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Task

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

Load data

Read the metadata file created in loading_data.html:

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

core_metadata <- 
  read_rds(file = paste0(path_argo_core_preprocessed, "/core_metadata.rds"))
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 = ""))

Spatial BGC-data coverage

Count profiles (BGC-Argo)

bgc_metadata <- inner_join(
  bgc_metadata,
  basinmask
)

##################################################################

bgc_profile_counts_year <- bgc_metadata %>% 
  select(platform_number, cycle_number, date, lon, lat,
         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)) %>% 
  filter(!is.na(profile_flag),
         profile_flag != "") %>% 
  count(lat, lon, year, parameter) # count the number of profiles per year in each lon/lat grid for each parameter 


###################################################################
# count the number of profiles which have flags A, B, C, D, or E (count the number of profiles which have usable data)

bgc_profile_counts_usable <- bgc_metadata %>% 
  select(platform_number, cycle_number, date, lon, lat,
         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)) %>% 
  filter(!is.na(profile_flag),
         profile_flag != "",
         profile_flag != 'F') %>% 
  count(lat, lon, parameter, profile_flag)  # count the number of profiles for flags A, B, C, D, and E (usable data) for each lon/lat grid 


##################################################################
# count the number of profiles which have QC flag A (100% of levels contain good data)

bgc_profile_counts_A <- bgc_metadata %>% 
  select(platform_number, cycle_number, date, lon, lat,
         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)) %>% 
  filter(profile_flag == 'A') %>% 
  count(lat, lon, parameter)


#####################################################################
# count the number of profiles which have a QC flag of F (0% of levels contain good data)

bgc_profile_counts_F <- bgc_metadata %>% 
  select(platform_number, cycle_number, date, lon, lat,
         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)) %>% 
  filter(profile_flag == 'F') %>% 
  count(lat, lon, parameter)

By Year

Map of profile locations for each parameter, per year

map +
  geom_tile(data = bgc_profile_counts_year,
              aes(lon, lat, fill = n)) +
  scale_fill_gradient(low = "blue", high = "red",
                      trans = "log10") +
  facet_grid(year ~ parameter)


# 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 = lon, y = lat, fill = n)) +
#   scale_fill_gradient(low="blue", high="red",
#                       trans = "log10") +
#   theme_bw() +
#   facet_grid(year ~ parameter)
# map the location of profiles for each parameter in each year 
bgc_profile_counts_year %>%
  group_split(parameter) %>%
  map(
    ~ map +
      geom_tile(data = .x, aes(
        x = lon, y = lat, fill = n
      )) +
      scale_fill_gradient(low = "blue", high = "red",
                          trans = "log10") +
      labs(
        x = 'lon',
        y = 'lat',
        fill = 'number of\nprofiles',
        title = paste('Parameter:', unique(.x$parameter))
      ) +
      theme(
        legend.position = "bottom",
        axis.text = element_blank(),
        axis.ticks = element_blank()
      ) +
      facet_wrap(~year, ncol = 3)
  )
[[1]]

Version Author Date
bdd516d pasqualina-vonlanthendinenna 2022-05-23
710edd4 jens-daniel-mueller 2022-05-11
68eff8b jens-daniel-mueller 2022-05-11
8805f99 pasqualina-vonlanthendinenna 2022-04-11
7f3cfe7 pasqualina-vonlanthendinenna 2021-12-17
123e5db pasqualina-vonlanthendinenna 2021-12-07
7a01367 pasqualina-vonlanthendinenna 2021-11-12
6276d6c pasqualina-vonlanthendinenna 2021-11-11
a103f60 pasqualina-vonlanthendinenna 2021-11-05
bba33bf pasqualina-vonlanthendinenna 2021-10-26
f7ef44f jens-daniel-mueller 2021-10-22
aa7280d jens-daniel-mueller 2021-10-22
701fffa pasqualina-vonlanthendinenna 2021-10-20 

[[2]]

Version Author Date
68eff8b jens-daniel-mueller 2022-05-11
8805f99 pasqualina-vonlanthendinenna 2022-04-11
7f3cfe7 pasqualina-vonlanthendinenna 2021-12-17
123e5db pasqualina-vonlanthendinenna 2021-12-07
7a01367 pasqualina-vonlanthendinenna 2021-11-12
6276d6c pasqualina-vonlanthendinenna 2021-11-11
a103f60 pasqualina-vonlanthendinenna 2021-11-05
bba33bf pasqualina-vonlanthendinenna 2021-10-26
f7ef44f jens-daniel-mueller 2021-10-22
aa7280d jens-daniel-mueller 2021-10-22
701fffa pasqualina-vonlanthendinenna 2021-10-20 

