Last updated: 2023-12-07

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Task

Map the location of oxygen, pH, and nitrate observations recorded by BGC-Argo floats

Load data

BGC-Argo data

Read the files created in loading_data.html:

bgc_temp <- read_rds(file = paste0(path_argo_preprocessed, "/temp_bgc_va.rds")) %>%
  filter(!is.na(year))

bgc_ph <- read_rds(file = paste0(path_argo_preprocessed, "/pH_bgc_va.rds")) %>%
  filter(!is.na(year))

bgc_doxy <- read_rds(file = paste0(path_argo_preprocessed, "/doxy_bgc_va.rds")) %>%
  filter(!is.na(year))

bgc_nitrate <- read_rds(file = paste0(path_argo_preprocessed, "/nitrate_bgc_va.rds")) %>%
  filter(!is.na(year))

bgc_chla <- read_rds(file = paste0(path_argo_preprocessed, "/chla_bgc_va.rds")) %>%
  filter(!is.na(year))

Core-Argo data

core_temp <- read_rds(file = paste0(path_argo_core_preprocessed, "/temp_core_va.rds")) %>%
  filter(!is.na(year))

map data

basinmask <-
  read_csv(paste(path_emlr_utilities,
                 "basin_mask_WOA18.csv",
                 sep = ""),
           col_types = cols("MLR_basins" = col_character()))

basinmask <- basinmask %>% 
  filter(MLR_basins == unique(basinmask$MLR_basins)[1]) %>% 
  select(lon, lat, basin_AIP)

map <-
  read_rds(paste(path_emlr_utilities,
                 "map_landmask_WOA18.rds",
                 sep = ""))

Core - temperature

# Number of measurements
core_count <- core_temp %>%
  group_by(year, file_id, lat, lon, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

# Number of profiles
core_count <- core_count %>%
  group_by(year, lat, lon, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

# core_count %>%
#   group_by (year, lat, lon) %>%
#   summarise(n = n()) %>%
#   filter (n == 1)
# 
# core_count <- rbind(
#   core_count %>%
#   filter (year == 2013, lat == -59.5, lon == 141.5),
#   core_count %>%
#   filter (year == 2013, lat == -63.5, lon == 149.5),
#   core_count %>%
#   filter (year == 2013, lat == -68.5, lon == 233.5))

# Aggregate profile range
core_count_agg <- core_count %>%
  group_by(year, lat, lon) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  mutate(profile_range = 1) %>%
  ungroup()

core_count_agg <- rbind(
  core_count_agg,
  core_count %>%
    filter (profile_range %in% c(2, 3)) %>%
    group_by(year, lat, lon) %>%
    summarise(count_profiles = sum(count_profiles)) %>%
    mutate(profile_range = 2) %>%
    ungroup()
)

core_count_agg <- rbind(
  core_count_agg,
  core_count %>%
    filter (profile_range == 3)
)

# measurement type
core_count_agg <- core_count_agg %>%
  mutate (prof_type = 'temperature')

spatial by year

# map the location of profiles for each profile in each year 
core_count_agg %>%
  group_split(profile_range) %>%
  map(
    ~ map +
    geom_tile(data = .x, aes(
      x = lon, y = lat, fill = count_profiles
    )) +
    scale_fill_gradient(low = "blue", high = "red",
                        trans = "log10") +
    labs(
      x = 'lon',
      y = 'lat',
      fill = 'number of\nprofiles',
      title = paste0('Core temperature by year and location ',
                     ifelse(unique(.x$profile_range) == 1, '600m', ifelse(unique(.x$profile_range) == 2, '1000m', '1500m')),
                     ' profiles')
    ) +
    theme(
      legend.position = "bottom",
      axis.text = element_blank(),
      axis.ticks = element_blank()
    ) +
    facet_wrap(~year, ncol = 3)
  )
[[1]]

Version Author Date
9d9224a ds2n19 2023-12-07
770b125 ds2n19 2023-10-11
13ae27f ds2n19 2023-10-09
6377b31 ds2n19 2023-10-02
7b3d8c5 pasqualina-vonlanthendinenna 2022-08-29 

[[2]]

Version Author Date
9d9224a ds2n19 2023-12-07
770b125 ds2n19 2023-10-11
13ae27f ds2n19 2023-10-09
6377b31 ds2n19 2023-10-02
7b3d8c5 pasqualina-vonlanthendinenna 2022-08-29 

