wq.Rmd

---
output: word_document
---

```{r wq_options setup, include=FALSE, warning=FALSE, message=FALSE, comment=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library("tidyverse")
library("lubridate")
library("waterData")
library("scales")
```
  
```{r salinity_data_processing, include=FALSE, warning=FALSE, message= FALSE}

# Sensor water quality carpentry
wq <- read.csv("data/wq/wq.csv", header= T) %>%
  filter(Site != 0) 

wq<-wq %>% 
  filter(Obs_Date > "2021-01-01 00:00:00" & Obs_Date < "2021-03-31 23:00:00")

wq$year <- strftime(wq$Obs_Date, format = "%Y")

#Remove 2017
wq <- wq %>%
  subset(year %in% c('2018','2019','2020', '2021')) %>%
  subset(Site %in% c(1:9))

#Calculate mean salinity by date and site
wq2 <- aggregate(Salinity ~ Obs_Date + Site, FUN = mean, data = wq) 

#Calculate the standard deviations for salinity by date and site
wq3 <- aggregate(Salinity ~ Obs_Date + Site, FUN = sd, data = wq)  

#Rename salinity sd to prevent redundant names
colnames(wq3)[colnames(wq3)=="Salinity"] <- "sal.sd"

#Add salinity sd vector to df with salinity mean values
wq2$sal.sd <- wq3$sal.sd

#Change the name of the mean salinity column
colnames(wq2)[colnames(wq2)=="Salinity"] <- "sal.mean"


wqs1<-wq2 %>%
  filter(Site == 1) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqs1['Site'] = as.numeric(1)

wqs2<-wq2 %>%
  filter(Site == 2) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqs2['Site'] = as.numeric(2)

wqs3<-wq2 %>%
  filter(Site == 3) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqs3['Site'] = as.numeric(3)

wqs4<-wq2 %>%
  filter(Site == 4) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqs4['Site'] = as.numeric(4)

wqs5<-wq2 %>%
  filter(Site == 5) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqs5['Site'] = as.numeric(5)

wqs6<-wq2 %>%
  filter(Site == 6) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqs6['Site'] = as.numeric(6)

wqs7<-wq2 %>%
  filter(Site == 7) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqs7['Site'] = as.numeric(7)

wqs8<-wq2 %>%
  filter(Site == 8) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqs8['Site'] = as.numeric(8)

wqs9<-wq2 %>%
  filter(Site == 9) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqs9['Site'] = as.numeric(9)

wq2<- rbind(wqs1,wqs2,wqs3,wqs4,wqs5,wqs6,wqs7,wqs8,wqs9)

#Change class? of date and year
wq2$Obs_Date <- as.POSIXct (wq2$Obs_Date)

#Factoring the site in the order that they are spatially
wq2$Site <- factor(wq2$Site, levels = c("6", "1", "7", "5", "2", "8","4", "3", "9"))

# Water discharge carpentry (dynamically updating local discharge file)
station = '02323500' 
stinfo  = siteInfo(station)
dis <- read_rds("data/wq/dis.rds")
if (max(dis$dates) < (Sys.Date() - 5)) {
  sdate <- max(dis$dates) + 1
  newdis <- importDVs(staid = station, code = '00060', stat = '00003', 
                      sdate= as.character(sdate))
  dis <- bind_rows(dis, newdis) %>%
    distinct() # In case there's repeated rows due to code failure
  write_rds(dis, "data/dis.rds")
}

# Start and end dates for all figures
startDate <- ymd_hms("2021-01-01 00:00:00")
endDate <- ymd_hms("2021-03-31 23:00:00")

dis2 <- dis %>%
  mutate(Datetime = paste(dates, " 12:00:00") %>% ymd_hms()) %>%
  filter(Datetime >= startDate & Datetime <= endDate)

dis3 <- dis2 %>%
 expand(Site=unique(wq2$Site), dates) %>%
  left_join(dis2) %>%
  mutate(Measure = "Discharge", minVal = -20, maxVal = val/1000-20) %>%
  select(Site, d2 = dates, Measure, minVal, maxVal)

