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results analysis 4spec, 1 high price.Rmd
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results analysis 4spec, 1 high price.Rmd
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---
title: "results analysis: 4spec, 1 high price"
author: "Kat Leigh"
date: "3/3/2021"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
library(tidyverse)
library(ggplot2)
library(here)
library(quadprog)
library(scales)
library(gridExtra)
library(grid)
library(patchwork)
```
# read in the data for best in NPV, best in av profit, and best in avstock for each assignment of species to prices.
```{r}
# get specific data for run identical
list <- here("working_model","price and cost explore",'model run result files',"4spec","prices","ident","pident11140")
temp = list.files(list, pattern="*.csv")
for (i in 1:length(temp))
{assign(paste("ident", temp[i], sep = "."), read_csv(here("working_model","price and cost explore",'model run result files',"4spec","prices","ident","pident11140", temp[i])))}
```
```{r}
# get specific data for run defaults
list2 <- here("working_model","price and cost explore",'model run result files',"4spec","prices","def","defp11140")
temp = list.files(list2,pattern="*.csv")
for (i in 1:length(temp))
{assign(paste("defaults", temp[i], sep = "."), read_csv(here("working_model","price and cost explore",'model run result files',"4spec","prices","def","defp11140", temp[i])))}
```
```{r}
# get specific data for uni def
uni_def <- read_csv("./working_model/price and cost explore/model run result files/uniident/unidef/4spec_opt_NPV_uni_def20.20.20.20.csv")
```
```{r}
# get control data (uni ident)
control <- read_csv("./working_model/price and cost explore/model run result files/uniident/uniident/4spec_opt_NPV_uni_ident20.20.20.20.csv")
```
# choose which assignment to focus on via exploratory graphs of all inputs for each of the 3 categories
- NPV graph (all assigns)
```{r}
# NPV
names_col<- c("s_1", "s_3", "s_4", "s_5", "pft_t1", "pft_t3", "pft_t4", "pft_t5", "mort_1", "mort_2", "tot_pft", "tot_biomass", "run")
control <- control %>%
select(-contains("s_1."),
-contains("s_3."),
-contains("s_4."),
-contains("s_5."),
-contains("t_3."),
-contains("t_4."),
-contains("t_5."),
-contains("t_1.1"),
-contains("t_2.3"),
-contains("t_2.1.1"),
-contains("t_2.2.1")) %>%
mutate(run = "control")
uni_defs <- uni_def %>%
select(-contains("s_1."),
-contains("s_3."),
-contains("s_4."),
-contains("s_5."),
-contains("t_3."),
-contains("t_4."),
-contains("t_5."),
-contains("t_1.1"),
-contains("t_2.3"),
-contains("t_2.1.1"),
-contains("t_2.2.1")) %>%
mutate(run = "uni_def")
#----
def_ps.1h <- mget(ls(pattern="defaults.4spec_opt_NPV_p1.1.1.40")) %>%
as.data.frame() %>%
select(-contains("s_1."),
-contains("s_3."),
-contains("s_4."),
-contains("s_5."),
-contains("t_3."),
-contains("t_4."),
-contains("t_5."),
-contains("t_1.1"),
-contains("t_2.3"),
-contains("t_2.1.1"),
-contains("t_2.2.1")) %>%
mutate(run = "def_ps1h")
