R/tidyALKdata.r

In einarhjorleifsson/datrasr: For analysing and visualising ICES datras trawl survey data

#' @title XXX
#'
#' @description XXX
#'
#' @export


tidyALKdata <- function(input = alk)

{

#Add on some stuff and tidy up

input$lngtclass[input$lngtcode == "."] <- input$lngtclass[input$lngtcode == '.']/10
input$lngtclass[input$lngtcode == 0] <- input$lngtclass[input$lngtcode == 0]/10

#Round down length classes & add 0.5


input$lngtclass[input$lngtcode != "5"] <- round(input$lngtclass[input$lngtcode != "5"])
input$lngtclass[input$lngtcode != "5"] <- input$lngtclass[input$lngtcode != "5"]+0.5


data(tsn)                 # itis database of ALL species in the world

data(DatrasSpeciesCodes)  # attach official database

#Dived data into two species code types
input$speccodetype <- tolower(input$speccodetype) # make sure they are all lowercase

input.t <- input[input$speccodetype == 't',]
input.w <- input[input$speccodetype == 'w',]

input.t$scientific.name <- as.character(tsn$completename[match(input.t$speccode,tsn$tsn)])   # match scientific names using tsn
input.w$scientific.name <- as.character(DatrasSpeciesCodes$scientific.name[match(input.w$speccode,DatrasSpeciesCodes$code_number)])   # match scientific names using tsn

#Reunite the data

input <- rbind(input.t,input.w)


input <- input[!duplicated(input),]     #  remove any duplicates
input <- input[input$speccode != -9,]   #  missing species codes
input <- input[input$lngtclass != -9,]  #  missing length classes
input <- input[input$age != -9,]        # missing ages
input <- input[!is.na(input$lngtcode),] #  missing length codes
input <- input[!is.na(input$scientific.name),] # missing species codes

input$age <- as.numeric(as.character(input$age))

input

}