Last updated: 2021-05-26

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Rmd 0ad30ba jens-daniel-mueller 2021-01-07 removed GLODAP gamma filter, target variable mapped by eras+era
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Rmd cfef575 jens-daniel-mueller 2020-12-23 Option to select era shift of A16 cruise
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1 Read files

Main data source for this project is the preprocessed version of the GLODAPv2.2020_Merged_Master_File.csv downloaded from glodap.info in June 2020.

CAVEAT: This file still contains neutral densities gamma calculated with a preliminary method. However, this is consistent with the way gamma is currently calculated in this script and should therefore be maintained until changed on all levels.

GLODAP <-
  read_csv(
    paste(
      path_preprocessing_model,
      "GLODAPv2.2020_preprocessed_model_runA_both.csv",
      sep = ""
    )
  )

GLODAP <- GLODAP %>%
  arrange(date) %>%
  relocate(row_number)

GLODAP_CB <-
  read_csv(paste(path_preprocessing,
                 "GLODAPv2.2020_Canyon-B.csv",
                 sep = ""))

2 Data preparation

2.1 Reference eras

Samples were assigned to following eras:

# create labels for era
era_labels <- bind_cols(
  start = params_local$era_start,
  end = params_local$era_end)

era_labels <- era_labels %>% 
  mutate(start = if_else(start == -Inf, max(GLODAP$year), start),
         end = if_else(end == Inf, max(GLODAP$year), end),
         era = as.factor(paste(start, end, sep = "-")))

# filter GLODAP data within eras
GLODAP <- expand_grid(
  GLODAP,
  era_labels
)

# select data within each era
GLODAP <- GLODAP %>% 
  filter(year >= start & year <= end)

GLODAP <- GLODAP %>% 
  select(-c(start, end))

rm(era_labels)

2.2 Spatial boundaries

2.2.1 Basin mask

The basin mask from the World Ocean Atlas was used. For details consult the data base subsection for WOA18 data.

Please note that some GLODAP observations were made outside the WOA18 basin mask (i.e. in marginal seas) and will be removed for further analysis.

# use only data inside basinmask
GLODAP <- inner_join(GLODAP, basinmask)

2.2.2 Depth

Observations collected shallower than:

  • minimum sampling depth: 150m

were excluded from the analysis to avoid seasonal bias.

GLODAP <- GLODAP %>% 
  filter(depth >= params_local$depth_min)

2.2.3 Bottomdepth

Observations collected in an area with a:

  • minimum bottom depth: 0m

were excluded from the analysis to avoid coastal impacts. Please note that minimum bottom depth criterion of 0m means that no filtering was applied here.

GLODAP <- GLODAP %>% 
  filter(bottomdepth >= params_local$bottomdepth_min)

2.3 Flags and missing data

Only rows (samples) for which all relevant parameters are available were selected, ie NA’s were removed.

According to Olsen et al (2020), flags within the merged master file identify:

  • f:

    • 2: Acceptable
    • 0: Interpolated (nutrients/oxygen) or calculated (CO[2] variables)
    • 9: Data not used (so, only NA data should have this flag)

  • qc:

    • 1: Adjusted or unadjusted data
    • 0: Data appear of good quality but have not been subjected to full secondary QC
    • data with poor or uncertain quality are excluded.

Following flagging criteria were taken into account:

  • flag_f: 2, 0
  • flag_qc: 1, 0

The cleaning process was performed successively and the maps below represent the data coverage at various cleaning levels.

Summary statistics were calculated during cleaning process.

2.3.1 tco2

2.3.1.1 NA

Rows with missing tco2 observations were already removed in the preprocessing. The map below shows the coverage of preprocessed GLODAP data.

GLODAP_stats <- GLODAP %>% 
  summarise(tco2_values = n())

GLODAP_obs_grid <- GLODAP %>% 
  count(lat, lon, era) %>% 
  mutate(cleaning_level = "tco2_values")
GLODAP_obs <- GLODAP %>% 
  group_by(lat, lon) %>% 
  summarise(n = n()) %>% 
  ungroup()

pdf(
  file = paste0(
    path_version_figures,
    "data_cleaning_maps_",
    params_local$Version_ID,
    ".pdf"
  ),
  width = 10,
  height = 5
)

map +
  geom_raster(data = basinmask, aes(lon, lat, fill = basin)) +
  geom_raster(data = GLODAP_obs, aes(lon, lat)) +
  scale_fill_brewer(palette = "Dark2") +
  theme(legend.position = "top",
        legend.title = element_blank())

dev.off()
png 
  2 
rm(GLODAP_obs)

2.3.1.2 f flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, tco2f)

print(
map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ tco2f) +
  theme(legend.position = "top")
)