# def_ps.2h <- mget(ls(pattern="opt_NPV_def_ps2-40")) %>%
# as.data.frame() %>%
# select(-contains("s_1."),
# -contains("s_2."),
# -contains("s_3."),
# -contains("s_4."),
# -contains("s_5."),
# -contains("t_3."),
# -contains("t_4."),
# -contains("t_5."),
# -contains("t_1.1"),
# -contains("t_2.4"),
# -contains("t_2.2.1"),
# -contains("t_2.1."),
# -contains("t_2.3.")) %>%
# mutate(run = "def_ps2h")
def_ps.3h <- mget(ls(pattern="defaults.4spec_opt_NPV_p1.40.1.1")) %>%
as.data.frame() %>%
select(-contains("s_1."),
-contains("s_3."),
-contains("s_4."),
-contains("s_5."),
-contains("t_3."),
-contains("t_4."),
-contains("t_5."),
-contains("t_1.1"),
-contains("t_2.3"),
-contains("t_2.1.1"),
-contains("t_2.2.1")) %>%
mutate(run = "def_ps3h")
def_ps.4h <- mget(ls(pattern="defaults.4spec_opt_NPV_p1.1.40.1")) %>%
as.data.frame() %>%
select(-contains("s_1."),
-contains("s_3."),
-contains("s_4."),
-contains("s_5."),
-contains("t_3."),
-contains("t_4."),
-contains("t_5."),
-contains("t_1.1"),
-contains("t_2.3"),
-contains("t_2.1.1"),
-contains("t_2.2.1")) %>%
mutate(run = "def_ps4h")
def_ps.5h <- mget(ls(pattern="defaults.4spec_opt_NPV_p1.1.1.40")) %>%
as.data.frame() %>%
select(-contains("s_1."),
-contains("s_3."),
-contains("s_4."),
-contains("s_5."),
-contains("t_3."),
-contains("t_4."),
-contains("t_5."),
-contains("t_1.1"),
-contains("t_2.3"),
-contains("t_2.1.1"),
-contains("t_2.2.1"))%>%
mutate(run = "def_ps5h")
# ----
ident_1h <- mget(ls(pattern=".ident.")) %>%
as.data.frame() %>%
select(-contains("s_1."),
-contains("s_3."),
-contains("s_4."),
-contains("s_5."),
-contains("t_3."),
-contains("t_4."),
-contains("t_5."),
-contains("t_1.1"),
-contains("t_2.3"),
-contains("t_2.1.1"),
-contains("t_2.2.1")) %>%
mutate(run = "ident_1h") %>%
select(1:12,49)
names(def_ps.5h) <- names_col
#names(def_ps.2h) <- names_col
names(def_ps.4h) <- names_col
names(def_ps.1h) <- names_col
names(def_ps.3h) <- names_col
names(ident_1h) <- names_col
names(control) <- names_col
names(uni_defs) <- names_col
```
```{r}
v1 <- ls(pattern = "ident_1|def_ps\\.|control|defs")
v2 <- do.call(rbind,mget(v1))
v2 <- v2 %>%
mutate(linetype = case_when(grepl("uni.", run) ~ "default species, uniform prices",
grepl("control", run) ~ "identical species, uniform prices",
grepl("def.", run) ~ "default species",
grepl("ident.", run) ~ "identical species"))
```
# make graphs
```{r}
#run_factors <- factor(v2$run, levels = unique(v2$run),
# labels = c("Ex: $20 all", "Ex: Sp1- $40", "Ex: Sp2- $40", "Ex: Sp3- $40", "Ex: Sp4- $40", "Ex: Sp5- $40"))
# tidy_av_biomass$run <- run_factors
# tidy_NPVs$run <- run_factors
# tidy_av_pft$run <- run_factors
#NPV
pft_g_NPV_v2 <- ggplot()+
geom_smooth(data = v2, aes(y=tot_pft, x=rep(0:30,7), color = as.factor(run),
linetype = as.factor(linetype)), se = FALSE, na.rm = TRUE)+ theme_minimal()+
labs(title= str_wrap("Profits for optimal-NPV arrangements, ", 80),
x= "Number of Years",
y= "Fishery-wide Annual Profits",
color= "Prices")+
scale_y_continuous(labels=dollar_format(prefix="$"))+
facet_wrap(~run)+
theme(plot.caption = element_text(hjust = 0))
```
what happens to each stock under these arrangements?
