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Task

Count the number of bgc-argo profiles, and plot their evolution over time.

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))

Core - temperature

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

# Number of profiles
core_temp_count <- core_temp_count %>%
  group_by(year, month, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

# Aggregate profile range
core_temp_count_agg <- core_temp_count %>%
  group_by(year, month) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  mutate(profile_range = 1) %>%
  ungroup()

core_temp_count_agg <- rbind(
  core_temp_count_agg,
  core_temp_count %>%
    filter (profile_range %in% c(2, 3)) %>%
    group_by(year, month) %>%
    summarise(count_profiles = sum(count_profiles)) %>%
    mutate(profile_range = 2) %>%
    ungroup()
)

core_temp_count_agg <- rbind(
  core_temp_count_agg,
  core_temp_count %>%
    filter (profile_range ==3)
)

# count of temperature profiles by year, month and profile range
core_temp_count_agg %>% 
  ggplot(aes(x = month, y = count_profiles, col = as.character(profile_range))) +
  geom_line() +
  geom_point() +
  facet_grid(. ~ year,
             scales = "free_y") +
  scale_x_continuous(breaks = seq(2,12,2)) +
  labs(x = 'month', 
       y = 'number of profiles',
       col = 'profile range',
       title = "Number of profiles",
       subtitle = "Complete core temperature profiles (1 = 600m, 2 = 1,000m, 3 = 1,500m)")

Version Author Date
e60ebd2 ds2n19 2023-12-07 
rm (core_temp, core_temp_count, core_temp_count_agg)

BGC - temperature

bgc_temp_count <- bgc_temp %>%
  group_by(year, month, file_id, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

bgc_temp_count <- bgc_temp_count %>%
  group_by(year, month, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

bgc_temp_count_agg <- bgc_temp_count %>%
  group_by(year, month) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  mutate(profile_range = 1) %>%
  ungroup()

bgc_temp_count_agg <- rbind(
  bgc_temp_count_agg,
  bgc_temp_count %>%
    filter (profile_range %in% c(2, 3)) %>%
    group_by(year, month) %>%
    summarise(count_profiles = sum(count_profiles)) %>%
    mutate(profile_range = 2) %>%
    ungroup()
)

bgc_temp_count_agg <- rbind(
  bgc_temp_count_agg,
  bgc_temp_count %>%
    filter (profile_range ==3)
)

# count of temperature profiles by year, month and profile range
bgc_temp_count_agg %>% 
  ggplot(aes(x = month, y = count_profiles, col = as.character(profile_range))) +
  geom_line() +
  geom_point() +
  facet_grid(. ~ year,
             scales = "free_y") +
  scale_x_continuous(breaks = seq(2,12,2)) +
  labs(x = 'month', 
       y = 'number of profiles',
       col = 'profile range',
       title = "Number of profiles",
       subtitle = "Complete bgc temperature profiles (1 = 600m, 2 = 1,000m, 3 = 1,500m)")

Version Author Date
e60ebd2 ds2n19 2023-12-07 
rm (bgc_temp_count, bgc_temp_count_agg)

BGC - pH

bgc_ph_count <- bgc_ph %>%
  group_by(year, month, file_id, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

bgc_ph_count <- bgc_ph_count %>%
  group_by(year, month, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

bgc_ph_count_agg <- bgc_ph_count %>%
  group_by(year, month) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  mutate(profile_range = 1) %>%
  ungroup()

bgc_ph_count_agg <- rbind(
  bgc_ph_count_agg,
  bgc_ph_count %>%
    filter (profile_range %in% c(2, 3)) %>%
    group_by(year, month) %>%
    summarise(count_profiles = sum(count_profiles)) %>%
    mutate(profile_range = 2) %>%
    ungroup()
)

bgc_ph_count_agg <- rbind(
  bgc_ph_count_agg,
  bgc_ph_count %>%
    filter (profile_range ==3)
)

# count of pH profiles by year, month and profile range
bgc_ph_count_agg %>% 
  ggplot(aes(x = month, y = count_profiles, col = as.character(profile_range))) +
  geom_line() +
  geom_point() +
  facet_grid(. ~ year,
             scales = "free_y") +
  scale_x_continuous(breaks = seq(2,12,2)) +
  labs(x = 'month', 
       y = 'number of profiles',
       col = 'profile range',
       title = "Number of profiles",
       subtitle = "Complete bgc pH profiles (1 = 600m, 2 = 1,000m, 3 = 1,500m)")

