HABSpring2024.Rmd

---
title: "HAB_Spring2024"
output: html_document
date: "2024-04-03"
editor_options: 
  chunk_output_type: console
  markdown: 
    wrap: 72
---

```{r}
library(dplyr)

```
# PRELIM STATS 

#FIND CRITICAL VALUES

```{r}
critical.r <- function( n, alpha = .05 ) {
  df <- n - 2
  critical.t <- qt(alpha/2, df, lower.tail = F)
  critical.r <- sqrt( (critical.t^2) / ( (critical.t^2) + df ) )
  return(critical.r)
}
# Example usage: Critical correlation coefficient at sample size of n = 100
critical.r( 100 )


if (file.exists("critical.r .rds")) {
  # If it's saved locally, load the data
  critical.r <- readRDS("critical.r.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
  saveRDS(critical.r , "critical.r .rds")
}

```


#TABLE AG_DF_CR

```{r}
# Assuming 'ag_df_CR' is your data frame

# Create an empty list to store the results
lm_list <- list()

# List of variables to include in the linear models
vars <- c("ph", "Celsius", "spcond", "do", "turb", "Salinity")

# Iterate over each variable
for (var in vars) {
  # Create the formula for the linear model
  formula_chl <- paste("chl ~", var)
  formula_phy <- paste("phy ~", var)
  
  # Fit the linear model for 'chl'
  lm_chl <- lm(formula_chl, data = ag_df_CR)
  # Fit the linear model for 'phy'
  lm_phy <- lm(formula_phy, data = ag_df_CR)
  
  # Store the results in the list
  lm_list[[paste(var, "chl", sep = "_")]] <- lm_chl
  lm_list[[paste(var, "phy", sep = "_")]] <- lm_phy
}

```

```{r}
# Create an empty data frame to store the results
results_df <- data.frame(variable = character(), response = character(), 
                          correlation = numeric(), r_squared = numeric(), p_value = numeric(),
                          stringsAsFactors = FALSE)

# Iterate over each variable
for (var in vars) {
  # Create the formula for the linear model
  formula_chl <- paste("chl ~", var)
  formula_phy <- paste("phy ~", var)
  
  # Fit the linear model for 'chl'
  lm_chl <- lm(formula_chl, data = ag_df_CR)
  # Fit the linear model for 'phy'
  lm_phy <- lm(formula_phy, data = ag_df_CR)
  
  # Extract the correlation coefficient, R-squared value, and p-value
  cor_chl <- cor(ag_df_CR[[var]], ag_df_CR$chl)
  cor_phy <- cor(ag_df_CR[[var]], ag_df_CR$phy)
  r_squared_chl <- summary(lm_chl)$r.squared
  r_squared_phy <- summary(lm_phy)$r.squared
  p_value_chl <- summary(lm_chl)$coefficients[2, 4]
  p_value_phy <- summary(lm_phy)$coefficients[2, 4]
  
  # Add the results to the data frame
  results_df <- rbind(results_df, 
                      data.frame(variable = var, response = "chl",
                                 correlation = cor_chl, r_squared = r_squared_chl, p_value = p_value_chl),
                      data.frame(variable = var, response = "phy",
                                 correlation = cor_phy, r_squared = r_squared_phy, p_value = p_value_phy))
}

# Print the results
print(results_df)

```

#TABLE All.Logan

#TABLE All.CR_nutrients

#TABLE All.bacteria

# Library

```{r}
library(tidyverse)
library(googlesheets4)
library(oce)
library(padr)
library(rEDM)
library(ggplot2)
library(zoo)
library(quantreg)
library(dplyr)

```

#List of all data

```{r}

#CR BUOY

ag_df_CR

ag_df_CR$chl

ag_df_CR$phy

ag_df_CR$ph

ag_df_CR$Celsius

ag_df_CR$spcond

ag_df_CR$do

ag_df_CR$turb

ag_df_CR$Salinity

#ag_Logan
                          
    ag_Logan$HourlyAltimeterSetting 

    ag_Logan$HourlyDewPointTemperature 
    
    ag_Logan$HourlyDryBulbTemperature 
    
    ag_Logan$HourlyPrecipitation 
    
   ag_Logan$HourlyRelativeHumidity 
   
    ag_Logan$HourlyStationPressure 
    
    ag_Logan$HourlyVisibility
    
    ag_Logan$HourlyWetBulbTemperature 
    
    ag_Logan$HourlyWindDirection 
    
    ag_Logan$HourlyWindGustSpeed
    
    ag_Logan$HourlyWindSpeed 
    
    #CAM017
    
    CAM017$MG
    
    #bacteria 
    
    ag_CR_bacteria$Enterococcus
    ag_CR_bacteria$E_coli
    ag_CR_nutrients$`Ammonium (uM)`
    
    #nutrients 
    ag_CR_nutrients$`Nitrate+nitrite (uM)`
    ag_CR_nutrients$`Phosphate (uM)`
    ag_CR_nutrients$`Phosphate (uM)`
    ag_CR_nutrients$`Total phosphorus (uM)`
    ag_CR_nutrients$`Chlorophyll a (ug/L)`
    ag_CR_nutrients$`Phaeophytin (ug/L)`
    
#aggregated
  
All.ag_Logan
All.CR_nutrients
All.CR_bacteria 


All.Var 

    
    
    
```

###9:12

```{r}

morndf_CR

morndf_CR$chl

mornddf_CR$phy

mornddf_CR$ph

mornddf_CR$Celsius

mornddf_CR$spcond

mornddf_CR$do

mornddf_CR$turb

mornddf_CR$Salinity


```

###HOURLY

```{r}

df_CR

df_CR$chl

df_CR$phy

df_CR$ph

df_CR$Celsius

df_CR$spcond

df_CR$do

df_CR$turb

df_CR$Salinity


```

##LOGAN

```{r}

    ag_Logan$HourlyAltimeterSetting 

    ag_Logan$HourlyDewPointTemperature 
    
    ag_Logan$HourlyDryBulbTemperature 
    
    ag_Logan$HourlyPrecipitation 
    
   ag_Logan$HourlyRelativeHumidity 
   
    ag_Logan$HourlyStationPressure 
    
    ag_Logan$HourlyVisibility
    
    ag_Logan$HourlyWetBulbTemperature 
    
    ag_Logan$HourlyWindDirection 
    
    ag_Logan$HourlyWindGustSpeed
    
    ag_Logan$HourlyWindSpeed 
    
  


```

##MWRA Non-CSO

```{r}

#ag_CR_secchi

ag_CR_secchi$`Total Suspended Solids (mg/L)`

#ag_CR_nutrients 

ag_CR_nutrients$`Ammonium (uM)`
ag_CR_nutrients$`Nitrate+nitrite (uM)`
ag_CR_nutrients$`Phosphate (uM)`
ag_CR_nutrients$`Total phosphorus (uM)`
ag_CR_nutrients$`Phaeophytin (ug/L)`

#ag_CR_bacteria

ag_CR_bacteria$Enterococcus
ag_CR_bacteria$E_coli
ag_CR_bacteria$coliform


```

##CSO

```{r}

CAM017$MG

```

```{r}

All.CR_bacteria

All.CR_secchi

All.CR_nutrients 

```

```{r}
library(tidyverse)
library(googlesheets4)
library(oce)
library(padr)
library(rEDM)
library(ggplot2)
library(zoo)
library(quantreg)

```

#Vignette

```{r}
vignette('rEDM-tutorial')

```

###LOAD DATA

##CR BUOY

```{r}

#CR2023

if (file.exists("CR2023.rds")) {
  # If it's saved locally, load the data
  CR2023 <- readRDS("CR2023.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CR2023 <- read_sheet('https://docs.google.com/spreadsheets/d/1sMKGzW71qErP8DIrxWDvlgqN3o9MrprLjjAFGsOie3I/edit?usp=sharing')
  saveRDS(CR2023, "CR2023.rds")
}

#CR2022

if (file.exists("CR2022.rds")) {
  # If it's saved locally, load the data
  CR2022 <- readRDS("CR2022.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CR2022 <- read_sheet('https://docs.google.com/spreadsheets/d/1F5l-1dWi7EFOaEKf9o3eUBi6BgQOrC1Rj_lVNRPrx5U/edit?usp=sharing', sheet = 2)
  saveRDS(CR2022, "CR2022.rds")
}

#CR2021

if (file.exists("CR2021.rds")) {
  # If it's saved locally, load the data
  CR2021 <- readRDS("CR2021.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CR2021 <- read_sheet('https://docs.google.com/spreadsheets/d/1vaDoymaAQmNFKqIgo1LStI7GQSKDeIqLy7We85IA3Oo/edit?usp=sharing', sheet = 2)
  saveRDS(CR2021, "CR2021.rds")
}

####FIX 2021

#CR2020


if (file.exists("CR2020.rds")) {
  # If it's saved locally, load the data
  CR2020 <- readRDS("CR2020.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CR2020 <- read_sheet('https://docs.google.com/spreadsheets/d/1Qvt0PIK4wPFPKbkM98pSQp9TbPsu_a6frTWFKv3aNQ4/edit?usp=sharing', sheet = 2)
  saveRDS(CR2020, "CR2020.rds")
}


#CR2019

if (file.exists("CR2019.rds")) {
  # If it's saved locally, load the data
  CR2019 <- readRDS("CR2019.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CR2019 <- read_sheet('https://docs.google.com/spreadsheets/d/1PGZCChA-8HU-cLvPSxqrxd89gLTletDYgogMGwv4Dg4/edit?usp=sharing')
  saveRDS(CR2019, "CR2019.rds")
}


#CR2018


if (file.exists("CR2018.rds")) {
  # If it's saved locally, load the data
  CR2018 <- readRDS("CR2018.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CR2018 <- read_sheet('https://docs.google.com/spreadsheets/d/1CO0DHuKb3C-hE9z-r5yR-SXwPnZWZg4vQDuEtRv2r7k/edit?usp=sharing', sheet = 2)
  saveRDS(CR2018, "CR2018.rds")
}


#CR2017

if (file.exists("CR2017.rds")) {
  # If it's saved locally, load the data
  CR2017 <- readRDS("CR2018.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CR2017 <- read_sheet('https://docs.google.com/spreadsheets/d/11SO-RxRhcc4qqn3gRt4n13JWClFoG--kISMoPAR2FRA/edit?usp=sharing', sheet = 2)
  saveRDS(CR2017, "CR2017.rds")
}

#CR2016


if (file.exists("CR2016.rds")) {
  # If it's saved locally, load the data
  CR2016 <- readRDS("CR2016.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CR2016 <- read_sheet('https://docs.google.com/spreadsheets/d/1sMcEOIzxPTUZ93-pTJBE0lQ3SI8kKihLZRdpZ32_Lyw/edit?usp=sharing', sheet = 2)
  saveRDS(CR2017, "CR2016.rds")
}


#CR2015

if (file.exists("CR2015.rds")) {
  # If it's saved locally, load the data
  CR2015 <- readRDS("CR2015.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CR2015 <- read_sheet('https://docs.google.com/spreadsheets/d/1t5joquxku9OfQGDdsabp-GdR2sUw8hPDcwatC3SL90Q/edit?usp=sharing', sheet = 2)
  saveRDS(CR2015, "CR2015.rds")
}


```

#df_CR

```{r}
#bind rows
df_CR <- bind_rows(
  CR2015,
  CR2016,
  CR2017, 
  CR2018,
  CR2019,
  CR2020,
  CR2021,
  CR2022,
  CR2023
)

#combine data
df_CR <- df_CR %>%
  mutate(hour = pmin(hour(`time est`),hour(`time edt`),na.rm=T),
         minute = pmin(minute(`time est`),minute(`time edt`),na.rm=T)) %>%
  select(date,hour,minute,chl=`chlorophyll (rfu)`,phy=`phycocyanin (rfu)`, ph=`ph`, Celsius =`temp c`,  spcond = `spcond (ms/cm)`, do = `do (mg/l)`,turb = `turbidity (fnu)` )
  
  
#%>% rename(`time est`=time)
#Aggregate Values Daily
df_CR$spcond = df_CR$spcond * 0.1 #mS to S/m

df_CR$Salinity <- 

  swSCTp(
  df_CR$spcond,
df_CR$Celsius,
  pressure = rep(10.1325,NROW(df_CR)),
  # "S/cm",
"",
  eos = getOption("oceEOS", default = "gsw")
)

if (file.exists("df_CR.rds")) {
  # If it's saved locally, load the data
  df_CR <- readRDS("df_CR.rds")
} else {
saveRDS(df_CR, "df_CR.rds")
}


```

#ag_df_CR

```{r}
#ag_df_CR


ag_df_CR <-df_CR %>%
  mutate(date = trunc(date,"days")) %>%
   group_by(date) %>%
  summarise(chl=mean(chl, na.rm = TRUE), phy=mean(phy, na.rm = TRUE), ph=mean(ph, na.rm = TRUE), Celsius=mean(Celsius,na.rm = TRUE), spcond=mean(spcond,na.rm = TRUE), do=mean(do,na.rm = TRUE), turb=mean(turb,na.rm = TRUE), Salinity = mean(Salinity,na.rm = TRUE))

## eventually find a way around this
ag_df_CR <- na.omit(ag_df_CR)


library(lubridate)

yday(as.Date("2024-03-01"))

df_example <- data.frame(seq(as.Date("2015-01-01"),by=1,length.out=5000))
df_example <- data.frame(Date=seq(as.Date("2015-01-01"),by=1,length.out=5000))
df_example <- df_example %>% mutate(yday = yday(Date))
head(df_example)
df_example <- df_example %>% mutate(sin_season = sin(2*pi*yday/365))
df_example %>% ggplot(aes(x=Date,y=sin_season)) + geom_line()

cycle <- df_example

if (file.exists("cycle.rds")) {
  # If it's saved locally, load the data
  cycle <- readRDS("cycle.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
  saveRDS(cycle, "cycle.rds")
}

merged_df <- merge(ag_df_CR, cycle, by.x = "date", by.y = "Date", all.x = TRUE)

ag_df_CR <- merged_df %>% select(-10)

if (file.exists("ag_df_CR.rds")) {
  # If it's saved locally, load the data
  ag_df_CR <- readRDS("ag_df_CR.rds")
} else {
  saveRDS(ag_df_CR, "ag_df_CR.rds")
}





```

# 9-12 Values

```{r}

df_CR <- readRDS("df_CR.rds")

morndf_CR <- df_CR %>%
  filter(hour %in% c(9, 10, 11))

morndf_CR <-morndf_CR %>% 
   mutate(date = trunc(date,"days")) %>%
   group_by(date) %>%
  summarise(chl=mean(chl, na.rm = TRUE), phy=mean(phy, na.rm = TRUE), ph=mean(ph, na.rm = TRUE), Celsius=mean(Celsius,na.rm = TRUE), spcond=mean(spcond,na.rm = TRUE), do=mean(do,na.rm = TRUE), turb=mean(turb,na.rm = TRUE), Salinity = mean(Salinity,na.rm = TRUE))

if (file.exists("morndf_CR.rds")) {
  # If it's saved locally, load the data
  morndf_CR <- readRDS("morndf_CR.rds")
} else {
  saveRDS(morndf_CR, "morndf_CR.rds")
}

```

###LOGAN

```{r}

Logan <- read_sheet('https://docs.google.com/spreadsheets/d/1h3mNXsGZxB4fhkZQGr2e8z6CxAQtumA5a208-GEYvBw/edit?usp=sharing')

# Convert to POSIXct object
Logan$DATE <- as.POSIXct(Logan$DATE, format = "%Y-%m-%dT%H:%M:%S")

Logan2 <- Logan

# Remove "s" suffixes from HourlyAltimeterSetting column
Logan2$HourlyAltimeterSetting <- gsub("s$", "", Logan2$HourlyAltimeterSetting)
Logan2$HourlySeaLevelPressure <- gsub("s$", "", Logan2$HourlySeaLevelPressure)
Logan2$HourlyDewPointTemperature <- gsub("s$", "", Logan2$HourlyDewPointTemperature)
Logan2$HourlyPrecipitation <- gsub("T", "0", Logan2$HourlyPrecipitation)


Logan2 <- Logan2 %>%
  mutate_at(vars(HourlyAltimeterSetting, HourlyDewPointTemperature, HourlyDryBulbTemperature,
                 HourlyPrecipitation, HourlyPressureChange, HourlyPressureTendency,
                 HourlyRelativeHumidity, HourlySeaLevelPressure, HourlyStationPressure,
                 HourlyVisibility, HourlyWetBulbTemperature, HourlyWindDirection,
                 HourlyWindGustSpeed, HourlyWindSpeed), ~ifelse(. == "NULL", NA, .))

# Convert columns to numeric (excluding DATE)
numeric_cols <- c("HourlyAltimeterSetting", "HourlyDewPointTemperature", "HourlyDryBulbTemperature",
                  "HourlyPrecipitation", "HourlyPressureChange", "HourlyPressureTendency",
                  "HourlyRelativeHumidity", "HourlySeaLevelPressure", "HourlyStationPressure",
                  "HourlyVisibility", "HourlyWetBulbTemperature", "HourlyWindDirection",
                  "HourlyWindGustSpeed", "HourlyWindSpeed")

Logan2 <- Logan2 %>%
  mutate(across(all_of(numeric_cols), as.numeric))


ag_Logan <- Logan2 %>%
  group_by(DATE) %>%
  summarise(
    HourlyAltimeterSetting = mean(HourlyAltimeterSetting, na.rm = TRUE),
    HourlyDewPointTemperature = mean(HourlyDewPointTemperature, na.rm = TRUE),
    HourlyDryBulbTemperature = mean(HourlyDryBulbTemperature, na.rm = TRUE), 
    HourlyPrecipitation = sum(HourlyPrecipitation, na.rm = TRUE),
   # HourlyPressureChange = mean(HourlyPressureChange, na.rm = TRUE), SKIP
   # HourlyPressureTendency = mean(HourlyPressureTendency, na.rm = TRUE), SKIP
    HourlyRelativeHumidity = mean(HourlyRelativeHumidity, na.rm = TRUE), 
   # HourlySeaLevelPressure = mean(HourlySeaLevelPressure, na.rm = TRUE), SKIP
    HourlyStationPressure = mean(HourlyStationPressure, na.rm = TRUE),
    HourlyVisibility = mean(HourlyVisibility, na.rm = TRUE),
    HourlyWetBulbTemperature = mean(HourlyWetBulbTemperature, na.rm = TRUE), 
    HourlyWindDirection = mean(HourlyWindDirection, na.rm = TRUE), 
    HourlyWindGustSpeed = mean(HourlyWindGustSpeed, na.rm = TRUE), 
    HourlyWindSpeed = mean(HourlyWindSpeed, na.rm = TRUE)
    
    )
  mutate(date = trunc(date,"days")) %>%


# Convert to POSIXct object
ag_Logan$DATE <- as.POSIXct(ag_Logan$DATE, format = "%Y-%m-%dT%H:%M:%S")

ag_Logan <-  ag_Logan %>%
   mutate(DATE = as.Date(DATE)) %>%
  group_by(DATE) %>%
  summarise(
    HourlyAltimeterSetting = mean(HourlyAltimeterSetting, na.rm = TRUE),
    HourlyDewPointTemperature = mean(HourlyDewPointTemperature, na.rm = TRUE),
    HourlyDryBulbTemperature = mean(HourlyDryBulbTemperature, na.rm = TRUE), 
    HourlyPrecipitation = sum(HourlyPrecipitation, na.rm = TRUE),
   # HourlyPressureChange = mean(HourlyPressureChange, na.rm = TRUE), SKIP
   # HourlyPressureTendency = mean(HourlyPressureTendency, na.rm = TRUE), SKIP
    HourlyRelativeHumidity = mean(HourlyRelativeHumidity, na.rm = TRUE), 
   # HourlySeaLevelPressure = mean(HourlySeaLevelPressure, na.rm = TRUE), SKIP
    HourlyStationPressure = mean(HourlyStationPressure, na.rm = TRUE),
    HourlyVisibility = mean(HourlyVisibility, na.rm = TRUE),
    HourlyWetBulbTemperature = mean(HourlyWetBulbTemperature, na.rm = TRUE), 
    HourlyWindDirection = mean(HourlyWindDirection, na.rm = TRUE), 
    HourlyWindGustSpeed = mean(HourlyWindGustSpeed, na.rm = TRUE), 
    HourlyWindSpeed = mean(HourlyWindSpeed, na.rm = TRUE)
    
    )


if (file.exists("ag_Logan.rds")) {
  # If it's saved locally, load the data
  ag_Logan <- readRDS("ag_Logan.rds")
} else {
  saveRDS(ag_Logan, "ag_Logan.rds")
}

#ag_Logan <- na.omit(ag_Logan)



