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Rmd	e03016e	jens-daniel-mueller	2020-07-14	split read in per data set

library(tidyverse)
library(lubridate)
library(patchwork)
library(cutr)

1 Read files

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

GLODAP <-
  read_csv(
    here::here(
      "data/GLODAPv2_2020/Merged_data_product",
      "GLODAPv2.2020_Merged_Master_File.csv"
    ),
    na = "-9999",
    col_types = cols(.default = col_double())
  )

# select relevant columns
GLODAP <- GLODAP %>%
  select(cruise:talkqc)

# create date column
GLODAP <- GLODAP %>%
  mutate(date = ymd(paste(year, month, day))) %>%
  #decade = as.factor(floor(year / 10) * 10)) %>%
  relocate(date)

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

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

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

2 Data preparation

2.1 Flags and missing data

Only rows (samples) for which all relevant parameters are available were selected. In additions, following flagging criteria were applied:

flag_f: 2
flag_qc: 1

Summary statistics were calculated during cleaning process.

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

##

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

GLODAP <- GLODAP %>% 
  filter(tco2f == parameters$flag_f) %>% 
  select(-tco2f)

GLODAP <- GLODAP %>% 
  filter(tco2qc == parameters$flag_qc) %>% 
  select(-tco2qc)

GLODAP_stats_temp <- GLODAP %>% 
  summarise(tco2 = n())

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

##

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

GLODAP <- GLODAP %>% 
  filter(talkf == parameters$flag_f) %>% 
  select(-talkf)

GLODAP <- GLODAP %>% 
  filter(talkqc == parameters$flag_qc) %>% 
  select(-talkqc)

##

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

GLODAP <- GLODAP %>% 
  filter(phosphatef == parameters$flag_f) %>% 
  select(-phosphatef)

GLODAP <- GLODAP %>% 
  filter(phosphateqc == parameters$flag_qc) %>% 
  select(-phosphateqc)

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

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

##

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

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

GLODAP <- GLODAP %>% 
  filter(salinityf == parameters$flag_f) %>% 
  select(-salinityf)

GLODAP <- GLODAP %>% 
  filter(salinityqc == parameters$flag_qc) %>% 
  select(-salinityqc)

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

GLODAP <- GLODAP %>% 
  filter(silicatef == parameters$flag_f) %>% 
  select(-silicatef)

GLODAP <- GLODAP %>% 
  filter(silicateqc == parameters$flag_qc) %>% 
  select(-silicateqc)

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

GLODAP <- GLODAP %>% 
  filter(oxygenf == parameters$flag_f) %>% 
  select(-oxygenf)

GLODAP <- GLODAP %>% 
  filter(oxygenqc == parameters$flag_qc) %>% 
  select(-oxygenqc)

##

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

GLODAP <- GLODAP %>% 
  filter(aouf == parameters$flag_f) %>% 
  select(-aouf)

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

GLODAP <- GLODAP %>% 
  filter(nitratef == parameters$flag_f) %>% 
  select(-nitratef)

GLODAP <- GLODAP %>% 
  filter(nitrateqc == parameters$flag_qc) %>% 
  select(-nitrateqc)

##

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)

2.2 Spatial boundaries

Only observations were taken into consideration with:

minimum sampling depth: 150m
minimum bottom depth: 500m
maximum latitude: 65°N

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

GLODAP_stats_temp <- GLODAP %>% 
  summarise(eMLR_variables_150m = n())

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

##

GLODAP <- GLODAP %>% 
  filter(lat <= parameters$lat_max)

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

GLODAP_stats_temp <- GLODAP %>% 
  summarise(eMLR_variables_150m_65N_500m = n())

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


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

GLODAP_stats_long  %>%  write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                                             "GLODAPv2.2020_stats.csv"))

rm(GLODAP_stats_long, GLODAP_stats)

2.3 Horizontal gridding

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

1° x 1°

GLODAP <- GLODAP %>% 
  mutate(lat = cut(lat, seq(-90, 90, 1), seq(-89.5, 89.5, 1)),
         lat = as.numeric(as.character(lat)),
         lon = cut(lon, seq(20, 380, 1), seq(20.5, 379.5, 1)),
         lon = as.numeric(as.character(lon))) %>% 
  arrange(date)

2.4 Basin mask

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

GLODAP_obs <- GLODAP %>% 
  group_by(lat, lon) %>% 
  summarise(n = n()) %>% 
  ungroup()

ggplot() +
  geom_raster(data = landmask %>% filter(region == "land"),
              aes(lon, lat), fill = "grey80") +
  geom_raster(data = basinmask, aes(lon, lat, fill = basin)) +
  geom_raster(data = GLODAP_obs, aes(lon, lat)) +
  scale_fill_brewer(palette = "Dark2") +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank())

rm(GLODAP_obs)

Please note that some GLODAP observations were made outside the WOA18 basin mask and will be removed for further analysis (eg North Sea, Sea of Japan).