```


```{r salinity_allsites, echo=FALSE, warning=FALSE, message = FALSE, fig.height= 10, fig.width=10}


ggplot() +

  geom_ribbon(data= wq2 , aes(x= Obs_Date, ymin = sal.mean - 1.96*sal.sd, ymax = sal.mean + 1.96*sal.sd, group =1), fill = "grey70") +
  
  geom_path(data= wq2, aes(x = Obs_Date, y = sal.mean, fill= "Salinity", group =1), na.rm = TRUE) +
  
  geom_ribbon(data=dis3, aes(x= as.POSIXct(d2), ymax=maxVal, ymin=minVal, fill=Measure), 
              alpha=0.4) +
  
  xlab("Date") +
  
  scale_y_continuous(name = "Salinity(ppt)", 
                     limits=c(-20,50), 
                     breaks = c(0, 10, 20, 30, 40),
                     sec.axis = sec_axis(~(.+20), 
                                         name = "River Discharge (1,000 cfs)
                                         ",
                                         breaks = waiver())) +
   
  scale_x_datetime(breaks = seq(as.POSIXct("2021-01-01"),
                 as.POSIXct("2021-03-31"), "2 weeks"), date_labels = "%b %d %Y",expand = c(0, 0)) +

  
  scale_fill_manual(values = c("#56B4E9", "#000000")) +
  
  theme(legend.position=("top"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = NA, colour = NA) ,
        panel.border = element_rect(color = "black", size = 1, fill = NA, linetype="solid"),
        axis.text=element_text(size=15),
        axis.title=element_text(size=17,face="bold"),
        plot.title =element_text(size=17, face='bold'),
        axis.text.x = element_text(angle = 65, hjust = 1, vjust = 1, face= "bold"),
        axis.text.y = element_text(face= "bold"),
        legend.title=element_blank(),
        legend.text=element_text(size=12),
        strip.text = element_text(size = 15),
        panel.spacing = unit(1, "lines")) +
  
 guides(fill = guide_legend(override.aes = list(colour = NULL, linetype = 0))) +
  
  facet_wrap(~ Site, ncol=3, labeller = label_both)


```
  
**Figure 1.**  Salinity and river discharge data collected from autonomous sensors from the Lone Cabbage Reef restoration site near Suwannee, FL. Each graph represents a sensor location, with top of page as north, and right of page as east. The center column of figures (Sites 1-3) represent the eastern side of the Lone Cabbage Reef restoration site. The left column of figures (Sites 4-6) represent the western side of the Lone Cabbage Reef restoration site. The right column of figures (Sites 7-9) represent sensors closest to shore in an area where salinity may be influenced by restoring Lone Cabbage Reef. The first two columns from the west (Sites 1-6) represent the inshore and offshore sides of the restoration reef. The primary y-axis is Salinity (ppt, parts per thousand), and the secondary y-axis is Suwannee River discharge (CFS, cubic feet per second) measured at USGS Wilcox station 02323500 on the Suwannee River. River discharge is graphed as a daily mean in the light blue filled shape near the bottom of each graph. Daily mean salinity values (black line) are depicted using a 95% confidence interval (grey shaded region). Missing river discharge values are due to corrupt readings or missing equipment.

```{r alltemp_plots_processing, include= FALSE, warning=FALSE}


wq_temp <- wq %>%
  subset(year %in% c('2018','2019', '2020', '2021')) %>%
  subset(Site %in% c(1:9))

# Sensor water quality carpentry
#Calculate mean salinity by date and site
wq_temp2 <- aggregate(Temperature ~ Obs_Date + Site, FUN = mean, data = wq) 

#Calculate the standard deviations for salinity by date and site
wq_temp3 <- aggregate(Temperature ~ Obs_Date + Site, FUN = sd, data = wq)  

#Rename salinity sd to prevent redundant names
colnames(wq_temp3)[colnames(wq_temp3)=="Temperature"] <- "temp.sd"

#Add salinity sd vector to df with salinity mean values
wq_temp2$temp.sd <- wq_temp3$temp.sd

#Change the name of the mean salinity column
colnames(wq_temp2)[colnames(wq_temp2)=="Temperature"] <- "temp.mean"

wqt1<-wq_temp2 %>%
  filter(Site == 1) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqt1['Site'] = as.numeric(1)

wqt2<-wq_temp2 %>%
  filter(Site == 2) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqt2['Site'] = as.numeric(2)

wqt3<-wq_temp2 %>%
  filter(Site == 3) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqt3['Site'] = as.numeric(3)

wqt4<-wq_temp2 %>%
  filter(Site == 4) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqt4['Site'] = as.numeric(4)

wqt5<-wq_temp2 %>%
  filter(Site == 5) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqt5['Site'] = as.numeric(5)

wqt6<-wq_temp2 %>%
  filter(Site == 6) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqt6['Site'] = as.numeric(6)

wqt7<-wq_temp2 %>%
  filter(Site == 7) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqt7['Site'] = as.numeric(7)

wqt8<-wq_temp2 %>%
  filter(Site == 8) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqt8['Site'] = as.numeric(8)

wqt9<-wq_temp2 %>%
  filter(Site == 9) %>% 
  mutate(Obs_Date = as.Date(Obs_Date)) %>%
  complete(Obs_Date = seq.Date(min(Obs_Date), max(Obs_Date), by="day"))

wqt9['Site'] = as.numeric(9)

wq_temp2<- rbind(wqt1,wqt2,wqt3,wqt4,wqt5,wqt6,wqt7,wqt8,wqt9)

wq_temp2$Site <- factor(wq_temp2$Site, levels = c("6", "1", "7", "5", "2", "8","4", "3", "9"))


#Change class? of date and year
wq_temp2$Obs_Date <- as.POSIXct (wq_temp2$Obs_Date)