Version Author Date
969e631 jens-daniel-mueller 2021-05-12
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
eb827c9 jens-daniel-mueller 2021-04-07
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
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33ba23c jens-daniel-mueller 2021-01-07
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0aa2b50 jens-daniel-mueller 2020-12-23
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02f0ee9 jens-daniel-mueller 2020-12-18
3ebff89 jens-daniel-mueller 2020-12-12
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
37e9dac jens-daniel-mueller 2020-12-02
7c25f7a jens-daniel-mueller 2020-12-02
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30 
rm(GLODAP_obs_grid_temp)

GLODAP <- GLODAP %>%
  filter(tco2f %in% params_local$flag_f)

2.3.1.3 qc flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, tco2qc)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ tco2qc) +
  theme(legend.position = "top")

Version Author Date
969e631 jens-daniel-mueller 2021-05-12
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
eb827c9 jens-daniel-mueller 2021-04-07
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
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a1d52ff jens-daniel-mueller 2021-03-15
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
6949b06 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
3ebff89 jens-daniel-mueller 2020-12-12
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
37e9dac jens-daniel-mueller 2020-12-02
7c25f7a jens-daniel-mueller 2020-12-02
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30 
##

GLODAP <- GLODAP %>%
  filter(tco2qc %in% params_local$flag_qc)

GLODAP_stats_temp <- GLODAP %>%
  summarise(tco2_flag = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era) %>%
  mutate(cleaning_level = "tco2_flag")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.2 talk

2.3.2.1 NA

GLODAP <- GLODAP %>% 
  mutate(talkna = if_else(is.na(talk), "NA", "Value"))

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, talkna)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ talkna) +
  theme(legend.position = "top")

Version Author Date
969e631 jens-daniel-mueller 2021-05-12
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
eb827c9 jens-daniel-mueller 2021-04-07
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
d7831b0 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
f155edd jens-daniel-mueller 2021-03-23
a1d52ff jens-daniel-mueller 2021-03-15
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
6949b06 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
3ebff89 jens-daniel-mueller 2020-12-12
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
37e9dac jens-daniel-mueller 2020-12-02
7c25f7a jens-daniel-mueller 2020-12-02
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30 
GLODAP <- GLODAP %>% 
  select(-talkna) %>% 
  filter(!is.na(talk))

##

GLODAP_stats_temp <- GLODAP %>% 
  summarise(talk_values = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>% 
  count(lat, lon, era) %>% 
  mutate(cleaning_level = "talk_values")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.2.2 f flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, talkf)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ talkf) +
  theme(legend.position = "top",
        legend.title = element_blank())

Version Author Date
969e631 jens-daniel-mueller 2021-05-12
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
eb827c9 jens-daniel-mueller 2021-04-07
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
d7831b0 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
f155edd jens-daniel-mueller 2021-03-23
a1d52ff jens-daniel-mueller 2021-03-15
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
6949b06 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
3ebff89 jens-daniel-mueller 2020-12-12
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
37e9dac jens-daniel-mueller 2020-12-02
7c25f7a jens-daniel-mueller 2020-12-02
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30 
# ###

GLODAP <- GLODAP %>%
  filter(talkf %in% params_local$flag_f)

2.3.2.3 qc flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, talkqc)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ talkqc) +
  theme(legend.position = "top",
        legend.title = element_blank())

Version Author Date
969e631 jens-daniel-mueller 2021-05-12
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
eb827c9 jens-daniel-mueller 2021-04-07
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
d7831b0 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
f155edd jens-daniel-mueller 2021-03-23
a1d52ff jens-daniel-mueller 2021-03-15
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
6949b06 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
3ebff89 jens-daniel-mueller 2020-12-12
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
37e9dac jens-daniel-mueller 2020-12-02
7c25f7a jens-daniel-mueller 2020-12-02
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30 
###

GLODAP <- GLODAP %>%
  filter(talkqc %in% params_local$flag_qc)

##

GLODAP_stats_temp <- GLODAP %>%
  summarise(talk_flag = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era) %>%
  mutate(cleaning_level = "talk_flag")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.3 Phosphate

2.3.3.1 NA

GLODAP <- GLODAP %>% 
  mutate(phosphatena = if_else(is.na(phosphate), "NA", "Value"))

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, phosphatena)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ phosphatena) +
  theme(legend.position = "top")

Version Author Date
969e631 jens-daniel-mueller 2021-05-12
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
eb827c9 jens-daniel-mueller 2021-04-07
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
d7831b0 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
f155edd jens-daniel-mueller 2021-03-23
a1d52ff jens-daniel-mueller 2021-03-15
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
6949b06 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
3ebff89 jens-daniel-mueller 2020-12-12
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
37e9dac jens-daniel-mueller 2020-12-02
7c25f7a jens-daniel-mueller 2020-12-02
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30 
GLODAP <- GLODAP %>% 
  select(-phosphatena) %>% 
  filter(!is.na(phosphate))

