GLODAPv2_2020

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Last updated: 2022-03-14

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Knit directory: emlr_obs_preprocessing/

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File	Version	Author	Date	Message
Rmd	19cd114	jens-daniel-mueller	2022-03-14	revised cruise mean offset plots
html	744b90f	jens-daniel-mueller	2022-03-11	Build site.
Rmd	aae5fc5	jens-daniel-mueller	2022-03-11	revised cruise mean offsets
html	84ca078	jens-daniel-mueller	2022-03-11	Build site.
Rmd	f9a4a5b	jens-daniel-mueller	2022-03-11	revised cruise-by_cruise
html	efd6581	jens-daniel-mueller	2022-03-11	Build site.
Rmd	a5262b7	jens-daniel-mueller	2022-03-11	revised cruise-by_cruise
html	25fef5b	jens-daniel-mueller	2022-03-11	Build site.
Rmd	064dea1	jens-daniel-mueller	2022-03-11	revised cruise-by_cruise
html	02a01ef	jens-daniel-mueller	2022-03-10	Build site.
Rmd	c6d5f07	jens-daniel-mueller	2022-03-10	revised crossover analysis
html	e3d1a2b	jens-daniel-mueller	2022-03-10	Build site.
Rmd	a706c3e	jens-daniel-mueller	2022-03-10	revised xover analysis
html	070ca03	jens-daniel-mueller	2022-03-09	Build site.
Rmd	204f92a	jens-daniel-mueller	2022-03-09	revised crossover analysis
html	9db485e	jens-daniel-mueller	2022-02-25	Build site.
Rmd	ad16b56	jens-daniel-mueller	2022-02-25	added cruise by cruise annual mean offset analysis
html	fecc329	jens-daniel-mueller	2022-02-25	Build site.
Rmd	4030fe6	jens-daniel-mueller	2022-02-25	added cruise by cruise offset analysis
html	29af13b	jens-daniel-mueller	2022-02-16	Build site.
Rmd	9755b16	jens-daniel-mueller	2022-02-16	cruise wise crossover analysis
html	6e65117	jens-daniel-mueller	2022-02-16	Build site.
Rmd	fc1cf80	jens-daniel-mueller	2022-02-15	rerun with flux products
html	cf43743	jens-daniel-mueller	2022-02-15	Build site.
Rmd	04014b7	jens-daniel-mueller	2022-02-15	decadal crossover evaluation pre subbasin
html	4a7550e	jens-daniel-mueller	2022-02-15	Build site.
Rmd	856705f	jens-daniel-mueller	2022-02-15	decadal crossover evaluation pre subbasin
html	8804a83	jens-daniel-mueller	2022-02-15	Build site.
Rmd	0c2d719	jens-daniel-mueller	2022-02-15	decadal crossover evaluation pre subbasin
html	e1243c2	jens-daniel-mueller	2022-02-15	Build site.
Rmd	8eced63	jens-daniel-mueller	2022-02-15	decadal crossover evaluation pre subbasin
html	efc2025	jens-daniel-mueller	2022-02-15	Build site.
Rmd	73fc278	jens-daniel-mueller	2022-02-15	decadal crossover evaluation pre subbasin
html	4d9d1cd	jens-daniel-mueller	2022-01-17	Build site.
Rmd	0a1ca07	jens-daniel-mueller	2022-01-17	rerun without saving expocodes
html	9075296	jens-daniel-mueller	2022-01-12	Build site.
Rmd	86182f0	jens-daniel-mueller	2022-01-12	data contribution per cruise
html	ecc669f	jens-daniel-mueller	2022-01-04	Build site.
Rmd	98d874a	jens-daniel-mueller	2022-01-04	calculate crossover of gap filled data
html	2620d02	jens-daniel-mueller	2022-01-03	Build site.
Rmd	ee1e44a	jens-daniel-mueller	2022-01-03	plot crossover of gap filled data
html	ca3a146	jens-daniel-mueller	2022-01-03	Build site.
Rmd	f71bc69	jens-daniel-mueller	2022-01-03	plot crossover of gap filled data
html	6e1b56c	jens-daniel-mueller	2022-01-03	Build site.
Rmd	c5258b1	jens-daniel-mueller	2022-01-03	plot crossover of gap filled data
html	9febbb8	jens-daniel-mueller	2022-01-03	Build site.
Rmd	cd89345	jens-daniel-mueller	2022-01-03	plot crossover of gap filled data
html	1a9c797	jens-daniel-mueller	2022-01-03	Build site.
Rmd	cde43c6	jens-daniel-mueller	2022-01-03	plot crossover of gap filled data
html	494beda	jens-daniel-mueller	2022-01-03	Build site.
Rmd	47811bd	jens-daniel-mueller	2022-01-03	plot crossover of gap filled data
html	51ec1fe	jens-daniel-mueller	2021-12-23	Build site.
Rmd	468c324	jens-daniel-mueller	2021-12-23	added crossover cruise subsetting
html	28ed51f	jens-daniel-mueller	2021-12-21	Build site.
Rmd	f99a7ce	jens-daniel-mueller	2021-12-21	print tables with flagging number
html	fcff192	jens-daniel-mueller	2021-12-21	Build site.
Rmd	e60be65	jens-daniel-mueller	2021-12-21	added flagging profiles
html	a87f8c7	jens-daniel-mueller	2021-12-20	Build site.
Rmd	7511f8c	jens-daniel-mueller	2021-12-20	revised IO analysis
html	2704ff6	jens-daniel-mueller	2021-12-20	Build site.
Rmd	f4696af	jens-daniel-mueller	2021-12-20	added cruise maps
html	7f65d3a	jens-daniel-mueller	2021-12-20	Build site.
Rmd	208283d	jens-daniel-mueller	2021-12-20	revised missing cruise crossover analysis
html	6106236	jens-daniel-mueller	2021-12-20	Build site.
Rmd	953ac0a	jens-daniel-mueller	2021-12-20	revised missing cruise crossover analysis
html	d5ef2c6	jens-daniel-mueller	2021-12-20	Build site.
Rmd	0b0800e	jens-daniel-mueller	2021-12-20	restructured IO crossover analysis
html	00227e6	jens-daniel-mueller	2021-12-20	Build site.
Rmd	8728169	jens-daniel-mueller	2021-12-20	added IO crossover analysis
html	e810585	jens-daniel-mueller	2021-12-16	Build site.
Rmd	aca9273	jens-daniel-mueller	2021-12-16	added maps per expocode
html	6aa4b75	jens-daniel-mueller	2021-12-16	Build site.
Rmd	3511fa7	jens-daniel-mueller	2021-12-16	f == 9 analysis added
html	163f976	jens-daniel-mueller	2021-12-16	Build site.
Rmd	7fa3a99	jens-daniel-mueller	2021-12-16	added cumulative data contribution as threshold
html	be0850d	jens-daniel-mueller	2021-12-16	Build site.
Rmd	8db3760	jens-daniel-mueller	2021-12-16	plot maps of f and qc data loss
html	61d5f49	jens-daniel-mueller	2021-12-15	Build site.
Rmd	be2f94e	jens-daniel-mueller	2021-12-15	analyse IO 1990 CRM data from Millero 1998 - TA only
html	d454df1	jens-daniel-mueller	2021-12-15	Build site.
Rmd	7802f47	jens-daniel-mueller	2021-12-15	analyse IO 1990 CRM data from Millero 1998
html	ce6cdae	jens-daniel-mueller	2021-12-15	Build site.
Rmd	acff553	jens-daniel-mueller	2021-12-15	plot qc data loss by cruise size
html	7ace7ab	jens-daniel-mueller	2021-12-15	Build site.
Rmd	554383a	jens-daniel-mueller	2021-12-15	plot qc data loss by cruise size
html	faa6b3c	jens-daniel-mueller	2021-12-15	Build site.
Rmd	be8751d	jens-daniel-mueller	2021-12-15	started data loss assesment
html	70923f2	jens-daniel-mueller	2021-12-14	Build site.
Rmd	1acf7ff	jens-daniel-mueller	2021-12-14	checked P18 nitrate data - quadratic fit
html	b68b58e	jens-daniel-mueller	2021-12-13	Build site.
Rmd	4c002c1	jens-daniel-mueller	2021-12-13	checked P18 nitrate data
html	de20732	jens-daniel-mueller	2021-12-08	Build site.
Rmd	badaed2	jens-daniel-mueller	2021-12-08	plotted f maps
html	daa43b9	jens-daniel-mueller	2021-12-06	Build site.
Rmd	b578bd9	jens-daniel-mueller	2021-12-06	plotted qc maps
html	2b22ffe	jens-daniel-mueller	2021-11-24	Build site.
Rmd	1b7ec1f	jens-daniel-mueller	2021-11-24	revised combined IO NS and EW analysis
html	0ef46e8	jens-daniel-mueller	2021-11-23	Build site.
Rmd	7fb15cf	jens-daniel-mueller	2021-11-23	combined IO NS and EW analysis
html	f2871b9	jens-daniel-mueller	2021-11-20	Build site.
Rmd	46c1246	jens-daniel-mueller	2021-11-19	rerun with GLODAP cast column
html	375d7c7	jens-daniel-mueller	2021-11-18	Build site.
Rmd	1839007	jens-daniel-mueller	2021-11-18	delta EW crossover values determined
html	f30883c	jens-daniel-mueller	2021-11-18	Build site.
Rmd	7acd48c	jens-daniel-mueller	2021-11-18	delta crossover values determined
html	2e6c3f1	jens-daniel-mueller	2021-11-18	Build site.
Rmd	49ca05c	jens-daniel-mueller	2021-11-18	delta crossover values determined
html	16dab59	jens-daniel-mueller	2021-11-18	Build site.
Rmd	620b6f4	jens-daniel-mueller	2021-11-18	delta crossover values determined
html	42965b9	jens-daniel-mueller	2021-11-18	Build site.
Rmd	69dbb5f	jens-daniel-mueller	2021-11-18	crossing checks
html	c9363ce	jens-daniel-mueller	2021-11-18	Build site.
Rmd	6bc79d6	jens-daniel-mueller	2021-11-18	crossing checks
html	0908ee5	jens-daniel-mueller	2021-11-15	Build site.
html	6d6a23e	jens-daniel-mueller	2021-11-01	Build site.
Rmd	2f36786	jens-daniel-mueller	2021-11-01	preprocess adjustment table, create new basinmaps
html	2a50fa9	jens-daniel-mueller	2021-10-28	Build site.
Rmd	67de9ab	jens-daniel-mueller	2021-10-28	preprocess tracers
html	a96bf9e	jens-daniel-mueller	2021-10-27	Build site.
Rmd	d99b131	jens-daniel-mueller	2021-10-27	added time series plots
html	fde6c32	jens-daniel-mueller	2021-10-27	Build site.
Rmd	db93d9f	jens-daniel-mueller	2021-10-27	added time series plots
html	7db7e6a	jens-daniel-mueller	2021-10-27	Build site.
Rmd	d6fb0dc	jens-daniel-mueller	2021-10-27	added time series plots
html	68d67e7	jens-daniel-mueller	2021-10-27	Build site.
Rmd	b4ea199	jens-daniel-mueller	2021-10-27	added time series plots
html	7987bb7	jens-daniel-mueller	2021-10-21	Build site.
Rmd	b64c54d	jens-daniel-mueller	2021-10-21	added inventory layer depth
html	8d1aaf8	jens-daniel-mueller	2021-10-20	Build site.
Rmd	5bce752	jens-daniel-mueller	2021-10-20	corrected qc flag in glodap
html	dc8d958	jens-daniel-mueller	2021-10-20	Build site.
Rmd	b2ccc04	jens-daniel-mueller	2021-10-20	corrected qc flag in glodap
html	2438c5a	jens-daniel-mueller	2021-08-30	Build site.
Rmd	4296433	jens-daniel-mueller	2021-08-30	rerun GLODAP preprocessing with officially released file
html	e49875a	jens-daniel-mueller	2021-07-07	Build site.
html	6312bd4	jens-daniel-mueller	2021-07-07	Build site.
Rmd	4905409	jens-daniel-mueller	2021-07-07	rerun with new setup_obs.Rmd file
html	58bc706	jens-daniel-mueller	2021-07-06	Build site.
Rmd	0db89e1	jens-daniel-mueller	2021-07-06	rerun with revised variable names
html	f600971	jens-daniel-mueller	2021-07-02	Build site.
html	98599d8	jens-daniel-mueller	2021-06-27	Build site.
Rmd	4f9c370	jens-daniel-mueller	2021-06-27	update to latest GLODAP pre-release
html	265c4ef	jens-daniel-mueller	2021-06-04	Build site.
html	c79346a	jens-daniel-mueller	2021-06-03	Build site.
html	9d8353f	jens-daniel-mueller	2021-05-31	Build site.
Rmd	b948168	jens-daniel-mueller	2021-05-31	ingest GLODAPv2_2021 beta data

---

path_glodapv2_2021  <- "/nfs/kryo/work/updata/glodapv2_2021/"
path_glodapv2_CRM  <- "/nfs/kryo/work/updata/glodapv2_CRM/"
path_crossover <- "/nfs/kryo/work/updata/glodapv2_crossover"

path_preprocessing  <- paste(path_root, "/observations/preprocessing/", sep = "")

1 Read files

1.1 Adjusted data

Main data source for this project is GLODAPv2.2021_Merged_Master_File.csv downloaded from https://www.ncei.noaa.gov/data/oceans/ncei/ocads/data/0237935/GLODAPv2.2021_Merged_Master_File.csv on Aug 30, 2021.

GLODAP <-
  read_csv(
    paste(
      path_glodapv2_2021,
      "GLODAPv2.2021_Merged_Master_File_20210830.csv",
      sep = ""
    ),
    na = "-9999",
    col_types = cols(.default = col_double())
  )


GLODAP <- GLODAP %>%
  rename_with(~str_remove(., 'G2'))

1.2 Adjustment table

GLODAP_adjustments <-
  read_csv(
    paste(
      path_glodapv2_2021,
      "GLODAPv2.2021_adjustments_last_updated_on_2021_05_10.csv",
      sep = ""
    ),
    na = c("-666", "-777", "-888", "-999"),
    skip = 2
  )

1.3 Expocodes

GLODAP_expocodes <-
  read_tsv(
    paste(
      path_glodapv2_2021,
      "EXPOCODES.txt",
      sep = ""
    ),
    col_names = c("cruise", "cruise_expocode")
  )

1.4 Crossover tables

# tables from glodapv2, provided by Steven van Heuven

glodapv2_xover_files <- fs::dir_ls(paste0(path_crossover, "/glodapv2"))

glodapv2_xover <- glodapv2_xover_files %>% 
  map_dfr(read_csv, .id = "parameter")

glodapv2_xover <- glodapv2_xover %>% 
  mutate(parameter = str_remove(parameter, ".csv"),
         parameter = str_sub(parameter, -3))


glodapv2_xover <- glodapv2_xover %>% 
  mutate(parameter = recode(parameter,
                            "ALK" = "talk",
                            "DIC" = "tco2",
                            "NO3" = "nitrate",
                            "_O2" = "oxygen",
                            "PO4" = "phosphate",
                            "SAL" = "salinity",
                            "SIL" = "silicate"))

# Note: In the files provided by Steven von Heuven
# the column names sigma_ratio and sigma_offset_sd were swapped

glodapv2_xover_absolute <- glodapv2_xover %>% 
  filter(parameter %in% c("salinity", "talk", "tco2")) %>% 
  select(parameter,
         offset = sigma_offset,
         offset_sd = sigma_ratio,
         cruise_A = CruiseA_EXPOCODE,
         cruise_B = CruiseB_EXPOCODE)

glodapv2_xover_ratio <- glodapv2_xover %>% 
  filter(!(parameter %in% c("salinity", "talk", "tco2"))) %>% 
  select(parameter,
         offset = sigma_offset_sd,
         offset_sd = sigma_ratio_sd,
         cruise_A = CruiseA_EXPOCODE,
         cruise_B = CruiseB_EXPOCODE)

glodapv2_xover <- bind_rows(
  glodapv2_xover_absolute,
  glodapv2_xover_ratio
)


rm(glodapv2_xover_files,
   glodapv2_xover_absolute, glodapv2_xover_ratio)

# tables created between glodapv2 and glodapv2.2021
# provided by Nico Lange

glodapv2_2021_xover_files <- fs::dir_ls(paste0(path_crossover, "/glodapv2_2021"))

glodapv2_2021_xover <- glodapv2_2021_xover_files %>% 
  map_dfr(readxl::read_excel)

glodapv2_2021_xover <- glodapv2_2021_xover %>%
  rename(parameter = Parameter) %>%
  mutate(parameter = recode(parameter,
                            "alkalinity" = "talk")) %>%
  filter(
    parameter %in%
      c(
        "tco2",
        "nitrate",
        "oxygen",
        "phosphate",
        "salinity",
        "silicate",
        "talk"
      )
  )


glodapv2_2021_xover <- glodapv2_2021_xover %>% 
  rename(offset = Offset,
         offset_sd = Std,
         cruise_A = Cruise_A,
         cruise_B = Cruise_B)

rm(glodapv2_2021_xover_files)

# tables for data not qc'ed in the regular GLODAP release
# provided by Nico Lange

glodapv2_2021_xover_files_add <-
  fs::dir_ls(paste0(path_crossover, "/glodapv2_2021_additional_crossover"))

glodapv2_2021_xover_add <- glodapv2_2021_xover_files_add %>% 
  map_dfr(readxl::read_excel)

glodapv2_2021_xover_add <- glodapv2_2021_xover_add %>%
  rename(parameter = Parameter) %>%
  mutate(parameter = recode(parameter,
                            "alkalinity" = "talk"))


glodapv2_2021_xover_add <- glodapv2_2021_xover_add %>% 
  rename(offset = Offset,
         offset_sd = Std,
         cruise_A = Cruise_A,
         cruise_B = Cruise_B)

rm(glodapv2_2021_xover_files_add)

1.5 Missing/flagged cruises

I generated this file manually based on the analysis presented in the Data loss section below.

GLODAP_cruises_missing <-
  read_csv(
    paste(
      path_glodapv2_2021,
      "GLODAPv2.2021_major_cruises_missing_flagged.csv",
      sep = ""
    )
  )

1.6 IO CRM data

CRM_IO_meas <-
  read_csv(
    paste(
      path_glodapv2_CRM,
      "/Millero_1998_Tab2.csv",
      sep = ""
    )
  )

CRM_ref_values <-
  read_csv(
    paste(
      path_glodapv2_CRM,
      "/Dickson_CRM_reference_values_20211215.csv",
      sep = ""
    )
  )

2 Data preparation

2.1 Correct talk qc flag

From an email conversation with Nico Lange

Yes, we are aware of these faulty(!) calculated TA data (using DIC and fCO2). It is linked to v2.2020 where we’ve added fCO2 to the “missing carbon calculation matrix”. Overall, including fCO2 in these calculations has worked great to fill some missing carbon gaps. However, for this cruise in particular the fCO2 values have most likely been converted wrongly to 20°C and are thus off! The problem of this all is that we haven’t really done a 2nd QC on the fCO2 values neither have we defined the corresponding “G2fCO2qc” variable, hence for the sake of consistency we kept all fCO2 values in. Again and unfortunately, in this particular case it led to the bad calculations of TA data…. We plan to do a full 2nd QC on all (!) fCO2 data for v3.

But you have indeed found a flaw in our merging script, as the corresponding calculated TA values should not have received a 2nd QC flag of 1! I missed out on adding a line to our merging script to accommodate for the non-existence of 2nd fCO2 flags in the carbon calculation matrix.

So long story short: Thank you very much for finding this flaw and letting me know of it!

and

Yes, the all calculated TA data from cruise 695 should have a talkqc of 0 (as they are based upon un QC’d fCO2 data…).

And no (thanks to your hint and questions), I figured that this wrongly assigned 2nd QC flag is a problem for all calculated carbon data, which used fCO2 for the calculations. However, luckily this is not really often the case.