```

### MWRA Non-CSO

```{r}
#READ DATA IN

#CR_bacteria

if (file.exists("CR_bacteria.rds")) {
  # If it's saved locally, load the data
  CR_bacteria <- readRDS("CR_bacteria.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CR_bacteria <- read_sheet('https://docs.google.com/spreadsheets/d/1V1DJc4er2gctuRN2bv3u3Db0bGxz566ec8j6xmXFZ70/edit?usp=sharing', range = 'cr_bacteria')  
saveRDS(CR_bacteria, "CR_bacteria.rds")
}

#CR_secchi


if (file.exists("CR_secchi.rds")) {
  # If it's saved locally, load the data
 CR_secchi <- readRDS("CR_secchi.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CR_secchi <- read_sheet('https://docs.google.com/spreadsheets/d/1fW4_60i-SYkdTPlFQQdN0b5nlZirc2uQyjDN_dvWA18/edit?usp=sharing', range = 'cr_secchi')
saveRDS(CR_secchi, "CR_secchi.rds")
}


#CR_physical 

if (file.exists("CR_physical.rds")) {
  # If it's saved locally, load the data
 CR_physical <- readRDS("CR_physical.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CR_physical <- read_sheet('https://docs.google.com/spreadsheets/d/1P5kzPHFABPTKffxM2yQBTIQWq9NAmnGd3PF6kOsSFNU/edit?usp=sharing', range = 'cr_physical')
saveRDS(CR_physical, "CR_physical.rds")
}

#CR_nutrients

if (file.exists("CR_nutrients.rds")) {
  # If it's saved locally, load the data
 CR_nutrients <- readRDS("CR_nutrients.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CR_nutrients <- read_sheet('https://docs.google.com/spreadsheets/d/1kPHmgJUHF7GtNJJDme_gWgB7hTS7i6SE7pqazChKMZ4/edit?usp=sharing')
saveRDS(CR_physical, "CR_nutrients.rds")
}



```

```{r}
#NEW NAMES 
## CRBacteria 
Bacteria_new_names <- c(
  "Project_ID",
  "Region",
  "Subregion",
  "DEP_Segment",
  "Station ID",
  "Date",
  "Depth_category",
  "Sample_depth",
  "Enterococcus",
  "Enterococcus_condition",
  "E_coli",
  "E_coli_condition",
  "coliform",
  "coliform_condition",
  "Rainfall_1d",
  "Rainfall_2d",
  "Rainfall_3d"
)

#CRSECCHI
Secchi_new_names <- c(
  "Project ID",
"Region",
"Subregion",
"DEP Segment",
"Station ID",
"Surface or Bottom",
"Depth to bottom (m)",
"Sample depth for TSS (m)",
"Date",
"Total Suspended Solids (mg/L)",
"Total Suspended Solids '<' or '>'",
"Secchi disk depth (m)"
)


#CRPHYSICAL
Physical_new_names <- c(
"Project ID",
"Region",
"Subregion",
"DEP segment",
"Station ID",
"Surface or Bottom",
"Date",
"Depth of measurement (m)",
"Temperature (C)",
"Salinity (PSU)",
"Specific Conductance (mS/cm)",
"Dissolved Oxygen (mg/L)",
"DO Pct Saturation (%)",
"pH",
"Turbidity (NTU)")



#CRNUTRIENTS
Nutrients_new_names <- c(

"Project ID",
"Region",
"Subregion",
"DEP segment",
"Station ID",
"Date",
"Surface or Bottom",
"Sample depth (m)",
"Ammonium (uM)",
"Ammonium '<' or '>'",
"Nitrate+nitrite (uM)",
"Nitrate+nitrite  '<' or '>'",
"Total dissolved N (uM)",
"Total dissolved N  '<' or '>'",
"Particulate nitrogen (uM)",
"Particulate nitrogen  '<' or '>'",
"Total Kjeldahl Nitrogen (uM)",
"TKN '<' or '>'",
"Phosphate (uM)",
"Phosphate '<' or '>'",
"Total dissolved P (uM)",
"Total dissolved P '<' or '>'",
"Particulate P (uM)",
"Particulate P '<' or '>'",
"Total phosphorus (uM)",
"Total phosphorus '<' or '>'",
"Particulate carbon (uM)",
"Particulate carbon     '<' or '>'",
"Chlorophyll a (ug/L)",
"Chlorophyll '<' or '>'",
"Phaeophytin (ug/L)",
"Phaeophytin '<' or '>'"
)

#Initial Data Wrangle 

#CRBACTERIA
#colnames(CR_bacteria) <- as.character(CR_bacteria[5,]) #set header
#CR_bacteria <- CR_bacteria[-c(1:5), ] #Remove 1st 5 Rows
#rename columns

CR_bacteria <- CR_bacteria %>% 
  set_names(Bacteria_new_names) %>%
  mutate(Date = map_vec(Date,as.Date))


#CR SECCHI

#colnames(CR_secchi) <- as.character(CR_secchi[5,]) #set header
#CR_secchi <- CR_secchi[-c(1:5), ] #Remove 1st 5 Rows
#rename columns

CR_secchi <- CR_secchi %>% 
  set_names(Secchi_new_names) %>%
  mutate(Date = map_vec(Date,as.Date))

#CRPHYSICAL

#colnames(CR_physical) <- as.character(CR_physical[5,]) #set header
#CR_physical <- CR_physical[-c(1:5), ] #Remove 1st 5 Rows
#rename columns

CR_physical <- CR_physical %>% 
  set_names(Physical_new_names) %>%
  mutate(Date = map_vec(Date,as.Date))

#CRNUTRIENTS
#colnames(CR_nutrients) <- as.character(CR_nutrients[5,]) #set header
#CR_nutrients <- CR_nutrients[-c(1:5), ] #Remove 1st 5 Rows
#rename columns

CR_nutrients <- CR_nutrients %>% 
  set_names(Nutrients_new_names) %>%
  mutate(Date = map_vec(Date,as.Date))


```

```{r}

#SITE SELECTION

#CR_bacteria
CR_bacteria_011 <- subset(CR_bacteria, CR_bacteria$`Station ID` == "011")


#CR_nutrients

CR_nutrients_166 <- subset(CR_nutrients, CR_nutrients$`Station ID` == "166")

#CR_physical
CR_physical_011 <- subset(CR_physical, CR_physical$`Station ID` == "011")


#CR_secchi

CR_secchi_011 <- subset(CR_secchi, CR_secchi$`Station ID` == "011")

#AGGREGATE DAILY

#CR_bacteria

ag_CR_bacteria <-CR_bacteria_011 %>%
   group_by(Date) %>%
  summarise(Enterococcus = mean(Enterococcus, na.rm = TRUE), E_coli = mean(E_coli, na.rm = TRUE),)
ag_CR_bacteria <- na.omit(ag_CR_bacteria)

if (file.exists("ag_CR_bacteria.rds")) {
  # If it's saved locally, load the data
ag_CR_bacteria <- readRDS("ag_CR_bacteria.rds")
} else {
saveRDS(ag_CR_bacteria, "ag_CR_bacteria.rds")
}

ag_CR_bacteria <- na.omit(ag_CR_bacteria)

#CR_nutrients

ag_CR_nutrients <- CR_nutrients_166 %>%
   group_by(Date) %>%
  summarise(`Ammonium (uM)` = mean(`Ammonium (uM)`, na.rm = TRUE), `Nitrate+nitrite (uM)` = mean(`Nitrate+nitrite (uM)`, na.rm = TRUE), `Phosphate (uM)` = mean(`Phosphate (uM)`, na.rm = TRUE), `Total phosphorus (uM)` = mean(`Total phosphorus (uM)`,na.rm = TRUE), `Chlorophyll a (ug/L)` = mean(`Chlorophyll a (ug/L)`,na.rm = TRUE), `Phaeophytin (ug/L)` = mean(`Phaeophytin (ug/L)`,na.rm = TRUE)) 


if (file.exists("ag_CR_nutrients.rds")) {
  # If it's saved locally, load the data
ag_CR_nutrients <- readRDS("ag_CR_nutrients.rds")
} else {
saveRDS(ag_CR_nutrients, "ag_CR_bacteria.rds")
  
}

ag_CR_nutrients <- na.omit(ag_CR_nutrients)

#CR_physical

ag_CR_physical <- CR_physical_011 %>%
  group_by(Date) %>%
  summarise(`Temperature (C)` = mean(`Temperature (C)`, na.rm = TRUE),
            `Salinity (PSU)` = mean(`Salinity (PSU)`, na.rm = TRUE),
            `Specific Conductance (mS/cm)` = mean(`Specific Conductance (mS/cm)`, na.rm = TRUE),
            `Dissolved Oxygen (mg/L)` = mean(`Dissolved Oxygen (mg/L)`, na.rm = TRUE),
            `DO Pct Saturation (%)` = mean(`DO Pct Saturation (%)`, na.rm = TRUE),
            `pH` = mean(`pH`, na.rm = TRUE),
            `Turbidity (NTU)` = mean(`Turbidity (NTU)`, na.rm = TRUE))



if (file.exists("ag_CR_physical.rds")) {
  # If it's saved locally, load the data
ag_CR_physical<- readRDS("ag_CR_physical.rds")
} else {
saveRDS(ag_CR_physical, "ag_CR_physical.rds")
  
}

ag_CR_physical <- na.omit(ag_CR_physical)


#CR_secchi



ag_CR_secchi <-CR_secchi_011 %>%
   group_by(Date) %>%
  summarise(`Total Suspended Solids (mg/L)`= mean(`Total Suspended Solids (mg/L)`, na.rm = TRUE ), `Secchi disk depth (m)` = mean(`Secchi disk depth (m)`, na.rm = TRUE))


if (file.exists("ag_CR_secchi.rds")) {
  # If it's saved locally, load the data
ag_CR_secchil<- readRDS("ag_CR_secchi.rds")
} else {
saveRDS(ag_CR_secchi, "ag_CR_secchi.rds")
  
}

ag_CR_secchi <- na.omit(ag_CR_secchi)




```

###MWRA CSO

```{r}

#MWRACSOData2016

if (file.exists("MWRACSOData2016.rds")) {
  # If it's saved locally, load the data
MWRACSOData2016 <- readRDS("MWRACSOData2016.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
MWRACSOData2016 <- read_sheet('https://docs.google.com/spreadsheets/d/1BF5lMyYrnILoS-rySwTGILiaqYfxA5-Pz5P2xUmOIq8/edit?usp=sharing')
saveRDS(MWRACSOData2016, "MWRACSOData2016.rds")
}

#MWRACSOData

if (file.exists("MWRACSOData.rds")) {
  # If it's saved locally, load the data
MWRACSOData <- readRDS("MWRACSOData.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
MWRACSOData <- read_sheet('https://docs.google.com/spreadsheets/d/1PRu1yvIuxOnMyazAVkXGv7dkOKUClNxl7CrvrctPREU/edit?usp=sharing')
saveRDS(MWRACSOData, "MWRACSOData.rds")
}

```

```{r}


#Extract Date
MWRACSOData$Date <- as.Date(MWRACSOData$`Start Time`, format = "%m/%d/%Y")


#Remove Excess Charecters

#Remove "Treated" in Volume Column
MWRACSOData$`Volume (MG)` <- gsub("Treated","",as.character(MWRACSOData$`Volume (MG)`))

#Remove "min" in Minutes Column

MWRACSOData$Minutes <- gsub("min","",as.character(MWRACSOData$Minutes))

#Remove Rows w/ "*"
MWRACSOData <- MWRACSOData %>% filter(!grepl("\\*", MWRACSOData$Duration))

#Replace NA w/ 0
MWRACSOData$Minutes[is.na(MWRACSOData$Minutes)] <- 0

#Make Duration Column
MWRACSOData$Minutes <- as.numeric(MWRACSOData$Minutes)
MWRACSOData$Hours <- as.numeric(MWRACSOData$Hours)

MWRACSOData$CombinedMinutes <- MWRACSOData$Minutes + (MWRACSOData$Hours * 60)

#Remove Columns 

MWRACSOData <- MWRACSOData[-c(4:10)]

#Check Dates

unique_dates <- unique(MWRACSOData$Date)


# Sanity Check: CombinedMinutes to numeric and Date to Date format
MWRACSOData <- MWRACSOData %>%
  mutate(CombinedMinutes = as.numeric(CombinedMinutes),
         Date = as.Date(Date))

MWRACSOData <- MWRACSOData %>%
  mutate(`Volume (MG)` = as.numeric(`Volume (MG)`),
         Date = as.Date(Date))

# Group by Date and calculate the sum of CombinedMinutes



MWRACSOData2 <- 
aggregate(MWRACSOData$CombinedMinutes, by=list(MWRACSOData$Date, MWRACSOData$`Outfall Location`), FUN = sum) 


MWRACSOData3 <- 
aggregate(MWRACSOData$`Volume (MG)`, by=list(MWRACSOData$Date, MWRACSOData$`Outfall Location`), FUN = sum) 

MWRACSOData20162 <- 
aggregate(MWRACSOData2016$`Duration (Min)`, by=list(MWRACSOData2016$Date, MWRACSOData2016$CSOName), FUN = sum) 

MWRACSOData20163 <- 
aggregate(MWRACSOData2016$`Volume (MG)`, by=list(MWRACSOData2016$Date, MWRACSOData2016$CSOName), FUN = sum) 


# Rename a column using colnames

colnames(MWRACSOData2)[1:3] <- c("Date","Outfall Location", "Total Minutes")

colnames(MWRACSOData20162)[1:3] <- c("Date","Outfall Location", "Total Minutes")

colnames(MWRACSOData3)[1:3] <- c("Date","Outfall Location", "Volume")

colnames(MWRACSOData20163)[1:3] <- c("Date","Outfall Location", "Volume")

#Set Columns to Be The Same

MWRACSOData <- MWRACSOData[-c(1,4,5,7)]

MWRACSOData2016 <- MWRACSOData2016[-c(3:8)]




#Make New Names

MWRA_new_names <- c(
  "CSO_ID",
  "Outfall Location",
  "Date"
)

MWRA2016_new_names <- c(
  "CSO_ID",
  "Outfall Location",
  "Date"
)

MWRACSOData <- MWRACSOData %>% 
  set_names(MWRA_new_names)
  
MWRACSOData2016 <- MWRACSOData2016 %>% 
  set_names(MWRA2016_new_names)

#Fix Date Format

MWRACSOData$Date <- format(as.Date(MWRACSOData$Date, format=
                           "%Y-%m-%d"          ), "%m-%d-%Y")

#Merge Columns 

MergedMWRACSO <- rbind(MWRACSOData,MWRACSOData2016)

#Order Date

MergedMWRACSO[order(as.Date(MergedMWRACSO$Date, format="%m/%d/%Y")),]

#Fix Date Formats of Future Joins
MWRACSOData2$Date <- format(as.Date(MWRACSOData2$Date, format=
                           "%Y-%m-%d"          ), "%m-%d-%Y")


MWRACSOData3$Date <- format(as.Date(MWRACSOData3$Date, format=
                           "%Y-%m-%d"          ), "%m-%d-%Y")

# Perform the joins

MergedMWRACSO <- merge(MergedMWRACSO, MWRACSOData2[, c("Date", "Outfall Location", "Total Minutes")], 
                       by.x = c("Date", "Outfall Location"), 
                       by.y = c("Date", "Outfall Location"), all.x = TRUE)

MergedMWRACSO <- merge(MergedMWRACSO, MWRACSOData20162[, c("Date", "Outfall Location", "Total Minutes")], 
                       by.x = c("Date", "Outfall Location"), 
                       by.y = c("Date", "Outfall Location"), all.x = TRUE)

# Combine the columns using coalesce
MergedMWRACSO <- MergedMWRACSO %>%
  mutate(TotalMinutes_combined = coalesce(`Total Minutes.x`, `Total Minutes.y`))


# Drop the individual columns if you no longer need them
MergedMWRACSO <- MergedMWRACSO %>%
  select(-`Total Minutes.x`, -`Total Minutes.y`)


MergedMWRACSO <- merge(MergedMWRACSO, MWRACSOData3[, c("Date", "Outfall Location", "Volume")], 
                       by.x = c("Date", "Outfall Location"), 
                       by.y = c("Date", "Outfall Location"), all.x = TRUE)

MergedMWRACSO <- merge(MergedMWRACSO, MWRACSOData20163[, c("Date", "Outfall Location", "Volume")], 
                       by.x = c("Date", "Outfall Location"), 
                       by.y = c("Date", "Outfall Location"), all.x = TRUE)

MergedMWRACSO <- MergedMWRACSO %>%
  mutate(Volume_combined = coalesce(`Volume.x`, `Volume.y`))


# Drop the individual columns if you no longer need them
MergedMWRACSO <- MergedMWRACSO %>%
  select(-`Volume.x`, -`Volume.y`)


```

```{r}

MWRACSOCoords <- read_sheet('https://docs.google.com/spreadsheets/d/1O9RONCW0lGEsPrt-srZU8M4xeNxDM3hJmkA1IWL2mW4/edit?usp=sharing')

MWRACSOCoords2 <- read_sheet('https://docs.google.com/spreadsheets/d/18PK0CcX6Ye5RZJtVUI8mmiaRwLIpRNIzGCwOupSqf-k/edit?usp=sharing')


```

```{r}

MWRACSOFinal <-  
left_join(MergedMWRACSO , MWRACSOCoords ,  by = c("CSO_ID" = "OUTFALL"  ))

#MWRACSOCoords2$OUTFALL_ID <- as.character(MWRACSOCoords2$OUTFALL_ID )

#MWRACSOFinal <-  
#left_join(MergedMWRACSO , MWRACSOCoords2 ,  by = c("CSO_ID" = "OUTFALL_ID"  ))
```

```{r}

if (file.exists("MWRACSOFinal.rds")) {
  # If it's saved locally, load the data
MWRACSOFinal <- readRDS("MWRACSOFinal.rds")
} else {
saveRDS(MWRACSOFinal, "MWRACSOFinal.rds")
}


```

```{r}
# List all objects in the environment
all_objects <- ls()

# Identify objects to keep
objects_to_keep <- c("MWRACSOFinal")

# Identify objects to remove
objects_to_remove <- setdiff(all_objects, objects_to_keep)

# Remove unwanted objects
rm(list = objects_to_remove)

rm(all_objects)
rm(objects_to_keep)
rm(objects_to_remove)