[[3]]

Version Author Date
68eff8b jens-daniel-mueller 2022-05-11
8805f99 pasqualina-vonlanthendinenna 2022-04-11
7f3cfe7 pasqualina-vonlanthendinenna 2021-12-17
123e5db pasqualina-vonlanthendinenna 2021-12-07
7a01367 pasqualina-vonlanthendinenna 2021-11-12
6276d6c pasqualina-vonlanthendinenna 2021-11-11
a103f60 pasqualina-vonlanthendinenna 2021-11-05
bba33bf pasqualina-vonlanthendinenna 2021-10-26
f7ef44f jens-daniel-mueller 2021-10-22
aa7280d jens-daniel-mueller 2021-10-22
701fffa pasqualina-vonlanthendinenna 2021-10-20 
ggsave("output/figures/maps_per_year.png",
       width = 7,
       height = 4)

# 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 = lon, y = lat, fill = n)) +
#   scale_fill_gradient(low="blue", high="red",
#                       trans = "log10") +
#   theme_bw() +
#   labs(x = 'lon', y = 'lat', fill = 'number of profiles', 
#        title = paste('Parameter:', unique(.x$parameter)))+
#   facet_grid(. ~ year)
#   )

By QC Flag

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 = lon, y = lat, fill = n)) +
#   scale_fill_gradient(low="blue", high="red",
#                       trans = "log10") +
#   theme_bw() +
#   facet_grid(profile_flag ~ parameter)

Flags A, B, C, D, and E

# map the location of profiles which contain usable data (profile QC flags A, B, C, D, and E)
# create a separate plot for each parameter  

bgc_profile_counts_usable %>%
  group_split(parameter) %>%
  map(
    ~ map +
      geom_tile(data = .x, aes(
        x = lon, y = lat, fill = n
      )) +
      scale_fill_gradient(low = "blue", high = "red",
                          trans = "log10") +
      labs(
        x = 'lon',
        y = 'lat',
        fill = 'number of\nprofiles',
        title = paste(unique(.x$parameter), 'Profile Flags A, B, C, D, E')
      ) +
      theme(
        legend.position = "bottom",
        axis.text = element_blank(),
        axis.ticks = element_blank()
      ) +
      facet_wrap(~ parameter)
)
[[1]]

Version Author Date
68eff8b jens-daniel-mueller 2022-05-11
8805f99 pasqualina-vonlanthendinenna 2022-04-11
7f3cfe7 pasqualina-vonlanthendinenna 2021-12-17
7a01367 pasqualina-vonlanthendinenna 2021-11-12
6276d6c pasqualina-vonlanthendinenna 2021-11-11
a103f60 pasqualina-vonlanthendinenna 2021-11-05 

[[2]]

Version Author Date
68eff8b jens-daniel-mueller 2022-05-11
8805f99 pasqualina-vonlanthendinenna 2022-04-11
7f3cfe7 pasqualina-vonlanthendinenna 2021-12-17
7a01367 pasqualina-vonlanthendinenna 2021-11-12
6276d6c pasqualina-vonlanthendinenna 2021-11-11
a103f60 pasqualina-vonlanthendinenna 2021-11-05 

[[3]]

Version Author Date
68eff8b jens-daniel-mueller 2022-05-11
8805f99 pasqualina-vonlanthendinenna 2022-04-11
7f3cfe7 pasqualina-vonlanthendinenna 2021-12-17
7a01367 pasqualina-vonlanthendinenna 2021-11-12
6276d6c pasqualina-vonlanthendinenna 2021-11-11
a103f60 pasqualina-vonlanthendinenna 2021-11-05 
ggsave("output/figures/maps_usable_data.png",
       width = 7,
       height = 4)

Flag A

# map the location of profiles with QC flag A for each parameter 
# only the highest-quality data, with 100% of levels with good data 

bgc_profile_counts_A %>%
  group_split(parameter) %>%
  map(
    ~ map +
      geom_tile(data = .x, aes(
        x = lon, y = lat, fill = n
      )) +
      scale_fill_gradient(low = "blue", high = "red",
                          trans = "log10") +
      labs(
        x = 'lon',
        y = 'lat',
        fill = 'number of\nprofiles',
        title = paste(unique(.x$parameter), 'Profile Flag A')
      ) +
      theme(
        legend.position = "bottom",
        axis.text = element_blank(),
        axis.ticks = element_blank()
      ) +
      facet_wrap(~ parameter)
)
[[1]]

