Global BGC temperature cluster analysis

workflowr

• Summary
• Checks
• Past versions

Last updated: 2024-04-26

Checks: 7 0

Knit directory: bgc_argo_r_argodata/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20211008)

The command set.seed(20211008) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: f37a13f

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version f37a13f. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  analysis/draft.Rmd
    Untracked:  load_argo_core_output.txt

Unstaged changes:
    Modified:   analysis/_site.yml
    Modified:   analysis/child/cluster_analysis_base.Rmd
    Modified:   analysis/load_argo_core.Rmd
    Modified:   analysis/load_broullon_DIC_TA_clim.Rmd
    Modified:   code/Workflowr_project_managment.R
    Modified:   code/start_background_job.R
    Modified:   code/start_background_job_load.R
    Modified:   code/start_background_job_partial.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/temp_cluster_analysis.Rmd) and HTML (docs/temp_cluster_analysis.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	a71690e	mlarriere	2024-04-22	Build site.
html	6b42f40	mlarriere	2024-04-13	Build site.
html	c076fba	mlarriere	2024-04-12	Build site.
html	91f08a6	mlarriere	2024-04-07	Build site.
html	53f0344	mlarriere	2024-04-06	Build site.
Rmd	cf926e3	mlarriere	2024-04-06	cluster analysis building test
html	f9de50e	ds2n19	2024-01-01	Build site.
html	07d4eb8	ds2n19	2023-12-20	Build site.
Rmd	f895ac9	ds2n19	2023-12-19	change to cluster analysis code to introduce child markdown files.
html	354592f	ds2n19	2023-12-19	Build site.
html	db7353e	ds2n19	2023-12-19	Build site.
Rmd	d80e73b	ds2n19	2023-12-19	combined cluster analysis.
html	2a3d9d2	ds2n19	2023-12-19	Build site.
Rmd	f2ae991	ds2n19	2023-12-19	combined cluster analysis.
html	fa6cf38	ds2n19	2023-12-14	Build site.
Rmd	64fd104	ds2n19	2023-12-14	revised coverage analysis and SO focused cluster analysis.
html	f110b74	ds2n19	2023-12-13	Build site.
Rmd	acb6523	ds2n19	2023-12-12	Added documentation added to tasks section at start of each script.
html	2d8fb44	ds2n19	2023-12-07	Build site.
html	087d6fe	ds2n19	2023-12-07	Build site.
Rmd	9e185d7	ds2n19	2023-12-07	Revised menu structure.
Rmd	c5005d7	ds2n19	2023-12-07	Revised menu structure.
html	e60ebd2	ds2n19	2023-12-07	Build site.
Rmd	23a24d1	ds2n19	2023-12-05	Revised extreme identification. Adjusted profiles for cluster analysis based on max anomaly.
html	12e23ab	ds2n19	2023-12-04	Build site.
Rmd	f2b7887	ds2n19	2023-12-04	Cluster under surface extreme.
html	31b8905	ds2n19	2023-12-04	Build site.
Rmd	20e0381	ds2n19	2023-12-04	Cluster under surface extreme.
html	2a330f3	ds2n19	2023-12-04	Build site.
Rmd	1231773	ds2n19	2023-12-04	Cluster under surface extreme.
html	cd75bb7	ds2n19	2023-12-04	Build site.
Rmd	d1088e0	ds2n19	2023-12-04	Cluster under surface extreme.
html	a6b9ac3	ds2n19	2023-12-04	Build site.
Rmd	c3b9451	ds2n19	2023-12-04	Cluster under surface extreme.
html	bb6c7b2	ds2n19	2023-12-03	Build site.
Rmd	b83178b	ds2n19	2023-12-03	Cluster under surface extreme.
html	cfa1850	ds2n19	2023-12-03	Build site.
Rmd	d93ff61	ds2n19	2023-12-03	Cluster under surface extreme.
html	9d9ac38	ds2n19	2023-12-01	Build site.
Rmd	27c706c	ds2n19	2023-12-01	Cluster under surface extreme.
html	ba42bd5	ds2n19	2023-11-30	Build site.
Rmd	964d84d	ds2n19	2023-11-30	Cluster under surface extreme.
html	1ad7f89	ds2n19	2023-11-30	Build site.
Rmd	0a8a718	ds2n19	2023-11-30	Cluster under surface extreme.
html	98f3220	ds2n19	2023-11-30	Build site.
Rmd	d9e17d4	ds2n19	2023-11-30	Cluster under surface extreme.
html	77cc54e	ds2n19	2023-11-24	Build site.
Rmd	b24f211	ds2n19	2023-11-24	Cluster under surface extreme.
Rmd	59f5cc4	ds2n19	2023-11-23	Moved spatiotemporal analysis to use aligned profiles.
html	80c16c2	ds2n19	2023-11-15	Build site.
Rmd	3eba518	ds2n19	2023-11-15	Introduction of vertical alignment and cluster analysis to github website.

---

Tasks

This markdown file carries out cluster analysis on previously created bgc temperature anomaly profiles.

The cluster_analysis_determine_k chunk is used to give an indication of an appropriate number of clusers and this can then set the option opt_num_clusters. Chunk cluster_analysis_cluster_details carried out the cluster analysis and the results are used in subsequent figures.

The cluster analysis is carried out on base anomaly profiles and well as adjusted profiles. Adjusted profiles have been normalised by dividing each anomaly in the profile by the max anomaly across the profile. This results in all Adjusted profiles having values in the rage -1 to +1.

cluster_analysis_base.Rmd - Child markdown file that does cluster analysis and can if set overlay additional attributes on clusters.

cluster_analysis_extreme.Rmd - Child markdown file that does cluster analysis under surface extremes conditions.

Dependencies

temp_anomaly_va.rds - bgc preprocessed folder, created by temp_align_climatology

OceanSODA_global_SST_anomaly_field_01.rds (or _02.rds) - bgc preprocessed folder, created by extreme_temp

Set directories

location of pre-prepared data

Category options

What category of data is the cluster analysis related to

# opt_category
opt_category <- "bgc_temp"

# # Global
# opt_lat_min <- -90
# opt_lat_max <- 90
# opt_lon_min <- 20
# opt_lon_max <- 380
# 
# # spatial restrictions
# # Southern ocean
# opt_lat_min <- -90
# opt_lat_max <- -30
# opt_lon_min <- 20
# opt_lon_max <- 380
# 
# # Mapping latitude limits
# opt_map_lat_limit <- c(-85, 85) # global
# opt_map_lat_limit <- c(-85,-30) # SO

Analysis options

Define options that are used to determine the nature of the cluster analysis that is carried out.

# Options

# opt_num_clusters
# How many clusters are used in the cluster analysis for each depth 1 (600 m), 2 (1000 m) and 3 (1500 m)
opt_num_clusters_min <- c(8, 8, 4)
opt_num_clusters_max <- c(8, 8, 5)
# Which profile range is used
opt_profile_range <- 3

# options relating to cluster analysis
opt_n_start <- 15
opt_max_iterations <- 500
opt_n_clusters <- 14 # Max number of clusters to try when determining optimal number of clusters

# opt_extreme_determination
# 1 - based on the trend of de-seasonal data - we believe this results in more summer extremes where variation tend to be greater.
# 2 - based on the trend of de-seasonal data by month. grouping is by lat, lon and month.
opt_extreme_determination <- 2

# Options associated with profiles under surface extreme conditions
extreme_type <- c('L', 'N', 'H')
opt_num_clusters_ext_min <- c(4, 4, 4)
opt_num_clusters_ext_max <- c(5, 5, 5)

# Option related to normalising the anomaly profiles.
# TRUE - anomaly profiles are normalised by the surface anomaly. Every depth anomaly is divided by the surface anomaly.
#      - The is only carried out for profiles where the abs(surface temp) > 1.
#      - This analysis is carried out in addition to the analysis on base anomaly profiles.  
# FALSE - The normalisation process is not carried out. 
opt_norm_anomaly <- TRUE

theme_set(theme_bw())

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

Preparation

Prepare data for cluster analysis

if (opt_category == "bgc_temp") {

  # ---------------------------------------------------------------------------------------------
  # spatial restrictions
  # ---------------------------------------------------------------------------------------------
  # Global
  opt_lat_min <- -90
  opt_lat_max <- 90
  opt_lon_min <- 20
  opt_lon_max <- 380
  
  # Mapping latitude limits
  opt_map_lat_limit <- c(-85, 85)
    
  # ---------------------------------------------------------------------------------------------
  # read data - bgc temperature must have a ph profile
  # ---------------------------------------------------------------------------------------------
  # read data, applying geographical limits and standardize field names.
  anomaly_va <-
    read_rds(file = paste0(path_argo_preprocessed, "/temp_anomaly_va.rds")) %>%
    filter (lat >= opt_lat_min &
            lat <= opt_lat_max &
            lon >= opt_lon_min &
            lon <= opt_lon_max) %>%
    select(file_id,
           date,
           year,
           month,
           lat,
           lon,
           profile_range,
           depth, 
           prof_measure = temp,
           clim_measure = clim_temp,
           anomaly
           )
  
