---
title: "Mapping Retrieval Sites"
author: "Chris"
output:
  pdf_document: default
  html_document: default
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Install anything missing & call the libraries we start with


```{r }

#install.packages("mapdata")
library(ggplot2)
#library(ggmap)
library(maps)
library("mapdata")

```
Testing the code from the website: https://eriqande.github.io/rep-res-web/lectures/making-maps-with-R.html

Going to start with direct cut & paste from site to replicate what I see. It works if you run the code without assigning it to object 'gg1'

```{r cars}
usa <- map_data("usa")
gg1 <-ggplot() + geom_polygon(data = usa, aes(x=long, y = lat, group = group)) + 
  coord_fixed(1.3)

```
Code to add points, here as a placeholder for me to add my own points after I know it can work


```{r, echo=FALSE}

labs <- data.frame(
  long = c(-122.064873, -122.306417),
  lat = c(36.951968, 47.644855),
  names = c("SWFSC-FED", "NWFSC"),
  stringsAsFactors = FALSE
  )  

gg1 + 
  geom_point(data = labs, aes(x = long, y = lat), color = "black", size = 5) +
  geom_point(data = labs, aes(x = long, y = lat), color = "yellow", size = 4)

```
Let's grab state data
```{r}
states <- map_data("state")
dim(states)
head(states)
```
Going to map the west coast
```{r}
west_coast <- subset(states, region %in% c("california", "oregon", "washington"))

ggplot(data = west_coast) + 
  geom_polygon(aes(x = long, y = lat, group = group), fill = "palegreen", color = "black") + 
  coord_fixed(1.3)
```
mapping WA
```{r}
library("cowplot")

wa_df <- subset(states, region== "washington")
head(wa_df)

counties <- map_data("county")
wa_county <- subset(counties, region== "washington")
head(wa_county)

wa_base <- ggplot(data = wa_df, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  geom_polygon(color = "black", fill = "gray")
wa_base + theme_nothing()

wac <- wa_base + theme_nothing() + 
  geom_polygon(data = wa_county, fill = NA, color = "white") +
  geom_polygon(color = "black", fill = NA)  # get the state border back on top

```
Now to plot the PS region
```{r}
wac + coord_fixed(xlim = c(-124, -122),  ylim = c(46, 49), ratio = 1.3)

```
Adding the retrieval points
```{r}

library(tidyverse)
#location<- read.csv("mussel.csv")
head(location)

# create plotting df
# gotta add site names
latitude <- location[,5]
longitude <- location[,6]
cage <- data.frame(longitude, latitude)

str(cage)

# Make sure 'wac' contains the necessary data for plotting

# Check the structure of wac to ensure it contains the necessary variables
str(wac)



wac + 
  geom_point(data = cage, aes(x = "longitude", y = "latitude"), color = "black", size = 5) +
  geom_point(data = cage, aes(x = "longitude", y = "latitude"), color = "yellow", size = 4)

```
this map code isn't working because the 
```{r, echo=FALSE}

library(ggplot2)


# Your base map code
wa_base <- ggplot(data = wa_df, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  geom_polygon(color = "black", fill = "gray") +
  theme_nothing()

# Adding county borders (assuming wa_county is already defined with lat, long, group)
wa_base <- wa_base + 
  geom_polygon(data = wa_county, aes(x = long, y = lat, group = group), fill = NA, color = "white") +
  geom_polygon(color = "black", fill = NA)  # Reinforcing state border

# Add each set of data points with different aesthetics
final_map <- wa_base +
  geom_point(data = df$p450, aes(x = long, y = lat), color = "red", size = 2) +  # Set 1 in red
  geom_point(data = df$SOD, aes(x = long, y = lat), color = "blue", size = 2)  # Set 2 in blue
  #geom_point(data = df$condition_factor, aes(x = long, y = lat), color = "green", size = 2)   # Set 3 in green

# Display the map
print(final_map)

```

trying a different map that maps the biomarker sites
```{r}
library(ggplot2)

# Assuming 'wa_base' is a ggplot object with your base map
# and 'data' is your dataframe with 'longitude', 'latitude', 'p450', and 'SOD' columns

# First, reshape your data from wide to long format
data_long <- tidyr::pivot_longer(data, cols = c("p450", "SOD"), names_to = "Variable", values_to = "Value")

wa_base + 
  geom_point(data = data_long, aes(x = longitude, y = latitude, color = Value), size = 2) +
  facet_wrap(~Variable, scales = "free", ncol = 1) + # Faceting by variable, with separate scales
  scale_color_gradient(low = "lightpink", high = "darkred", name = "Value", aesthetics = "color") +
  labs(title = "Map with p450 and SOD values", x = "Longitude", y = "Latitude") +
  theme_minimal()

```




this is a map
```{r}
library(ggplot2)

# Assuming wa_base is your prepared base map

# Plot p450 values with a gradient from light to dark red
wa_base + 
  geom_point(data = data, aes(x = longitude, y = latitude, color = p450), size = 2) +
  scale_color_gradient(low = "lightpink", high = "darkred", name = "p450 Value") +
  geom_point(data = data, aes(x = longitude, y = latitude, color = SOD), size = 2) +
  scale_color_gradient(low = "lightblue", high = "darkblue", name = "SOD Value") +
  labs(title = "Map with p450 and SOD values", x = "Longitude", y = "Latitude") +
  theme_minimal()

```
simplified map
```{r}
# Start with a simple plot
wa_base + 
  geom_point(data = data, aes(x = longitude, y = latitude), size = 2) +
  theme_minimal()

```



Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.