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Rmd	0e92cf9	jens-daniel-mueller	2020-09-25	enabled chunks for merging and saving
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Rmd	75e8c80	jens-daniel-mueller	2020-09-25	plot with 10% sample size
html	616c27f	jens-daniel-mueller	2020-09-25	updated repo manually
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Rmd	7f497e4	jens-daniel-mueller	2020-09-23	updated tau lm fit procedure
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Rmd	e78c435	jens-daniel-mueller	2020-05-04	finalized time sync check
html	f95bf94	jens-daniel-mueller	2020-05-04	Build site.
Rmd	56f6c8a	jens-daniel-mueller	2020-05-04	corrected dep_maxgap removel criterion
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Rmd	3067532	jens-daniel-mueller	2020-05-04	revise time sync
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Rmd	52090bf	jens-daniel-mueller	2020-04-29	correct interpolation, new d pco2 plot range
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Rmd	70bd3f0	jens-daniel-mueller	2020-04-29	correct interpolation, new d pco2 plot
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html	3042ff3	jens-daniel-mueller	2019-12-19	Build site.
Rmd	282c3ac	jens-daniel-mueller	2019-12-19	whole data set RT corrected
html	78710ee	jens-daniel-mueller	2019-12-09	Build site.
Rmd	c6cfca5	jens-daniel-mueller	2019-12-09	RT correction incl OGB data
html	c6cfca5	jens-daniel-mueller	2019-12-09	RT correction incl OGB data
html	bc6f19b	jens-daniel-mueller	2019-11-22	Build site.
Rmd	03b1b97	jens-daniel-mueller	2019-11-22	updated RT determination
html	874dac5	jens-daniel-mueller	2019-11-22	Build site.
Rmd	f875795	jens-daniel-mueller	2019-11-22	now clean
html	d921065	jens-daniel-mueller	2019-11-14	Build site.
Rmd	252f84d	jens-daniel-mueller	2019-11-14	included EDA in data base
html	d61a468	jens-daniel-mueller	2019-11-14	Build site.
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Rmd	6cb1935	jens-daniel-mueller	2019-11-08	response_time updated
html	33e3659	jens-daniel-mueller	2019-10-22	Build site.
Rmd	efcafd1	jens-daniel-mueller	2019-10-22	Added data base, merging, and RT determination
html	1595fe9	jens-daniel-mueller	2019-10-21	Build site.
Rmd	4131b9c	jens-daniel-mueller	2019-10-21	finisehd read CTD and HydroC, created merging Rmd

library(tidyverse)
library(lubridate)
library(zoo)

1 CTD (ts) + HydroC CO₂ data (th)

1.1 Merging summarized data sets

# Load Sensor and HydroC data ---------------------------------------------
ts <- read_csv(here::here("data/intermediate/_summarized_data_files",
                          "ts.csv"),
               col_types = list("pCO2_analog" = col_double()))

th <- read_csv(here::here("data/intermediate/_summarized_data_files",
                          "th.csv"))

# Time offset correction ----------------------------------------------

# Time offset was determined by comparing zeroing reads from Sensor and th
# in the plots produced in the section Time stamp synchronicity below
# before applying this correction

ts <- ts %>% 
  mutate(day = yday(date_time),
         date_time = if_else(day >= 206 & day <= 220,
                             date_time - 80, date_time - 10)) %>% 
  select(-day)

# Merge Sensor and HydroC data --------------------------------------------

ts_th <- full_join(ts, th) %>% 
  arrange(date_time)

rm(th, ts)

1.2 Interpolation to common time stamp

CTD and auxillary recordings (15 sec measurment interval) are interpolated to HydroC time stamps (first 10 sec, than 1 sec measurement interval) when gaps between observations are not larger than 20. Thereafter, HydroC readings not falling in regular transects/profilings are removed, by removing rows with NA depth values. Furthermore, CTD readings without corresponding HydroC reading are removed, except during periods when HydroC was not operating.