```


```{r alltemp_sites, echo=FALSE, warning= FALSE, message= FALSE, fig.height= 10, fig.width=10}
ggplot() +

  geom_ribbon(data= wq_temp2, aes( x= Obs_Date, ymin = temp.mean - 1.96*temp.sd, ymax = temp.mean + 1.96*temp.sd), fill = "#D55E00", alpha= 0.4) +
  
  geom_line(data= wq_temp2, aes(x = Obs_Date, y = temp.mean, fill= "Temperature"), color= "#D55E00") +
  
  geom_ribbon(data=dis3, aes(x= as.POSIXct(d2), ymax=maxVal, ymin=minVal, fill=Measure), 
              alpha=0.4) +
  
  xlab("Date") +
  
  scale_y_continuous(name = "Temperature(C)", 
                     limits=c(-20,50), 
                     breaks = c(0, 10,20,30,40),
                     sec.axis = sec_axis(~(.+20), 
                                         name = "River Discharge (1,000 cfs)
                                         ",
                                         breaks = waiver())) +
  
  scale_x_datetime(breaks = seq(as.POSIXct("2021-01-01"),
                 as.POSIXct("2021-03-31"), "2 weeks"), date_labels = "%b %d %Y",expand = c(0, 0)) +

  
  scale_fill_manual(values = c("#56B4E9", "#D55E00")) +
  
  theme(legend.position=("top"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = NA, colour = NA) ,
        panel.border = element_rect(color = "black", size = 1, fill = NA, linetype="solid"),
        axis.text=element_text(size=15),
        axis.title=element_text(size=17,face="bold"),
        plot.title =element_text(size=17, face='bold'),
        axis.text.x = element_text(angle = 65, hjust = 1, vjust = 1, face= "bold"),
        axis.text.y = element_text(face= "bold"),
        legend.title=element_blank(),
        legend.text=element_text(size=12),
        strip.text = element_text(size = 15),
        panel.spacing = unit(1, "lines")) +
  
 guides(fill = guide_legend(override.aes = list(colour = NULL, linetype = 0))) +
  
  facet_wrap(~ Site, ncol=3, labeller = label_both)

