Data base
Jens Daniel Müller
21 November, 2019
Last updated: 2019-11-21
Checks: 6 1
Knit directory: BloomSail/
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library(tidyverse)
library(data.table)
library(lubridate)
library(DataExplorer)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)
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/")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.
# 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")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") %>%
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") %>%
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)
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$Zero_ID)
# Flush: Identification
HC <- HC %>%
mutate(Flush = 0) %>%
group_by(Zero, Zero_ID) %>%
mutate(start = min(date_time),
duration = date_time - start,
Flush = if_else(Zero == 0 & duration < 600, "1", "0")) %>%
ungroup()
# Flush: Identify equilibration and internal gas mixing periods
HC <- HC %>%
mutate(mixing = if_else(duration < 20, "mixing", "equilibration"))A pdf with plots of all Flush periods (mixing and equilibration identified) can be found here:
pdf(file=here::here("output/Plots/data_base",
"HydroC_pCO2_deployments.pdf"), onefile = TRUE, width = 7, height = 4)
for (i in unique(HC$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:
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.csv"))Tasks / open questions
Include data around Ostergarnsholm island and 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)
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] DataExplorer_0.8.0 lubridate_1.7.4 data.table_1.12.6
[4] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3
[7] purrr_0.3.3 readr_1.3.1 tidyr_1.0.0
[10] tibble_2.1.3 ggplot2_3.2.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 xfun_0.10 haven_2.1.1
[4] lattice_0.20-35 colorspace_1.4-1 vctrs_0.2.0
[7] generics_0.0.2 htmltools_0.4.0 yaml_2.2.0
[10] rlang_0.4.1 pillar_1.4.2 glue_1.3.1
[13] withr_2.1.2 modelr_0.1.5 readxl_1.3.1
[16] networkD3_0.4 lifecycle_0.1.0 munsell_0.5.0
[19] gtable_0.3.0 workflowr_1.4.0 cellranger_1.1.0
[22] rvest_0.3.4 htmlwidgets_1.5.1 evaluate_0.14
[25] labeling_0.3 knitr_1.25 parallel_3.5.0
[28] highr_0.8 broom_0.5.2 Rcpp_1.0.2
[31] scales_1.0.0 backports_1.1.5 jsonlite_1.6
[34] fs_1.3.1 gridExtra_2.3 hms_0.5.1
[37] digest_0.6.22 stringi_1.4.3 grid_3.5.0
[40] rprojroot_1.3-2 here_0.1 cli_1.1.0
[43] tools_3.5.0 magrittr_1.5 lazyeval_0.2.2
[46] crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3
[49] zeallot_0.1.0 xml2_1.2.2 assertthat_0.2.1
[52] rmarkdown_1.16 httr_1.4.1 rstudioapi_0.10
[55] R6_2.4.0 igraph_1.2.4.1 nlme_3.1-137
[58] git2r_0.26.1 compiler_3.5.0