You can check if thats the case by looking at which other carbon parameters are measured, i.e. by checking their primary flags (e.g. G2talkf, G2tco2f and G2phts25p0f and G2fco2f). If only two are measured and one of them is fCO2, it means that the other carbon parameters (the ones with a primary flag of 0) are calculated using fCO2. Hence, for these instances no 2nd QC is done and the corresponding qc flag should be 0 and not 1.

# calculate number of measured co2 system variables

GLODAP <- GLODAP %>%
  mutate(measured_CO2_vars = rowSums(select(., c(
    tco2f, talkf, fco2f, phts25p0f
  )) == 2))

# identify cruises on which talk/tco2 was calculated

talk_qc_error_cruises <- GLODAP %>%
  select(cruise, tco2:phtsqc, measured_CO2_vars) %>% 
  filter(measured_CO2_vars == 2,
         fco2f == 2,
         talkf == 0) %>% 
  distinct(cruise, talkf, talkqc, fco2f)

tco2_qc_error_cruises <- GLODAP %>%
  select(cruise, tco2:phtsqc, measured_CO2_vars) %>% 
  filter(measured_CO2_vars == 2,
         fco2f == 2,
         tco2f == 0) %>% 
  distinct(cruise, tco2f, tco2qc, fco2f)

talk_qc_error_cruises %>% 
  write_csv("data/talk_qc_error_cruises_GLODAPv2_2021.csv")

tco2_qc_error_cruises %>% 
  write_csv("data/tco2_qc_error_cruises_GLODAPv2_2021.csv")

rm(talk_qc_error_cruises, tco2_qc_error_cruises)


# set qc = 0 for tco2 and talk values calculated from fco2   

GLODAP <- GLODAP %>%
  mutate(tco2qc = if_else(measured_CO2_vars == 2 &
                            fco2f == 2 & tco2f == 0,
                          0,
                          tco2qc))

GLODAP <- GLODAP %>%
  mutate(talkqc = if_else(measured_CO2_vars == 2 &
                            fco2f == 2 & talkf == 0,
                          0,
                          talkqc))

GLODAP <- GLODAP %>% 
  select(-measured_CO2_vars)

2.2 Harmonize nomenclature

# create date column
GLODAP <- GLODAP %>%
  mutate(date = ymd(paste(year, month, day))) %>%
  relocate(date)

# harmonize column names
GLODAP <- GLODAP  %>%
  rename(sal = salinity,
         temp = temperature)

# harmonize coordinates
GLODAP <- GLODAP  %>%
  rename(lon = longitude,
         lat = latitude) %>%
  mutate(lon = if_else(lon < 20, lon + 360, lon))

2.3 Horizontal gridding

For merging with other data sets, all observations were grouped into latitude intervals of:

• 1° x 1°

GLODAP <- m_grid_horizontal(GLODAP)

2.4 Apply basin mask

# use only three basin to assign general basin mask
# ie this is not specific to the MLR fitting
basinmask_5 <- basinmask %>% 
  filter(MLR_basins == "5") %>% 
  select(lat, lon, basin)

basinmask <- basinmask %>% 
  filter(MLR_basins == "2") %>% 
  select(lat, lon, basin_AIP)

GLODAP <- inner_join(GLODAP, basinmask)

2.5 Add expocodes

GLODAP <- right_join(
  GLODAP_expocodes,
  GLODAP)

2.6 Add row number

GLODAP <- GLODAP  %>%  
  mutate(row_number = row_number()) %>% 
  relocate(row_number)

2.7 Split CO2 and tracers

Measurements of CO2 system and other biogeochemical parameters are separated from the measurements of halogenated tracers.

# remove irrelevant columns
GLODAP <- GLODAP %>%
  select(-c(region,
            month:minute,
            maxsampdepth, sigma0:sigma4,
            nitrite:nitritef))


GLODAP_tracer <- GLODAP %>% 
  select(row_number:gamma,
         cfc11:sf6f,
         basin_AIP)

# select relevant columns
GLODAP <- GLODAP %>%
  select(row_number:talkqc,
         basin_AIP)

2.8 Subset measured data

2.8.1 tco2

The vast majority of rows is removed due to missing tco2 observations.

GLODAP <- GLODAP %>% 
  filter(!is.na(tco2))

2.8.2 tracer

Rows are removed if no tracer observation is available.

GLODAP_tracer <- GLODAP_tracer %>%
  filter(if_any(
    c(
      cfc11,
      cfc12,
      cfc113,
      ccl4,
      sf6,
      pcfc11,
      pcfc12,
      pcfc113,
      pccl4,
      psf6
    ),
    ~ !is.na(.)
  ))

2.9 Create clean observations grid

2.9.1 tco2

GLODAP_obs_grid <- GLODAP %>% 
  count(lat, lon)

GLODAP_grid_year <- GLODAP %>%
  count(lat, lon, year)

map +
  geom_tile(data = GLODAP_grid_year,
              aes(lon, lat)) +
  facet_wrap(~ year, ncol=3)

Past versions of grid_by_year-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
dc8d958	jens-daniel-mueller	2021-10-20

2.9.2 tracer

GLODAP_obs_grid_tracer <- GLODAP_tracer %>% 
  count(lat, lon)

GLODAP_grid_year_tracer <- GLODAP_tracer %>%
  count(lat, lon, year)

map +
  geom_tile(data = GLODAP_grid_year_tracer,
              aes(lon, lat)) +
  facet_wrap(~ year, ncol=3)

Past versions of grid_by_year_tracer-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
2a50fa9	jens-daniel-mueller	2021-10-28

3 Flagging

In this sections, I explore the data coverage with respect to the flagging scheme. Data are not manipulated in this section.

3.1 qc

qc_flag <- GLODAP %>%
  mutate(decade = m_grid_decade(year),
         .after = year) %>%
  filter(!is.na(decade)) %>%
  select(lon, lat, basin_AIP, decade, cruise_expocode, ends_with("qc"))

qc_flag_grid <- qc_flag %>%
  pivot_longer(ends_with("qc"),
               names_to = "parameter",
               values_to = "value") %>%
  count(lon, lat, decade, parameter, value)

p_qc_flag_map <- qc_flag_grid %>%
  group_split(value) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = n)) +
      facet_grid(parameter ~ decade) +
      labs(title = paste("qc flag =", unique(.x$value))) +
      scale_fill_viridis_c(
        option = "magma",
        direction = -1,
        trans = "log10"
      )
  )

p_qc_flag_map

[[1]]

Past versions of qc_flagging-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
daa43b9	jens-daniel-mueller	2021-12-06

[[2]]

Past versions of qc_flagging-2.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
daa43b9	jens-daniel-mueller	2021-12-06

pdf("output/qc_flag_coverage_maps.pdf")
p_qc_flag_map

[[1]]

[[2]]

dev.off()

png 
  2 

qc_flag_grid_all_1 <- qc_flag %>%
  filter(
    if_all(ends_with("qc"), ~ . == 1)) %>%
  count(lon, lat, decade)

map +
  geom_tile(data = qc_flag_grid_all_1,
            aes(lon, lat, fill = n)) +
  facet_grid(decade ~ .) +
  labs(title = "All parameters qc == 1") +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10")

Past versions of qc_flagging-3.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10

rm(qc_flag, qc_flag_grid, p_qc_flag_map, qc_flag_grid_all_1)

3.2 f

f_flag <- GLODAP %>%
  mutate(decade = m_grid_decade(year),
         .after = year) %>%
  filter(!is.na(decade)) %>%
  select(lon, lat, basin_AIP, decade, cruise_expocode, ends_with("f"))

f_flag_grid <- f_flag %>%
  pivot_longer(ends_with("f"),
               names_to = "parameter",
               values_to = "value") %>%
  count(lon, lat, decade, parameter, value)

p_f_flag_map <- f_flag_grid %>% 
  group_split(value) %>%
  # head(1) %>%
  map(
    ~map +
  geom_tile(data = .x,
            aes(lon, lat, fill=n)) +
  facet_grid(parameter ~ decade) +
  labs(title = paste("f flag =", unique(.x$value))) +
    scale_fill_viridis_c(option = "magma",
                         direction = -1,
                         trans = "log10")
  )

p_f_flag_map

[[1]]

Past versions of f_flagging-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
de20732	jens-daniel-mueller	2021-12-08

[[2]]

Past versions of f_flagging-2.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
de20732	jens-daniel-mueller	2021-12-08

[[3]]

Past versions of f_flagging-3.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
de20732	jens-daniel-mueller	2021-12-08

pdf("output/f_flag_coverage_maps.pdf")
p_f_flag_map

[[1]]

[[2]]

[[3]]

dev.off()

png 
  2 

f_flag_grid_all_2 <- f_flag %>%
  filter(
    if_all(ends_with("f"), ~ . == 2)) %>%
  count(lon, lat, decade)

map +
  geom_tile(data = f_flag_grid_all_2,
            aes(lon, lat, fill = n)) +
  facet_grid(decade ~ .) +
  labs(title = "All parameters f == 2") +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10")

Past versions of f_flagging-4.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10

rm(f_flag, f_flag_grid, p_f_flag_map, f_flag_grid_all_2)

4 Data loss

In this section, I explore the potential loss of data if certain quality quality flag criteria are not met by the observations.

loss_all <- GLODAP %>%
  mutate(decade = m_grid_decade(year),
         .after = year) %>%
  filter(!is.na(decade))


loss <- loss_all %>%
  filter(if_all(ends_with("f"), ~ . != 9))

map +
  geom_tile(data = loss_all %>% distinct(lon, lat, decade),
            aes(lon, lat, fill = "incl f = 9")) +
  geom_tile(data = loss %>% distinct(lon, lat, decade),
            aes(lon, lat, fill = "excl f = 9")) +
  scale_fill_brewer(palette = "Set1") +
  facet_grid(decade ~ .) +
  labs(title = "All available data") +
  theme(legend.title = element_blank())

Past versions of data_loss_prepartion-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
e810585	jens-daniel-mueller	2021-12-16

loss_all_n <- loss_all %>%
  count(basin_AIP, decade)

loss_n <- loss %>%
  count(basin_AIP, decade)

4.1 qc

Here, I analysis the loss of data due to qc flagging, based on the samples were all parameters are available (i.e. where f-flag != 9).

# prepare qc loss data
loss_qc <- loss %>%
  select(lon, lat, basin_AIP, decade, cruise_expocode, ends_with("qc")) %>%
  pivot_longer(ends_with("qc"),
               names_to = "parameter",
               values_to = "value") %>%
  mutate(parameter = str_remove(parameter, "qc"))

# compute fraction of qc loss per parameters and cruise
loss_qc <- loss_qc %>%
  count(cruise_expocode, basin_AIP, decade, parameter, value) %>%
  pivot_wider(
    names_from = value,
    names_prefix = "qc_",
    values_from = n,
    values_fill = 0
  ) %>%
  mutate(n_cruise = qc_0 + qc_1,
         category = if_else(qc_0 <= 0.1 * (n_cruise), "OK", "loss"))

# calculate number of parameters with loss
# separately for target/predictor variables
loss_qc_cruise <- loss_qc %>%
  mutate(parameter_class = if_else(
    parameter %in% c("tco2", "talk", "phosphate"),
    "target",
    "predictor"
  )) %>%
  count(cruise_expocode,
        basin_AIP,
        decade,
        n_cruise,
        parameter_class,
        category) %>%
  pivot_wider(names_from = category,
              values_from = n,
              values_fill = 0) %>%
  select(-OK) %>%
  pivot_wider(names_from = parameter_class,
              values_from = loss) %>%
  group_by(basin_AIP, decade) %>%
  mutate(rank_n_cruise = rank(-n_cruise)) %>%
  ungroup()

# combine with total number of observations
loss_qc_cruise <- full_join(loss_qc_cruise, loss_n)

# calculate relative contribution of cruise samples to total
loss_qc_cruise <- loss_qc_cruise %>% 
  mutate(n_cruise_rel = 100 * n_cruise / n) %>% 
  arrange(basin_AIP, decade, -n_cruise_rel) %>% 
  group_by(basin_AIP, decade) %>% 
  mutate(n_cruise_rel_cum = cumsum(n_cruise_rel)) %>% 
  ungroup() %>% 
  select(-n)

loss_qc_cruise <- loss_qc_cruise %>% 
  pivot_longer(predictor:target,
               names_to = "parameter_class",
               values_to = "loss") %>% 
  mutate(loss = as.factor(loss))

grey_plasma <- c("grey80", viridisLite::plasma(4))

# filter large cruises
loss_qc_cruise <- loss_qc_cruise %>%
  filter(n_cruise_rel >= 3)

loss_qc_cruise %>%
  group_split(basin_AIP) %>%
  # head(3) %>%
  map(
    ~ ggplot(data = .x,
             aes(rank_n_cruise, n_cruise_rel, fill = loss)) +
      geom_point(shape = 21, size = 2) +
      scale_fill_manual(values = grey_plasma,
                        name = "variables missing") +
      facet_grid(decade ~ parameter_class) +
      labs(title = paste("basin_AIP:", unique(.x$basin_AIP))) +
      ylim(0, NA)
  )

[[1]]

Past versions of qc_loss-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
6aa4b75	jens-daniel-mueller	2021-12-16
163f976	jens-daniel-mueller	2021-12-16
be0850d	jens-daniel-mueller	2021-12-16
ce6cdae	jens-daniel-mueller	2021-12-15

[[2]]

Past versions of qc_loss-2.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
6aa4b75	jens-daniel-mueller	2021-12-16
163f976	jens-daniel-mueller	2021-12-16
be0850d	jens-daniel-mueller	2021-12-16
ce6cdae	jens-daniel-mueller	2021-12-15

[[3]]

Past versions of qc_loss-3.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
6aa4b75	jens-daniel-mueller	2021-12-16
163f976	jens-daniel-mueller	2021-12-16
be0850d	jens-daniel-mueller	2021-12-16
ce6cdae	jens-daniel-mueller	2021-12-15

loss_qc_cruise %>%
  filter(loss != 0) %>%
  select(basin_AIP,
         decade,
         parameter_class,
         rank_n_cruise,
         cruise_expocode,
         loss) %>%
  arrange(basin_AIP, decade, parameter_class, rank_n_cruise) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

basin_AIP	decade	parameter_class	rank_n_cruise	cruise_expocode	loss
Atlantic	1989-1999	target	11	06MT19900123	1
Atlantic	1989-1999	target	12	33LK19960415	1
Atlantic	2000-2009	predictor	8	35TH20010823	3
Atlantic	2000-2009	predictor	14	33RO20070710	1
Atlantic	2000-2009	target	8	35TH20010823	2
Atlantic	2000-2009	target	9	74DI20040404	1
Atlantic	2000-2009	target	10	35TH20080610	1
Atlantic	2000-2009	target	12	35TH20040604	1
Atlantic	2000-2009	target	13	35TH20020611	1
Atlantic	2010-2020	predictor	5	74EQ20151206	1
Atlantic	2010-2020	target	12	35TH20100608	1
Atlantic	2010-2020	target	13	29AH20160617	1
Indian	1989-1999	target	11	320619960503	1
Pacific	1989-1999	predictor	2	31DS19940126	1
Pacific	1989-1999	predictor	4	31DS19920907	3
Pacific	1989-1999	target	4	31DS19920907	3
Pacific	1989-1999	target	6	316N19930222	1
Pacific	1989-1999	target	7	316N19921006	1
Pacific	1989-1999	target	8	90KD19920214	1

loss_grid <- loss %>% distinct(lon, lat, cruise_expocode)

loss_qc_grid <- left_join(loss_qc_cruise,
                          loss_grid)

map +
  geom_tile(data = loss_qc_grid,
            aes(lon, lat, fill = loss)) +
  facet_grid(decade ~ parameter_class) +
  scale_fill_manual(values = grey_plasma)

Past versions of qc_loss_maps-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
6aa4b75	jens-daniel-mueller	2021-12-16
163f976	jens-daniel-mueller	2021-12-16
be0850d	jens-daniel-mueller	2021-12-16

loss_qc_grid %>% filter(loss != 0) %>%
  group_split(parameter_class, decade) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = cruise_expocode)) +
      scale_fill_brewer(palette = "Paired") +
      facet_grid(decade ~ parameter_class)
  )

[[1]]

Past versions of qc_loss_maps-2.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
e810585	jens-daniel-mueller	2021-12-16

[[2]]

Past versions of qc_loss_maps-3.png

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e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
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[[3]]

Past versions of qc_loss_maps-4.png

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e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
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[[4]]

Past versions of qc_loss_maps-5.png

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[[5]]

Past versions of qc_loss_maps-6.png

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e3d1a2b	jens-daniel-mueller	2022-03-10
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[[6]]

Past versions of qc_loss_maps-7.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10

rm(loss_qc_cruise, loss_qc_grid)

4.2 f

Here, I analysis the loss of data due to f flagging, based on the samples were all parameters are available (i.e. where f-flag != 9).

# prepare qc loss data
loss_f <- loss %>% 
  select(lon, lat, basin_AIP, decade, cruise_expocode, ends_with("f")) %>%
  pivot_longer(ends_with("f"),
               names_to = "parameter",
               values_to = "value") %>% 
  mutate(parameter = str_remove(parameter, "f"))

# compute fraction of qc loss per parameters and cruise
loss_f <- loss_f %>%
  count(cruise_expocode, basin_AIP, decade, parameter, value) %>%
  pivot_wider(
    names_from = value,
    names_prefix = "f_",
    values_from = n,
    values_fill = 0
  ) %>%
  mutate(n_cruise = f_0 + f_2,
         category = if_else(f_0 <= 0.1 * (n_cruise), "OK", "loss"))

# calculate number of parameters with loss
# separately for target/predictor variables
loss_f_cruise <- loss_f %>%
  mutate(parameter_class = if_else(
    parameter %in% c("tco2", "talk", "phosphate"),
    "target",
    "predictor"
  )) %>%
  count(cruise_expocode,
        basin_AIP,
        decade,
        n_cruise,
        parameter_class,
        category) %>% 
  pivot_wider(names_from = category,
              values_from = n,
              values_fill = 0) %>% 
  select(-OK) %>% 
  pivot_wider(names_from = parameter_class,
              values_from = loss) %>% 
  group_by(basin_AIP, decade) %>%
  mutate(rank_n_cruise = rank(-n_cruise)) %>%
  ungroup()

# combine with total number of observations
loss_f_cruise <- full_join(loss_f_cruise, loss_n)

# calculate relative contribution of cruise samples to total
loss_f_cruise <- loss_f_cruise %>% 
  mutate(n_cruise_rel = 100 * n_cruise / n) %>% 
  arrange(basin_AIP, decade, -n_cruise_rel) %>% 
  group_by(basin_AIP, decade) %>% 
  mutate(n_cruise_rel_cum = cumsum(n_cruise_rel)) %>% 
  ungroup() %>% 
  select(-n)

loss_f_cruise <- loss_f_cruise %>% 
  pivot_longer(predictor:target,
               names_to = "parameter_class",
               values_to = "loss") %>% 
  mutate(loss = as.factor(loss))

grey_plasma <- c("grey80", viridisLite::plasma(4))

# filter large cruises
loss_f_cruise <- loss_f_cruise %>%
    filter(n_cruise_rel >= 3)

loss_f_cruise %>%
  group_split(basin_AIP) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(rank_n_cruise, n_cruise, fill = loss)) +
      geom_point(shape = 21, size = 2) +
      scale_fill_manual(values = grey_plasma,
                        name = "variables missing") +
      facet_grid(decade ~ parameter_class) +
      labs(title = paste("basin_AIP:", unique(.x$basin_AIP))) +
      ylim(0, NA)
  )

[[1]]

Past versions of f_loss-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
6aa4b75	jens-daniel-mueller	2021-12-16
163f976	jens-daniel-mueller	2021-12-16
ce6cdae	jens-daniel-mueller	2021-12-15

[[2]]

Past versions of f_loss-2.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
6aa4b75	jens-daniel-mueller	2021-12-16
163f976	jens-daniel-mueller	2021-12-16
ce6cdae	jens-daniel-mueller	2021-12-15

[[3]]