```
  
**Figure 2.**  Temperature and discharge data collected from autonomous sensors from the Lone Cabbage Reef restoration site near Suwannee, FL. Each graph represents a sensor location, with top of page as north, and right of page as east. The center column of figures (Sites 1-3) represent the eastern side of the Lone Cabbage Reef restoration site. The left column of figures (Sites 4-6) represent the western side of the Lone Cabbage Reef restoration site. The right column of figures (Sites 7-9) represent sensors close to shore in an area where salinity may be influenced by restoring Lone Cabbage Reef. The first two columns from the west (Sites 1-6) represent the inshore and offshore sides of the restoration reef. The primary y-axis is Temperature (C, Celsius), and the secondary y-axis is Suwannee River discharge (CFS, cubic feet per second) measured at USGS Wilcox station 02323500 on the Suwannee River. River discharge is graphed as a daily mean in the light blue filled shape near the bottom of each graph. Daily mean temperature values (orange line) are depicted with a 95% confidence interval (shaded orange region). Missing river discharge values are due to corrupt readings or missing equipment.


```{r sal_789, warning= FALSE, message= FALSE, include= FALSE}
wq_789 <- wq2 %>% 
  filter( Site == c("7", "8", "9"))

wq_789$Site <- factor(wq_789$Site, levels = c("7", "8","9"))

dis_789 <- dis2 %>%
 expand(Site=unique(wq_789$Site), dates) %>%
  left_join(dis2) %>%
  mutate(Measure = "Discharge", minVal = -20, maxVal = val/1000-20) %>%
  select(Site, d2 = dates, Measure, minVal, maxVal)


```


```{r sal_plot789, echo= FALSE, warning= FALSE, message= FALSE, fig.height= 10, fig.width=10}
ggplot() +

  geom_ribbon(data= wq_789, aes( x= Obs_Date, ymin = sal.mean - 1.96*sal.sd, ymax = sal.mean + 1.96*sal.sd), fill = "grey70") +
  
  geom_line(data= wq_789, aes(x = Obs_Date, y = sal.mean, fill= "Salinity")) +
  
  geom_ribbon(data=dis_789, aes(x= as.POSIXct(d2), ymax=maxVal, ymin=minVal, fill=Measure), 
              alpha=0.4) +
  
  xlab("Date") +
  
  scale_y_continuous(name = "Salinity(ppt)", 
                     limits=c(-20,50), 
                     breaks = c(0, 10,20,30,40),
                     sec.axis = sec_axis(~(.+20), 
                                         name = "River Discharge (1,000 cfs)
                                         ",
                                         breaks = waiver())) +
  
  scale_x_datetime(breaks = seq(as.POSIXct("2021-01-01"),
                 as.POSIXct("2021-03-31"), "2 weeks"), date_labels = "%b %d %Y",expand = c(0, 0)) +

  
  scale_fill_manual(values = c("#56B4E9", "#000000")) +
  
  theme(legend.position=("top"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = NA, colour = NA) ,
        panel.border = element_rect(color = "black", size = 1, fill = NA, linetype="solid"),
        axis.text=element_text(size=15),
        axis.title=element_text(size=17,face="bold"),
        plot.title =element_text(size=17, face='bold'),
        axis.text.x = element_text(angle = 65, hjust = 1, vjust = 1, face= "bold"),
        axis.text.y = element_text(face= "bold"),
        legend.title=element_blank(),
        legend.text=element_text(size=12),
        strip.text = element_text(size = 15),
        panel.spacing = unit(1, "lines")) +
  
 guides(fill = guide_legend(override.aes = list(colour = NULL, linetype = 0))) +
  
  facet_wrap(~ Site, ncol=1, labeller = label_both)

```
  
**Figure 3.** Salinity and discharge data collected from autonomous sensors from the Lone Cabbage Reef restoration site near Suwannee, FL. Each graph represents a sensor location, with top of page as north, and right of page as east. The figures (Sites 7-9) represent sensors close to shore in an area where salinity may be influenced by restoring Lone Cabbage Reef. The primary y-axis is Salinity (ppt, parts per thousand), and the secondary y-axis is Suwannee River discharge (CFS, cubic feet per second) measured at USGS Wilcox station 02323500 on the Suwannee River. River discharge is graphed as a daily mean in the light blue filled shape near the bottom of each graph. Daily mean salinity values (black line) are depicted using a 95% confidence interval (grey shaded region). Missing river discharge values are due to corrupt readings or missing equipment.


```{r temp_789, warning=FALSE, include= FALSE}

wq_temp789 <- wq_temp2 %>% 
  filter( Site == c("7", "8", "9"))

wq_temp789$Site <- factor(wq_temp789$Site, levels = c("7", "8","9"))

dis_temp789 <- dis2 %>%
 expand(Site=unique(wq_temp789$Site), dates) %>%
  left_join(dis2) %>%
  mutate(Measure = "Discharge", minVal = -20, maxVal = val/1000-20) %>%
  select(Site, d2 = dates, Measure, minVal, maxVal)


