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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, O₂, Chla and pCO₂ sensors were recorded with a measurement frequency of 15 sec. (In addition, pCO₂ 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.)

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/")

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/")

CTD <- bind_rows(CTD, OGB) %>% 
  arrange(date)

rm(OGB)

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

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

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.

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)

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

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

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)

Temperature profiles recorded on regular stations P01-P13. ID refers to the starting date of each cruise.

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~ 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.

pCO₂ profiles (analog output from HydroC) recorded on regular stations P01-P13. ID refers to the starting date of each cruise. Please note that pCO₂ 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.

pCO₂ data

HydroC pCO₂ 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.

# 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.

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))

# 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)

# 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

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 pCO₂ timeseries plots

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 pCO₂ data data

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”.

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 pCO₂ date were written to file.

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

CO₂

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.

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

pCO₂ data were recorded on VOS Finnmaid in summer 2018.

### 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

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)
check interpolation method na.approx

sessionInfo()

R version 3.6.3 (2020-02-29)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_Germany.1252  LC_CTYPE=English_Germany.1252   
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C                    
[5] LC_TIME=English_Germany.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gsubfn_0.7         proto_1.0.0        readxl_1.3.1       leaflet_2.0.3     
 [5] DataExplorer_0.8.1 lubridate_1.7.4    data.table_1.12.8  forcats_0.5.0     
 [9] stringr_1.4.0      dplyr_0.8.5        purrr_0.3.3        readr_1.3.1       
[13] tidyr_1.0.2        tibble_3.0.0       ggplot2_3.3.0      tidyverse_1.3.0   
[17] workflowr_1.6.1   

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4              whisker_0.4             knitr_1.28             
 [4] xml2_1.3.0              magrittr_1.5            hms_0.5.3              
 [7] rvest_0.3.5             tidyselect_1.0.0        here_0.1               
[10] colorspace_1.4-1        lattice_0.20-41         R6_2.4.1               
[13] rlang_0.4.5             fansi_0.4.1             tcltk_3.6.3            
[16] parallel_3.6.3          broom_0.5.5             xfun_0.12              
[19] dbplyr_1.4.2            modelr_0.1.6            withr_2.1.2            
[22] git2r_0.26.1            ellipsis_0.3.0          htmltools_0.4.0        
[25] assertthat_0.2.1        rprojroot_1.3-2         digest_0.6.25          
[28] lifecycle_0.2.0         haven_2.2.0             rmarkdown_2.1          
[31] compiler_3.6.3          cellranger_1.1.0        pillar_1.4.3           
[34] leaflet.providers_1.9.0 scales_1.1.0            backports_1.1.5        
[37] generics_0.0.2          jsonlite_1.6.1          httpuv_1.5.2           
[40] pkgconfig_2.0.3         igraph_1.2.5            rstudioapi_0.11        
[43] munsell_0.5.0           highr_0.8               httr_1.4.1             
[46] tools_3.6.3             networkD3_0.4           grid_3.6.3             
[49] nlme_3.1-145            gtable_0.3.0            DBI_1.1.0              
[52] cli_2.0.2               crosstalk_1.1.0.1       yaml_2.2.1             
[55] crayon_1.3.4            gridExtra_2.3           farver_2.0.3           
[58] later_1.0.0             promises_1.1.0          htmlwidgets_1.5.1      
[61] fs_1.4.0                vctrs_0.2.4             glue_1.3.2             
[64] evaluate_0.14           labeling_0.3            reprex_0.3.0           
[67] stringi_1.4.6

Data base

Jens Daniel Müller

28 April, 2020

CTD Sensor data

Regular profiles and transects

pCO₂ data

Bottle data

CO₂

GPS Track

Finnmaid

Interactive map

Tasks / open questions

Data base

Jens Daniel Müller

28 April, 2020

CTD Sensor data

Regular profiles and transects

pCO2 data

Bottle data

CO2

GPS Track

Finnmaid

Interactive map

Tasks / open questions

pCO₂ data

CO₂