Past versions of f_loss-3.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
6aa4b75	jens-daniel-mueller	2021-12-16
163f976	jens-daniel-mueller	2021-12-16
ce6cdae	jens-daniel-mueller	2021-12-15

loss_f_cruise %>%
  filter(loss != 0) %>%
  select(basin_AIP,
         decade,
         parameter_class,
         rank_n_cruise,
         cruise_expocode,
         loss) %>%
  arrange(basin_AIP, decade, parameter_class, rank_n_cruise) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

basin_AIP	decade	parameter_class	rank_n_cruise	cruise_expocode	loss
Atlantic	1989-1999	target	1	323019940104	1
Atlantic	1989-1999	target	7	33RO19980123	1
Atlantic	1989-1999	target	9	35A319950113	1
Atlantic	1989-1999	target	11	06MT19900123	1
Atlantic	1989-1999	target	12	33LK19960415	1
Atlantic	2000-2009	target	8	35TH20010823	1
Atlantic	2000-2009	target	9	74DI20040404	1
Atlantic	2000-2009	target	10	35TH20080610	1
Atlantic	2000-2009	target	12	35TH20040604	1
Atlantic	2000-2009	target	13	35TH20020611	1
Atlantic	2010-2020	target	9	33RO20110926	1
Atlantic	2010-2020	target	12	35TH20100608	1
Atlantic	2010-2020	target	13	29AH20160617	1
Indian	1989-1999	target	11	320619960503	1
Indian	2000-2009	target	3	33RR20080204	1
Pacific	1989-1999	target	3	31DS19960105	1
Pacific	1989-1999	target	6	316N19930222	1
Pacific	1989-1999	target	7	316N19921006	1
Pacific	1989-1999	target	8	90KD19920214	1
Pacific	2000-2009	target	1	33RO20071215	1
Pacific	2000-2009	target	6	318M20091121	1
Pacific	2010-2020	target	4	320620170703	1
Pacific	2010-2020	target	9	318M20091121	2

rm(loss_n)

loss_grid <- loss %>% distinct(lon, lat, cruise_expocode)

loss_f_grid <- left_join(loss_f_cruise,
                          loss_grid)
map +
  geom_tile(data = loss_f_grid,
            aes(lon, lat, fill = loss)) +
  facet_grid(decade ~ parameter_class) +
  scale_fill_manual(values = grey_plasma)

Past versions of f_loss_maps-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
6aa4b75	jens-daniel-mueller	2021-12-16
163f976	jens-daniel-mueller	2021-12-16
be0850d	jens-daniel-mueller	2021-12-16

loss_f_grid %>% filter(loss != 0) %>%
  group_split(parameter_class, decade) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = cruise_expocode)) +
      scale_fill_brewer(palette = "Paired") +
      facet_grid(decade ~ parameter_class)
  )

[[1]]

Past versions of f_loss_maps-2.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
e810585	jens-daniel-mueller	2021-12-16

[[2]]

Past versions of f_loss_maps-3.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
e810585	jens-daniel-mueller	2021-12-16

[[3]]

Past versions of f_loss_maps-4.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
e810585	jens-daniel-mueller	2021-12-16

rm(loss_f_cruise, loss_f_grid)
rm(loss_grid)

4.3 f == 9

Here, I analysis the loss of data due to unavailability (i.e. where f-flag == 9).

loss_f9 <- loss_all %>% 
  select(lon, lat, basin_AIP, decade, cruise_expocode, ends_with("f")) %>%
  pivot_longer(ends_with("f"),
               names_to = "parameter",
               values_to = "value") %>% 
  mutate(parameter = str_remove(parameter, "f"))

loss_f9 <- loss_f9 %>%
  count(cruise_expocode, basin_AIP, decade, parameter, value) %>%
  pivot_wider(
    names_from = value,
    names_prefix = "f_",
    values_from = n,
    values_fill = 0
  ) %>%
  mutate(n_cruise = f_0 + f_2 + f_9,
         category = if_else(f_9 <= 0.1 * (n_cruise), "OK", "loss"))

loss_f9_cruise <- loss_f9 %>%
  mutate(parameter_class = if_else(
    parameter %in% c("tco2", "talk", "phosphate"),
    "target",
    "predictor"
  )) %>%
  count(cruise_expocode,
        basin_AIP,
        decade,
        n_cruise,
        parameter_class,
        category) %>% 
  pivot_wider(names_from = category,
              values_from = n,
              values_fill = 0) %>% 
  select(-OK) %>% 
  pivot_wider(names_from = parameter_class,
              values_from = loss) %>% 
  group_by(basin_AIP, decade) %>%
  mutate(rank_n_cruise = rank(-n_cruise)) %>%
  ungroup()

loss_f9_cruise <- full_join(loss_f9_cruise, loss_all_n)

loss_f9_cruise <- loss_f9_cruise %>% 
  mutate(n_cruise_rel = 100 * n_cruise / n) %>% 
  arrange(basin_AIP, decade, -n_cruise_rel) %>% 
  group_by(basin_AIP, decade) %>% 
  mutate(n_cruise_rel_cum = cumsum(n_cruise_rel)) %>% 
  ungroup() %>% 
  select(-n)

loss_f9_cruise <- loss_f9_cruise %>% 
  pivot_longer(predictor:target,
               names_to = "parameter_class",
               values_to = "loss") %>% 
  mutate(loss = as.factor(loss))

grey_plasma <- c("grey80", viridisLite::plasma(4))

loss_f9_cruise <- loss_f9_cruise %>%
    filter(n_cruise_rel >= 3)

loss_f9_cruise %>%
  group_split(basin_AIP) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(rank_n_cruise, n_cruise, fill = loss)) +
      geom_point(shape = 21, size = 2) +
      scale_fill_manual(values = grey_plasma,
                        name = "variables missing") +
      facet_grid(decade ~ parameter_class) +
      labs(title = paste("basin_AIP:", unique(.x$basin_AIP))) +
      ylim(0, NA)
  )

[[1]]

Past versions of f9_loss-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
6aa4b75	jens-daniel-mueller	2021-12-16

[[2]]

Past versions of f9_loss-2.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
6aa4b75	jens-daniel-mueller	2021-12-16

[[3]]

Past versions of f9_loss-3.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
6aa4b75	jens-daniel-mueller	2021-12-16

loss_f9_cruise %>% 
  filter(loss != 0) %>% 
  select(basin_AIP, decade, parameter_class, rank_n_cruise, cruise_expocode) %>% 
  arrange(basin_AIP, decade, parameter_class, rank_n_cruise) %>% 
  kable() %>% 
  kable_styling() %>% 
  scroll_box(height = "300px")

basin_AIP	decade	parameter_class	rank_n_cruise	cruise_expocode
Atlantic	1989-1999	predictor	2	316N19871123
Atlantic	1989-1999	predictor	4	06AQ19980328
Atlantic	1989-1999	predictor	6	74DI19970807
Atlantic	1989-1999	target	2	316N19871123
Atlantic	1989-1999	target	3	33RO19980123
Atlantic	1989-1999	target	4	06AQ19980328
Atlantic	1989-1999	target	6	74DI19970807
Atlantic	1989-1999	target	7	33MW19930704
Atlantic	2000-2009	target	1	33RO20050111
Atlantic	2000-2009	target	2	33RO20030604
Atlantic	2000-2009	target	3	06AQ20050122
Atlantic	2000-2009	target	4	06AQ20080210
Atlantic	2010-2020	predictor	10	06M220170104
Atlantic	2010-2020	predictor	11	06AQ20120107
Atlantic	2010-2020	target	3	33RO20110926
Atlantic	2010-2020	target	6	29HE20130320
Atlantic	2010-2020	target	10	06M220170104
Indian	1989-1999	predictor	1	316N19951202
Indian	1989-1999	predictor	3	316N19950310
Indian	1989-1999	predictor	7	35MF19960220
Indian	1989-1999	target	1	316N19951202
Indian	1989-1999	target	5	316N19941201
Indian	1989-1999	target	8	320619960503
Indian	1989-1999	target	10	316N19950611
Indian	1989-1999	target	12	35MF19930123
Indian	2000-2009	predictor	10	09AR20071216
Indian	2000-2009	target	7	09AR20060102
Indian	2010-2020	predictor	8	09AR20141205
Indian	2010-2020	target	5	325020190403
Indian	2010-2020	target	8	09AR20141205
Pacific	1989-1999	predictor	6	33MW19920224
Pacific	1989-1999	target	1	316N19920502
Pacific	1989-1999	target	6	33MW19920224
Pacific	2000-2009	predictor	8	325020060213

loss_all_grid <- loss_all %>% distinct(lon, lat, cruise_expocode)

loss_f9_grid <- left_join(loss_f9_cruise,
                          loss_all_grid)
map +
  geom_tile(data = loss_f9_grid,
            aes(lon, lat, fill = loss)) +
  facet_grid(decade ~ parameter_class) +
  scale_fill_manual(values = grey_plasma)

Past versions of f9_loss_maps-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
6aa4b75	jens-daniel-mueller	2021-12-16

loss_f9_grid %>% filter(loss != 0) %>%
  group_split(parameter_class, decade) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = cruise_expocode)) +
      scale_fill_brewer(palette = "Paired") +
      facet_grid(decade ~ parameter_class)
  )

[[1]]

Past versions of f9_loss_maps-2.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
e810585	jens-daniel-mueller	2021-12-16

[[2]]

Past versions of f9_loss_maps-3.png

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e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
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[[3]]

Past versions of f9_loss_maps-4.png

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e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
e810585	jens-daniel-mueller	2021-12-16

[[4]]

Past versions of f9_loss_maps-5.png

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e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
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[[5]]

Past versions of f9_loss_maps-6.png

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e3d1a2b	jens-daniel-mueller	2022-03-10
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[[6]]

Past versions of f9_loss_maps-7.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
e810585	jens-daniel-mueller	2021-12-16

rm(loss_f9_cruise, loss_f9_grid)
rm(loss_all_grid)
rm(loss_all_n)
rm(loss)

4.4 Relevant cruises

Below, I plot the most relevant cruises that would be lost when applying the strictest quality flagging criteria. These cruises were hand-picked, based on the relevance analysis shown above.

expocodes_missing <- GLODAP_cruises_missing %>%
  distinct(cruise_expocode) %>%
  pull()

missing_cruise_grid <- loss_all %>%
  filter(cruise_expocode %in% expocodes_missing) %>% 
  distinct(cruise_expocode, decade, lon, lat)

missing_cruise_grid %>%
  group_split(decade) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = str_sub(
                  cruise_expocode, 1, 4
                ))) +
      facet_grid(decade ~ .) +
      scale_fill_brewer(palette = "Paired",
                        name = "RV")
  )

[[1]]

Past versions of relevant_lost_cruises-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10

[[2]]

Past versions of relevant_lost_cruises-2.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10

[[3]]

Past versions of relevant_lost_cruises-3.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10

4.5 P18 phosphate

Here I analyse the phosphate data from section P18, which was repeated 3 times.

P18 <- GLODAP %>% 
  filter(cruise_expocode %in% c("33RO20161119",
                                "33RO20071215",
                                "31DS19940126"))
# plot raw data section
P18 %>% 
  filter(!is.na(nitrate)) %>% 
  ggplot(aes(lat, depth, col= nitrate)) +
  geom_point() +
  scale_color_viridis_c() +
  scale_y_reverse() +
  facet_grid(cruise_expocode ~.)

Past versions of P18_nitrate-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
70923f2	jens-daniel-mueller	2021-12-14
b68b58e	jens-daniel-mueller	2021-12-13

# grid section data
P18_grid <- P18 %>% 
  select(lat, lon, depth, cruise_expocode, nitrate) %>% 
  mutate(depth = as.numeric(as.character(cut(depth,
                     seq(0,1e4, 500), 
                     seq(250,1e4,500))))) %>% 
  group_by(lat, depth, cruise_expocode) %>% 
  summarise(nitrate = mean(nitrate, na.rm=TRUE)) %>% 
  ungroup()

P18_grid %>% 
  ggplot(aes(lat, depth, fill= nitrate)) +
  geom_tile() +
  scale_fill_viridis_c() +
  scale_y_reverse() +
  facet_grid(cruise_expocode ~.)

Past versions of P18_nitrate-2.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
70923f2	jens-daniel-mueller	2021-12-14
b68b58e	jens-daniel-mueller	2021-12-13

# calculate gridded offsets
P18_grid_offset <- P18_grid %>%
  pivot_wider(names_from = cruise_expocode,
              values_from = nitrate) %>%
  mutate(
    delta_nitrate_1994_2007 = (`31DS19940126` - `33RO20071215`) / `33RO20071215`,
    delta_nitrate_1994_2016 = (`31DS19940126` - `33RO20161119`) / `33RO20071215`,
    delta_nitrate_2007_2016 = (`33RO20071215` - `33RO20161119`) / `33RO20071215`
  ) %>%
  select(lat, depth, starts_with("delta")) %>%
  pivot_longer(
    starts_with("delta"),
    values_to = "delta_nitrate",
    names_to = "years",
    names_prefix = "delta_nitrate_"
  ) %>%
  filter(delta_nitrate > -20,
         depth > 1500)

P18_grid_offset %>% 
  ggplot(aes(lat, depth, fill = delta_nitrate)) +
  geom_tile() +
  scale_fill_divergent() +
  scale_y_reverse() +
  facet_grid(years ~.)

Past versions of P18_nitrate-3.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
70923f2	jens-daniel-mueller	2021-12-14
b68b58e	jens-daniel-mueller	2021-12-13

P18_grid_offset %>%
  group_by(lat, years) %>%
  summarise(delta_nitrate = mean(delta_nitrate, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(lat, delta_nitrate, col = years, fill = years)) +
  geom_hline(yintercept = 0) +
  stat_smooth(method = "lm", formula = y ~ x + I(x ^ 2)) +
  geom_point() +
  geom_line()

Past versions of P18_nitrate-4.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
70923f2	jens-daniel-mueller	2021-12-14

rm(P18, P18_grid, P18_grid_offset)

4.6 A16

A16 <- GLODAP %>% 
  filter(cruise_expocode %in% c(
    "33MW19930704" #A16N-1993
    ))

map + 
  geom_tile(data = A16 %>% distinct(lon, lat),
            aes(lon, lat))

Past versions of A16-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
70923f2	jens-daniel-mueller	2021-12-14

A16 %>% 
  select(ends_with(c("qc"))) %>% 
  pivot_longer(everything(),
               names_to = "flag",
               values_to = "value") %>% 
  distinct(flag, value)

# A tibble: 9 × 2
  flag        value
  <chr>       <dbl>
1 salinityqc      1
2 oxygenqc        1
3 nitrateqc       1
4 silicateqc      1
5 phosphateqc     1
6 tco2qc          1
7 talkqc          1
8 talkqc          0
9 tco2qc          0

rm(A16)

5 Adjustments

Typically, the reasons for multiple expocode entries of the same cruise in the adjustment table list are:

1. The cruise adjustments are different for different station, i.e. station split (e.g. 316N19821201)

-> How to merge? Based on first and last station? Cruise_ID not in GLODAP merged master file.

2. The cruise adjustments are different for different legs (e.g. 316N19871123.6) but have been merged into one cruise (316N19871123) for the product

-> How to merge? Based on first and last station?

3. The cruise adjustments have been updated/changed through the versions, here always look for the most recent entry (see table below) (e.g. 320620180309)

For the expocodes not listed in the expocode list the reason is that INDIGO has been splitted into three cruises: 35MF1985-1987 and the same holds for SAVE (316N1987 - 6legs). Further 49HH20011208 has been assigned wrongly and corrected to 49HH20011127.

Remove expocode INDIGO and maintain only 35MF19850224. Remove expocode SAVE and maintain only 316N1987.

GLODAP_adjustments <- GLODAP_adjustments %>%
  select(cruise_expocode,
         first_station, last_station,
         version,
         calculated_carbon_parameter,
         ends_with("_adj")) %>% 
  rename(talk_adj = alkalinity_adj)

# Remove cruises INDIGO and SAVE

GLODAP_adjustments <- 
  GLODAP_adjustments %>% 
  filter(!(cruise_expocode %in% c("INDIGO", "SAVE")))

# correct expocode 49HH20011208 to 49HH20011127

GLODAP_adjustments <- 
  GLODAP_adjustments %>% 
  mutate(cruise_expocode = if_else(
    cruise_expocode == "49HH20011208",
    "49HH20011127",
    cruise_expocode
  ))


# select latest adjustment versions
GLODAP_adjustments <- 
  GLODAP_adjustments %>% 
  group_by(cruise_expocode, first_station) %>% 
  mutate(n = n(),
         version_max = max(version)) %>%
  ungroup() %>% 
  filter(version == version_max | is.na(version)) %>% 
  select(-c(version_max, version, n))

# harmonize multiple cruise expocodes of 316N1987
GLODAP_adjustments <- GLODAP_adjustments %>% 
  mutate(cruise_expocode = str_split(cruise_expocode,
                                     "\\.",
                                     simplify = TRUE)[,1])

# correct one wrong last_cruise label
GLODAP_adjustments <- GLODAP_adjustments %>%
  mutate(
    last_station = if_else(
      cruise_expocode == "318M20091121" &
        first_station == 1,
      127,
      last_station
    )
  )

# merge with expocode table
GLODAP_adjustments <-
  full_join(GLODAP_adjustments, GLODAP_expocodes) %>%
  relocate(cruise)


GLODAP_adjustments_NA_cruises <- 
  GLODAP_adjustments %>% 
  filter(is.na(cruise))

GLODAP_adjustments_duplicated_cruises <- 
  GLODAP_adjustments %>% 
  group_by(cruise_expocode, cruise) %>% 
  mutate(n = n()) %>%
  ungroup() %>% 
  filter(n != 1)


GLODAP_adjustments %>% 
  pivot_longer(salinity_adj:c13_adj,
               names_to = "parameter",
               values_to = "adjustment") %>% 
  ggplot(aes(adjustment)) +
  geom_histogram() +
  scale_y_log10() +
  facet_wrap(~ parameter, scales = "free_x")

Past versions of prepare_adjustments-1.png

Version	Author	Date
6d6a23e	jens-daniel-mueller	2021-11-01

rm(GLODAP_adjustments_duplicated_cruises,
   GLODAP_adjustments_NA_cruises)

6 Crossover preparation

6.1 Histograms

GLODAP_adjustments_long <- GLODAP_adjustments %>%
  select(
    cruise_expocode,
    first_station,
    last_station,
    tco2_adj,
    talk_adj,
    phosphate_adj,
    nitrate_adj,
    oxygen_adj,
    silicate_adj,
    salinity_adj
  ) %>%
  pivot_longer(tco2_adj:salinity_adj,
               names_to = "parameter",
               values_to = "adjustment") %>% 
  mutate(parameter = str_remove(parameter, "_adj"))

p_adjustment_histo <- GLODAP_adjustments_long %>% 
  ggplot(aes(adjustment)) +
  geom_histogram() +
  scale_y_log10() +
  facet_wrap(~ parameter, scales = "free_x", ncol = 1)

p_xover_histo <- 
  ggplot() +
  geom_histogram(data = glodapv2_xover,
                 aes(offset)) +
  labs(title = "v2") +
  scale_y_log10() +
  facet_wrap(~ parameter, scales = "free_x", ncol = 1)

p_xover_histo_2021 <- 
  ggplot() +
  geom_histogram(data = glodapv2_2021_xover,
                 aes(offset)) +
  labs(title = "v2_2021") +
  scale_y_log10() +
  facet_wrap(~ parameter, scales = "free_x", ncol = 1)

p_xover_histo + p_xover_histo_2021 + p_adjustment_histo

Past versions of crossover_histograms-1.png

Version	Author	Date
a87f8c7	jens-daniel-mueller	2021-12-20
d5ef2c6	jens-daniel-mueller	2021-12-20

rm(p_xover_histo, p_xover_histo_2021, p_adjustment_histo)

6.2 Adjustment correction

The crossover analysis I received refer to unadjusted data. In order to analyse remaining crossover biases that are relevant for the adjusted data, the crossover results are adjusted with the same value that was also applied to the data.