```

```{r temp_plot789, echo= FALSE, warning= FALSE, message= FALSE, fig.height= 10, fig.width=10}

ggplot() +

  geom_ribbon(data= wq_temp789, aes( x= Obs_Date, ymin = temp.mean - 1.96*temp.sd, ymax = temp.mean + 1.96*temp.sd), fill = "#D55E00", alpha= 0.3) +
  
  geom_line(data= wq_temp789, aes(x = Obs_Date, y = temp.mean, fill= "Temperature"), color= "#D55E00") +
  
  geom_ribbon(data=dis_temp789, aes(x= as.POSIXct(d2), ymax=maxVal, ymin=minVal, fill=Measure), 
              alpha=0.4) +
  
  xlab("Date") +
  
  scale_y_continuous(name = "Temperature(C)", 
                     limits=c(-20,50), 
                     breaks = c(0, 10,20,30,40),
                     sec.axis = sec_axis(~(.+20), 
                                         name = "River Discharge (1,000 cfs)
                                         ",
                                         breaks = waiver())) +
   
  scale_x_datetime(breaks = seq(as.POSIXct("2021-01-01"),
                 as.POSIXct("2021-03-31"), "2 weeks"), date_labels = "%b %d %Y",expand = c(0, 0)) +

  
  scale_fill_manual(values = c("#56B4E9", "#D55E00")) +
  
  theme(legend.position=("top"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = NA, colour = NA) ,
        panel.border = element_rect(color = "black", size = 1, fill = NA, linetype="solid"),
        axis.text=element_text(size=15),
        axis.title=element_text(size=17,face="bold"),
        plot.title =element_text(size=17, face='bold'),
        axis.text.x = element_text(angle = 65, hjust = 1, vjust = 1, face= "bold"),
        axis.text.y = element_text(face= "bold"),
        legend.title=element_blank(),
        legend.text=element_text(size=12),
        strip.text = element_text(size = 15),
        panel.spacing = unit(1, "lines")) +
  
 guides(fill = guide_legend(override.aes = list(colour = NULL, linetype = 0))) +
  
  facet_wrap(~ Site, ncol=1, labeller = label_both)

```
  
**Figure 4.**  Temperature and discharge data collected from autonomous sensors from the Lone Cabbage Reef restoration site near Suwannee, FL. Each graph represents a sensor location, with top of page as north, and right of page as east. The figures (Sites 7-9) represent sensors close to shore in an area where salinity may be influenced by restoring Lone Cabbage Reef. The primary y-axis is Temperature (C, Celsius), and the secondary y-axis is Suwannee River discharge (CFS, cubic feet per second) measured at USGS Wilcox station 02323500 on the Suwannee River. River discharge is graphed as a daily mean in the light blue filled shape near the bottom of each graph. Daily mean temperature values (orange line) are depicted with a 95% confidence interval (shaded orange region). Missing river discharge values are due to corrupt readings or missing equipment.

```{r sal_654, warning= FALSE, message= FALSE, include= FALSE}

# Sensor water quality carpentry
wq_654 <- wq2 %>% 
  filter( Site == c("6", "5", "4"))

wq_654$Site <- factor(wq_654$Site, levels = c("6", "5","4"))

dis_654 <- dis2 %>%
 expand(Site=unique(wq_654$Site), dates) %>%
  left_join(dis2) %>%
  mutate(Measure = "Discharge", minVal = -20, maxVal = val/1000-20) %>%
  select(Site, d2 = dates, Measure, minVal, maxVal)

