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library(tidyverse)
library(lubridate)
library(tidync)
library(stars)
library(metR)
library(patchwork)

1 Read ncdf, write csv, plot

file_list <- list.files(path= "data/GLODAPv2_2016b_Mappedclimatologies", pattern = "*.nc")

print("files used:")
[1] "files used:"
print(file_list)
[1] "GLODAPv2.2016b.Cant.nc"        "GLODAPv2.2016b.NO3.nc"        
[3] "GLODAPv2.2016b.oxygen.nc"      "GLODAPv2.2016b.PO4.nc"        
[5] "GLODAPv2.2016b.salinity.nc"    "GLODAPv2.2016b.silicate.nc"   
[7] "GLODAPv2.2016b.TAlk.nc"        "GLODAPv2.2016b.TCO2.nc"       
[9] "GLODAPv2.2016b.temperature.nc"
#file <- file_list[1]

for (file in file_list) {
 
clim_stars <- read_stars(here::here("data/GLODAPv2_2016b_Mappedclimatologies",
                          file))

parameter <- str_split(file, pattern = "6b.", simplify = TRUE)[2]
parameter <- str_split(parameter, pattern = ".nc", simplify = TRUE)[1]

map_clim_stars <- clim_stars %>% select(all_of(parameter))

map_clim_stars %>% 
  as_tibble() %>% 
  rename(lat = y, lon = x, depth = depth_surface) %>% 
  mutate(lon = if_else(lon>180, lon - 360, lon)) %>% 
  write_csv(here::here("data/GLODAPv2_2016b_MappedClimatologies/_summarized_files",
                       paste(parameter,".csv", sep = "")))

map_clim_stars_horizons <- map_clim_stars %>% 
  filter(depth_surface %in% c(0,100,500,2000))

print(
ggplot()+
  geom_stars(data = map_clim_stars_horizons)+
  geom_vline(xintercept = c(190.5,335.5), col="white")+
  scale_fill_viridis_b(n.breaks = 8)+
  labs(title = file)+
  facet_wrap(~depth_surface, ncol = 2)+
  coord_quickmap(expand = 0)
)

Atl_section <- map_clim_stars %>% 
  as_tibble() %>% 
  rename(lat = y, lon = x, depth = depth_surface, parameter = 4) %>% 
  filter(lon == 335.5)

Atl_section_bathy <- Atl_section %>% 
  filter(is.na(parameter)) %>% 
  group_by(lat) %>% 
  summarise(bottom_depth = min(depth)) %>% 
  ungroup()

p_Atl_section <-
ggplot()+
  geom_contour_fill(data = Atl_section,
                    aes(lat, depth, z=parameter),
                    na.fill = TRUE)+
  geom_ribbon(data = Atl_section_bathy,
              aes(x = lat, ymin = bottom_depth, ymax = 5500),
              fill = "grey80",
              col="black")+
  labs(title = "Atlantic ocean N-S section")+
  scale_y_reverse()+
  scale_fill_viridis_b(name = parameter,
                       n.breaks = 8)+
  coord_cartesian(expand = 0)

Pac_section <- map_clim_stars %>% 
  as_tibble() %>% 
  rename(lat = y, lon = x, depth = depth_surface, parameter = 4) %>% 
  filter(lon == 190.5)

Pac_section_bathy <- Pac_section %>% 
  filter(is.na(parameter)) %>% 
  group_by(lat) %>% 
  summarise(bottom_depth = min(depth)) %>% 
  ungroup()

p_Pac_section <-
ggplot()+
  geom_contour_fill(data = Pac_section,
                    aes(lat, depth, z=parameter),
                    na.fill = TRUE)+
  geom_ribbon(data = Pac_section_bathy,
              aes(x = lat, ymin = bottom_depth, ymax = 5500),
              fill = "grey80",
              col="black")+
  labs(title = "Pacific ocean N-S section")+
  scale_y_reverse()+
  scale_fill_viridis_b(name = parameter,
                       n.breaks = 8)+
  coord_cartesian(expand = 0)

print(
p_Atl_section / p_Pac_section
)

# clim_tidync <- tidync(here::here("data/GLODAPv2_2016b_Mappedclimatologies",
#                                  file))
# print(clim_tidync)
# 
# Atl_sec_clim <- clim_tidync %>% hyper_filter(lon = lon == 335.5)
# Atl_sec_clim_tibble <- Atl_sec_clim %>% hyper_tibble()
# 
# Atl_sec_clim_tibble %>% 
#   ggplot(aes(lat, depth_surface, fill=Cant))+
#   geom_point()+
#   scale_y_reverse()+
#   scale_fill_viridis_b()