Version Author Date
e60ebd2 ds2n19 2023-12-07 
rm (bgc_ph_count, bgc_ph_count_agg)

BGC - temp AND pH

bgc_ph_temp <- inner_join(bgc_ph, bgc_temp %>% distinct(file_id)) 

bgc_ph_temp_count <- bgc_ph_temp %>%
  group_by(year, month, file_id, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

bgc_ph_temp_count <- bgc_ph_temp_count %>%
  group_by(year, month, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

bgc_ph_temp_count_agg <- bgc_ph_temp_count %>%
  group_by(year, month) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  mutate(profile_range = 1) %>%
  ungroup()

bgc_ph_temp_count_agg <- rbind(
  bgc_ph_temp_count_agg,
  bgc_ph_temp_count %>%
    filter (profile_range %in% c(2, 3)) %>%
    group_by(year, month) %>%
    summarise(count_profiles = sum(count_profiles)) %>%
    mutate(profile_range = 2) %>%
    ungroup()
)

bgc_ph_temp_count_agg <- rbind(
  bgc_ph_temp_count_agg,
  bgc_ph_temp_count %>%
    filter (profile_range ==3)
)

# count of temperature AND pH profiles by year, month and profile range
bgc_ph_temp_count_agg %>% 
  ggplot(aes(x = month, y = count_profiles, col = as.character(profile_range))) +
  geom_line() +
  geom_point() +
  facet_grid(. ~ year,
             scales = "free_y") +
  scale_x_continuous(breaks = seq(2,12,2)) +
  labs(x = 'month', 
       y = 'number of profiles',
       col = 'profile range',
       title = "Number of profiles",
       subtitle = "Complete bgc temperature AND pH profiles (1 = 600m, 2 = 1,000m, 3 = 1,500m)")

Version Author Date
e60ebd2 ds2n19 2023-12-07 
rm (bgc_ph_temp, bgc_ph_temp_count, bgc_ph_temp_count_agg)

BGC - dissolved oxygen

bgc_doxy_count <- bgc_doxy %>%
  group_by(year, month, file_id, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

bgc_doxy_count <- bgc_doxy_count %>%
  group_by(year, month, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

bgc_doxy_count_agg <- bgc_doxy_count %>%
  group_by(year, month) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  mutate(profile_range = 1) %>%
  ungroup()

bgc_doxy_count_agg <- rbind(
  bgc_doxy_count_agg,
  bgc_doxy_count %>%
    filter (profile_range %in% c(2, 3)) %>%
    group_by(year, month) %>%
    summarise(count_profiles = sum(count_profiles)) %>%
    mutate(profile_range = 2) %>%
    ungroup()
)

bgc_doxy_count_agg <- rbind(
  bgc_doxy_count_agg,
  bgc_doxy_count %>%
    filter (profile_range ==3)
)

# count of dissolved oxygen profiles by year, month and profile range
bgc_doxy_count_agg %>% 
  ggplot(aes(x = month, y = count_profiles, col = as.character(profile_range))) +
  geom_line() +
  geom_point() +
  facet_grid(. ~ year,
             scales = "free_y") +
  scale_x_continuous(breaks = seq(2,12,2)) +
  labs(x = 'month', 
       y = 'number of profiles',
       col = 'profile range',
       title = "Number of profiles",
       subtitle = "Complete bgc dissolved oxygen profiles (1 = 600m, 2 = 1,000m, 3 = 1,500m)")

Version Author Date
e60ebd2 ds2n19 2023-12-07 
rm (bgc_doxy_count, bgc_doxy_count_agg)