  # ---------------------------------------------------------------------------------------------
  # read data extreme data for later use
  # ---------------------------------------------------------------------------------------------
  # load previously created OceanSODA extreme data. date, position and nature of extreme
  if (opt_extreme_determination == 1){
    extreme_data <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_global_SST_anomaly_field_01.rds")) %>%
      select(lon, lat, date, extreme_flag = temp_extreme)
  } else if (opt_extreme_determination == 2){
    extreme_data <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_global_SST_anomaly_field_02.rds")) %>%
      select(lon, lat, date, extreme_flag = temp_extreme)
  }

  # ---------------------------------------------------------------------------------------------
  # Associated data restrictions and formatting
  # ---------------------------------------------------------------------------------------------
  # What is the max depth of each profile_range
  opt_max_depth <- c(600, 1000, 1500)
  
  # opt_measure_label, opt_xlim and opt_xbreaks are associated with formatting
  opt_measure_label <- "temperature anomaly (°C)"
  opt_xlim <- c(-4.5, 4.5)
  opt_xbreaks <- c(-4, -2, 0, 2, 4)
  
  # adjusted to be in scale -1 to 1
  opt_measure_label_adjusted <- "adjusted temperature anomaly"
  opt_xlim_adjusted <- c(-1, 1)
  opt_xbreaks_adjusted <- c(-1.0, -0.5, 0, 0.5, 1.0)

  # oxygen formatting
  opt_doxy_measure_label <- expression("dissolved oxygen anomaly ( µmol kg"^"-1"~")")

  # pH formatting
  opt_ph_h_plus_measure_label <- expression("[H]"^"+" ~ "anomaly")

  # Under extreme analysis
  opt_extreme_analysis <- TRUE
  
}

Data preparation

# select profile based on profile_range and the appropriate max depth
anomaly_va <- anomaly_va %>% 
  filter(profile_range == opt_profile_range & depth <= opt_max_depth[opt_profile_range])

# Simplified table ready to pivot
anomaly_va_id <- anomaly_va %>%
  select(file_id,
         depth,
         anomaly,
         year, 
         month, 
         lat, 
         lon)

# wide table with each depth becoming a column
anomaly_va_wide <- anomaly_va_id %>%
  select(file_id, depth, anomaly) %>%
  pivot_wider(names_from = depth, values_from = anomaly)

# Drop any rows with missing values N/A caused by gaps in climatology data
anomaly_va_wide <- anomaly_va_wide %>% 
  drop_na()

# Table for cluster analysis
points <- anomaly_va_wide %>%
  column_to_rownames(var = "file_id")

# normalisation?
if (opt_norm_anomaly) {
  
  # Get the maximum anomaly for each profile - the normalisation will then fit -1 to 1
  anomaly_va_id_normalised <- anomaly_va_id %>%
    group_by(file_id) %>%
    mutate(abs_ma = max(abs(anomaly))) %>%
    ungroup()
  
  # divide each anomaly by the maximum anomaly
  anomaly_va_id_normalised <- anomaly_va_id_normalised %>%
    mutate(anomaly = anomaly/abs_ma)
    
  # wide table with each depth becoming a column
  anomaly_va_wide <- anomaly_va_id_normalised %>%
    select(file_id, depth, anomaly) %>%
    pivot_wider(names_from = depth, values_from = anomaly)
  
  # Drop any rows with missing values N/A caused by gaps in climatology data
  anomaly_va_wide <- anomaly_va_wide %>% 
    drop_na()
  
  # Table for cluster analysis
  points_normalised <- anomaly_va_wide %>%
    column_to_rownames(var = "file_id")

}

Cluster analysis

Cluster means

Based on all floats regardless of surface condition.

for (iType in 1:2) {
  for (inum_clusters in opt_num_clusters_min[opt_profile_range]:opt_num_clusters_max[opt_profile_range]) {
    if (iType == 1) {

      set.seed(1)
      kclusts <-
        tibble(k = inum_clusters) %>%
        mutate(kclust = map(k, ~ kmeans(points, .x, iter.max = opt_max_iterations, nstart = opt_n_start)),
          tidied = map(kclust, tidy),
          glanced = map(kclust, glance),
          augmented = map(kclust, augment, points)
        )
      
      profile_id <-
        kclusts %>%
        unnest(cols = c(augmented)) %>%
        select(file_id = .rownames,
               cluster = .cluster) %>%
        mutate(file_id = as.numeric(file_id),
               cluster = as.character(cluster))
      
      # Add cluster to anomaly_va_id
      anomaly_cluster <-
        full_join(anomaly_va_id, profile_id)
      
      # Add profile_type field
      anomaly_cluster <- anomaly_cluster %>%
        mutate(profile_type = 'base')
      
      # Check null clusters
      anomaly_cluster <- anomaly_cluster %>%
        filter(!is.na(cluster))
      
      # Create table to be used for later analysis and Set the number of clusters field
      if (!exists('anomaly_cluster_all')) {
        anomaly_cluster_all <- anomaly_cluster %>%
          mutate(num_clusters = inum_clusters)
      } else {
        anomaly_cluster_all <-
          rbind(
            anomaly_cluster_all,
            anomaly_cluster %>%
              mutate(num_clusters = inum_clusters)
          )
      }
      
    } else if (iType == 2 & opt_norm_anomaly) {

      set.seed(1)
      kclusts <-
        tibble(k = inum_clusters) %>%
        mutate(kclust = map(k, ~ kmeans(points_normalised, .x, iter.max = opt_max_iterations, nstart = opt_n_start)),
          tidied = map(kclust, tidy),
          glanced = map(kclust, glance),
          augmented = map(kclust, augment, points)
        )
      
      profile_id <-
        kclusts %>%
        unnest(cols = c(augmented)) %>%
        select(file_id = .rownames,
               cluster = .cluster) %>%
        mutate(file_id = as.numeric(file_id),
               cluster = as.character(cluster))
      
      # Add cluster to anomaly_va
      anomaly_cluster_norm <-
        full_join(anomaly_va_id_normalised %>% select(-c(abs_ma)) ,
                  profile_id)
      
      # Add profile_type field
      anomaly_cluster_norm <- anomaly_cluster_norm %>%
        mutate(profile_type = 'adjusted')
      
      # Check null clusters
      anomaly_cluster_norm <- anomaly_cluster_norm %>%
        filter(!is.na(cluster))
      
      # Create table to be used for later analysis and Set the number of clusters field
      if (!exists('anomaly_cluster_all')) {
        anomaly_cluster_all <- anomaly_cluster_norm %>%
          mutate(num_clusters = inum_clusters)
      } else {
        anomaly_cluster_all <-
          rbind(
            anomaly_cluster_all,
            anomaly_cluster_norm %>%
              mutate(num_clusters = inum_clusters)
          )
      }
      
    }
    
  }
}

# Prepare to plot cluster mean
anomaly_cluster_mean <- anomaly_cluster_all %>%
  group_by(profile_type, num_clusters, cluster, depth) %>%
  summarise(
    count_cluster = n(),
    anomaly_mean = mean(anomaly, na.rm = TRUE),
    anomaly_sd = sd(anomaly, na.rm = TRUE)
  ) %>%
  ungroup()

anomaly_cluster_mean_year <- anomaly_cluster_all %>%
  group_by(profile_type, num_clusters, cluster, depth, year) %>%
  summarise(
    count_cluster = n(),
    anomaly_mean = mean(anomaly, na.rm = TRUE),
    anomaly_sd = sd(anomaly, na.rm = TRUE)
  ) %>%
  ungroup()

anomaly_year_mean <- anomaly_cluster_all %>%
  group_by(profile_type, num_clusters, cluster, year) %>%
  summarise(
    count_cluster = n(),
    anomaly_mean = mean(anomaly, na.rm = TRUE),
    anomaly_sd = sd(anomaly, na.rm = TRUE)
  ) %>%
  ungroup()

anomaly_year_mean <- anomaly_year_mean %>%
  group_by(profile_type, num_clusters, year) %>%
  summarise(anomaly_mean = mean(anomaly_mean, na.rm = TRUE)) %>%
  ungroup ()

# Determine profile count by cluster and year
# Count the measurements
cluster_by_year <- anomaly_cluster_all %>% 
  count(profile_type, num_clusters, file_id, cluster, year,
        name = "count_cluster")

# Convert to profiles
cluster_by_year <- cluster_by_year %>% 
  count(profile_type, num_clusters, cluster, year,
        name = "count_cluster")

# total of each type of cluster
cluster_count <- cluster_by_year %>%
  group_by(profile_type, num_clusters, cluster) %>% 
  summarise(count_profiles = sum(count_cluster)) %>%
  ungroup()

anomaly_cluster_mean <- left_join(anomaly_cluster_mean, cluster_count)