# join crossover and adjustments
glodapv2_xover <- left_join(
  glodapv2_xover,
  GLODAP_adjustments_long %>%
    select(
      cruise_A = cruise_expocode,
      parameter,
      first_station_A = first_station,
      last_station_A = last_station,
      adjustment_A = adjustment
    )
)

glodapv2_xover <- left_join(
  glodapv2_xover,
  GLODAP_adjustments_long %>%
    select(
      cruise_B = cruise_expocode,
      parameter,
      first_station_B = first_station,
      last_station_B = last_station,
      adjustment_B = adjustment
    )
)

glodapv2_xover <- glodapv2_xover %>% 
  mutate(adjustment_A = if_else(
    parameter %in% c("salinity", "talk", "tco2"),
    replace_na(adjustment_A, 0),
    replace_na(adjustment_A, 1)
  )) %>% 
  mutate(adjustment_B = if_else(
    parameter %in% c("salinity", "talk", "tco2"),
    replace_na(adjustment_B, 0),
    replace_na(adjustment_B, 1)
  ))



# apply adjustment to crossover
glodapv2_xover <- glodapv2_xover  %>%
  mutate(offset_adj = 
           if_else(parameter %in% c("salinity", "talk", "tco2"),
                   offset + adjustment_A - adjustment_B,
                   offset * adjustment_A / adjustment_B))

# join crossover and adjustments
glodapv2_2021_xover <- left_join(
  glodapv2_2021_xover,
  GLODAP_adjustments_long %>%
    select(
      cruise_A = cruise_expocode,
      parameter,
      first_station_A = first_station,
      last_station_A = last_station,
      adjustment_A = adjustment
    )
)

glodapv2_2021_xover <- left_join(
  glodapv2_2021_xover,
  GLODAP_adjustments_long %>%
    select(
      cruise_B = cruise_expocode,
      parameter,
      first_station_B = first_station,
      last_station_B = last_station,
      adjustment_B = adjustment
    )
)

glodapv2_2021_xover <- glodapv2_2021_xover %>% 
  mutate(adjustment_A = if_else(
    parameter %in% c("salinity", "talk", "tco2"),
    replace_na(adjustment_A, 0),
    replace_na(adjustment_A, 1)
  )) %>% 
  mutate(adjustment_B = if_else(
    parameter %in% c("salinity", "talk", "tco2"),
    replace_na(adjustment_B, 0),
    replace_na(adjustment_B, 1)
  ))


# apply adjustment to crossover
glodapv2_2021_xover <- glodapv2_2021_xover  %>%
  mutate(offset_adj = 
           if_else(parameter %in% c("salinity", "talk", "tco2"),
                   offset + adjustment_A,
                   offset * adjustment_A))

xover <- bind_rows(glodapv2_xover,
                   glodapv2_2021_xover)

rm(glodapv2_xover,
   glodapv2_2021_xover)


xover <- xover %>%
  mutate(date_A = ymd(str_sub(cruise_A, 5, 12)),
         date_B = ymd(str_sub(cruise_B, 5, 12)))

# Remove cruises with expocodes starting with "OMEX"
# for which dates cannot be extracted from expocode

xover <- xover %>%
  filter(!is.na(date_A),
         !is.na(date_B))


xover <- xover %>% 
  filter(!is.na(offset_adj))

7 Crossover analysis

7.0.1 Functions

# reverse cruise A and B
m_xover_reverse <- function(df) {
  df_rev <- df %>%
    rename(
      cruise_A_back = cruise_A,
      cruise_A = cruise_B,
      date_A_back = date_A,
      date_A = date_B,
      n_A_back = n_A,
      n_A = n_B,
      adjustment_A_back = adjustment_A,
      adjustment_A = adjustment_B
    ) %>%
    rename(cruise_B = cruise_A_back,
           date_B = date_A_back,
           n_B = n_A_back,
           adjustment_B = adjustment_A_back) %>%
    mutate(
      offset = if_else(
        parameter %in% c("salinity", "talk", "tco2"),
        -offset,
        1 / offset
      ),
      offset_adj = if_else(
        parameter %in% c("salinity", "talk", "tco2"),
        -offset_adj,
        1 / offset_adj
      )
    )
  return(df_rev)
}


# extract cruise based on expocode
m_xover_cruise_extractation <- function (df, expocode) {
  
  xover_cruise_A <- df %>%
    filter(cruise_A %in% expocode)
  
  xover_cruise_B <- df %>%
    filter(cruise_B %in% expocode)
  
  xover_cruise_B_rev <- m_xover_reverse(df = xover_cruise_B)
  
  xover_cruise <- bind_rows(xover_cruise_A,
                            xover_cruise_B_rev)
  
  return(xover_cruise)
}

7.1 Missing/flagged data

Analyse crossover results for cruises that cause a relevant data gap, with the aim to inform the use of data from these cruises.

hline_intercept <-
  tibble(parameter = unique(xover$parameter)) %>%
  mutate(intercept = if_else(parameter %in% c("salinity", "talk", "tco2"),
                             0,
                             1))

for (i_expocodes_missing in expocodes_missing) {
  # i_expocodes_missing <- expocodes_missing[1]
  
  cruise <- GLODAP %>%
    filter(cruise_expocode == i_expocodes_missing) %>% 
    rename(salinity = sal)
  
  # extract parameter that cause qc loss
  parameter_qc <- loss_qc %>%
    filter(cruise_expocode == i_expocodes_missing,
           category == "loss") 
  
  parameter_qc <- parameter_qc %>%
    pull(parameter)
  
  print(paste("qc parameter:", parameter_qc))
  
  if (length(parameter_qc) > 0) {
    parameter_qc <- parameter_qc %>% str_c(.,"qc")
  }
  
  
  # extract parameter that cause f loss
  parameter_f <- loss_f %>%
    filter(cruise_expocode == i_expocodes_missing,
           category == "loss")
  
  parameter_f <- parameter_f %>%
    pull(parameter)
  
  print(paste("f parameter:", parameter_f))
    
  if (length(parameter_f) > 0) {
    parameter_f <- parameter_f %>% str_c(.,"f")
  }
  
  # extract parameter that cause f9 loss
  parameter_f9 <- loss_f9 %>%
    filter(cruise_expocode == i_expocodes_missing,
           category == "loss")
  
  parameter_f9 <- parameter_f9 %>%
    pull(parameter)
  
  print(paste("f9 parameter:", parameter_f9))
  
  if (length(parameter_f9) > 0) {
    parameter_f9 <- parameter_f9 %>% str_c(.,"f")
  }
  
  # extract unique loss parameters
  parameter_check <-
    unique(c(parameter_qc, parameter_f, parameter_f9))
  
  rm(parameter_qc, parameter_f, parameter_f9)
  

  xover_cruise <- m_xover_cruise_extractation(
    df = xover %>% mutate(n_A = 0,
                          n_B = 0),
    expocode = i_expocodes_missing
  )

  for (i_parameter_check in parameter_check) {
    # i_parameter_check <- parameter_check[1]
    
    cruise_flag_count <- cruise %>%
      count(lon, lat, !!sym(i_parameter_check)) %>%
      group_by(lon, lat) %>%
      mutate(n_rel = 100 * n / sum(n)) %>%
      ungroup()
    
    print(
      map +
        geom_tile(data = cruise_flag_count,
                  aes(lon, lat, fill = n_rel)) +
        scale_fill_viridis_c(option = "magma",
                             direction = -1) +
        facet_wrap(i_parameter_check, ncol = 2) +
        labs(title = i_expocodes_missing,
             subtitle = i_parameter_check)
    )
    
    i_parameter_check_var <- str_remove(i_parameter_check, "f")
    i_parameter_check_var <- str_remove(i_parameter_check_var, "qc")
    
    print(
      cruise %>% 
        ggplot(aes(!!sym(i_parameter_check_var), depth, fill=station)) +
        geom_point(alpha = 0.2, shape = 21) +
        scale_fill_viridis_c() +
        scale_y_reverse() +
        facet_wrap(i_parameter_check, ncol = 2) +
        labs(title = i_expocodes_missing,
             subtitle = i_parameter_check)
    )
    
  }

  p_crossover_ts <- xover_cruise %>%
    ggplot(aes(date_B, offset_adj)) +
    geom_vline(xintercept = ymd(str_sub(i_expocodes_missing, 5)),
               col = "red") +
    geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
    geom_point() +
    facet_grid(parameter ~ ., scales = "free_y") +
    labs(title = i_expocodes_missing,
         subtitle = str_c(parameter_check, collapse = "+")) +
    theme(
      legend.position = "bottom",
      legend.direction = "vertical",
      axis.title.x = element_blank()
    )
  
  xover_cruise_decade <- xover_cruise %>%
    mutate(decade = m_grid_decade(year(date_B))) %>%
    filter(!is.na(decade)) %>%
    group_by(parameter, decade) %>%
    mutate(n = n()) %>%
    ungroup() %>% 
    filter(n > 2)
  
  
  p_crossover_decadal <-
    ggplot() +
    geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
    geom_violin(
      data = xover_cruise_decade,
      aes(x = decade, y = offset_adj),
      fill = "gold"
    ) +
    geom_boxplot(
      data = xover_cruise_decade,
      aes(x = decade, y = offset_adj),
      width = 0.2
    ) +
    labs(title = "Decadal averages") +
    facet_grid(parameter ~ ., scales = "free_y") +
    theme(axis.title.x = element_blank(),
          axis.text.x = element_text(angle = 90))
  
  print(
  p_crossover_ts + p_crossover_decadal +
    plot_layout(widths = c(2, 1))
  )
  
  rm(p_crossover_ts, p_crossover_decadal)
  
}

[1] "qc parameter: "
[1] "f parameter: talk"
[1] "f9 parameter: "

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[1] "qc parameter: "
[1] "f parameter: talk"
[1] "f9 parameter: "

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[1] "qc parameter: "
[1] "f parameter: "
[1] "f9 parameter: aou"

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[1] "qc parameter: "
[1] "f parameter: "
[1] "f9 parameter: nitrate"   "f9 parameter: phosphate"
[3] "f9 parameter: silicate"  "f9 parameter: talk"     

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[1] "qc parameter: talk" "qc parameter: talk"
[1] "f parameter: talk" "f parameter: talk"
[1] "f9 parameter: talk" "f9 parameter: aou"  "f9 parameter: talk"

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[1] "qc parameter: "
[1] "f parameter: "
[1] "f9 parameter: aou"  "f9 parameter: talk"

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[1] "qc parameter: talk"
[1] "f parameter: talk"
[1] "f9 parameter: phosphate" "f9 parameter: talk"     

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[1] "qc parameter: talk"
[1] "f parameter: talk"
[1] "f9 parameter: talk"

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Past versions of crossover_missing_flagged_data-40.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-41.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-42.png

Version	Author	Date
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

[1] "qc parameter: "
[1] "f parameter: "
[1] "f9 parameter: talk"

Past versions of crossover_missing_flagged_data-43.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-44.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-45.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

[1] "qc parameter: "
[1] "f parameter: "
[1] "f9 parameter: aou"      "f9 parameter: salinity"

Past versions of crossover_missing_flagged_data-46.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-47.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-48.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-49.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-50.png

Version	Author	Date
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

[1] "qc parameter: "
[1] "f parameter: "
[1] "f9 parameter: talk"

Past versions of crossover_missing_flagged_data-51.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-52.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-53.png

Version	Author	Date
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

[1] "qc parameter: "
[1] "f parameter: tco2" "f parameter: talk" "f parameter: tco2"
[1] "f9 parameter: "

Past versions of crossover_missing_flagged_data-54.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-55.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-56.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-57.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-58.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

[1] "qc parameter: nitrate"
[1] "f parameter: "
[1] "f9 parameter: "

Past versions of crossover_missing_flagged_data-59.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-60.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-61.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

[1] "qc parameter: "
[1] "f parameter: talk"
[1] "f9 parameter: "

Past versions of crossover_missing_flagged_data-62.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-63.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-64.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

[1] "qc parameter: talk"
[1] "f parameter: talk"
[1] "f9 parameter: phosphate"

Past versions of crossover_missing_flagged_data-65.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-66.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-67.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-68.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-69.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-70.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-71.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

[1] "qc parameter: "
[1] "f parameter: talk"
[1] "f9 parameter: phosphate"

Past versions of crossover_missing_flagged_data-72.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-73.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-74.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-75.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-76.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

[1] "qc parameter: "
[1] "f parameter: talk"
[1] "f9 parameter: "

Past versions of crossover_missing_flagged_data-77.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-78.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-79.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

[1] "qc parameter: "
[1] "f parameter: tco2"
[1] "f9 parameter: phosphate"

Past versions of crossover_missing_flagged_data-80.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-81.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-82.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-83.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-84.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

[1] "qc parameter: "
[1] "f parameter: tco2"
[1] "f9 parameter: "

Past versions of crossover_missing_flagged_data-85.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-86.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-87.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

[1] "qc parameter: "
[1] "f parameter: tco2"
[1] "f9 parameter: aou"

Past versions of crossover_missing_flagged_data-88.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-89.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-90.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-91.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

Past versions of crossover_missing_flagged_data-92.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
fcff192	jens-daniel-mueller	2021-12-21
2704ff6	jens-daniel-mueller	2021-12-20

[1] "qc parameter: "
[1] "f parameter: "
[1] "f9 parameter: phosphate"

Past versions of crossover_missing_flagged_data-93.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-94.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-95.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
fcff192	jens-daniel-mueller	2021-12-21

[1] "qc parameter: "
[1] "f parameter: "
[1] "f9 parameter: silicate"

Past versions of crossover_missing_flagged_data-96.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-97.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-98.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

[1] "qc parameter: tco2"
[1] "f parameter: tco2"
[1] "f9 parameter: "

Past versions of crossover_missing_flagged_data-99.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-100.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-101.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-102.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-103.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21

[1] "qc parameter: tco2"
[1] "f parameter: tco2"
[1] "f9 parameter: nitrate"

Past versions of crossover_missing_flagged_data-104.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-105.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-106.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-107.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-108.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-109.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-110.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21

[1] "qc parameter: tco2"
[1] "f parameter: tco2"
[1] "f9 parameter: "

Past versions of crossover_missing_flagged_data-111.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-112.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-113.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-114.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-115.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21

[1] "qc parameter: "
[1] "f parameter: "
[1] "f9 parameter: nitrate"   "f9 parameter: phosphate"
[3] "f9 parameter: silicate" 

Past versions of crossover_missing_flagged_data-116.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-117.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-118.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-119.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-120.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-121.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-122.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21

[1] "qc parameter: talk"
[1] "f parameter: talk"
[1] "f9 parameter: "

Past versions of crossover_missing_flagged_data-123.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-124.png

Version	Author	Date
fcff192	jens-daniel-mueller	2021-12-21

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e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

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fcff192	jens-daniel-mueller	2021-12-21

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744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21

[1] "qc parameter: nitrate"
[1] "f parameter: "
[1] "f9 parameter: "

Past versions of crossover_missing_flagged_data-128.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

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fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-130.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
fcff192	jens-daniel-mueller	2021-12-21

[1] "qc parameter: "
[1] "f parameter: "
[1] "f9 parameter: phosphate"

Past versions of crossover_missing_flagged_data-131.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-132.png

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fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-133.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21

[1] "qc parameter: "
[1] "f parameter: "
[1] "f9 parameter: phosphate"

Past versions of crossover_missing_flagged_data-134.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-135.png

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fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-136.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21

[1] "qc parameter: "
[1] "f parameter: nitrate"   "f parameter: phosphate" "f parameter: silicate" 
[1] "f9 parameter: talk"      "f9 parameter: nitrate"  
[3] "f9 parameter: phosphate" "f9 parameter: silicate" 
[5] "f9 parameter: talk"     

Past versions of crossover_missing_flagged_data-137.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

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fcff192	jens-daniel-mueller	2021-12-21

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e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

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fcff192	jens-daniel-mueller	2021-12-21

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e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

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fcff192	jens-daniel-mueller	2021-12-21

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e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

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fcff192	jens-daniel-mueller	2021-12-21

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744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21

[1] "qc parameter: "
[1] "f parameter: "
[1] "f9 parameter: talk"

Past versions of crossover_missing_flagged_data-146.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

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fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-148.png

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744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21

[1] "qc parameter: "
[1] "f parameter: "
[1] "f9 parameter: phosphate"

Past versions of crossover_missing_flagged_data-149.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-150.png

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fcff192	jens-daniel-mueller	2021-12-21

Past versions of crossover_missing_flagged_data-151.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
fcff192	jens-daniel-mueller	2021-12-21

rm(xover_cruise, xover_cruise_decade)

7.2 IO 1990 data

IO_1990_expocodes <- GLODAP %>%
  filter(str_detect(cruise_expocode, "316N199") &
           basin_AIP == "Indian") %>%
  distinct(cruise_expocode) %>%
  pull()

xover_IO_1990 <-
  m_xover_cruise_extractation(df = xover %>% mutate(n_A = 0,
                                                    n_B = 0),
                              expocode = IO_1990_expocodes)


xover_IO_1990 <- xover_IO_1990 %>%
  mutate(RV = if_else(str_detect(cruise_B, "316N"),
                      "316N",
                      "other"))


xover_IO_1990_decade <- xover_IO_1990 %>%
    mutate(decade = m_grid_decade(year(date_B))) %>%
    filter(!is.na(decade),
           RV != "316N") %>% 
  arrange(date_B)


xover_IO_1990_decade %>%
  group_by(parameter, decade) %>%
  summarise(offset_adj_mean = mean(offset_adj, na.rm = TRUE),
            offset_adj_median = median(offset_adj, na.rm = TRUE)) %>%
  ungroup() %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

parameter	decade	offset_adj_mean	offset_adj_median
nitrate	1989-1999	0.9959899	0.9952475
nitrate	2000-2009	1.0018716	1.0034000
nitrate	2010-2020	0.9942423	0.9954335
oxygen	1989-1999	0.9986282	0.9974067
oxygen	2000-2009	0.9983700	1.0002000
oxygen	2010-2020	0.9992518	0.9981748
phosphate	1989-1999	0.9926563	0.9962076
phosphate	2000-2009	1.0050862	1.0052750
phosphate	2010-2020	1.0016116	1.0031516
salinity	1989-1999	-0.0016439	-0.0012000
salinity	2000-2009	-0.0009812	-0.0012000
salinity	2010-2020	-0.0008173	-0.0009442
silicate	1989-1999	0.9994382	1.0012621
silicate	2000-2009	1.0045634	1.0058000
silicate	2010-2020	1.0076910	1.0101825
talk	1989-1999	3.0075385	2.5782000
talk	2000-2009	2.3614576	2.9766000
talk	2010-2020	3.3860085	3.8736858
tco2	1989-1999	-0.7801709	-0.3723461
tco2	2000-2009	-2.6973312	-2.5524500
tco2	2010-2020	-2.0787148	-1.9810457

p_crossover_ts <- xover_IO_1990 %>%
  ggplot(aes(date_B, offset, col = RV)) +
  geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
  geom_point(shape = 21) +
  scale_color_brewer(palette = "Set1") +
  facet_grid(parameter ~ ., scales = "free_y") +
  labs(title = "Crossover 316N199XXXXX") +
  theme(
    legend.position = "bottom",
    legend.direction = "vertical",
    axis.title.x = element_blank()
  )

p_crossover_decadal <-
  ggplot() +
  geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
  geom_violin(data = xover_IO_1990_decade,
               aes(x = decade, y = offset), fill="gold") +
  geom_boxplot(data = xover_IO_1990_decade,
               aes(x = decade, y = offset),
               width = 0.2) +
  facet_grid(parameter ~ ., scales = "free_y") +
  labs(title = "Decadal offsets") +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 90))


p_crossover_ts + p_crossover_decadal +
  plot_layout(widths = c(2, 1))

Past versions of crossover_analysis_data-1.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
a87f8c7	jens-daniel-mueller	2021-12-20
d5ef2c6	jens-daniel-mueller	2021-12-20
00227e6	jens-daniel-mueller	2021-12-20

rm(p_crossover_ts, p_crossover_decadal)

p_crossover_ts <- xover_IO_1990 %>%
  ggplot(aes(date_B, offset_adj, col = RV)) +
  geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
  geom_point(shape = 21) +
  scale_color_brewer(palette = "Set1") +
  facet_grid(parameter ~ ., scales = "free_y") +
  labs(title = "Crossover 316N199XXXXX") +
  theme(
    legend.position = "bottom",
    legend.direction = "vertical",
    axis.title.x = element_blank()
  )

p_crossover_decadal <-
  ggplot() +
  geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
  geom_violin(data = xover_IO_1990_decade,
               aes(x = decade, y = offset_adj), fill="gold") +
  geom_boxplot(data = xover_IO_1990_decade,
               aes(x = decade, y = offset_adj),
               width = 0.2) +
  facet_grid(parameter ~ ., scales = "free_y") +
  labs(title = "Decadal offsets") +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 90))

p_crossover_ts + p_crossover_decadal +
  plot_layout(widths = c(2, 1))

Past versions of crossover_analysis_data-2.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
51ec1fe	jens-daniel-mueller	2021-12-23
a87f8c7	jens-daniel-mueller	2021-12-20
d5ef2c6	jens-daniel-mueller	2021-12-20
00227e6	jens-daniel-mueller	2021-12-20

rm(p_crossover_ts, p_crossover_decadal)
rm(xover_IO_1990, xover_IO_1990_decade)

7.3 5 basin

In this section, I analyse GLODAP’s crossover data separately for each of 5 subbasins. For this purpose, each cruise is taken into account that provided at least one measurement in the respective subbasin, irrespective of measurements done outside this subbasin.

