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# Use the devtools package (on CRAN) to get the most up to date version of rICES package (very much in the works) here:
# https://github.com/ices-dk
devtools::install_github("ICES-dk/rICES", force = TRUE)
library(rICES)
# You can get the HL, HH, or CA "EXCHANGE" data using the following function. HH is info on the haul (used to calculate a survey index)
# and HL is the length data. CA is for age info.
# You can make a loop or a function to get data from other surveys. Let it run overnight because there is a lot of data!!!
d <-  getDATRAS(record = "HL",
                survey="NS-IBTS",
                startyear = 2010,

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In the shell:

git clone https://github.com/hadley/adv-r.git
gem install jekyll mime-types

In R:

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tt <- data.frame(YEAR = seq(1981, 2010, by = 1),
                 INDICATOR = rnorm(n = 30))

library(zoo)

dolm <- function(x) {
  SE <- summary(lm(INDICATOR ~ ., data = as.data.frame(x)))$sigma
  SLOPE <- summary(lm(INDICATOR ~ ., data = as.data.frame(x)))$coefficients[1]
  return(list(standard.error = SE, slope = SLOPE))
}

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rm(list = ls())
# Downloads and parses Calinus finmarchicus abundance data from EMODnet OOPS for the Greater North Sea
# 10 yr rolling mean and 1 yr seasonal data are provided. The latter is probably most relevant for fisheries applications. 
# Citation for data: "European Marine Observation Data Network (EMODnet) Biology project (www.emodnet-biology.eu), funded by the European Commission's Directorate - General for Maritime Affairs and Fisheries (DG MARE)"
# 
#Install package
install.packages("jsonlite")
library(jsonlite)
# 
emodDatJSON <- fromJSON("http://geo.vliz.be/geoserver/Emodnetbio/ows?service=WFS&version=1.0.0&request=GetFeature&typeName=Emodnetbio:OOPS_summaries&outputFormat=json")

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library(dplyr)
tt <- read.csv("~/AreasEcoregionsStocks.csv",
                      stringsAsFactors = FALSE)

td <- tt %>%
  select(StockCode, Ecoregion1_major, Ecoregion2_minor, Ecoregion3_more_minor) %>%
  melt(id.vars = "StockCode") %>%
  select(-variable) %>%
  filter(value != 0) %>%
  distinct()

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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) #

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############################
# How many pages of Advice #
############################

##SOURCE
#----pdftools package
#https://cran.rstudio.com/web/packages/pdftools

#INSTALL PACKAGES
#install.packages("pdftools", dependencies=TRUE)  #only once

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rm(list = ls())

library(data.table)

# Unzip each of the InterCatch download files to a folder "/data" in your working directory
wd <- getwd()
file_list <- paste0(wd,
                    "/data/",
                    grep("CatchAndSampleDataTables.txt", 
                         list.files("data/", recursive = TRUE),

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rm(list = ls())
# Load this package, install it if you don't have it already
library(jsonlite)

# Change the active year.
ActiveYear <- 2016
rawsl <- jsonlite::fromJSON(paste0("http://sd.ices.dk/services/odata3/StockListDWs3?$filter=ActiveYear%20eq%20",
                                   ActiveYear),
                            simplifyDataFrame = TRUE)$value
# colnames(rawsl)

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---
title: "UK Salmon"
author: ""
date: "28 March 2017"
output:
  pdf_document: default
  html_document: default
---

```{r setup, include=FALSE}