Version Author Date
68eff8b jens-daniel-mueller 2022-05-11
8805f99 pasqualina-vonlanthendinenna 2022-04-11
7f3cfe7 pasqualina-vonlanthendinenna 2021-12-17
7a01367 pasqualina-vonlanthendinenna 2021-11-12
6276d6c pasqualina-vonlanthendinenna 2021-11-11
a103f60 pasqualina-vonlanthendinenna 2021-11-05 

[[2]]

Version Author Date
68eff8b jens-daniel-mueller 2022-05-11
8805f99 pasqualina-vonlanthendinenna 2022-04-11
7f3cfe7 pasqualina-vonlanthendinenna 2021-12-17
7a01367 pasqualina-vonlanthendinenna 2021-11-12
6276d6c pasqualina-vonlanthendinenna 2021-11-11
a103f60 pasqualina-vonlanthendinenna 2021-11-05 

[[3]]

Version Author Date
68eff8b jens-daniel-mueller 2022-05-11
8805f99 pasqualina-vonlanthendinenna 2022-04-11
7f3cfe7 pasqualina-vonlanthendinenna 2021-12-17
7a01367 pasqualina-vonlanthendinenna 2021-11-12
6276d6c pasqualina-vonlanthendinenna 2021-11-11
a103f60 pasqualina-vonlanthendinenna 2021-11-05 
ggsave("output/figures/maps_A_flag.png",
       width = 7,
       height = 4)

Flag F

# map the location of profiles with QC flag F (not usable data)
bgc_profile_counts_F %>%
  group_split(parameter) %>%
  map(
    ~ map +
      geom_tile(data = .x, aes(
        x = lon, y = lat, fill = n
      )) +
      scale_fill_gradient(low = "blue", high = "red",
                          trans = "log10") +
      labs(
        x = 'lon',
        y = 'lat',
        fill = 'number of\nprofiles',
        title = paste(unique(.x$parameter), 'Profile Flag F')
      ) +
      theme(
        legend.position = "bottom",
        axis.text = element_blank(),
        axis.ticks = element_blank()
      ) +
      facet_wrap(~ parameter)
)
[[1]]

Version Author Date
68eff8b jens-daniel-mueller 2022-05-11
8805f99 pasqualina-vonlanthendinenna 2022-04-11
7f3cfe7 pasqualina-vonlanthendinenna 2021-12-17
7a01367 pasqualina-vonlanthendinenna 2021-11-12
6276d6c pasqualina-vonlanthendinenna 2021-11-11
a103f60 pasqualina-vonlanthendinenna 2021-11-05 

[[2]]

Version Author Date
68eff8b jens-daniel-mueller 2022-05-11
8805f99 pasqualina-vonlanthendinenna 2022-04-11
7f3cfe7 pasqualina-vonlanthendinenna 2021-12-17
6276d6c pasqualina-vonlanthendinenna 2021-11-11
a103f60 pasqualina-vonlanthendinenna 2021-11-05 

[[3]]

Version Author Date
68eff8b jens-daniel-mueller 2022-05-11
8805f99 pasqualina-vonlanthendinenna 2022-04-11
7f3cfe7 pasqualina-vonlanthendinenna 2021-12-17
6276d6c pasqualina-vonlanthendinenna 2021-11-11
a103f60 pasqualina-vonlanthendinenna 2021-11-05 
ggsave("output/figures/maps_flag_F.png",
       width = 7,
       height = 4)
# create a separate plot for each QC flag (instead of multiple panels in one plot) 

# bgc_profile_counts_flag %>%
#   group_split(profile_flag) %>%
#   map(
#     ~ map +
#       geom_tile(data = .x, aes(
#         x = lon, y = lat, fill = n
#       )) +
#       scale_fill_gradient(low = "blue", high = "red",
#                           trans = "log10") +
#       labs(
#         x = 'lon',
#         y = 'lat',
#         fill = 'number of\nprofiles',
#         title = paste('Profile QC flag', unique(.x$profile_flag))
#       ) +
#       theme(
#         legend.position = "bottom",
#         axis.text = element_blank(),
#         axis.ticks = element_blank()
#       ) +
#       facet_grid(parameter ~ .)
#   )

ggsave("output/figures/maps_per_flag.png",
       width = 7,
       height = 4)
ph_profile_counts_year <- bgc_metadata %>%      # count the number of A-flag pH profiles 
  select(platform_number, cycle_number, date, lon, lat,
        profile_ph_in_situ_total_qc) %>% 
  pivot_longer(profile_ph_in_situ_total_qc,
               names_to = "parameter",
               values_to = "profile_flag",
               names_prefix = "profile_") %>% 
  mutate(year = year(date)) %>% 
  filter(profile_flag == "A") %>% 
  count(lat, lon, year, parameter)