# Interpolate Sensor data to HydroC time stamp

ts_th <- ts_th %>%
  mutate(
    dep_maxgap = na.approx(dep, na.rm = FALSE, maxgap = 20),
    dep = approxfun(date_time, dep)(date_time),
    sal = approxfun(date_time, sal)(date_time),
    tem = approxfun(date_time, tem)(date_time),
    pCO2_analog = approxfun(date_time, pCO2_analog)(date_time)
  ) %>%
  filter(!is.na(dep_maxgap)) %>% #remove HC readings not falling in regular transects/profiling
  select(-dep_maxgap) %>%
  fill(ID, type, station) %>%
  filter(!is.na(deployment),!is.na(pCO2_analog)) # removes CTD readings without corresponding HydroC reading

# filter(!is.na(deployment) | is.na(pCO2_analog)) # removes CTD readings without corresponding HydroC reading, except during periods when HydroC was not operating


# Time stamp synchronicity

ts_th_Zero <- ts_th %>%
  filter(Zero == 1 | Flush == 1 & duration < 120)

pdf(
  file = here::here(
    "output/Plots/merging_interpolation",
    "Zero_time_synchronization.pdf"
  ),
  onefile = TRUE,
  width = 5,
  height = 5
)

for (i_deployment in unique(ts_th$deployment)) {
  #i_deployment <- unique(ts_th_Zero$deployment)[1]
  
  ts_th_Zero_deployment <- ts_th_Zero %>%
    filter(deployment == i_deployment)
  
  for (i_Zero_counter in unique(ts_th_Zero_deployment$Zero_counter)) {
    #i_Zero_counter <- unique(ts_th_Zero_deployment$Zero_counter)[1]
    
    print(
      ts_th_Zero_deployment %>%
        filter(Zero_counter == i_Zero_counter) %>%
        ggplot() +
        geom_point(aes(date_time, pCO2_corr, col = "HydroC")) +
        geom_point(aes(date_time, pCO2_analog, col = "analog")) +
        labs(
          title = paste("Depl: ", i_deployment,
                        " | Zero_counter: ", i_Zero_counter)
        )
      
    )
    
  }
}

dev.off()

png 
  2

rm(ts_th_Zero,
   ts_th_Zero_deployment,
   i_deployment,
   i_Zero_counter)

1.3 Time series pCO₂

1.3.1 Read cleaned processed 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, based on cleaned recordings

th_new_withAW <-
  read_csv2(
    here::here(
      "data/input/TinaV/Sensor/HydroC-pCO2/corrected_Contros",
      "parameter&pCO2s(method 43)_new_withAW.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))
  )

th_new_withAW <- th_new_withAW %>%
  slice(seq(1, n(), 10))

# Read Contros corrected data file, based on cleaned recordings without water vapor correction

th_new_withoutAW_all <-
  read_csv2(
    here::here(
      "data/input/TinaV/Sensor/HydroC-pCO2/corrected_Contros",
      "parameter&pCO2s(method 43)_new_withoutAW.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))
  )

th_new_withoutAW <- th_new_withoutAW_all %>%
  slice(seq(1, n(), 10))

th_pre_cleaning <-
  read_csv(here::here(
    "data/intermediate/_summarized_data_files",
    "th_pre_cleaning.csv"
  ))

th_pre_cleaning <- th_pre_cleaning %>%
  slice(seq(1, n(), 10))

ts_th_sub <- ts_th %>%
  slice(seq(1, n(), 10))

1.3.2 Comparison of preliminary pCO2 data

1.3.2.1 Analog vs internal

ggplot() +
  #geom_path(data = th_pre_cleaning, aes(date_time, pCO2_corr, col = "pre cleaning")) +
  geom_path(data = ts_th_sub, aes(date_time, pCO2_corr, col = "HydroC, drift corrected")) +
  geom_path(data = ts_th_sub, aes(date_time, pCO2_analog, col = "analog CTD")) +
  scale_color_brewer(palette = "Set1", name = "pCO2 record") +
  coord_cartesian(ylim = c(0, 600)) +
  labs(y = expression(pCO[2] ~ (µatm)), x = "") +
  facet_wrap( ~ deployment, scales = "free_x", ncol = 1)

pCO~2~ record after interpolation to HydroC timestamp (analog output from HydroC and drift corrected data provided by Contos). ID refers to the starting date of each cruise. Please note that pCO2_analog measurement range is technically restricted to 100-500 µatm. Zeroing periods are included.

pCO₂ record after interpolation to HydroC timestamp (analog output from HydroC and drift corrected data provided by Contos). ID refers to the starting date of each cruise. Please note that pCO2_analog measurement range is technically restricted to 100-500 µatm. Zeroing periods are included.

