Read raw data
Jens Daniel Müller
29 September, 2020
Last updated: 2020-09-29
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Knit directory: BloomSail/
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library(tidyverse)
library(data.table)
library(lubridate)
library(DataExplorer)
library(leaflet)
library(readxl)
library(gsubfn)In this document, raw data files are read, merged into one file with harmonized column names and written as summarized data file.
1 Sea-Bird SBE 16 sensor data (ts)
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.)
1.1 Read regular profiles and transects
files <-
list.files(path = "data/input/TinaV/Sensor/Profiles_Transects/", pattern = "[.]cnv$")
#file <- files[1]
for (file in files) {
start_date <-
data.table(read.delim(
here::here("data/input/TinaV/Sensor/Profiles_Transects/", file),
sep = "#",
nrows = 160
))[[78, 1]]
start_date <- substr(start_date, 15, 34)
start_date <- mdy_hms(start_date, tz = "UTC")
temp <-
read.delim(
here::here("data/input/TinaV/Sensor/Profiles_Transects/", file),
sep = "",
skip = 160,
header = FALSE
)
temp <- data.table(temp[, c(2, 3, 4, 5, 6, 7, 9, 11, 13)])
names(temp) <-
c("date_time",
"dep",
"tem",
"sal",
"V_pH",
"pH",
"Chl",
"O2",
"pCO2_analog")
temp$start_date <- start_date
temp$date_time <- temp$date_time + temp$start_date
temp$ID <- substr(file, 1, 6)
temp$type <- substr(file, 8, 8)
temp$station <- substr(file, 8, 10)
temp$cast <- "up"
temp[date_time < mean(temp[dep == max(temp$dep)]$date_time)]$cast <-
"down"
if (exists("dataset")) {
dataset <- rbind(dataset, temp)
}
if (!exists("dataset")) {
dataset <- temp
}
rm(start_date)
rm(temp)
}
ts <- dataset
rm(dataset, file, files)1.2 Read profiles and transects around Ostergarnsholm
files <-
list.files(path = "data/input/TinaV/Sensor/Ostergarnsholm/", pattern = "[.]cnv$")
for (file in files) {
start_date <-
data.table(read.delim(
here::here("data/input/TinaV/Sensor/Ostergarnsholm/", file),
sep = "#",
nrows = 160
))[[78, 1]]
start_date <- substr(start_date, 15, 34)
start_date <- mdy_hms(start_date, tz = "UTC")
temp <-
read.delim(
here::here("data/input/TinaV/Sensor/Ostergarnsholm/", file),
sep = "",
skip = 160,
header = FALSE
)
temp <- data.table(temp[, c(2, 3, 4, 5, 6, 7, 9, 11, 13)])
names(temp) <-
c("date_time",
"dep",
"tem",
"sal",
"V_pH",
"pH",
"Chl",
"O2",
"pCO2_analog")
temp$start_date <- start_date
temp$date_time <- temp$date_time + temp$start_date
temp$ID <- substr(file, 1, 6)
temp$type <- substr(file, 8, 8)
temp$station <- substr(file, 11, 12)
temp$cast <- "up"
temp[date_time < mean(temp[dep == max(temp$dep)]$date_time)]$cast <-
"down"
if (exists("dataset")) {
dataset <- rbind(dataset, temp)
}
if (!exists("dataset")) {
dataset <- temp
}
rm(start_date)
rm(temp)
}
ts_OGB <- dataset
rm(dataset, file, files)
ts_OGB <- ts_OGB %>%
mutate(
type = if_else(station == "bo", "P", "T"),
station = if_else(station == "bo", "P14", station),
station = if_else(station == "in", "T14", station),
station = if_else(station == "ou", "T15", station)
)ts <- bind_rows(ts, ts_OGB) %>%
arrange(date_time)
rm(ts_OGB)1.3 EDA raw data
source("code/eda.R")
eda(ts, "ts-raw")
rm(eda)The output of an automated Exploratory Data Analysis (EDA) performed with the package DataExplorer can be accessed here:
1.4 Clean data set
Sensor recordings were cleaned from obviously erroneous readings, by setting suspecious values to NA.
