In UofTCoders/eeb430.2017.JuniorCoders: Team Junior Coders Final Report for EEB430 at UofT

knitr::opts_chunk$set(echo = TRUE)

Setup

library(dplyr)

Dataset

Based off the "Pred_farhan.Rmd" file in the /doc.

head(Pred)

Day 1 - Nov 16

Pred %>% 
    group_by(Geographic_location) %>% 
    tally()

Pred %>% 
    group_by(Predator, Predator_common_name) %>%
    tally() %>% 
    filter(n > 1000)

Pred %>% 
    group_by(Predator, Predator_common_name, Geographic_location) %>% 
    tally() %>% 
    filter(n > 1000)

Pred %>% 
    group_by(Geographic_location) %>% 
    filter(Predator_common_name == 'Bluefish') %>% 
    tally()

Pred %>% 
    group_by(Geographic_location) %>% 
    filter(Predator_common_name == 'Atlantic bluefin tuna') %>% 
    tally()

Using above work; we see that tuna is a species that is very ubiquitous. Looking at the wikipedia article on Albacore, we have a phylogeny of the genus Thunnus.

The four species in question: (from most derived to basal) - Yellowfin tuna (T. albacares) - Bigeye tuna (T. obesus) - Atlantic bluefin tuna (T. thynnus) - Albacore (T. alalunga)

Pred %>% 
    group_by(Predator_common_name, Geographic_location, Latitude, Specific_habitat) %>% 
    filter(Predator_common_name == 'Albacore'| 
           Predator_common_name == 'Atlantic bluefin tuna'|
           Predator_common_name == 'Yellowfin tuna'|
           Predator_common_name == 'Bigeye tuna') %>% 
    tally()

Looking at Latitude data, we see that these four species of tuna reside within two distinct latitudes: (approx)40N and 12S In other words, they are either in the North Atlantic, or South Pacific

Leila's data: grouped by depth seems that three of spp are deep sea spp (Abyssopelagic). Other one is shallow depth (Epi/Mesopelagic) -> Bluefin.

Pred %>% 
    group_by(Predator_common_name, Predator_mass) %>% 
    filter(Predator_common_name == 'Albacore'| 
           Predator_common_name == 'Atlantic bluefin tuna'|
           Predator_common_name == 'Yellowfin tuna'|
           Predator_common_name == 'Bigeye tuna') %>% 
    ggplot(aes(x = Predator_common_name, y = log(Predator_mass))) +
    geom_boxplot()

Day 2 - Nov 17

Pred %>% 
    group_by(Predator_common_name, Prey_common_name) %>% 
    filter(Predator_common_name == 'Albacore'| 
           Predator_common_name == 'Atlantic bluefin tuna'|
           Predator_common_name == 'Yellowfin tuna'|
           Predator_common_name == 'Bigeye tuna') %>% 
    tally()

Pred %>% 
    group_by(Predator, Predator_common_name) %>%
    tally() %>% 
    filter(n > 1000)

Pred %>% 
    group_by(Depth) %>%
    filter(Depth > 200 & Depth < 1000) %>% 
    summarise(mean_mass = mean(Predator_mass)) %>% 
    ggplot(aes(x = Depth, y = mean_mass)) +
    geom_point() +
    geom_smooth(method = glm)


Pred %>% 
    group_by(Depth, Mean_annual_temp, Latitude) %>%
    filter(Depth > 200 & Depth < 1000) %>% 
    summarise(mean_mass = mean(Predator_mass))

Pred %>% 
    group_by(Predator_common_name, Type_of_feeding_interaction, Specific_habitat) %>% 
    tally() %>% 
    filter(Type_of_feeding_interaction == "predacious", Specific_habitat == "offshelf and on shelf")

Pred %>% 
    group_by(Specific_habitat) %>% 
    ggplot(aes(x = Specific_habitat, y = Depth)) +
    geom_boxplot() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1))

Day 3 - Nov 21

install.packages("lme4")
install.packages("MuMIn")

library(lme4)
library(MuMIn)

lmer(log(Pred$Prey_mass) ~ log(Pred$Predator_mass) + (Pred$Specific_habitat|Pred$Specific_habitat))

Day 4 - Nov 23

?lmer

UofTCoders/eeb430.2017.JuniorCoders documentation built on May 28, 2019, 3:19 p.m.