##

GLODAP_stats_temp <- GLODAP %>% 
  summarise(phosphate_values = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>% 
  count(lat, lon, era) %>% 
  mutate(cleaning_level = "phosphate_values")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.3.2 f flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, phosphatef)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
    scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era~phosphatef) +
  theme(legend.position = "top",
        legend.title = element_blank())

Version Author Date
969e631 jens-daniel-mueller 2021-05-12
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
eb827c9 jens-daniel-mueller 2021-04-07
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
d7831b0 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
f155edd jens-daniel-mueller 2021-03-23
a1d52ff jens-daniel-mueller 2021-03-15
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
6949b06 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
3ebff89 jens-daniel-mueller 2020-12-12
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
37e9dac jens-daniel-mueller 2020-12-02
7c25f7a jens-daniel-mueller 2020-12-02
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30 
###

GLODAP <- GLODAP %>%
  filter(phosphatef %in% params_local$flag_f)

2.3.3.3 qc flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, phosphateqc)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
    scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era~phosphateqc) +
  theme(legend.position = "top",
        legend.title = element_blank())

Version Author Date
969e631 jens-daniel-mueller 2021-05-12
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
eb827c9 jens-daniel-mueller 2021-04-07
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
d7831b0 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
f155edd jens-daniel-mueller 2021-03-23
a1d52ff jens-daniel-mueller 2021-03-15
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
6949b06 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
3ebff89 jens-daniel-mueller 2020-12-12
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
37e9dac jens-daniel-mueller 2020-12-02
7c25f7a jens-daniel-mueller 2020-12-02
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30 
###

GLODAP <- GLODAP %>%
  filter(phosphateqc %in% params_local$flag_qc)

##

GLODAP_stats_temp <- GLODAP %>%
  summarise(phosphate_flag = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era) %>%
  mutate(cleaning_level = "phosphate_flag")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.4 eMLR variables

Variables required as predictors for the MLR fits, are subsetted for NAs and flags.

if ("temp" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(temp))
}

##

if ("sal" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(sal))
  
  GLODAP <- GLODAP %>%
    filter(salinityf %in% params_local$flag_f)
  
  GLODAP <- GLODAP %>%
    filter(salinityqc %in% params_local$flag_qc)
}

##

if ("silicate" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(silicate))
  
  GLODAP <- GLODAP %>%
    filter(silicatef %in% params_local$flag_f)
  
  GLODAP <- GLODAP %>%
    filter(silicateqc %in% params_local$flag_qc)
}

##

if ("oxygen" %in% params_local$MLR_predictors |
    "phosphate_star" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(oxygen))
  
  GLODAP <- GLODAP %>%
    filter(oxygenf %in% params_local$flag_f)
  
  GLODAP <- GLODAP %>%
    filter(oxygenqc %in% params_local$flag_qc)
}

##

if ("aou" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(aou))
  
  GLODAP <- GLODAP %>%
    filter(aouf %in% params_local$flag_f)
}

##

if ("nitrate" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(nitrate))
  
  GLODAP <- GLODAP %>%
    filter(nitratef %in% params_local$flag_f)
  
  GLODAP <- GLODAP %>%
    filter(nitrateqc %in% params_local$flag_qc)
}

##

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

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

##

GLODAP_stats_temp <- GLODAP %>%
  summarise(eMLR_variables = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)

rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era) %>%
  mutate(cleaning_level = "eMLR_variables")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)
GLODAP <- GLODAP %>% 
  select(-ends_with(c("f", "qc")))

2.4 CANYON-B comparison

Cruises are removed, when the mean offset of the observation from the value predicted with CANYON-B is higher than 10 times the standard deviation of all cruise mean offsets. This critrion is evaluated individually for each variable involved in the eMLR approach.