1.3.2.2 Raw vs clean

th_comparison <- full_join(
  ts_th_sub %>% select(date_time, deployment, pCO2_corr),
  th_new_withAW %>% select(date_time, pCO2_corr) %>% rename(pCO2_withAW = pCO2_corr)
)

th_comparison <- full_join(
  th_comparison,
  th_new_withoutAW %>% select(date_time, pCO2_corr) %>% rename(pCO2_withoutAW = pCO2_corr)
)


th_comparison %>%
  ggplot() +
  geom_path(data = th_pre_cleaning, aes(date_time, pCO2_corr, col = "pre cleaning")) +
  geom_path(aes(date_time, pCO2_corr, col = "HydroC, drift corrected")) +
  geom_path(aes(date_time, pCO2_withAW, col = "withAW")) +
  geom_path(aes(date_time, pCO2_withoutAW, col = "withoutAW")) +
  scale_color_brewer(palette = "Set1", name = "pCO2 record") +
  coord_cartesian(ylim = c(0, 600)) +
  labs(y = expression(pCO[2] ~ (µatm)), x = "") +
  facet_wrap( ~ deployment, scales = "free_x", ncol = 1)

1.3.2.3 Water vapor correction

th_comparison %>%
  ggplot() +
  geom_path(data = th_pre_cleaning %>% slice_sample(prop = 0.1),
            aes(date_time, 0, col = "pre runtime")) +
  geom_path(aes(date_time, pCO2_corr - pCO2_withAW, col = "orig - with AW")) +
  scale_color_brewer(palette = "Set1", name = "pCO2 record") +
  labs(y = expression(pCO[2] ~ (µatm)), x = "") +
  facet_wrap( ~ deployment, scales = "free_x", ncol = 1)

th_comparison %>%
  ggplot() +
  geom_path(data = th_pre_cleaning, aes(date_time, 0, col = "pre runtime")) +
  geom_path(aes(date_time, pCO2_withoutAW - pCO2_withAW, col = "without - with AW")) +
  scale_color_brewer(palette = "Set1", name = "pCO2 record") +
  labs(y = expression(pCO[2] ~ (µatm)), x = "") +
  facet_wrap( ~ deployment, scales = "free_x", ncol = 1)

rm(ts_th_sub,
   th_pre_cleaning,
   th_new_withAW,
   th_new_withoutAW,
   th_comparison)

1.3.3 replace pCO2 data

th_new_withoutAW_all <- th_new_withoutAW_all %>% 
  select(date_time, pCO2_corr)

ts_th <- ts_th %>% 
  select(-pCO2_corr)

ts_th <- full_join(ts_th, th_new_withoutAW_all) 

rm(th_new_withoutAW_all)

1.4 Write merged file

ts_th %>% 
  write_csv(here::here("data/intermediate/_merged_data_files/merging_interpolation", "ts_th.csv"))

1.4.1 Offset analog vs post-processed pCO₂

ts_th %>%
  ggplot() +
  geom_path(aes(date_time, pCO2_corr - pCO2_analog)) +
  ylim(-30, 0) +
  labs(y = expression(pCO[2] ~ (µats_th)), x = "") +
  facet_wrap( ~ ID, scales = "free_x", ncol = 1)

pCO~2~ difference betweeb HydroC and drift corrected data provided by Contos. Please note that pCO2 range is restricted to +/- 50 µatm.

pCO₂ difference betweeb HydroC and drift corrected data provided by Contos. Please note that pCO2 range is restricted to +/- 50 µatm.