class(ts)[1] "data.table" "data.frame"ts <- data.table(ts)
# Profiling data
# temperature
# ts %>%
# filter(type == "P") %>%
# ggplot(aes(tem, dep, col=station, linetype = cast))+
# geom_line()+
# scale_y_reverse()+
# geom_vline(xintercept = c(10, 20))+
# facet_wrap(~ID)
ts[ID == "180723" & station == "P07" & dep < 2 & cast == "up"]$tem <- NA
# salinity
# ts %>%
# filter(type == "P") %>%
# ggplot(aes(sal, dep, col=station, linetype = cast))+
# geom_path()+
# scale_y_reverse()+
# facet_wrap(~ID)
ts[sal < 6]$sal <- NA
# pH
# ts %>%
# filter(type == "P") %>%
# ggplot(aes(pH, dep, col=station, linetype=cast))+
# geom_path()+
# scale_y_reverse()+
# facet_wrap(~ID)
#
# ts %>%
# filter(type == "P") %>%
# ggplot(aes(V_pH, dep, col=station, linetype=cast))+
# geom_path()+
# scale_y_reverse()+
# facet_wrap(~ID)
ts[pH < 7.5]$V_pH <- NA
ts[pH < 7.5]$pH <- NA
ts[ID == "180709" & station == "P03" & dep < 5 & cast == "down"]$pH <- NA
ts[ID == "180709" & station == "P05" & dep < 10 & cast == "down"]$pH <- NA
ts[ID == "180718" & station == "P10" & dep < 3 & cast == "down"]$pH <- NA
ts[ID == "180815" & station == "P03" & dep < 2 & cast == "down"]$pH <- NA
ts[ID == "180820" & station == "P11" & dep < 15 & cast == "down"]$pH <- NA
ts[ID == "180709" & station == "P03" & dep < 5 & cast == "down"]$V_pH <- NA
ts[ID == "180709" & station == "P05" & dep < 10 & cast == "down"]$V_pH <- NA
ts[ID == "180718" & station == "P10" & dep < 3 & cast == "down"]$V_pH <- NA
ts[ID == "180815" & station == "P03" & dep < 2 & cast == "down"]$V_pH <- NA
ts[ID == "180820" & station == "P11" & dep < 15 & cast == "down"]$V_pH <- NA
# pCO2
# ts %>%
# filter(type == "P") %>%
# ggplot(aes(pCO2, dep, col=station, linetype = cast))+
# geom_path()+
# scale_y_reverse()+
# facet_wrap(~ID)
ts[ID == "180616"]$pCO2_analog <- NA
# O2
# ts %>%
# filter(type == "P") %>%
# ggplot(aes(O2, dep, col=station, linetype = cast))+
# geom_path()+
# scale_y_reverse()+
# facet_wrap(~ID)
# Chlorophyll
# ts %>%
# filter(type == "P") %>%
# ggplot(aes(Chl, dep, col=station, linetype = cast))+
# geom_path()+
# scale_y_reverse()+
# facet_wrap(~ID)
ts[Chl > 100]$Chl <- NA
#### Surface transect data
# ts %>%
# filter(type == "T") %>%
# ggplot(aes(date, dep, col=station))+
# geom_point()+
# scale_y_reverse()+
# facet_wrap(~ID, scales = "free_x")
#
# ts %>%
# filter(type == "T") %>%
# ggplot(aes(date, tem, col=station))+
# geom_point()+
# facet_wrap(~ID, scales = "free_x")
#
# ts %>%
# filter(type == "T") %>%
# ggplot(aes(date, sal, col=station))+
# geom_point()+
# facet_wrap(~ID, scales = "free_x")
#
# ts %>%
# filter(type == "T") %>%
# ggplot(aes(date, pCO2, col=station))+
# geom_point()+
# facet_wrap(~ID, scales = "free_x")
#
# ts %>%
# filter(type == "T") %>%
# ggplot(aes(date, pH, col=station))+
# geom_point()+
# facet_wrap(~ID, scales = "free_x")
#
# ts %>%
# filter(type == "T") %>%
# ggplot(aes(date, Chl, col=station))+
# geom_point()+
# facet_wrap(~ID, scales = "free_x")
ts[type == "T" & Chl > 10]$Chl <- NA
# ts %>%
# filter(type == "T") %>%
# ggplot(aes(date, O2, col=station))+
# geom_point()+
# facet_wrap(~ID, scales = "free_x")1.5 Write summary file
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.