Base profiles

# create figure of cluster mean profiles
anomaly_cluster_mean %>%
  filter (profile_type == "base") %>%
  group_split(profile_type, num_clusters) %>%
  map(
    ~ ggplot(data = .x,) +
      geom_path(aes(x = anomaly_mean,
                    y = depth)) +
      geom_ribbon(
        aes(
          xmax = anomaly_mean + anomaly_sd,
          xmin = anomaly_mean - anomaly_sd,
          y = depth
        ),
        alpha = 0.2
      ) +
      geom_vline(xintercept = 0) +
      scale_y_reverse() +
      facet_wrap(~ paste0(cluster, " (", formatC(count_profiles, big.mark=",") , ")")) +
      coord_cartesian(xlim = opt_xlim) +
      scale_x_continuous(breaks = opt_xbreaks) +
      labs(
        title = paste0(
          'Overall mean anomaly profiles by cluster \n',
          'type = ', unique(.x$profile_type), ', ', 
          'num clusters = ', unique(.x$num_clusters)
        ),
        x = opt_measure_label,
        y = 'depth (m)'
      )
  )

[[1]]

Past versions of cluster_analysis_means-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19

[[2]]

Past versions of cluster_analysis_means-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19

Adjusted profiles

if (opt_norm_anomaly) {

  # repeat for adjusted profiles profiles
  anomaly_cluster_mean %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters) %>%
    map(
      ~ ggplot(data = .x,) +
        geom_path(aes(x = anomaly_mean,
                      y = depth)) +
        geom_ribbon(
          aes(
            xmax = anomaly_mean + anomaly_sd,
            xmin = anomaly_mean - anomaly_sd,
            y = depth
          ),
          alpha = 0.2
        ) +
        geom_vline(xintercept = 0) +
        scale_y_reverse() +
        facet_wrap(~ paste0(cluster, " (", formatC(count_profiles, big.mark=",") , ")")) +
        coord_cartesian(xlim = opt_xlim_adjusted) +
        scale_x_continuous(breaks = opt_xbreaks_adjusted) +
        labs(
          title = paste0(
            'Overall mean anomaly profiles by cluster \n',
            'type = ', unique(.x$profile_type), ', ', 
            'num clusters = ', unique(.x$num_clusters)
          ),
          x = opt_measure_label_adjusted,
          y = 'depth (m)'
        )
    )
}

[[1]]

Past versions of cluster_analysis_means_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19

[[2]]

Past versions of cluster_analysis_means_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19

Cluster mean by year

# cluster means by year
anomaly_cluster_mean_year %>%
  filter (profile_type == "base") %>%
  mutate(year = as.factor(year)) %>%
  group_split(profile_type, num_clusters) %>%
  map(
    ~ ggplot(data = .x, ) +
      geom_path(aes(
        x = anomaly_mean,
        y = depth,
        col = year
      )) +
      geom_vline(xintercept = 0) +
      scale_y_reverse() +
      facet_wrap(~ cluster) +
      coord_cartesian(xlim = opt_xlim) +
      scale_x_continuous(breaks = opt_xbreaks) +
      scale_color_viridis_d() +
      labs(
        title = paste0(
          'Overall mean anomaly profiles by cluster \n',
          'type = ', unique(.x$profile_type), ', ', 
          'num clusters = ', unique(.x$num_clusters)
        ),
        x = opt_measure_label,
        y = 'depth (m)'
      )
  )

[[1]]

Past versions of cluster_mean_year-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
cd75bb7	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[2]]

Past versions of cluster_mean_year-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
cd75bb7	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

Adjusted profiles

if (opt_norm_anomaly) {
  
  # Repeat for adjusted profiles
  anomaly_cluster_mean_year %>%
    filter (profile_type == "adjusted") %>%
    mutate(year = as.factor(year)) %>%
    group_split(profile_type, num_clusters) %>%
    map(
      ~ ggplot(data = .x, ) +
        geom_path(aes(
          x = anomaly_mean,
          y = depth,
          col = year
        )) +
        geom_vline(xintercept = 0) +
        scale_y_reverse() +
        facet_wrap(~ cluster) +
        coord_cartesian(xlim = opt_xlim_adjusted) +
        scale_x_continuous(breaks = opt_xbreaks_adjusted) +
        scale_color_viridis_d() +
        labs(
          title = paste0(
            'Overall mean anomaly profiles by cluster \n',
            'type = ', unique(.x$profile_type), ', ', 
            'num clusters = ', unique(.x$num_clusters)
          ),
          x = opt_measure_label_adjusted,
          y = 'depth (m)'
        )
    )
  
}

[[1]]

Past versions of cluster_mean_year_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[2]]

Past versions of cluster_mean_year_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

Cluster by year

count of each cluster by year

year_min <- min(cluster_by_year$year)
year_max <- max(cluster_by_year$year)

# create figure
cluster_by_year %>%
  filter (profile_type == "base") %>%
  group_split(profile_type, num_clusters) %>%
  map(
    ~ ggplot(data = .x, aes(
        x = year,
        y = count_cluster,
        col = cluster,
        group = cluster
      )) +
      geom_point() +
      geom_line() +
      scale_x_continuous(breaks = seq(year_min, year_max, 2)) +
      scale_color_brewer(palette = 'Dark2') +
      labs(
        title = paste0(
          'Count of profiles by year and cluster \n',
          'type = ', unique(.x$profile_type), ', ', 
          'num clusters = ', unique(.x$num_clusters)
        ),
        x = 'year',
        y = 'number of profiles',
        col = 'cluster'
      )
  )

[[1]]

Past versions of cluster_by_year-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
80c16c2	ds2n19	2023-11-15

[[2]]

Past versions of cluster_by_year-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

Adjusted profiles

if (opt_norm_anomaly) {

  year_min <- min(cluster_by_year$year)
  year_max <- max(cluster_by_year$year)
  
  # create figure
  cluster_by_year %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters) %>%
    map(
      ~ ggplot(data = .x, aes(
          x = year,
          y = count_cluster,
          col = cluster,
          group = cluster
        )) +
        geom_point() +
        geom_line() +
        scale_x_continuous(breaks = seq(year_min, year_max, 2)) +
        scale_color_brewer(palette = 'Dark2') +
        labs(
          title = paste0(
            'Count of profiles by year and cluster \n',
            'type = ', unique(.x$profile_type), ', ', 
            'num clusters = ', unique(.x$num_clusters)
          ),
          x = 'year',
          y = 'number of profiles',
          col = 'cluster'
        )
    )
  
}

[[1]]

Past versions of cluster_by_year_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[2]]

Past versions of cluster_by_year_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

Cluster by month

count of each cluster by month of year

# Determine profile count by cluster and year
# Count the measurements
cluster_by_year <- anomaly_cluster_all %>% 
  count(profile_type, num_clusters, file_id, cluster, month,
        name = "count_cluster")

# Convert to profiles
cluster_by_year <- cluster_by_year %>% 
  count(profile_type, num_clusters, cluster, month,
        name = "count_cluster")

# create figure
cluster_by_year %>%
  filter (profile_type == "base") %>%
  group_split(profile_type, num_clusters) %>%
  map(
    ~ ggplot(
      data = .x,
      aes(
        x = month,
        y = count_cluster,
        col = cluster,
        group = cluster
      )
    ) +
      geom_point() +
      geom_line() +
      scale_x_continuous(breaks = seq(1, 12, 2)) +
      scale_color_brewer(palette = 'Dark2') +
      labs(
        title = paste0(
          'Count of profiles by month and cluster \n',
          'type = ', unique(.x$profile_type), ', ', 
          'num clusters = ', unique(.x$num_clusters)
        ),
        x = 'month',
        y = 'number of profiles',
        col = 'cluster'
      )
  )

[[1]]

Past versions of cluster_by_month-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
80c16c2	ds2n19	2023-11-15

[[2]]

Past versions of cluster_by_month-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

Adjusted profiles

if (opt_norm_anomaly) {

  # create figure
  cluster_by_year %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters) %>%
    map(
      ~ ggplot(
        data = .x,
        aes(
          x = month,
          y = count_cluster,
          col = cluster,
          group = cluster
        )
      ) +
        geom_point() +
        geom_line() +
        scale_x_continuous(breaks = seq(1, 12, 2)) +
        scale_color_brewer(palette = 'Dark2') +
        labs(
          title = paste0(
            'Count of profiles by month and cluster \n',
            'type = ', unique(.x$profile_type), ', ', 
            'num clusters = ', unique(.x$num_clusters)
          ),
          x = 'month',
          y = 'number of profiles',
          col = 'cluster'
        )
    )
  
}

[[1]]

Past versions of cluster_by_month_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[2]]

Past versions of cluster_by_month_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

Cluster spatial

location of each cluster on map, spatial analysis

# create figure
anomaly_cluster_all %>%
  filter (profile_type == "base") %>%
  group_split(profile_type, num_clusters) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(
                  x = lon,
                  y = lat,
                  fill = cluster
                )) +
      lims(y = opt_map_lat_limit) +
      scale_fill_brewer(palette = 'Dark2') +
      labs(
        title = paste0(
          'cluster spatial distribution \n',
          'type = ', unique(.x$profile_type), ', ', 
          'num clusters = ', unique(.x$num_clusters)
        ),
      )
  )

[[1]]

Past versions of spatial_cluster_01-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[2]]

Past versions of spatial_cluster_01-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

