read-in.Rmd

---
title: "Data base"
author: "Jens Daniel Müller"
date:  "`r format(Sys.time(), '%d %B, %Y')`"
output: workflowr::wflow_html
editor_options:
  chunk_output_type: console
---
```{r global_options, include = FALSE}
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
```


```{r packages}
library(tidyverse)
library(data.table)
library(lubridate)
library(DataExplorer)
library(leaflet)
library(readxl)
library(gsubfn)

```


# CTD Sensor data

## Regular profiles and transects

CTD sensor data including recordings from auxiliary pH, O~2~, Chla and pCO~2~ sensors were recorded with a measurement frequency of 15 sec. (In addition, pCO~2~ data were also internally recorded on the Contros HydroC instrument with higher temporal resolution and will later be used for further analysis after merging with CTD data.)

```{r read_profile_transect_CTD_data, eval=FALSE}

setwd("C:/Mueller_Jens_Data/Research/Projects/BloomSail/data/TinaV/Sensor/Profiles_Transects")
files <- list.files(pattern = "[.]cnv$")
#file <- files[1]

for (file in files){
  
  start.date <- data.table(read.delim(file, sep="#", nrows = 160))[[78,1]]
  start.date <- substr(start.date, 15, 34)
  start.date <- mdy_hms(start.date, tz="UTC")
  
  tempo <- read.delim(file, sep="", skip = 160, header = FALSE)
  tempo <- data.table(tempo[,c(2,3,4,5,6,7,9,11,13)])
  names(tempo) <- c("date", "Dep.S", "Tem.S", "Sal.S", "V_pH", "pH", "Chl", "O2", "pCO2")
  tempo$start.date <- start.date
  tempo$date <- tempo$date + tempo$start.date
  
  tempo$transect.ID <- substr(file, 1, 6)
  tempo$type <- substr(file, 8,8)
  tempo$label <- substr(file, 8,10)
  
  tempo$cast <- "up"
  tempo[date < mean(tempo[Dep.S == max(tempo$Dep.S)]$date)]$cast <- "down"
  
  if (exists("dataset")){
    dataset <- rbind(dataset, tempo)
  }
  
  if (!exists("dataset")){
    dataset <- tempo
  }
  
  rm(start.date)
  rm(tempo)
}


CTD <- dataset
rm(dataset, file, files)


setwd("C:/Mueller_Jens_Data/Research/Projects/BloomSail/")

```

```{r read_profile_transect_CTD_data_Ostergarnsholm, eval=FALSE}

setwd("C:/Mueller_Jens_Data/Research/Projects/BloomSail/data/TinaV/Sensor/Ostergarnsholm")
files <- list.files(pattern = "[.]cnv$")
#file <- files[1]

for (file in files){
  
  start.date <- data.table(read.delim(file, sep="#", nrows = 160))[[78,1]]
  start.date <- substr(start.date, 15, 34)
  start.date <- mdy_hms(start.date, tz="UTC")
  
  tempo <- read.delim(file, sep="", skip = 160, header = FALSE)
  tempo <- data.table(tempo[,c(2,3,4,5,6,7,9,11,13)])
  names(tempo) <- c("date", "Dep.S", "Tem.S", "Sal.S", "V_pH", "pH", "Chl", "O2", "pCO2")
  tempo$start.date <- start.date
  tempo$date <- tempo$date + tempo$start.date
  
  tempo$transect.ID <- substr(file, 1, 6)
  tempo$type <- substr(file, 8,8)
  tempo$label <- substr(file, 11,12)
    
  tempo$cast <- "up"
  tempo[date < mean(tempo[Dep.S == max(tempo$Dep.S)]$date)]$cast <- "down"
  
  if (exists("dataset")){
    dataset <- rbind(dataset, tempo)
  }
  
  if (!exists("dataset")){
    dataset <- tempo
  }
  
  rm(start.date)
  rm(tempo)
}


OGB <- dataset
rm(dataset, file, files)

OGB <- OGB %>% 
  mutate(type = if_else(label=="bo", "P", "T"),
         label = if_else(label == "bo", "P14", label),
         label = if_else(label == "in", "T14", label),
         label = if_else(label == "ou", "T15", label))

setwd("C:/Mueller_Jens_Data/Research/Projects/BloomSail/")

