Last updated: 2021-10-21

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Task

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

library(tidyverse)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
✓ ggplot2 3.3.5     ✓ purrr   0.3.4
✓ tibble  3.1.3     ✓ dplyr   1.0.5
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
library(argodata)
library(ggplot2)
library(lubridate)

Attaching package: 'lubridate'
The following objects are masked from 'package:base':

    date, intersect, setdiff, union
library(sf)
Linking to GEOS 3.6.2, GDAL 3.0.4, PROJ 7.0.0
library(rnaturalearth)
library(rnaturalearthdata)
# load in coastline data (uses sf and rnaturalearthdata packages)
world = ne_coastline(scale = 'medium', returnclass = 'sf')

Load data

Read the files created in loading_data.html:

path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")

bgc_subset <-
  read_rds(file = paste0(path_argo_preprocessed, "/bgc_subset.rds"))

bgc_metadata <-
  read_rds(file = paste0(path_argo_preprocessed, "/bgc_metadata.rds"))

bgc_data <-
  read_rds(file = paste0(path_argo_preprocessed, "/bgc_data.rds"))

bgc_merge <-
  read_rds(file = paste0(path_argo_preprocessed, "/bgc_merge.rds"))

Spatial data coverage

Count the number of profiles per year

bgc_profile_counts_year <- bgc_metadata %>% 
  select(platform_number, cycle_number, date, longitude, latitude,
         profile_doxy_qc, profile_ph_in_situ_total_qc, profile_nitrate_qc) %>% 
  pivot_longer(profile_doxy_qc:profile_nitrate_qc,
               names_to = "parameter",
               values_to = "profile_flag",
               names_prefix = "profile_") %>% 
  mutate(year = year(date),
         latitude = round(latitude, digits = 0),
         longitude = round(longitude, digits = 0)) %>% 
  filter(!is.na(profile_flag),
         profile_flag != "") %>% 
  count(latitude, longitude, year, parameter) # count the number of profiles per year in each lon/lat grid for each parameter 

bgc_profile_counts_flag <- bgc_metadata %>% 
  select(platform_number, cycle_number, date, longitude, latitude,
         profile_doxy_qc, profile_ph_in_situ_total_qc, profile_nitrate_qc) %>% 
  pivot_longer(profile_doxy_qc:profile_nitrate_qc,
               names_to = "parameter",
               values_to = "profile_flag",
               names_prefix = "profile_") %>% 
  mutate(year = year(date),
         latitude = round(latitude, digits = 0),
         longitude = round(longitude, digits = 0)) %>% 
  filter(!is.na(profile_flag),
         profile_flag != "") %>% 
  count(latitude, longitude, parameter, profile_flag)  # count the number of profiles for each profile QC flag in each lon/lat area and for each parameter

Map the profile locations

Map of profile locations for each parameter, per year

bgc_profile_counts_year %>%
  ggplot() +
  geom_sf(data = ne_countries(returnclass = "sf"),
          fill = "gray90",
          color = NA) +
  geom_sf(data = ne_coastline(returnclass = "sf")) +
  geom_tile(aes(x = longitude, y = latitude, fill = n)) +
  scale_fill_gradient(low="blue", high="red",
                      trans = "log10") +
  theme_bw() +
  facet_grid(year ~ parameter)
bgc_profile_counts_year %>%
  group_split(parameter) %>% 
  map( 
  ~ ggplot() +
  geom_sf(data = ne_countries(returnclass = "sf"),
          fill = "gray90",
          color = NA) +
  geom_sf(data = ne_coastline(returnclass = "sf")) +
  geom_tile(data = .x, aes(x = longitude, y = latitude, fill = n)) +
  scale_fill_gradient(low="blue", high="red",
                      trans = "log10") +
  theme_bw() +
  labs(x = 'longitude', y = 'latitude', fill = 'number of profiles', 
       title = paste('Parameter:', unique(.x$parameter)))+
  facet_grid(. ~ year)
  )
[[1]]
Warning: Removed 3 rows containing missing values (geom_tile).

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[[2]]
Warning: Removed 1 rows containing missing values (geom_tile).