```

###CAMCSO

```{r}

#CAM2015CSO

if (file.exists("CAM2015CSO.rds")) {
  # If it's saved locally, load the data
CAM2015CSO <- readRDS("CAM2015CSO.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CAM2015CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2015CAMCSO')
saveRDS(CAM2015CSO, "CAM2015CSO.rds")
}


#CAM2016CSO

if (file.exists("CAM2016CSO.rds")) {
  # If it's saved locally, load the data
CAM2016CSO <- readRDS("CAM2016CSO.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CAM2016CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2016CAMCSO')
saveRDS(CAM2016CSO , "CAM2016CSO.rds")
}

CAM2016CSO <- subset(CAM2016CSO, select = c("Date", "CAM017* (MG)"))

#CAM2017CSO

if (file.exists("CAM2017CSO.rds")) {
  # If it's saved locally, load the data
CAM2017CSO <- readRDS("CAM2017CSO.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CAM2017CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2017CAMCSO')
saveRDS(CAM2017CSO , "CAM2017CSO.rds")
}

CAM2017CSO <- subset(CAM2017CSO, select = c("Date", "CAM017* (MG)"))


#CAM2018CSO

if (file.exists("CAM2018CSO.rds")) {
  # If it's saved locally, load the data
CAM2018CSO <- readRDS("CAM2018CSO.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CAM2018CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2018CAMCSO')
saveRDS(CAM2018CSO , "CAM2018CSO.rds")
}



CAM2018CSO <- subset(CAM2018CSO, select = c("Date", "CAM017* (MG)"))

#CAM2019CSO

if (file.exists("CAM2019CSO.rds")) {
  # If it's saved locally, load the data
CAM2019CSO <- readRDS("CAM2019CSO.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CAM2019CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2019CAMCSO')
saveRDS(CAM2019CSO , "CAM2019CSO.rds")
}

CAM2019CSO <- subset(CAM2019CSO, select = c("Date", "CAM017* (MG)"))


#CAM2020CSO

if (file.exists("CAM2020CSO.rds")) {
  # If it's saved locally, load the data
CAM2020CSO <- readRDS("CAM2020CSO.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CAM2020CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2020CAMCSO')
saveRDS(CAM2020CSO , "CAM2020CSO.rds")
}

CAM2020CSO <- subset(CAM2020CSO, select = c("Date", "CAM017* (MG)"))



#CAM2021CSO

if (file.exists("CAM2021CSO.rds")) {
  # If it's saved locally, load the data
CAM2021CSO <- readRDS("CAM2021CSO.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CAM2021CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2021CAMCSO')
saveRDS(CAM2021CSO , "CAM2021CSO.rds")
}

CAM2021CSO <- subset(CAM2021CSO, select = c("Date", "CAM017* (MG)"))

#CAM2022CSO

if (file.exists("CAM2022CSO.rds")) {
  # If it's saved locally, load the data
CAM2022CSO <- readRDS("CAM2022CSO.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
CAM2022CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2022CAMCSO')
saveRDS(CAM2022CSO , "CAM2022CSO.rds")
}

CAM2022CSO <- subset(CAM2022CSO, select = c("Date", "CAM017* (MG)"))


```

```{r}
# Combine all values into a single vector and sort them

library(dplyr)

# Ensure column names are consistent across all data frames
colnames(CAM2015CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2016CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2017CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2018CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2019CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2020CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2021CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2022CSO) <- c("Date", "CAM017* (MG)")

CAM2015CSO$`CAM017* (MG)` <- as.numeric(CAM2015CSO$`CAM017* (MG)`)
CAM2016CSO$`CAM017* (MG)` <- as.numeric(CAM2016CSO$`CAM017* (MG)`)
CAM2017CSO$`CAM017* (MG)` <- as.numeric(CAM2017CSO$`CAM017* (MG)`)
CAM2018CSO$`CAM017* (MG)` <- as.numeric(CAM2018CSO$`CAM017* (MG)`)
CAM2019CSO$`CAM017* (MG)` <- as.numeric(CAM2019CSO$`CAM017* (MG)`)
CAM2020CSO$`CAM017* (MG)` <- as.numeric(CAM2020CSO$`CAM017* (MG)`)
CAM2021CSO$`CAM017* (MG)` <- as.numeric(CAM2021CSO$`CAM017* (MG)`)
CAM2022CSO$`CAM017* (MG)` <- as.numeric(CAM2022CSO$`CAM017* (MG)`)


# Make sure that the "CAM017* (MG)" column is of type double


CAM017 <- bind_rows(
 CAM2016CSO,
  CAM2017CSO, 
  CAM2018CSO,
  CAM2019CSO,
  CAM2020CSO,
  CAM2021CSO,
  CAM2022CSO,
)


CAM017 <- CAM017 %>% rename(MG = `CAM017* (MG)`)

CAM017_discharges <- subset(CAM017, MG != 0)



if (file.exists("CAM017.rds")) {
  # If it's saved locally, load the data
  CAM017 <- readRDS("All.CAM017.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
  saveRDS(All.CAM017, "CAM017.rds")
} 
  


```

### ALL CSOs

```{r}

ag_df_CR$Date <- ag_df_CR$date

ag_df_CR <- ag_df_CR[,-c(1) ]

#rm(CR2023, CR2022, CR2020, CR2021, CR2020, CR2019, CR2018, CR2017, CR2016, CR2015, df_CR)

ag_df_CR2 <- ag_df_CR


#Fix Date Formats of Future Joins
MWRACSOFinal$Date <- format(as.Date(MWRACSOFinal$Date, format=
                           "%m-%d-%Y"         ), "%m-%d-%Y")


ag_df_CR2$Date <- format(as.Date(ag_df_CR2$Date , format=
                           "%Y-%m-%d"          ), "%m-%d-%Y")


#join

All <- merge(MWRACSOFinal, ag_df_CR2, by = "Date", all = TRUE)

# Assuming your date column is named "Date"
All$Date <- as.Date(All$Date, format="%m-%d-%Y")

# Order the data frame by Year, Month, and Day
All <- All[order(year(All$Date), month(All$Date), day(All$Date)), ]

```

Join CAM + CSO

```{r}


CAM017 #Top CAM CSOs site 
All #ALL MWRA CSOs

All.CSOs <- merge(All, CAM017, by = "Date", all = TRUE)


```

###CSO OCCURRANCES

```{r}

# Assuming your data frame is named MWRACSOFinal and the column with CSO IDs is named CSO_ID
cso_counts <- as.data.frame(table(MWRACSOFinal$CSO_ID))

#011

```

#COMBINE ALL DATA

##MWRA X EPA

```{r}
ag_df_CR <- ag_df_CR %>%
    mutate(date = as.POSIXct(date, format = "%Y-%m-%d"))
ag_df_CR <- ag_df_CR %>% pad()

#All.CR_bacteria
ag_CR_bacteria <- ag_CR_bacteria %>% pad()
All.CR_bacteria <- left_join(ag_CR_bacteria, ag_df_CR,  by = c("Date" = "date"  ))

if (file.exists("All.CR_bacteria.rds")) {
  # If it's saved locally, load the data
 All.CR_bacteria <- readRDS("All.CR_bacteria.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
  saveRDS(All.CR_bacteria, "All.CR_bacteria.rds")
}
                            
#All.CR_secchi

ag_CR_secchi <- ag_CR_secchi %>% pad()
All.CR_secchi <- left_join(ag_CR_secchi, ag_df_CR,  by = c("Date" = "date"  ))


if (file.exists("All.CR_secchi.rds")) {
  # If it's saved locally, load the data
  CR2023 <- readRDS("All.secchi.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
  saveRDS(All.CR_secchi, "All.CR_secchi.rds")
}
                            

#All.CR.nutrients

ag_CR_nutrients <- ag_CR_nutrients %>% pad()
All.CR_nutrients <- left_join(ag_CR_nutrients, ag_df_CR,  by = c("Date" = "date"  ))

if (file.exists("All.CR_nutrients.rds")) {
  # If it's saved locally, load the data
  All.CR_nutrients <- readRDS("All.nutrients.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
  saveRDS(All.CR_nutrients, "All.CR_nutrients.rds")
}

                          
```

#LOGAN X EPA

```{r}

#TO BE FIXED 

#All.CR_bacteria
ag_Logan <- ag_Logan %>% pad()
All.ag_Logan <- left_join(ag_Logan, ag_df_CR,  by = c("DATE" = "date"  ))

if (file.exists("All.ag_Logan.rds")) {
  # If it's saved locally, load the data
  All.ag_Logan <- readRDS("All.ag_Logan.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
  saveRDS(All.ag_Logan, "All.ag_Logan.rds")
}

    
  

```

#CSOS

```{r}

CAM017 <- CAM017 %>% pad()
All.CAM017 <- left_join(CAM017, ag_df_CR,  by  = c("Date" = "date"  ))

if (file.exists("All.CAM017.rds")) {
  # If it's saved locally, load the data
  All.CAM017 <- readRDS("All.CAM017.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
  saveRDS(All.CAM017, "All.CAM017.rds")
} 
  
  
```

#Combine All Var

```{r}
All.Var1 <-  left_join(CAM017, ag_df_CR,  by  = c("Date" = "date"  ))

ag_Logan <- ag_Logan %>% pad()
All.Var1 <- left_join(ag_Logan, ag_df_CR,  by = c("DATE" = "date"  ))
All.Var2 <- left_join(All.Var1, CAM017,  by = c("DATE" = "Date"  ))
All.Var3 <- left_join(All.Var2, ag_CR_bacteria,  by = c("DATE" = "Date"  ))
All.Var4 <- left_join(All.Var3, ag_CR_nutrients,  by = c("DATE" = "Date"  ))
All.Var5 <- left_join(All.Var4, ag_CR_nutrients,  by = c("DATE" = "Date"  ))


All.Var <- All.Var5


if (file.exists("All.Var.rds")) {
  # If it's saved locally, load the data
  All.Var <- readRDS("All.Var.rds")
} else {
  # If it's not saved locally, read it from Google Sheets and save it locally
  saveRDS(All.Var, "All.Var.rds")
} 
  
  
```

#SURROGATE

```{r}

ag_df_CR <- na.omit(ag_df_CR)
ag_Logan <- na.omit(ag_Logan)
ag_CR_nutrients
ag_CR_secchi
ag_CR_bacteria


#ag_CR_secchi

ag_CR_secchi$`Total Suspended Solids (mg/L)`

#ag_CR_nutrients 

ag_CR_nutrients$`Ammonium (uM)`
ag_CR_nutrients$`Nitrate+nitrite (uM)`
ag_CR_nutrients$`Phosphate (uM)`
ag_CR_nutrients$`Total phosphorus (uM)`
ag_CR_nutrients$`Phaeophytin (ug/L)`

#ag_CR_bacteria

ag_CR_bacteria$Enterococcus
ag_CR_bacteria$E_coli
ag_CR_bacteria$coliform


    
  


#ADD CSOs

# Convert ag_df_CR to a time series object if necessary
Surrogate_phy <- as.ts(ag_df_CR$phy)
Surrogate_chl <- as.ts(ag_df_CR$chl)
Surrogate_ph <- as.ts(ag_df_CR$ph)
Surrogate_Celsius <- as.ts(ag_df_CR$Celsius)
Surrogate_spcond <- as.ts(ag_df_CR$spcond)
Surrogate_do <- as.ts(ag_df_CR$do)
Surrogate_turb <- as.ts(ag_df_CR$turb)
Surrogate_Salinity <- as.ts(ag_df_CR$Salinity)
Surrogate_del_phy <- as.ts(ag_df_CR$Salinity)
Surrogate_TSS <- as.ts(ag_CR_secchi$`Total Suspended Solids (mg/L)`)
Surrogate_Ammonium <- as.ts(ag_CR_nutrients$`Ammonium (uM)`)
Surrogate_nitrate+nitrite <- as.ts(ag_CR_nutrients$`Nitrate+nitrite (uM)`)
Surrogate_phosphate <- as.ts(ag_CR_nutrients$`Phosphate (uM)`)
Surrogate_phosphorous <- as.ts(ag_CR_nutrients$`Total phosphorus (uM)`)
Surrogate_Phaeophytin <- as.ts(ag_CR_nutrients$`Phaeophytin (ug/L)`)
Surrogate_Enterococcus <- as.ts(ag_CR_bacteria$Enterococcus)
Surrogate_E_coli <- as.ts(ag_CR_bacteria$E_coli)
Surrogate_coliform <- as.ts(ag_CR_bacteria$coliform)
Surrogate_alt <-  as.ts(ag_Logan$HourlyAltimeterSetting)
Surrogate_Dew <-  as.ts(ag_Logan$HourlyDewPointTemperature)
Surrogate_Precip <-  as.ts(ag_Logan$HourlyPrecipitation)
Surrogate_Humidity <-  as.ts(ag_Logan$HourlyRelativeHumidity)
Surrogate_Station <-  as.ts(ag_Logan$HourlyStationPressure)
Surrogate_Visibility <-  as.ts(ag_Logan$HourlyVisibility)
Surrogate_WindDirection <-  as.ts(ag_Logan$HourlyWindDirection)
Surrogate_WindGustSpeed <-  as.ts(ag_Logan$HourlyWindGustSpeed)
Surrogate_WindSpeed <-  as.ts(ag_Logan$HourlyWindSpeed)



#Surrogate

Surrogate_phy <- SurrogateData(Surrogate_phy, method = c("ebisuzaki"), num_surr = 1, alpha = 0 )
Surrogate_chl <- SurrogateData(Surrogate_chl, method = c("ebisuzaki"), num_surr = 1, alpha = 0 )
Surrogate_ph <- SurrogateData(Surrogate_ph, method = c("ebisuzaki"), num_surr = 1, alpha = 0 )
Surrogate_Celsius <- SurrogateData(Surrogate_Celsius, method = c("ebisuzaki"), num_surr = 1, alpha = 0 )
Surrogate_spcond <- SurrogateData(Surrogate_spcond, method = c("ebisuzaki"), num_surr = 1, alpha = 0 )
Surrogate_do <- SurrogateData(Surrogate_do, method = c("ebisuzaki"), num_surr = 1, alpha = 0 )
Surrogate_turb <- SurrogateData(Surrogate_turb, method = c("ebisuzaki"), num_surr = 1, alpha = 0 )
Surrogate_Salinity <- SurrogateData(Surrogate_Salinity, method = c("ebisuzaki"), num_surr = 1, alpha = 0 )
Surrogate_del_phy <- SurrogateData(Surrogate_del_phy, method = c("ebisuzaki"), num_surr = 1, alpha = 0 )
Surrogate_TSS <- SurrogateData(as.ts(ag_CR_secchi$`Total Suspended Solids (mg/L)`), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_Ammonium <- SurrogateData(as.ts(ag_CR_nutrients$`Ammonium (uM)`), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_nitrate_nitrite <- SurrogateData(as.ts(ag_CR_nutrients$`Nitrate+nitrite (uM)`), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_phosphate <- SurrogateData(as.ts(ag_CR_nutrients$`Phosphate (uM)`), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_phosphorous <- SurrogateData(as.ts(ag_CR_nutrients$`Total phosphorus (uM)`), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_Phaeophytin <- SurrogateData(as.ts(ag_CR_nutrients$`Phaeophytin (ug/L)`), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_Enterococcus <- SurrogateData(as.ts(ag_CR_bacteria$Enterococcus), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_E_coli <- SurrogateData(as.ts(ag_CR_bacteria$E_coli), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_coliform <- SurrogateData(as.ts(ag_CR_bacteria$coliform), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_alt <- SurrogateData(as.ts(ag_Logan$HourlyAltimeterSetting), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_Dew <- SurrogateData(as.ts(ag_Logan$HourlyDewPointTemperature), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_Precip <- SurrogateData(as.ts(ag_Logan$HourlyPrecipitation), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_Humidity <- SurrogateData(as.ts(ag_Logan$HourlyRelativeHumidity), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_Station <- SurrogateData(as.ts(ag_Logan$HourlyStationPressure), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_Visibility <- SurrogateData(as.ts(ag_Logan$HourlyVisibility), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_WindDirection <- SurrogateData(as.ts(ag_Logan$HourlyWindDirection), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_WindGustSpeed <- SurrogateData(as.ts(ag_Logan$HourlyWindGustSpeed), method = c("ebisuzaki"), num_surr = 1, alpha = 0)
Surrogate_WindSpeed <- SurrogateData(as.ts(ag_Logan$HourlyWindSpeed), method = c("ebisuzaki"), num_surr = 1, alpha = 0)






# Check the class of each object
sapply(list(Surrogate_phy, Surrogate_chl, Surrogate_ph, Surrogate_Celsius, 
            Surrogate_spcond, Surrogate_do, Surrogate_turb, Surrogate_Salinity, 
            Surrogate_del_phy, 
Surrogate_TSS  ,
Surrogate_Ammonium ,
Surrogate_nitrate+nitrite ,
Surrogate_phosphate  ,
Surrogate_phosphorous ,
Surrogate_Phaeophytin  ,
Surrogate_Enterococcus  ,
Surrogate_E_coli ,
Surrogate_coliform ,
Surrogate_alt ,
Surrogate_Dew ,
Surrogate_Precip ,
Surrogate_Humidity  ,
Surrogate_Station , 
Surrogate_Visibility ,
Surrogate_WindDirection ,
Surrogate_WindGustSpeed ,
Surrogate_WindSpeed ) 
       
       class)

Surrogate_phy <- as.data.frame(Surrogate_phy)
Surrogate_chl <- as.data.frame(Surrogate_chl)
Surrogate_ph <- as.data.frame(Surrogate_ph)
Surrogate_Celsius <- as.data.frame(Surrogate_Celsius)
Surrogate_spcond <- as.data.frame(Surrogate_spcond)
Surrogate_do <- as.data.frame(Surrogate_do)
Surrogate_turb <- as.data.frame(Surrogate_turb)
Surrogate_Salinity <- as.data.frame(Surrogate_Salinity)
Surrogate_del_phy <- as.data.frame(Surrogate_del_phy)
Surrogate_TSS <- as.data.frame(Surrogate_TSS)
Surrogate_Ammonium <- as.data.frame(Surrogate_Ammonium)
Surrogate_nitrate_nitrite <- as.data.frame(Surrogate_nitrate_nitrite)
Surrogate_phosphate <- as.data.frame(Surrogate_phosphate)
Surrogate_phosphorous <- as.data.frame(Surrogate_phosphorous)
Surrogate_Phaeophytin <- as.data.frame(Surrogate_Phaeophytin)
Surrogate_Enterococcus <- as.data.frame(Surrogate_Enterococcus)
Surrogate_E_coli <- as.data.frame(Surrogate_E_coli)
Surrogate_coliform <- as.data.frame(Surrogate_coliform)
Surrogate_alt <- as.data.frame(Surrogate_alt)
Surrogate_Dew <- as.data.frame(Surrogate_Dew)
Surrogate_Precip <- as.data.frame(Surrogate_Precip)
Surrogate_Humidity <- as.data.frame(Surrogate_Humidity)
Surrogate_Station <- as.data.frame(Surrogate_Station)
Surrogate_Visibility <- as.data.frame(Surrogate_Visibility)
Surrogate_WindDirection <- as.data.frame(Surrogate_WindDirection)
Surrogate_WindGustSpeed <- as.data.frame(Surrogate_WindGustSpeed)
Surrogate_WindSpeed <- as.data.frame(Surrogate_WindSpeed)



Surrogate <- bind_cols(
  Surrogate_phy,
  Surrogate_chl,
  Surrogate_ph,
  Surrogate_Celsius,
  Surrogate_spcond,
  Surrogate_do,
  Surrogate_turb,
  Surrogate_Salinity,
  Surrogate_del_phy,
Surrogate_TSS ,
Surrogate_Ammonium,
Surrogate_nitrate+nitrite,
Surrogate_phosphate ,
Surrogate_phosphorous,
Surrogate_Phaeophytin ,
Surrogate_Enterococcus ,
Surrogate_E_coli,
Surrogate_coliform ,
Surrogate_alt ,
Surrogate_Dew,
Surrogate_Precip,
Surrogate_Humidity ,
Surrogate_Station ,
Surrogate_Visibility ,
Surrogate_WindDirection ,
Surrogate_WindGustSpeed,
Surrogate_WindSpeed 

)

# Set the column names
colnames(Surrogate) <- c(
  "phy",
  "chl",
  "ph",
  "Celsius",
  "spcond",
  "do",
  "turb",
  "Salinity",
  "del_phy",
  "TSS",
"Ammonium",
"nitrate+nitrite",
"phosphate",
"phosphorous",
"Phaeophytin",
"Enterococcus",
"E_coli",
"coliform",
"alt",
"Dew",
"Precip",
"Humidity",
"Station",
"Visibility",
"WindDirection",
"WindGustSpeed",
"WindSpeed"

)