GLODAP <- inner_join(GLODAP, basinmask)
rm(basinmask)

2.5 Reference eras

Samples were assigned to following eras:

JGOFS_WOCE: 1981 - 1999
GO_SHIP: 2000 - 2012
new_era: > 2013

GLODAP <- GLODAP %>%
  filter(year >= parameters$year_JGOFS_start) %>% 
  mutate(era = "JGOFS_WOCE",
         era = if_else(year > parameters$year_JGOFS_end, "GO_SHIP", era),
         era = if_else(year > parameters$year_GOSHIP_end, "new_era", era))

2.5.1 Manual adjustment A16 cruise

For harmonization with Gruber et al. (2019), cruises 1041 (A16N) and 1042 (A16S) were grouped into the GO_SHIP area despite taking place in 2013/14.

GLODAP_cruises <- GLODAP %>% 
  filter(basin == "Atlantic",
         year %in% c(2013, 2014)) %>% 
  group_by(lat, lon, cruise) %>% 
  summarise(cruise = as.factor(mean(cruise))) %>% 
  ungroup()

ggplot() +
  geom_raster(data = landmask %>% filter(region == "land"),
              aes(lon, lat), fill = "grey80") +
  geom_raster(data = GLODAP_cruises, aes(lon, lat, fill = cruise)) +
  scale_fill_brewer(palette = "Dark2") +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank())

rm(GLODAP_cruises)

GLODAP <- GLODAP %>%
  mutate(era = if_else(cruise %in% c(1041, 1042),
                       "GO_SHIP", era))

2.6 Observations grid

A 1x1 grid containing all coordinates with at least one GLODAP observation was created.

GLODAP_obs_grid <- GLODAP %>% 
  group_by(lat, lon) %>% 
  summarise(n = n()) %>% 
  ungroup()

2.7 Write summary file

GLODAP_obs_grid  %>%  write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                                           "GLODAPv2.2020_clean_obs_grid.csv"))

GLODAP  %>%  write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                                  "GLODAPv2.2020_clean.csv"))

rm(GLODAP, GLODAP_obs_grid)

3 Overview plots

3.1 Cleaning stats

Number of observations at various steps of data cleaning.

GLODAP_stats_long <-  read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                                             "GLODAPv2.2020_stats.csv"))

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

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

rm(GLODAP_stats_long)

3.2 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 <- read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                              "GLODAPv2.2020_clean.csv"))

GLODAP <- GLODAP %>%
  mutate(
    lat_grid = smart_cut(
      lat,
      seq(-90, 90, parameters$coarse_grid_resolution),
      labels = ~ mean(.y)
    ),
    lat_grid = as.numeric(as.character(lat_grid)),
    lon_grid = smart_cut(
      lon,
      seq(-1000, 1000, parameters$coarse_grid_resolution),
      labels = ~ mean(.y)
    ),
    lon_grid = as.numeric(as.character(lon_grid))
  ) %>%
  arrange(date)

GLODAP <- GLODAP %>% 
  mutate(lat_grid = cut(lat, seq(-90, 90, 5), seq(-87.5, 87.5, 5)),
         lat_grid = as.numeric(as.character(lat_grid)),
         lon_grid = cut(lon, seq(20, 380, 5), seq(22.5, 377.5, 5)),
         lon_grid = as.numeric(as.character(lon_grid))) %>% 
  arrange(date)

3.3 Histogram Zonal coverage

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

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

rm(GLODAP_histogram_lat)

3.4 Histogram temporal coverage

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

era_median_year <- GLODAP %>%
  group_by(era) %>%
  summarise(t_ref = median(year)) %>%
  ungroup()

GLODAP_histogram_year %>%
  ggplot() +
  geom_vline(xintercept = c(
    parameters$year_JGOFS_end + 0.5,
    parameters$year_GOSHIP_end + 0.5
  )) +
  geom_col(aes(year, n, fill = basin)) +
  geom_point(
    data = era_median_year,
    aes(t_ref, 0, shape = "Median year"),
    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()
  )

rm(GLODAP_histogram_year,
   era_median_year)