ts <- ts %>%
select(date_time,
ID,
type,
station,
dep,
sal,
tem,
pCO2_analog)
# ts <- ts %>%
# filter( !(station %in% c("PX1", "PX2", "TX1", "TX2") ))
ts %>%
write_csv(here::here("data/intermediate/_summarized_data_files", "ts.csv"))1.6 EDA clean data
source("code/eda.R")
eda(ts, "ts_clean")
rm(eda)The output of an automated Exploratory Data Analysis (EDA) performed with the package DataExplorer can be accessed here:
1.7 Overview plots
ts %>%
arrange(date_time) %>%
filter(type == "P", !(station %in% c("PX1", "PX2"))) %>%
ggplot(aes(tem, dep, col=ymd(ID), group=ID))+
geom_path()+
scale_y_reverse()+
scale_color_viridis_c(trans = "date", name="")+
labs(x="temperature (°C)", y="Depth (m)")+
facet_wrap(~station)
temperature profiles by stations. Color refers to the starting date of each cruise.
ts %>%
arrange(date_time) %>%
filter(type == "P", !(station %in% c("PX1", "PX2"))) %>%
ggplot(aes(pCO2_analog, dep, col=ymd(ID), group=ID))+
geom_path()+
scale_y_reverse()+
scale_color_viridis_c(trans = "date", name="")+
labs(x="temperature (°C)", y="Depth (m)")+
facet_wrap(~station)
pCO2 (analog signal) profiles by stations. Color refers to the starting date of each cruise.
2 HydroC CO2 data (th)
2.1 Read data
Originally, 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. Those data are read-in here. However, later, the post-processing was repeated based on a cleaned data set.
# Read Contros corrected data file, based on all recordings
th <-
read_csv2(here::here("data/input/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_corr", "Runtime", "nr.ave")) %>%
mutate(date_time = dmy_hms(date_time),
Flush = as.factor(as.character(Flush)),
Zero = as.factor(as.character(Zero)))2.2 Deployment identification and subsetting
Individual deployments (periods of observations with less than 30 sec between recordings) were identified and relevant deployment periods were subsetted. This procedure removes only recordings attributable to sensor testing and set-up.
th <- th %>%
arrange(date_time) %>%
mutate(deployment = cumsum(c(TRUE, diff(date_time) >= 30)))
th %>%
select(date_time, pCO2_corr, deployment) %>%
write_csv(here::here(
"data/intermediate/_summarized_data_files",
"th_pre_cleaning.csv"
))
th <- th %>%
filter(deployment %in% c(2, 6, 9, 14, 17, 21, 23, 27, 31, 33, 34, 35, 37))2.3 Removal of duplicated time stamps
# add counter for date_time observations
th <- th %>%
add_count(date_time)
# find triplicated time stamp and select only first observation, and merge
th_no_triple <- th %>%
filter(n <= 2)
th_triple_clean <- th %>%
filter(n > 2) %>%
slice(1)
th <- full_join(th_no_triple, th_triple_clean)
rm(list = setdiff(ls(), c("th", "parameters")))
# find duplicated time stamps and shift first by one second backward, and merge
th %>%
distinct(date_time)# A tibble: 977,668 x 1
date_time
<dttm>
1 2018-07-05 19:05:14
2 2018-07-05 19:05:24
3 2018-07-05 19:05:34
4 2018-07-05 19:05:44
5 2018-07-05 19:05:54
6 2018-07-05 19:06:04
7 2018-07-05 19:06:14
8 2018-07-05 19:06:24
9 2018-07-05 19:06:34
10 2018-07-05 19:06:44
# ... with 977,658 more rowsth <- th %>%
select(-n) %>%
add_count(date_time)
unique(th$n)[1] 1 2th_no_duplicated <- th %>%
filter(n == 1)
th_duplicated <- th %>%
filter(n == 2)
th_duplicated_first <- th_duplicated %>%
group_by(date_time) %>%
slice(1) %>%
ungroup() %>%
mutate(date_time = date_time - 1)
th_duplicated_second <- th_duplicated %>%
group_by(date_time) %>%
slice(2) %>%
ungroup()
th_duplicated_clean <-
full_join(th_duplicated_first, th_duplicated_second) %>%
arrange(date_time)
th <- full_join(th_no_duplicated, th_duplicated_clean)
th %>%
distinct(date_time)# A tibble: 983,236 x 1
date_time
<dttm>
1 2018-07-05 19:05:14
2 2018-07-05 19:05:24
3 2018-07-05 19:05:34
4 2018-07-05 19:05:44
5 2018-07-05 19:05:54
6 2018-07-05 19:06:04
7 2018-07-05 19:06:14
8 2018-07-05 19:06:24
9 2018-07-05 19:06:34
10 2018-07-05 19:06:44