```

```{r merge_regular_CTD_and_Ostergarnsholm, eval=FALSE}
CTD <- bind_rows(CTD, OGB) %>% 
  arrange(date)

rm(OGB)
```


```{r CTD_EDA_raw, eval=FALSE}

source("code/eda.R")
eda(CTD, "CTD-raw")
rm(eda)

```

The output of an automated Exploratory Data Analysis (EDA) performed with the package `DataExplorer` can be accessed here:

[Link to EDA report of CTD raw data](https://jens-daniel-mueller.github.io/BloomSail/EDA_report_CTD-raw.html){target="_blank}

CTD recordings were cleaned from obviously erroneous readings, by setting values to NA. 

```{r clean_CTD_data, eval=FALSE}

class(CTD)
CTD <- data.table(CTD)

# Profiling data

# Temperature

# CTD %>%
#   filter(type == "P") %>%
#   ggplot(aes(Tem.S, Dep.S, col=label, linetype = cast))+
#   geom_line()+
#   scale_y_reverse()+
#   geom_vline(xintercept = c(10, 20))+
#   facet_wrap(~transect.ID)

CTD[transect.ID == "180723" & label == "P07" & Dep.S < 2 & cast == "up"]$Tem.S <- NA

# Salinity

# CTD %>%
#   filter(type == "P") %>%
#   ggplot(aes(Sal.S, Dep.S, col=label, linetype = cast))+
#   geom_path()+
#   scale_y_reverse()+
#   facet_wrap(~transect.ID)

CTD[Sal.S < 6]$Sal.S <- NA

# pH

# CTD %>%
#   filter(type == "P") %>%
#   ggplot(aes(pH, Dep.S, col=label, linetype=cast))+
#   geom_path()+
#   scale_y_reverse()+
#   facet_wrap(~transect.ID)
# 
# CTD %>%
#   filter(type == "P") %>%
#   ggplot(aes(V_pH, Dep.S, col=label, linetype=cast))+
#   geom_path()+
#   scale_y_reverse()+
#   facet_wrap(~transect.ID)

CTD[pH < 7.5]$V_pH <- NA
CTD[pH < 7.5]$pH <- NA

CTD[transect.ID == "180709" & label == "P03" & Dep.S < 5 & cast == "down"]$pH <- NA
CTD[transect.ID == "180709" & label == "P05" & Dep.S < 10 & cast == "down"]$pH <- NA
CTD[transect.ID == "180718" & label == "P10" & Dep.S < 3 & cast == "down"]$pH <- NA
CTD[transect.ID == "180815" & label == "P03" & Dep.S < 2 & cast == "down"]$pH <- NA
CTD[transect.ID == "180820" & label == "P11" & Dep.S < 15 & cast == "down"]$pH <- NA

CTD[transect.ID == "180709" & label == "P03" & Dep.S < 5 & cast == "down"]$V_pH <- NA
CTD[transect.ID == "180709" & label == "P05" & Dep.S < 10 & cast == "down"]$V_pH <- NA
CTD[transect.ID == "180718" & label == "P10" & Dep.S < 3 & cast == "down"]$V_pH <- NA
CTD[transect.ID == "180815" & label == "P03" & Dep.S < 2 & cast == "down"]$V_pH <- NA
CTD[transect.ID == "180820" & label == "P11" & Dep.S < 15 & cast == "down"]$V_pH <- NA


# pCO2

# CTD %>%
#   filter(type == "P") %>%
#   ggplot(aes(pCO2, Dep.S, col=label, linetype = cast))+
#   geom_path()+
#   scale_y_reverse()+
#   facet_wrap(~transect.ID)

CTD[transect.ID == "180616"]$pCO2 <- NA

# O2

# CTD %>% 
#   filter(type == "P") %>% 
#   ggplot(aes(O2, Dep.S, col=label, linetype = cast))+
#   geom_path()+
#   scale_y_reverse()+
#   facet_wrap(~transect.ID)

# Chlorophyll

# CTD %>%
#   filter(type == "P") %>%
#   ggplot(aes(Chl, Dep.S, col=label, linetype = cast))+
#   geom_path()+
#   scale_y_reverse()+
#   facet_wrap(~transect.ID)

CTD[Chl > 100]$Chl <- NA


#### Surface transect data

# CTD %>%
#   filter(type == "T") %>%
#   ggplot(aes(date, Dep.S, col=label))+
#   geom_point()+
#   scale_y_reverse()+
#   facet_wrap(~transect.ID, scales = "free_x")
# 
# CTD %>%
#   filter(type == "T") %>%
#   ggplot(aes(date, Tem.S, col=label))+
#   geom_point()+
#   facet_wrap(~transect.ID, scales = "free_x")
# 
# CTD %>%
#   filter(type == "T") %>%
#   ggplot(aes(date, Sal.S, col=label))+
#   geom_point()+
#   facet_wrap(~transect.ID, scales = "free_x")
# 
# CTD %>%
#   filter(type == "T") %>%
#   ggplot(aes(date, pCO2, col=label))+
#   geom_point()+
#   facet_wrap(~transect.ID, scales = "free_x")
# 
# CTD %>%
#   filter(type == "T") %>%
#   ggplot(aes(date, pH, col=label))+
#   geom_point()+
#   facet_wrap(~transect.ID, scales = "free_x")
# 
# CTD %>%
#   filter(type == "T") %>%
#   ggplot(aes(date, Chl, col=label))+
#   geom_point()+
#   facet_wrap(~transect.ID, scales = "free_x")

CTD[type == "T" & Chl > 10]$Chl <- NA


# CTD %>% 
#   filter(type == "T") %>% 
#   ggplot(aes(date, O2, col=label))+
#   geom_point()+
#   facet_wrap(~transect.ID, scales = "free_x")