Adjusted profiles

if (opt_norm_anomaly) {

  # create figure
  anomaly_cluster_all %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters) %>%
    map(
      ~ map +
        geom_tile(data = .x,
                  aes(
                    x = lon,
                    y = lat,
                    fill = cluster
                  )) +
        lims(y = opt_map_lat_limit) +
        scale_fill_brewer(palette = 'Dark2') +
        labs(
          title = paste0(
            'cluster spatial distribution \n',
            'type = ', unique(.x$profile_type), ', ', 
            'num clusters = ', unique(.x$num_clusters)
          ),
        )
    )
  
}

[[1]]

Past versions of spatial_cluster_01_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[2]]

Past versions of spatial_cluster_01_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

Cluster spatial counts

count of measurements for each cluster on separate maps, spatial analysis

# Count profiles    
cluster_by_location <- anomaly_cluster_all %>%
  count(profile_type, num_clusters, file_id, lat, lon, cluster,
        name = "count_cluster")

# # Add cluster counts to 
cluster_by_location <- left_join(cluster_by_location, cluster_count)
    
# create figure
cluster_by_location %>%
  filter (profile_type == "base") %>%
  group_split(profile_type, num_clusters) %>%
  map(
    ~ map +
      geom_tile(data = .x %>%
                  count(lat, lon, cluster, count_profiles),
                aes(
                  x = lon,
                  y = lat,
                  fill = n
                )) +
      lims(y = opt_map_lat_limit) +
      scale_fill_gradient(low = "blue",
                          high = "red",
                          trans = "log10") +
      facet_wrap(~ paste0(cluster, " (", formatC(count_profiles, big.mark=",") , ")"), ncol = 2) +
      labs(
        title = paste0(
          'cluster spatial distribution \n',
          'type = ', unique(.x$profile_type), ', ', 
          'num clusters = ', unique(.x$num_clusters)
        )
      )
  )

[[1]]

Past versions of spatial_cluster_03-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[2]]

Past versions of spatial_cluster_03-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

Adjusted profiles

if (opt_norm_anomaly) {

  # create figure
  cluster_by_location %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters) %>%
    map(
      ~ map +
        geom_tile(data = .x %>%
                    count(lat, lon, cluster, count_profiles),
                  aes(
                    x = lon,
                    y = lat,
                    fill = n
                  )) +
        lims(y = opt_map_lat_limit) +
        scale_fill_gradient(low = "blue",
                            high = "red",
                            trans = "log10") +
        facet_wrap(~ paste0(cluster, " (", formatC(count_profiles, big.mark=",") , ")"), ncol = 2) +
        labs(
          title = paste0(
            'cluster spatial distribution \n',
            'type = ', unique(.x$profile_type), ', ', 
            'num clusters = ', unique(.x$num_clusters)
          )
        )
    )
  
}

[[1]]

Past versions of spatial_cluster_03_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[2]]

Past versions of spatial_cluster_03_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

Overlay oxygen anomaly

for each cluster identified show with second attribute overlayed

# Read doxy data
overlay_va <- read_rds(file = paste0(path_argo_preprocessed, "/doxy_anomaly_va.rds")) %>%
  select (file_id,
          date,
          year,
          month,
          lat,
          lon,
          profile_range,
          measure_depth = depth,
          measure = anomaly)

if (opt_category == "bgc_ph_ph"){
  overlay_factor <- 250
  overlay_offset <- 0
  overlay_factor_adjusted <- 20
  overlay_offset_adjusted <- 0
} else if (opt_category == "bgc_ph_h_plus"){
  overlay_factor <- 1e10
  overlay_offset <- 0
  overlay_factor_adjusted <- 20
  overlay_offset_adjusted <- 0
} else if (opt_category == "bgc_temp" | opt_category == "bgc_temp_SO"){
  overlay_factor <- 5
  overlay_offset <- 0
  overlay_factor_adjusted <- 20
  overlay_offset_adjusted <- 0
}

overlay_color <- "#5B9BD5"
opt_overlay_measure_label <- opt_doxy_measure_label
  
# link overlay data to cluster details
overlay_cluster <-
  full_join(
    overlay_va,
    anomaly_cluster_all %>% distinct (file_id, cluster, profile_type, num_clusters)
  ) %>% filter(!is.na(cluster))

# summarise by cluster
overlay_cluster_mean <- overlay_cluster %>%
  group_by(cluster, profile_type, num_clusters, measure_depth) %>%
  summarise(
    measure_mean = mean(measure, na.rm = TRUE),
    measure_sd = sd(measure, na.rm = TRUE)
  ) %>%
  ungroup() %>%
  select(
    cluster,
    profile_type,
    num_clusters,
    measure_depth,
    measure_mean,
    measure_sd
  )

# join base anomaly with overlay
cluster_base_overlay <- full_join(anomaly_cluster_mean, overlay_cluster_mean)

# Add the cluster count information
cluster_base_overlay <- left_join(cluster_base_overlay, cluster_count)

Base profiles

  cluster_base_overlay %>% 
    filter (profile_type == "base") %>%
    group_split(profile_type, num_clusters) %>%
    map(
    ~ ggplot(data = .x,) +
      geom_path(aes(x = anomaly_mean,
                    y = depth)) +
      geom_ribbon(aes(
        xmax = anomaly_mean + anomaly_sd,
        xmin = anomaly_mean - anomaly_sd,
        y = depth
      ),
      alpha = 0.2) +
      geom_path(aes(
        x = (measure_mean - overlay_offset) / overlay_factor,
        y = measure_depth
      ), color = overlay_color) +
      geom_ribbon(
        aes(
          xmax = (measure_mean + measure_sd - overlay_offset) / overlay_factor,
          xmin = (measure_mean - measure_sd - overlay_offset) / overlay_factor,
          y = measure_depth
        ),
        fill = overlay_color,
        alpha = 0.2
      ) +
      geom_vline(xintercept = 0) +
      # scale_y_continuous(trans = trans_reverser("sqrt"),
      #                    breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
      scale_y_reverse() +
      facet_wrap(~ paste0(cluster, " (", formatC(count_profiles, big.mark=",") , ")"),
                  strip.position = "right") +
      coord_cartesian(xlim = opt_xlim) +
      scale_x_continuous(
        # First axis
        name = opt_measure_label,
        breaks = opt_xbreaks,
        # Second axis
        sec.axis = sec_axis(
          trans =  ~ . * overlay_factor + overlay_offset,
          name = opt_overlay_measure_label
        )
      ) +
      labs(
        title = paste0(
          'Overall mean anomaly profiles by cluster \n',
          'type = ', unique(.x$profile_type), ', ', 
          'num clusters = ', unique(.x$num_clusters)
        ),
        x = opt_measure_label,
        y = 'depth (m)'
      ) +
      theme(axis.title.x.top = element_text(color = overlay_color),
            axis.text.x.top = element_text(color = overlay_color))
    )

[[1]]

Past versions of overlay_doxy_display-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
db7353e	ds2n19	2023-12-19

[[2]]

Past versions of overlay_doxy_display-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
db7353e	ds2n19	2023-12-19

Adjusted profiles

  cluster_base_overlay %>% 
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters) %>%
    map(
    ~ ggplot(data = .x,) +
      geom_path(aes(x = anomaly_mean,
                    y = depth)) +
      geom_ribbon(aes(
        xmax = anomaly_mean + anomaly_sd,
        xmin = anomaly_mean - anomaly_sd,
        y = depth
      ),
      alpha = 0.2) +
      geom_path(aes(
        x = (measure_mean - overlay_offset_adjusted) / overlay_factor_adjusted,
        y = measure_depth
      ), color = overlay_color) +
      geom_ribbon(
        aes(
          xmax = (measure_mean + measure_sd - overlay_offset_adjusted) / overlay_factor_adjusted,
          xmin = (measure_mean - measure_sd - overlay_offset_adjusted) / overlay_factor_adjusted,
          y = measure_depth
        ),
        fill = overlay_color,
        alpha = 0.2
      ) +
      geom_vline(xintercept = 0) +
      # scale_y_continuous(trans = trans_reverser("sqrt"),
      #                    breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
      scale_y_reverse() +
      facet_wrap(~ paste0(cluster, " (", formatC(count_profiles, big.mark=",") , ")"),
                  strip.position = "right") +
      coord_cartesian(xlim = opt_xlim_adjusted) +
      scale_x_continuous(
        # First axis
        name = opt_measure_label_adjusted,
        breaks = opt_xbreaks_adjusted,
        # Second axis
        sec.axis = sec_axis(
          trans =  ~ . * overlay_factor_adjusted + overlay_offset_adjusted,
          name = opt_overlay_measure_label
        )
      ) +
      labs(
        title = paste0(
          'Overall mean anomaly profiles by cluster \n',
          'type = ', unique(.x$profile_type), ', ', 
          'num clusters = ', unique(.x$num_clusters)
        ),
        x = opt_measure_label_adjusted,
        y = 'depth (m)'
      ) +
      theme(axis.title.x.top = element_text(color = overlay_color),
            axis.text.x.top = element_text(color = overlay_color))
    )

[[1]]

Past versions of overlay_doxy_display_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
db7353e	ds2n19	2023-12-19

[[2]]

Past versions of overlay_doxy_display_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
db7353e	ds2n19	2023-12-19