7.3.1 Cruise-by-cruise

Here, I filter all crossover and use only those were both cruises covered the basin of interest.

# reformat basin labels
basinmask_5 <- basinmask_5 %>%
  mutate(
    basin = str_replace(basin, "_", ". "),
    basin = fct_relevel(
      basin,
      "N. Pacific",
      "S. Pacific",
      "N. Atlantic",
      "S. Atlantic",
      "Indian"
    )
  )

basins <- unique(basinmask_5$basin)

GLODAP <- inner_join(GLODAP, basinmask_5)

GLODAP <- GLODAP %>%
  mutate(decade = m_grid_decade(year))

# loop over all 5 subbasins
for (i_basin in basins) {
  # i_basin <- basins[5]
  
  # retrieve subbasin expocodes
  GLODAP_basin <- GLODAP %>%
    filter(basin == i_basin,
           !is.na(decade))
  
  expocodes_basin <- GLODAP_basin %>%
    count(cruise_expocode)
  
  # subset cruise with all qc flag = 1
  expocodes_basin_qc <- GLODAP_basin %>%
    select(cruise_expocode, ends_with("qc")) %>%
    filter(if_all(ends_with("qc"), ~ . == 1)) %>%
    distinct(cruise_expocode) %>%
    pull(cruise_expocode)
  
  # subset cruise with all f flag = 2
  expocodes_basin_f <- GLODAP_basin %>%
    select(cruise_expocode, ends_with("f")) %>%
    filter(if_all(ends_with("f"), ~ . == 2)) %>%
    distinct(cruise_expocode) %>%
    pull(cruise_expocode)
  
  # join qc and f cruises and identify lower number of observations
  expocodes_basin <- expocodes_basin %>%
    mutate(
      parameter_coverage = if_else(
        cruise_expocode %in% expocodes_basin_qc &
          cruise_expocode %in% expocodes_basin_f,
        "full",
        "partial"
      )
    )
  
  rm(expocodes_basin_f, expocodes_basin_qc)
  
  GLODAP_basin_grid <- GLODAP_basin %>%
    count(cruise_expocode, lat, lon, decade)
  
  print(
    map +
      geom_tile(data = GLODAP_basin_grid,
                aes(lon, lat, fill = n)) +
      scale_fill_viridis_c(
        option = "magma",
        direction = -1,
        trans = "log10"
      ) +
      labs(title = i_basin) +
      facet_grid(decade ~ .) +
      theme(legend.title = element_blank())
  )
  
  GLODAP_basin_grid <- full_join(GLODAP_basin_grid %>% select(-n),
                                 expocodes_basin)
  
  print(
    map +
      geom_tile(
        data = GLODAP_basin_grid %>% filter(parameter_coverage == "partial"),
        aes(lon, lat, fill = "partial")
      ) +
      geom_tile(
        data = GLODAP_basin_grid %>% filter(parameter_coverage == "full"),
        aes(lon, lat, fill = "full")
      ) +
      scale_fill_brewer(palette = "Set1") +
      labs(title = i_basin) +
      facet_grid(decade ~ .) +
      theme(legend.title = element_blank())
  )
  
  # filter crossover with both cruises falling into subbasin
  xover_basin <- xover %>%
    filter(
      cruise_A %in% expocodes_basin$cruise_expocode &
        cruise_B %in% expocodes_basin$cruise_expocode
    )

  xover_basin <- xover_basin %>%
    mutate(basin = i_basin)

  # combine with cruise meta data
  xover_basin <- left_join(
    xover_basin,
    expocodes_basin %>%
      rename(
        cruise_A = cruise_expocode,
        n_A = n,
        parameter_coverage_A = parameter_coverage
      )
  )
  
  xover_basin <- left_join(
    xover_basin,
    expocodes_basin %>%
      rename(
        cruise_B = cruise_expocode,
        n_B = n,
        parameter_coverage_B = parameter_coverage
      )
  )
  
  xover_basin <- xover_basin %>%
    mutate(
      parameter_coverage = if_else(
        parameter_coverage_A == "full" & parameter_coverage_B == "full",
        "full",
        "partial"
      ),
      n = n_A + n_B
    ) %>%
    select(-c(parameter_coverage_A, parameter_coverage_B))
  
  
  # reverse later cruise to cruise A
  xover_basin_A <- xover_basin %>%
    filter(date_A > date_B)
  
  xover_basin_B <- xover_basin %>%
    filter(date_A <= date_B)
  
  xover_basin_B_rev <- m_xover_reverse(df = xover_basin_B)
  
  
  xover_basin <- bind_rows(xover_basin_A,
                           xover_basin_B_rev)
  
  rm(xover_basin_A,
     xover_basin_B,
     xover_basin_B_rev)
  
  
  if (exists("xover_basin_all")) {
    xover_basin_all <-
      bind_rows(xover_basin_all, xover_basin)
  }
  
  if (!exists("xover_basin_all")) {
    xover_basin_all <- xover_basin
  }
  
  print(
    xover_basin %>%
      filter(
        !is.na(offset_adj),
        parameter %in% c("talk", "tco2"),
        parameter_coverage == "full"
      ) %>%
      mutate(offset_adj = cut(
        offset_adj, c(-Inf, -5, -2, -1, 1, 2, 5, Inf)
      )) %>%
      group_split(parameter) %>%
      # head(1) %>%
      map(
        ~ ggplot(data = .x,
                 aes(
                   date_A, date_B, fill = offset_adj, size = n
                 )) +
          geom_point(alpha = 0.5, shape = 21) +
          scale_fill_discrete_diverging(palette = "Blue-Red") +
          labs(title = paste(i_basin, "|", .x$parameter, "| full")) +
          coord_fixed(xlim = c(ymd("1990-01-01"), ymd("2021-01-01")),
                      ylim = c(ymd("1990-01-01"), ymd("2021-01-01")))
      )
  )
  
  print(
    xover_basin %>%
      filter(
        !is.na(offset_adj),
        parameter %in% c("phosphate"),
        parameter_coverage == "full"
      ) %>%
      mutate(offset_adj = cut(
        offset_adj, 1 + c(-Inf, -5, -2, -1, 1, 2, 5, Inf) /
          100
      )) %>%
      group_split(parameter) %>%
      # head(1) %>%
      map(
        ~ ggplot(data = .x,
                 aes(
                   date_A, date_B, fill = offset_adj, size = n
                 )) +
          geom_point(alpha = 0.5, shape = 21) +
          scale_fill_discrete_diverging(palette = "Blue-Red") +
          labs(title = paste(i_basin, "|", .x$parameter, "| full")) +
          coord_fixed(xlim = c(ymd("1990-01-01"), ymd("2021-01-01")),
                      ylim = c(ymd("1990-01-01"), ymd("2021-01-01")))
      )
  )
  
  print(
    xover_basin %>%
      filter(!is.na(offset_adj),
             parameter %in% c("talk", "tco2")) %>%
      mutate(offset_adj = cut(
        offset_adj, c(-Inf, -5, -2, -1, 1, 2, 5, Inf)
      )) %>%
      group_split(parameter) %>%
      # head(1) %>%
      map(
        ~ ggplot(data = .x,
                 aes(
                   date_A, date_B, fill = offset_adj, size = n
                 )) +
          geom_point(alpha = 0.5, shape = 21) +
          scale_fill_discrete_diverging(palette = "Blue-Red") +
          labs(title = paste(i_basin, "|", .x$parameter, "| partial")) +
          coord_fixed(xlim = c(ymd("1990-01-01"), ymd("2021-01-01")),
                      ylim = c(ymd("1990-01-01"), ymd("2021-01-01")))
      )
  )
  
  print(
    xover_basin %>%
      filter(!is.na(offset_adj),
             parameter %in% c("phosphate")) %>%
      mutate(offset_adj = cut(
        offset_adj, 1 + c(-Inf, -5, -2, -1, 1, 2, 5, Inf) /
          100
      )) %>%
      group_split(basin, parameter) %>%
      # head(1) %>%
      map(
        ~ ggplot(data = .x,
                 aes(
                   date_A, date_B, fill = offset_adj, size = n
                 )) +
          geom_point(alpha = 0.5, shape = 21) +
          scale_fill_discrete_diverging(palette = "Blue-Red") +
          labs(title = paste(i_basin, "|", .x$parameter, "| partial")) +
          coord_fixed(xlim = c(ymd("1990-01-01"), ymd("2021-01-01")),
                      ylim = c(ymd("1990-01-01"), ymd("2021-01-01")))
      )
  )
  
}

Past versions of filter_xover_basin-1.png

Version	Author	Date
25fef5b	jens-daniel-mueller	2022-03-11

Past versions of filter_xover_basin-2.png

Version	Author	Date
25fef5b	jens-daniel-mueller	2022-03-11

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Past versions of filter_xover_basin-3.png

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84ca078	jens-daniel-mueller	2022-03-11
25fef5b	jens-daniel-mueller	2022-03-11

[[2]]

Past versions of filter_xover_basin-4.png

Version	Author	Date
84ca078	jens-daniel-mueller	2022-03-11
25fef5b	jens-daniel-mueller	2022-03-11

[[1]]

Past versions of filter_xover_basin-5.png

Version	Author	Date
84ca078	jens-daniel-mueller	2022-03-11
25fef5b	jens-daniel-mueller	2022-03-11

[[1]]

Past versions of filter_xover_basin-6.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
84ca078	jens-daniel-mueller	2022-03-11
25fef5b	jens-daniel-mueller	2022-03-11

[[2]]

Past versions of filter_xover_basin-7.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
84ca078	jens-daniel-mueller	2022-03-11
25fef5b	jens-daniel-mueller	2022-03-11

[[1]]

Past versions of filter_xover_basin-8.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
84ca078	jens-daniel-mueller	2022-03-11
25fef5b	jens-daniel-mueller	2022-03-11

Past versions of filter_xover_basin-9.png

Version	Author	Date
84ca078	jens-daniel-mueller	2022-03-11
25fef5b	jens-daniel-mueller	2022-03-11

Past versions of filter_xover_basin-10.png

Version	Author	Date
84ca078	jens-daniel-mueller	2022-03-11
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rm(xover_basin, GLODAP_basin, GLODAP_basin_grid, expocodes_basin)

xover_basin <- xover_basin_all
rm(xover_basin_all)

7.3.2 Cruise mean offsets

The aim of the decadal scale analysis is to investigate mean crossover offsets between all cruises from two decades.

expocodes_basin <- unique(c(xover_basin$cruise_A, xover_basin$cruise_B)) 


# loop over each cruises
# determine the mean decadal crossover from other cruises
for (i_cruise_expocode in expocodes_basin) {
  # i_cruise_expocode <- expocodes_basin[1]
  
  xover_cruise <- m_xover_cruise_extractation(df = xover_basin,
                                              expocode = i_cruise_expocode)
  
  # calculate mean cruise-by-cruise offset (in case more than one leg was compared)
  xover_cruise <- xover_cruise %>%
    group_by(basin,
             cruise_A,
             cruise_B,
             date_A,
             date_B,
             n_A,
             n_B,
             n,
             parameter,
             parameter_coverage) %>%
    summarise(
      offset = mean(offset, na.rm = TRUE),
      offset_adj = mean(offset_adj, na.rm = TRUE)
    ) %>%
    ungroup()
  
  
  # calculate long-term mean offsets for cruise
  # Note: weighting is only done based on size of cruise B
  xover_cruise_partial <- xover_cruise %>%
    group_by(cruise_A, date_A, n_A, parameter, basin) %>%
    summarise(
      offset_adj_mean = mean(offset_adj, na.rm = TRUE),
      offset_adj_mean_weighted = weighted.mean(x = offset_adj, w = n_B, na.rm = TRUE)
    ) %>%
    ungroup()
  
  xover_cruise_full <- xover_cruise %>%
    filter(parameter_coverage == "full") %>%
    group_by(cruise_A, date_A, n_A, parameter, basin) %>%
    summarise(
      offset_adj_mean = mean(offset_adj, na.rm = TRUE),
      offset_adj_mean_weighted = weighted.mean(x = offset_adj, w = n_B, na.rm = TRUE)
    ) %>%
    ungroup()
  
  xover_cruise_long_term <- bind_rows(
    xover_cruise_full %>% mutate(parameter_coverage = "full"),
    xover_cruise_partial %>% mutate(parameter_coverage = "partial")
  )
  
  
  rm(xover_cruise_full,
     xover_cruise_partial)
  
  if (exists("xover_cruise_long_term_all")) {
    xover_cruise_long_term_all <-
      bind_rows(xover_cruise_long_term_all, xover_cruise_long_term)
  }
  
  if (!exists("xover_cruise_long_term_all")) {
    xover_cruise_long_term_all <- xover_cruise_long_term
  }
  
  
  # cut cruise B date into decades
  xover_cruise <- xover_cruise %>%
    mutate(decade = m_grid_decade(year(date_B))) %>%
    arrange(date_B)
  
  # calculate decadal mean offsets for cruise
  # Note: weighting is only done based on size of cruise B
  xover_cruise_decade_partial <- xover_cruise %>%
    group_by(cruise_A, date_A, n_A, parameter, decade, basin) %>%
    summarise(
      offset_adj_mean = mean(offset_adj, na.rm = TRUE),
      offset_adj_mean_weighted = weighted.mean(x = offset_adj, w = n_B, na.rm = TRUE)
    ) %>%
    ungroup()
  
  xover_cruise_decade_full <- xover_cruise %>%
    filter(parameter_coverage == "full") %>%
    group_by(cruise_A, date_A, n_A, parameter, decade, basin) %>%
    summarise(
      offset_adj_mean = mean(offset_adj, na.rm = TRUE),
      offset_adj_mean_weighted = weighted.mean(x = offset_adj, w = n_B, na.rm = TRUE)
    ) %>%
    ungroup()
  
  xover_cruise_decade <- bind_rows(
    xover_cruise_decade_full %>% mutate(parameter_coverage = "full"),
    xover_cruise_decade_partial %>% mutate(parameter_coverage = "partial")
  )
  
  
  rm(xover_cruise_decade_full,
     xover_cruise_decade_partial)
  
  if (exists("xover_cruise_decade_all")) {
    xover_cruise_decade_all <-
      bind_rows(xover_cruise_decade_all, xover_cruise_decade)
  }
  
  if (!exists("xover_cruise_decade_all")) {
    xover_cruise_decade_all <- xover_cruise_decade
  }
  
}


hline_intercept <- hline_intercept %>% 
  filter(parameter %in% c("talk", "tco2", "phosphate"))

xover_cruise_long_term_all %>%
  filter(parameter %in% c("talk", "tco2", "phosphate")) %>%
  group_split(basin, parameter_coverage) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(date_A, offset_adj_mean_weighted, size = n_A)) +
      geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
      geom_point(alpha = 0.3) +
      labs(title = paste(.x$basin, "|", .x$parameter_coverage)) +
      facet_grid(parameter ~ ., scales = "free_y")
  )

[[1]]

Past versions of crossover_analysis_5_basin_decadal-1.png

Version	Author	Date
744b90f	jens-daniel-mueller	2022-03-11
25fef5b	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
070ca03	jens-daniel-mueller	2022-03-09
9db485e	jens-daniel-mueller	2022-02-25
4a7550e	jens-daniel-mueller	2022-02-15
8804a83	jens-daniel-mueller	2022-02-15
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25fef5b	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
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8804a83	jens-daniel-mueller	2022-02-15
e1243c2	jens-daniel-mueller	2022-02-15

xover_cruise_decade_all %>%
  filter(parameter %in% c("talk", "tco2", "phosphate")) %>%
  group_split(basin, parameter_coverage) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(date_A, offset_adj_mean_weighted, size = n_A)) +
      geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
      geom_point(alpha = 0.3) +
      labs(title = paste(.x$basin, "|", .x$parameter_coverage)) +
      facet_grid(parameter ~ decade, scales = "free_y")
  )

[[1]]

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744b90f	jens-daniel-mueller	2022-03-11
25fef5b	jens-daniel-mueller	2022-03-11
e3d1a2b	jens-daniel-mueller	2022-03-10
070ca03	jens-daniel-mueller	2022-03-09
6e65117	jens-daniel-mueller	2022-02-16
4a7550e	jens-daniel-mueller	2022-02-15
8804a83	jens-daniel-mueller	2022-02-15
e1243c2	jens-daniel-mueller	2022-02-15

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070ca03	jens-daniel-mueller	2022-03-09
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e3d1a2b	jens-daniel-mueller	2022-03-10
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9db485e	jens-daniel-mueller	2022-02-25
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e3d1a2b	jens-daniel-mueller	2022-03-10
070ca03	jens-daniel-mueller	2022-03-09
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e3d1a2b	jens-daniel-mueller	2022-03-10
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8804a83	jens-daniel-mueller	2022-02-15
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#
#     print(
#       xover_subbasin_decade_stats %>%
#         kable(caption = paste0(i_basin, " | ", i_decade_start, "s")) %>%
#         kable_styling() %>%
#         scroll_box(height = "300px")
#     )
#
#
#     p_crossover_ts <- xover_subbasin_decade %>%
#       ggplot(aes(date_B, offset_adj)) +
#       geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
#       geom_point(shape = 21) +
#       scale_color_brewer(palette = "Set1") +
#       facet_grid(parameter ~ ., scales = "free_y") +
#       labs(title = paste0(i_basin, " | ", i_decade_start, "s")) +
#       theme(
#         legend.position = "bottom",
#         legend.direction = "vertical",
#         axis.title.x = element_blank()
#       )
#
#     p_crossover_decadal <-
#       ggplot() +
#       geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
#       geom_violin(data = xover_subbasin_decade,
#                   aes(x = decade, y = offset_adj),
#                   fill = "gold") +
#       geom_boxplot(data = xover_subbasin_decade,
#                    aes(x = decade, y = offset_adj),
#                    width = 0.2) +
#       facet_grid(parameter ~ ., scales = "free_y") +
#       labs(title = "Decadal offsets") +
#       theme(axis.title.x = element_blank(),
#             axis.text.x = element_text(angle = 90))
#
#     print(p_crossover_ts + p_crossover_decadal +
#             plot_layout(widths = c(2, 1)))
#
#     rm(p_crossover_ts, p_crossover_decadal)
#
#   }
# }
#
# rm(xover_subbasin, xover_cruise_decade, xover_subbasin_decade, xover_subbasin_decade_stats)

xover_cruise_long_term_all %>%
  filter(parameter %in% c("talk", "tco2", "phosphate")) %>%
  group_split(basin, parameter_coverage) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(n_A, offset_adj_mean_weighted, size = n_A)) +
      geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
      geom_point(alpha = 0.3) +
      labs(title = paste(.x$basin, "|", .x$parameter_coverage)) +
      facet_grid(parameter ~ ., scales = "free_y")
  )