BGC - temp AND dissolved oxygen

bgc_doxy_temp <- inner_join(bgc_doxy, bgc_temp %>% distinct(file_id)) 

bgc_doxy_temp_count <- bgc_doxy_temp %>%
  group_by(year, month, file_id, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

bgc_doxy_temp_count <- bgc_doxy_temp_count %>%
  group_by(year, month, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

bgc_doxy_temp_count_agg <- bgc_doxy_temp_count %>%
  group_by(year, month) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  mutate(profile_range = 1) %>%
  ungroup()

bgc_doxy_temp_count_agg <- rbind(
  bgc_doxy_temp_count_agg,
  bgc_doxy_temp_count %>%
    filter (profile_range %in% c(2, 3)) %>%
    group_by(year, month) %>%
    summarise(count_profiles = sum(count_profiles)) %>%
    mutate(profile_range = 2) %>%
    ungroup()
)

bgc_doxy_temp_count_agg <- rbind(
  bgc_doxy_temp_count_agg,
  bgc_doxy_temp_count %>%
    filter (profile_range ==3)
)

# count of temperature AND dissolved oxygen profiles by year, month and profile range
bgc_doxy_temp_count_agg %>% 
  ggplot(aes(x = month, y = count_profiles, col = as.character(profile_range))) +
  geom_line() +
  geom_point() +
  facet_grid(. ~ year,
             scales = "free_y") +
  scale_x_continuous(breaks = seq(2,12,2)) +
  labs(x = 'month', 
       y = 'number of profiles',
       col = 'profile range',
       title = "Number of profiles",
       subtitle = "Complete bgc temperature AND dissolved oxygen profiles (1 = 600m, 2 = 1,000m, 3 = 1,500m)")

Version Author Date
e60ebd2 ds2n19 2023-12-07 
rm (bgc_doxy_temp, bgc_doxy_temp_count, bgc_doxy_temp_count_agg)

BGC - nitrate

bgc_nitrate_count <- bgc_nitrate %>%
  group_by(year, month, file_id, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

bgc_nitrate_count <- bgc_nitrate_count %>%
  group_by(year, month, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

bgc_nitrate_count_agg <- bgc_nitrate_count %>%
  group_by(year, month) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  mutate(profile_range = 1) %>%
  ungroup()

bgc_nitrate_count_agg <- rbind(
  bgc_nitrate_count_agg,
  bgc_nitrate_count %>%
    filter (profile_range %in% c(2, 3)) %>%
    group_by(year, month) %>%
    summarise(count_profiles = sum(count_profiles)) %>%
    mutate(profile_range = 2) %>%
    ungroup()
)

bgc_nitrate_count_agg <- rbind(
  bgc_nitrate_count_agg,
  bgc_nitrate_count %>%
    filter (profile_range ==3)
)

# count of nitrate profiles by year, month and profile range
bgc_nitrate_count_agg %>% 
  ggplot(aes(x = month, y = count_profiles, col = as.character(profile_range))) +
  geom_line() +
  geom_point() +
  facet_grid(. ~ year,
             scales = "free_y") +
  scale_x_continuous(breaks = seq(2,12,2)) +
  labs(x = 'month', 
       y = 'number of profiles',
       col = 'profile range',
       title = "Number of profiles",
       subtitle = "Complete bgc nitrate profiles (1 = 600m, 2 = 1,000m, 3 = 1,500m)")

Version Author Date
e60ebd2 ds2n19 2023-12-07 
rm (bgc_nitrate_count, bgc_nitrate_count_agg)

BGC - temp AND nitrate

bgc_nitrate_temp <- inner_join(bgc_nitrate, bgc_temp %>% distinct(file_id)) 

bgc_nitrate_temp_count <- bgc_nitrate_temp %>%
  group_by(year, month, file_id, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

bgc_nitrate_temp_count <- bgc_nitrate_temp_count %>%
  group_by(year, month, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

bgc_nitrate_temp_count_agg <- bgc_nitrate_temp_count %>%
  group_by(year, month) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  mutate(profile_range = 1) %>%
  ungroup()

bgc_nitrate_temp_count_agg <- rbind(
  bgc_nitrate_temp_count_agg,
  bgc_nitrate_temp_count %>%
    filter (profile_range %in% c(2, 3)) %>%
    group_by(year, month) %>%
    summarise(count_profiles = sum(count_profiles)) %>%
    mutate(profile_range = 2) %>%
    ungroup()
)

bgc_nitrate_temp_count_agg <- rbind(
  bgc_nitrate_temp_count_agg,
  bgc_nitrate_temp_count %>%
    filter (profile_range ==3)
)

