Last updated: 2020-10-13

Checks: 7 0

Knit directory: BloomSail/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20191021)

The command set.seed(20191021) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 439d733

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 439d733. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/input/
    Ignored:    data/intermediate/
    Ignored:    data/output_submission/
    Ignored:    output/Plots/Figures_publication/.tmp.drivedownload/

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/merging_interpolation.Rmd) and HTML (docs/merging_interpolation.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	439d733	jens-daniel-mueller	2020-10-13	removed various Contros correction plots
html	6896725	jens-daniel-mueller	2020-10-01	Build site.
html	9f66019	jens-daniel-mueller	2020-10-01	Build site.
html	27c5431	jens-daniel-mueller	2020-09-29	Build site.
Rmd	2e0f902	jens-daniel-mueller	2020-09-29	all parameters separate, rebuild
html	1d01685	jens-daniel-mueller	2020-09-28	Build site.
Rmd	d28129f	jens-daniel-mueller	2020-09-28	republish after tau factor set to 1 and using final pCO2 data
html	4cfc1ad	jens-daniel-mueller	2020-09-25	Build site.
Rmd	0e92cf9	jens-daniel-mueller	2020-09-25	enabled chunks for merging and saving
html	02a1609	jens-daniel-mueller	2020-09-25	Build site.
Rmd	99e69cf	jens-daniel-mueller	2020-09-25	activated read-in of th and ts data
html	16554bc	jens-daniel-mueller	2020-09-25	Build site.
Rmd	75e8c80	jens-daniel-mueller	2020-09-25	plot with 10% sample size
html	616c27f	jens-daniel-mueller	2020-09-25	updated repo manually
Rmd	118f99e	jens-daniel-mueller	2020-09-25	comparison of pCO2 data included
html	264566c	jens-daniel-mueller	2020-09-25	Build site.
Rmd	abc5bac	jens-daniel-mueller	2020-09-25	comparison of pCO2 data included
html	904f0f7	jens-daniel-mueller	2020-09-23	Build site.
Rmd	7f497e4	jens-daniel-mueller	2020-09-23	updated tau lm fit procedure
html	8951791	jens-daniel-mueller	2020-09-23	Build site.
Rmd	9e87621	jens-daniel-mueller	2020-09-23	included postprocessed cleaned data
html	ddd2d3e	jens-daniel-mueller	2020-09-23	Build site.
Rmd	3ad71b0	jens-daniel-mueller	2020-09-23	included postprocessed cleaned data
html	aea9be2	jens-daniel-mueller	2020-09-23	Build site.
Rmd	ed17078	jens-daniel-mueller	2020-09-23	included postprocessed cleaned data
html	66bf52a	jens-daniel-mueller	2020-09-23	Build site.
Rmd	0c8eed6	jens-daniel-mueller	2020-09-23	included postprocessed cleaned data
html	c919fb7	jens-daniel-mueller	2020-06-29	Build site.
Rmd	1461cb6	jens-daniel-mueller	2020-06-29	Fig update for talk
html	603af23	jens-daniel-mueller	2020-05-25	Build site.
html	3414c23	jens-daniel-mueller	2020-05-25	Build site.
html	772e588	jens-daniel-mueller	2020-05-04	Build site.
Rmd	2ab39d7	jens-daniel-mueller	2020-05-04	All profiles and timeseries in one plot pdf
html	1ae50d3	jens-daniel-mueller	2020-05-04	Build site.
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
html	c23350e	jens-daniel-mueller	2020-05-04	Build site.
Rmd	3067532	jens-daniel-mueller	2020-05-04	revise time sync
html	3832733	jens-daniel-mueller	2020-04-30	Build site.
Rmd	4f4ab08	jens-daniel-mueller	2020-04-30	harmonized code until RT determination
html	6465570	jens-daniel-mueller	2020-04-29	Build site.
Rmd	0bbf0e6	jens-daniel-mueller	2020-04-29	revised nomenclature
html	ebd1948	jens-daniel-mueller	2020-04-29	Build site.
Rmd	52090bf	jens-daniel-mueller	2020-04-29	correct interpolation, new d pco2 plot range
html	d9248a6	jens-daniel-mueller	2020-04-29	Build site.
Rmd	70bd3f0	jens-daniel-mueller	2020-04-29	correct interpolation, new d pco2 plot
html	b5722a7	jens-daniel-mueller	2020-04-28	Build site.
html	472c2b4	jens-daniel-mueller	2020-04-21	Build site.
html	f8fcf50	jens-daniel-mueller	2020-04-19	created pub figures for time series
html	a6c4c22	jens-daniel-mueller	2020-03-30	Build site.
html	80c78b3	jens-daniel-mueller	2020-03-30	Build site.
html	5f8ca30	jens-daniel-mueller	2020-03-20	Build site.
html	2a20453	jens-daniel-mueller	2020-03-20	Build site.
html	473ab25	jens-daniel-mueller	2020-03-19	Build site.
html	81f022e	jens-daniel-mueller	2020-03-18	Build site.
html	1e39d85	jens-daniel-mueller	2020-03-18	Build site.
html	2105236	jens-daniel-mueller	2020-03-18	Build site.
html	05b9bdc	jens-daniel-mueller	2020-03-17	Build site.
html	0202742	jens-daniel-mueller	2020-03-16	Build site.
html	8e83afd	jens-daniel-mueller	2020-03-12	Build site.
html	a3ddea4	jens-daniel-mueller	2020-03-12	Build site.
html	52621ea	jens-daniel-mueller	2020-03-12	Build site.
html	e43a6f2	jens-daniel-mueller	2019-12-19	Build site.
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.
html	f3277a5	jens-daniel-mueller	2019-11-08	Build site.
html	4256bcf	jens-daniel-mueller	2019-11-08	Build site.
html	72687ee	jens-daniel-mueller	2019-11-08	Build site.
html	74212a6	jens-daniel-mueller	2019-11-08	Build site.
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