphia_id <int>, prey_valid_name <chr>, prey_valid_aphia_id <int>,
#> # prey_size <chr>, prey_age_group <chr>, freq_biomass <dbl>,
#> # sample_size <dbl>, sample_type <chr>, ref_n <dbl>, ref_ids <chr>,
#> # source <chr>, notes <chr>
filter prey
sbd_filter(prey_taxon = c("Cottidae", "Actinopterygii"))
#> # A tibble: 124 x 33
#> id year startyear endyear multiyear location latitude longitude
#> <int> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
#> 1 360 1982 1982 1982 FALSE Isle of… 56.2 -2.56
#> 2 366 1983 1983 1983 FALSE Isle of… 56.2 -2.56
#> 3 374 1984 1984 1984 FALSE Isle of… 56.2 -2.56
#> 4 899 1990 1990 1990 FALSE Isle of… 56.2 -2.56
#> 5 911 1992 1992 1992 FALSE Isle of… 56.2 -2.56
#> 6 1055 2006 2006 2006 FALSE Isle of… 56.2 -2.56
#> 7 1197 2009 2006 2011 TRUE Anglesey 53.3 -4.43
#> 8 1201 2009 2006 2011 TRUE Bempton 54.1 -0.17
#> 9 1214 2009 2006 2011 TRUE Bullers… 57.4 -1.82
#> 10 1218 2009 2006 2011 TRUE Burravoe 60.6 -1.05
#> # … with 114 more rows, and 25 more variables: pred_common_name <chr>,
#> # pred_species <chr>, pred_rank <chr>, pred_aphia_id <int>,
#> # pred_valid_name <chr>, pred_valid_aphia_id <int>,
#> # pred_breeding_status <chr>, pred_age_group <chr>,
#> # prey_orig_descr <chr>, prey_taxon <chr>, prey_rank <chr>,
#> # prey_aphia_id <int>, prey_valid_name <chr>, prey_valid_aphia_id <int>,
#> # prey_size <chr>, prey_age_group <chr>, freq_occ <dbl>, freq_num <dbl>,
#> # freq_biomass <dbl>, sample_size <dbl>, sample_type <chr>, ref_n <dbl>,
#> # ref_ids <chr>, source <chr>, notes <chr>
filter year
sbd_filter(year = 1973:1976)
#> # A tibble: 207 x 33
#> id year startyear endyear multiyear location latitude longitude
#> <int> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
#> 1 1 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 2 2 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 3 3 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 4 4 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 5 5 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 6 6 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 7 7 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 8 11 1974 1974 1974 FALSE Fair Is… 59.6 -1.63
#> 9 12 1974 1974 1974 FALSE Fair Is… 59.6 -1.63
#> 10 13 1974 1974 1974 FALSE Fair Is… 59.6 -1.63
#> # … with 197 more rows, and 25 more variables: pred_common_name <chr>,
#> # pred_species <chr>, pred_rank <chr>, pred_aphia_id <int>,
#> # pred_valid_name <chr>, pred_valid_aphia_id <int>,
#> # pred_breeding_status <chr>, pred_age_group <chr>,
#> # prey_orig_descr <chr>, prey_taxon <chr>, prey_rank <chr>,
#> # prey_aphia_id <int>, prey_valid_name <chr>, prey_valid_aphia_id <int>,
#> # prey_size <chr>, prey_age_group <chr>, freq_occ <dbl>, freq_num <dbl>,
#> # freq_biomass <dbl>, sample_size <dbl>, sample_type <chr>, ref_n <dbl>,
#> # ref_ids <chr>, source <chr>, notes <chr>
filter multiple
sbd_filter(year = 1973:1976, pred_species = "Uria aalge", prey_taxon = "Gadidae")
#> # A tibble: 3 x 33
#> id year startyear endyear multiyear location latitude longitude
#> <int> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
#> 1 1333 1973 1973 1973 FALSE Co. Ker… 52.2 -9.75
#> 2 1699 1973 1973 1973 FALSE Skomer 51.6 -5.29
#> 3 1704 1975 1975 1975 FALSE Skomer 51.6 -5.29
#> # … with 25 more variables: pred_common_name <chr>, pred_species <chr>,
#> # pred_rank <chr>, pred_aphia_id <int>, pred_valid_name <chr>,
#> # pred_valid_aphia_id <int>, pred_breeding_status <chr>,
#> # pred_age_group <chr>, prey_orig_descr <chr>, prey_taxon <chr>,
#> # prey_rank <chr>, prey_aphia_id <int>, prey_valid_name <chr>,
#> # prey_valid_aphia_id <int>, prey_size <chr>, prey_age_group <chr>,
#> # freq_occ <dbl>, freq_num <dbl>, freq_biomass <dbl>, sample_size <dbl>,
#> # sample_type <chr>, ref_n <dbl>, ref_ids <chr>, source <chr>,
#> # notes <chr>
Plot data
There are additional helpers for interactive plotting of data
sbd_plot_predators(year = 1973)
To cite use
citation("seabirddietDB")
#>
#> To cite package 'seabirddietDB' in publications use:
#>
#> Anna Krystalli, Agnes Olin, James Grecian and Ruedi Nager
#> (2019). seabirddietDB: Seabird Diet Database. R package version
#> 0.0.1. https://github.com/annakrystalli/seabirddietDB
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {seabirddietDB: Seabird Diet Database},
#> author = {Anna Krystalli and Agnes Olin and James Grecian and Ruedi Nager},
#> year = {2019},
#> note = {R package version 0.0.1},
#> url = {https://github.com/annakrystalli/seabirddietDB},
#> }
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