}
Cant, Cant_error, Input_mean, Input_std, Input_N, Cant_relerr, 

NO3, NO3_error, Input_mean, Input_std, Input_N, NO3_relerr, 

oxygen, oxygen_error, Input_mean, Input_std, Input_N, oxygen_relerr, 

PO4, PO4_error, Input_mean, Input_std, Input_N, PO4_relerr, 

salinity, salinity_error, Input_mean, Input_std, Input_N, salinity_relerr, 

silicate, silicate_error, Input_mean, Input_std, Input_N, silicate_relerr, 

TAlk, TAlk_error, Input_mean, Input_std, Input_N, TAlk_relerr, 

TCO2, TCO2_error, Input_mean, Input_std, Input_N, TCO2_relerr, 

temperature, temperature_error, Input_mean, Input_std, Input_N, temperature_relerr, 

2 Plot Cant section

Cant <-   read_csv(here::here("data/GLODAPv2_2016b_MappedClimatologies/_summarized_files",
                              "Cant.csv"))

Cant_Atl_section <- Cant %>% 
  filter(x == 335.5)

Cant_Atl_section_bathy <- Cant_Atl_section %>% 
  filter(is.na(Cant)) %>% 
  group_by(y) %>% 
  summarise(depth_surface = min(depth_surface)) %>% 
  ungroup()

ggplot()+
  geom_contour_fill(data = Cant_Atl_section,
                    aes(y, depth_surface, z=Cant),
                    na.fill = TRUE)+
  geom_ribbon(data = Cant_Atl_section_bathy,
              aes(x = y, ymin = depth_surface, ymax = 5500),
              fill = "grey30",
              col="black")+
  scale_y_reverse()+
  scale_fill_viridis_b(breaks = seq(0,100,10))+
  coord_cartesian(expand = 0)

3 Open tasks

  • none

4 Questions

  • none

sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: i386-w64-mingw32/i386 (32-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] patchwork_1.0.1 metR_0.7.0      stars_0.4-3     sf_0.9-4       
 [5] abind_1.4-5     tidync_0.2.4    lubridate_1.7.9 forcats_0.5.0  
 [9] stringr_1.4.0   dplyr_1.0.0     purrr_0.3.4     readr_1.3.1    
[13] tidyr_1.1.0     tibble_3.0.3    ggplot2_3.3.2   tidyverse_1.3.0
[17] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5         whisker_0.4        knitr_1.29         xml2_1.3.2        
 [5] magrittr_1.5       units_0.6-7        hms_0.5.3          rvest_0.3.5       
 [9] tidyselect_1.1.0   lattice_0.20-41    viridisLite_0.3.0  here_0.1          
[13] colorspace_1.4-1   R6_2.4.1           rlang_0.4.7        fansi_0.4.1       
[17] parallel_3.6.3     broom_0.7.0        xfun_0.15          e1071_1.7-3       
[21] ncmeta_0.2.5       cubelyr_1.0.0      dbplyr_1.4.4       modelr_0.1.8      
[25] withr_2.2.0        git2r_0.27.1       ellipsis_0.3.1     htmltools_0.5.0   
[29] class_7.3-17       assertthat_0.2.1   rprojroot_1.3-2    digest_0.6.25     
[33] lifecycle_0.2.0    RNetCDF_2.3-1      haven_2.3.1        rmarkdown_2.3     
[37] sp_1.4-2           compiler_3.6.3     cellranger_1.1.0   pillar_1.4.6      
[41] scales_1.1.1       backports_1.1.5    generics_0.0.2     classInt_0.4-3    
[45] jsonlite_1.7.0     httpuv_1.5.4       pkgconfig_2.0.3    rstudioapi_0.11   
[49] munsell_0.5.0      plyr_1.8.6         blob_1.2.1         httr_1.4.1        
[53] tools_3.6.3        grid_3.6.3         data.table_1.12.8  checkmate_2.0.0   
[57] gtable_0.3.0       KernSmooth_2.23-16 DBI_1.1.0          cli_2.0.2         
[61] readxl_1.3.1       yaml_2.2.1         lwgeom_0.2-5       crayon_1.3.4      
[65] farver_2.0.3       later_1.1.0.1      promises_1.1.1     fs_1.4.2          
[69] vctrs_0.3.1        ncdf4_1.17         memoise_1.1.0      glue_1.4.1        
[73] evaluate_0.14      labeling_0.3       reprex_0.3.0       stringi_1.4.6