# ... with 983,226 more rowsrm(list = setdiff(ls(), c("th", "parameters")))
# find duplicated time stamps and shift first by two seconds forward, and merge
th %>%
distinct(date_time)# A tibble: 983,236 x 1
date_time
<dttm>
1 2018-07-05 19:05:14
2 2018-07-05 19:05:24
3 2018-07-05 19:05:34
4 2018-07-05 19:05:44
5 2018-07-05 19:05:54
6 2018-07-05 19:06:04
7 2018-07-05 19:06:14
8 2018-07-05 19:06:24
9 2018-07-05 19:06:34
10 2018-07-05 19:06:44
# ... with 983,226 more rowsth <- th %>%
select(-n) %>%
add_count(date_time)
unique(th$n)[1] 1 2th_no_duplicated <- th %>%
filter(n == 1)
th_duplicated <- th %>%
filter(n == 2)
th_duplicated_first <- th_duplicated %>%
group_by(date_time) %>%
slice(1) %>%
ungroup() %>%
mutate(date_time = date_time + 2)
th_duplicated_second <- th_duplicated %>%
group_by(date_time) %>%
slice(2) %>%
ungroup()
th_duplicated_clean <-
full_join(th_duplicated_first, th_duplicated_second) %>%
arrange(date_time)
th <- full_join(th_no_duplicated, th_duplicated_clean)
th %>%
distinct(date_time)# A tibble: 983,241 x 1
date_time
<dttm>
1 2018-07-05 19:05:14
2 2018-07-05 19:05:24
3 2018-07-05 19:05:34
4 2018-07-05 19:05:44
5 2018-07-05 19:05:54
6 2018-07-05 19:06:04
7 2018-07-05 19:06:14
8 2018-07-05 19:06:24
9 2018-07-05 19:06:34
10 2018-07-05 19:06:44
# ... with 983,231 more rowsrm(list = setdiff(ls(), c("th", "parameters")))
# remaining duplicates are observations where other observations with a +/- 1 sec timestamp exist
# for those cases, only the first duplicated observation is selected (similar to triplicate treatment)
th %>%
distinct(date_time)# A tibble: 983,241 x 1
date_time
<dttm>
1 2018-07-05 19:05:14
2 2018-07-05 19:05:24
3 2018-07-05 19:05:34
4 2018-07-05 19:05:44
5 2018-07-05 19:05:54
6 2018-07-05 19:06:04
7 2018-07-05 19:06:14
8 2018-07-05 19:06:24
9 2018-07-05 19:06:34
10 2018-07-05 19:06:44
# ... with 983,231 more rowsth <- th %>%
select(-n) %>%
add_count(date_time)
unique(th$n)[1] 1 2th_still_no_duplicated <- th %>%
filter(n == 1)
th_still_duplicated_first <- th %>%
filter(n == 2) %>%
group_by(date_time) %>%
slice(1)
th <- full_join(th_still_no_duplicated, th_still_duplicated_first)
th %>%
distinct(date_time)# A tibble: 983,241 x 1
date_time
<dttm>
1 2018-07-05 19:05:14
2 2018-07-05 19:05:24
3 2018-07-05 19:05:34
4 2018-07-05 19:05:44
5 2018-07-05 19:05:54
6 2018-07-05 19:06:04
7 2018-07-05 19:06:14
8 2018-07-05 19:06:24
9 2018-07-05 19:06:34
10 2018-07-05 19:06:44
# ... with 983,231 more rowsrm(list = setdiff(ls(), c("th", "parameters")))
th <- th %>%
select(-n)2.4 Flush and Zeroing identification
# Zeroing ID labeling
th <- th %>%
arrange(date_time) %>%
group_by(Zero) %>%
mutate(Zero_counter = as.factor(cumsum(c(
TRUE, diff(date_time) >= 30
)))) %>%
ungroup()
unique(th$Zero_counter) [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
[51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
[76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97
97 Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ... 97# Flush: Identification
th <- th %>%
mutate(Flush = 0) %>%
group_by(Zero, Zero_counter) %>%
mutate(
start = min(date_time),
duration = date_time - start,
Flush = if_else(Zero == 0 &
duration < parameters$HC_flush_duration, "1", "0")
) %>%
ungroup()
# Flush: Identify equilibration and internal gas mixing periods
th <- th %>%
mutate(mixing = if_else(
duration < parameters$HC_mixing_duration,
"mixing",
"equilibration"
))2.5 Deployment plots
pdf(file=here::here("output/Plots/read_in",
"th_deployments.pdf"), onefile = TRUE, width = 7, height = 4)
for (i in unique(th$deployment)) {
#i <- unique(th$deployment)[3]
sub <- th %>%
filter(deployment == i)
start_date <- min(sub$date_time)
print(
sub %>%
ggplot(aes(date_time, pCO2_corr, col=Zero_counter))+
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(th, "th")
rm(eda)The output of an automated Exploratory Data Analysis (EDA) performed with the package DataExplorer can be accessed here:
2.6 Write summary file
Summarized pCO2 date were written to file.
