###Overall goal is to calculate and plot (both histogram and time series) CPUE of the most commonly caught fish in the dataset.  
#CPUE = # of individuals / angler / hour
#Each data point is a trip

#Basic strategy
# 1) Calculate average frequency of fish being caught by species over all trips and find the max -- this is the most commonly caught species.
# 2) Calulate CPUE (#fish angler-1 hr-1) for that species for each trip
# 3) Make histogram (CPUE on x-axis and frequency on y-axis) 
# 4) Split data by port and make scatter plot (with date on x-axis, CPUE on y-axis). Pretty the plot with titles and a legend.

########################
# Set up: 
########################
# Clear the workspace

#set working directory (or use a project)

#load data

#Convert Date column in tripData to Date class for plotting (cleaner)

#Convert TripNum, DropNum, SpeciesCode to factors (why?)

########################
# Step 1: calculate most commonly caught species
########################
#Make data frame of counts for each species by trip;  use table() which creates a contingency table

#convert to data frame which is easier to work with (for me at least)
  
#Use tapply to calculate the mean number caught for each species over all trips

#sort in decending order

#now add names from speciesCode data frame using merge

#get the species codes which as stored as column names; use names()

#grab only species that are in our data set -- find using match()

#assemble into new data frame

#print top five species for visual inspection


#########################
# Step 2: For the most common species calculate CPUE
########################
#pull out the most common species.  This is the first row of our sorted vector 'codes'

#run table() again and store as data frame
   
#Merge with tripData data frame
    
#Calculate CPUE, note units


#########################
# Step 3: Plot histogram 
########################
# Define title for plots (you can do this directly in the plot command also)
# Advanced: pluck common and scientific name from data sets and paste() together for a title
    
#Make historgam of CPUE


#########################
# Step 4: Make scatter plot 
########################
#split dataset into Morro Bay and San Luis
   
#define colors for plot
    
#Make scatter plot with title and axis labels

# Add points

#Add legend


#DONE!