[[3]]

spatial all years

# sum across years
core_count_agg <- core_count_agg %>%
  group_by(profile_range, lat, lon) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  ungroup()

# map the location of profiles for each profile in each year 
core_count_agg %>%
  group_split(profile_range) %>%
  map(
    ~ map +
    geom_tile(data = .x, aes(
      x = lon, y = lat, fill = count_profiles
    )) +
    scale_fill_gradient(low = "blue", high = "red",
                        trans = "log10") +
    labs(
      x = 'lon',
      y = 'lat',
      fill = 'number of\nprofiles',
      title = paste0('Core temperature by location ',
                     ifelse(unique(.x$profile_range) == 1, '600m', ifelse(unique(.x$profile_range) == 2, '1000m', '1500m')),
                     ' profiles')
    ) +
    theme(
      legend.position = "bottom",
      axis.text = element_blank(),
      axis.ticks = element_blank()
    )
  )
[[1]]

Version Author Date
9d9224a ds2n19 2023-12-07 

[[2]]


[[3]]

BGC

# ----------------------------------------------------------------------------------------------
# temperature 
# ----------------------------------------------------------------------------------------------

# Number of measurements
bgc_temp_count <- bgc_temp %>%
  group_by(year, file_id, lat, lon, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

# Number of profiles
bgc_temp_count <- bgc_temp_count %>%
  group_by(year, lat, lon, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

# measurement type
bgc_temp_count <- bgc_temp_count %>%
  mutate (prof_order = 1,
          prof_type = 'temperature')

# ----------------------------------------------------------------------------------------------
# ph 
# ----------------------------------------------------------------------------------------------

# Number of measurements
bgc_ph_count <- bgc_ph %>%
  group_by(year, file_id, lat, lon, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

# Number of profiles
bgc_ph_count <- bgc_ph_count %>%
  group_by(year, lat, lon, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

# measurement type
bgc_ph_count <- bgc_ph_count %>%
  mutate (prof_order = 2,
          prof_type = 'pH')

# ----------------------------------------------------------------------------------------------
# doxy
# ----------------------------------------------------------------------------------------------

# Number of measurements
bgc_doxy_count <- bgc_doxy %>%
  group_by(year, file_id, lat, lon, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

# Number of profiles
bgc_doxy_count <- bgc_doxy_count %>%
  group_by(year, lat, lon, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

# measurement type
bgc_doxy_count <- bgc_doxy_count %>%
  mutate (prof_order = 3,
          prof_type = 'dissolved oxygen')

# ----------------------------------------------------------------------------------------------
# nitrate
# ----------------------------------------------------------------------------------------------

# Number of measurements
bgc_nitrate_count <- bgc_nitrate %>%
  group_by(year, file_id, lat, lon, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

# Number of profiles
bgc_nitrate_count <- bgc_nitrate_count %>%
  group_by(year, lat, lon, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

# measurement type
bgc_nitrate_count <- bgc_nitrate_count %>%
  mutate (prof_order = 4,
          prof_type = 'nitrate')

# ----------------------------------------------------------------------------------------------
# chla
# ----------------------------------------------------------------------------------------------

# Number of measurements
bgc_chla_count <- bgc_chla %>%
  group_by(year, file_id, lat, lon, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

# Number of profiles
bgc_chla_count <- bgc_chla_count %>%
  group_by(year, lat, lon, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

# measurement type
bgc_chla_count <- bgc_chla_count %>%
  mutate (prof_order = 5,
          prof_type = 'chlorophyll a')

# combine
bgc_count <- rbind(bgc_temp_count, bgc_ph_count, bgc_doxy_count, bgc_nitrate_count, bgc_chla_count)

# Aggregate profile range
bgc_count_agg <- bgc_count %>%
  group_by(prof_order, prof_type, year, lat, lon) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  mutate(profile_range = 1) %>%
  ungroup()

bgc_count_agg <- rbind(
  bgc_count_agg,
  bgc_count %>%
    filter (profile_range %in% c(2, 3)) %>%
    group_by(prof_order, prof_type, year, lat, lon) %>%
    summarise(count_profiles = sum(count_profiles)) %>%
    mutate(profile_range = 2) %>%
    ungroup()
)

bgc_count_agg <- rbind(
  bgc_count_agg,
  bgc_count %>%
    filter (profile_range == 3)
)