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[[3]]

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Map the profile locations for each profile QC flag of each parameter

bgc_profile_counts_flag %>%
  ggplot() +
  geom_sf(data = ne_countries(returnclass = "sf"),
          fill = "gray90",
          color = NA) +
  geom_sf(data = ne_coastline(returnclass = "sf")) +
  geom_tile(aes(x = longitude, y = latitude, fill = n)) +
  scale_fill_gradient(low="blue", high="red",
                      trans = "log10") +
  theme_bw() +
  facet_grid(profile_flag ~ parameter)
# create a separate plot for each QC flag (instead of multiple panels in one plot) 

bgc_profile_counts_flag %>%
  group_split(profile_flag) %>% 
  map(
  ~ ggplot() +
  geom_sf(data = ne_countries(returnclass = "sf"),
          fill = "gray90",
          color = NA) +
  geom_sf(data = ne_coastline(returnclass = "sf")) +
  geom_tile(data = .x, aes(x = longitude, y = latitude, fill = n)) +
  scale_fill_gradient(low="blue", high="red",
                      trans = "log10") +
  theme_bw() +
  labs(x = 'longitude', y = 'latitude', fill = 'number of profiles',
       title = paste('Profile QC flag', unique(.x$profile_flag)))+
  facet_grid(. ~ parameter)
  )
[[1]]
Warning: Removed 2 rows containing missing values (geom_tile).

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[[2]]
Warning: Removed 1 rows containing missing values (geom_tile).

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[[3]]

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[[4]]

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[[5]]

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[[6]]
Warning: Removed 2 rows containing missing values (geom_tile).

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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] rnaturalearthdata_0.1.0 rnaturalearth_0.1.0     sf_1.0-2               
 [4] lubridate_1.7.9         argodata_0.0.0.9000     forcats_0.5.0          
 [7] stringr_1.4.0           dplyr_1.0.5             purrr_0.3.4            
[10] readr_1.4.0             tidyr_1.1.3             tibble_3.1.3           
[13] ggplot2_3.3.5           tidyverse_1.3.0         workflowr_1.6.2        

loaded via a namespace (and not attached):
 [1] httr_1.4.2         sass_0.4.0         jsonlite_1.7.2     modelr_0.1.8      
 [5] bslib_0.2.5.1      assertthat_0.2.1   highr_0.8          sp_1.4-4          
 [9] blob_1.2.1         cellranger_1.1.0   yaml_2.2.1         pillar_1.6.2      
[13] backports_1.1.10   lattice_0.20-41    glue_1.4.2         digest_0.6.27     
[17] promises_1.2.0.1   rvest_0.3.6        colorspace_2.0-2   htmltools_0.5.1.1 
[21] httpuv_1.6.2       pkgconfig_2.0.3    broom_0.7.9        haven_2.3.1       
[25] s2_1.0.6           scales_1.1.1       whisker_0.4        later_1.3.0       
[29] git2r_0.27.1       proxy_0.4-26       generics_0.1.0     farver_2.1.0      
[33] ellipsis_0.3.2     withr_2.4.2        cli_3.0.1          magrittr_2.0.1    
[37] crayon_1.4.1       readxl_1.3.1       evaluate_0.14      fs_1.5.0          
[41] fansi_0.5.0        xml2_1.3.2         class_7.3-17       tools_4.0.3       
[45] hms_0.5.3          lifecycle_1.0.0    munsell_0.5.0      reprex_0.3.0      
[49] compiler_4.0.3     jquerylib_0.1.4    e1071_1.7-8        RNetCDF_2.4-2     
[53] rlang_0.4.11       classInt_0.4-3     units_0.7-2        grid_4.0.3        
[57] rstudioapi_0.13    rmarkdown_2.10     wk_0.5.0           gtable_0.3.0      
[61] DBI_1.1.1          R6_2.5.1           knitr_1.33         rgeos_0.5-5       
[65] utf8_1.2.2         rprojroot_2.0.2    KernSmooth_2.23-17 stringi_1.5.3     
[69] Rcpp_1.0.7         vctrs_0.3.8        dbplyr_1.4.4       tidyselect_1.1.0  
[73] xfun_0.25