This R Markdown document is designed to transform data that is not in CWP format into CWP format. Initially, it changes the format of the data; subsequently, it maps the data to adhere to CWP standards. This markdown is automatically created from the function: https://raw.githubusercontent.com/eblondel/geoflow-tunaatlas/master/R/tunaatlas_scripts/pre-harmonization/southern_hemisphere_oceans_catch_1deg_1m_tunaatlasccsbt_level0__surface.R, the documentation keeps the format of roxygen2 skeleton.
A summary of the mapping process is provided. The path to the dataset is specified. You will find on this same repository on GitHub the first line of each dataset. The datasets are named after the historical name provided by tRFMOs while exporting and may change. The information provided in the Rmd allows understanding correctly which dataset should be used in this markdown.
Additional operations are performed next to verify other aspects of the data, such as the consistency of the geolocation, the values, and the reported catches in numbers and tons.
If you are interested in further details, the results and codes are available for review.
Each .Rmd script requires the user to knit the
dataset at the beginning of the script in order to execute the
harmonization process correctly. It is also possible to run the code
chunk by chunk but be sure to be in the correct working directory (i.e.,
the one of the .Rmd).
path_to_raw_dataset <- here::here('R/tunaatlas_scripts/pre-harmonization', 'ccsbt', 'catch', 'data', 'CEData_Surface.xlsx')Harmonize CCSBT Surface Catch Datasets
This function harmonizes the CCSBT Surface catch datasets, preparing them for integration into the Tuna Atlas database, according to specified format requirements.
@return None; the function outputs files directly, including harmonized datasets, optional metadata, and code lists for integration within the Tuna Atlas database.
@details This function modifies the dataset to ensure compliance with the standardized format, including renaming, reordering, and recalculating specific fields as necessary. Metadata integration is contingent on the intended use within the Tuna Atlas database.
@import dplyr @import readxl @importFrom stringr str_replace @seealso @export @keywords data harmonization, fisheries, CCSBT, tuna @author Paul Taconet, IRD @author Bastien Grasset, IRD Input data sample (after importing as data.frame in R):
# YEAR MONTH COUNTRY_CODE GEAR_CODE CCSBT_STATISTICAL_AREA LATITUDE LONGITUDE NUMBER_OF_HOURS_SEARCHED WEIGHT_(Kg)_OF_SBT_RETAINED
# 1975 12 AU PS 4 -37 150 17 59800
# 1976 8 AU BB 4 -36 150 8 0
# 1976 9 AU BB 4 -37 150 14 36200
# 1976 9 AU BB 4 -35 150 4 0
# 1976 9 AU BB 4 -34 151 21 0
# 1976 10 AU BB 4 -37 150 51 41000
# Catch: final data sample:
# FishingFleet Gear time_start time_end AreaName School Species CatchType CatchUnits Catch
# AU BB 1976-09-01 1976-10-01 6337150 ALL SBT ALL MT 36.2
# AU BB 1976-10-01 1976-11-01 6337150 ALL SBT ALL MT 41.0
# AU BB 1976-12-01 1977-01-01 6332132 ALL SBT ALL MT 167.5
# AU BB 1976-12-01 1977-01-01 6333134 ALL SBT ALL MT 35.6
# AU BB 1976-12-01 1977-01-01 6334134 ALL SBT ALL MT 56.5
# AU BB 1976-12-01 1977-01-01 6334135 ALL SBT ALL MT 37.0
source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/R/sardara_functions/harmo_time_2.R")
source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/R/sardara_functions/harmo_spatial_5.R")
source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/R/sardara_functions/format_time_db_format.R")
#packages
if(!require(readxl)){
install.packages("readxl")
require(readxl)
}Historical name for the dataset at source CEData_Surface.xlsx
opts <- options()
options(encoding = "UTF-8")RFMO_CE<-readxl::read_excel(path_to_raw_dataset, sheet = "CEData_Surface", col_names = TRUE, col_types = NULL,na = "")
colnames(RFMO_CE)<-gsub("\r\n", "_", colnames(RFMO_CE))
colnames(RFMO_CE)<-gsub(" ", "_", colnames(RFMO_CE))
colnames(RFMO_CE)<-gsub("\\(", "", colnames(RFMO_CE))
colnames(RFMO_CE)<-gsub("\\)", "", colnames(RFMO_CE))
RFMO_CE<-as.data.frame(RFMO_CE)Remove lines that are read in the Excel but that are not real
RFMO_CE<- RFMO_CE[!is.na(RFMO_CE$YEAR),]
RFMO_CE$WEIGHT_Kg_OF_SBT_RETAINED<-as.numeric(RFMO_CE$WEIGHT_Kg_OF_SBT_RETAINED)
RFMO_CE$NUMBER_OF_HOURS_SEARCHED<-as.numeric(RFMO_CE$NUMBER_OF_HOURS_SEARCHED)FishingFleet
RFMO_CE$FishingFleet<-RFMO_CE$COUNTRY_CODEGear
