Last updated: 2019-10-21

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Knit directory: BloomSail/

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File	Version	Author	Date	Message
Rmd	eff54ce	jens-daniel-mueller	2019-10-21	Added CTD read-in
html	32ec4f7	jens-daniel-mueller	2019-10-21	Build site.
Rmd	b2d2bbb	jens-daniel-mueller	2019-10-21	Structured data base and response time Rmd
html	bafa88f	jens-daniel-mueller	2019-10-21	Build site.
html	076a36b	jens-daniel-mueller	2019-10-21	Build site.
Rmd	3e8a32e	jens-daniel-mueller	2019-10-21	Structured data base and response time Rmd
html	b2d0164	jens-daniel-mueller	2019-10-21	Build site.
Rmd	53ae361	jens-daniel-mueller	2019-10-21	Added data base and response time Rmd

library(tidyverse)

Warning: package 'tidyverse' was built under R version 3.5.3

-- Attaching packages ------------------------------------------------------------------------------------------------------------------------ tidyverse 1.2.1 --

v ggplot2 3.2.1     v purrr   0.3.2
v tibble  2.1.3     v dplyr   0.8.3
v tidyr   1.0.0     v stringr 1.4.0
v readr   1.3.1     v forcats 0.4.0

Warning: package 'ggplot2' was built under R version 3.5.3

Warning: package 'tibble' was built under R version 3.5.3

Warning: package 'tidyr' was built under R version 3.5.3

Warning: package 'readr' was built under R version 3.5.3

Warning: package 'purrr' was built under R version 3.5.3

Warning: package 'dplyr' was built under R version 3.5.3

Warning: package 'stringr' was built under R version 3.5.3

Warning: package 'forcats' was built under R version 3.5.3

-- Conflicts --------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(data.table)

Warning: package 'data.table' was built under R version 3.5.3


Attaching package: 'data.table'

The following objects are masked from 'package:dplyr':

    between, first, last

The following object is masked from 'package:purrr':

    transpose

library(lubridate)


Attaching package: 'lubridate'

The following objects are masked from 'package:data.table':

    hour, isoweek, mday, minute, month, quarter, second, wday,
    week, yday, year

The following object is masked from 'package:base':

    date

CTD Sensor data

Regular profiles and transects

Reading the CTD Sensor data including recordings from auxillary pH, O₂, Chla and pCO₂ data. pCO₂ data were also internally recorded on the Contros HydroC instrument and will later be replaced.

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)
}


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


#### plots to check succesful read-in and data-quality ####
#### removal of errornous recordings ####

#### Profiling data

# Temperature

# df %>% 
#   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)

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

# Salinity

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

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

# pH

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

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

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

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


# pCO2

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

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

# O2

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

# Chlorophyll

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

df[Chl > 100]$Chl <- NA


#### Surface transect data

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

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


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

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

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

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

Around Ostergarnsholm island

Crossing large vessels

pCO₂ data

Merging observations

pCO₂ & CTD

Time lag correction

Temporal interpolation

sessionInfo()

R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17763)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

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

other attached packages:
 [1] lubridate_1.7.4   data.table_1.12.2 forcats_0.4.0    
 [4] stringr_1.4.0     dplyr_0.8.3       purrr_0.3.2      
 [7] readr_1.3.1       tidyr_1.0.0       tibble_2.1.3     
[10] ggplot2_3.2.1     tidyverse_1.2.1  

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 xfun_0.9         haven_2.1.1      lattice_0.20-35 
 [5] colorspace_1.3-2 vctrs_0.2.0      generics_0.0.2   htmltools_0.3.6 
 [9] yaml_2.2.0       rlang_0.4.0      pillar_1.3.1     glue_1.3.1      
[13] withr_2.1.2      modelr_0.1.5     readxl_1.3.1     lifecycle_0.1.0 
[17] plyr_1.8.4       munsell_0.5.0    gtable_0.2.0     workflowr_1.4.0 
[21] cellranger_1.1.0 rvest_0.3.4      evaluate_0.14    knitr_1.25      
[25] highr_0.7        broom_0.5.2      Rcpp_1.0.1       backports_1.1.2 
[29] scales_0.5.0     jsonlite_1.6     fs_1.3.1         hms_0.5.1       
[33] digest_0.6.18    stringi_1.1.7    grid_3.5.0       rprojroot_1.3-2 
[37] here_0.1         cli_1.1.0        tools_3.5.0      magrittr_1.5    
[41] lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2 
[45] zeallot_0.1.0    xml2_1.2.2       assertthat_0.2.0 rmarkdown_1.15  
[49] httr_1.4.1       rstudioapi_0.10  R6_2.2.2         nlme_3.1-137    
[53] git2r_0.23.0     compiler_3.5.0

data_base

Jens Daniel Müller

21 October, 2019

CTD Sensor data

Regular profiles and transects

Around Ostergarnsholm island

Crossing large vessels

pCO₂ data

Merging observations

pCO₂ & CTD

Time lag correction

Temporal interpolation

data_base

Jens Daniel Müller

21 October, 2019

CTD Sensor data

Regular profiles and transects

Around Ostergarnsholm island

Crossing large vessels

pCO2 data

Merging observations

pCO2 & CTD

Time lag correction

Temporal interpolation

pCO₂ data

pCO₂ & CTD