# map the location of pH profiles with QC flag A each year
ph_profile_counts_year %>%
  group_split(parameter) %>%
  map(
    ~ map +
      geom_tile(data = .x, aes(
        x = lon, y = lat, fill = n
      )) +
      scale_fill_gradient(low = "blue", high = "red",
                          trans = "log10") +
      labs(
        x = 'lon',
        y = 'lat',
        fill = 'number of\nprofiles',
        title = paste('Parameter:', unique(.x$parameter), 'flag A')
      ) +
      theme(
        legend.position = "bottom",
        axis.text = element_blank(),
        axis.ticks = element_blank()
      ) +
      facet_wrap(~year, ncol = 3)
  )

ggsave("output/figures/map_pH_flag_A_per_year.png",
       width = 7,
       height = 4)

Remove BGC data

rm(list = ls(pattern = 'bgc_'))

Spatial Core-data coverage

Count profiles (Core-Argo)

core_metadata <- inner_join(core_metadata, basinmask)

#################################################
# count the number of core profiles in each lat/lon grid for each year and each parameter (temp and sal)

core_profile_counts_year <- core_metadata %>% 
  select(platform_number, cycle_number, date, lon, lat,
         profile_temp_qc, profile_psal_qc) %>% 
  pivot_longer(profile_temp_qc:profile_psal_qc,
               names_to = 'parameter',
               values_to = 'profile_flag',
               names_prefix = 'profile_') %>% 
  mutate(year = year(date)) %>% 
  filter(!is.na(profile_flag),
         profile_flag != "") %>% 
  count(lat, lon, year, parameter)


#############################################################
# count the number of profiles with usable data (flags A-E) for each lat/lon grid, per parameter 

core_profile_counts_usable <- core_metadata %>% 
  select(platform_number, cycle_number, date, lon, lat,
         profile_temp_qc, profile_psal_qc) %>% 
  pivot_longer(profile_temp_qc:profile_psal_qc,
               names_to = 'parameter',
               values_to = 'profile_flag',
               names_prefix = 'profile_') %>% 
  mutate(year = year(date)) %>% 
  filter(!is.na(profile_flag),
         profile_flag != "",
         profile_flag != 'F') %>% 
  count(lat, lon, parameter, profile_flag)


##############################################################
# count the number of core-profiles with QC flag A for each lat/lon grid, per parameter 

core_profile_counts_A <- core_metadata %>% 
  select(platform_number, cycle_number, date, lon, lat,
         profile_temp_qc, profile_psal_qc) %>% 
  pivot_longer(profile_temp_qc:profile_psal_qc,
               names_to = 'parameter',
               values_to = 'profile_flag',
               names_prefix = 'profile_') %>% 
  mutate(year = year(date)) %>% 
  filter(profile_flag == 'A') %>% 
  count(lat, lon, parameter)


##############################################################
# count the number of core-profiles with QC flag A for each lat/lon grid, per parameter 

core_profile_counts_F <- core_metadata %>% 
  select(platform_number, cycle_number, date, lon, lat,
         profile_temp_qc, profile_psal_qc) %>% 
  pivot_longer(profile_temp_qc:profile_psal_qc,
               names_to = "parameter",
               values_to = "profile_flag",
               names_prefix = "profile_") %>% 
  mutate(year = year(date)) %>% 
  filter(profile_flag == 'F') %>% 
  count(lat, lon, parameter)

By Year

# map the location of profiles for each parameter in each year 
core_profile_counts_year %>%
  group_split(parameter) %>%
  map(
    ~ map +
      geom_tile(data = .x, aes(
        x = lon, y = lat, fill = n
      )) +
      scale_fill_gradient(low = "blue", high = "red",
                          trans = "log10") +
      labs(
        x = 'lon',
        y = 'lat',
        fill = 'number of\n core profiles',
        title = paste('Parameter:', unique(.x$parameter))
      ) +
      theme(
        legend.position = "bottom",
        axis.text = element_blank(),
        axis.ticks = element_blank()
      ) +
      facet_wrap(~year, ncol = 3)
  )
[[1]]


[[2]]

ggsave("output/figures/core_maps_per_year.png",
       width = 7,
       height = 4)