th %>%
select(date_time, Zero, Flush, pCO2_corr, deployment, Zero_counter, duration, mixing) %>%
write_csv(here::here("data/intermediate/_summarized_data_files",
"th.csv"))
rm(th)3 Bottle data CO2 (tb)
Discrete samples were collected with a Niskin bottle and analyzed for C[T] and A[T] at IOW CO2 lab.
3.1 Read data
tb <- read_csv(here::here("data/input/TinaV/Bottle/Tracegases", "BloomSail_bottle_CO2_all.csv"),
col_types = list("c","c","n","n","n","n","n"))
tb <- tb %>%
select(ID = transect.ID,
station = label,
dep = Dep,
sal = Sal,
CT,
AT)3.2 Write summary file
tb %>% write_csv(here::here("data/intermediate/_summarized_data_files",
"tb.csv"))
rm(tb)4 Bottle data plankton (tp)
Discrete samples were collected with a Niskin bottle and analysed for phytoplankton composition and biomass at IOW CO2 lab.
4.1 Read data
tp <- read_csv(here::here("data/input/TinaV/Bottle/Phytoplankton",
"181205_BloomSail_Plankton_counts.csv"))
#### delete colomns that contain counts, not calculated biomass
tp <- tp[,-seq(4, 21,1)]
#### assign new column names
#### for species: nr = size class, HV = Heterocyst per Volume, Hl = Heterocyst per length, t = total
names(tp) <-
c("date", "station", "dep",
"Aphanizomenon.1", "Aphanizomenon.2", "Aphanizomenon.3",
"Aphanizomenon.t", "Aphanizomenon.HV", "Aphanizomenon.Hl",
"Dolichospermum.1", "Dolichospermum.2", "Dolichospermum.3", "Dolichospermum.4",
"Dolichospermum.t", "Dolichospermum.HV", "Dolichospermum.Hl",
"Nodularia.1", "Nodularia.2", "Nodularia.3",
"Nodularia.t", "Nodularia.HV", "Nodularia.Hl",
"Nodulariadead.1", "Nodulariadead.2", "Nodulariadead.3",
"Nodulariadead.t",
"total.t"
)
#### change format of data table and seperate into 2 columns for species and class
tp <- gather(tp, para, value, Aphanizomenon.1:total.t, factor_key = TRUE)
tp <- separate(tp, col = para, into = c("Species", "class"))
#### change class of columns
tp <- tp %>%
mutate(ID = date,
date = ymd(date))4.2 Write summary file
tp %>% write_csv(here::here("data/intermediate/_summarized_data_files",
"tp.csv"))
rm(tp)5 GPS track (tt)
GPS track data were recorded with a Samsung Galaxy tablet.