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xover_cruise_decade_all %>%
  filter(parameter %in% c("talk", "tco2", "phosphate")) %>%
  group_split(basin, parameter_coverage) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(n_A, offset_adj_mean_weighted, size = n_A)) +
      geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
      geom_point(alpha = 0.3) +
      labs(title = paste(.x$basin, "|", .x$parameter_coverage)) +
      facet_grid(parameter ~ decade, scales = "free_y")
  )

[[1]]

[[2]]

[[3]]

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[[8]]

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[[10]]

xover_cruise_long_term_all %>%
  filter(parameter %in% c("talk", "tco2", "phosphate")) %>%
  group_split(basin, parameter_coverage) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(n_A, offset_adj_mean_weighted, size = n_A)) +
      geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
      geom_point(alpha = 0.3) +
      labs(title = paste(.x$basin, "|", .x$parameter_coverage)) +
      facet_grid(parameter ~ ., scales = "free_y")
  )

xover_cruise_decade_all %>%
  filter(parameter %in% c("talk", "tco2", "phosphate")) %>%
  group_split(basin, parameter_coverage) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(n_A, offset_adj_mean_weighted, size = n_A)) +
      geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
      geom_point(alpha = 0.3) +
      labs(title = paste(.x$basin, "|", .x$parameter_coverage)) +
      facet_grid(parameter ~ decade, scales = "free_y")
  )

7.3.3 Decadal averages

xover_subbasin_decade_stats_all <- xover_subbasin_decade_stats_all %>%
    mutate(
    basin = fct_relevel(
      basin,
      "N. Pacific",
      "S. Pacific",
      "N. Atlantic",
      "S. Atlantic",
      "Indian"
    )
  )


xover_subbasin_decade_stats_all_cstar <- xover_subbasin_decade_stats_all %>%
  filter(parameter %in% c("talk", "phosphate", "tco2")) %>% 
  select(
    parameter,
    basin,
    decade,
    decade_start,
    offset_adj_mean_weighted,
  ) %>%
  pivot_wider(names_from = parameter,
              values_from = offset_adj_mean_weighted) %>%
  mutate(cstar = tco2  - 
           (117 * (1 - phosphate) * 2e-6)  - 0.5 * 
           (talk - (16 * (1 - phosphate) * 2e-6))) %>%
  pivot_longer(phosphate:cstar,
               values_to = "offset_adj_mean_weighted",
               names_to = "parameter")



xover_subbasin_decade_stats_all_cstar %>%
  ggplot(aes(decade_start, offset_adj_mean_weighted)) +
  geom_hline(data = hline_intercept %>%
               filter(parameter %in% c("talk", "tco2", "phosphate")),
             aes(yintercept = intercept)) +
  geom_point(aes(fill = basin), shape = 21) +
  geom_line(aes(col = basin)) +
  scale_fill_brewer(palette = "Paired") +
  scale_color_brewer(palette = "Paired") +
  scale_x_continuous(breaks = seq(1990, 2010, 10)) +
  facet_grid(parameter ~ decade, scales = "free_y")

xover_subbasin_decade_stats_all %>% 
  filter(parameter %in% c("talk", "tco2", "phosphate")) %>%
  ggplot(aes(decade_start, offset_adj_sd)) +
  geom_point(aes(fill = basin), shape = 21) +
  geom_line(aes(col = basin)) +
  scale_fill_brewer(palette = "Paired") +
  scale_color_brewer(palette = "Paired") +
  scale_x_continuous(breaks = seq(1990, 2010, 10)) +
  facet_grid(parameter ~ decade, scales = "free_y")

7.3.4 Cruise mean offsets

xover_cruise_decade_all <- xover_cruise_decade_all %>%
  mutate(basin = fct_relevel(
    basin,
    "N. Pacific",
    "S. Pacific",
    "N. Atlantic",
    "S. Atlantic",
    "Indian"
  ))

expocodes <- inner_join(GLODAP, basinmask_5)

expocodes <- expocodes %>%
  count(basin, cruise_expocode)


xover_cruise_decade_all <- left_join(xover_cruise_decade_all,
                                    expocodes)

xover_cruise_decade_all_cstar <- xover_cruise_decade_all %>%
  filter(parameter %in% c("talk", "phosphate", "tco2")) %>%
  select(parameter,
         date_A,
         basin,
         decade,
         cruise_expocode,
         n,
         offset_adj_mean_weighted,) %>%
  pivot_wider(names_from = parameter,
              values_from = offset_adj_mean_weighted) %>%
  mutate(cstar = tco2  -
           (117 * (1 - phosphate) * 2e-6)  - 0.5 *
           (talk - (16 * (1 - phosphate) * 2e-6))) %>%
  pivot_longer(phosphate:cstar,
               values_to = "offset_adj_mean_weighted",
               names_to = "parameter")

hline_intercept <-
  tibble(parameter = unique(xover_cruise_decade_all_cstar$parameter)) %>%
  mutate(intercept = if_else(parameter %in% c("cstar", "talk", "tco2"),
                             0,
                             1))

xover_cruise_decade_all_cstar %>%
  filter(year(date_A) >= 1989) %>%
  group_by(basin) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(date_A, offset_adj_mean_weighted, size = n)) +
      geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
      geom_point(alpha = 0.3) +
      labs(title = .x$basin) +
      facet_grid(parameter ~ decade, scales = "free_y")
  )

7.3.5 Cruise by cruise

for (i_basin in unique(basinmask_5$basin)) {
  # i_basin <- unique(basinmask_5$basin)[4]
  
  expocodes_subbasin <- inner_join(GLODAP, basinmask_5) %>%
    filter(basin == i_basin)
  
  # subset cruise with all qc flag = 1
  expocodes_subbasin_qc <- expocodes_subbasin %>%
    select(row_number, cruise_expocode, ends_with("qc")) %>%
    pivot_longer(ends_with("qc"),
                 names_to = "parameter",
                 values_to = "value") %>%
    filter(value == 1) %>%
    pivot_wider(names_from = parameter,
                values_from = value) %>%
    drop_na() %>%
    count(cruise_expocode)
  
  # subset cruise with all f flag = 2
  expocodes_subbasin_f <- expocodes_subbasin %>%
    select(row_number, cruise_expocode, ends_with("f")) %>%
    pivot_longer(ends_with("f"),
                 names_to = "parameter",
                 values_to = "value") %>%
    filter(value == 2) %>%
    pivot_wider(names_from = parameter,
                values_from = value) %>%
    drop_na() %>%
    count(cruise_expocode)
  
  # join qc and f cruises and identify lower number of observations
  expocodes_subbasin <-
    inner_join(expocodes_subbasin_f %>% rename(n_f = n),
               expocodes_subbasin_qc %>% rename(n_qc = n)) %>%
    mutate(n = if_else(n_f < n_qc, n_f, n_qc)) %>% 
    select(-starts_with("n_"))
  
  rm(expocodes_subbasin_f, expocodes_subbasin_qc)
  
  expocodes_subbasin_all <- inner_join(GLODAP, basinmask_5) %>%
    filter(basin == i_basin)
  
  expocodes_subbasin_clean <- inner_join(GLODAP, basinmask_5) %>%
    filter(basin == i_basin,
           cruise_expocode %in% expocodes_subbasin$cruise_expocode)
  
  print(
    map +
      geom_tile(
        data = expocodes_subbasin_all %>%
          mutate(decade = m_grid_decade(year)) %>%
          distinct(lon, lat, decade) %>% 
          drop_na(),
        aes(lon, lat, fill="all")
      ) +
      geom_tile(
        data = expocodes_subbasin_clean %>%
          mutate(decade = m_grid_decade(year)) %>%
          distinct(lon, lat, decade) %>% 
          drop_na(),
        aes(lon, lat, fill="filtered")
      ) +
      scale_fill_brewer(palette = "Set1", direction = -1) +
      labs(title = i_basin) +
      facet_grid(decade ~ .) +
      theme(legend.title = element_blank())
  )
  
  rm(expocodes_subbasin_all, expocodes_subbasin_clean)
  
  
  xover_subbasin <- xover %>%
    filter(
      cruise_A %in% expocodes_subbasin$cruise_expocode &
        cruise_B %in% expocodes_subbasin$cruise_expocode
    )
  
  
  xover_subbasin <- xover_subbasin %>%
    mutate(date_A = ymd(str_sub(cruise_A, 5, 12)),
           date_B = ymd(str_sub(cruise_B, 5, 12)))
  
  
  xover_subbasin <- left_join(
    xover_subbasin,
    expocodes_subbasin %>% rename(cruise_A = cruise_expocode,
                                  n_A = n)
  )
  
  xover_subbasin <- left_join(
    xover_subbasin,
    expocodes_subbasin %>% rename(cruise_B = cruise_expocode,
                                  n_B = n)
  )
  
  xover_subbasin <- xover_subbasin %>%
    mutate(n = n_A + n_B) %>%
    select(-starts_with("n_"))
  
  
  xover_cruise_A <- xover_subbasin %>%
    filter(date_A > date_B)
  
  xover_cruise_B <- xover_subbasin %>%
    filter(date_A <= date_B)
  
  xover_cruise_B_rev <- xover_cruise_B %>%
    rename(
      cruise_A_back = cruise_A,
      cruise_A = cruise_B,
      date_A_back = date_A,
      date_A = date_B,
      adjustment_A_back = adjustment_A,
      adjustment_A = adjustment_B
    ) %>%
    rename(cruise_B = cruise_A_back,
           date_B = date_A_back,
           adjustment_B = adjustment_A_back) %>%
    mutate(
      offset = if_else(
        parameter %in% c("salinity", "talk", "tco2"),
        -offset,
        1 / offset
      ),
      offset_adj = if_else(
        parameter %in% c("salinity", "talk", "tco2"),
        -offset_adj,
        1 / offset_adj
      )
    )
  
  xover_cruise <- bind_rows(xover_cruise_A,
                            xover_cruise_B_rev)
  
  rm(xover_cruise_A, xover_cruise_B, xover_cruise_B_rev)
  
  print(
  xover_cruise %>%
    filter(date_A > ymd("1989-01-01") &
             date_B > ymd("1989-01-01") &
             !is.na(offset_adj)) %>%
    filter(parameter %in% c("talk", "tco2")) %>%
    mutate(offset_adj = cut(offset_adj, c(-Inf, -5, -2, -1, 1, 2, 5, Inf))) %>% 
    group_split(parameter) %>%
    map(
      ~ ggplot(data = .x,
               aes(date_A, date_B, col = offset_adj, size = n)) +
        geom_point(alpha = 0.5) +
        scale_color_discrete_diverging(palette = "Blue-Red") +
        labs(title = paste(i_basin, "|", .x$parameter)) +
        coord_fixed()
    )
  )
  
    
  print(
  xover_cruise %>%
    filter(date_A > ymd("1989-01-01") &
             date_B > ymd("1989-01-01") &
             !is.na(offset_adj)) %>%
    filter(parameter %in% c("phosphate")) %>%
    mutate(offset_adj = cut(offset_adj, 1 + c(-Inf, -5, -2, -1, 1, 2, 5, Inf)/100)) %>% 
    group_split(parameter) %>%
    map(
      ~ ggplot(data = .x,
               aes(date_A, date_B, col = offset_adj, size = n)) +
        geom_point(alpha = 0.5) +
        scale_color_discrete_diverging(palette = "Blue-Red") +
        labs(title = paste(i_basin, "|", .x$parameter)) +
        coord_fixed()
    )
  )
    
  print(
  xover_cruise %>%
    filter(date_A > ymd("1989-01-01") &
             date_B > ymd("1989-01-01") &
             !is.na(offset_adj)) %>%
    filter(parameter %in% c("talk", "tco2")) %>%
    mutate(yearA = year(date_A),
           yearB = year(date_B)) %>%
    group_by(parameter, yearA, yearB) %>%
    summarise(offset_adj_weighted_mean = weighted.mean(x = offset_adj, w=n, na.rm = TRUE),
              n = sum(n)) %>%
    ungroup() %>% 
    mutate(offset_adj_weighted_mean = cut(offset_adj_weighted_mean, c(-Inf, -5, -2, -1, 1, 2, 5, Inf))) %>% 
    group_split(parameter) %>%
    map(
      ~ ggplot(data = .x,
               aes(yearA, yearB, col = offset_adj_weighted_mean, size =n)) +
        geom_point(alpha = 0.5) +
        scale_color_discrete_diverging(palette = "Blue-Red") +
        labs(title = paste(i_basin, "|", .x$parameter)) +
        coord_fixed()
    )
  )

  print(
  xover_cruise %>%
    filter(date_A > ymd("1989-01-01") &
             date_B > ymd("1989-01-01") &
             !is.na(offset_adj)) %>%
    filter(parameter %in% c("phosphate")) %>%
    mutate(yearA = year(date_A),
           yearB = year(date_B)) %>%
    group_by(parameter, yearA, yearB) %>%
    summarise(offset_adj_weighted_mean = weighted.mean(x = offset_adj, w=n, na.rm = TRUE),
              n = sum(n)) %>%
    ungroup() %>% 
    mutate(offset_adj_weighted_mean = cut(offset_adj_weighted_mean, 1 + c(-Inf, -5, -2, -1, 1, 2, 5, Inf)/100)) %>% 
    group_split(parameter) %>%
    map(
      ~ ggplot(data = .x,
               aes(yearA, yearB, col = offset_adj_weighted_mean, size = n)) +
        geom_point(alpha = 0.5) +
        scale_color_discrete_diverging(palette = "Blue-Red") +
        labs(title = paste(i_basin, "|", .x$parameter)) +
        coord_fixed()
    )
  )
      
}

7.4 N Pacific 2010s

7.4.1 Cruise mean offsets

xover_cruise_decade_all_cstar %>%
  filter(basin == "N. Pacific",
         decade == "2000-2009",
         year(date_A) >= 1989) %>%
  group_by(basin) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(date_A, offset_adj_mean_weighted, size = n)) +
      geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
      geom_point(alpha = 0.3) +
      labs(title = .x$basin) +
      facet_grid(parameter ~ decade, scales = "free_y")
  )


xover_cruise_decade_all_cstar %>%
  filter(basin == "N. Pacific",
         decade == "2000-2009",
         year(date_A) >= 2010) %>%
  group_by(basin) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(n, offset_adj_mean_weighted, size = n)) +
      geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
      geom_point(alpha = 0.3) +
      labs(title = .x$basin) +
      facet_grid(parameter ~ decade, scales = "free_y")
  )

xover_cruise_decade_all_cstar %>%
  filter(basin == "N. Pacific",
         decade == "2010-2019",
         year(date_A) >= 1989) %>%
  group_by(basin) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(date_A, offset_adj_mean_weighted, size = n)) +
      geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
      geom_point(alpha = 0.3) +
      labs(title = .x$basin) +
      facet_grid(parameter ~ decade, scales = "free_y")
  )

xover_cruise_decade_all_cstar %>%
  filter(basin == "N. Pacific",
         decade == "2010-2019",
         year(date_A) >= 2000,
         year(date_A) < 2010) %>%
  group_by(basin) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(n, offset_adj_mean_weighted, size = n)) +
      geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
      geom_point(alpha = 0.3) +
      labs(title = .x$basin) +
      facet_grid(parameter ~ decade, scales = "free_y")
  )

8 RV activity

GLODAP_counts <- GLODAP %>%
  mutate(decade = m_grid_decade(year),
         .after = year) %>% 
  filter(!is.na(decade))

GLODAP_counts <- GLODAP_counts %>% 
  mutate(RV = str_sub(cruise_expocode, 1, 4))

RV_activity <- GLODAP_counts %>% 
  count(decade, basin_AIP, RV) %>% 
  group_by(decade, basin_AIP) %>% 
  mutate(n_total = sum(n)) %>% 
  ungroup() %>% 
  mutate(n_prop = 100* n / n_total)

RV_activity <-RV_activity %>% 
  group_by(decade, basin_AIP) %>% 
  mutate(rank = rank(-n_prop)) %>% 
  ungroup()

RV_activity %>% 
  ggplot(aes(rank, n_prop)) +
  geom_line() +
  geom_point() +
  geom_text(data = RV_activity %>% filter(n_prop > 20),
             aes(rank, n_prop, label = RV),
             nudge_x = 5) +
  labs(y = "proportion of tco2 samples (%)") +
  facet_grid(decade ~ basin_AIP)

Past versions of RV_activity-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
daa43b9	jens-daniel-mueller	2021-12-06

rm(RV_activity)

9 Largest cruises

large_cruises <- GLODAP_counts %>% 
  count(decade, basin_AIP, cruise_expocode) %>% 
  group_by(decade, basin_AIP) %>% 
  mutate(n_total = sum(n)) %>% 
  ungroup() %>% 
  mutate(n_prop = 100* n / n_total)

large_cruises <- large_cruises %>% 
  group_by(decade, basin_AIP) %>% 
  mutate(rank = rank(-n_prop)) %>% 
  ungroup()


large_cruises %>%
  group_split(decade, basin_AIP) %>%
  head(1) %>%
  map(
    ~
      ggplot(data = .x,
             aes(rank, n_prop)) +
      geom_line() +
      geom_point(
        data = .x %>% filter(rank <= 5),
        aes(rank, n_prop, fill = cruise_expocode), shape = 21) +
      scale_fill_brewer(palette = "Set1") +
      xlim(0, max(large_cruises$rank)) +
      labs(y = "proportion of tco2 samples (%)") +
      facet_grid(decade ~ basin_AIP)
  )

[[1]]

Past versions of large_cruises-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9075296	jens-daniel-mueller	2022-01-12

large_cruises %>%
  filter(rank <= 5) %>% 
  select(decade, basin_AIP, rank, n_prop, cruise_expocode) %>% 
  mutate(n_prop = round(n_prop, 1)) %>% 
  arrange(decade, basin_AIP, rank) %>% 
  kable() %>% 
  kable_styling() %>% 
  scroll_box(height = "300px")

decade	basin_AIP	rank	n_prop	cruise_expocode
1989-1999	Atlantic	1	5.0	323019940104
1989-1999	Atlantic	2	4.9	316N19871123
1989-1999	Atlantic	3	3.5	33RO19980123
1989-1999	Atlantic	4	3.5	06AQ19980328
1989-1999	Atlantic	5	3.3	316N19970530
1989-1999	Indian	1	7.8	316N19951202
1989-1999	Indian	2	7.8	316N19950124
1989-1999	Indian	3	7.5	316N19950310
1989-1999	Indian	4	7.1	316N19950829
1989-1999	Indian	5	6.8	316N19941201
1989-1999	Pacific	1	5.7	316N19920502
1989-1999	Pacific	2	5.6	31DS19960105
1989-1999	Pacific	3	5.6	31DS19940126
1989-1999	Pacific	4	5.5	318M19940327
1989-1999	Pacific	5	4.2	31DS19920907
2000-2009	Atlantic	1	4.6	33RO20050111
2000-2009	Atlantic	2	4.5	33RO20030604
2000-2009	Atlantic	3	4.1	06AQ20050122
2000-2009	Atlantic	4	3.5	06AQ20080210
2000-2009	Atlantic	5	2.7	35MF20080207
2000-2009	Indian	1	19.4	33RR20090320
2000-2009	Indian	2	10.4	33RR20070322
2000-2009	Indian	3	9.1	33RR20070204
2000-2009	Indian	4	9.0	33RR20080204
2000-2009	Indian	5	8.2	49NZ20031209
2000-2009	Pacific	1	7.0	33RO20071215
2000-2009	Pacific	2	6.0	318M20040615
2000-2009	Pacific	3	4.9	49NZ20030803
2000-2009	Pacific	4	4.6	49NZ20090410
2000-2009	Pacific	5	4.6	49NZ20051031
2010-2020	Atlantic	1	6.8	33RO20100308
2010-2020	Atlantic	2	6.3	33RO20130803
2010-2020	Atlantic	3	5.7	33RO20110926
2010-2020	Atlantic	4	5.5	740H20180228
2010-2020	Atlantic	5	5.1	33RO20131223
2010-2020	Indian	1	17.7	33RO20180423
2010-2020	Indian	2	16.8	33RR20160321
2010-2020	Indian	3	11.4	33RR20160208
2010-2020	Indian	4	9.6	096U20180111
2010-2020	Indian	5	8.8	325020190403
2010-2020	Pacific	1	5.9	33RO20161119
2010-2020	Pacific	2	4.1	318M20130321
2010-2020	Pacific	3	4.1	320620180309
2010-2020	Pacific	4	4.0	320620170703
2010-2020	Pacific	5	3.6	49RY20110515

rm(GLODAP_count, large_cruises)

