Takes ICES catch statistics and aggregates according to ICES area and species.

aggregateICEScatchData.R

rm(list = ls())
# 
library(dplyr, quietly = TRUE)
library(reshape2, quietly = TRUE)
library(ggplot2, quietly = TRUE)
# 
options(scipen = 5)
# 
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
# DATA SOURCE: ICES official catch statistics (2006-2014) #
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
catchURL <- "http://ices.dk/marine-data/Documents/CatchStats/OfficialNominalCatches.zip"
tmpFileCatch <- tempfile(fileext = ".zip")
download.file(catchURL, destfile = tmpFileCatch, mode = "wb", quiet = TRUE)
catchDat2010 <- read.csv(unz(tmpFileCatch,
                             "ICESCatchDataset2006-2014.csv"),
                         stringsAsFactors = FALSE, header = TRUE, fill = TRUE)

#(NOTE: this file was created manually - with stock list database this should not be necessary - SL)
areaDat <- read.csv("~/git/ices-dk/fisheryO/inst/extdata/areaList.csv",
                    stringsAsFactors = FALSE)
# 
# Clean up the area data. n.b., there is more to this list than necessary, also has ecoregions, STECF and historical areas.
areaID <- areaDat %>%
  filter(areaType %in% c("ICESarea"),
         FO2016 == TRUE) %>%
  select(-ICESarea,
         -areaType,
         -FO2016,
         -Ecoregion) %>%
  mutate(value = tolower(value),
         value = gsub("_nk", "_NK", value))

# Pick out the areas that you want to look at
head(areaID)

areaID <- areaID %>%
  filter(value %in% c("27.6_NK", "27.7.c_NK")) # ... and whatever else you want to see.
# 
catchDat2010Clean <- catchDat2010 %>%
  Filter(f = function(x)!all(is.na(x))) %>% # Get rid of extra columns of NA
  select(-Units,
         ICES_Area = Area) %>%
  melt(id.vars = c("Species", "ICES_Area", "Country"), # Turn into a long data frame
       variable.name = "YEAR",
       value.name = "VALUE") %>%
  mutate(YEAR = as.numeric(gsub("X", "", YEAR)), # Clean up year column
         VALUE = as.numeric(VALUE)) %>% 
  filter(ICES_Area %in% areaID$value,
         Species %in% c("HER", "POS", "MAC")) %>%
  group_by(YEAR, ICES_Area, Species) %>%
  summarize(VALUE = sum(VALUE))

ggplot(catchDat2010Clean, aes(x=YEAR, y = VALUE)) +
  geom_area(aes(fill = Species)) +
  facet_wrap(~ICES_Area)