5.1 Read data
files <-
list.files(path = "data/input/TinaV/Track/GPS_Logger_Track/", pattern = "[.]txt$")
for (file in files) {
# if the merged dataset does exist, append to it
if (exists("dataset")) {
temp <-
data.table(read.delim(
here::here("data/input/TinaV/Track/GPS_Logger_Track", file),
sep = ","
)[, c(2, 3, 4)])
names(temp) <- c("date_time", "lat", "lon")
temp$date_time <- ymd_hms(temp$date, tz = "UTC")
dataset <- rbind(dataset, temp)
rm(temp)
}
# if the merged dataset doesn't exist, create it
if (!exists("dataset")) {
dataset <-
data.table(read.delim(
here::here("data/input/TinaV/Track/GPS_Logger_Track", file),
sep = ","
)[, c(2, 3, 4)])
names(dataset) <- c("date_time", "lat", "lon")
dataset$date_time <- ymd_hms(dataset$date_time, tz = "UTC")
}
}
tt <- dataset
rm(dataset, file, files)5.2 Write summary file
tt %>%
write_csv(here::here("data/intermediate/_summarized_data_files",
"tt.csv"))
rm(tt)6 Ostergarnsholm
Atmospheric data were recorded at the ICOS station on Osterganrsholm.
6.1 Read data
og <-
read_delim(
here::here(
"data/input/Ostergarnsholm/Tower",
"Oes_Jens_atm_water_June_to_August_2018.csv"
),
delim = ";"
)
og <- og %>%
mutate(date_time = ymd_hms(paste(
paste(year, month, day, sep = "/"),
paste(hour, min, sec, sep = ":")
))) %>%
select(
"date_time",
"CO2 12m [ppm]",
"w_c [ppm m/s]",
"WS 12m [m/s]",
"WD 12m [degrees]",
"T 12m [degrees C]",
"RIS [W/m^2]"
)
og <- og %>%
select(date_time, pCO2_atm = "CO2 12m [ppm]", wind = "WS 12m [m/s]")6.2 Write summary file
og %>%
write_csv(here::here("data/intermediate/_summarized_data_files",
"og.csv"))
rm(og)7 Finnmaid
pCO2 data were recorded on VOS Finnmaid in summer 2018.
7.1 Read data
### June - August 2018
files <-
list.files(path = "data/input/Finnmaid_2018", pattern = "[.]xls$")
#file <-files[1]
for (file in files) {
temp <- read_excel(here::here("data/input/Finnmaid_2018", file))
temp <- temp[c(1, 2, 3, 12, 7, 4, 15, 8, 5, 17)]
names(temp) <-
c("date_time",
"lon",
"lat",
"pCO2",
"sal",
"tem",
"cO2",
"patm",
"Teq",
"xCO2")
temp <- temp[-c(1), ]
temp$date_time <-
as.POSIXct(as.numeric(temp$date_time) * 60 * 60 * 24,
origin = "1899-12-30",
tz = "GMT")
temp$lon <- as.numeric(as.character(temp$lon))
temp$lat <- as.numeric(as.character(temp$lat))
temp$pCO2 <- as.numeric(as.character(temp$pCO2))
temp$sal <- as.numeric(as.character(temp$sal))
temp$tem <- as.numeric(as.character(temp$tem))
temp$cO2 <- as.numeric(as.character(temp$cO2))
temp$patm <- as.numeric(as.character(temp$patm))
temp$Teq <- as.numeric(as.character(temp$Teq))
temp$xCO2 <- as.numeric(as.character(temp$xCO2))
temp <- data.table(temp)
temp$route <-
strapplyc(as.character(file), ".*(.).xls*", simplify = TRUE)
temp$ID <- substr(as.character(file), 3, 10)
if (exists("dataset")) {
dataset <- rbind(dataset, temp)
} else{
dataset <- temp
}
}
rm(temp, files, file)
dataset <- dataset[pCO2 != 0]
#### Los Gatos data
files <-
list.files(path = "data/input/Finnmaid_2018/LGR", pattern = "[.]xls$")
#file <-files[1]
for (file in files) {
temp <- read_excel(here::here("data/input/Finnmaid_2018/LGR", file))
temp <- temp[c(2, 3, 4, 8, 6, 5, 14, 7, 15, 9)]
names(temp) <-
c("date_time",
"lon",
"lat",
"pCO2",
"sal",