3.5 Zonal temporal coverage (Hovmoeller)

GLODAP_hovmoeller_year <- GLODAP %>%
  group_by(year, lat_grid, basin) %>%
  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) +
  facet_wrap( ~ basin, ncol = 1) +
  theme(legend.position = "top",
        axis.title.x = element_blank())

rm(GLODAP_hovmoeller_year)

3.6 Coverage maps by era

GLODAP <- GLODAP %>% 
  mutate(era = factor(era, c("JGOFS_WOCE", "GO_SHIP", "new_era")))

GLODAP %>%
  ggplot(aes(lon, lat)) +
  geom_raster(data = landmask %>% filter(region == "land"),
              aes(lon, lat), fill = "grey80") +
  geom_bin2d(binwidth = c(1,1)) +
  scale_fill_viridis_c(option = "magma", direction = -1, trans = "log10",
                       name = "log10(n)") +
  coord_quickmap(expand = FALSE) +
  facet_wrap(~era, ncol = 1) +
  theme(legend.position = "top",
        axis.title = element_blank())

# library("rnaturalearth")
# library("rnaturalearthdata")
# library("sf")
# 
# world <- ne_countries(scale = "small", returnclass = "sf")
# class(world)
# 
# GLODAP_map <- GLODAP %>% 
#   group_by(lat_grid, lon_grid) %>% 
#   tally() %>% 
#   ungroup()
# 
# ggplot() +
#   geom_raster(data = GLODAP_map, aes(lon_grid, lat_grid)) +
#   geom_sf(data = world) +
#   coord_sf(crs = "+proj=robin +lat_0=0 +lon_0=0 +x0=0 +y0=0")


# https://gist.github.com/clauswilke/783e1a8ee3233775c9c3b8bfe531e28a
# https://twitter.com/clauswilke/status/1066024436208406529
# https://www.r-spatial.org/r/2018/10/25/ggplot2-sf.html

4 Open tasks

5 Questions

Apply coastal filter?
Keep horizontal gridding to 1°x1° (thereby discarding some horizontal information)?
Quality of WOA18 basin mask
- excluded areas (e.g. Bay of Bengal, South Norwegian Sea, Sea of Japan)
- Pacific-Atlantic boundary

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_Germany.1252  LC_CTYPE=English_Germany.1252   
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C                    
[5] LC_TIME=English_Germany.1252    

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

other attached packages:
 [1] cutr_0.0.0.9000 patchwork_1.0.1 lubridate_1.7.9 forcats_0.5.0  
 [5] stringr_1.4.0   dplyr_1.0.0     purrr_0.3.4     readr_1.3.1    
 [9] tidyr_1.1.0     tibble_3.0.3    ggplot2_3.3.2   tidyverse_1.3.0
[13] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5         here_0.1           assertthat_0.2.1   rprojroot_1.3-2   
 [5] digest_0.6.25      R6_2.4.1           cellranger_1.1.0   backports_1.1.8   
 [9] reprex_0.3.0       evaluate_0.14      httr_1.4.2         pillar_1.4.6      
[13] rlang_0.4.7        readxl_1.3.1       rstudioapi_0.11    whisker_0.4       
[17] blob_1.2.1         rmarkdown_2.3      labeling_0.3       munsell_0.5.0     
[21] broom_0.7.0        compiler_4.0.2     httpuv_1.5.4       modelr_0.1.8      
[25] xfun_0.16          pkgconfig_2.0.3    htmltools_0.5.0    tidyselect_1.1.0  
[29] viridisLite_0.3.0  fansi_0.4.1        crayon_1.3.4       dbplyr_1.4.4      
[33] withr_2.2.0        later_1.1.0.1      grid_4.0.2         jsonlite_1.7.0    
[37] gtable_0.3.0       lifecycle_0.2.0    DBI_1.1.0          git2r_0.27.1      
[41] magrittr_1.5       scales_1.1.1       cli_2.0.2          stringi_1.4.6     
[45] farver_2.0.3       fs_1.4.2           promises_1.1.1     xml2_1.3.2        
[49] ellipsis_0.3.1     generics_0.0.2     vctrs_0.3.2        RColorBrewer_1.1-2
[53] tools_4.0.2        glue_1.4.1         hms_0.5.3          yaml_2.2.1        
[57] colorspace_1.4-1   rvest_0.3.6        knitr_1.29         haven_2.3.1

GLODAPv2_2020

Jens Daniel Müller

26 August, 2020