```

```{r sal_plot654, echo=FALSE, warning= FALSE, message= FALSE, fig.height= 10, fig.width=10}
ggplot() +

  geom_ribbon(data= wq_654, aes( x= Obs_Date, ymin = sal.mean - 1.96*sal.sd, ymax = sal.mean + 1.96*sal.sd), fill = "grey70") +
  
  geom_line(data= wq_654, aes(x = Obs_Date, y = sal.mean, fill= "Salinity")) +
  
  geom_ribbon(data=dis_654, aes(x= as.POSIXct(d2), ymax=maxVal, ymin=minVal, fill=Measure), 
              alpha=0.4) +
  
  xlab("Date") +
  
  scale_y_continuous(name = "Salinity(ppt)", 
                     limits=c(-20,50), 
                     breaks = c(0, 10,20,30,40),
                     sec.axis = sec_axis(~(.+20), 
                                         name = "River Discharge (1,000 cfs)
                                         ",
                                         breaks = waiver())) +
   
  scale_x_datetime(breaks = seq(as.POSIXct("2021-01-01"),
                 as.POSIXct("2021-03-31"), "2 weeks"), date_labels = "%b %d %Y",expand = c(0, 0)) +

  
  scale_fill_manual(values = c("#56B4E9", "#000000")) +
  
  theme(legend.position=("top"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = NA, colour = NA) ,
        panel.border = element_rect(color = "black", size = 1, fill = NA, linetype="solid"),
        axis.text=element_text(size=15),
        axis.title=element_text(size=17,face="bold"),
        plot.title =element_text(size=17, face='bold'),
        axis.text.x = element_text(angle = 65, hjust = 1, vjust = 1, face= "bold"),
        axis.text.y = element_text(face= "bold"),
        legend.title=element_blank(),
        legend.text=element_text(size=12),
        strip.text = element_text(size = 15),
        panel.spacing = unit(1, "lines")) +
  
 guides(fill = guide_legend(override.aes = list(colour = NULL, linetype = 0))) +
  
  facet_wrap(~ Site, ncol=1, labeller = label_both)

```
  
**Figure 5.**  Salinity and discharge data collected from autonomous sensors from the Lone Cabbage Reef restoration site near Suwannee, FL. Each graph represents a sensor location, with top of page as north, and right of page as east. The figures (Sites 4-6) represent the western side of the Lone Cabbage Reef restoration site. The primary y-axis is Salinity (ppt, parts per thousand), and the secondary y-axis is Suwannee River discharge (CFS, cubic feet per second) measured at USGS Wilcox station 02323500 on the Suwannee River. River discharge is graphed as a daily mean in the light blue filled shape near the bottom of each graph. Daily mean salinity values (black line) are depicted using a 95% confidence interval (grey shaded region). Missing river discharge values are due to corrupt readings or missing equipment.

```{r temp_654, warning= FALSE, message= FALSE, include= FALSE}
# Sensor water quality carpentry

wq_temp654 <- wq_temp2 %>% 
  filter( Site == c("6", "5", "4"))

wq_temp654$Site <- factor(wq_temp654$Site, levels = c("6", "5","4"))

dis_temp654 <- dis2 %>%
 expand(Site=unique(wq_temp654$Site), dates) %>%
  left_join(dis2) %>%
  mutate(Measure = "Discharge", minVal = -20, maxVal = val/1000-20) %>%
  select(Site, d2 = dates, Measure, minVal, maxVal)

```



```{r temp_plot654, echo= FALSE, warning= FALSE, message= FALSE, fig.height= 10, fig.width=10}
ggplot() +

  geom_ribbon(data= wq_temp654, aes( x= Obs_Date, ymin = temp.mean - 1.96*temp.sd, ymax = temp.mean + 1.96*temp.sd), fill = "#D55E00", alpha= 0.3) +
  
  geom_line(data= wq_temp654, aes(x = Obs_Date, y = temp.mean, fill= "Temperature"), color= "#D55E00") +
  
  geom_ribbon(data=dis_temp654, aes(x= as.POSIXct(d2), ymax=maxVal, ymin=minVal, fill=Measure), 
              alpha=0.4) +
  
  xlab("Date") +
  
  scale_y_continuous(name = "Temperature(C)", 
                     limits=c(-20,50), 
                     breaks = c(0, 10,20,30,40),
                     sec.axis = sec_axis(~(.+20), 
                                         name = "River Discharge (1,000 cfs)
                                         ",
                                         breaks = waiver())) +
 
  scale_x_datetime(breaks = seq(as.POSIXct("2021-01-01"),
                 as.POSIXct("2021-03-31"), "2 weeks"), date_labels = "%b %d %Y",expand = c(0, 0)) +
  
  scale_fill_manual(values = c("#56B4E9", "#D55E00")) +
  
  theme(legend.position=("top"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = NA, colour = NA) ,
        panel.border = element_rect(color = "black", size = 1, fill = NA, linetype="solid"),
        axis.text=element_text(size=15),
        axis.title=element_text(size=17,face="bold"),
        plot.title =element_text(size=17, face='bold'),
        axis.text.x = element_text(angle = 65, hjust = 1, vjust = 1, face= "bold"),
        axis.text.y = element_text(face= "bold"),
        legend.title=element_blank(),
        legend.text=element_text(size=12),
        strip.text = element_text(size = 15),
        panel.spacing = unit(1, "lines")) +
  
 guides(fill = guide_legend(override.aes = list(colour = NULL, linetype = 0))) +
  
  facet_wrap(~ Site, ncol=1, labeller = label_both)
```
    