```

#PLOT 2021 DRIVERS

####Visualize Salinity

```{r}
#Visualize Salinity 2021 

df_CR <- df_CR %>% pad()

df_CR %>%
  mutate(date = as.Date(date)) %>%
  group_by(date) %>%
  filter(date >= as.Date("2021-05-01") & date <= as.Date("2021-11-01"))  %>%
  ggplot(aes(x = date)) + 
  geom_path(aes(y = Salinity, color = "Salinity")) +
  scale_x_date(date_labels = "%b", date_breaks = "1 month") +
  ggtitle("Salinity 2021") +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(x = "Month", y = "Salinity (ppm)")

#Visualize Chl 2021 

df_CR %>%
  mutate(date = as.Date(date)) %>%
  group_by(date) %>%
  filter(date >= as.Date("2021-05-01") & date <= as.Date("2021-11-01"))  %>%
  ggplot(aes(x = date)) + 
  geom_path(aes(y = chl, color = "chl")) +
  scale_x_date(date_labels = "%b", date_breaks = "1 month") +
  ggtitle("Chlorophyll 2021") +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(x = "Month", y = "Chlorophyll (RFU)")
  

#Visualize Phy 2021 

df_CR %>%
  mutate(date = as.Date(date)) %>%
  group_by(date) %>%
  filter(date >= as.Date("2021-05-01") & date <= as.Date("2021-11-01"))  %>%
  ggplot(aes(x = date)) + 
  geom_path(aes(y = chl, color = "phy")) +
  scale_x_date(date_labels = "%b", date_breaks = "1 month") +
  ggtitle("Phycocanin 2021") +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(x = "Month", y = "Phycocanin (RFU)")


df_CR %>% group_by(date,hour) %>%
    mutate(date = as.Date(date)) %>%
  group_by(date) %>%
  filter(date >= as.Date("2021-05-01") & date <= as.Date("2021-11-01"))  %>%
  ggplot(aes(x=date)) + 
  geom_path(aes(y=chl,color="chl")) + 
  geom_path(aes(y=phy,color="phy")) +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle(" < 2021 Chlorophyll V. Phycocanin ") + 
    labs(x = "Dates", y = "RFU")



ag_df_CR %>% 
  group_by(date) %>%
  filter(date >= as.Date("2021-05-01") & date <= as.Date("2021-11-01"))  %>%
  ggplot(aes(x=date)) + 
  geom_path(aes(y=chl,color="Chlorophyll (RFU)")) + 
  geom_path(aes(y=phy,color="Phycocanin (RFU)")) +
    scale_color_manual(values=c("Chlorophyll (RFU)"="darkgreen", "Phycocanin (RFU)"="orange")) +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("2021 Chlorophyll V. Phycocanin ") + 
    labs(x = "Dates", y = "RFU (Relative Fluorescence Units)")


  ag_df_CR <- ag_df_CR %>% pad()


ag_df_CR %>% 
  group_by(date) %>%
  filter(date >= as.Date("2015-05-01") & date <= as.Date("2021-11-01"))  %>%
  ggplot(aes(x=date)) + 
  geom_path(aes(y=chl,color="Chlorophyll (RFU)")) + 
  geom_path(aes(y=phy,color="Phycocanin (RFU)")) +
    scale_color_manual(values=c("Chlorophyll (RFU)"="darkgreen", "Phycocanin (RFU)"="orange")) +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("< 2021 Chlorophyll V. Phycocanin ") + 
    labs(x = "Dates", y = "RFU (Relative Fluorescence Units)")

ag_df_CR <- na.omit(ag_df_CR)


```

### Plot All EPA Buoy Vars All Time

```{r}
#df_CR
df_CR %>%
  group_by(date, hour) %>%
  summarise(across(c("chl", "phy", "ph", "Celsius", "spcond", "do", "turb", "Salinity"), mean), .groups = "keep") %>%
  mutate(date = date + hours(hour)) %>%
  ggplot(aes(x = date)) +
  geom_path(aes(y = chl, color = "Chlorophyll (RFU)")) +
  geom_path(aes(y = phy, color = "Phycocanin (RFU)")) +
  geom_path(aes(y = ph, color = "pH")) +
  geom_path(aes(y = Celsius, color = "Temperature (Celsius)")) +
  geom_path(aes(y = spcond, color = "Specific Conductance Ratio (mS/cm)")) +
  geom_path(aes(y = do, color = "Dissolved Oxygen (mg/L)")) +
  geom_path(aes(y = turb, color = "Turbidity (FNU)")) +
  geom_path(aes(y = Salinity, color = "Salinity (ppm)")) +
  scale_color_manual(
    values = c("Chlorophyll (RFU)" = "green",
               "Phycocanin (RFU)" = "orange",
               "pH" = "brown",
               "Temperature (Celsius)" = "red",
               "Specific Conductance Ratio (mS/cm)" = "pink",
               "Dissolved Oxygen (mg/L)" = "yellow",
               "Turbidity (FNU)" = "purple",
               "Salinity (ppm)" = "blue"),
    name = "Color"
  ) + 
      labs(x = "Dates", y = "Units") +
theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("All EPA Buoy Vars")


ag_df_CR <- ag_df_CR %>%
    mutate(date = as.POSIXct(date, format = "%Y-%m-%d"))
  ag_df_CR <- ag_df_CR %>% pad()
  
#ag_df_CR
ag_df_CR %>%
    mutate(date = as.POSIXct(date, format = "%Y-%m-%d")) %>%
  group_by(date) %>%
  summarise(across(c("chl", "phy", "ph", "Celsius", "spcond", "do", "turb", "Salinity"), mean), .groups = "keep") %>%
  ggplot(aes(x = date)) +
  geom_path(aes(y = chl, color = "Chlorophyll (RFU)")) +
  geom_path(aes(y = phy, color = "Phycocanin (RFU)")) +
  geom_path(aes(y = ph, color = "pH")) +
  geom_path(aes(y = Celsius, color = "Temperature (Celsius)")) +
  geom_path(aes(y = spcond, color = "Specific Conductance Ratio (mS/cm)")) +
  geom_path(aes(y = do, color = "Dissolved Oxygen (mg/L)")) +
  geom_path(aes(y = turb, color = "Turbidity (FNU)")) +
  geom_path(aes(y = Salinity, color = "Salinity (ppm)")) +
  scale_color_manual(
    values = c("Chlorophyll (RFU)" = "green",
               "Phycocanin (RFU)" = "orange",
               "pH" = "brown",
               "Temperature (Celsius)" = "red",
               "Specific Conductance Ratio (mS/cm)" = "pink",
               "Dissolved Oxygen (mg/L)" = "yellow",
               "Turbidity (FNU)" = "purple",
               "Salinity (ppm)" = "blue"),
    name = "Color"
  ) + 
      labs(x = "Dates", y = "Units") +
theme(plot.title = element_text(hjust = 0.5)) +
    ylim(0, 31) +
  ggtitle("2015 - 2023 EPA Buoy Variables")


ag_df_CR <- na.omit(ag_df_CR)


```

####CR BUOY

```{r}
ag_df_CR

ag_df_CR$chl

ag_df_CR$phy

ag_df_CR$ph

ag_df_CR$Celsius

ag_df_CR$spcond

ag_df_CR$do

ag_df_CR$turb

ag_df_CR$Salinity

```

###9:12

```{r}

morndf_CR

morndf_CR$chl

mornddf_CR$phy

mornddf_CR$ph

mornddf_CR$Celsius

mornddf_CR$spcond

mornddf_CR$do

mornddf_CR$turb

mornddf_CR$Salinity


```

#ACFs

```{r}

ag_df_CR
morndf_CR
df_CR



```

###ACF Aggreggated

```{r}
ag_df_CR <- na.omit(ag_df_CR)

#2021
ts_chl_2021 <- ag_df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date) %>%
  summarise(across(c("chl","phy"),mean),.groups = "keep") %>%
  pull(chl)

ts_phy_2021 <- ag_df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date) %>%
  summarise(across(c("chl","phy"),mean),.groups = "keep") %>%
  pull(phy)


#MORNDF
morndf_chl_2021 <- ag_df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date) %>%
  summarise(across(c("chl","phy"),mean),.groups = "keep") %>%
  pull(chl)

morndf_phy_2021 <- ag_df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date) %>%
  summarise(across(c("chl","phy"),mean),.groups = "keep") %>%
  pull(phy)

length(ts_chl_2021)
acf(ts_chl_2021,lag.max = 24*30, main = " Hourly Chlorophyll ACF")
abline(h = c(0.3,0.6) , col = c("red","red")) #change to lagmax = 30 if want days 
#determine lag -talk to deyle?
acf(ts_phy_2021,lag.max = 24*30, main = "Hourly Phycocanin ACF")
abline(h = c(0.3,0.6) , col = c("red","red"))

acf(ts_chl_2021,lag.max = 30, main = "Daily Chlorophyll ACF")
abline(h = c(0.3,0.6) , col = c("red","red")) #change to lagmax = 30 if want days 
#determine lag -talk to deyle?
acf(ts_phy_2021,lag.max = 30, main = "Daily Phycocanin  ACF")
abline(h = c(0.3,0.6) , col = c("red","red"))

length(ts_chl_2021)
acf(ts_chl_2021,lag.max = 24*30, main = " 9 AM - 12 PM Chlorophyll ACF")
abline(h = c(0.3,0.6) , col = c("red","red")) #change to lagmax = 30 if want days 
#determine lag -talk to deyle?
acf(ts_phy_2021,lag.max = 24*30, main = "9 AM - 12 PM Phycocanin ACF")
abline(h = c(0.3,0.6) , col = c("red","red"))

## you can check an individual value as follows
cor(ts_phy_2021,lag(ts_phy_2021,6),use="pairwise")

df_CR <- na.omit(df_CR)
#2021
ts_chl_2021 <- df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date) %>%
  summarise(across(c("chl","phy"),mean),.groups = "keep") %>%
  pull(chl)

ts_phy_2021 <- df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date) %>%
  summarise(across(c("chl","phy"),mean),.groups = "keep") %>%
  pull(phy)

length(ts_chl_2021)
acf(ts_chl_2021,lag.max = 24*30, main = " Hourly Chlorophyll ACF")
abline(h = c(0.3,0.6) , col = c("red","red")) #change to lagmax = 30 if want days 
#determine lag -talk to deyle?
acf(ts_phy_2021,lag.max = 24*30, main = "Hourly Phycocanin ACF")
abline(h = c(0.3,0.6) , col = c("red","red"))




```

#PACFs

```{r}

morndf_CR <- na.omit(morndf_CR)
ag_df_CR<- na.omit(ag_df_CR)

#2021
ts_chl_2021 <- ag_df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date) %>%
  summarise(across(c("chl","phy"),mean),.groups = "keep") %>%
  pull(chl)

ts_phy_2021 <- ag_df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date) %>%
  summarise(across(c("chl","phy"),mean),.groups = "keep") %>%
  pull(phy)


#MORNDF
morndf_chl_2021 <- ag_df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date) %>%
  summarise(across(c("chl","phy"),mean),.groups = "keep") %>%
  pull(chl)

morndf_phy_2021 <- ag_df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date) %>%
  summarise(across(c("chl","phy"),mean),.groups = "keep") %>%
  pull(phy)


acf(ts_chl_2021,lag.max = 20, main = "Daily Chlorophyll ACF")
abline(h = c(0.3,0.6) , col = c("red","red")) #change to lagmax = 30 if want days 
#determine lag -talk to deyle?
acf(ts_phy_2021,lag.max = 20, main = "Daily Phycocanin  ACF")
abline(h = c(0.3,0.6) , col = c("red","red"))

length(ts_chl_2021)
acf(ts_chl_2021,lag.max = 20, main = " 9 AM - 12 PM Chlorophyll ACF")
abline(h = c(0.3,0.6) , col = c("red","red")) #change to lagmax = 30 if want days 
#determine lag -talk to deyle?
acf(ts_phy_2021,lag.max = 20, main = "9 AM - 12 PM Phycocanin ACF")
abline(h = c(0.3,0.6) , col = c("red","red"))

## you can check an individual value as follows
cor(ts_phy_2021,lag(ts_phy_2021,6),use="pairwise")


#PACF 2021
pacf2021 <- morndf_CR %>% 
  filter(year(date)==2021) %>%
  pull(chl) %>%
  pacf(lag.max = 7 )
plot(pacf2021, main = " 9 AM - 12 PM Chlorophyll PACF")

pacf2021 <- morndf_CR %>% 
  filter(year(date)==2021) %>%
  pull(phy) %>%
  pacf(lag.max= 7)
plot(pacf2021, main = "9 AM - 12 PM Phycocanin PACF")


pacf2021 <- ag_df_CR %>% 
  filter(year(date)==2021) %>%
  pull(chl) %>%
  pacf(lag.max= 7)
plot(pacf2021, main = "Daily Chlorophyll PACF")

pacf2021 <- ag_df_CR %>% 
  filter(year(date)==2021) %>%
  pull(phy) %>%
  pacf(lag.max= 7)
plot(pacf2021, main = "Daily Phyocanin PACF")




```

###HOURLY

```{r}

df_CR


#CHL
acf_chl_2021 <- df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("chl", "phy", "ph", "Celsius", "spcond", "do", "turb", "Salinity",),mean),.groups = "keep") %>%
  pull(chl)

#PHY
acf_phy_2021 <- df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("chl", "phy", "ph", "Celsius", "spcond", "do", "turb", "Salinity",),mean),.groups = "keep") %>%
  pull(phy)

#ph
acf_ph_2021 <- df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("chl", "phy", "ph", "Celsius", "spcond", "do", "turb", "Salinity",),mean),.groups = "keep") %>%
  pull(ph)

#Celsius
acf_Celsius_2021 <- df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("chl", "phy", "ph", "Celsius", "spcond", "do", "turb", "Salinity",),mean),.groups = "keep") %>%
  pull(Celsius)

#spcond
acf_spcond_2021 <- df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("chl", "phy", "ph", "Celsius", "spcond", "do", "turb", "Salinity",),mean),.groups = "keep") %>%
  pull(spcond)

#spcond
acf_do_2021 <- df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("chl", "phy", "ph", "Celsius", "spcond", "do", "turb", "Salinity",),mean),.groups = "keep") %>%
  pull(do)

#turb
acf_turb_2021 <- df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("chl", "phy", "ph", "Celsius", "spcond", "do", "turb", "Salinity",),mean),.groups = "keep") %>%
  pull(turb)

#Salinity 
acf_Salinity_2021 <- df_CR %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("chl", "phy", "ph", "Celsius", "spcond", "do", "turb", "Salinity",),mean),.groups = "keep") %>%
  pull(Salinity)


#DO LEGENDS


length(ts_chl_2021)
acf(ts_chl_2021,lag.max = 24*30, main = "Cholorophyll Hourly Non-Aggregated ACF")
abline(h = c(0.3,0.6) , col = c("red","red")) #change to lagmax = 30 if want days 

#determine lag -talk to deyle?
acf(ts_phy_2021,lag.max = 24*30, main = "Phycocanin Hourly Non-Aggregated ACF")
abline(h = c(0.3,0.6) , col = c("red","red"))

acf(ts_chl_2021,lag.max = 30, main = "Cholorophyll Daily Non-Aggregated ACF")
abline(h = c(0.3,0.6) , col = c("red","red")) #change to lagmax = 30 if want days 
#determine lag -talk to deyle?
acf(ts_phy_2021,lag.max = 30, main = "Phycocanin Daily Non-Aggregated ACF")
abline(h = c(0.3,0.6) , col = c("red","red"))


## you can check an individual value as follows
cor(ts_phy_2021,lag(ts_phy_2021,6),use="pairwise")



#df_CR$chl

#df_CR$phy

#df_CR$ph

#df_CR$Celsius

#df_CR$spcond

#df_CR$do

#df_CR$turb

#df_CR$Salinity


```

##LOGAN

```{r}


#ag_Logan$HourlyAltimeterSetting 


acf_ag_Logan$HourlyAltimeterSetting <- ag_Logan %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("    ag_Logan$HourlyAltimeterSetting ",
"HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(HourlyAltimeterSetting)


     #acf_ag_Logan$HourlyDewPointTemperature 
     
     acf_ag_Logan$HourlyDewPointTemperature <- ag_Logan %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(HourlyDewPointTemperature)
    
     #acf_ag_Logan$HourlyDryBulbTemperature 
     
      acf_ag_Logan$HourlyDryBulbTemperature  <- ag_Logan %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(HourlyDryBulbTemperature )
    
     #acf_ag_Logan$HourlyPrecipitation 
  
   acf_ag_Logan$HourlyPrecipitation  <- ag_Logan %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(HourlyPrecipitation)
    
    #acf_ag_Logan$HourlyRelativeHumidity 
  
   acf_ag_Logan$HourlyRelativeHumidity <- ag_Logan %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(HourlyRelativeHumidity )
   
     #acf_ag_Logan$HourlyStationPressure 
  
   acf_ag_Logan$HourlyStationPressure  <- ag_Logan %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(HourlyStationPressure)
    
     #acf_ag_Logan$HourlyVisibility
  
   acf_ag_Logan$HourlyVisibility <- ag_Logan %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(HourlyVisibility)
    
    #acf_ag_Logan$HourlyWetBulbTemperature 
  
   acf_ag_LoganHourlyWetBulbTemperature <- ag_Logan %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(HourlyWetBulbTemperature )
    
     #acf_ag_Logan$HourlyWindDirection 
  
   acf_ag_Logan$HourlyWindDirection  <- ag_Logan %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(HourlyWindDirection )
    
     #acf_ag_Logan$HourlyWindGustSpeed
  
   acf_ag_Logan$HourlyWindGustSpeed <- ag_Logan %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(HourlyWindGustSpeed)
    
     #acf_ag_Logan$HourlyWindSpeed 
  
   acf_ag_Logan$HourlyWindSpeed <- ag_Logan %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(HourlyWindSpeed)
    
  


```

##MWRA Non-CSO

```{r}

#acf_ag_CR_secchi$`Total Suspended Solids (mg/L)`
 
   acf_ag_CR_secchi$`Total Suspended Solids (mg/L)` <- ag_CR_secchi %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(`Total Suspended Solids (mg/L)`)

#acf_ag_CR_nutrients$`Ammonium (uM)`
 
   acf_ag_CR_nutrients$`Ammonium (uM)` <- ag_CR_nutrients %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(`Ammonium (uM)`)
  
  #acf_ag_CR_nutrients$`Nitrate+nitrite (uM)`
 
   acf_ag_CR_nutrients$`Nitrate+nitrite (uM)` <-ag_CR_nutrients %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(`Nitrate+nitrite (uM)`)
  
  #acf_ag_CR_nutrients$`Phosphate (uM)`
 
   acf_ag_CR_nutrients$`Phosphate (uM)` <- ag_CR_nutrients %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(`Phosphate (uM)`)
  
  #acf_ag_CR_nutrients$`Total phosphorus (uM)`
 
   acf_ag_CR_nutrients$`Total phosphorus (uM)` <- ag_CR_nutrients %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(`Total phosphorus (uM)`)
  
    #acf_ag_CR_nutrients$`Phaeophytin (ug/L)`
 
   acf_ag_CR_nutrients$`Phaeophytin (ug/L)` <- ag_CR_nutrients %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(`Phaeophytin (ug/L)`)
  

  

    #acf_ag_CR_bacteria$Enterococcus

 
 acf_ag_CR_bacteria$Enterococcus <- ag_CR_bacteria %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(Enterococcus)
  
      #acf_ag_CR_bacteria$E_coli

 acf_ag_CR_bacteria$E_coli <- ag_CR_bacteria %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(E_coli)
  
     #acf_ag_CR_bacteria$coliform

 acf_ag_CR_bacteria$coliform <- ag_CR_bacteria %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  summarise(across(c("HourlyDewPointTemperature ", "HourlyDryBulbTemperature ", "HourlyPrecipitation", "HourlyRelativeHumidity", "HourlyStationPressure ", "HourlyVisibility", "HourlyWetBulbTemperature ", "HourlyWindDirection ", "HourlyWindGustSpeed", "HourlyWindSpeed") %>%
  pull(coliform)
  


```

##CSO

```{r}

 #acf_CAM017$MG 

 acf_CAM017$MG <- CAM017 %>% 
  filter(year(date) == 2021) %>%
  group_by(date,hour) %>%
  pull(MG)