10 Indian Ocean 1990 CRM

CRM_IO_meas <- CRM_IO_meas %>%
  fill(cruise:batch) %>% 
  select(-starts_with("ph")) %>% 
  rename(talk_meas = talk_ave,
         tco2_meas = tco2_ave)
  
CRM_ref_values <- CRM_ref_values %>% 
  select(-c(date, comment, sal)) %>% 
  rename(talk_ref = talk,
         tco2_ref = tco2)

IO_CRM_offset <-
  left_join(CRM_IO_meas,
            CRM_ref_values) %>% 
  mutate(batch = as.factor(batch))

IO_CRM_offset <- IO_CRM_offset %>% 
  mutate(talk_offset = talk_meas - talk_ref,
         tco2_offset = tco2_meas - tco2_ref)

IO_CRM_offset <- IO_CRM_offset %>% 
  select(-c(talk_meas:talk_ref)) %>% 
  pivot_longer(ends_with("_offset"),
               values_to = "offset",
               names_to = "parameter") %>% 
  mutate(parameter = str_remove(parameter, "_offset"),
         start_date = mdy(start_date))

IO_CRM_offset %>% 
  ggplot(aes(offset)) +
  geom_histogram(binwidth = 1) +
  facet_wrap(~ parameter)

Past versions of IO_1990_CRM-1.png

Version	Author	Date
d454df1	jens-daniel-mueller	2021-12-15

IO_CRM_offset <- IO_CRM_offset %>% 
  filter(cell != "All")

IO_CRM_offset_mean <- IO_CRM_offset %>% 
  group_by(parameter) %>% 
  summarise(offset_mean = mean(offset),
            offset_sd = sd(offset)) %>% 
  ungroup()

IO_CRM_offset %>%
  filter(parameter == "talk") %>%
  ggplot() +
  scale_fill_brewer(palette = "Set1",
                    name = "CRM batch") +
  geom_hline(data = IO_CRM_offset_mean %>% filter(parameter == "talk"),
             aes(yintercept = offset_mean)) +
  geom_hline(
    data = IO_CRM_offset_mean %>% filter(parameter == "talk"),
    aes(yintercept = offset_mean - offset_sd),
    linetype = 2
  ) +
  geom_hline(
    data = IO_CRM_offset_mean %>% filter(parameter == "talk"),
    aes(yintercept = offset_mean + offset_sd),
    linetype = 2
  ) +
  geom_point(aes(start_date, offset, fill = batch, size=n),
             shape = 21) +
  scale_size(name = "Nr of\nmeasurements") +
  labs(x = "Cruise start date",
       y = "TA offset meas-CRM (µmol/kg)",
       title = "RV Knorr IO 1990 - TA reference measurements",
       subtitle = "Data source: Tables 1 and 2 from Millero et al. (1998)")

Past versions of IO_1990_CRM-2.png

Version	Author	Date
61d5f49	jens-daniel-mueller	2021-12-15
d454df1	jens-daniel-mueller	2021-12-15

11 Write files

GLODAP  %>%
  select(-cruise_expocode) %>% 
  write_csv(paste(path_preprocessing,
                  "GLODAPv2.2021_preprocessed.csv",
                  sep = ""))

GLODAP_tracer  %>%
  write_csv(paste(
    path_preprocessing,
    "GLODAPv2.2021_preprocessed_tracer.csv",
    sep = ""
  ))

GLODAP_adjustments  %>%
  write_csv(paste(path_preprocessing,
                  "GLODAPv2.2021_adustments.csv",
                  sep = ""))

# GLODAP_adjustments_NA_cruises  %>%
#   select(cruise_expocode, cruise) %>%
#   write_csv(paste(
#     path_preprocessing,
#     "GLODAPv2.2021_adustments_NA_cruises.csv",
#     sep = ""
#   ))
# 
# GLODAP_adjustments_duplicated_cruises  %>%
#   drop_na() %>%
#   write_csv(
#     paste(
#       path_preprocessing,
#       "GLODAPv2.2021_adustments_duplicated_cruises.csv",
#       sep = ""
#     )
#   )

12 Overview plots

12.1 Assign coarse spatial grid

For the following plots, the cleaned data set was re-opened and observations were gridded spatially to intervals of:

• 5° x 5°

GLODAP <- m_grid_horizontal_coarse(GLODAP)

12.2 Histogram Zonal coverage

GLODAP_histogram_lat <- GLODAP %>%
  group_by(lat_grid) %>%
  tally() %>%
  ungroup()

GLODAP_histogram_lat %>%
  ggplot(aes(lat_grid, n)) +
  geom_col() +
  coord_flip() +
  theme(legend.title = element_blank())

Past versions of coverage_histogram_zonal-1.png

Version	Author	Date
98599d8	jens-daniel-mueller	2021-06-27
9d8353f	jens-daniel-mueller	2021-05-31

rm(GLODAP_histogram_lat)

12.3 Histogram temporal coverage

GLODAP_histogram_year <- GLODAP %>%
  group_by(year) %>%
  tally() %>%
  ungroup()

GLODAP_histogram_year %>%
  ggplot() +
  geom_col(aes(year, n)) +
  theme(
    axis.title.x = element_blank()
  )

Past versions of coverage_histogram_temporal-1.png

Version	Author	Date
98599d8	jens-daniel-mueller	2021-06-27
9d8353f	jens-daniel-mueller	2021-05-31

rm(GLODAP_histogram_year)

12.4 Zonal temporal coverage (Hovmoeller)

GLODAP_hovmoeller_year <- GLODAP %>%
  group_by(year, lat_grid) %>%
  tally() %>%
  ungroup()

GLODAP_hovmoeller_year %>%
  ggplot(aes(year, lat_grid, fill = log10(n))) +
  geom_tile() +
  geom_vline(xintercept = c(1999.5, 2012.5)) +
  scale_fill_viridis_c(option = "magma", direction = -1) +
  theme(legend.position = "top",
        axis.title.x = element_blank())

Past versions of coverage_hovmoeller-1.png

Version	Author	Date
98599d8	jens-daniel-mueller	2021-06-27
9d8353f	jens-daniel-mueller	2021-05-31

rm(GLODAP_hovmoeller_year)

12.5 Coverage map

map +
  geom_raster(data = GLODAP_obs_grid,
              aes(lon, lat, fill = log10(n))) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1)

Past versions of coverage_map-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
98599d8	jens-daniel-mueller	2021-06-27
9d8353f	jens-daniel-mueller	2021-05-31

GLODAP_obs_grid_all_vars <- GLODAP %>% 
  select(year, lat, lon, cruise, sal, temp, oxygen,
         phosphate, nitrate, silicate, tco2, talk) %>% 
  pivot_longer(cols = sal:talk,
               names_to = "parameter",
               values_to = "value") %>% 
  mutate(presence = if_else(is.na(value), "missing", "available")) %>% 
  count(year, lat, lon, parameter, presence)

GLODAP_obs_grid_all_vars_wide <- GLODAP_obs_grid_all_vars %>% 
  pivot_wider(names_from = "presence",
              values_from = n,
              values_fill = 0) %>% 
  mutate(ratio_available = available/(available+missing))

all_plots <- GLODAP_obs_grid_all_vars_wide %>%
  # mutate(cruise = as.factor(cruise)) %>%
  group_split(year) %>%
  # tail(3) %>%
  map(
    ~ map +
      geom_tile(
        data = .x,
        aes(
          x = lon,
          y = lat,
          width = 1,
          height = 1,
          fill = ratio_available
        )
      ) +
      scale_fill_scico(palette = "berlin",
                       limits = c(0,1)) +
      labs(title = unique(.x$year)) +
      facet_wrap(~ parameter)
  )


pdf(file = paste0(path_preprocessing, "GLODAPv2.2021_preprocessed_coverage_maps.pdf"),
    width = 10, 
    height = 5)
all_plots

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dev.off()

png 
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12.6 Time series

GLODAP_time_series <- GLODAP %>% 
  select(year, basin_AIP, lat, depth, sal, temp,
         oxygen, aou, nitrate, silicate, phosphate,
         tco2, talk)

GLODAP_time_series <- GLODAP_time_series %>% 
  mutate(depth_grid = cut(depth, seq(0,1e4,1000)))

GLODAP_time_series <- GLODAP_time_series %>% 
  pivot_longer(sal:talk,
               names_to = "parameter",
               values_to = "value") %>% 
  filter(!is.na(value),
         !is.na(depth_grid))

GLODAP_time_series %>%
  group_split(basin_AIP, depth_grid) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(year, value, col = lat)) +
      geom_jitter(alpha = 0.1) +
      scale_color_divergent() +
      facet_grid(parameter ~ depth_grid,
                 scales = "free_y") +
      labs(title = paste(
        "basin_AIP:",
        unique(.x$basin_AIP),
        "| depth_grid:",
        unique(.x$depth_grid)
      ))
  )

13 CANYON-B

13.1 Prediction

source("/net/kryo/work/uptools/co2_calculation/CANYON-B/CANYONB.R")

GLODAP_CB <- GLODAP %>%
  mutate(lon = if_else(lon > 180, lon - 360, lon)) %>%
  arrange(year) %>% 
  select(row_number, year, date, lat, lon, depth, basin_AIP,
         temp, sal, oxygen,
         talk, tco2, nitrate, phosphate, silicate)

# filter rows with essential variables for Canyon-B
GLODAP_CB <- GLODAP_CB %>%
  filter(across(c(lat, lon, depth,
                  temp, sal, oxygen), ~ !is.na(.x)))

GLODAP_CB <- GLODAP_CB %>%
  mutate(as_tibble(
    CANYONB(
      date = paste0(as.character(date), " 12:00"),
      lat = lat,
      lon = lon,
      pres = depth,
      temp = temp,
      psal = sal,
      doxy = oxygen,
      param = c("AT", "CT", "NO3", "PO4", "SiOH4")
    )
  ))

GLODAP_CB <- GLODAP_CB %>%
  select(-ends_with(c("_cim", "_cin", "_cii")))


GLODAP_CB <- GLODAP_CB %>%
  rename(
    "talk_CANYONB" = "AT",
    "tco2_CANYONB" = "CT",
    "nitrate_CANYONB" = "NO3",
    "phosphate_CANYONB" = "PO4",
    "silicate_CANYONB" = "SiOH4"
  )

13.2 Comparison to observations

variables <- c("talk", "tco2", "nitrate", "phosphate", "silicate")

for (i_variable in variables) {
  # i_variable <- variables[1]
  
  # calculate equal axis limits and binwidth
  axis_lims <- GLODAP_CB %>%
    drop_na() %>% 
    summarise(max_value = max(c(max(
      !!sym(i_variable)
    ),
    max(!!sym(
      paste0(i_variable, "_CANYONB")
    )))),
    min_value = min(c(min(
      !!sym(i_variable)
    ),
    min(!!sym(
      paste0(i_variable, "_CANYONB")
    )))))
  
  binwidth_value <- (axis_lims$max_value - axis_lims$min_value) / 60
  axis_lims <- c(axis_lims$min_value, axis_lims$max_value)
  
  print(
    ggplot(GLODAP_CB, aes(
      x = !!sym(i_variable),
      y = !!sym(paste0(i_variable, "_CANYONB"))
    )) +
      geom_bin2d(binwidth = binwidth_value) +
      scale_fill_viridis_c(trans = "log10") +
      geom_abline(slope = 1, col = 'red') +
      coord_equal(xlim = axis_lims,
                  ylim = axis_lims) +
      facet_wrap( ~ basin_AIP) +
      labs(title = "All years")
  ) 
  
  
  # for (i_year in unique(GLODAP_CB$year)) {
  #   # i_year <- 2017
  #   
  #   print(
  #     ggplot(
  #       GLODAP_CB %>% filter(year == i_year),
  #       aes(x = !!sym(i_variable),
  #           y = !!sym(paste0(
  #             i_variable, "_CANYONB"
  #           )))
  #     ) +
  #       geom_bin2d(binwidth = binwidth_value) +
  #       scale_fill_viridis_c(trans = "log10") +
  #       geom_abline(slope = 1, col = 'red') +
  #       coord_equal(xlim = axis_lims,
  #                   ylim = axis_lims) +
  #       facet_wrap( ~ basin_AIP) +
  #       labs(title = paste("Year:", i_year))
  #   )
  # }
  
}

13.3 Write files

GLODAP_CB %>% 
  select(row_number,
         talk_CANYONB, tco2_CANYONB,
         nitrate_CANYONB, phosphate_CANYONB, silicate_CANYONB) %>% 
  write_csv(paste(path_preprocessing,
                             "GLODAPv2.2021_Canyon-B.csv",
                             sep = ""))

14 Crossover data

GLODAP_CB <-
  read_csv(paste(path_preprocessing,
                 "GLODAPv2.2021_Canyon-B.csv",
                 sep = ""))

cruises_phosphate_gap_fill <-
  c("33MW19930704",
    "33RO20030604",
    "33RO20050111",
    "33RO19980123")

cruises_talk_gap_fill <-
  c("06AQ19980328")

cruises_tco2_calc <-
  c("35TH20040604",
    "29AH20160617")

cruises_talk_calc <-
  c("06MT19900123",
    "316N19920502",
    "316N19921006")

xover_add_decade <- glodapv2_2021_xover_add %>%
  mutate(date_A = ymd(str_sub(cruise_A, 5, 12)),
         date_B = ymd(str_sub(cruise_B, 5, 12))) %>%
  mutate(decade = cut(
    year(date_B),
    c(1989, 1999, 2009, 2019),
    labels = c("1989-1999", "2000-2009", "2010-2019")
  )) %>%
  filter(!is.na(decade),
         !is.na(offset)) %>%
  arrange(date_B)

xover_add_decade %>%
  group_by(parameter, cruise_A) %>%
  summarise(offset_mean = mean(offset, na.rm = TRUE)) %>%
  ungroup() %>%
  kable(caption = "Long-term average per cruise and parameter") %>%
  kable_styling() %>%
  scroll_box(height = "250px")

Long-term average per cruise and parameter

parameter	cruise_A	offset_mean
phosphate	06AQ19980328	1.0076477
phosphate	06MT19900123	0.9952731
phosphate	29AH20160617	0.9848944
phosphate	316N19920502	1.0081928
phosphate	316N19921006	1.0099657
phosphate	33MW19930704	0.9890182
phosphate	33RO19980123	0.9965710
phosphate	33RO20030604	0.9966745
phosphate	33RO20050111	1.0019318
phosphate	35TH20040604	0.9740753
talk	06AQ19980328	0.5163006
talk	06MT19900123	-3.5574819
talk	29AH20160617	1.5479474
talk	316N19920502	-4.3333542
talk	316N19921006	-1.0527798
talk	33MW19930704	-0.6041569
talk	33RO19980123	-0.8518579
talk	33RO20030604	-1.8164213
talk	33RO20050111	1.6308865
talk	35TH20040604	0.3672771
tco2	06AQ19980328	-0.5510192
tco2	06MT19900123	-2.6515513
tco2	29AH20160617	6.3313796
tco2	316N19920502	0.2705865
tco2	316N19921006	0.7551445
tco2	33MW19930704	-1.3349429
tco2	33RO19980123	0.5594899
tco2	33RO20030604	-0.7151763
tco2	33RO20050111	-0.3622474
tco2	35TH20040604	1.2796712

xover_add_decade %>%
  group_by(parameter, decade, cruise_A) %>%
  summarise(offset_mean = mean(offset, na.rm = TRUE)) %>%
  ungroup() %>%
  kable(caption = "Decadal average per cruise and parameter") %>%
  kable_styling() %>%
  scroll_box(height = "250px")

Decadal average per cruise and parameter

parameter	decade	cruise_A	offset_mean
phosphate	1989-1999	06AQ19980328	1.0088276
phosphate	1989-1999	06MT19900123	0.9925257
phosphate	1989-1999	29AH20160617	0.9796380
phosphate	1989-1999	316N19920502	1.0067578
phosphate	1989-1999	316N19921006	1.0017277
phosphate	1989-1999	33MW19930704	0.9882398
phosphate	1989-1999	33RO19980123	0.9952849
phosphate	1989-1999	33RO20030604	1.0012928
phosphate	1989-1999	33RO20050111	1.0009821
phosphate	1989-1999	35TH20040604	0.9712452
phosphate	2000-2009	06AQ19980328	1.0053414
phosphate	2000-2009	06MT19900123	1.0010033
phosphate	2000-2009	29AH20160617	0.9899738
phosphate	2000-2009	316N19920502	1.0075795
phosphate	2000-2009	316N19921006	1.0137857
phosphate	2000-2009	33MW19930704	0.9846140
phosphate	2000-2009	33RO19980123	1.0037495
phosphate	2000-2009	33RO20030604	0.9921003
phosphate	2000-2009	33RO20050111	1.0025604
phosphate	2000-2009	35TH20040604	0.9765408
phosphate	2010-2019	06AQ19980328	1.0092836
phosphate	2010-2019	06MT19900123	0.9922316
phosphate	2010-2019	29AH20160617	0.9904749
phosphate	2010-2019	316N19920502	1.0127004
phosphate	2010-2019	316N19921006	1.0143839
phosphate	2010-2019	33MW19930704	0.9964602
phosphate	2010-2019	33RO19980123	0.9950980
phosphate	2010-2019	33RO20030604	0.9944871
phosphate	2010-2019	33RO20050111	1.0032793
phosphate	2010-2019	35TH20040604	0.9766304
talk	1989-1999	06AQ19980328	4.2986091
talk	1989-1999	06MT19900123	-1.7866112
talk	1989-1999	29AH20160617	1.5510114
talk	1989-1999	316N19920502	-0.0551690
talk	1989-1999	316N19921006	0.4878711
talk	1989-1999	33MW19930704	-0.6029753
talk	1989-1999	33RO19980123	0.1289735
talk	1989-1999	33RO20030604	-2.6727990
talk	1989-1999	33RO20050111	1.2382129
talk	1989-1999	35TH20040604	0.3314651
talk	2000-2009	06AQ19980328	-0.2162355
talk	2000-2009	06MT19900123	-4.0659303
talk	2000-2009	29AH20160617	2.3884257
talk	2000-2009	316N19920502	-4.0265274
talk	2000-2009	316N19921006	-0.9215473
talk	2000-2009	33MW19930704	-0.3195600
talk	2000-2009	33RO19980123	-5.2032065
talk	2000-2009	33RO20030604	-0.9500663
talk	2000-2009	33RO20050111	1.2662351
talk	2000-2009	35TH20040604	1.1230941
talk	2010-2019	06AQ19980328	0.7206032
talk	2010-2019	06MT19900123	-3.9344689
talk	2010-2019	29AH20160617	-0.1395747
talk	2010-2019	316N19920502	-6.9326869
talk	2010-2019	316N19921006	-2.7246632
talk	2010-2019	33MW19930704	-0.9855887
talk	2010-2019	33RO19980123	0.3001826
talk	2010-2019	33RO20030604	-1.7612909
talk	2010-2019	33RO20050111	1.8786579
talk	2010-2019	35TH20040604	-1.0676170
tco2	1989-1999	06AQ19980328	1.1144077
tco2	1989-1999	06MT19900123	-0.2131875
tco2	1989-1999	29AH20160617	8.1120081
tco2	1989-1999	316N19920502	0.1582007
tco2	1989-1999	316N19921006	0.2100602
tco2	1989-1999	33MW19930704	-0.1342253
tco2	1989-1999	33RO19980123	0.4187870
tco2	1989-1999	33RO20030604	0.8379332
tco2	1989-1999	33RO20050111	1.4562572
tco2	1989-1999	35TH20040604	2.7288076
tco2	2000-2009	06AQ19980328	-1.3551591
tco2	2000-2009	06MT19900123	-3.4099140
tco2	2000-2009	29AH20160617	6.1180167
tco2	2000-2009	316N19920502	0.5997351
tco2	2000-2009	316N19921006	0.8386702
tco2	2000-2009	33MW19930704	-1.6191444
tco2	2000-2009	33RO19980123	1.0463619
tco2	2000-2009	33RO20030604	-1.2259491
tco2	2000-2009	33RO20050111	1.0973981
tco2	2000-2009	35TH20040604	1.2641341
tco2	2010-2019	06AQ19980328	-1.1823577
tco2	2010-2019	06MT19900123	-3.9523709
tco2	2010-2019	29AH20160617	1.1923250
tco2	2010-2019	316N19920502	-0.1669434
tco2	2010-2019	316N19921006	1.2167030
tco2	2010-2019	33MW19930704	-3.8756236
tco2	2010-2019	33RO19980123	0.4491628
tco2	2010-2019	33RO20030604	-2.5484791
tco2	2010-2019	33RO20050111	-2.0013224
tco2	2010-2019	35TH20040604	-3.0344442

xover_add_decade %>%
  filter(cruise_A %in% cruises_talk_calc,
         parameter == "talk") %>% 
  group_by(parameter, decade, cruise_A) %>%
  summarise(offset_mean = mean(offset, na.rm = TRUE)) %>%
  ungroup() %>%
  kable(caption = "Decadal talk average per cruise") %>%
  kable_styling() %>%
  scroll_box(height = "250px")