```

Relevant columns were selected and renamed, only observations from regular stations (P01-P13) and transects (T01-T13) were selected and summarized data were written to file.

```{r subsetting_writing_summary_file, eval=FALSE}

CTD <-
  CTD %>% 
  select(date_time=date,
         ID=transect.ID,
         type,
         station=label,
         dep=Dep.S,
         sal=Sal.S,
         tem=Tem.S,
         pCO2,pH,V_pH,O2,Chl)

# CTD <- CTD %>% 
#   filter( !(station %in% c("PX1", "PX2", "TX1", "TX2") ))

CTD %>% 
  write_csv(here::here("data/_summarized_data_files", "Tina_V_Sensor_Profiles_Transects.csv"))

rm(CTD)
```

```{r reopen_CTD_data}
CTD <- 
  read_csv(here::here("data/_summarized_data_files", "Tina_V_Sensor_Profiles_Transects.csv"),
           col_types = cols(pCO2 = col_double()))
```


```{r CTD_EDA, eval=FALSE}

source("code/eda.R")
eda(CTD, "CTD")

rm(eda)
```


The output of an automated Exploratory Data Analysis (EDA) performed with the package `DataExplorer` can be accessed here:

[Link to EDA report of CTD clean data](https://jens-daniel-mueller.github.io/BloomSail/EDA_report_CTD.html){target="_blank}


```{r Overview_temperature_profiles, fig.cap="Temperature profiles recorded on regular stations P01-P13. ID refers to the starting date of each cruise.", fig.asp = 1.3}

CTD %>%
  arrange(date_time) %>% 
  filter(type == "P", !(station %in% c("PX1", "PX2"))) %>%
  ggplot(aes(tem, dep, col=station))+
  geom_path()+
  scale_y_reverse()+
  labs(x="Temperature (°C)", y="Depth (m)")+
  facet_wrap(~ID, labeller = label_both)

```

```{r Overview_pCO2_analog_profiles, fig.cap="pCO~2~ profiles (analog output from HydroC) recorded on regular stations P01-P13. ID refers to the starting date of each cruise. Please note that pCO~2~ measurement range is restricted to 100-500  µatm here due to the settings of the analog voltage output of the sensor. Zeroing periods are included.", fig.asp = 1.3}

CTD %>%
  arrange(date_time) %>% 
  filter(type == "P", !(station %in% c("PX1", "PX2"))) %>%
  ggplot(aes(pCO2, dep, col=station))+
  geom_path()+
  scale_y_reverse()+
  labs(x=expression(pCO[2]~(µatm)), y="Depth (m)")+
  facet_wrap(~ID, labeller = label_both)