```

#ARIMA

#COEFFICENTS OF CORRELATION

#REGRESSIONS

```{r}

ag_df_CR %>% 
  ggplot(aes(x=ph, y=phy, color = "Points")) + 
  geom_point() +
  geom_hline(aes(yintercept = mean(phy), color = "Mean Phy Trendline"), linetype = "dashed") +
  geom_vline(aes(xintercept = mean(ph), color = "Mean Ph Trendline"), linetype = "dashed") +
  geom_smooth(aes(color = "Best Fit Line"), method = "lm") +
  scale_color_manual(values = c("Points" = "red", 
                                 "Mean Phy Trendline" = "blue", 
                                 "Best Fit Line" = "purple", 
                                 "Mean Ph Trendline" = "orange"), 
                     breaks = c("Points", "Mean Phy Trendline", 
                                "Best Fit Line", "Mean Ph Trendline"),
                     labels = c("Points", "Mean Phy Trendline", 
                                "Best Fit Line", "Mean Ph Trendline")) +
  guides(color = guide_legend(override.aes = list(
    shape = c(19, NA, NA, NA),
    linetype = c("blank", "dotted", "solid", "dotted")
  ))) +
  ggtitle("pH vs. Phycocanin (RFU)") +
  labs(color = "Legend",
       x = "pH",
       y = "Phycocanin (RFU")


ag_df_CR %>% 
  ggplot(aes(x=ph, y=phy, color = "Points")) + 
  geom_point() +
  geom_hline(aes(yintercept = mean(phy), color = "Mean Phycocanin Trendline"), linetype = "dashed") +
  geom_vline(aes(xintercept = mean(ph), color = "Mean pH Trendline"), linetype = "dashed") +
  geom_smooth(aes(color = "Best Fit Line"), method = "lm") +
  scale_color_manual(values = c("Points" = "red", 
                                 "Mean pH Trendline" = "blue", 
                                 "Best Fit Line" = "purple", 
                                 "Mean Phycocanin Trendline" = "orange"), 
                     breaks = c("Points", "Mean Phycocanin Trendline", 
                                "Best Fit Line", "Mean pH Trendline"),
                     labels = c("Points", "Mean Phycocanin Trendline", 
                                "Best Fit Line", "Mean pH Trendline")) +
  guides(color = guide_legend(override.aes = list(
    shape = c(19, NA, NA, NA),
    linetype = c("blank", "dotted", "solid", "dotted")
  ))) +
  ggtitle("pH vs. Phycocanin (RFU)") +
  labs(color = "Legend",
       x = "pH",
       y = "Phycocanin (RFU")



```

##PLOTS W/ CORR \> 0.5

```{r}
phphy
dochl
turbphy



ag_df_CR %>% 
  ggplot(aes(x=ph, y=phy, color = "Points")) + 
  geom_point() +
  geom_hline(aes(yintercept = mean(ag_df_CR$phy), color = "Phy Mean Trendline"), linetype = "dashed") +
  geom_hline(aes(yintercept = median(ag_df_CR$phy), color = "Phy Median Trendline"), linetype = "dashed") +
  geom_smooth(aes(color = "Best Fit Line"), method = "lm", ) +
  geom_vline(aes(xintercept = mean(ag_df_CR$ph), color = "Ph Mean Trendline"), linetype = "dashed") +
  geom_vline(aes(xintercept = median(ag_df_CR$ph), color = "Ph Median Trendline"), linetype = "dashed") +
  scale_color_manual(values = c("Points" = "red", 
                                 "Phy Mean Trendline" = "blue", "Phy Median Trendline" = "green", 
                                 "Best Fit Line" = "purple", 
                                 "Ph Mean Trendline" = "orange", "Ph Median Trendline" = "brown"), 
                     breaks = c("Points", "Phy Mean Trendline", "Phy Median Trendline", 
                                "Best Fit Line", "Ph Mean Trendline", "Ph Median Trendline"),
                     labels = c("Points", "Phy Mean Trendline", "Phy Median Trendline", 
                                "Best Fit Line", "Ph Mean Trendline", "Ph Median Trendline")) +
  guides(color = guide_legend(override.aes = list(
    shape = c(19, NA, NA, NA, NA, NA),
    linetype = c(1, 0, 0, 0, 0 ,0)
  ))) +
  ggtitle("Ph vs. Phy") +
  labs(color = "Legend Title")






```

#PLOTS

```{r}

ag_df_CR <- na.omit(ag_df_CR)
ag_Logan <- na.omit(ag_Logan)
ag_CR_nutrients
ag_CR_secchi
ag_CR_bacteria


#ag_CR_secchi

ag_CR_secchi$`Total Suspended Solids (mg/L)`

#ag_CR_nutrients 

ag_CR_nutrients$`Ammonium (uM)`
ag_CR_nutrients$`Nitrate+nitrite (uM)`
ag_CR_nutrients$`Phosphate (uM)`
ag_CR_nutrients$`Total phosphorus (uM)`
ag_CR_nutrients$`Phaeophytin (ug/L)`

#ag_CR_bacteria

ag_CR_bacteria$Enterococcus
ag_CR_bacteria$E_coli
ag_CR_bacteria$coliform


#Ph MEDIAN AND MEAN ARE THE SAME?


#PH VS. PHY

ag_df_CR %>% 
  ggplot(aes(x=ph, y=phy, color = "Points")) + 
  geom_point() +
  geom_hline(aes(yintercept = mean(ag_df_CR$phy), color = "Phy Mean Trendline"), linetype = "dashed") +
  geom_hline(aes(yintercept = median(ag_df_CR$phy), color = "Phy Median Trendline"), linetype = "dashed") +
  geom_smooth(aes(color = "Best Fit Line"), method = "lm", ) +
  geom_vline(aes(xintercept = mean(ag_df_CR$ph), color = "Ph Mean Trendline"), linetype = "dashed") +
  geom_vline(aes(xintercept = median(ag_df_CR$ph), color = "Ph Median Trendline"), linetype = "dashed") +
  scale_color_manual(values = c("Points" = "red", 
                                 "Phy Mean Trendline" = "blue", "Phy Median Trendline" = "green", 
                                 "Best Fit Line" = "purple", 
                                 "Ph Mean Trendline" = "orange", "Ph Median Trendline" = "brown"), 
                     breaks = c("Points", "Phy Mean Trendline", "Phy Median Trendline", 
                                "Best Fit Line", "Ph Mean Trendline", "Ph Median Trendline"),
                     labels = c("Points", "Phy Mean Trendline", "Phy Median Trendline", 
                                "Best Fit Line", "Ph Mean Trendline", "Ph Median Trendline")) +
  guides(color = guide_legend(override.aes = list(
    shape = c(19, NA, NA, NA, NA, NA),
    linetype = c(1, 0, 0, 0, 0 ,0)
  ))) +
  ggtitle("Ph vs. Phy") +
  labs(color = "Legend Title")

#PH VS. CHL


ag_df_CR %>% 
  ggplot(aes(x=ph, y=chl, color = "Points")) + 
  geom_point() +
  geom_hline(aes(yintercept = mean(ag_df_CR$chl), color = "Chl Mean Trendline"), linetype = "dashed") +
  geom_hline(aes(yintercept = median(ag_df_CR$chl), color = "Chl Median Trendline"), linetype = "dashed") +
  geom_smooth(aes(color = "Best Fit Line"), method = "lm", ) +
  geom_vline(aes(xintercept = mean(ag_df_CR$ph), color = "Ph Mean Trendline"), linetype = "dashed") +
  geom_vline(aes(xintercept = median(ag_df_CR$ph), color = "Ph Median Trendline"), linetype = "dashed") +
  scale_color_manual(values = c("Points" = "red", 
                                 "Chl Mean Trendline" = "blue", "Chl Median Trendline" = "green", 
                                 "Best Fit Line" = "purple", 
                                 "Ph Mean Trendline" = "orange", "Ph Median Trendline" = "brown"), 
                     breaks = c("Points", "Chl Mean Trendline", "Chl Median Trendline", 
                                "Best Fit Line", "Ph Mean Trendline", "Ph Median Trendline"),
                     labels = c("Points", "Chl Mean Trendline", "Chl Median Trendline", 
                                "Best Fit Line", "Ph Mean Trendline", "Ph Median Trendline")) +
  guides(color = guide_legend(override.aes = list(
    shape = c(19, NA, NA, NA, NA, NA),
    linetype = c(1, 1, 1, 1, 1 ,1)
  ))) +
  ggtitle("Ph vs. Chl") +
  labs(color = "Legend Title")


# Ph vs. Del_phy
ag_df_CR %>% 
  ggplot(aes(x=ph, y=del_phy, color = "Points")) + 
  geom_point() +
  geom_hline(aes(yintercept = mean(ag_df_CR$del_phy), color = "Del_phy Mean Trendline"), linetype = "dashed") +
  geom_hline(aes(yintercept = median(ag_df_CR$del_phy), color = "Del_phy Median Trendline"), linetype = "dashed") +
  geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
  geom_vline(aes(xintercept = mean(ag_df_CR$ph), color = "Ph Mean Trendline"), linetype = "dashed") +
  geom_vline(aes(xintercept = median(ag_df_CR$ph), color = "Ph Median Trendline"), linetype = "dashed") +
  scale_color_manual(values = c("Points" = "red", 
                                 "Del_phy Mean Trendline" = "blue", "Del_phy Median Trendline" = "green", 
                                 "Best Fit Line" = "purple", 
                                 "Ph Mean Trendline" = "orange", "Ph Median Trendline" = "brown"), 
                     breaks = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline", 
                                "Best Fit Line", "Ph Mean Trendline", "Ph Median Trendline"),
                     labels = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline", 
                                "Best Fit Line", "Ph Mean Trendline", "Ph Median Trendline")) +
  guides(color = guide_legend(override.aes = list(
    shape = c(19, NA, NA, NA, NA, NA),
    linetype = c(0, 0, 0, 0, 1, 1)
  ))) +
  ggtitle("Ph vs. Del_phy") +
  labs(color = "Legend Title")


# Chl vs. Phy
ag_df_CR %>% 
  ggplot(aes(x=chl, y=phy, color = "Points")) + 
  geom_point() +
  geom_hline(aes(yintercept = mean(ag_df_CR$phy), color = "Phy Mean Trendline"), linetype = "dashed") +
  geom_hline(aes(yintercept = median(ag_df_CR$phy), color = "Phy Median Trendline"), linetype = "dashed") +
  geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
  geom_vline(aes(xintercept = mean(ag_df_CR$chl), color = "Chl Mean Trendline"), linetype = "dashed") +
  geom_vline(aes(xintercept = median(ag_df_CR$chl), color = "Chl Median Trendline"), linetype = "dashed") +
  scale_color_manual(values = c("Points" = "red", 
                                 "Phy Mean Trendline" = "blue", "Phy Median Trendline" = "green", 
                                 "Best Fit Line" = "purple", 
                                 "Chl Mean Trendline" = "orange", "Chl Median Trendline" = "brown"), 
                     breaks = c("Points", "Phy Mean Trendline", "Phy Median Trendline", 
                                "Best Fit Line", "Chl Mean Trendline", "Chl Median Trendline"),
                     labels = c("Points", "Phy Mean Trendline", "Phy Median Trendline", 
                                "Best Fit Line", "Chl Mean Trendline", "Chl Median Trendline")) +
  guides(color = guide_legend(override.aes = list(
    shape = c(19, NA, NA, NA, NA, NA),
    linetype = c(0, 1, 1, 1, 1 ,1)
  ))) +
  ggtitle("Chl vs. Phy") +
  labs(color = "Legend Title")

# Celsius vs. Phy
ag_df_CR %>% 
  ggplot(aes(x=Celsius, y=phy, color = "Points")) + 
  geom_point() +
  geom_hline(aes(yintercept = mean(ag_df_CR$phy), color = "Phy Mean Trendline"), linetype = "dashed") +
  geom_hline(aes(yintercept = median(ag_df_CR$phy), color = "Phy Median Trendline"), linetype = "dashed") +
  geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
  geom_vline(aes(xintercept = mean(ag_df_CR$Celsius), color = "Celsius Mean Trendline"), linetype = "dashed") +
  geom_vline(aes(xintercept = median(ag_df_CR$Celsius), color = "Celsius Median Trendline"), linetype = "dashed") +
  scale_color_manual(values = c("Points" = "red", 
                                 "Phy Mean Trendline" = "blue", "Phy Median Trendline" = "green", 
                                 "Best Fit Line" = "purple", 
                                 "Celsius Mean Trendline" = "orange", "Celsius Median Trendline" = "brown"), 
                     breaks = c("Points", "Phy Mean Trendline", "Phy Median Trendline", 
                                "Best Fit Line", "Celsius Mean Trendline", "Celsius Median Trendline"),
                     labels = c("Points", "Phy Mean Trendline", "Phy Median Trendline", 
                                "Best Fit Line", "Celsius Mean Trendline", "Celsius Median Trendline")) +
  guides(color = guide_legend(override.aes = list(
    shape = c(19, NA, NA, NA, NA, NA),
    linetype = c(0, 1, 1, 1, 1 ,1)
  ))) +
  ggtitle("Celsius vs. Phy") +
  labs(color = "Legend Title")

# Celsius vs. Chl
ag_df_CR %>% 
  ggplot(aes(x=Celsius, y=chl, color = "Points")) + 
  geom_point() +
  geom_hline(aes(yintercept = mean(ag_df_CR$chl), color = "Chl Mean Trendline"), linetype = "dashed") +
  geom_hline(aes(yintercept = median(ag_df_CR$chl), color = "Chl Median Trendline"), linetype = "dashed") +
  geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
  geom_vline(aes(xintercept = mean(ag_df_CR$Celsius), color = "Celsius Mean Trendline"), linetype = "dashed") +
geom_vline(aes(xintercept = median(ag_df_CR$Celsius), color = "Celsius Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Chl Mean Trendline" = "blue", "Chl Median Trendline" = "green",
"Best Fit Line" = "purple",
"Celsius Mean Trendline" = "orange", "Celsius Median Trendline" = "brown"),
breaks = c("Points", "Chl Mean Trendline", "Chl Median Trendline",
"Best Fit Line", "Celsius Mean Trendline", "Celsius Median Trendline"),
labels = c("Points", "Chl Mean Trendline", "Chl Median Trendline",
"Best Fit Line", "Celsius Mean Trendline", "Celsius Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 0, 0, 1, 1 ,1)
))) +
ggtitle("Celsius vs. Chl") +
labs(color = "Legend Title")


#Celsius vs. Del_phy

ag_df_CR %>%
ggplot(aes(x=Celsius, y=del_phy, color = "Points")) +
geom_point() +
geom_hline(aes(yintercept = mean(ag_df_CR$del_phy), color = "Del_phy Mean Trendline"), linetype = "dashed") +
geom_hline(aes(yintercept = median(ag_df_CR$del_phy), color = "Del_phy Median Trendline"), linetype = "dashed") +
geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
geom_vline(aes(xintercept = mean(ag_df_CR$Celsius), color = "Celsius Mean Trendline"), linetype = "dashed") +
geom_vline(aes(xintercept = median(ag_df_CR$Celsius), color = "Celsius Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Del_phy Mean Trendline" = "blue", "Del_phy Median Trendline" = "green",
"Best Fit Line" = "purple",
"Celsius Mean Trendline" = "orange", "Celsius Median Trendline" = "brown"),
breaks = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline",
"Best Fit Line", "Celsius Mean Trendline", "Celsius Median Trendline"),
labels = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline",
"Best Fit Line", "Celsius Mean Trendline", "Celsius Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 0, 0, 1, 1 ,1)
))) +
ggtitle("Celsius vs. Del_phy") +
labs(color = "Legend Title")

#Spcond vs. Phy
ag_df_CR %>%
ggplot(aes(x=spcond, y=phy, color = "Points")) +
geom_point() +
geom_hline(aes(yintercept = mean(ag_df_CR$phy), color = "Phy Mean Trendline"), linetype = "dashed") +
geom_hline(aes(yintercept = median(ag_df_CR$phy), color = "Phy Median Trendline"), linetype = "dashed") +
geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
geom_vline(aes(xintercept = mean(ag_df_CR$spcond), color = "Spcond Mean Trendline"), linetype = "dashed") +
geom_vline(aes(xintercept = median(ag_df_CR$spcond), color = "Spcond Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Phy Mean Trendline" = "blue", "Phy Median Trendline" = "green",
"Best Fit Line" = "purple",
"Spcond Mean Trendline" = "orange", "Spcond Median Trendline" = "brown"),
breaks = c("Points", "Phy Mean Trendline", "Phy Median Trendline",
"Best Fit Line", "Spcond Mean Trendline", "Spcond Median Trendline"),
labels = c("Points", "Phy Mean Trendline", "Phy Median Trendline",
"Best Fit Line", "Spcond Mean Trendline", "Spcond Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 1, 1, 1, 1 ,1)
))) +
ggtitle("Spcond vs. Phy") +
labs(color = "Legend Title")

#Spcond vs. Chl
ag_df_CR %>%
ggplot(aes(x=spcond, y=chl, color = "Points")) +
geom_point() +
geom_hline(aes(yintercept = mean(ag_df_CR$chl), color = "Chl Mean Trendline"), linetype = "dashed") +
geom_hline(aes(yintercept = median(ag_df_CR$chl), color = "Chl Median Trendline"), linetype = "dashed") +
geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
geom_vline(aes(xintercept = mean(ag_df_CR$spcond), color = "Spcond Mean Trendline"), linetype = "dashed") +
geom_vline(aes(xintercept = median(ag_df_CR$spcond), color = "Spcond Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Chl Mean Trendline" = "blue", "Chl Median Trendline" = "green",
"Best Fit Line" = "purple",
"Spcond Mean Trendline" = "orange", "Spcond Median Trendline" = "brown"),
breaks = c("Points", "Chl Mean Trendline", "Chl Median Trendline",
"Best Fit Line", "Spcond Mean Trendline", "Spcond Median Trendline"),
labels = c("Points", "Chl Mean Trendline", "Chl Median Trendline",
"Best Fit Line", "Spcond Mean Trendline", "Spcond Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 0, 0, 1, 1 ,1)
))) +
ggtitle("Spcond vs. Chl") +
labs(color = "Legend Title")


#Spcond vs. Del_phy

ag_df_CR %>%
ggplot(aes(x=spcond, y=del_phy, color = "Points")) +
geom_point() +
geom_hline(aes(yintercept = mean(ag_df_CR$del_phy), color = "Del_phy Mean Trendline"), linetype = "dashed") +
geom_hline(aes(yintercept = median(ag_df_CR$del_phy), color = "Del_phy Median Trendline"), linetype = "dashed") +
geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
geom_vline(aes(xintercept = mean(ag_df_CR$spcond), color = "Spcond Mean Trendline"), linetype = "dashed") +
geom_vline(aes(xintercept = median(ag_df_CR$spcond), color = "Spcond Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Del_phy Mean Trendline" = "blue", "Del_phy Median Trendline" = "green",
"Best Fit Line" = "purple",
"Spcond Mean Trendline" = "orange", "Spcond Median Trendline" = "brown"),
breaks = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline",
"Best Fit Line", "Spcond Mean Trendline", "Spcond Median Trendline"),
labels = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline",
"Best Fit Line", "Spcond Mean Trendline", "Spcond Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 0, 0, 1, 1 ,1)
))) +
ggtitle("Spcond vs. Del_phy") +
labs(color = "Legend Title")

#Do vs. Phy
ag_df_CR %>%
ggplot(aes(x=do, y=phy, color = "Points")) +
geom_point() +
geom_hline(aes(yintercept = mean(ag_df_CR$phy), color = "Phy Mean Trendline"), linetype = "dashed") +
geom_hline(aes(yintercept = median(ag_df_CR$phy), color = "Phy Median Trendline"), linetype = "dashed") +
geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
geom_vline(aes(xintercept = mean(ag_df_CR$do), color = "Do Mean Trendline"), linetype = "dashed") +
geom_vline(aes(xintercept = median(ag_df_CR$do), color = "Do Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Phy Mean Trendline" = "blue", "Phy Median Trendline" = "green",
"Best Fit Line" = "purple",
"Do Mean Trendline" = "orange", "Do Median Trendline" = "brown"),
breaks = c("Points", "Phy Mean Trendline", "Phy Median Trendline",
"Best Fit Line", "Do Mean Trendline", "Do Median Trendline"),
labels = c("Points", "Phy Mean Trendline", "Phy Median Trendline",
"Best Fit Line", "Do Mean Trendline", "Do Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 1, 1, 1, 1 ,1)
))) +
ggtitle("Do vs. Phy") +
labs(color = "Legend Title")

#Do vs. Chl
ag_df_CR %>%
ggplot(aes(x=do, y=chl, color = "Points")) +
geom_point() +
geom_hline(aes(yintercept = mean(ag_df_CR$chl), color = "Chl Mean Trendline"), linetype = "dashed") +
geom_hline(aes(yintercept = median(ag_df_CR$chl), color = "Chl Median Trendline"), linetype = "dashed") +
geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
geom_vline(aes(xintercept = mean(ag_df_CR$do), color = "Do Mean Trendline"), linetype = "dashed
") +
geom_vline(aes(xintercept = median(ag_df_CR$do), color = "Do Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Chl Mean Trendline" = "blue", "Chl Median Trendline" = "green",
"Best Fit Line" = "purple",
"Do Mean Trendline" = "orange", "Do Median Trendline" = "brown"),
breaks = c("Points", "Chl Mean Trendline", "Chl Median Trendline",
"Best Fit Line", "Do Mean Trendline", "Do Median Trendline"),
labels = c("Points", "Chl Mean Trendline", "Chl Median Trendline",
"Best Fit Line", "Do Mean Trendline", "Do Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 0, 0, 1, 1 ,1)
))) +
ggtitle("Do vs. Chl") +
labs(color = "Legend Title")

#Do vs. Del_phy
ag_df_CR %>%
ggplot(aes(x=do, y=del_phy, color = "Points")) +
geom_point() +
geom_hline(aes(yintercept = mean(ag_df_CR$del_phy), color = "Del_phy Mean Trendline"), linetype = "dashed") +
geom_hline(aes(yintercept = median(ag_df_CR$del_phy), color = "Del_phy Median Trendline"), linetype = "dashed") +
geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
geom_vline(aes(xintercept = mean(ag_df_CR$do), color = "Do Mean Trendline"), linetype = "dashed") +
geom_vline(aes(xintercept = median(ag_df_CR$do), color = "Do Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Del_phy Mean Trendline" = "blue", "Del_phy Median Trendline" = "green",
"Best Fit Line" = "purple",
"Do Mean Trendline" = "orange", "Do Median Trendline" = "brown"),
breaks = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline",
"Best Fit Line", "Do Mean Trendline", "Do Median Trendline"),
labels = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline",
"Best Fit Line", "Do Mean Trendline", "Do Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 0, 0, 1, 1 ,1)
))) +
ggtitle("Do vs. Del_phy") +
labs(color = "Legend Title")

#Turb vs. Chl
ag_df_CR %>%
ggplot(aes(x=turb, y=chl, color = "Points")) +
geom_point() +
geom_hline(aes(yintercept = mean(ag_df_CR$chl), color = "Chl Mean Trendline"), linetype = "dashed") +
geom_hline(aes(yintercept = median(ag_df_CR$chl), color = "Chl Median Trendline"), linetype = "dashed") +
geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
geom_vline(aes(xintercept = mean(ag_df_CR$turb), color = "Turb Mean Trendline"), linetype = "dashed") +
geom_vline(aes(xintercept = median(ag_df_CR$turb), color = "Turb Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Chl Mean Trendline" = "blue", "Chl Median Trendline" = "green",
"Best Fit Line" = "purple",
"Turb Mean Trendline" = "orange", "Turb Median Trendline" = "brown"),
breaks = c("Points", "Chl Mean Trendline", "Chl Median Trendline",
"Best Fit Line", "Turb Mean Trendline", "Turb Median Trendline"),
labels = c("Points", "Chl Mean Trendline", "Chl Median Trendline",
"Best Fit Line", "Turb Mean Trendline", "Turb Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 0, 0, 1, 1 ,1)
))) +
ggtitle("Turb vs. Chl") +
labs(color = "Legend Title")


#Turb vs. Phy
ag_df_CR %>%
ggplot(aes(x=turb, y=phy, color = "Points")) +
geom_point() +
geom_hline(aes(yintercept = mean(ag_df_CR$phy), color = "Phy Mean Trendline"), linetype = "dashed") +
geom_hline(aes(yintercept = median(ag_df_CR$phy), color = "Phy Median Trendline"), linetype = "dashed") +
geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
geom_vline(aes(xintercept = mean(ag_df_CR$turb), color = "Turb Mean Trendline"), linetype = "dashed") +
geom_vline(aes(xintercept = median(ag_df_CR$turb), color = "Turb Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Phy Mean Trendline" = "blue", "Phy Median Trendline" = "green",
"Best Fit Line" = "purple",
"Turb Mean Trendline" = "orange", "Turb Median Trendline" = "brown"),
breaks = c("Points", "Phy Mean Trendline", "Phy Median Trendline",
"Best Fit Line", "Turb Mean Trendline", "Turb Median Trendline"),
labels = c("Points", "Phy Mean Trendline", "Phy Median Trendline",
"Best Fit Line", "Turb Mean Trendline", "Turb Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 1, 1, 1, 1 ,1)
))) +
ggtitle("Turb vs. Phy") +
labs(color = "Legend Title")

#Turb vs. Del_phy
ag_df_CR %>%
ggplot(aes(x=turb, y=del_phy, color = "Points")) +
geom_point() +
geom_hline(aes(yintercept = mean(ag_df_CR$del_phy), color = "Del_phy Mean Trendline"), linetype = "dashed") +
geom_hline(aes(yintercept = median(ag_df_CR$del_phy), color = "Del_phy Median Trendline"), linetype = "dashed") +
geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
geom_vline(aes(xintercept = mean(ag_df_CR$turb), color = "Turb Mean Trendline"), linetype = "dashed") +
geom_vline(aes(xintercept = median(ag_df_CR$turb), color = "Turb Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Del_phy Mean Trendline" = "blue", "Del_phy Median Trendline" = "green",
"Best Fit Line" = "purple",
"Turb Mean Trendline" = "orange", "Turb Median Trendline" = "brown"),
breaks = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline",
"Best Fit Line", "Turb Mean Trendline", "Turb Median Trendline"),
labels = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline",
"Best Fit Line", "Turb Mean Trendline", "Turb Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 1, 1, 1, 1 ,1)
))) +
ggtitle("Turb vs. Del_phy") +
labs(color = "Legend Title")

#Salinity vs. Phy
ag_df_CR %>%
ggplot(aes(x=Salinity, y=phy, color = "Points")) +
geom_point() +
geom_hline(aes(yintercept = mean(ag_df_CR$phy), color = "Phy Mean Trendline"), linetype = "dashed") +
geom_hline(aes(yintercept = median(ag_df_CR$phy), color = "Phy Median Trendline"), linetype = "dashed") +
geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
geom_vline(aes(xintercept = mean(ag_df_CR$Salinity), color = "Salinity Mean Trendline"), linetype = "dashed") +
geom_vline(aes(xintercept = median(ag_df_CR$Salinity), color = "Salinity Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Phy Mean Trendline" = "blue", "Phy Median Trendline" = "green",
"Best Fit Line" = "purple",
"Salinity Mean Trendline" = "orange", "Salinity Median Trendline" = "brown"),
breaks = c("Points", "Phy Mean Trendline", "Phy Median Trendline",
"Best Fit Line", "Salinity Mean Trendline", "Salinity Median Trendline"),
labels = c("Points", "Phy Mean Trendline", "Phy Median Trendline",
"Best Fit Line", "Salinity Mean Trendline", "Salinity Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 0, 0, 1, 1 ,1)
))) +
ggtitle("Salinity vs. Phy") +
labs(color = "Legend Title")

#Salinity vs. Chl
ag_df_CR %>%
ggplot(aes(x=Salinity, y=chl, color = "Points")) +
geom_point() +
geom_hline(aes(yintercept = mean(ag_df_CR$chl), color = "Chl Mean Trendline"), linetype = "dashed") +
geom_hline(aes(yintercept = median(ag_df_CR$chl), color = "Chl Median Trendline"), linetype = "dashed") +
geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
geom_vline(aes(xintercept = mean(ag_df_CR$Salinity), color = "Salinity Mean Trendline"), linetype = "dashed") +
geom_vline(aes(xintercept = median(ag_df_CR$Salinity), color = "Salinity Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Chl Mean Trendline" = "blue", "Chl Median Trendline" = "green",
"Best Fit Line" = "purple",
"Salinity Mean Trendline" = "orange", "Salinity Median Trendline" = "brown"),
breaks = c("Points", "Chl Mean Trendline", "Chl Median Trendline",
"Best Fit Line", "Salinity Mean Trendline", "Salinity Median Trendline"),
labels = c("Points", "Chl Mean Trendline", "Chl Median Trendline",
"Best Fit Line", "Salinity Mean Trendline", "Salinity Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 0, 0, 1, 1 ,1)
))) +
ggtitle("Salinity vs. Chl") +
labs(color = "Legend Title")



#Salinity vs. Del_phy

ag_df_CR %>%
ggplot(aes(x=Salinity, y=del_phy, color = "Points")) +
geom_point() +
geom_hline(aes(yintercept = mean(ag_df_CR$del_phy), color = "Del_phy Mean Trendline"), linetype = "dashed") +
geom_hline(aes(yintercept = median(ag_df_CR$del_phy), color = "Del_phy Median Trendline"), linetype = "dashed") +
geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
geom_vline(aes(xintercept = mean(ag_df_CR$Salinity), color = "Salinity Mean Trendline"), linetype = "dashed") +
geom_vline(aes(xintercept = median(ag_df_CR$Salinity), color = "Salinity Median Trendline"), linetype = "dashed") +
scale_color_manual(values = c("Points" = "red",
"Del_phy Mean Trendline" = "blue", "Del_phy Median Trendline" = "green",
"Best Fit Line" = "purple",
"Salinity Mean Trendline" = "orange", "Salinity Median Trendline" = "brown"),
breaks = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline",
"Best Fit Line", "Salinity Mean Trendline", "Salinity Median Trendline"),
labels = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline",
"Best Fit Line", "Salinity Mean Trendline", "Salinity Median Trendline")) +
guides(color = guide_legend(override.aes = list(
shape = c(19, NA, NA, NA, NA, NA),
linetype = c(0, 0, 0, 1, 1 ,1)
))) +
ggtitle("Salinity vs. Del_phy") +
labs(color = "Legend Title")


# Phy vs. Chl
ag_df_CR %>% 
  ggplot(aes(x=phy, y=chl, color = "Points")) + 
  geom_point() +
  geom_hline(aes(yintercept = mean(ag_df_CR$chl), color = "Chl Mean Trendline"), linetype = "dashed") +
  geom_hline(aes(yintercept = median(ag_df_CR$chl), color = "Chl Median Trendline"), linetype = "dashed") +
  geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
  geom_vline(aes(xintercept = mean(ag_df_CR$phy), color = "Phy Mean Trendline"), linetype = "dashed") +
  geom_vline(aes(xintercept = median(ag_df_CR$phy), color = "Phy Median Trendline"), linetype = "dashed") +
  scale_color_manual(values = c("Points" = "red", 
                                 "Chl Mean Trendline" = "blue", "Chl Median Trendline" = "green", 
                                 "Best Fit Line" = "purple", 
                                 "Phy Mean Trendline" = "orange", "Phy Median Trendline" = "brown"), 
                     # breaks = c("Points", "Chl Mean Trendline", "Chl Median Trendline", 
                     #            "Best Fit Line", "Phy Mean Trendline", "Phy Median Trendline"),
                     labels = c("Points", "Chl Mean Trendline", "Chl Median Trendline", 
                                "Best Fit Line", "Phy Mean Trendline", "Phy Median Trendline")) +
  guides(color = guide_legend(override.aes = list(
    shape = c(19, NA, NA, NA, NA, NA),
    linetype = c(0, 0, 0, 1, 1 ,1)
  ))) +
  ggtitle("Phy vs. Chl") +
  labs(color = "Legend Title")

# Phy vs. Del_phy
ag_df_CR %>% 
  ggplot(aes(x=phy, y=del_phy, color = "Points")) + 
  geom_point() +
  geom_hline(aes(yintercept = mean(ag_df_CR$del_phy), color = "Del_phy Mean Trendline"), linetype = "dashed") +
  geom_hline(aes(yintercept = median(ag_df_CR$del_phy), color = "Del_phy Median Trendline"), linetype = "dashed") +
  geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
  geom_vline(aes(xintercept = mean(ag_df_CR$phy), color = "Phy Mean Trendline"), linetype = "dashed") +
  geom_vline(aes(xintercept = median(ag_df_CR$phy), color = "Phy Median Trendline"), linetype = "dashed") +
  scale_color_manual(values = c("Points" = "red", 
                                 "Del_phy Mean Trendline" = "blue", "Del_phy Median Trendline" = "green", 
                                 "Best Fit Line" = "purple", 
                                 "Phy Mean Trendline" = "orange", "Phy Median Trendline" = "brown"), 
                     breaks = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline", 
                                "Best Fit Line", "Phy Mean Trendline", "Phy Median Trendline"),
                     labels = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline", 
                                "Best Fit Line", "Phy Mean Trendline", "Phy Median Trendline")) +
  # guides(color = guide_legend(override.aes = list(
  #   shape = c(19, NA, NA, NA, NA, NA),
  #   linetype = c(0, 0, 0, 1, 1 ,1)
  # ))) +
  ggtitle("Phy vs. Del_phy") +
  labs(color = "Legend Title")

# Chl vs. Del_phy
ag_df_CR %>% 
  ggplot(aes(x=chl, y=del_phy, color = "Points")) + 
  geom_point() +
  geom_hline(aes(yintercept = mean(ag_df_CR$del_phy), color = "Del_phy Mean Trendline"), linetype = "dashed") +
  geom_hline(aes(yintercept = median(ag_df_CR$del_phy), color = "Del_phy Median Trendline"), linetype = "dashed") +
  geom_smooth(aes(color = "Best Fit Line"), method = "lm", se = FALSE) +
  geom_vline(aes(xintercept = mean(ag_df_CR$chl), color = "Chl Mean Trendline"), linetype = "dashed") +
  geom_vline(aes(xintercept = median(ag_df_CR$chl), color = "Chl Median Trendline"), linetype = "dashed") +
  scale_color_manual(values = c("Points" = "red", 
                                 "Del_phy Mean Trendline" = "blue", "Del_phy Mean Trendline" = "green", 
                                 "Best Fit Line" = "purple", 
                                 "Chl Mean Trendline" = "orange", "Chl Median Trendline" = "brown"), 
                     breaks = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline", 
                                "Best Fit Line", "Chl Mean Trendline", "Chl Median Trendline"),
                     labels = c("Points", "Del_phy Mean Trendline", "Del_phy Median Trendline", 
                                "Best Fit Line", "Chl Mean Trendline", "Chl Median Trendline")) +
  # guides(color = guide_legend(override.aes = list(
  #   shape = c(19, NA, NA, NA, NA, NA),
  #   linetype = c(0, 0, 0, 1, 1 ,1)
  # ))) +
  ggtitle("Chl vs. Del_phy") +
  labs(color = "Legend Title")





#median: do 0.5 quantile regression