Decadal talk average per cruise

parameter	decade	cruise_A	offset_mean
talk	1989-1999	06MT19900123	-1.7866112
talk	1989-1999	316N19920502	-0.0551690
talk	1989-1999	316N19921006	0.4878711
talk	2000-2009	06MT19900123	-4.0659303
talk	2000-2009	316N19920502	-4.0265274
talk	2000-2009	316N19921006	-0.9215473
talk	2010-2019	06MT19900123	-3.9344689
talk	2010-2019	316N19920502	-6.9326869
talk	2010-2019	316N19921006	-2.7246632

xover_add_decade %>%
  filter(cruise_A %in% cruises_talk_calc,
         parameter == "talk") %>% 
  group_by(parameter, decade) %>%
  summarise(offset_mean = mean(offset, na.rm = TRUE)) %>%
  ungroup() %>%
  kable(caption = "Decadal talk average") %>%
  kable_styling() %>%
  scroll_box(height = "250px")

Decadal talk average

parameter	decade	offset_mean
talk	1989-1999	-0.7851301
talk	2000-2009	-3.6581063
talk	2010-2019	-4.6182733

xover_add_decade %>%
  filter(cruise_A %in% cruises_talk_calc,
         parameter == "talk") %>% 
  group_by(parameter) %>%
  summarise(offset_mean = mean(offset, na.rm = TRUE)) %>%
  ungroup() %>%
  kable(caption = "talk average") %>%
  kable_styling() %>%
  scroll_box(height = "250px")

talk average

parameter	offset_mean
talk	-3.407015

xover_add_decade %>%
  filter(cruise_A %in% cruises_talk_calc,
         parameter == "talk") %>% 
  group_by(parameter, cruise_A) %>%
  summarise(offset_mean = mean(offset, na.rm = TRUE)) %>%
  ungroup() %>%
  kable(caption = "talk average per cruise") %>%
  kable_styling() %>%
  scroll_box(height = "250px")

talk average per cruise

parameter	cruise_A	offset_mean
talk	06MT19900123	-3.557482
talk	316N19920502	-4.333354
talk	316N19921006	-1.052780

hline_intercept <- hline_intercept %>% 
  filter(parameter %in% unique(xover_add_decade$parameter))


p_crossover_ts <- xover_add_decade %>%
  ggplot(aes(date_B, offset)) +
  geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
  geom_point(shape = 21) +
  scale_color_brewer(palette = "Set1") +
  facet_grid(parameter ~ ., scales = "free_y") +
  theme(
    legend.position = "bottom",
    legend.direction = "vertical",
    axis.title.x = element_blank()
  )

p_crossover_decadal <-
  ggplot() +
  geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
  geom_violin(data = xover_add_decade,
               aes(x = decade, y = offset), fill="gold") +
  geom_boxplot(data = xover_add_decade,
               aes(x = decade, y = offset),
               width = 0.2) +
  facet_grid(parameter ~ ., scales = "free_y") +
  labs(title = "Decadal offsets") +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 90))


p_crossover_ts + p_crossover_decadal +
  plot_layout(widths = c(2, 1))

Past versions of prepare_additional_crossover_data-1.png

Version	Author	Date
ecc669f	jens-daniel-mueller	2022-01-04
494beda	jens-daniel-mueller	2022-01-03

14.1 Gap filling

GLODAP <- left_join(GLODAP,
                    GLODAP_CB %>% 
                      select(row_number, ends_with("_CANYONB")))

# fill missing phosphate with CANYON-B estimate

GLODAP_phosphate_fill <- GLODAP %>%
  filter(cruise_expocode %in% cruises_phosphate_gap_fill,
         is.na(phosphate),
         oxygenqc == 1)

GLODAP_phosphate_fill <- GLODAP_phosphate_fill %>% 
  mutate(phosphate = phosphate_CANYONB) %>% 
  filter(!is.na(phosphate))

map +
  geom_tile(data = GLODAP_phosphate_fill %>%
              distinct(lon, lat, cruise_expocode),
            aes(lon, lat, fill = cruise_expocode)) +
  scale_fill_brewer(palette = "Set1")

Past versions of CANYON-B_gap_filling-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
494beda	jens-daniel-mueller	2022-01-03

for (i_cruise in cruises_phosphate_gap_fill) {
  # i_cruise <- cruises_phosphate_gap_fill[1]
  
  p_crossover_ts <- xover_add_decade %>%
    filter(cruise_A %in% i_cruise) %>%
    ggplot(aes(date_B, offset)) +
    geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
    geom_point(shape = 21) +
    scale_color_brewer(palette = "Set1") +
    facet_grid(parameter ~ ., scales = "free_y") +
    labs(title = i_cruise) +
    theme(
      legend.position = "bottom",
      legend.direction = "vertical",
      axis.title.x = element_blank()
    )
  
  p_crossover_decadal <-
    ggplot() +
    geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
    geom_violin(
      data = xover_add_decade %>%
        filter(cruise_A %in% i_cruise),
      aes(x = decade, y = offset),
      fill = "gold"
    ) +
    geom_boxplot(
      data = xover_add_decade %>%
        filter(cruise_A %in% i_cruise),
      aes(x = decade, y = offset),
      width = 0.2
    ) +
    facet_grid(parameter ~ ., scales = "free_y") +
    theme(axis.title.x = element_blank(),
          axis.text.x = element_text(angle = 90))
  
  print(
  p_crossover_ts + p_crossover_decadal +
    plot_layout(widths = c(2, 1))
  )
  
}

Past versions of CANYON-B_gap_filling-2.png

Version	Author	Date
1a9c797	jens-daniel-mueller	2022-01-03
494beda	jens-daniel-mueller	2022-01-03

Past versions of CANYON-B_gap_filling-3.png

Version	Author	Date
1a9c797	jens-daniel-mueller	2022-01-03
494beda	jens-daniel-mueller	2022-01-03

Past versions of CANYON-B_gap_filling-4.png

Version	Author	Date
1a9c797	jens-daniel-mueller	2022-01-03
494beda	jens-daniel-mueller	2022-01-03

Past versions of CANYON-B_gap_filling-5.png

Version	Author	Date
1a9c797	jens-daniel-mueller	2022-01-03
494beda	jens-daniel-mueller	2022-01-03

# fill missing talk with CANYON-B estimate

GLODAP_talk_fill <- GLODAP %>%
  filter(cruise_expocode %in% cruises_talk_gap_fill,
         is.na(talk),
         oxygenqc == 1)

GLODAP_talk_fill <- GLODAP_talk_fill %>% 
  mutate(talk = talk_CANYONB) %>% 
  filter(!is.na(talk))


map +
  geom_tile(data = GLODAP_talk_fill %>%
              distinct(lon, lat, cruise_expocode),
            aes(lon, lat, fill = cruise_expocode)) +
  scale_fill_brewer(palette = "Set1")

Past versions of CANYON-B_gap_filling-6.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
494beda	jens-daniel-mueller	2022-01-03

for (i_cruise in cruises_talk_gap_fill) {
  # i_cruise <- cruises_phosphate_gap_fill[1]
  
  p_crossover_ts <- xover_add_decade %>%
    filter(cruise_A %in% i_cruise) %>%
    ggplot(aes(date_B, offset)) +
    geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
    geom_point(shape = 21) +
    scale_color_brewer(palette = "Set1") +
    facet_grid(parameter ~ ., scales = "free_y") +
    labs(title = i_cruise) +
    theme(
      legend.position = "bottom",
      legend.direction = "vertical",
      axis.title.x = element_blank()
    )
  
  p_crossover_decadal <-
    ggplot() +
    geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
    geom_violin(
      data = xover_add_decade %>%
        filter(cruise_A %in% i_cruise),
      aes(x = decade, y = offset),
      fill = "gold"
    ) +
    geom_boxplot(
      data = xover_add_decade %>%
        filter(cruise_A %in% i_cruise),
      aes(x = decade, y = offset),
      width = 0.2
    ) +
    facet_grid(parameter ~ ., scales = "free_y") +
    theme(axis.title.x = element_blank(),
          axis.text.x = element_text(angle = 90))
  
  print(p_crossover_ts + p_crossover_decadal +
          plot_layout(widths = c(2, 1)))
  
}

Past versions of CANYON-B_gap_filling-7.png

Version	Author	Date
1a9c797	jens-daniel-mueller	2022-01-03
494beda	jens-daniel-mueller	2022-01-03

GLODAP_gap_fill <- bind_rows(
  GLODAP_phosphate_fill,
  GLODAP_talk_fill
)

14.2 Flagging

GLODAP_tco2_calc <- GLODAP %>% 
  filter(cruise_expocode %in% cruises_tco2_calc,
         tco2f == 0)


map +
  geom_tile(data = GLODAP_tco2_calc %>%
              distinct(lon, lat, cruise_expocode),
            aes(lon, lat, fill = cruise_expocode)) +
  scale_fill_brewer(palette = "Set1")

Past versions of flagged_cruises-1.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
494beda	jens-daniel-mueller	2022-01-03

for (i_cruise in cruises_tco2_calc) {
  # i_cruise <- cruises_phosphate_gap_fill[1]
  
  p_crossover_ts <- xover_add_decade %>%
    filter(cruise_A %in% i_cruise) %>%
    ggplot(aes(date_B, offset)) +
    geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
    geom_point(shape = 21) +
    scale_color_brewer(palette = "Set1") +
    facet_grid(parameter ~ ., scales = "free_y") +
    labs(title = i_cruise) +
    theme(
      legend.position = "bottom",
      legend.direction = "vertical",
      axis.title.x = element_blank()
    )
  
  p_crossover_decadal <-
    ggplot() +
    geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
    geom_violin(
      data = xover_add_decade %>%
        filter(cruise_A %in% i_cruise),
      aes(x = decade, y = offset),
      fill = "gold"
    ) +
    geom_boxplot(
      data = xover_add_decade %>%
        filter(cruise_A %in% i_cruise),
      aes(x = decade, y = offset),
      width = 0.2
    ) +
    facet_grid(parameter ~ ., scales = "free_y") +
    theme(axis.title.x = element_blank(),
          axis.text.x = element_text(angle = 90))
  
  print(p_crossover_ts + p_crossover_decadal +
          plot_layout(widths = c(2, 1)))
  
}

Past versions of flagged_cruises-2.png

Version	Author	Date
1a9c797	jens-daniel-mueller	2022-01-03
494beda	jens-daniel-mueller	2022-01-03

Past versions of flagged_cruises-3.png

Version	Author	Date
1a9c797	jens-daniel-mueller	2022-01-03
494beda	jens-daniel-mueller	2022-01-03

GLODAP_talk_calc <- GLODAP %>% 
  filter(cruise_expocode %in% cruises_talk_calc,
         talkf == 0)


map +
  geom_tile(data = GLODAP_talk_calc %>%
              distinct(lon, lat, cruise_expocode),
            aes(lon, lat, fill = cruise_expocode)) +
  scale_fill_brewer(palette = "Set1")

Past versions of flagged_cruises-4.png

Version	Author	Date
e3d1a2b	jens-daniel-mueller	2022-03-10
9db485e	jens-daniel-mueller	2022-02-25
494beda	jens-daniel-mueller	2022-01-03

for (i_cruise in cruises_talk_calc) {
  # i_cruise <- cruises_phosphate_gap_fill[1]
  
  p_crossover_ts <- xover_add_decade %>%
    filter(cruise_A %in% i_cruise) %>%
    ggplot(aes(date_B, offset)) +
    geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
    geom_point(shape = 21) +
    scale_color_brewer(palette = "Set1") +
    facet_grid(parameter ~ ., scales = "free_y") +
    labs(title = i_cruise) +
    theme(
      legend.position = "bottom",
      legend.direction = "vertical",
      axis.title.x = element_blank()
    )
  
  p_crossover_decadal <-
    ggplot() +
    geom_hline(data = hline_intercept, aes(yintercept = intercept)) +
    geom_violin(
      data = xover_add_decade %>%
        filter(cruise_A %in% i_cruise),
      aes(x = decade, y = offset),
      fill = "gold"
    ) +
    geom_boxplot(
      data = xover_add_decade %>%
        filter(cruise_A %in% i_cruise),
      aes(x = decade, y = offset),
      width = 0.2
    ) +
    facet_grid(parameter ~ ., scales = "free_y") +
    theme(axis.title.x = element_blank(),
          axis.text.x = element_text(angle = 90))
  
  print(
  p_crossover_ts + p_crossover_decadal +
    plot_layout(widths = c(2, 1))
  )
    
}

Past versions of flagged_cruises-5.png

Version	Author	Date
1a9c797	jens-daniel-mueller	2022-01-03
494beda	jens-daniel-mueller	2022-01-03

Past versions of flagged_cruises-6.png

Version	Author	Date
1a9c797	jens-daniel-mueller	2022-01-03
494beda	jens-daniel-mueller	2022-01-03

Past versions of flagged_cruises-7.png

Version	Author	Date
1a9c797	jens-daniel-mueller	2022-01-03
494beda	jens-daniel-mueller	2022-01-03

GLODAP_calc <- bind_rows(
  GLODAP_tco2_calc,
  GLODAP_talk_calc
)

14.3 Write files

GLODAP_crossover <- bind_rows(
  GLODAP_gap_fill,
  GLODAP_calc
) 

GLODAP_crossover_write <- GLODAP_crossover %>% 
  select(
    EXPOCODE = cruise_expocode,
    STNNBR = station,
    CASTNO = cast,
    BTLNBR = bottle,
    DATE = date,
    LATITUDE = lat,
    LONGITUDE = lon,
    CTDPRS = pressure,
    CTDTMP = temp,
    CTDSAL = sal,
    CTDSAL_FLAG_W = salinityf,
    PHSPHT = phosphate,
    PHSPHT_FLAG_W = phosphatef,
    TCARBN = tco2,
    TCARBN_FLAG_W = tco2f,
    ALKALI = talk,
    ALKALI_FLAG_W = talkf)

GLODAP_crossover_write <- GLODAP_crossover_write %>% 
  mutate(DATE = format(DATE,  "%Y%m%d"))


last_line <- "END_DATA"

for (i_EXPOCODE in unique(GLODAP_crossover_write$EXPOCODE)) {
  # i_EXPOCODE <- unique(GLODAP_crossover_write$EXPOCODE)[1]
  
  temp <- GLODAP_crossover_write %>%
    filter(EXPOCODE == i_EXPOCODE) %>%
    add_row(.before = 1)
  
  cat("Bottle",
      "\n",
      file = paste0(
        path_preprocessing,
        "crossover_cruises/",
        i_EXPOCODE,
        ".exc.csv"
      )
    )
  
  temp %>%
    write_csv(
      file = paste0(
        path_preprocessing,
        "crossover_cruises/",
        i_EXPOCODE,
        ".exc.csv"
      ),
      na = "",
      append = TRUE,
      col_names = TRUE
    )
  
  write(
    last_line,
    file = paste0(
      path_preprocessing,
      "crossover_cruises/",
      i_EXPOCODE,
      ".exc.csv"
    ),
    append = TRUE
  )
  
  
}

Session information

sessionInfo()

R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3

Matrix products: default
BLAS:   /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] colorspace_2.0-2 kableExtra_1.3.4 ggrepel_0.9.1    lubridate_1.8.0 
 [5] marelac_2.1.10   shape_1.4.6      ggforce_0.3.3    metR_0.11.0     
 [9] scico_1.3.0      patchwork_1.1.1  collapse_1.7.0   forcats_0.5.1   
[13] stringr_1.4.0    dplyr_1.0.7      purrr_0.3.4      readr_2.1.1     
[17] tidyr_1.1.4      tibble_3.1.6     ggplot2_3.3.5    tidyverse_1.3.1 
[21] workflowr_1.7.0 

loaded via a namespace (and not attached):
 [1] nlme_3.1-155       fs_1.5.2           bit64_4.0.5        gsw_1.0-6         
 [5] RColorBrewer_1.1-2 webshot_0.5.2      httr_1.4.2         rprojroot_2.0.2   
 [9] tools_4.1.2        backports_1.4.1    bslib_0.3.1        utf8_1.2.2        
[13] R6_2.5.1           mgcv_1.8-38        DBI_1.1.2          withr_2.4.3       
[17] tidyselect_1.1.1   processx_3.5.2     bit_4.0.4          compiler_4.1.2    
[21] git2r_0.29.0       cli_3.1.1          rvest_1.0.2        xml2_1.3.3        
[25] labeling_0.4.2     sass_0.4.0         scales_1.1.1       checkmate_2.0.0   
[29] SolveSAPHE_2.1.0   callr_3.7.0        systemfonts_1.0.3  digest_0.6.29     
[33] svglite_2.0.0      rmarkdown_2.11     oce_1.5-0          pkgconfig_2.0.3   
[37] htmltools_0.5.2    highr_0.9          dbplyr_2.1.1       fastmap_1.1.0     
[41] rlang_0.4.12       readxl_1.3.1       rstudioapi_0.13    jquerylib_0.1.4   
[45] generics_0.1.1     farver_2.1.0       jsonlite_1.7.3     vroom_1.5.7       
[49] magrittr_2.0.1     Matrix_1.4-0       Rcpp_1.0.8         munsell_0.5.0     
[53] fansi_1.0.2        lifecycle_1.0.1    stringi_1.7.6      whisker_0.4       
[57] yaml_2.2.1         MASS_7.3-55        grid_4.1.2         parallel_4.1.2    
[61] promises_1.2.0.1   crayon_1.4.2       lattice_0.20-45    splines_4.1.2     
[65] haven_2.4.3        hms_1.1.1          seacarb_3.3.0      knitr_1.37        
[69] ps_1.6.0           pillar_1.6.4       reprex_2.0.1       glue_1.6.0        
[73] evaluate_0.14      getPass_0.2-2      data.table_1.14.2  modelr_0.1.8      
[77] vctrs_0.3.8        tzdb_0.2.0         tweenr_1.0.2       httpuv_1.6.5      
[81] cellranger_1.1.0   gtable_0.3.0       polyclip_1.10-0    assertthat_0.2.1  
[85] xfun_0.29          broom_0.7.11       later_1.3.0        viridisLite_0.4.0 
[89] ellipsis_0.3.2