# count of temperature AND nitrate profiles by year, month and profile range
bgc_nitrate_temp_count_agg %>% 
  ggplot(aes(x = month, y = count_profiles, col = as.character(profile_range))) +
  geom_line() +
  geom_point() +
  facet_grid(. ~ year,
             scales = "free_y") +
  scale_x_continuous(breaks = seq(2,12,2)) +
  labs(x = 'month', 
       y = 'number of profiles',
       col = 'profile range',
       title = "Number of profiles",
       subtitle = "Complete bgc temperature AND nitrate profiles (1 = 600m, 2 = 1,000m, 3 = 1,500m)")

Version Author Date
e60ebd2 ds2n19 2023-12-07 
rm (bgc_nitrate_temp, bgc_nitrate_temp_count, bgc_nitrate_temp_count_agg)

BGC - chlorophyll a

bgc_chla_count <- bgc_chla %>%
  group_by(year, month, file_id, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

bgc_chla_count <- bgc_chla_count %>%
  group_by(year, month, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

bgc_chla_count_agg <- bgc_chla_count %>%
  group_by(year, month) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  mutate(profile_range = 1) %>%
  ungroup()

bgc_chla_count_agg <- rbind(
  bgc_chla_count_agg,
  bgc_chla_count %>%
    filter (profile_range %in% c(2, 3)) %>%
    group_by(year, month) %>%
    summarise(count_profiles = sum(count_profiles)) %>%
    mutate(profile_range = 2) %>%
    ungroup()
)

bgc_chla_count_agg <- rbind(
  bgc_chla_count_agg,
  bgc_chla_count %>%
    filter (profile_range ==3)
)

# count of chlorophyll a profiles by year, month and profile range
bgc_chla_count_agg %>% 
  ggplot(aes(x = month, y = count_profiles, col = as.character(profile_range))) +
  geom_line() +
  geom_point() +
  facet_grid(. ~ year,
             scales = "free_y") +
  scale_x_continuous(breaks = seq(2,12,2)) +
  labs(x = 'month', 
       y = 'number of profiles',
       col = 'profile range',
       title = "Number of profiles",
       subtitle = "Complete bgc chlorophyll a profiles (1 = 600m, 2 = 1,000m, 3 = 1,500m)")

Version Author Date
e60ebd2 ds2n19 2023-12-07 
rm (bgc_chla_count, bgc_chla_count_agg)

BGC - temp AND chlorophyll a

bgc_chla_temp <- inner_join(bgc_chla, bgc_temp %>% distinct(file_id)) 

bgc_chla_temp_count <- bgc_chla_temp %>%
  group_by(year, month, file_id, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

bgc_chla_temp_count <- bgc_chla_temp_count %>%
  group_by(year, month, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

bgc_chla_temp_count_agg <- bgc_chla_temp_count %>%
  group_by(year, month) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  mutate(profile_range = 1) %>%
  ungroup()

bgc_chla_temp_count_agg <- rbind(
  bgc_chla_temp_count_agg,
  bgc_chla_temp_count %>%
    filter (profile_range %in% c(2, 3)) %>%
    group_by(year, month) %>%
    summarise(count_profiles = sum(count_profiles)) %>%
    mutate(profile_range = 2) %>%
    ungroup()
)

bgc_chla_temp_count_agg <- rbind(
  bgc_chla_temp_count_agg,
  bgc_chla_temp_count %>%
    filter (profile_range ==3)
)

# count of temperature AND chlorophyll a profiles by year, month and profile range
bgc_chla_temp_count_agg %>% 
  ggplot(aes(x = month, y = count_profiles, col = as.character(profile_range))) +
  geom_line() +
  geom_point() +
  facet_grid(. ~ year,
             scales = "free_y") +
  scale_x_continuous(breaks = seq(2,12,2)) +
  labs(x = 'month', 
       y = 'number of profiles',
       col = 'profile range',
       title = "Number of profiles",
       subtitle = "Complete bgc temperature AND chlorophyll a profiles (1 = 600m, 2 = 1,000m, 3 = 1,500m)")

Version Author Date
e60ebd2 ds2n19 2023-12-07 
rm (bgc_chla_temp, bgc_chla_temp_count, bgc_chla_temp_count_agg)