```

#EDM UNIVARIATE

#S-MAP

#SIMPLEX

#CMAP

#CCM MWRA Non-CSO

***CR_bacteria***

#Enteroccus

```{r}

cmap.Enteroccus.del_phy <- CCM(dataFrame = ag_CR_bacteria, E = 4, Tp = 4, columns = "Enteroccus", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.Enteroccus.phy <- CCM(dataFrame = ag_CR_bacteria, E = 4, Tp = 4, columns = "Enteroccus", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.Enteroccus.chl <- CCM(dataFrame = ag_CR_bacteria, E = 4, Tp = 4, columns = "Enteroccus", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


#Surrogates


Surr.cmap.Enteroccus.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Enteroccus", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.Enteroccus.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Enteroccus", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


Surr.cmap.Enteroccus.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Enteroccus", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)



```

#E_coli

```{r}


cmap.E_coli.del_phy <- CCM(dataFrame = ag_CR_bacteria, E = 4, Tp = 4, columns = "E_coli", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.E_coli.phy <- CCM(dataFrame = ag_CR_bacteria, E = 4, Tp = 4, columns = "E_coli", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.E_coli.chl <- CCM(dataFrame = ag_CR_bacteria, E = 4, Tp = 4, columns = "E_coli", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)




Surr.cmap.E_coli.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "E_coli", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.E_coli.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "E_coli", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.E_coli.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "E_coli", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)



```

***CR_Secchi***

#Total Suspended Solids (mg/L)

```{r}


cmap.TSS.del_phy <- CCM(dataFrame = ag_CR_secchi, E = 4, Tp = 4, columns = "Total Suspended Solids (mg/L)", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


cmap.TSS.phy <- CCM(dataFrame = ag_CR_secchi, E = 4, Tp = 4, columns = "Total Suspended Solids (mg/L)", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.TSS.chl <- CCM(dataFrame = ag_CR_secchi, E = 4, Tp = 4, columns = "Total Suspended Solids (mg/L)", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


Surr.cmap.TSS.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Total Suspended Solids (mg/L)", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


Surr.cmap.TSS.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Total Suspended Solids (mg/L)", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.TSS.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Total Suspended Solids (mg/L)", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


```

***CR_Nutrients***

#Ammonium (uM)

```{r}


cmap.Ammonium.del_phy <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Ammonium (uM)", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


cmap.Ammonium.phy <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Ammonium (uM)", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.Ammonium.chl <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Ammonium (uM)", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


Surr.cmap.Ammonium.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Ammonium (uM)", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


Surr.cmap.Ammonium.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Ammonium (uM)", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.Ammonium.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Ammonium (uM)", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)
```

\#"Nitrate+nitrite (uM)"

```{r}


cmap.nitrate_nitrite.del_phy <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Nitrate+nitrite (uM)", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.nitrate_nitrite.phy <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Nitrate+nitrite (uM)", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.nitrate_nitrite.chl <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Nitrate+nitrite (uM)", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


Surr.cmap.nitrate_nitrite.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Nitrate+nitrite (uM)", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.nitrate_nitrite.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Nitrate+nitrite (uM)", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.nitrate_nitrite.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Nitrate+nitrite (uM)", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


```

#Phosphate (uM)

```{r}


cmap.phosphate.del_phy <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Phosphate (uM)", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.phosphate.phy <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Phosphate (uM)", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.phosphate.chl <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Phosphate (uM)", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


Surr.cmap.phosphate.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Phosphate (uM)", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.phosphate.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Phosphate (uM)", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.phosphate.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Phosphate (uM)", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


```

#Total phosphorus (uM)

```{r}


cmap.phosphorus.del_phy <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Total phosphorus (uM)", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.phosphorus.phy <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Total phosphorus (uM)", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.phosphorus.chl <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Total phosphorus (uM)", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.phosphorus.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Total phosphorus (uM)", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.phosphorus.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Total phosphorus (uM)", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.phosphorus.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Total phosphorus (uM)", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

```

#Phaeophytin

#Total phosphorus (uM)

```{r}


cmap.Phaeophytin.del_phy <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Phaeophytin (ug/L)", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.Phaeophytin.phy <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Phaeophytin (ug/L)", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.Phaeophytin.chl <- CCM(dataFrame = ag_CR_nutrients, E = 4, Tp = 4, columns = "Phaeophytin (ug/L)", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


Surr.cmap.Phaeophytin.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Phaeophytin (ug/L)", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.Phaeophytin.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Phaeophytin (ug/L)", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.Phaeophytin.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "Phaeophytin (ug/L)", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

```

###CAMCSO

```{r}

CAM2015CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2015CAMCSO')


CAM2016CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2016CAMCSO')

CAM2016CSO <- subset(CAM2016CSO, select = c("Date", "CAM017* (MG)"))



CAM2017CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2017CAMCSO')


CAM2017CSO <- subset(CAM2017CSO, select = c("Date", "CAM017* (MG)"))




CAM2018CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2018CAMCSO')


CAM2018CSO <- subset(CAM2018CSO, select = c("Date", "CAM017* (MG)"))

CAM2019CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2019CAMCSO')



CAM2019CSO <- subset(CAM2019CSO, select = c("Date", "CAM017* (MG)"))

CAM2020CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2020CAMCSO')


CAM2020CSO <- subset(CAM2020CSO, select = c("Date", "CAM017* (MG)"))

CAM2021CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2021CAMCSO')


CAM2021CSO <- subset(CAM2021CSO, select = c("Date", "CAM017* (MG)"))

CAM2022CSO <- read_sheet('https://docs.google.com/spreadsheets/d/11v15YsnizQBilaTK-mzJWz-qp1PLJGqQwrxQ2AV7WdA/edit?usp=sharing', sheet = '2022CAMCSO')

CAM2022CSO <- subset(CAM2022CSO, select = c("Date", "CAM017* (MG)"))