Cluster by surface Extreme

if (opt_extreme_analysis){
  
# date to match to ocean SODA
anomaly_va <- anomaly_va %>% 
  mutate(date = ymd(format(date, "%Y-%m-15")))

# Add the OceanSODA extreme condition
anomaly_va <- left_join(anomaly_va, extreme_data)

# If extreme is NA set it to N
anomaly_va <- anomaly_va %>% replace_na(list(extreme_flag = 'N'))
anomaly_va <- anomaly_va %>% mutate (profile_type = 'base')

# Create a replica data set with profile_type = adjusted 
if (opt_norm_anomaly){
  
  # mark as adjusted
  anomaly_va_norm <- anomaly_va %>% mutate (profile_type = 'adjusted')

  # Get the maximum anomaly for each profile - the normalisation will then fit -1 to 1
  anomaly_va_norm <- anomaly_va_norm %>%
    group_by(file_id) %>%
    mutate(abs_ma = max(abs(anomaly))) %>%
    ungroup()

  # Carry out the adjustment
  anomaly_va_norm <- anomaly_va_norm %>%
    mutate(anomaly = anomaly/abs_ma)
  
  #remove the surface anomaly field
  anomaly_va_norm <- anomaly_va_norm %>% select(-c(abs_ma))
  
  # Append to base profiles
  anomaly_va <- rbind(anomaly_va, anomaly_va_norm)
  
}

profile_types <- c('adjusted', 'base')

# loop through profile_type
for (iprofile_type in 1:2) {
  
  sel_profile_type = profile_types[iprofile_type]
    
  # loop through surface condition
  for (i in 1:3) {

    # ---------------------------------------------------------------------------------------------
    # Preparation
    # ---------------------------------------------------------------------------------------------
    # select profile based on profile_range and he appropriate max depth
    anomaly_va_id <- anomaly_va %>%
      filter(profile_range == opt_profile_range & depth <= opt_max_depth[opt_profile_range] & extreme_flag == extreme_type[i] & profile_type == sel_profile_type)

    # Simplified table ready to pivot
    anomaly_va_id <- anomaly_va_id %>%
      select(file_id,
             depth,
             anomaly,
             year, 
             month, 
             lat, 
             lon)
    
    # wide table with each depth becoming a column
    anomaly_va_wide <- anomaly_va_id %>%
      select(file_id, depth, anomaly) %>%
      pivot_wider(names_from = depth, values_from = anomaly)
    
    # Drop any rows with missing values N/A caused by gaps in climatology data
    anomaly_va_wide <- anomaly_va_wide %>% 
      drop_na()
    
    # Table for cluster analysis
    points <- anomaly_va_wide %>%
      column_to_rownames(var = "file_id")
    
    # ---------------------------------------------------------------------------------------------
    # cluster analysis
    # ---------------------------------------------------------------------------------------------
    # loop through number of clusters
    for (inum_clusters in opt_num_clusters_ext_min[i]:opt_num_clusters_ext_max[i]) {    

      set.seed(1)
      kclusts <-
        tibble(k = inum_clusters) %>%
        mutate(
          kclust = map(k, ~ kmeans(points, .x, iter.max = opt_max_iterations, nstart = opt_n_start)),
          tidied = map(kclust, tidy),
          glanced = map(kclust, glance),
          augmented = map(kclust, augment, points)
        )
      
      profile_id <-
        kclusts %>%
        unnest(cols = c(augmented)) %>%
        select(file_id = .rownames,
               cluster = .cluster) %>% 
        mutate(file_id = as.numeric(file_id),
               cluster = as.character(cluster))
      
      # Add cluster to anomaly_va
      anomaly_cluster <- full_join(anomaly_va_id, profile_id)
      
      # Plot cluster mean
      anomaly_cluster <- anomaly_cluster %>% 
        filter(!is.na(cluster))
      
      # cluster mean
      anomaly_cluster_mean <- anomaly_cluster %>%
        group_by(cluster, depth) %>%
        summarise(
          count_cluster = n(),
          anomaly_mean = mean(anomaly, na.rm = TRUE),
          anomaly_sd = sd(anomaly, na.rm = TRUE)
        ) %>%
        ungroup()
      
      anomaly_cluster_mean_year <- anomaly_cluster %>%
        group_by(cluster, depth, year) %>%
        summarise(
          count_cluster = n(),
          anomaly_mean = mean(anomaly, na.rm = TRUE),
          anomaly_sd = sd(anomaly, na.rm = TRUE)
        ) %>%
        ungroup()
      
      anomaly_year_mean <- anomaly_cluster %>%
        group_by(cluster, year) %>%
        summarise(
          count_cluster = n(),
          anomaly_mean = mean(anomaly, na.rm = TRUE),
          anomaly_sd = sd(anomaly, na.rm = TRUE)
        ) %>%
        ungroup()
      
      anomaly_year_mean <- anomaly_year_mean %>%
        group_by(year) %>%
        summarise(anomaly_mean = mean(anomaly_mean, na.rm = TRUE)) %>%
        ungroup ()
      
      if (!exists('anomaly_cluster_mean_ext')) {
        anomaly_cluster_mean_ext <-
          anomaly_cluster_mean %>% mutate(
            extreme_order = i,
            extreme = extreme_type[i],
            num_clusters = inum_clusters,
            profile_type = sel_profile_type
          )
        anomaly_cluster_mean_year_ext <-
          anomaly_cluster_mean_year %>% mutate(
            extreme_order = i,
            extreme = extreme_type[i],
            num_clusters = inum_clusters,
            profile_type = sel_profile_type
          )
        anomaly_year_mean_ext <-
          anomaly_year_mean %>% mutate(
            extreme_order = i,
            extreme = extreme_type[i],
            num_clusters = inum_clusters,
            profile_type = sel_profile_type
          )
        anomaly_cluster_ext <-
          anomaly_cluster %>% mutate(
            extreme_order = i,
            extreme = extreme_type[i],
            num_clusters = inum_clusters,
            profile_type = sel_profile_type
          )
      } else {
        anomaly_cluster_mean_ext <-
          rbind(
            anomaly_cluster_mean_ext,
            anomaly_cluster_mean %>% mutate(
              extreme_order = i,
              extreme = extreme_type[i],
              num_clusters = inum_clusters,
              profile_type = sel_profile_type
            )
          )
        anomaly_cluster_mean_year_ext <-
          rbind(
            anomaly_cluster_mean_year_ext,
            anomaly_cluster_mean_year %>% mutate(
              extreme_order = i,
              extreme = extreme_type[i],
              num_clusters = inum_clusters,
              profile_type = sel_profile_type
            )
          )
        anomaly_year_mean_ext <-
          rbind(
            anomaly_year_mean_ext,
            anomaly_year_mean %>% mutate(
              extreme_order = i,
              extreme = extreme_type[i],
              num_clusters = inum_clusters,
              profile_type = sel_profile_type
            )
          )
        anomaly_cluster_ext <-
          rbind(
            anomaly_cluster_ext,
            anomaly_cluster_ext <-
              anomaly_cluster %>% mutate(
                extreme_order = i,
                extreme = extreme_type[i],
                num_clusters = inum_clusters,
                profile_type = sel_profile_type
              )
          )
      }
  
    }
    
  }
  
}
}

Cluster means

if (opt_extreme_analysis){

# Determine profile count by cluster and year
# Count the measurements
cluster_by_year <- anomaly_cluster_ext %>% 
  count(profile_type, num_clusters, extreme, extreme_order, file_id, cluster, year,
        name = "count_cluster")

# Convert to profiles
cluster_by_year <- cluster_by_year %>% 
  count(profile_type, num_clusters, extreme, extreme_order, cluster, year,
        name = "count_cluster")

# total of each type of cluster
cluster_count <- cluster_by_year %>%
  group_by(profile_type, num_clusters, extreme, extreme_order, cluster) %>% 
  summarise(count_profiles = sum(count_cluster)) %>%
  ungroup()

anomaly_cluster_mean_ext <- left_join(anomaly_cluster_mean_ext, cluster_count)
  
# create figure of cluster mean profiles
anomaly_cluster_mean_ext %>%
  filter (profile_type == "base") %>%
  group_split(profile_type, num_clusters, extreme_order) %>%
  map(
    ~  ggplot(data = .x, ) +
      geom_path(aes(x = anomaly_mean,
                    y = depth)) +
      geom_ribbon(
        aes(
          xmax = anomaly_mean + anomaly_sd,
          xmin = anomaly_mean - anomaly_sd,
          y = depth
        ),
        alpha = 0.2
      ) +
      geom_vline(xintercept = 0) +
      scale_y_reverse() +
      #facet_wrap(~ cluster) +
      facet_wrap(~ paste0(cluster, " (", formatC(count_profiles, big.mark=",") , ")")) +
      coord_cartesian(xlim = opt_xlim) +
      scale_x_continuous(breaks = opt_xbreaks) +
      labs(
        title = paste0(
          'Overall mean anomaly profiles by cluster \n',
          'profile type: ', unique(.x$profile_type), ', ', 
          'surface extreme: ', unique(.x$extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        ),
        x = opt_measure_label,
        y = 'depth (m)'
      )
  )
}

[[1]]