```



# pCO~2~ data

HydroC pCO~2~ data  were provided by KM Contros after applying a drift correction to the raw data, which was based on pre- and post-deployment calibration results. 

```{r read_HydroC_raw_data, eval=FALSE}

# Read Contros corrected data file

HC <-
  read_csv2(here::here("Data/TinaV/Sensor/HydroC-pCO2/corrected_Contros",
                       "parameter&pCO2s(method 43).txt"),
            col_names = c("date_time", "Zero", "Flush", "p_NDIR",
                          "p_in", "T_control", "T_gas", "%rH_gas",
                          "Signal_raw", "Signal_ref", "T_sensor",
                          "pCO2", "Runtime", "nr.ave")) %>% 
  mutate(date_time = dmy_hms(date_time),
         Flush = as.factor(as.character(Flush)),
         Zero = as.factor(as.character(Zero)))

```

Individual deployments (periods of observations with less than 30 sec between recordings) were identified and relevant deployment periods were subsetted.

```{r HydroC_deployment_identification_selection, eval=FALSE}

HC <- HC %>% 
  arrange(date_time) %>% 
  mutate(deployment = cumsum(c(TRUE,diff(date_time)>=30)))

HC <- HC %>% 
  filter(deployment %in% c(2,6,9,14,17,21,23,27,31,33,34,35,37))

```

```{r duplicated_timestamp_correction, eval=FALSE}


# add counter for date_time observations

HC <- HC %>% 
  add_count(date_time)

# find triplicated time stamp and select only first observation, and merge

HC_no_triple <- HC %>% 
  filter(n <= 2)

HC_triple_clean <- HC %>% 
  filter(n > 2) %>% 
  slice(1)

HC <- full_join(HC_no_triple, HC_triple_clean)

rm(list=setdiff(ls(), "HC"))


# find duplicated time stamps and shift first by one second backward, and merge

HC %>% 
  distinct(date_time)

HC <- HC %>% 
  select(-n) %>% 
  add_count(date_time)

unique(HC$n)

HC_no_duplicated <- HC %>%
  filter(n == 1)

HC_duplicated <- HC %>% 
  filter(n == 2)

HC_duplicated_first <- HC_duplicated %>% 
  group_by(date_time) %>% 
  slice(1) %>% 
  ungroup() %>% 
  mutate(date_time = date_time - 1)

HC_duplicated_second <- HC_duplicated %>% 
  group_by(date_time) %>% 
  slice(2) %>% 
  ungroup()

HC_duplicated_clean <- full_join(HC_duplicated_first, HC_duplicated_second) %>% 
  arrange(date_time)

HC <- full_join(HC_no_duplicated, HC_duplicated_clean)

HC %>% 
  distinct(date_time)

rm(list=setdiff(ls(), "HC"))



# find duplicated time stamps and shift first by two seconds forward, and merge

HC %>% 
  distinct(date_time)

HC <- HC %>% 
  select(-n) %>% 
  add_count(date_time)

unique(HC$n)

HC_no_duplicated <- HC %>%
  filter(n == 1)

HC_duplicated <- HC %>% 
  filter(n == 2)

HC_duplicated_first <- HC_duplicated %>% 
  group_by(date_time) %>% 
  slice(1) %>% 
  ungroup() %>% 
  mutate(date_time = date_time + 2)

HC_duplicated_second <- HC_duplicated %>% 
  group_by(date_time) %>% 
  slice(2) %>% 
  ungroup()

HC_duplicated_clean <- full_join(HC_duplicated_first, HC_duplicated_second) %>% 
  arrange(date_time)

HC <- full_join(HC_no_duplicated, HC_duplicated_clean)

HC %>% 
  distinct(date_time)

rm(list=setdiff(ls(), "HC"))

# remaining duplicates are observations where other observations with a +/- 1 sec timestamp exist
# for those cases, only the first duplicated observation is selected (similar to triplicate treatment)


HC %>% 
  distinct(date_time)

HC <- HC %>% 
  select(-n) %>% 
  add_count(date_time)

unique(HC$n)


HC_still_no_duplicated <- HC %>% 
  filter(n == 1)

HC_still_duplicated_first <- HC %>% 
  filter(n == 2) %>% 
  group_by(date_time) %>% 
  slice(1)

HC <- full_join(HC_still_no_duplicated, HC_still_duplicated_first)

HC %>% 
  distinct(date_time)

rm(list=setdiff(ls(), "HC"))

HC <- HC %>% 
  select(-n)