**Figure 6.**  Temperature and discharge data collected from autonomous sensors from the Lone Cabbage Reef restoration site near Suwannee, FL. Each graph represents a sensor location, with top of page as north, and right of page as east. The figures (Sites 4-6) represent the western side of the Lone Cabbage Reef restoration site. The primary y-axis is Temperature (C, Celsius), and the secondary y-axis is Suwannee River discharge (CFS, cubic feet per second) measured at USGS Wilcox station 02323500 on the Suwannee River. River discharge is graphed as a daily mean in the light blue filled shape near the bottom of each graph. Daily mean temperature values (orange line) are depicted with a 95% confidence interval (shaded orange region). Missing river discharge values are due to corrupt readings or missing equipment.


```{r sal_123, warning= FALSE, message= FALSE, include= FALSE}

wq_123 <- wq2 %>% 
  filter( Site == c("1", "2", "3"))

wq_123$Site <- factor(wq_123$Site, levels = c("1", "2","3"))

dis_123 <- dis2 %>%
 expand(Site=unique(wq_123$Site), dates) %>%
  left_join(dis2) %>%
  mutate(Measure = "Discharge", minVal = -20, maxVal = val/1000-20) %>%
  select(Site, d2 = dates, Measure, minVal, maxVal)



```

```{r sal_plot123, echo= FALSE, warning= FALSE, message= FALSE, fig.height= 10, fig.width=10}

ggplot() +

  geom_ribbon(data= wq_123, aes( x= Obs_Date, ymin = sal.mean - 1.96*sal.sd, ymax = sal.mean + 1.96*sal.sd), fill = "grey70") +
  
  geom_line(data= wq_123, aes(x = Obs_Date, y = sal.mean, fill= "Salinity")) +
  
  geom_ribbon(data=dis_123, aes(x= as.POSIXct(d2), ymax=maxVal, ymin=minVal, fill=Measure), 
              alpha=0.4) +
  
  xlab("Date") +
  
  scale_y_continuous(name = "Salinity(ppt)", 
                     limits=c(-20,50), 
                     breaks = c(0, 10,20,30,40),
                     sec.axis = sec_axis(~(.+20), 
                                         name = "River Discharge (1,000 cfs)
                                         ",
                                         breaks = waiver())) +
   
  scale_x_datetime(breaks = seq(as.POSIXct("2021-01-01"),
                 as.POSIXct("2021-03-31"), "2 weeks"), date_labels = "%b %d %Y",expand = c(0, 0)) +

  
  scale_fill_manual(values = c("#56B4E9", "#000000")) +
  
  theme(legend.position=("top"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = NA, colour = NA) ,
        panel.border = element_rect(color = "black", size = 1, fill = NA, linetype="solid"),
        axis.text=element_text(size=15),
        axis.title=element_text(size=17,face="bold"),
        plot.title =element_text(size=17, face='bold'),
        axis.text.x = element_text(angle = 65, hjust = 1, vjust = 1, face= "bold"),
        axis.text.y = element_text(face= "bold"),
        legend.title=element_blank(),
        legend.text=element_text(size=12),
        strip.text = element_text(size = 15),
        panel.spacing = unit(1, "lines")) +
  
 guides(fill = guide_legend(override.aes = list(colour = NULL, linetype = 0))) +
  
  facet_wrap(~ Site, ncol=1, labeller = label_both, scales= "free_y")