```

```{r}
# Combine all values into a single vector and sort them

library(dplyr)

# Ensure column names are consistent across all data frames
colnames(CAM2015CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2016CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2017CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2018CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2019CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2020CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2021CSO) <- c("Date", "CAM017* (MG)")
colnames(CAM2022CSO) <- c("Date", "CAM017* (MG)")

CAM2015CSO$`CAM017* (MG)` <- as.numeric(CAM2015CSO$`CAM017* (MG)`)
CAM2016CSO$`CAM017* (MG)` <- as.numeric(CAM2016CSO$`CAM017* (MG)`)
CAM2017CSO$`CAM017* (MG)` <- as.numeric(CAM2017CSO$`CAM017* (MG)`)
CAM2018CSO$`CAM017* (MG)` <- as.numeric(CAM2018CSO$`CAM017* (MG)`)
CAM2019CSO$`CAM017* (MG)` <- as.numeric(CAM2019CSO$`CAM017* (MG)`)
CAM2020CSO$`CAM017* (MG)` <- as.numeric(CAM2020CSO$`CAM017* (MG)`)
CAM2021CSO$`CAM017* (MG)` <- as.numeric(CAM2021CSO$`CAM017* (MG)`)
CAM2022CSO$`CAM017* (MG)` <- as.numeric(CAM2022CSO$`CAM017* (MG)`)


# Make sure that the "CAM017* (MG)" column is of type double


CAM017 <- bind_rows(
 CAM2016CSO,
  CAM2017CSO, 
  CAM2018CSO,
  CAM2019CSO,
  CAM2020CSO,
  CAM2021CSO,
  CAM2022CSO,
)


CAM017 <- CAM017 %>% rename(MG = `CAM017* (MG)`)

CAM017_discharges <- subset(CAM017, MG != 0)



```

\### ALL CSOs

```{r}

ag_df_CR$Date <- ag_df_CR$date

ag_df_CR <- ag_df_CR[,-c(1) ]

#rm(CR2023, CR2022, CR2020, CR2021, CR2020, CR2019, CR2018, CR2017, CR2016, CR2015, df_CR)

ag_df_CR2 <- ag_df_CR


#Fix Date Formats of Future Joins
MWRACSOFinal$Date <- format(as.Date(MWRACSOFinal$Date, format=
                           "%m-%d-%Y"         ), "%m-%d-%Y")


ag_df_CR2$Date <- format(as.Date(ag_df_CR2$Date , format=
                           "%Y-%m-%d"          ), "%m-%d-%Y")


#join

All <- merge(MWRACSOFinal, ag_df_CR2, by = "Date", all = TRUE)

# Assuming your date column is named "Date"
All$Date <- as.Date(All$Date, format="%m-%d-%Y")

# Order the data frame by Year, Month, and Day
All <- All[order(year(All$Date), month(All$Date), day(All$Date)), ]

```

Join CAM + CSO

```{r}
All <- merge(All, CAM017, by = "Date", all = TRUE)
All <- All %>%
  rename(CAM017MG = MG)

```

```{r}
All <- na.omit(All)
```

###CCM LOGAN

#HourlyAltimeterSetting

```{r}

cmap.alt.del_phy < - CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyAltimeterSetting", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.alt.chl <- CCM(dataFrame = ag_Logan, E = 4, Tp = 0, columns = "HourlyAltimeterSetting", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.alt.phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 0, columns = "HourlyAltimeterSetting", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.alt.del_phy < - CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyAltimeterSetting", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.alt.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 0, columns = "HourlyAltimeterSetting", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.alt.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 0, columns = "HourlyAltimeterSetting", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)



```

#HourlyDryBulbTemperature

```{r}

cmap.drybulb.del_phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyDryBulbTemperature", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.drybulb.chl <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyDryBulbTemperature", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.drybulb.phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyDryBulbTemperature", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.drybulb.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyDryBulbTemperature", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.drybulb.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyDryBulbTemperature", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.drybulb.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyDryBulbTemperature", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

```

#HourlyPrecipitation

```{r}

cmap.precip.del_phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyPrecipitation", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.precip.chl <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyPrecipitation", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.precip.phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyPrecipitation", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.precip.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyPrecipitation", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.precip.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyPrecipitation", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.precip.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyPrecipitation", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


```

#HourlyRelativeHumidity

```{r}

cmap.humidity.del_phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyRelativeHumidity", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.humidity.chl <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = " HourlyRelativeHumidity", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.humidity.phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = " HourlyRelativeHumidity", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


Surr.cmap.humidity.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyRelativeHumidity", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.humidity.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = " HourlyRelativeHumidity", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.humidity.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = " HourlyRelativeHumidity", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

```

#HourlyStationPressure

```{r}

cmap.station.del_phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyStationPressure", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.station.chl <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyStationPressure", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.station.phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyStationPressure", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


Surr.cmap.station.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyStationPressure", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.station.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyStationPressure", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.station.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyStationPressure", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

```

#HourlyVisibility keep bc effect incident radiation

```{r}

cmap.visibility.del_phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyVisibility", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.visibility.phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyVisibility", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.visibility.chl <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyVisibility", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.visibility.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyVisibility", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


Surr.cmap.visibility.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyVisibility", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.visibility.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyVisibility", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)
```

#HourlyWindDirection - could be matter but direction of wind may not
tell how wind is pushing around in charles around. Dan Li. Andy?

```{r}

cmap.wind_direction.del_phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyWindDirection", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


cmap.wind_direction.phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyWindDirection", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.wind_direction.chl <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyWindDirection", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.wind_direction.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyWindDirection", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.wind_direction.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyWindDirection", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.wind_direction.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyWindDirection", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

```

#HourlyWindGustSpeed

```{r}

cmap.wind_gust.del_phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyWindGustSpeed", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.wind_gust.phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyWindGustSpeed", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.wind_gust.chl <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyWindGustSpeed", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.wind_gust.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyWindGustSpeed", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.wind_gust.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyWindGustSpeed", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.wind_gust.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyWindGustSpeed", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)


#Correlations 

windspeed.phy.corr <- cor(cmap.windspeed.phy)[1, 3]
windspeed.del_phy.corr <- cor(cmap.windspeed.del_phy)[1, 3]
windspeed.chl.corr <- cor(cmap.windspeed.chl)[1, 3]

#Surrogate Correlations

Surr.windspeed.phy.corr <- cor(Surr.cmap.windspeed.phy)[1, 3]
Surr.windspeed.del_phy.corr <- cor(Surr.cmap.windspeed.del_phy)[1, 3]
Surr.windspeed.chl.corr <- cor(Surr.cmap.windspeed.chl)[1, 3]


```

#HourlyWindSpeed - synioptic variable

```{r}

cmap.windspeed.del_phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyWindSpeed", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.windspeed.phy <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyWindSpeed", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

cmap.windspeed.chl <- CCM(dataFrame = ag_Logan, E = 4, Tp = 4, columns = "HourlyWindSpeed", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

#SURROGATE

Surr.cmap.windspeed.del_phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyWindSpeed", target = "del_phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.windspeed.phy <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyWindSpeed", target = "phy", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

Surr.cmap.windspeed.chl <- CCM(dataFrame = Surrogate, E = 4, Tp = 4, columns = "HourlyWindSpeed", target = "chl", libSizes = "20 200 10", sample = 100, showPlot = TRUE)

#Correlations 

windspeed.phy.corr <- cor(cmap.windspeed.phy)[1, 3]
windspeed.del_phy.corr <- cor(cmap.windspeed.del_phy)[1, 3]
windspeed.chl.corr <- cor(cmap.windspeed.chl)[1, 3]

#Surrogate Correlations

Surr.windspeed.phy.corr <- cor(Surr.cmap.windspeed.phy)[1, 3]
Surr.windspeed.del_phy.corr <- cor(Surr.cmap.windspeed.del_phy)[1, 3]
Surr.windspeed.chl.corr <- cor(Surr.cmap.windspeed.chl)[1, 3]




```

```{r}
#1 is get the tau/Tp sorted


But when we get to multivariate, can include the seasonal cycle:

yday(as.Date("2024-03-01"))

df_example <- data.frame(Date=seq(as.Date("2015-01-01"),by=1,length.out=3300))
df_example <- df_example %>% mutate(yday = yday(Date))
head(df_example)
df_example <- df_example %>% mutate(sin_season = sin(2*pi*yday/365))
df_example %>% ggplot(aes(x=Date,y=sin_season)) + geom_line()

cycle <- df_example 


```

#FIND CRITICAL VALUES

```{r}
critical.r <- function( n, alpha = .05 ) {
  df <- n - 2
  critical.t <- qt(alpha/2, df, lower.tail = F)
  critical.r <- sqrt( (critical.t^2) / ( (critical.t^2) + df ) )
  return(critical.r)
}
# Example usage: Critical correlation coefficient at sample size of n = 100
critical.r( 100 )


```

#EDM UNIVARIATE

# EDM

## EDM Uni-variate Analysis

## EDM 9-12 PHY

```{r}

#Without NA Values

morndf_CR <- readRDS("morndf_CR.rds")

# Find the first row number for the first date in 2023
first_row_2023 <- which(format(morndf_CR$date, "%Y") == "2023")[1]

print(first_row_2023)

# Find the last value 
last_value <- nrow(morndf_CR)

print(last_value)

```

```{r}
lib_ins <- c(1,1084) # data up through end of 2022; this is the training set
lib_oos <- c(1085,1258) # leave this alone until we're nearly done with the project

rho_E <- EmbedDimension(dataFrame = morndf_CR, columns = "phy", target = "phy",
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 

# we can use S-map with E=1 and theta=0 to get the AR(1) forecast skill
preds_AR <- SMap(dataFrame = morndf_CR, columns = "phy", target = "phy",
lib = lib_ins, pred = lib_ins, E=1,theta=0) #lib train, #pred is pred 

stats_AR <- ComputeError(preds_AR$predictions$Observations,
                         preds_AR$predictions$Predictions)


rho_E %>%
  ggplot(aes(x=E,y=rho)) + 
  geom_line(color="blue") +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,1)
  
```

4-days:

```{r}
tau_i <- 4

# Same as before but using a larger tau and setting Tp = tau

rho_E_tau_i_0 <- EmbedDimension(dataFrame = morndf_CR, columns = "phy", target = "phy",
                        tau=-tau_i,Tp=tau_i,
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


rho_E_tau_i <- EmbedDimension(dataFrame = morndf_CR, columns = "phy", target = "phy",
                        tau=-tau_i,Tp=tau_i,
                        exclusionRadius = tau_i, # exclude 2*tau additional points in cross-val
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


# we can use S-map with E=1 and theta=0 to get the AR(1) forecast skill
preds_AR <- SMap(dataFrame = morndf_CR, columns = "phy", target = "phy",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR <- ComputeError(preds_AR$predictions$Observations,
                         preds_AR$predictions$Predictions)


rho_E_tau_i %>%
  ggplot(aes(x=E,y=rho)) + 
  geom_line(aes(color="correct")) +
  geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,1) +
  labs(title=paste0("Simplex with tau = ",tau_i))
  
```

###Predict Nonlinear

```{r}
rho_theta_tau_i <- PredictNonlinear(dataFrame = morndf_CR, columns = "phy", target = "phy",
                                    E=3,
                                    tau=-tau_i,Tp=tau_i,exclusionRadius = tau_i,
                                    lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 

preds_AR1 <- SMap(dataFrame = morndf_CR, columns = "del_phy", target = "phy",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR1 <- ComputeError(preds_AR1$predictions$Observations,
                         preds_AR1$predictions$Predictions)


rho_theta_tau_i %>%
  ggplot(aes(x=Theta,y=rho)) + 
  geom_line() +
  # geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  # ylim(0,0.5) +
  labs(title=paste0("Simplex with tau = ",tau_i))

```

## EDM 9-12 1st Diff PHY

```{r}

#Without NA Values

morndf_CR <- na.omit(morndf_CR) 


# Find the first row number for the first date in 2023
first_row_2023 <- which(format(morndf_CR$date, "%Y") == "2023")[1]

print(first_row_2023)

# Find the last value 
last_value <- nrow(morndf_CR)

print(last_value)

```

```{r}

rho_E <- EmbedDimension(dataFrame = morndf_CR, columns = "del_phy", target = "del_phy",
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 

# we can use S-map with E=1 and theta=0 to get the AR(1) forecast skill
preds_AR <- SMap(dataFrame = morndf_CR, columns = "del_phy", target = "del_phy",
lib = lib_ins, pred = lib_ins, E=1,theta=0) #lib train, #pred is pred 

stats_AR <- ComputeError(preds_AR$predictions$Observations,
                         preds_AR$predictions$Predictions)


rho_E %>%
  ggplot(aes(x=E,y=rho)) + 
  geom_line(color="blue") +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,1)
  

#rho_E <- EmbedDimension(dataFrame = ag_df_CR, columns = "chl", target = "chl",
#lib = "1 1075", pred = "1076 1223", showPlot = TRUE) #lib train, #pred is pred 

#lib = "1 100 101 200 301 400", - leaving one out

```

4-days:

```{r}
tau_i <- 4

# Same as before but using a larger tau and setting Tp = tau

rho_E_tau_i_0 <- EmbedDimension(dataFrame = morndf_CR, columns = "del_phy", target = "del_phy",
                        tau=-tau_i,Tp=tau_i,
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


rho_E_tau_i <- EmbedDimension(dataFrame = morndf_CR, columns = "del_phy", target = "del_phy",
                        tau=-tau_i,Tp=tau_i,
                        exclusionRadius = tau_i, # exclude 2*tau additional points in cross-val
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


# we can use S-map with E=1 and theta=0 to get the AR(1) forecast skill
preds_AR <- SMap(dataFrame = morndf_CR, columns = "del_phy", target = "del_phy",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR <- ComputeError(preds_AR$predictions$Observations,
                         preds_AR$predictions$Predictions)


rho_E_tau_i %>%
  ggplot(aes(x=E,y=rho)) + 
  geom_line(aes(color="correct")) +
  geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,1) +
  labs(title=paste0("Simplex with tau = ",tau_i))
  
```

###Predict Nonlinear

```{r}
rho_theta_tau_i <- PredictNonlinear(dataFrame = morndf_CR, columns = "del_phy", target = "del_phy",
                                    E=8,
                                    tau=-tau_i,Tp=tau_i,exclusionRadius = tau_i,
                                    lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 

preds_AR1 <- SMap(dataFrame = morndf_CR, columns = "del_phy", target = "del_phy",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR1 <- ComputeError(preds_AR1$predictions$Observations,
                         preds_AR1$predictions$Predictions)


rho_theta_tau_i %>%
  ggplot(aes(x=Theta,y=rho)) + 
  geom_line() +
  # geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  # ylim(0,0.5) +
  labs(title=paste0("Simplex with tau = ",tau_i))

```

## EDM 9-12 CHL

```{r}

#Without NA Values

morndf_CR <- na.omit(morndf_CR) 


# Find the first row number for the first date in 2023
first_row_2023 <- which(format(morndf_CR$date, "%Y") == "2023")[1]

print(first_row_2023)

# Find the last value 
last_value <- nrow(morndf_CR)

print(last_value)

```

```{r}

rho_E <- EmbedDimension(dataFrame = morndf_CR, columns = "chl", target = "chl",
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 

# we can use S-map with E=1 and theta=0 to get the AR(1) forecast skill
preds_AR <- SMap(dataFrame = morndf_CR, columns = "chl", target = "chl",
lib = lib_ins, pred = lib_ins, E=1,theta=0) #lib train, #pred is pred 

stats_AR <- ComputeError(preds_AR$predictions$Observations,
                         preds_AR$predictions$Predictions)


rho_E %>%
  ggplot(aes(x=E,y=rho)) + 
  geom_line(color="blue") +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,1)
  

#rho_E <- EmbedDimension(dataFrame = ag_df_CR, columns = "chl", target = "chl",
#lib = "1 1075", pred = "1076 1223", showPlot = TRUE) #lib train, #pred is pred 

#lib = "1 100 101 200 301 400", - leaving one out

```

4-days:

```{r}
tau_i <- 4

# Same as before but using a larger tau and setting Tp = tau

rho_E_tau_i_0 <- EmbedDimension(dataFrame = morndf_CR, columns = "chl", target = "chl",
                        tau=-tau_i,Tp=tau_i,
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


rho_E_tau_i <- EmbedDimension(dataFrame = morndf_CR, columns = "chl", target = "chl",
                        tau=-tau_i,Tp=tau_i,
                        exclusionRadius = tau_i, # exclude 2*tau additional points in cross-val
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


# we can use S-map with E=1 and theta=0 to get the AR(1) forecast skill
preds_AR <- SMap(dataFrame = morndf_CR, columns = "chl", target = "chl",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR <- ComputeError(preds_AR$predictions$Observations,
                         preds_AR$predictions$Predictions)


rho_E_tau_i %>%
  ggplot(aes(x=E,y=rho)) + 
  geom_line(aes(color="correct")) +
  geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,1) +
  labs(title=paste0("Simplex with tau = ",tau_i))
  
```

###Predict Nonlinear

```{r}
rho_theta_tau_i <- PredictNonlinear(dataFrame = morndf_CR, columns = "chl", target = "chl",
                                    E=9,
                                    tau=-tau_i,Tp=tau_i,exclusionRadius = tau_i,
                                    lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 

preds_AR1 <- SMap(dataFrame = morndf_CR, columns = "chl", target = "chl",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR1 <- ComputeError(preds_AR1$predictions$Observations,
                         preds_AR1$predictions$Predictions)


rho_theta_tau_i %>%
  ggplot(aes(x=Theta,y=rho)) + 
  geom_line() +
  # geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  # ylim(0,0.5) +
  labs(title=paste0("Simplex with tau = ",tau_i))

```

## EDM Daily CHL

```{r}

#Without NA Values
ag_df_CR <- readRDS("ag_df_CR.rds")
ag_df_CR <- na.omit(ag_df_CR) 


# Find the first row number for the first date in 2023
first_row_2023 <- which(format(ag_df_CR$date, "%Y") == "2023")[1]

print(first_row_2023)

# Find the last value 
last_value <- nrow(ag_df_CR)

print(last_value)

```

### Optimal Embedding Dimension

-   rho_E

    -   E

    -   Rho

```{r}
lib_ins <- c(1,1014) # data up through end of 2022; this is the training set
lib_oos <- c(1025,1189) # leave this alone until we're nearly done with the project

rho_E <- EmbedDimension(dataFrame = ag_df_CR, columns = "chl", target = "chl",
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 

# we can use S-map with E=1 and theta=0 to get the AR(1) forecast skill
preds_AR <- SMap(dataFrame = ag_df_CR, columns = "chl", target = "chl",
lib = lib_ins, pred = lib_ins, E=1,theta=0) #lib train, #pred is pred 

stats_AR <- ComputeError(preds_AR$predictions$Observations,
                         preds_AR$predictions$Predictions)


rho_E %>%
  ggplot(aes(x=E,y=rho)) + 
  geom_line(color="blue") +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,1)
  

#rho_E <- EmbedDimension(dataFrame = ag_df_CR, columns = "chl", target = "chl",
#lib = "1 1075", pred = "1076 1223", showPlot = TRUE) #lib train, #pred is pred 

#lib = "1 100 101 200 301 400", - leaving one out

```

"At a daily time-scale, EDM predictions do not beat the strong
day-to-day autocorrelation in the signal." This is no surprise. Let's
look again with a larger tau, informed by the previous ACF analysis; we
saw a signal somewhere in the window of 4 days - 1 week.