spatial by year

# map the location of profiles for each profile in each year 
bgc_count_agg %>%
  group_split(prof_order, profile_range) %>%
  map(
    ~ map +
    geom_tile(data = .x, aes(
      x = lon, y = lat, fill = count_profiles
    )) +
    scale_fill_gradient(low = "blue", high = "red",
                        trans = "log10") +
    labs(
      x = 'lon',
      y = 'lat',
      fill = 'number of\nprofiles',
      title = paste0('BGC ',
                     unique(.x$prof_type),
                     ' by year and location ',
                     ifelse(unique(.x$profile_range) == 1, '600m', ifelse(unique(.x$profile_range) == 2, '1000m', '1500m')),
                     ' profiles')
    ) +
    theme(
      legend.position = "bottom",
      axis.text = element_blank(),
      axis.ticks = element_blank()
    ) +
    facet_wrap(~year, ncol = 3)
  )
[[1]]

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spatial all years

# sum across years
bgc_count_agg <- bgc_count_agg %>%
  group_by(prof_order, prof_type, profile_range, lat, lon) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  ungroup()

# map the location of profiles for each profile in each year 
bgc_count_agg %>%
  group_split(prof_order, profile_range) %>%
  map(
    ~ map +
    geom_tile(data = .x, aes(
      x = lon, y = lat, fill = count_profiles
    )) +
    scale_fill_gradient(low = "blue", high = "red",
                        trans = "log10") +
    labs(
      x = 'lon',
      y = 'lat',
      fill = 'number of\nprofiles',
      title = paste0('BGC ',
                     unique(.x$prof_type),
                     ' by location ',
                     ifelse(unique(.x$profile_range) == 1, '600m', ifelse(unique(.x$profile_range) == 2, '1000m', '1500m')),
                     ' profiles')
    ) +
    theme(
      legend.position = "bottom",
      axis.text = element_blank(),
      axis.ticks = element_blank()
    )
  )
[[1]]

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9d9224a ds2n19 2023-12-07 

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sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5

Matrix products: default
BLAS:   /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/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] lubridate_1.9.0  timechange_0.1.1 argodata_0.1.0   forcats_0.5.2   
 [5] stringr_1.5.0    dplyr_1.1.3      purrr_1.0.2      readr_2.1.3     
 [9] tidyr_1.3.0      tibble_3.2.1     ggplot2_3.4.4    tidyverse_1.3.2 

loaded via a namespace (and not attached):
 [1] httr_1.4.4          sass_0.4.4          bit64_4.0.5        
 [4] vroom_1.6.0         jsonlite_1.8.3      modelr_0.1.10      
 [7] bslib_0.4.1         assertthat_0.2.1    highr_0.9          
[10] googlesheets4_1.0.1 cellranger_1.1.0    yaml_2.3.6         
[13] pillar_1.9.0        backports_1.4.1     glue_1.6.2         
[16] digest_0.6.30       promises_1.2.0.1    rvest_1.0.3        
[19] colorspace_2.0-3    htmltools_0.5.3     httpuv_1.6.6       
[22] pkgconfig_2.0.3     broom_1.0.5         haven_2.5.1        
[25] scales_1.2.1        whisker_0.4         later_1.3.0        
[28] tzdb_0.3.0          git2r_0.30.1        googledrive_2.0.0  
[31] generics_0.1.3      farver_2.1.1        ellipsis_0.3.2     
[34] cachem_1.0.6        withr_2.5.0         cli_3.6.1          
[37] magrittr_2.0.3      crayon_1.5.2        readxl_1.4.1       
[40] evaluate_0.18       fs_1.5.2            fansi_1.0.3        
[43] xml2_1.3.3          tools_4.2.2         hms_1.1.2          
[46] gargle_1.2.1        lifecycle_1.0.3     munsell_0.5.0      
[49] reprex_2.0.2        compiler_4.2.2      jquerylib_0.1.4    
[52] RNetCDF_2.6-1       rlang_1.1.1         grid_4.2.2         
[55] rstudioapi_0.15.0   labeling_0.4.2      rmarkdown_2.18     
[58] gtable_0.3.1        DBI_1.1.3           R6_2.5.1           
[61] knitr_1.41          fastmap_1.1.0       bit_4.0.5          
[64] utf8_1.2.2          workflowr_1.7.0     rprojroot_2.0.3    
[67] stringi_1.7.8       parallel_4.2.2      Rcpp_1.0.10        
[70] vctrs_0.6.4         dbplyr_2.2.1        tidyselect_1.2.0   
[73] xfun_0.35