Past versions of cluster_mean_ext-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[2]]

Past versions of cluster_mean_ext-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[3]]

Past versions of cluster_mean_ext-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[4]]

Past versions of cluster_mean_ext-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

[[5]]

Past versions of cluster_mean_ext-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

[[6]]

Past versions of cluster_mean_ext-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

Adjusted profiles

if (opt_extreme_analysis){

if (opt_norm_anomaly) {

  # create figure of cluster mean profiles
  anomaly_cluster_mean_ext %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters, extreme_order) %>%
    map(
      ~  ggplot(data = .x, ) +
        geom_path(aes(x = anomaly_mean,
                      y = depth)) +
        geom_ribbon(
          aes(
            xmax = anomaly_mean + anomaly_sd,
            xmin = anomaly_mean - anomaly_sd,
            y = depth
          ),
          alpha = 0.2
        ) +
        geom_vline(xintercept = 0) +
        scale_y_reverse() +
        #facet_wrap(~ cluster) +
        facet_wrap(~ paste0(cluster, " (", formatC(count_profiles, big.mark=",") , ")")) +
        coord_cartesian(xlim = opt_xlim_adjusted) +
        scale_x_continuous(breaks = opt_xbreaks_adjusted) +
        labs(
          title = paste0(
            'Overall mean anomaly profiles by cluster \n',
            'profile type: ', unique(.x$profile_type), ', ', 
            'surface extreme: ', unique(.x$extreme), ', ', 
            'number clusters: ', unique(.x$num_clusters)
          ),
          x = opt_measure_label_adjusted,
          y = 'depth (m)'
        )
    )

}
}

[[1]]

Past versions of cluster_mean_ext_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[2]]

Past versions of cluster_mean_ext_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[3]]

Past versions of cluster_mean_ext_adj-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[4]]

Past versions of cluster_mean_ext_adj-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[5]]

Past versions of cluster_mean_ext_adj-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[6]]

Past versions of cluster_mean_ext_adj-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

Clusters mean by year

if (opt_extreme_analysis){

# cluster means by year
anomaly_cluster_mean_year_ext %>%
  filter (profile_type == "base") %>%
  mutate(year = as.factor(year)) %>%
  group_split(profile_type, num_clusters, extreme_order) %>%
  map(
    ~  ggplot(data = .x,) +
      geom_path(aes(
        x = anomaly_mean,
        y = depth,
        col = year
      )) +
      geom_vline(xintercept = 0) +
      scale_y_reverse() +
      facet_wrap(~ cluster) +
      coord_cartesian(xlim = opt_xlim) +
      scale_x_continuous(breaks = opt_xbreaks) +
      scale_color_viridis_d() +
      labs(
        title = paste0(
          'Overall mean anomaly profiles by cluster by year \n',
          'profile type: ', unique(.x$profile_type), ', ', 
          'surface extreme: ', unique(.x$extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        ),
        x = opt_measure_label,
        y = 'depth (m)'
      )
  )
}

[[1]]

Past versions of cluster_mean_year_ext-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[2]]

Past versions of cluster_mean_year_ext-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[3]]

Past versions of cluster_mean_year_ext-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[4]]

Past versions of cluster_mean_year_ext-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

[[5]]

Past versions of cluster_mean_year_ext-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

[[6]]

Past versions of cluster_mean_year_ext-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

Adjusted profiles

if (opt_extreme_analysis){

if (opt_norm_anomaly) {

  # cluster means by year
  anomaly_cluster_mean_year_ext %>%
    filter (profile_type == "adjusted") %>%
    mutate(year = as.factor(year)) %>%
    group_split(profile_type, num_clusters, extreme_order) %>%
    map(
      ~  ggplot(data = .x,) +
        geom_path(aes(
          x = anomaly_mean,
          y = depth,
          col = year
        )) +
        geom_vline(xintercept = 0) +
        scale_y_reverse() +
        facet_wrap(~ cluster) +
        coord_cartesian(xlim = opt_xlim_adjusted) +
        scale_x_continuous(breaks = opt_xbreaks_adjusted) +
        scale_color_viridis_d() +
        labs(
          title = paste0(
            'Overall mean anomaly profiles by cluster by year \n',
            'profile type: ', unique(.x$profile_type), ', ', 
            'surface extreme: ', unique(.x$extreme), ', ', 
            'number clusters: ', unique(.x$num_clusters)
          ),
          x = opt_measure_label_adjusted,
          y = 'depth (m)'
        )
    )

}
}

[[1]]

Past versions of cluster_mean_year_ext_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[2]]

Past versions of cluster_mean_year_ext_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[3]]

Past versions of cluster_mean_year_ext_adj-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[4]]

Past versions of cluster_mean_year_ext_adj-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[5]]

Past versions of cluster_mean_year_ext_adj-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[6]]

Past versions of cluster_mean_year_ext_adj-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

Cluster by year

count of each cluster by year

if (opt_extreme_analysis){

# Determine profile count by extreme and cluster and year
# Count the measurements
cluster_by_year <- anomaly_cluster_ext %>% 
  count(file_id, profile_type, num_clusters, extreme_order, extreme, cluster, year,
        name = "count_cluster")
# Convert to profiles
cluster_by_year <- cluster_by_year %>% 
  count(profile_type, num_clusters, extreme_order, extreme, cluster, year,
        name = "count_cluster")

year_min <- min(cluster_by_year$year)
year_max <- max(cluster_by_year$year)

# create figure
cluster_by_year %>%
  filter (profile_type == "base") %>%
  group_split(profile_type, num_clusters, extreme_order) %>%
  map(
    ~ ggplot(
      data = .x,
      aes(
        x = year,
        y = count_cluster,
        col = cluster,
        group = cluster
      )
    ) +
      geom_point() +
      geom_line() +
      scale_x_continuous(breaks = seq(year_min, year_max, 2)) +
      scale_color_brewer(palette = 'Dark2') +
      labs(
        x = 'year',
        y = 'number of profiles',
        col = 'cluster',
        title = paste0(
          'Count of profiles by year and cluster \n',
          'profile type: ', unique(.x$profile_type), ', ', 
          'surface extreme: ', unique(.x$extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        )
      )
  )
}

[[1]]

Past versions of cluster_by_year_ext-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[2]]

Past versions of cluster_by_year_ext-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[3]]

Past versions of cluster_by_year_ext-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[4]]

Past versions of cluster_by_year_ext-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

[[5]]

Past versions of cluster_by_year_ext-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

[[6]]

Past versions of cluster_by_year_ext-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

Adjusted profiles

if (opt_extreme_analysis){

if (opt_norm_anomaly) {

  # create figure
  cluster_by_year %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters, extreme_order) %>%
    map(
      ~ ggplot(
        data = .x,
        aes(
          x = year,
          y = count_cluster,
          col = cluster,
          group = cluster
        )
      ) +
        geom_point() +
        geom_line() +
        scale_x_continuous(breaks = seq(year_min, year_max, 2)) +
        scale_color_brewer(palette = 'Dark2') +
        labs(
          x = 'year',
          y = 'number of profiles',
          col = 'cluster',
          title = paste0(
            'Count of profiles by year and cluster \n',
            'profile type: ', unique(.x$profile_type), ', ', 
            'surface extreme: ', unique(.x$extreme), ', ', 
            'number clusters: ', unique(.x$num_clusters)
          )
        )
    )
    
}
}

[[1]]

Past versions of cluster_by_year_ext_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[2]]

Past versions of cluster_by_year_ext_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[3]]

Past versions of cluster_by_year_ext_adj-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[4]]

Past versions of cluster_by_year_ext_adj-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[5]]

Past versions of cluster_by_year_ext_adj-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[6]]

Past versions of cluster_by_year_ext_adj-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

Cluster by month

count of each cluster by month of year

if (opt_extreme_analysis){

# Determine profile count by cluster and year
# Count the measurements
cluster_by_year <- anomaly_cluster_ext %>% 
  count(file_id, profile_type, num_clusters, extreme_order, extreme, cluster, month,
        name = "count_cluster")
# Convert to profiles
cluster_by_year <- cluster_by_year %>% 
  count(profile_type, num_clusters, extreme_order, extreme, cluster, month,
        name = "count_cluster")

# create figure
cluster_by_year %>%
  filter (profile_type == "base") %>%
  group_split(profile_type, num_clusters, extreme_order) %>%
  map(
    ~ ggplot(
      data = .x,
      aes(
        x = month,
        y = count_cluster,
        col = cluster,
        group = cluster
      )
    ) +
      geom_point() +
      geom_line() +
      scale_x_continuous(breaks = seq(1, 12, 2)) +
      scale_color_brewer(palette = 'Dark2') +
      labs(
        x = 'month',
        y = 'number of profiles',
        col = 'cluster',
        title = paste0(
          'Count of profiles by month and cluster \n',
          'profile type: ', unique(.x$profile_type), ', ', 
          'surface extreme: ', unique(.x$extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        )
      )
  )
}

[[1]]

Past versions of cluster_by_month_ext-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[2]]

Past versions of cluster_by_month_ext-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[3]]

Past versions of cluster_by_month_ext-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[4]]