# join data frames
GLODAP_combined_raw <- left_join(GLODAP,
                                 GLODAP_CB)

# calculate offset by parameter
GLODAP_combined <- GLODAP_combined_raw %>%
  mutate(
    offset_talk = talk - talk_CANYONB,
    offset_tco2 = tco2 - tco2_CANYONB,
    offset_nitrate = nitrate - nitrate_CANYONB,
    offset_phosphate = phosphate - phosphate_CANYONB,
    offset_silicate = silicate - silicate_CANYONB
  ) %>%
  select(row_number,
         year,
         cruise,
         basin_AIP,
         lat,
         lon,
         starts_with("offset_"))

# pivot to long format
GLODAP_combined <- GLODAP_combined %>%
  pivot_longer(
    starts_with("offset"),
    names_to = "parameter",
    names_prefix = "offset_",
    values_to = "offset"
  )

2.4.1 Residual histograms

GLODAP_combined %>%
  ggplot(aes(offset)) +
  geom_histogram() +
  scale_y_continuous(trans = "log10") +
  facet_grid(basin_AIP ~ parameter, scales = "free_x")

Version Author Date
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
6482ed7 jens-daniel-mueller 2021-03-11

2.4.2 Property-property plots

# GLODAP_combined_raw <- GLODAP_combined_raw %>%
#   select(ends_with(c("_CANYONB", "_model")))

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_combined_raw %>%
    drop_na() %>% 
    summarise(max_value = max(c(max(!!sym(
      paste0(i_variable)
    )),
    max(!!sym(
      paste0(i_variable, "_CANYONB")
    )))),
    min_value = min(c(min(!!sym(
      paste0(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_combined_raw, aes(
      x = !!sym(paste0(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")
  ) 

}

Version Author Date
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
87c1aee jens-daniel-mueller 2021-05-11

Version Author Date
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
87c1aee jens-daniel-mueller 2021-05-11

Version Author Date
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
87c1aee jens-daniel-mueller 2021-05-11

Version Author Date
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
87c1aee jens-daniel-mueller 2021-05-11

Version Author Date
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
87c1aee jens-daniel-mueller 2021-05-11 
rm(variables, GLODAP_combined_raw)

2.4.3 Residual time series

GLODAP_combined %>%
  ggplot(aes(year, offset)) +
  geom_bin2d() +
  scale_fill_viridis_c(trans = "log10") +
  facet_grid(parameter ~ basin_AIP, scales = "free_y")

Version Author Date
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
aa676ca jens-daniel-mueller 2021-05-12

2.4.4 Residual distribution

# calculate mean cruise offset by parameter
cruise_all <- GLODAP_combined %>%
  group_by(cruise, parameter) %>%
  summarise(
    mean_offset = mean(offset, na.rm = TRUE),
    sd_offset = sd(offset, na.rm = TRUE)
  ) %>%
  ungroup()

# rank offsets and calculate offset thresholds
cruise_all <- cruise_all %>%
  group_by(parameter) %>%
  mutate(
    rank_offset = rank(mean_offset),
    threshold_offset = sd(mean_offset) * params_local$CANYON_B_max,
    cruise = as.factor(cruise)
  ) %>%
  ungroup() %>%
  arrange(parameter, rank_offset)

cruise_out <- cruise_all %>%
  filter(parameter %in% c("tco2", "talk", params_local$MLR_predictors)) %>% 
  filter(abs(mean_offset) > threshold_offset)

for (i_parameter in unique(cruise_all$parameter)) {
  # i_parameter <- unique(cruise_all$parameter)[1]
  
  i_cruise_all <- cruise_all %>%
    filter(parameter == i_parameter)
  
  i_cruise_out <- cruise_out %>%
    filter(parameter == i_parameter)
  
  print(
  ggplot() +
    geom_hline(data = i_cruise_all,
               aes(yintercept = c(-1, 1) * threshold_offset),
               lty = 2) +
    geom_ribbon(
      data = i_cruise_all,
      aes(
        x = rank_offset,
        ymax = mean_offset + sd_offset,
        ymin = mean_offset-+sd_offset
      ),
      alpha = 0.3
    ) +
    geom_path(data = i_cruise_all,
              aes(rank_offset, mean_offset)) +
    geom_point(data = i_cruise_out,
               aes(rank_offset, mean_offset, col = cruise)) +
    labs(title = i_parameter)
  )
  
    print(
  ggplot() +
    geom_point(data = i_cruise_all,
              aes(mean_offset, sd_offset)) +
    geom_point(data = i_cruise_out,
               aes(mean_offset, sd_offset, col = cruise)) +
    labs(title = i_parameter)
  )
  
}

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26 
rm(i_parameter, i_cruise_all, i_cruise_out)

2.4.5 Remove cruises

GLODAP_out <- GLODAP %>%
  filter(cruise %in% cruise_out$cruise)

if (nrow(GLODAP_out) > 0) {
  map +
  geom_raster(data = GLODAP_out %>% distinct(lat, lon, era),
              aes(lon, lat)) +
  facet_wrap( ~ era, ncol = 1) +
  labs(title = "Maps of removed cruises")

} else {
  print("no cruises removed")
  