By QC Flag

Flags A-E

# map the location of profiles which contain usable data (profile QC flags A, B, C, D, and E)
# create a separate plot for each parameter  

core_profile_counts_usable %>%
  group_split(parameter) %>%
  map(
    ~ map +
      geom_tile(data = .x, aes(
        x = lon, y = lat, fill = n
      )) +
      scale_fill_gradient(low = "blue", high = "red",
                          trans = "log10") +
      labs(
        x = 'lon',
        y = 'lat',
        fill = 'number of\ncore profiles',
        title = paste(unique(.x$parameter), 'Profile Flags A, B, C, D, E')
      ) +
      theme(
        legend.position = "bottom",
        axis.text = element_blank(),
        axis.ticks = element_blank()
      ) +
      facet_wrap(~ parameter)
)
[[1]]


[[2]]

ggsave("output/figures/maps_usable_core_data.png",
       width = 7,
       height = 4)

Flag A

# map the location of profiles with QC flag A for each parameter 
# only the highest-quality data, with 100% of levels with good data 

core_profile_counts_A %>%
  group_split(parameter) %>%
  map(
    ~ map +
      geom_tile(data = .x, aes(
        x = lon, y = lat, fill = n
      )) +
      scale_fill_gradient(low = "blue", high = "red",
                          trans = "log10") +
      labs(
        x = 'lon',
        y = 'lat',
        fill = 'number of\ncore profiles',
        title = paste(unique(.x$parameter), 'Profile Flag A')
      ) +
      theme(
        legend.position = "bottom",
        axis.text = element_blank(),
        axis.ticks = element_blank()
      ) +
      facet_wrap(~ parameter)
)
[[1]]


[[2]]

ggsave("output/figures/maps_A_flag_core.png",
       width = 7,
       height = 4)

Flag F

core_profile_counts_F %>%
  group_split(parameter) %>%
  map(
    ~ map +
      geom_tile(data = .x, aes(
        x = lon, y = lat, fill = n
      )) +
      scale_fill_gradient(low = "blue", high = "red",
                          trans = "log10") +
      labs(
        x = 'lon',
        y = 'lat',
        fill = 'number of\ncore profiles',
        title = paste(unique(.x$parameter), 'Profile Flag F')
      ) +
      theme(
        legend.position = "bottom",
        axis.text = element_blank(),
        axis.ticks = element_blank()
      ) +
      facet_wrap(~ parameter)
)
[[1]]


[[2]]

ggsave("output/figures/maps_flag_F_core.png",
       width = 7,
       height = 4)
rm(list = ls(pattern = 'core_'))

sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3

Matrix products: default
BLAS:   /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/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_1.8.0 argodata_0.1.0  forcats_0.5.1   stringr_1.4.0  
 [5] dplyr_1.0.7     purrr_0.3.4     readr_2.1.1     tidyr_1.1.4    
 [9] tibble_3.1.6    ggplot2_3.3.5   tidyverse_1.3.1 workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8       getPass_0.2-2    ps_1.6.0         assertthat_0.2.1
 [5] rprojroot_2.0.2  digest_0.6.29    utf8_1.2.2       R6_2.5.1        
 [9] cellranger_1.1.0 backports_1.4.1  reprex_2.0.1     evaluate_0.14   
[13] highr_0.9        httr_1.4.2       pillar_1.6.4     rlang_1.0.2     
[17] readxl_1.3.1     rstudioapi_0.13  whisker_0.4      callr_3.7.0     
[21] jquerylib_0.1.4  rmarkdown_2.11   labeling_0.4.2   bit_4.0.4       
[25] munsell_0.5.0    broom_0.7.11     compiler_4.1.2   httpuv_1.6.5    
[29] modelr_0.1.8     xfun_0.29        pkgconfig_2.0.3  htmltools_0.5.2 
[33] tidyselect_1.1.1 fansi_1.0.2      withr_2.4.3      crayon_1.4.2    
[37] tzdb_0.2.0       dbplyr_2.1.1     later_1.3.0      grid_4.1.2      
[41] jsonlite_1.7.3   gtable_0.3.0     lifecycle_1.0.1  DBI_1.1.2       
[45] git2r_0.29.0     magrittr_2.0.1   scales_1.1.1     vroom_1.5.7     
[49] cli_3.1.1        stringi_1.7.6    farver_2.1.0     fs_1.5.2        
[53] promises_1.2.0.1 xml2_1.3.3       bslib_0.3.1      ellipsis_0.3.2  
[57] generics_0.1.1   vctrs_0.3.8      tools_4.1.2      bit64_4.0.5     
[61] glue_1.6.0       RNetCDF_2.5-2    hms_1.1.1        parallel_4.1.2  
[65] processx_3.5.2   fastmap_1.1.0    yaml_2.2.1       colorspace_2.0-2
[69] rvest_1.0.2      knitr_1.37       haven_2.4.3      sass_0.4.0