```
  
**Figure 7.**  Salinity and discharge data collected from autonomous sensors from the Lone Cabbage Reef restoration site near Suwannee, FL. The figures (Sites 1-3) represent the eastern side of the Lone Cabbage Reef restoration site. The primary y-axis is Salinity (ppt, parts per thousand), and the secondary y-axis is Suwannee River discharge (CFS, cubic feet per second) measured at USGS Wilcox station 02323500 on the Suwannee River. River discharge is graphed as a daily mean in the light blue filled shape near the bottom of each graph. Daily mean salinity values (black line) are depicted using a 95% confidence interval (grey shaded region). Missing river discharge values are due to corrupt readings or missing equipment.


```{r temp_123, warning= FALSE, message= FALSE, include = FALSE}

wq_temp123 <- wq_temp2 %>% 
  filter( Site == c("1", "2", "3"))

wq_temp123$Site <- factor(wq_temp123$Site, levels = c("1", "2","3"))

dis_temp123 <- dis2 %>%
 expand(Site=unique(wq_temp123$Site), dates) %>%
  left_join(dis2) %>%
  mutate(Measure = "Discharge", minVal = -20, maxVal = val/1000-20) %>%
  select(Site, d2 = dates, Measure, minVal, maxVal)
```


```{r temp_plot123, echo=FALSE, warning= FALSE, message= FALSE, fig.height= 10, fig.width=10}

ggplot() +

  geom_ribbon(data= wq_temp123, aes( x= Obs_Date, ymin = temp.mean - 1.96*temp.sd, ymax = temp.mean + 1.96*temp.sd), fill = "#D55E00", alpha= 0.3) +
  
  geom_line(data= wq_temp123, aes(x = Obs_Date, y = temp.mean, fill= "Temperature"), color= "#D55E00") +
  
  geom_ribbon(data=dis_temp123, aes(x= as.POSIXct(d2), ymax=maxVal, ymin=minVal, fill=Measure), 
              alpha=0.4) +
  
  xlab("Date") +
  
  scale_y_continuous(name = "Temperature(C)", 
                     limits=c(-20,50), 
                     breaks = c(0, 10,20,30,40),
                     sec.axis = sec_axis(~(.+20), 
                                         name = "River Discharge (1,000 cfs)
                                         ",
                                         breaks = waiver())) +
   
  scale_x_datetime(breaks = seq(as.POSIXct("2021-01-01"),
                 as.POSIXct("2021-03-31"), "2 weeks"), date_labels = "%b %d %Y",expand = c(0, 0)) +

  
  scale_fill_manual(values = c("#56B4E9", "#D55E00")) +
  
  theme(legend.position=("top"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = NA, colour = NA) ,
        panel.border = element_rect(color = "black", size = 1, fill = NA, linetype="solid"),
        axis.text=element_text(size=15),
        axis.title=element_text(size=17,face="bold"),
        plot.title =element_text(size=17, face='bold'),
        axis.text.x = element_text(angle = 65, hjust = 1, vjust = 1, face= "bold"),
        axis.text.y = element_text(face= "bold"),
        legend.title=element_blank(),
        legend.text=element_text(size=12),
        strip.text = element_text(size = 15),
        panel.spacing = unit(1, "lines")) +
  
 guides(fill = guide_legend(override.aes = list(colour = NULL, linetype = 0))) +
  
  facet_wrap(~ Site, ncol=1, labeller = label_both)


```
  
  
**Figure 8.**  Temperature and discharge data collected from autonomous sensors from the Lone Cabbage Reef restoration site near Suwannee, FL. The figures (Sites 1-3) represent the eastern side of the Lone Cabbage Reef restoration site. The primary y-axis is Temperature (C, Celsius), and the secondary y-axis is Suwannee River discharge (CFS, cubic feet per second) measured at USGS Wilcox station 02323500 on the Suwannee River. River discharge is graphed as a daily mean in the light blue filled shape near the bottom of each graph. Daily mean temperature values (orange line) are depicted with a 95% confidence interval (shaded orange region).