Past versions of cluster_by_month_ext-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

[[5]]

Past versions of cluster_by_month_ext-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

[[6]]

Past versions of cluster_by_month_ext-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

Adjusted profiles

if (opt_extreme_analysis){

if (opt_norm_anomaly) {

  # create figure
  cluster_by_year %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters, extreme_order) %>%
    map(
      ~ ggplot(
        data = .x,
        aes(
          x = month,
          y = count_cluster,
          col = cluster,
          group = cluster
        )
      ) +
        geom_point() +
        geom_line() +
        scale_x_continuous(breaks = seq(1, 12, 2)) +
        scale_color_brewer(palette = 'Dark2') +
        labs(
          x = 'month',
          y = 'number of profiles',
          col = 'cluster',
          title = paste0(
            'Count of profiles by month and cluster \n',
            'profile type: ', unique(.x$profile_type), ', ', 
            'surface extreme: ', unique(.x$extreme), ', ', 
            'number clusters: ', unique(.x$num_clusters)
          )
        )
    )
  
}
}

[[1]]

Past versions of cluster_by_month_ext_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[2]]

Past versions of cluster_by_month_ext_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[3]]

Past versions of cluster_by_month_ext_adj-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[4]]

Past versions of cluster_by_month_ext_adj-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[5]]

Past versions of cluster_by_month_ext_adj-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[6]]

Past versions of cluster_by_month_ext_adj-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

Cluster spatial

location of each cluster on map, spatial analysis

if (opt_extreme_analysis){

# create figure combined
anomaly_cluster_ext %>%
  filter (profile_type == "base") %>%
  group_split(profile_type, num_clusters, extreme_order) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(
                  x = lon,
                  y = lat,
                  fill = cluster
                )) +
      lims(y = opt_map_lat_limit) +
      scale_fill_brewer(palette = 'Dark2') +
      labs(
        title = paste0(
          'cluster spatial distribution \n',
          'profile type: ', unique(.x$profile_type), ', ', 
          'surface extreme: ', unique(.x$extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        )
      )
  )
}

[[1]]

Past versions of spatial_cluster_combined_ext-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[2]]

Past versions of spatial_cluster_combined_ext-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[3]]

Past versions of spatial_cluster_combined_ext-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[4]]

Past versions of spatial_cluster_combined_ext-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

[[5]]

Past versions of spatial_cluster_combined_ext-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

[[6]]

Past versions of spatial_cluster_combined_ext-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

Adjusted profiles

if (opt_extreme_analysis){

if (opt_norm_anomaly) {

  # create figure combined
  anomaly_cluster_ext %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters, extreme_order) %>%
    map(
      ~ map +
        geom_tile(data = .x,
                  aes(
                    x = lon,
                    y = lat,
                    fill = cluster
                  )) +
        lims(y = opt_map_lat_limit) +
        scale_fill_brewer(palette = 'Dark2') +
        labs(
          title = paste0(
            'cluster spatial distribution \n',
            'profile type: ', unique(.x$profile_type), ', ', 
            'surface extreme: ', unique(.x$extreme), ', ', 
            'number clusters: ', unique(.x$num_clusters)
          )
        )
    )

}
}

[[1]]

Past versions of spatial_cluster_combined_ext_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[2]]

Past versions of spatial_cluster_combined_ext_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[3]]

Past versions of spatial_cluster_combined_ext_adj-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[4]]

Past versions of spatial_cluster_combined_ext_adj-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[5]]

Past versions of spatial_cluster_combined_ext_adj-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[6]]

Past versions of spatial_cluster_combined_ext_adj-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

Cluster spatial counts

location of each cluster on map, spatial analysis

if (opt_extreme_analysis){

# Count profiles    
cluster_by_location <- anomaly_cluster_ext %>%
  count(profile_type, num_clusters, extreme_order, extreme, file_id, lat, lon, cluster,
        name = "count_cluster")

# # Add cluster counts to 
cluster_by_location <- left_join(cluster_by_location, cluster_count)
    
# create figure
cluster_by_location %>%
  filter (profile_type == "base") %>%
  group_split(profile_type, num_clusters, extreme_order) %>%
  map(
    ~ map +
      geom_tile(data = .x %>%
                  count(lat, lon, cluster, count_profiles),
                aes(
                  x = lon,
                  y = lat,
                  fill = n
                )) +
      lims(y = opt_map_lat_limit) +
      scale_fill_gradient(low = "blue",
                          high = "red",
                          trans = "log10") +
    facet_wrap(~ paste0(cluster, " (", formatC(count_profiles, big.mark=",") , ")"), ncol = 2) +
      labs(
        title = paste0(
          'cluster spatial distribution \n',
          'profile type: ', unique(.x$profile_type), ', ', 
          'surface extreme: ', unique(.x$extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        )
      )
  )
}

[[1]]

Past versions of spatial_cluster_count_ext-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[2]]

Past versions of spatial_cluster_count_ext-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[3]]

Past versions of spatial_cluster_count_ext-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30
77cc54e	ds2n19	2023-11-24

[[4]]

Past versions of spatial_cluster_count_ext-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

[[5]]

Past versions of spatial_cluster_count_ext-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

[[6]]

Past versions of spatial_cluster_count_ext-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04
a6b9ac3	ds2n19	2023-12-04
bb6c7b2	ds2n19	2023-12-03
cfa1850	ds2n19	2023-12-03
9d9ac38	ds2n19	2023-12-01
ba42bd5	ds2n19	2023-11-30
1ad7f89	ds2n19	2023-11-30
98f3220	ds2n19	2023-11-30

Adjusted profiles

if (opt_extreme_analysis){

if (opt_norm_anomaly) {

  cluster_by_location %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters, extreme_order) %>%
    map(
      ~ map +
        geom_tile(data = .x %>%
                    count(lat, lon, cluster, count_profiles),
                  aes(
                    x = lon,
                    y = lat,
                    fill = n
                  )) +
        lims(y = opt_map_lat_limit) +
        scale_fill_gradient(low = "blue",
                            high = "red",
                            trans = "log10") +
        facet_wrap(~ paste0(cluster, " (", formatC(count_profiles, big.mark=",") , ")"), ncol = 2) +
        labs(
          title = paste0(
            'cluster spatial distribution \n',
            'profile type: ', unique(.x$profile_type), ', ', 
            'surface extreme: ', unique(.x$extreme), ', ', 
            'number clusters: ', unique(.x$num_clusters)
          )
        )
    )
    
}    
}

[[1]]

Past versions of spatial_cluster_count_ext_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[2]]

Past versions of spatial_cluster_count_ext_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[3]]

Past versions of spatial_cluster_count_ext_adj-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[4]]

Past versions of spatial_cluster_count_ext_adj-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[5]]

Past versions of spatial_cluster_count_ext_adj-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

[[6]]

Past versions of spatial_cluster_count_ext_adj-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
2a3d9d2	ds2n19	2023-12-19
2d8fb44	ds2n19	2023-12-07
89075e8	ds2n19	2023-12-07
087d6fe	ds2n19	2023-12-07
12e23ab	ds2n19	2023-12-04
31b8905	ds2n19	2023-12-04
2a330f3	ds2n19	2023-12-04

Overlay oxygen anomaly

for each cluster identified show with second attribute overlayed

# Read doxy data
overlay_va <- read_rds(file = paste0(path_argo_preprocessed, "/doxy_anomaly_va.rds")) %>%
  select (file_id,
          date,
          year,
          month,
          lat,
          lon,
          profile_range,
          measure_depth = depth,
          measure = anomaly)

if (opt_category == "bgc_ph_ph"){
  overlay_factor <- 250
  overlay_offset <- 0
  overlay_factor_adjusted <- 20
  overlay_offset_adjusted <- 0
} else if (opt_category == "bgc_ph_h_plus"){
  overlay_factor <- 1e10
  overlay_offset <- 0
  overlay_factor_adjusted <- 20
  overlay_offset_adjusted <- 0
} else if (opt_category == "bgc_temp" | opt_category == "bgc_temp_SO"){
  overlay_factor <- 5
  overlay_offset <- 0
  overlay_factor_adjusted <- 20
  overlay_offset_adjusted <- 0
}

overlay_color <- "#5B9BD5"
opt_overlay_measure_label <- opt_doxy_measure_label
  
# link overlay data to cluster details
overlay_cluster <-
  full_join(
    overlay_va,
    anomaly_cluster_ext %>% distinct (file_id, cluster, extreme_order, extreme, profile_type, num_clusters)
  ) %>% filter(!is.na(cluster))

# summarise by cluster
overlay_cluster_mean <- overlay_cluster %>%
  group_by(cluster, extreme_order, extreme, profile_type, num_clusters, measure_depth) %>%
  summarise(
    measure_mean = mean(measure, na.rm = TRUE),
    measure_sd = sd(measure, na.rm = TRUE)
  ) %>%
  ungroup() %>%
  select(
    cluster,
    extreme_order, 
    extreme,
    profile_type,
    num_clusters,
    measure_depth,
    measure_mean,
    measure_sd
  )