}

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
eb827c9 jens-daniel-mueller 2021-04-07
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
555750f jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
f155edd jens-daniel-mueller 2021-03-23
a1d52ff jens-daniel-mueller 2021-03-15
0bade3b jens-daniel-mueller 2021-03-15
27c1f4b jens-daniel-mueller 2021-03-14
af75ebf jens-daniel-mueller 2021-03-14
5017709 jens-daniel-mueller 2021-03-11
6482ed7 jens-daniel-mueller 2021-03-11

The ratio (%) of removed cruises is:

nrow(GLODAP_out) / nrow(GLODAP) * 100
[1] 0.005788191
GLODAP <- GLODAP %>%
  filter(!(cruise %in% cruise_out$cruise))

2.5 Median years (tref)

Median years of each era (tref) were determined as:

# calculate reference year
tref <- GLODAP %>%
  group_by(era) %>%
  summarise(median_year = median(year)) %>%
  ungroup()

tref
# A tibble: 2 x 2
  era       median_year
  <fct>           <dbl>
1 2000-2009        2006
2 2010-2019        2014

2.6 Manual cruise duplication

map +
  geom_raster(data =
                GLODAP %>%
                distinct(year, lat, lon, basin_AIP),
              aes(lon, lat, fill=basin_AIP)) +
  scale_fill_brewer(palette = "Set1") +
  facet_wrap(~ year, ncol = 3)

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16 
# Duplicate Indian ocean cruises manually

# GLODAP_dup <- GLODAP %>% 
#   filter(basin_AIP == "Indian",
#          year >=2007,
#          year <=2009) %>% 
#   mutate(era = sort(unique(GLODAP$era))[2])
# 
# GLODAP <- bind_rows(GLODAP, GLODAP_dup)
# rm(GLODAP_dup)

# Duplicate overlapping eras manually

GLODAP_dup_era1 <- GLODAP %>%
  filter(year >= params_local$dup_era1[1],
         year <= params_local$dup_era1[2]) %>%
  mutate(era = sort(unique(GLODAP$era))[2])

GLODAP_dup_era2 <- GLODAP %>%
  filter(year >= params_local$dup_era2[1],
         year <= params_local$dup_era2[2]) %>%
  mutate(era = sort(unique(GLODAP$era))[1])

GLODAP <- bind_rows(GLODAP %>% mutate(duplicate = "no"),
                    GLODAP_dup_era1 %>% mutate(duplicate = "yes"),
                    GLODAP_dup_era2 %>% mutate(duplicate = "yes"))

GLODAP_grid_dup <- GLODAP %>%
  distinct(era, duplicate, lon, lat)
  
GLODAP <- GLODAP %>% 
  select(-duplicate)

rm(GLODAP_dup_era1, GLODAP_dup_era2)

2.7 Create clean observations grid

Grid containing all grid cells where at least one observation remains available after cleaning.

GLODAP_obs_grid_clean <- GLODAP %>% 
  distinct(lat, lon)

2.8 Write summary file

GLODAP_obs_grid_clean  %>%
  write_csv(paste(path_version_data,
                  "GLODAPv2.2020_clean_obs_grid.csv",
                  sep = ""))

GLODAP_grid_dup  %>%
  write_csv(paste(path_version_data,
                  "GLODAPv2.2020_clean_obs_grid_duplicates.csv",
                  sep = ""))

# select relevant columns for further analysis
GLODAP <- GLODAP %>% 
  select(year, date, era, basin, basin_AIP, lat, lon, cruise,
         bottomdepth, depth,
         temp, sal, gamma,
         tco2, talk, phosphate,
         oxygen, aou, nitrate, silicate,
         temp_model, sal_model = so_model, gamma_model,
         tco2_model = dissic_model, talk_model, phosphate_model = po4_model,
         oxygen_model = o2_model, aou_model,
         nitrate_model = no3_model, silicate_model = si_model
         )


GLODAP  %>%  write_csv(paste(path_version_data,
                             "GLODAPv2.2020_clean.csv",
                             sep = ""))

tref  %>%  write_csv(paste(path_version_data,
                           "tref.csv",
                           sep = ""))

3 Overview plots

3.1 Number of overservations

GLODAP %>% 
  group_by(era, basin_AIP) %>% 
  count() %>% 
  ggplot(aes(basin_AIP, n, fill = era)) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2")

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13

3.2 Cleaning stats

Number of observations at various steps of data cleaning.