4-days:

```{r}
tau_i <- 4

# Same as before but using a larger tau and setting Tp = tau

rho_E_tau_i_0 <- EmbedDimension(dataFrame = ag_df_CR, columns = "chl", target = "chl",
                        tau=-tau_i,Tp=tau_i,
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


rho_E_tau_i <- EmbedDimension(dataFrame = ag_df_CR, columns = "chl", target = "chl",
                        tau=-tau_i,Tp=tau_i,
                        exclusionRadius = tau_i, # exclude 2*tau additional points in cross-val
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


# we can use S-map with E=1 and theta=0 to get the AR(1) forecast skill
preds_AR <- SMap(dataFrame = ag_df_CR, columns = "chl", target = "chl",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR <- ComputeError(preds_AR$predictions$Observations,
                         preds_AR$predictions$Predictions)


rho_E_tau_i %>%
  ggplot(aes(x=E,y=rho)) + 
  geom_line(aes(color="correct")) +
  geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,1) +
  labs(title=paste0("Simplex with tau = ",tau_i))
  
```

```{r}
rho_theta_tau_i <- PredictNonlinear(dataFrame = ag_df_CR, columns = "chl", target = "chl",
                                    E=5,
                                    tau=-tau_i,Tp=tau_i,exclusionRadius = tau_i,
                                    lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 

preds_AR1 <- SMap(dataFrame = ag_df_CR, columns = "chl", target = "chl",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR1 <- ComputeError(preds_AR1$predictions$Observations,
                         preds_AR1$predictions$Predictions)


rho_theta_tau_i %>%
  ggplot(aes(x=Theta,y=rho)) + 
  geom_line() +
  # geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  # ylim(0,0.5) +
  labs(title=paste0("Simplex with tau = ",tau_i))

```

1 week:

```{r}
tau_i <- 7

# Same as before but using a larger tau and setting Tp = tau

rho_E_tau_i_0 <- EmbedDimension(dataFrame = ag_df_CR, columns = "chl", target = "chl",
                        tau=-tau_i,Tp=tau_i,
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


rho_E_tau_i <- EmbedDimension(dataFrame = ag_df_CR, columns = "chl", target = "chl",
                        tau=-tau_i,Tp=tau_i,exclusionRadius = tau_i,
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


# we can use S-map with E=1 and theta=0 to get the AR(1) forecast skill
preds_AR <- SMap(dataFrame = ag_df_CR, columns = "chl", target = "chl",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR <- ComputeError(preds_AR$predictions$Observations,
                         preds_AR$predictions$Predictions)


rho_E_tau_i %>%
  ggplot(aes(x=E,y=rho)) + 
  geom_line(aes(color="correct")) +
  geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,1) +
  labs(title=paste0("Simplex with tau = ",tau_i))
  
```

```{r}

rho_theta_tau_i <- PredictNonlinear(dataFrame = ag_df_CR, columns = "chl", target = "chl",
                                    E=5,
                                    tau=-tau_i,Tp=tau_i,exclusionRadius = tau_i,
                                    lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 

preds_AR1 <- SMap(dataFrame = ag_df_CR, columns = "chl", target = "chl",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR1 <- ComputeError(preds_AR1$predictions$Observations,
                         preds_AR1$predictions$Predictions)


rho_theta_tau_i %>%
  ggplot(aes(x=Theta,y=rho)) + 
  geom_line() +
  # geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,0.5) +
  labs(title=paste0("Simplex with tau = ",tau_i))

```

## EDM Daily PHY

```{r}

#Without NA Values

ag_df_CR <- na.omit(ag_df_CR) 


# Find the first row number for the first date in 2023
first_row_2023 <- which(format(ag_df_CR$date, "%Y") == "2023")[1]

print(first_row_2023)

# Find the last value 
last_value <- nrow(ag_df_CR)

print(last_value)

```

### Optimal Embedding Dimension

-   rho_E

    -   E

    -   Rho

```{r}

rho_E <- EmbedDimension(dataFrame = ag_df_CR, columns = "phy", target = "phy",
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 

# we can use S-map with E=1 and theta=0 to get the AR(1) forecast skill
preds_AR <- SMap(dataFrame = ag_df_CR, columns = "phy", target = "phy",
lib = lib_ins, pred = lib_ins, E=1,theta=0) #lib train, #pred is pred 

stats_AR <- ComputeError(preds_AR$predictions$Observations,
                         preds_AR$predictions$Predictions)


rho_E %>%
  ggplot(aes(x=E,y=rho)) + 
  geom_line(color="blue") +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,1)
  
```

"At a daily time-scale, EDM predictions do not beat the strong
day-to-day autocorrelation in the signal." This is no surprise. Let's
look again with a larger tau, informed by the previous ACF analysis; we
saw a signal somewhere in the window of 4 days - 1 week.

4-days:

```{r}
tau_i <- 4

# Same as before but using a larger tau and setting Tp = tau

rho_E_tau_i_0 <- EmbedDimension(dataFrame = ag_df_CR, columns = "phy", target = "phy",
                        tau=-tau_i,Tp=tau_i,
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


rho_E_tau_i <- EmbedDimension(dataFrame = ag_df_CR, columns = "phy", target = "phy",
                        tau=-tau_i,Tp=tau_i,
                        exclusionRadius = tau_i, # exclude 2*tau additional points in cross-val
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


# we can use S-map with E=1 and theta=0 to get the AR(1) forecast skill
preds_AR <- SMap(dataFrame = ag_df_CR, columns = "phy", target = "phy",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR <- ComputeError(preds_AR$predictions$Observations,
                         preds_AR$predictions$Predictions)


rho_E_tau_i %>%
  ggplot(aes(x=E,y=rho)) + 
  geom_line(aes(color="correct")) +
  geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,1) +
  labs(title=paste0("Simplex with tau = ",tau_i))
  
```

**E = 6 here?**

```{r}
rho_theta_tau_i <- PredictNonlinear(dataFrame = ag_df_CR, columns = "phy", target = "phy",
                                    E=4,
                                    tau=-tau_i,Tp=tau_i,exclusionRadius = tau_i,
                                    lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 

preds_AR1 <- SMap(dataFrame = ag_df_CR, columns = "phy", target = "phy",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR1 <- ComputeError(preds_AR1$predictions$Observations,
                         preds_AR1$predictions$Predictions)


rho_theta_tau_i %>%
  ggplot(aes(x=Theta,y=rho)) + 
  geom_line() +
  # geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  # ylim(0,0.5) +
  labs(title=paste0("Simplex with tau = ",tau_i))

```

1 week:

```{r}
tau_i <- 7

# Same as before but using a larger tau and setting Tp = tau

rho_E_tau_i_0 <- EmbedDimension(dataFrame = ag_df_CR, columns = "phy", target = "phy",
                        tau=-tau_i,Tp=tau_i,
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


rho_E_tau_i <- EmbedDimension(dataFrame = ag_df_CR, columns = "phy", target = "phy",
                        tau=-tau_i,Tp=tau_i,exclusionRadius = tau_i,
lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 


# we can use S-map with E=1 and theta=0 to get the AR(1) forecast skill
preds_AR <- SMap(dataFrame = ag_df_CR, columns = "phy", target = "phy",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR <- ComputeError(preds_AR$predictions$Observations,
                         preds_AR$predictions$Predictions)


rho_E_tau_i %>%
  ggplot(aes(x=E,y=rho)) + 
  geom_line(aes(color="correct")) +
  geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,1) +
  labs(title=paste0("Simplex with tau = ",tau_i))
  
```

```{r}

rho_theta_tau_i <- PredictNonlinear(dataFrame = ag_df_CR, columns = "phy", target = "phy",
                                    E=2,
                                    tau=-tau_i,Tp=tau_i,exclusionRadius = tau_i,
                                    lib = lib_ins, pred = lib_ins, showPlot = TRUE) #lib train, #pred is pred 

preds_AR1 <- SMap(dataFrame = ag_df_CR, columns = "phy", target = "phy",
lib = lib_ins, pred = lib_ins, E=1,theta=0,tau=-tau_i,Tp=tau_i,exclusionRadius=tau_i) #lib train, #pred is pred 

stats_AR1 <- ComputeError(preds_AR1$predictions$Observations,
                         preds_AR1$predictions$Predictions)


rho_theta_tau_i %>%
  ggplot(aes(x=Theta,y=rho)) + 
  geom_line() +
  # geom_line(data=rho_E_tau_i_0,aes(color="wrong")) +
  geom_hline(aes(yintercept = stats_AR$rho),lty=2,color="black") +
  ylim(0,0.5) +
  labs(title=paste0("Simplex with tau = ",tau_i))

```

### Prediction Decay

-   rho_Tp

    -   Tp

    -   Rho

```{r}

rho_Tp <- PredictInterval(dataFrame = ag_df_CR, lib = lib_ins, pred = lib_ins, target = "chl",
columns = "chl", E = 6)
#long-term memory in system (ACF), long term -persistent auto-correlation 


```

### S-Map

-   rho_theta_e3

    -   Theta

    -   Rho

```{r}

rho_theta_e3 = PredictNonlinear(dataFrame = ag_df_CR, columns = "chl",
target = "chl", lib = lib_ins, pred = lib_ins, E = 4)

```

### **Evaluate Simplex Prediction**

-   ag_df_CRPredict

    -   Observations

    -   Predictions

    -   Pred_Variance

```{r}

ag_df_CRPredict <- Simplex(dataFrame = ag_df_CR,lib = lib_ins, pred = lib_ins, pred = "1076 1223", target = "chl",
columns = "chl", E = 4)

#S-Map = Theta



```

```{r}

 ComputeError(ag_df_CRPredict$Observations, ag_df_CRPredict$Predictions)
 
```

### Evaluate S-Map

It can be advantageous to combine different time series to create the
phase-space embedding, providing they are observed from the same system.

-   Smap

    -   predictions

    -   coefficents

    -   singularvalues

    -   ∂chl/∂chl(t-1,2,3,4)

**#Where does TentMapNoise come from? SMapPredict?**

**Tentmap Noise = Theta?**

**What is columns?**

```{r}

 #If θ= 0, all library points have the same weight regardless of the local state of the predictee; 

smap = SMap(dataFrame = ag_df_CR, lib = lib_ins, pred = lib_ins, target = "chl",
columns = "chl", E = 4, theta = 5)

#if h > θ, the forecast given by the S-map depends on the local state of the predictee, and thus produces locally different fittings.
#smap = SMap(dataFrame = ag_df_CR, lib = "1 1075", pred = "1076 1223", target = "chl",
#columns = "chl", E = 2, theta = 0)

```

```{r}
head(cbind(smap$predictions, smap$coefficients), 2)

tail(cbind(smap$predictions, smap$coefficients))


```

### Convergent Cross Mapping (CCM)

find out multivariate CSO - reflects and episodic threshold reponse to
weather

```{r}


cmap <- CCM(dataFrame = ag_df_CR, E = 4, Tp = 2, columns = "chl", target = "turb", libSizes = "10 70 5", sample = 100, showPlot = TRUE)



#tp - 2012 paper 
```

# EDM Mulit-variate Analysis

### Generalized Takens Theorem

smplx_3CR

multivar

```{r}

multivar <- Simplex(Embedded = TRUE, columns = c("phy"))



```

### Save as New

```{r}
block_4CR <- smap$coefficients  

_new_names <- c(   "date",   "C0",   "t-0",   "t-1",   "t-2",   "t-3" )  

block_4CR <- block_4CR %>%   set_names(v_new_names)  

head(block_4CR)
```

**What is target? t-0?**

smplx_3species

-   observations

-   predicitons

-   pred variance

```{r}


smplx_3CR = Simplex(dataFrame = block_4CR, lib = "1 74", pred = "75 149", E = 4, columns = "t-0 t-1 t-2 t-3", target = "t-0", embedded = TRUE)

err = ComputeError(smplx_3CR$Observations, smplx_3CR$Predictions)
plot(smplx_3CR$Observations, smplx_3CR$Predictions, pch = 19, cex = 0.5,
xlab = "Observations", ylab = "Predictions", main = "3 CR x_t")
abline(a = 0, b = 1, lty = 2, col = "blue")
text(-1, 1, paste(capture.output(cbind(err)), collapse = "\n"))
```

```{r}
#**is this for one variable?**

#head(ag_df_CR,3)

#simplexforecasting

#smplx_3CR = Simplex(dataFrame = ag_df_CR, lib = "1 100", pred = "101 190",
#E = 2, columns = "ph Celsius chl", target = "chl", embedded = TRUE)
#are we using all the columns?
```

Plot

```{r}
#err = ComputeError(smplx_3CR$Observations, smplx_3CR$Predictions)
#plot(smplx_3CR$Observations, smplx_3CR$Predictions, pch = 19, cex = 0.5,
#xlab = "Observations", ylab = "Predictions", main = "3 CR x_t")
#abline(a = 0, b = 1, lty = 2, col = "blue")
#text(-1, 1, paste(capture.output(cbind(err)), collapse = "\n"))

```

### S-Map Coefficents

The Lorenz5D data.frame contains a N=5 dimensional system with F=8 from
(Lorenz 1996). Here, we use SMap() to compute a 4-dimensional forecast
at Tp=1:

```{r}
colnames(ag_df_CR) #pull col names
head(ag_df_CR)
smap_CR <- SMap(dataFrame = ag_df_CR, lib = "1 500", pred = "601 900", E = 4,
theta = 5, columns = "chl phy ph Celsius spcond do turb", target = "chl", embedded = TRUE)
```

```{r}
head(cbind(smap_CR$predictions, smap_CR$coefficients[, 2:6]), 3)

```

```{r}
#**review w/ Deyle**
#View(predictions)
predictions = smap_CR$predictions
coefficients = smap_CR$coefficients
Time = predictions$Time
plot(Time, predictions$Observations, type = "l", col = "blue", ylab = "chl", xlab = "",
lwd = 2, cex.lab = 1.3, cex.axis = 1.3)
lines(Time, predictions$Predictions, lwd = 2, col = "red")
legend("topright", legend = c("observed", "predicted"), fill = c("blue", "red"),
bty = "n", cex = 1.3)

plot(Time, coefficients[, 6], type = "l", col = "brown", ylab = paste("", "V4/",
"", "chl", sep = ""), xlab = "", lwd = 2, cex.lab = 1.3, cex.axis = 1.3)

plot(Time, coefficients[, 5], type = "l", col = "darkgreen", ylab = paste("",
"V3/", "", "chl", sep = ""), xlab = "", lwd = 2, cex.lab = 1.3, cex.axis = 1.3)

plot(Time, coefficients[, 4], type = "l", col = "blue", ylab = paste("", "V2/",
"", "chl", sep = ""), xlab = "", lwd = 2, cex.lab = 1.3, cex.axis = 1.3)

```

```{r}
Mview = Multiview(dataFrame = df_CR, lib = "1 100", pred = "101 190", E = 3,
columns = "chl phy ph Celsius spcond do turb", target = "chl")
```

```{r}

Mview$View[which(Mview$View$rho > 0.91), ]

```

Next Steps

Convergent Cross Mapping All possible Covariates in one data frame as
different columns other variables all the endogenous bigoeochemical and
human drivers management driver and - rainfall and CSOs know now half
week to weekly time scale we see has signature until we have the
corrected values to keep the diff 9 to noon 4 days negative auto, use
linear s-map To make forecasts: predict 1st differences and add back
into data prediction for tomorrow is value for tomorrow + value for
today

list of measurments and list of weekly measurments look at Tahoe +
geneva

plot minutes vs. volume CSOs - rainfall causal associations? analysis:
same trend = synchornity not = one is low and flat is oneway causal\
number at end matters, consider as correlation coeff, rho ccm at max
library size

look at full matrix with nutrient time series later on

2 different data matrixes 1. time-scale 2. timespan

#EDM MULTIVARIATE

#MULTIVIEW

ccm uses y to predict x, if true, x can affect y ccm wraps (Simplex
target = x, columns = y)

multivariate Simplex ( Embedded = True, columns = c("x1, "x2)) set CCM
samples to 10 to see result


#Multiview

Here, we demonstrate this idea using the Multiview() function with the
3-species data used above. Multiview() operates by constructing all
possible embeddings of dimension E with lag up to E-1. These embeddings
are ranked by forecast skill (rho) over the lib portion of the data. The
individual forecasts for the top multiview embeddings are then averaged
together. If multiview is not specified it is set to sqrt(C) where C is
the number of E-dimensional combinations created from all data vectors.

View lists the various combinations of data embedding vectors used for
the forecasts along with their prediction statistics. Predictions
returns the final averaged multiview projections.

E is amounts of lags. univar time lags D is dimension of individual
multivariate models, number of vars we're taking at a time. axis to
embed. Additional Analysis fills in mechnaistically. Can set lib = pred
At least E of 2, after E \> 5 com sci problem

```{r}
Multiview2 = Multiview(dataFrame = ag_df_CR, lib = "1 1048", pred = "1 1048", E = 3, D= 5,
                       
  columns = c("chl", "ph", "Celsius", "spcond", "do", "turb", "Salinity"), 
  target = "phy")
Multiview2()


**DIFFERENCE BETWEEN Multiview 2 and Multiview**?

```

\#**TEST**

# Check if the file exists

#if(file.exists("Multi_E2_D3.rds")) { \# Load the file if it exists
Multi_E2_D3 \<- readRDS("Multi_E2_D3.rds") #} else { \# If the file does
not exist, create Multi_E2_D3 Multi_E2_D3 \<- Multiview(dataFrame =
ag_df_CR, \# lib = "1 1014", pred = "1 1014", \# in-sample \# lib = "1
1014", pred = "1015 1189", \# out-sample \# trainLib = TRUE, \# E = 2,
D=3, \# tau = -7, Tp = 7, \# columns = "chl phy ph Celsius spcond do
turb Salinity", #target = "phy") \# Save Multi_E2_D3 to file \#
saveRDS(Multi_E2_D3, "Multi_E2_D3.rds") #}

```{r}


#Mutltiview

#Omit NAs 

ag_df_CR <- na.omit(ag_df_CR)


# Find the first row number for the first date in 2023
first_row_2023 <- which(format(ag_df_CR$date, "%Y") == "2023")[1]

print(first_row_2023)

#Check class

ag_df_CR$date <- as.numeric(ag_df_CR$date)
library(rEDM)


#**E3 D5 IN SAMPLE**

# Check if the file exists
if(file.exists("Multi_E3_D5.rds")) {
  # Load the file if it exists
  Multi_E3_D5 <- readRDS("Mutliview_E3D5.rds")
} else {
  # If the file does not exist, create Multi_E2_D3
  Multi_E3_D5 <- Multiview(dataFrame = ag_df_CR, lib = "1 1014", pred = "1 1014", E =3, D=5,
                            columns = "chl phy ph Celsius spcond do turb Salinity sin_season", target = "phy")
  # Save Multi_E2_D3 to file
  saveRDS(Multi_E3_D5, "Mutliview_E2D5.rds")
}



#**E2 D3 IN SAMPLE**

Multi_E2_D3 <- "Multi_E2_D3.rds"
if(!file.exists("Multi_E2_D3.rds")){
 Multi_E2_D3 <- Multiview(dataFrame = ag_df_CR, lib = "1 1014", pred = "1 1014", E = 2, D=3,
                            columns = "chl phy ph Celsius spcond do turb Salinity sin_season", target = "phy")
    save(Multi_E2_D3, file = "Multi_E2_D3.rds")
} else{
  load(Multi_E2_D3)
}


#HEAD


head(Multi_E2_D3)
  
head(Multi_E3_D5)

  
  
  

```

#HISTOGRAM

ccm uses y to predict x, if true, x can affect y ccm wraps (Simplex
target = x, columns = y)

multivariate Simplex ( Embedded = True, columns = c("x1, "x2)) set CCM
samples to 10 to see result

#PULL GRAPHS

```{r}
plots.dir.path <- list.files(tempdir("C:\\Users\\mfman\\AppData\\Local\\Temp\\RtmpaE80Ug"), pattern="rs-graphics", full.names = TRUE); 
plots.png.paths <- list.files(plots.dir.path, pattern=".png", full.names = TRUE)
```

```{r}
file.copy(from=plots.png.paths, to="C:/Users/mfman/OneDrive/Desktop/HAB_PLOTS")

```