RFMO_CE$Gear<-RFMO_CE$GEAR_CODEreplace PS by Purse Seine and BB by Pole and Line
RFMO_CE$Gear[RFMO_CE$Gear == "PS"] <- "Purse Seine"
RFMO_CE$Gear[RFMO_CE$Gear == "BB"] <- "Pole and Line"Year and period
RFMO_CE<-harmo_time_2(RFMO_CE, "YEAR", "MONTH")Format inputDataset time to have the time format of the DB, which is one column time_start and one time_end
RFMO_CE<-format_time_db_format(RFMO_CE)Area
RFMO_CE<-harmo_spatial_5(RFMO_CE,"LATITUDE","LONGITUDE",1,5)School
RFMO_CE$School<-"UNK"Species
RFMO_CE$Species<-"SBF"CatchType
RFMO_CE$CatchType<-"RC" #retained catchesCatch
RFMO_CE$Catch<-RFMO_CE$WEIGHT_Kg_OF_SBT_RETAINED/1000
RFMO_CE$CatchUnits<-"t"
colToKeep_captures <- c("FishingFleet","Gear","time_start","time_end","AreaName","School","Species","CatchType","CatchUnits","Catch")
catches <-RFMO_CE[,colToKeep_captures]remove whitespaces on columns that should not have withespace
catches[,c("AreaName","FishingFleet")]<-as.data.frame(apply(catches[,c("AreaName","FishingFleet")],2,function(x){gsub(" *$","",x)}),stringsAsFactors=FALSE)remove 0 and NA values
catches <- catches[!is.na(catches$Catch),]
catches <- catches[catches$Catch != 0,]
catches <- aggregate(catches$Catch, FUN = sum,
by = list(
FishingFleet = catches$FishingFleet,
Gear = catches$Gear,
time_start = catches$time_start,
time_end = catches$time_end,
AreaName = catches$AreaName,
School = catches$School,
Species = catches$Species,
CatchType = catches$CatchType,
CatchUnits = catches$CatchUnits
)
)
colnames(catches)<-c("fishing_fleet","gear_type","time_start","time_end","geographic_identifier","fishing_mode","species","measurement_type","measurement_unit","measurement_value")
catches$source_authority<-"CCSBT"
catches$measurement_type <- "RC" # Retained catches
catches$measurement <- "catch"
catches$measurement_processing_level <- "unknown"
catches$time_start <- as.Date(catches$time_start)
catches$time_end <- as.Date(catches$time_end)
dataset_temporal_extent <- paste(
paste0(format(min(catches$time_start), "%Y"), "-01-01"),
paste0(format(max(catches$time_end), "%Y"), "-12-31"),
sep = "/"
)output in same folder as path_to_raw_dataset
output_name_dataset <- here::here('R/tunaatlas_scripts/pre-harmonization', 'ccsbt', 'catch', 'data', 'CEData_Surface_harmonized.csv')
write.csv(catches, output_name_dataset, row.names = FALSE)
georef_dataset <- catches@ Load pre-harmonization scripts and apply mappings
download.file('https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/R/tunaatlas_scripts/pre-harmonization/map_codelists_no_DB.R', destfile = 'local_map_codelists_no_DB.R')
source('local_map_codelists_no_DB.R')
fact <- "catch"
mapping_codelist <- map_codelists_no_DB(fact, mapping_dataset = "https://raw.githubusercontent.com/fdiwg/fdi-mappings/main/global/firms/gta/codelist_mapping_rfmos_to_global.csv", dataset_to_map = georef_dataset, mapping_keep_src_code = FALSE, summary_mapping = TRUE, source_authority_to_map = c("IATTC", "CCSBT", "WCPFC"))##
## mapping dimension gear_type with code list mapping##
## mapping dimension species with code list mapping##
## mapping dimension fishing_fleet with code list mapping##
## mapping dimension fishing_mode with code list mapping@ Handle unmapped values and save the results
georef_dataset <- mapping_codelist$dataset_mapped %>% dplyr::mutate(fishing_fleet = ifelse(fishing_fleet == 'UNK', 'NEI', fishing_fleet), gear_type = ifelse(gear_type == 'UNK', '99.9', gear_type))
data.table::fwrite(mapping_codelist$recap_mapping, here::here('R/tunaatlas_scripts/pre-harmonization', 'ccsbt', 'catch', 'data', 'CEData_Surface_recap_mapping.csv'))
data.table::fwrite(mapping_codelist$not_mapped_total, here::here('R/tunaatlas_scripts/pre-harmonization', 'ccsbt', 'catch', 'data', 'CEData_Surface_not_mapped_total.csv'))
data.table::fwrite(georef_dataset, here::here('R/tunaatlas_scripts/pre-harmonization', 'ccsbt', 'catch', 'data', 'CEData_Surface_CWP_dataset.csv'))Display the first few rows of the mapping summaries
print(head(mapping_codelist$recap_mapping))## # A tibble: 5 × 5
## src_code trg_code src_codingsystem trg_codingsystem source_authority
## <chr> <chr> <chr> <chr> <chr>
## 1 UNK UNK schooltype_ccsbt schooltype_rfmos CCSBT
## 2 AU AUS flag_ccsbt fishingfleet_firms CCSBT
## 3 SBF SBF species_ccsbt species_asfis CCSBT
## 4 Pole and Line 09.1 gear_ccsbt isscfg_revision_1 CCSBT
## 5 Purse Seine 01.1 gear_ccsbt isscfg_revision_1 CCSBT