# join base anomaly with overlay
cluster_base_overlay <- full_join(anomaly_cluster_mean_ext, overlay_cluster_mean)

# Add the cluster count information
#cluster_base_overlay <- left_join(cluster_base_overlay, cluster_count)

Base profiles

  cluster_base_overlay %>% 
    filter (profile_type == "base") %>%
    group_split(profile_type, num_clusters, extreme_order) %>%
    map(
    ~ ggplot(data = .x,) +
      geom_path(aes(x = anomaly_mean,
                    y = depth)) +
      geom_ribbon(aes(
        xmax = anomaly_mean + anomaly_sd,
        xmin = anomaly_mean - anomaly_sd,
        y = depth
      ),
      alpha = 0.2) +
      geom_path(aes(
        x = (measure_mean - overlay_offset) / overlay_factor,
        y = measure_depth
      ), color = overlay_color) +
      geom_ribbon(
        aes(
          xmax = (measure_mean + measure_sd - overlay_offset) / overlay_factor,
          xmin = (measure_mean - measure_sd - overlay_offset) / overlay_factor,
          y = measure_depth
        ),
        fill = overlay_color,
        alpha = 0.2
      ) +
      geom_vline(xintercept = 0) +
      # scale_y_continuous(trans = trans_reverser("sqrt"),
      #                    breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
      scale_y_reverse() +
      facet_wrap(~ paste0(cluster, " (", formatC(count_profiles, big.mark=",") , ")"),
                  strip.position = "right") +
      coord_cartesian(xlim = opt_xlim) +
      scale_x_continuous(
        # First axis
        name = opt_measure_label,
        breaks = opt_xbreaks,
        # Second axis
        sec.axis = sec_axis(
          trans =  ~ . * overlay_factor + overlay_offset,
          name = opt_overlay_measure_label
        )
      ) +
      labs(
        title = paste0(
          'Overall mean anomaly profiles by cluster \n',
          'profile type: ', unique(.x$profile_type), ', ', 
          'surface extreme: ', unique(.x$extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        ),
        x = opt_measure_label,
        y = 'depth (m)'
      ) +
      theme(axis.title.x.top = element_text(color = overlay_color),
            axis.text.x.top = element_text(color = overlay_color))
    )

[[1]]

Past versions of extreme_overlay_doxy_display-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
354592f	ds2n19	2023-12-19

[[2]]

Past versions of extreme_overlay_doxy_display-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
354592f	ds2n19	2023-12-19

[[3]]

Past versions of extreme_overlay_doxy_display-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
354592f	ds2n19	2023-12-19

[[4]]

Past versions of extreme_overlay_doxy_display-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
354592f	ds2n19	2023-12-19

[[5]]

Past versions of extreme_overlay_doxy_display-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
354592f	ds2n19	2023-12-19

[[6]]

Past versions of extreme_overlay_doxy_display-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
354592f	ds2n19	2023-12-19

Adjusted profiles

  cluster_base_overlay %>% 
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters, extreme_order) %>%
    map(
    ~ ggplot(data = .x,) +
      geom_path(aes(x = anomaly_mean,
                    y = depth)) +
      geom_ribbon(aes(
        xmax = anomaly_mean + anomaly_sd,
        xmin = anomaly_mean - anomaly_sd,
        y = depth
      ),
      alpha = 0.2) +
      geom_path(aes(
        x = (measure_mean - overlay_offset_adjusted) / overlay_factor_adjusted,
        y = measure_depth
      ), color = overlay_color) +
      geom_ribbon(
        aes(
          xmax = (measure_mean + measure_sd - overlay_offset_adjusted) / overlay_factor_adjusted,
          xmin = (measure_mean - measure_sd - overlay_offset_adjusted) / overlay_factor_adjusted,
          y = measure_depth
        ),
        fill = overlay_color,
        alpha = 0.2
      ) +
      geom_vline(xintercept = 0) +
      # scale_y_continuous(trans = trans_reverser("sqrt"),
      #                    breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
      scale_y_reverse() +
      facet_wrap(~ paste0(cluster, " (", formatC(count_profiles, big.mark=",") , ")"),
                  strip.position = "right") +
      coord_cartesian(xlim = opt_xlim_adjusted) +
      scale_x_continuous(
        # First axis
        name = opt_measure_label_adjusted,
        breaks = opt_xbreaks_adjusted,
        # Second axis
        sec.axis = sec_axis(
          trans =  ~ . * overlay_factor_adjusted + overlay_offset_adjusted,
          name = opt_overlay_measure_label
        )
      ) +
      labs(
        title = paste0(
          'Overall mean anomaly profiles by cluster \n',
          'profile type: ', unique(.x$profile_type), ', ', 
          'surface extreme: ', unique(.x$extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        ),
        x = opt_measure_label_adjusted,
        y = 'depth (m)'
      ) +
      theme(axis.title.x.top = element_text(color = overlay_color),
            axis.text.x.top = element_text(color = overlay_color))
    )

[[1]]

Past versions of extreme_overlay_doxy_display_adj-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
354592f	ds2n19	2023-12-19

[[2]]

Past versions of extreme_overlay_doxy_display_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
354592f	ds2n19	2023-12-19

[[3]]

Past versions of extreme_overlay_doxy_display_adj-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
354592f	ds2n19	2023-12-19

[[4]]

Past versions of extreme_overlay_doxy_display_adj-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
354592f	ds2n19	2023-12-19

[[5]]

Past versions of extreme_overlay_doxy_display_adj-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
354592f	ds2n19	2023-12-19

[[6]]

Past versions of extreme_overlay_doxy_display_adj-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
354592f	ds2n19	2023-12-19

Session information

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] yardstick_1.2.0    workflowsets_1.0.1 workflows_1.1.3    tune_1.1.2        
 [5] rsample_1.2.0      recipes_1.0.8      parsnip_1.1.1      modeldata_1.2.0   
 [9] infer_1.0.5        dials_1.2.0        scales_1.2.1       broom_1.0.5       
[13] tidymodels_1.1.1   ggforce_0.4.1      gsw_1.1-1          gridExtra_2.3     
[17] lubridate_1.9.0    timechange_0.1.1   argodata_0.1.0     forcats_0.5.2     
[21] stringr_1.5.0      dplyr_1.1.3        purrr_1.0.2        readr_2.1.3       
[25] tidyr_1.3.0        tibble_3.2.1       ggplot2_3.4.4      tidyverse_1.3.2   

loaded via a namespace (and not attached):
 [1] googledrive_2.0.0   colorspace_2.0-3    ellipsis_0.3.2     
 [4] class_7.3-20        rprojroot_2.0.3     fs_1.5.2           
 [7] rstudioapi_0.15.0   listenv_0.8.0       furrr_0.3.1        
[10] farver_2.1.1        prodlim_2019.11.13  fansi_1.0.3        
[13] xml2_1.3.3          codetools_0.2-18    splines_4.2.2      
[16] cachem_1.0.6        knitr_1.41          polyclip_1.10-4    
[19] jsonlite_1.8.3      workflowr_1.7.0     dbplyr_2.2.1       
[22] compiler_4.2.2      httr_1.4.4          backports_1.4.1    
[25] assertthat_0.2.1    Matrix_1.5-3        fastmap_1.1.0      
[28] gargle_1.2.1        cli_3.6.1           later_1.3.0        
[31] tweenr_2.0.2        htmltools_0.5.8.1   tools_4.2.2        
[34] gtable_0.3.1        glue_1.6.2          Rcpp_1.0.10        
[37] cellranger_1.1.0    jquerylib_0.1.4     RNetCDF_2.6-1      
[40] DiceDesign_1.9      vctrs_0.6.4         iterators_1.0.14   
[43] timeDate_4021.106   xfun_0.35           gower_1.0.0        
[46] globals_0.16.2      rvest_1.0.3         lifecycle_1.0.3    
[49] googlesheets4_1.0.1 future_1.29.0       MASS_7.3-58.1      
[52] ipred_0.9-13        hms_1.1.2           promises_1.2.0.1   
[55] parallel_4.2.2      RColorBrewer_1.1-3  yaml_2.3.6         
[58] sass_0.4.4          rpart_4.1.19        stringi_1.7.8      
[61] highr_0.9           foreach_1.5.2       lhs_1.1.6          
[64] hardhat_1.3.0       lava_1.7.0          rlang_1.1.1        
[67] pkgconfig_2.0.3     evaluate_0.18       lattice_0.20-45    
[70] labeling_0.4.2      tidyselect_1.2.0    here_1.0.1         
[73] parallelly_1.32.1   magrittr_2.0.3      R6_2.5.1           
[76] generics_0.1.3      DBI_1.2.2           pillar_1.9.0       
[79] haven_2.5.1         whisker_0.4         withr_2.5.0        
[82] survival_3.4-0      nnet_7.3-18         future.apply_1.10.0
[85] modelr_0.1.10       crayon_1.5.2        utf8_1.2.2         
[88] tzdb_0.3.0          rmarkdown_2.18      grid_4.2.2         
[91] readxl_1.4.1        git2r_0.30.1        reprex_2.0.2       
[94] digest_0.6.30       httpuv_1.6.6        GPfit_1.0-8        
[97] munsell_0.5.0       viridisLite_0.4.1   bslib_0.4.1