"tem",
"cO2",
"patm",
"Teq",
"xCO2")
temp <- temp[-c(1), ]
temp$date_time <- dmy_hms(temp$date_time)
temp <- data.table(temp)
temp$route <- substr(as.character(file), 12, 12)
temp$ID <- substr(as.character(file), 3, 10)
if (exists("dataset.LGR")) {
dataset.LGR <- rbind(dataset.LGR, temp)
} else{
dataset.LGR <- temp
}
}
rm(temp, files, file)7.2 Convert O2 concentration units
source(here::here("code", "O2stoO2c.R"))
dataset.LGR <- dataset.LGR %>%
filter() %>%
mutate(cO2 = O2stoO2c(
O2sat = cO2,
T = tem,
S = sal,
P = 3 / 10,
p_atm = 1013.5
))
rm(O2stoO2c, pH2Osat, sca_T, Scorr, TCorr, R, Vm)dataset$sensor <- "LICOR"
dataset.LGR$sensor <- "LosGatos"
fm <- bind_rows(dataset, dataset.LGR)
rm(dataset, dataset.LGR)7.3 Write summary file
fm %>%
write_csv(here::here("data/intermediate/_summarized_data_files",
"fm.csv"))
rm(fm)8 Interactive map
fm <-
read_csv(here::here("data/intermediate/_summarized_data_files",
"fm.csv"))
fm_sub <- fm %>%
arrange(date_time) %>%
slice(which(row_number() %% 20 == 1))
tt <-
read_csv(here::here("data/intermediate/_summarized_data_files", "tt.csv"))
tt_sub <- tt %>%
slice(which(row_number() %% 20 == 1))
rm(tt, fm)
leaflet() %>%
setView(lng = 20, lat = 57.3, zoom = 8) %>%
addLayersControl(
baseGroups = c("Ocean Basemap",
"Satellite"),
overlayGroups = c("BloomSail", "Finnmaid"),
options = layersControlOptions(collapsed = FALSE),
position = 'topright'
) %>%
addProviderTiles("Esri.WorldImagery", group = "Satellite") %>%
addProviderTiles(providers$Esri.OceanBasemap, group = "Ocean Basemap") %>%
addScaleBar(position = 'topright') %>%
addMeasure(
primaryLengthUnit = "kilometers",
secondaryLengthUnit = 'miles',
primaryAreaUnit = "sqmeters",
secondaryAreaUnit = "acres",
position = 'topleft'
) %>%
addCircles(data = fm_sub,
~ lon,
~ lat,
color = "white",
group = "Finnmaid") %>%
addPolylines(data = tt_sub,
~ lon,
~ lat,
color = "red",
group = "BloomSail")rm(fm_sub, tt_sub)
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-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_1.7.9 data.table_1.13.0 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
[13] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 jsonlite_1.7.0 viridisLite_0.3.0
[4] here_0.1 modelr_0.1.8 assertthat_0.2.1
[7] highr_0.8 blob_1.2.1 cellranger_1.1.0
[10] yaml_2.2.1 pillar_1.4.6 backports_1.1.8
[13] glue_1.4.1 digest_0.6.25 promises_1.1.1
[16] rvest_0.3.6 leaflet.providers_1.9.0 colorspace_1.4-1
[19] htmltools_0.5.0 httpuv_1.5.4 pkgconfig_2.0.3
[22] broom_0.7.0 haven_2.3.1 scales_1.1.1
[25] whisker_0.4 later_1.1.0.1 git2r_0.27.1
[28] generics_0.0.2 farver_2.0.3 ellipsis_0.3.1
[31] withr_2.2.0 cli_2.0.2 magrittr_1.5
[34] crayon_1.3.4 evaluate_0.14 fs_1.4.2
[37] fansi_0.4.1 xml2_1.3.2 tools_4.0.2
[40] hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0
[43] reprex_0.3.0 networkD3_0.4 compiler_4.0.2
[46] rlang_0.4.7 grid_4.0.2 rstudioapi_0.11
[49] htmlwidgets_1.5.1 crosstalk_1.1.0.1 igraph_1.2.5
[52] tcltk_4.0.2 labeling_0.3 rmarkdown_2.3
[55] gtable_0.3.0 DBI_1.1.0 R6_2.4.1
[58] gridExtra_2.3 knitr_1.29 utf8_1.1.4
[61] rprojroot_1.3-2 stringi_1.4.6 parallel_4.0.2
[64] Rcpp_1.0.5 vctrs_0.3.2 dbplyr_1.4.4
[67] tidyselect_1.1.0 xfun_0.16