GLODAP_stats_long <- GLODAP_stats %>%
  pivot_longer(1:length(GLODAP_stats),
               names_to = "parameter",
               values_to = "n")

GLODAP_stats_long <- GLODAP_stats_long %>%
  mutate(parameter = fct_reorder(parameter, n))

GLODAP_stats_long %>% 
  ggplot(aes(parameter, n/1000)) +
  geom_col() +
  coord_flip() +
  theme(axis.title.y = element_blank())

Version Author Date
969e631 jens-daniel-mueller 2021-05-12
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
6949b06 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
5d452fe jens-daniel-mueller 2020-12-18
158fe26 jens-daniel-mueller 2020-12-15
3ebff89 jens-daniel-mueller 2020-12-12
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30 
rm(GLODAP_stats_long)

3.3 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)

3.4 Histogram Zonal coverage

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

GLODAP_histogram_lat %>%
  ggplot(aes(lat_grid, n, fill = era)) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2") +
  facet_wrap( ~ basin_AIP) +
  coord_flip(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank())

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
bb44686 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
555750f jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
f155edd jens-daniel-mueller 2021-03-23
a1d52ff jens-daniel-mueller 2021-03-15
0bade3b jens-daniel-mueller 2021-03-15
27c1f4b jens-daniel-mueller 2021-03-14
af75ebf jens-daniel-mueller 2021-03-14
5017709 jens-daniel-mueller 2021-03-11
85a5ed2 jens-daniel-mueller 2021-03-10
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
6949b06 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
158fe26 jens-daniel-mueller 2020-12-15
984697e jens-daniel-mueller 2020-12-12
3ebff89 jens-daniel-mueller 2020-12-12
c8acfcb jens-daniel-mueller 2020-12-11
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
7c25f7a jens-daniel-mueller 2020-12-02
0ff728b jens-daniel-mueller 2020-12-01
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30 
rm(GLODAP_histogram_lat)

3.5 Histogram temporal coverage

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

GLODAP_histogram_year %>%
  ggplot() +
  geom_vline(xintercept = sort(params_local$era_end)[1] + 0.5) +
  geom_col(aes(year, n,
               fill = basin_AIP)) +
  geom_point(
    data = tref,
    aes(median_year, 100, shape = "tref"),
    size = 2,
    fill = "white"
  ) +
  scale_fill_brewer(palette = "Dark2") +
  scale_shape_manual(values = 24, name = "") +
  scale_y_continuous() +
  coord_cartesian(expand = 0) +
  theme(
    legend.position = "top",
    legend.direction = "vertical",
    legend.title = element_blank(),
    axis.title.x = element_blank()
  )

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
7e4f671 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
099d566 jens-daniel-mueller 2021-04-14
bb44686 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
555750f jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
f155edd jens-daniel-mueller 2021-03-23
a1d52ff jens-daniel-mueller 2021-03-15
0bade3b jens-daniel-mueller 2021-03-15
27c1f4b jens-daniel-mueller 2021-03-14
af75ebf jens-daniel-mueller 2021-03-14
5017709 jens-daniel-mueller 2021-03-11
85a5ed2 jens-daniel-mueller 2021-03-10
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
6949b06 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
158fe26 jens-daniel-mueller 2020-12-15
984697e jens-daniel-mueller 2020-12-12
3ebff89 jens-daniel-mueller 2020-12-12
c8acfcb jens-daniel-mueller 2020-12-11
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
0ff728b jens-daniel-mueller 2020-12-01
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30 
rm(GLODAP_histogram_year,
   era_median_year)

3.6 Zonal temporal coverage (Hovmoeller)

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

GLODAP_hovmoeller_year %>%
  ggplot(aes(year, lat_grid, fill = n)) +
  geom_tile() +
  geom_vline(xintercept = sort(params_local$era_end)[1] + 0.5) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  coord_cartesian(expand = 0) +
  facet_wrap( ~ basin_AIP, ncol = 1) +
  theme(legend.position = "top",
        axis.title.x = element_blank())

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
099d566 jens-daniel-mueller 2021-04-14
bb44686 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
555750f jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
a1d52ff jens-daniel-mueller 2021-03-15
0bade3b jens-daniel-mueller 2021-03-15
27c1f4b jens-daniel-mueller 2021-03-14
af75ebf jens-daniel-mueller 2021-03-14
5017709 jens-daniel-mueller 2021-03-11
85a5ed2 jens-daniel-mueller 2021-03-10
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
6949b06 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
158fe26 jens-daniel-mueller 2020-12-15
984697e jens-daniel-mueller 2020-12-12
3ebff89 jens-daniel-mueller 2020-12-12
c8acfcb jens-daniel-mueller 2020-12-11
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
37e9dac jens-daniel-mueller 2020-12-02
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30 
rm(GLODAP_hovmoeller_year)

3.7 Coverage maps by era

3.7.1 Subsetting process

The following plots show the remaining data after individual cleaning steps, separately for each era.