```


```{r HC_Zeroing_Flush_Identification, eval=FALSE}

# Zeroing ID labelling

HC <- HC %>% 
  arrange(date_time) %>% 
  group_by(Zero) %>% 
  mutate(Zero_ID = as.factor(cumsum(c(TRUE,diff(date_time)>=30)))) %>% 
  ungroup()

unique(HC$Zero_ID)

# Flush: Identification

HC <- HC %>% 
  mutate(Flush = 0) %>% 
  group_by(Zero, Zero_ID) %>% 
  mutate(start = min(date_time),
         duration = date_time - start,
         Flush = if_else(Zero == 0 & duration < 600, "1", "0")) %>% 
  ungroup()


#  Flush: Identify equilibration and internal gas mixing periods

HC <- HC %>% 
  mutate(mixing = if_else(duration < 20, "mixing", "equilibration"))



```

A pdf with plots of all Flush periods (mixing and equilibration identified) can be found here:

[Link to Flush period plots](https://github.com/jens-daniel-mueller/BloomSail/tree/master/output/Plots/data_base/Flush_periods_all.pdf){target="_blank"}

```{r HydroC_deployment_plots, eval=FALSE, eval=FALSE}

pdf(file=here::here("output/Plots/data_base",
    "HydroC_pCO2_deployments.pdf"), onefile = TRUE, width = 7, height = 4)

for (i in unique(HC$deployment)) {
  
  #i <- unique(HC$deployment)[3]
  
  sub <-  HC %>%
      filter(deployment == i)
  start_date <- min(sub$date_time)
  
  print(
    sub %>% 
      ggplot(aes(date_time, pCO2, col=Zero_ID))+
      geom_line()+
      labs(title = paste("Deployment: ",i, "| Start time: ", start_date))
)
  
}

dev.off()

rm(sub, start_date, i)

```

A pdf with pCO2 timeseries plots of all deployments can be found here:

[Link to pCO~2~ timeseries plots](https://github.com/jens-daniel-mueller/BloomSail/tree/master/output/Plots/data_base/HydroC_pCO2_deployments.pdf){target="_blank"}


```{r HydroC_EDA, eval=FALSE}

source("code/eda.R")
eda(HC, "HydroC-pCO2")
rm(eda)

```

The output of an automated Exploratory Data Analysis (EDA) performed with the package `DataExplorer` can be accessed here:

[Link to EDA report of HydroC pCO~2~ data data](https://jens-daniel-mueller.github.io/BloomSail/EDA_report_HydroC-pCO2.html){target="_blank}

Individual zeroings were labeled with a counter and a period of 10 min after the zeroing was labelled as Flush period (overriding the internal Flush flag). A mixing flag was introduced, flagging the initial 20 sec of each Flush period as "mixing" and the rest as "equilibration".

```{r Plot_all_Flush_periods, eval=FALSE}


Flush <- HC %>% 
 filter(Flush == 1)

# Flush: Plot individual periods

pdf(file=here::here("output/Plots/data_base",
    "Flush_periods_all.pdf"), onefile = TRUE, width = 7, height = 4)

for (i in unique(Flush$Zero_ID)) {
  
  #i <- unique(Flush$Zero_ID)[5]
print(
  Flush %>%
    filter(Zero_ID == i) %>%
    ggplot(aes(duration, pCO2, col=mixing))+
    geom_point() +
    scale_color_brewer(palette = "Set1")+
    labs(y=expression(pCO[2]~(µatm)), x="Duration of Flush period (s)",
             title = paste("Zero_ID: ", i))
)

}

dev.off()

rm(Flush,i)

```





Summarized pCO~2~ date were written to file.

```{r writing_summary_file, eval=FALSE}

HC %>% 
  write_csv(here::here("Data/_summarized_data_files",
                       "Tina_V_HydroC_full.csv"))

HC %>% 
  select(date_time, Zero, Flush, pCO2, deployment, Zero_ID, duration, mixing) %>% 
  write_csv(here::here("Data/_summarized_data_files",
                       "Tina_V_HydroC.csv"))