BGC - temp, pH, doxy, nitrate AND chl a

bgc_all <- inner_join(bgc_ph, bgc_temp %>% distinct(file_id)) 
bgc_all <- inner_join(bgc_all, bgc_doxy %>% distinct(file_id)) 
bgc_all <- inner_join(bgc_all, bgc_nitrate %>% distinct(file_id)) 
bgc_all <- inner_join(bgc_all, bgc_chla %>% distinct(file_id)) 

bgc_all_count <- bgc_all %>%
  group_by(year, month, file_id, profile_range) %>%
  summarise(count_measures = n()) %>%
  ungroup()

bgc_all_count <- bgc_all_count %>%
  group_by(year, month, profile_range) %>%
  summarise(count_profiles = n()) %>%
  ungroup()

bgc_all_count_agg <- bgc_all_count %>%
  group_by(year, month) %>%
  summarise(count_profiles = sum(count_profiles)) %>%
  mutate(profile_range = 1) %>%
  ungroup()

bgc_all_count_agg <- rbind(
  bgc_all_count_agg,
  bgc_all_count %>%
    filter (profile_range %in% c(2, 3)) %>%
    group_by(year, month) %>%
    summarise(count_profiles = sum(count_profiles)) %>%
    mutate(profile_range = 2) %>%
    ungroup()
)

bgc_all_count_agg <- rbind(
  bgc_all_count_agg,
  bgc_all_count %>%
    filter (profile_range ==3)
)

# count of temp, pH, doxy, nitrate AND chl a profiles by year, month and profile range
bgc_all_count_agg %>% 
  ggplot(aes(x = month, y = count_profiles, col = as.character(profile_range))) +
  geom_line() +
  geom_point() +
  facet_grid(. ~ year,
             scales = "free_y") +
  scale_x_continuous(breaks = seq(2,12,2)) +
  labs(x = 'month', 
       y = 'number of profiles',
       col = 'profile range',
       title = "Number of profiles",
       subtitle = "Complete bgc temp, pH, doxy, nitrate AND chl a profiles (1 = 600m, 2 = 1,000m, 3 = 1,500m)")

Version Author Date
e60ebd2 ds2n19 2023-12-07 
rm (bgc_all, bgc_all_count, bgc_all_count_agg)

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] Rcpp_1.0.10         assertthat_0.2.1    rprojroot_2.0.3    
 [4] digest_0.6.30       utf8_1.2.2          R6_2.5.1           
 [7] cellranger_1.1.0    backports_1.4.1     reprex_2.0.2       
[10] evaluate_0.18       highr_0.9           httr_1.4.4         
[13] pillar_1.9.0        rlang_1.1.1         googlesheets4_1.0.1
[16] readxl_1.4.1        rstudioapi_0.15.0   whisker_0.4        
[19] jquerylib_0.1.4     rmarkdown_2.18      labeling_0.4.2     
[22] googledrive_2.0.0   munsell_0.5.0       broom_1.0.5        
[25] compiler_4.2.2      httpuv_1.6.6        modelr_0.1.10      
[28] xfun_0.35           pkgconfig_2.0.3     htmltools_0.5.3    
[31] tidyselect_1.2.0    workflowr_1.7.0     fansi_1.0.3        
[34] crayon_1.5.2        withr_2.5.0         tzdb_0.3.0         
[37] dbplyr_2.2.1        later_1.3.0         grid_4.2.2         
[40] jsonlite_1.8.3      gtable_0.3.1        lifecycle_1.0.3    
[43] DBI_1.1.3           git2r_0.30.1        magrittr_2.0.3     
[46] scales_1.2.1        cli_3.6.1           stringi_1.7.8      
[49] cachem_1.0.6        farver_2.1.1        fs_1.5.2           
[52] promises_1.2.0.1    xml2_1.3.3          bslib_0.4.1        
[55] ellipsis_0.3.2      generics_0.1.3      vctrs_0.6.4        
[58] tools_4.2.2         glue_1.6.2          RNetCDF_2.6-1      
[61] hms_1.1.2           fastmap_1.1.0       yaml_2.3.6         
[64] colorspace_2.0-3    gargle_1.2.1        rvest_1.0.3        
[67] knitr_1.41          haven_2.5.1         sass_0.4.4