getting ICES 1903-1949 catch statistics into R

January 5, 2012
By

(This article was first published on fishvice, and kindly contributed to R-bloggers)


in the last post i showed how to get the nominal north east atlantic landings data from 1950 onwards into a usable format in R. this time around a script that shows how to import zipped excel files from 1903 to 1949 is provided. finalizing with a ggplot of the total reported landings over the period.

again the data is available at the web site (http://www.ices.dk/fish/CATChSTATISTICS.asp) as excel files. the twist is that the files are stored in a zipped format and there are separate excel files for each country. and within each country file there is a separate worksheet for each of the years. within each worksheet the data are again stored in a wide format, the species recorded in the landings is by rows and the area fished is by columns. the number of rows (species recorded) and the number of columns (area fished) differs among different worksheet. and then there are some other nasties here and there in the worksheets, something that took a little trial and error to circumvent in the final script provided.

this is what the final work looked like:
################################################################################
# stuff needed
require(gdata)
require(reshape2)
require(stringr)
require(plyr)

################################################################################
# the source
path <- "YOURDIRECTORY"
URL <-  "http://www.ices.dk/"
PATH <- "fish/statlant/"



################################################################################
# Downloading and extraction of 1903 to 1949 data
FILE <- "ICES1903-49.zip"
download.file(paste(URL,PATH,FILE,sep=""),
              paste(path,"ICES19030-49.zip",sep=""))
dir <- unzip(paste(path,"ICES19030-49.zip",sep=""))

files <- sort(dir[grep("xls",dir)])
files <- substr(files,3,nchar(files))
cntr <- substr(files,1,3)

dat <- NULL
for (i in 1:length(cntr)) {
  print(cntr[i])
for (j in c(2:48)) {
  print(j+1901)
  if (i == 1 & j == 44)  {                     # problem with header character
    tmp <- read.xls(files[i],sheet=j,strip.white=T,stringsAsFactors=F,
                  header=F,skip=1)
    names(tmp) <- c("species","alpha","IVc","IV","VIIa,f","VIId,e","UNK","Total")
  } else {
  tmp <- read.xls(files[i],sheet=j,strip.white=T,stringsAsFactors=F,blank.lines.skip = TRUE)
  }
  if(i == 19 & j == 25) tmp <- tmp[1,1:2]    # Russia 1926 (empty spaces)
  #print(tmp)
  if(ncol(tmp)>1 & nrow(tmp)> 1) {
    rowTotal <- grep("Total",tmp[,1]) # find the location of row Total
    colTotal <- grep("Total",names(tmp)) # find the location of column Total
    if(i == 6  & j == 22) {rowTotal <- 22 ; colTotal <- 8} # France  1923
    if(i == 12 & j == 7)  {rowTotal <- 18 ; colTotal <- 7} # Ireland 1908
    tmp <- tmp[1:(rowTotal-1),1:(colTotal-1)] # exclude anything below and to
                                              #  the right of Total, including the Total
    names(tmp)[1:2] <- c("species","alfa")    # the species acronym column is in most cases left blank
                                        # in some cases it is however names something odd
    tmp <- melt(tmp,id.var=c("species","alfa"))   # turn into long format (kind of like inverse of "Pivot table"
    tmp$variable <- as.character(tmp$variable)
    tmp$value <- str_replace_all(tmp$value,",","")
    tmp$value <- ifelse(tmp$value %in% "-",0,tmp$value)
    tmp$value <- ifelse(tmp$value %in% ".",NA,tmp$value)
    tmp$value <- ifelse(tmp$value %in% "<0.5",0.25,tmp$value)
    tmp$value <- ifelse(tmp$value %in% "",NA,tmp$value)
    tmp$value <- as.numeric(tmp$value)
    tmp$year <- rep(j+1901,nrow(tmp))
    tmp$cUN2 <- cntr[i]
    dat <- rbind(dat,tmp)
  }
} # end loop through sheets (years/worksheet)
} # end loop through countries (xls file)
require(ggplot2)
ggplot(dat,aes(factor(year),weight=value/1e6))+ geom_bar() + xlab("") +
  ylab("Nominal landings [million tonnes]")



the focus here has been on just getting the data into R. more work is needed, in particular when it comes to combining the data from 1903-1949 and 1950-2010. hope i will have some time to work and report on that later.




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