```


# Bottle data

## CO~2~

```{r read_bottle_CO2, eval=FALSE}


Bottle <- read_csv(here::here("Data/TinaV/Bottle/Tracegases", "BloomSail_bottle_CO2_all.csv"),
                   col_types = list("c","c","n","n","n","n","n"))

Bottle <- Bottle %>% 
  select(ID=transect.ID,
         station=label,
         dep=Dep,
         sal=Sal,
         CT, AT,
         pH_Mosley = pH.Mosley)

Bottle %>% 
  write_csv(here::here("Data/_summarized_data_files", "Tina_V_Bottle_CO2_lab.csv"))

```



# GPS Track

GPS track data were recorded.

```{r read_GPS, eval=FALSE}

setwd("C:/Mueller_Jens_Data/Research/Projects/BloomSail/data/TinaV/Track/GPS_Logger_Track")
files <- list.files(pattern = "[.]txt$")


for (file in files){
  
  # if the merged dataset does exist, append to it
  if (exists("dataset")){
    
    tempo<-data.table ( read.delim(file, sep=",")[,c(2,3,4)])
    names(tempo) <- c("date_time", "lat", "lon")
    tempo$date_time<- ymd_hms(tempo$date, tz="UTC")
    
    dataset<-rbind(dataset, tempo)
    rm(tempo)
  }
  
  # if the merged dataset doesn't exist, create it
  if (!exists("dataset")){
    dataset<-data.table ( read.delim(file, sep=",")[,c(2,3,4)])
    names(dataset) <- c("date_time", "lat", "lon")
    dataset$date_time<- ymd_hms(dataset$date_time, tz="UTC")
    
  }
}

track <- dataset
rm(dataset, file, files)

setwd("C:/Mueller_Jens_Data/Research/Projects/BloomSail")

track %>% 
  write_csv(here::here("Data/_summarized_data_files",
                       "TinaV_Track.csv"))

# track_sub <- track %>%
#  slice(which(row_number() %% 20 == 1))
# 
# bathy <- read_csv(here::here("data/Maps","Bathymetry_Gotland_east.csv"))
# 
# track_sub %>% 
#   ggplot()+
#   geom_raster(data=bathy, aes(lon, lat, fill=elev))+
#   scale_fill_continuous(na.value = "black", name="Tiefe [m]")+
#   geom_path(aes(lon, lat), col="grey80")+
#   labs(x="Längengrad (°E)", y="Breitengrad (°N)")+
#   coord_quickmap(expand = 0, ylim = c(57.25,57.6), xlim = c(18.6, 19.8))+
#   theme_bw()+
#   guides(col = guide_legend(nrow = 5))