GLODAP_obs_grid <- GLODAP_obs_grid %>%
  mutate(cleaning_level = factor(
           cleaning_level,
           unique(GLODAP_obs_grid$cleaning_level)
         ))

map +
  geom_raster(data = GLODAP_obs_grid %>%
                filter(cleaning_level == "tco2_values") %>%
                select(-cleaning_level),
              aes(lon, lat, fill = "tco2_values")) +
  geom_raster(data = GLODAP_obs_grid %>%
                filter(cleaning_level != "tco2_values"),
              aes(lon, lat, fill = "subset")) +
  scale_fill_brewer(palette = "Set1", name = "") +
  facet_grid(cleaning_level ~ era) +
  theme(legend.position = "top",
        axis.title = element_blank())

Version Author Date
969e631 jens-daniel-mueller 2021-05-12
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
eb827c9 jens-daniel-mueller 2021-04-07
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
f155edd jens-daniel-mueller 2021-03-23
a1d52ff jens-daniel-mueller 2021-03-15
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
6949b06 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
158fe26 jens-daniel-mueller 2020-12-15
3ebff89 jens-daniel-mueller 2020-12-12
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
37e9dac jens-daniel-mueller 2020-12-02
7c25f7a jens-daniel-mueller 2020-12-02
d5c5378 jens-daniel-mueller 2020-12-02
0ff728b jens-daniel-mueller 2020-12-01
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30

3.7.2 Final input data

The following plots show the remaining data density in each grid cell after all cleaning steps, separately for each era.

GLODAP_tco2_grid <- GLODAP %>%
  count(lat, lon)

map +
  geom_bin2d(data = GLODAP,
             aes(lon, lat),
             binwidth = c(1,1)) +
  scale_fill_viridis_c(option = "magma", direction = -1, trans = "log10") +
  facet_wrap(~era, ncol = 1) +
  labs(title = "Cleaned GLODAP observations",
       subtitle = paste("Version:", params_local$Version_ID)) +
  theme(axis.title = element_blank())

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
84b1fe3 jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
eb827c9 jens-daniel-mueller 2021-04-07
05cc66b jens-daniel-mueller 2021-03-24
03b6009 jens-daniel-mueller 2021-03-23
555750f jens-daniel-mueller 2021-03-23
685338e jens-daniel-mueller 2021-03-23
886f523 jens-daniel-mueller 2021-03-23
10c1346 jens-daniel-mueller 2021-03-23
f155edd jens-daniel-mueller 2021-03-23
a1d52ff jens-daniel-mueller 2021-03-15
0bade3b jens-daniel-mueller 2021-03-15
27c1f4b jens-daniel-mueller 2021-03-14
af75ebf jens-daniel-mueller 2021-03-14
5017709 jens-daniel-mueller 2021-03-11
85a5ed2 jens-daniel-mueller 2021-03-10
7b672f7 jens-daniel-mueller 2021-01-11
33ba23c jens-daniel-mueller 2021-01-07
318609d jens-daniel-mueller 2020-12-23
6949b06 jens-daniel-mueller 2020-12-23
0aa2b50 jens-daniel-mueller 2020-12-23
2886da0 jens-daniel-mueller 2020-12-19
02f0ee9 jens-daniel-mueller 2020-12-18
158fe26 jens-daniel-mueller 2020-12-15
3ebff89 jens-daniel-mueller 2020-12-12
2fd0a2a jens-daniel-mueller 2020-12-11
24a632f jens-daniel-mueller 2020-12-07
6a8004b jens-daniel-mueller 2020-12-07
70bf1a5 jens-daniel-mueller 2020-12-07
7555355 jens-daniel-mueller 2020-12-07
143d6fa jens-daniel-mueller 2020-12-07
37e9dac jens-daniel-mueller 2020-12-02
7c25f7a jens-daniel-mueller 2020-12-02
d5c5378 jens-daniel-mueller 2020-12-02
083b3b0 jens-daniel-mueller 2020-12-02
0ff728b jens-daniel-mueller 2020-12-01
b02b7a4 jens-daniel-mueller 2020-12-01
196be51 jens-daniel-mueller 2020-11-30 
ggsave(path = path_version_figures,
       filename = "data_distribution_era.png",
       height = 8,
       width = 5)

3.8 Model vs observation

variables <-
  c(
    "temp",
    "sal",
    "gamma",
    "tco2",
    "talk",
    "phosphate",
    "oxygen",
    "aou",
    "nitrate",
    "silicate"
  )

for (i_var in variables) {
  #  i_var <- variables[1]
  
