Example data records - ices-taf/doc GitHub Wiki
See also: Creating a TAF analysis, Bib entries.
This page was based on using the icesTAF
package version 4.2.0
dated
2023-03-21
.
In this guide
Using a script to download a zip file
We will start with an empty repository
test_datasets
¦--boot
¦ °--initial
¦ °--data
¦--data.R
¦--model.R
¦--output.R
°--report.R
Consider the following script that downloads a zip file containing ESRI shapefiles of ICES areas, unzips it and deletes the zip file
filename <- "ICES_areas.zip"
# download and unzip
download(paste0("http://gis.ices.dk/shapefiles/", filename))
unzip(filename)
# delete zip file
unlink(filename)
This code can be used to download shapefiles for use in a TAF assessment
by specifying script
as the source
of the data. This is done by
creating the following metadata record in DATA.bib
@Misc{icesareas,
originator = {ICES},
year = {2023},
title = {ICES Areas ESRI Shapefile},
access = {Public},
source = {script},
}
and an accompanying R script. The R script must have the same name as
the ‘key’ field. In this case the key is icesareas
so the script must
be called icesareas.R
. The DATA.bib
entry can be created using the
draft.data
function
draft.data(
data.files = "icesareas",
originator = "ICES",
title = "ICES Areas ESRI Shapefile",
period = FALSE,
source = "script",
file = TRUE
)
After you have created the script in the boot folder called
icesareas.R
the directory structure should look like this:
test_datasets
¦--boot
¦ ¦--DATA.bib
¦ ¦--icesareas.R
¦ °--initial
¦ °--data
¦--data.R
¦--model.R
¦--output.R
°--report.R
Now we can run
taf.boot
to process
the script to download the process the zip file and now the directory
structure will look like this
taf.boot()
test_datasets
¦--boot
¦ ¦--data
¦ ¦ °--icesareas
¦ ¦ ¦--DISCLAIMER_GIS.txt
¦ ¦ ¦--ICES_Areas_20160601_cut_dense_3857.cpg
¦ ¦ ¦--ICES_Areas_20160601_cut_dense_3857.dbf
¦ ¦ ¦--ICES_Areas_20160601_cut_dense_3857.prj
¦ ¦ ¦--ICES_Areas_20160601_cut_dense_3857.sbn
¦ ¦ ¦--ICES_Areas_20160601_cut_dense_3857.sbx
¦ ¦ ¦--ICES_Areas_20160601_cut_dense_3857.shp
¦ ¦ ¦--ICES_Areas_20160601_cut_dense_3857.shp.xml
¦ ¦ °--ICES_Areas_20160601_cut_dense_3857.shx
¦ ¦--DATA.bib
¦ ¦--icesareas.R
¦ °--initial
¦ °--data
¦--data.R
¦--model.R
¦--output.R
°--report.R
Get the ICES Word template
We will start with an empty repository
test_datasets
¦--boot
¦ °--initial
¦ °--data
¦--data.R
¦--model.R
¦--output.R
°--report.R
To download this file for use in an automated report for a TAF
assessment, specify the location of the file as the source
of the data
record. The file is held in the
ices-taf/doc repository and is called
reportTemplate.docx. This is done by creating the following meta-data
record in DATA.bib
@Misc{reportTemplate.docx,
originator = {ICES},
year = {2023},
title = {ICES TAF Word template for report automation},
access = {Public},
source = {https://github.com/ices-taf/doc/raw/master/reportTemplate.docx},
}
The DATA.bib
entry can be created using the
draft.data
function
draft.data(
data.files = "reportTemplate.docx",
originator = "ICES",
title = "ICES TAF Word template for report automation",
period = FALSE,
source = "https://github.com/ices-taf/doc/raw/master/reportTemplate.docx",
file = TRUE
)
The directory structure should now look like this:
test_datasets
¦--boot
¦ ¦--DATA.bib
¦ °--initial
¦ °--data
¦--data.R
¦--model.R
¦--output.R
°--report.R
Now we can run
taf.boot
to process
the script to download the Word template file and now the directory
structure will look like this
taf.boot()
test_datasets
¦--boot
¦ ¦--data
¦ ¦ °--reportTemplate.docx
¦ ¦--DATA.bib
¦ °--initial
¦ °--data
¦--data.R
¦--model.R
¦--output.R
°--report.R
DATA.bib
entries
Further ICES Statistical Rectangles mapped to Ecoregions
DATA.bib
entry:
@Misc{ICES_StatRec_mapto_Ecoregions,
originator = {DTU Aqua},
year = {2019},
title = {ICES Stat rec ESRI Shapefile},
url = {https://gis.ices.dk/geonetwork/srv/metadata/81f68a99-9b91-4762-80d3-31c069731f44},
source = {script},
}
with the R script: ICES_StatRec_mapto_Ecoregions.R
filename <- "ICES_StatRec_mapto_Ecoregions.zip"
# download and unzip
download(paste0("http://gis.ices.dk/shapefiles/", filename))
unzip(filename)
# delete zip file
unlink(filename)
ICES Ecoregion
DATA.bib
entry:
@Misc{ICES_ecoregions,
originator = {ICES},
year = {2019},
title = {ICES Ecoregion ESRI Shapefile},
url = {https://gis.ices.dk/geonetwork/srv/metadata/4745e824-a612-4a1f-bc56-b540772166eb},
source = {script},
}
with the R script: ICES_ecoregions.R
filename <- "ICES_ecoregions.zip"
# download and unzip
download(paste0("http://gis.ices.dk/shapefiles/", filename))
unzip(filename)
# delete zip file
unlink(filename)
Species lookup table from ICES SD database
DATA.bib
entry:
@Misc{ICES_SD_species_lookup,
originator = {ICES},
year = {2019},
title = {ICES Fisheries Guild lookup table},
url = {https://gis.ices.dk/geonetwork/srv/metadata/30541cf4-0236-437f-9757-596c5f793cff},
source = {script},
}
with the R script: ICES_SD_species_lookup.R
library(icesSD)
library(magrittr)
sid <- getSD()
# get lookup table for species, common name and Fisheries guild from SID
species_lookup <-
sid %>%
filter(ActiveYear > 2018) %>%
select(
SpeciesScientificName,
SpeciesCommonName,
FisheriesGuild
) %>%
mutate(
FisheriesGuild = tolower(FisheriesGuild)
) %>%
filter(
!is.na(FisheriesGuild) &
!is.na(SpeciesScientificName)
) %>%
unique()
write.taf(species_lookup, quote = TRUE)