```{r}
longer_comp <- v2 %>%
rownames_to_column() %>%
select(c(rowname,s_1, s_3, s_4, s_5, tot_biomass, run, linetype)) %>%
pivot_longer(cols = c(s_1, s_3, s_4, s_5, tot_biomass), names_to = "Species", values_to = "Stock_Level")
longer_comp$Species <- factor(longer_comp$Species, levels = c("s_1", #'s_2',
's_3', 's_4', 's_5', 'tot_biomass'),
labels = c("Species 1", #"Species 2",
"Species 3", 'Species 4', 'Species 5', 'Fishery Wide'))
df <- split(longer_comp,f = longer_comp$run)
view(longer_comp)
p1 <- ggplot(filter(df$control, Species != "Fishery Wide"), aes(x = rep(0:30,4),y = Stock_Level, colour = Species, linetype = linetype)) +
geom_smooth(method = "loess", se = FALSE, formula = 'y ~ x') + theme_minimal()+
facet_wrap(~run)+
labs(x= NULL,
y=NULL,
color= "Species")+
scale_color_viridis_d()+
scale_y_continuous(limits = c(0,100))
p2 <- p1 %+% filter(df$def_ps1h, Species != "Fishery Wide") + theme(legend.position = "none")
#p3 <- p1 %+% filter(df$def_ps2h, Species != "Fishery Wide")+ theme(legend.position = #"none")
p4 <- p1 %+% filter(df$def_ps3h, Species != "Fishery Wide")+ theme(legend.position = "none")
p5<- p1 %+% filter(df$def_ps4h, Species != "Fishery Wide")+ theme(legend.position = "none")
p6<- p1 %+% filter(df$def_ps5h, Species != "Fishery Wide")+ theme(legend.position = "none")
p7<- p1 %+% filter(df$ident_1h, Species != "Fishery Wide")+ theme(legend.position = "none")
p8<- p1 %+% filter(df$uni_def, Species != "Fishery Wide")+ theme(legend.position = "none")
stock_comp_g <- (p1|p8|p7) / (p2|p4|p5|p6) + plot_annotation(
title = str_wrap("Predicted annual stock levels for a fishery with 1 high price under 2 quota baskets", 100),
caption = str_wrap("Results of a 2 quota basket model for 5 species and 5 technologies parameterized such that species are identical in the following parameters: initial stock size = 50, fishing costs = 1, carrying capacities = 100, coefficient for quota basket limits = 0.2. The coefficient for quota basket limit defines the total combined biomass that can be extracted per year as a proportion of the total available biomass for that year. Species differ in terms of: their respective market prices, growth rates = 0.15, 0.2, 0.2, 0.3, 0.4 (respective to each species, 1-5), catchabilities per technology = all 0.01 except for t1 catches species1 at 0.04, t2 catches species2 at 0.04, t3 catches species3 at 0.04, t4 catches species4 at 0.04, and t5 catches species5 at 0.04. Model was run for 6 different price arrangements (see legend).", 140)
)
```
### GABE GRAPH
```{r}
unique(longer_comp$run)
view(v2)
plot_df <- v2 %>%
filter(run %in% c("def_ps1h", "def_ps3h", "def_ps4h", "def_ps5h")) %>%
mutate(year = rep(0:30, 4)) %>%
pivot_longer(1:4,
values_to = "biomass",
names_to = "species") %>%
mutate(value = case_when(
run %in% "def_ps1h" & species %in% "s_1" ~ "high",
run %in% "def_ps1h" & species %in% "s_3" ~ "low",
run %in% "def_ps1h" & species %in% "s_4" ~ "low",
run %in% "def_ps1h" & species %in% "s_5" ~ "low",
run %in% "def_ps3h" & species %in% "s_1" ~ "low",
run %in% "def_ps3h" & species %in% "s_3" ~ "high",
run %in% "def_ps3h" & species %in% "s_4" ~ "low",
run %in% "def_ps3h" & species %in% "s_5" ~ "low",
run %in% "def_ps4h" & species %in% "s_1" ~ "low",
run %in% "def_ps4h" & species %in% "s_3" ~ "low",
run %in% "def_ps4h" & species %in% "s_4" ~ "high",
run %in% "def_ps4h" & species %in% "s_5" ~ "low",
run %in% "def_ps5h" & species %in% "s_1" ~ "low",
run %in% "def_ps5h" & species %in% "s_3" ~ "low",
run %in% "def_ps5h" & species %in% "s_4" ~ "low",
run %in% "def_ps5h" & species %in% "s_5" ~ "high"))
price_plot <- ggplot(data = plot_df) +
geom_line(aes(y = biomass, x = year, linetype = species, color = value), size = 0.8) +
facet_wrap(~run) +
theme_bw() +
scale_x_continuous(expand = c(0,0)) +
scale_y_continuous(expand = c(0,0), limits = c(0,85)) +
scale_color_manual(values = c("firebrick1", "grey50")) +
labs(title = "One species has a high price")
ggsave(plot = price_plot, here("working_model", "scratch", "kat_figs", "figs", "price_one_hi.png"))
```
```{r}
morts <- v2 %>%
group_by(run) %>%
dplyr::select(mort_1,mort_2, tot_pft:run,linetype) %>%
dplyr::summarise(tot_pft = mean(tot_pft, na.rm = TRUE),
tot_biomass = mean(tot_biomass),
mort_1 = unique(mort_1),
mort_2 = unique(mort_2))
```
# export results
- graphs of the most interesting comparison
- summary table of results for each item in graph