```



# Finnmaid

pCO~2~ data were recorded on VOS Finnmaid in summer 2018.

```{r read_Finnmaid_data, eval=FALSE}


### June - August 2018

setwd("C:/Mueller_Jens_Data/Research/Projects/BloomSail/data/Finnmaid_2018")
files <- list.files(pattern = "[.]xls$")
#file <-files[1]

for (file in files){
  
  
  df <- read_excel(file)
  df <- df[c(1,2,3,12,7,4,15,8,5,17)]
  names(df) <- c("date","Lon","Lat","pCO2","Sal","Tem","cO2","patm", "Teq","xCO2")
  df <- df[-c(1),]
  df$date <- as.POSIXct(as.numeric(df$date)*60*60*24, origin="1899-12-30", tz="GMT")
  df$Lon <- as.numeric(as.character(df$Lon))
  df$Lat <- as.numeric(as.character(df$Lat))
  df$pCO2 <- as.numeric(as.character(df$pCO2))
  df$Sal <- as.numeric(as.character(df$Sal))
  df$Tem <- as.numeric(as.character(df$Tem))
  df$cO2 <- as.numeric(as.character(df$cO2))
  df$patm <- as.numeric(as.character(df$patm))
  df$Teq <- as.numeric(as.character(df$Teq))
  df$xCO2 <- as.numeric(as.character(df$xCO2))
  df <- data.table(df)
  
  df$route <- strapplyc(as.character(file), ".*(.).xls*", simplify = TRUE)
  df$ID <- substr(as.character(file), 3, 10)
  
  if (exists("temp")){
    temp <- rbind (temp, df)
  } else{temp <- df}
  
}


rm(df, files, file)
temp <- temp[pCO2 != 0]


#### Los Gatos data

setwd("C:/Mueller_Jens_Data/Research/Projects/BloomSail/data/Finnmaid_2018/LGR")
files <- list.files(pattern = "[.]xls$")
#file <-files[1]

for (file in files){
  
  
  df <- read_excel(file)
  df <- df[c(2,3,4,8,6,5,14,7,15,9)]
  names(df) <- c("date","Lon","Lat","pCO2","Sal","Tem","cO2","patm", "Teq","xCO2")
  df <- df[-c(1),]
  df$date <- dmy_hms(df$date)
  df <- data.table(df)
  
  df$route <- substr(as.character(file), 12, 12)
  df$ID <- substr(as.character(file), 3, 10)
  
  if (exists("temp.LGR")){
    temp.LGR <- rbind (temp.LGR, df)
  } else{temp.LGR <- df}
  
}


#Convert O2 sat to O2 concentration #

source(here::here("code", "O2stoO2c.R"))

temp.LGR <- temp.LGR %>%
  filter() %>% 
  mutate(cO2 = O2stoO2c(O2sat = cO2, T=Tem, S=Sal, P=3/10, p_atm = 1013.5))


#### Merge Los Gator and LICOR data files ####

temp$sensor <- "LICOR"
temp.LGR$sensor <- "LosGatos"

temp <- rbind(temp, temp.LGR)

rm(temp.LGR, df, file, files)


#### Assign subareas according to Schneider and Mueller (2018) ####

temp$Area <- with(temp,
                  ifelse(Lon>12 & Lon<12.6, "1.MEB",
                  ifelse(Lon>13.1 & Lon<14.3, "2.ARK",
                  ifelse(Lat>57.5 & Lat<58.5 & route %in% c("E", "G"), "4.EGS",
                  ifelse(Lat>57.3 & Lat<57.5 & route %in% c("E"), "BS",
                  ifelse(Lat>56.8 & Lat<57.5 & route=="W", "3.WGS",
                  ifelse(Lat>58.5 & Lat<59 & Lon>20, "5.NGS",
                  ifelse(Lon>22 & Lon<24, "6.WGF",
                  ifelse(Lon>24 & Lon<24.5, "7.HGF", "NaN")))))))))


temp <-temp[complete.cases(temp[,pCO2]),]

temp %>% 
  write_csv(here::here("Data/_summarized_data_files",
                       "Finnmaid.csv"))

fm_bs <- temp %>% 
  filter(Area == "BS")

```

# Interactive map.

```{r map}

fm_bs <-
  read_csv(here::here("Data/_summarized_data_files",
                       "Finnmaid.csv")) %>% 
  filter(Area == "BS")


track <- 
  read_csv(here::here("Data/_summarized_data_files",
                       "TinaV_Track.csv"))

track_sub <- track %>%
  slice(which(row_number() %% 20 == 1))

rm(track)

leaflet() %>% 
  setView(lng = 20, lat = 57.3, zoom = 8) %>%
  addLayersControl(baseGroups = c("Ocean Basemap",
                                  "Satellite"),
                   overlayGroups = c("Track", "Finnmaid"),
                   options = layersControlOptions(collapsed = FALSE),
                   position = 'topright') %>% 
  addProviderTiles("Esri.WorldImagery", group = "Satellite") %>%
  addProviderTiles(providers$Esri.OceanBasemap, group = "Ocean Basemap") %>%
  addScaleBar(position = 'topright') %>%
  addMeasure(
    primaryLengthUnit = "kilometers",
    secondaryLengthUnit = 'miles', 
    primaryAreaUnit = "sqmeters",
    secondaryAreaUnit="acres", 
    position = 'topleft') %>% 
  addCircles(data = fm_bs, ~Lon, ~Lat,
               color = "white",
               group = "Finnmaid") %>% 
  addPolylines(data = track_sub, ~lon, ~lat,
               color = "red",
               group = "Track")


```


# Tasks / open questions

- Include data from crossing large vessels 

- Include information about HydroC calibration results and raw data correction by Contros

- Check results from field response time experiment (high zeroing frequency)