2 Merges sensor (ts_th) + track (tt) data

tt <- read_csv(here::here("data/intermediate/_summarized_data_files",
                          "tt.csv"))


tm <- full_join(ts_th, tt) %>% 
  arrange(date_time)

# interpolate tt data and than remove columns that originate from tt time stamp
tm <- tm %>%
  mutate(lat = approxfun(date_time, lat)(date_time),
         lon = approxfun(date_time, lon)(date_time)) %>% 
  filter(!is.na(dep))

tm %>% write_csv(here::here("data/intermediate/_merged_data_files/merging_interpolation",
                            "tm.csv"))

rm(tm, ts_th, tt)

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] zoo_1.8-8       lubridate_1.7.9 forcats_0.5.0   stringr_1.4.0  
 [5] dplyr_1.0.0     purrr_0.3.4     readr_1.3.1     tidyr_1.1.0    
 [9] tibble_3.0.3    ggplot2_3.3.2   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5         here_0.1           lattice_0.20-41    assertthat_0.2.1  
 [5] rprojroot_1.3-2    digest_0.6.25      R6_2.4.1           cellranger_1.1.0  
 [9] backports_1.1.8    reprex_0.3.0       evaluate_0.14      highr_0.8         
[13] httr_1.4.2         pillar_1.4.6       rlang_0.4.7        readxl_1.3.1      
[17] rstudioapi_0.11    whisker_0.4        blob_1.2.1         rmarkdown_2.3     
[21] labeling_0.3       munsell_0.5.0      broom_0.7.0        compiler_4.0.2    
[25] httpuv_1.5.4       modelr_0.1.8       xfun_0.16          pkgconfig_2.0.3   
[29] htmltools_0.5.0    tidyselect_1.1.0   fansi_0.4.1        crayon_1.3.4      
[33] dbplyr_1.4.4       withr_2.2.0        later_1.1.0.1      grid_4.0.2        
[37] jsonlite_1.7.0     gtable_0.3.0       lifecycle_0.2.0    DBI_1.1.0         
[41] git2r_0.27.1       magrittr_1.5       scales_1.1.1       cli_2.0.2         
[45] stringi_1.4.6      farver_2.0.3       fs_1.4.2           promises_1.1.1    
[49] xml2_1.3.2         ellipsis_0.3.1     generics_0.0.2     vctrs_0.3.2       
[53] RColorBrewer_1.1-2 tools_4.0.2        glue_1.4.1         hms_0.5.3         
[57] yaml_2.2.1         colorspace_1.4-1   rvest_0.3.6        knitr_1.30        
[61] haven_2.3.1

Merging and interpolation of observations

Jens Daniel Müller

13 October, 2020

1 CTD (ts) + HydroC CO₂ data (th)

1.1 Merging summarized data sets

1.2 Interpolation to common time stamp

1.3 Time series pCO₂

1.3.1 Read cleaned processed data

1.3.2 Comparison of preliminary pCO2 data

1.3.2.1 Analog vs internal

1.3.2.2 Raw vs clean

1.3.2.3 Water vapor correction

1.3.3 replace pCO2 data

1.4 Write merged file

1.4.1 Offset analog vs post-processed pCO₂

2 Merges sensor (ts_th) + track (tt) data

Merging and interpolation of observations

Jens Daniel Müller

13 October, 2020

1 CTD (ts) + HydroC CO2 data (th)

1.1 Merging summarized data sets

1.2 Interpolation to common time stamp

1.3 Time series pCO2

1.3.1 Read cleaned processed data

1.3.2 Comparison of preliminary pCO2 data

1.3.2.1 Analog vs internal

1.3.2.2 Raw vs clean

1.3.2.3 Water vapor correction

1.3.3 replace pCO2 data

1.4 Write merged file

1.4.1 Offset analog vs post-processed pCO2

2 Merges sensor (ts_th) + track (tt) data

1 CTD (ts) + HydroC CO₂ data (th)

1.3 Time series pCO₂

1.4.1 Offset analog vs post-processed pCO₂