  # calculate equal axis limits and binwidth
  axis_lims <- GLODAP %>%
    summarise(max_value = max(c(max(!!sym(
      i_var
    )),
    max(!!sym(
      paste0(i_var, "_model")
    )))),
    min_value = min(c(min(!!sym(
      i_var
    )),
    min(!!sym(
      paste0(i_var, "_model")
    )))))
  
  binwidth_value <- (axis_lims$max_value - axis_lims$min_value) / 40
  axis_lims <- c(axis_lims$min_value, axis_lims$max_value)
  
  # obs-model plot (season)
  print(
    ggplot(GLODAP, aes(
      x = !!sym(i_var),
      y = !!sym(paste0(i_var, "_model"))
    )) +
      geom_bin2d(binwidth = binwidth_value) +
      scale_fill_viridis_c() +
      geom_abline(slope = 1,
                  col = 'red') +
      coord_equal(xlim = axis_lims,
                  ylim = axis_lims) +
      labs(x = "observation", y = "model", title = i_var)
  )
}

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
857bad3 jens-daniel-mueller 2021-03-24
05cc66b jens-daniel-mueller 2021-03-24

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
857bad3 jens-daniel-mueller 2021-03-24
05cc66b jens-daniel-mueller 2021-03-24

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
857bad3 jens-daniel-mueller 2021-03-24
05cc66b jens-daniel-mueller 2021-03-24

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
857bad3 jens-daniel-mueller 2021-03-24
05cc66b jens-daniel-mueller 2021-03-24

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
857bad3 jens-daniel-mueller 2021-03-24
05cc66b jens-daniel-mueller 2021-03-24

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
857bad3 jens-daniel-mueller 2021-03-24
05cc66b jens-daniel-mueller 2021-03-24

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
857bad3 jens-daniel-mueller 2021-03-24
05cc66b jens-daniel-mueller 2021-03-24

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
857bad3 jens-daniel-mueller 2021-03-24
05cc66b jens-daniel-mueller 2021-03-24

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
857bad3 jens-daniel-mueller 2021-03-24
05cc66b jens-daniel-mueller 2021-03-24

Version Author Date
cf773c5 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
0a84f2a jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
858c4e6 jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
9f31fe3 jens-daniel-mueller 2021-04-13
338dd3c jens-daniel-mueller 2021-04-09
a79ca2c jens-daniel-mueller 2021-04-09
857bad3 jens-daniel-mueller 2021-03-24
05cc66b jens-daniel-mueller 2021-03-24 

sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2

Matrix products: default
BLAS:   /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/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] gt_0.2.2        lubridate_1.7.9 metR_0.9.0      scico_1.2.0    
 [5] patchwork_1.1.1 collapse_1.5.0  forcats_0.5.0   stringr_1.4.0  
 [9] dplyr_1.0.5     purrr_0.3.4     readr_1.4.0     tidyr_1.1.2    
[13] tibble_3.0.4    ggplot2_3.3.3   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2               viridisLite_0.3.0        jsonlite_1.7.1          
 [4] here_0.1                 modelr_0.1.8             assertthat_0.2.1        
 [7] blob_1.2.1               cellranger_1.1.0         yaml_2.2.1              
[10] pillar_1.4.7             backports_1.1.10         lattice_0.20-41         
[13] glue_1.4.2               RcppEigen_0.3.3.7.0      digest_0.6.27           
[16] RColorBrewer_1.1-2       promises_1.1.1           checkmate_2.0.0         
[19] rvest_0.3.6              colorspace_1.4-1         htmltools_0.5.0         
[22] httpuv_1.5.4             Matrix_1.2-18            pkgconfig_2.0.3         
[25] broom_0.7.5              haven_2.3.1              scales_1.1.1            
[28] whisker_0.4              later_1.1.0.1            git2r_0.27.1            
[31] generics_0.0.2           farver_2.0.3             ellipsis_0.3.1          
[34] withr_2.3.0              cli_2.1.0                magrittr_1.5            
[37] crayon_1.3.4             readxl_1.3.1             evaluate_0.14           
[40] fs_1.5.0                 fansi_0.4.1              xml2_1.3.2              
[43] RcppArmadillo_0.10.1.2.0 tools_4.0.3              data.table_1.13.2       
[46] hms_0.5.3                lifecycle_1.0.0          munsell_0.5.0           
[49] reprex_0.3.0             compiler_4.0.3           rlang_0.4.10            
[52] grid_4.0.3               rstudioapi_0.13          labeling_0.4.2          
[55] rmarkdown_2.5            gtable_0.3.0             DBI_1.1.0               
[58] R6_2.5.0                 knitr_1.30               utf8_1.1.4              
[61] rprojroot_2.0.2          stringi_1.5.3            parallel_4.0.3          
[64] Rcpp_1.0.5               vctrs_0.3.5              dbplyr_1.4.4            
[67] tidyselect_1.1.0         xfun_0.18