Oxygen cluster analysis

workflowr

• Summary
• Checks
• Past versions

Last updated: 2024-04-07

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: 53f0344

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 53f0344. 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/

Unstaged changes:
    Modified:   analysis/_site.yml
    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/doxy_cluster_analysis.Rmd) and HTML (docs/doxy_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	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	9b78fd8	ds2n19	2023-12-20	Build site.
Rmd	f1fce59	ds2n19	2023-12-20	builing generic cluster analysis.
html	07d4eb8	ds2n19	2023-12-20	Build site.
html	70dd838	ds2n19	2023-12-18	Build site.
html	7b7b3b3	ds2n19	2023-12-18	Build site.
Rmd	96126b2	ds2n19	2023-12-18	combined cluster analysis.

---

Tasks

Carried out cluster analysis having first set options and determined the data sets on which that cluster analysis should be based on.

Set directories

location of pre-prepared data

Category options

What category of data is the cluster analysis related to

# opt_category
opt_category <- "bgc_doxy"

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_doxy") {
  
  # ---------------------------------------------------------------------------------------------
  # 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) # global
  
  # ---------------------------------------------------------------------------------------------
  # read data - bgc oxygen must have a ph profile
  # ---------------------------------------------------------------------------------------------
  # read data, applying geographical limits and standardize field names.
  anomaly_va <-
    read_rds(file = paste0(path_argo_preprocessed, "/doxy_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 = doxy,
           clim_measure = clim_doxy,
           anomaly
           )
  
  # ---------------------------------------------------------------------------------------------
  # 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 <- expression("dissolved oxygen anomaly ( µmol kg"^"-1"~")")
  opt_xlim <- c(-40, 40)
  opt_xbreaks <- c(-40, -20, 0, 20, 40)

  # adjusted to be in scale -1 to 1
  opt_measure_label_adjusted <- "adjusted oxygen anomaly"
  opt_xlim_adjusted <- c(-1, 1)
  opt_xbreaks_adjusted <- c(-1.0, -0.5, 0, 0.5, 1.0)

  # Under extreme analysis
  opt_extreme_analysis <- FALSE
  
}

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
7b7b3b3	ds2n19	2023-12-18

[[2]]

Past versions of cluster_analysis_means-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
7b7b3b3	ds2n19	2023-12-18

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
7b7b3b3	ds2n19	2023-12-18

[[2]]

Past versions of cluster_analysis_means_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
7b7b3b3	ds2n19	2023-12-18

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
7b7b3b3	ds2n19	2023-12-18

[[2]]

Past versions of cluster_mean_year-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
7b7b3b3	ds2n19	2023-12-18

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
7b7b3b3	ds2n19	2023-12-18

[[2]]

Past versions of cluster_mean_year_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
7b7b3b3	ds2n19	2023-12-18

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
7b7b3b3	ds2n19	2023-12-18

[[2]]

Past versions of cluster_by_year-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
7b7b3b3	ds2n19	2023-12-18

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
7b7b3b3	ds2n19	2023-12-18

[[2]]

Past versions of cluster_by_year_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
7b7b3b3	ds2n19	2023-12-18

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
7b7b3b3	ds2n19	2023-12-18

[[2]]

Past versions of cluster_by_month-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
7b7b3b3	ds2n19	2023-12-18

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
7b7b3b3	ds2n19	2023-12-18

[[2]]

Past versions of cluster_by_month_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
7b7b3b3	ds2n19	2023-12-18

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
7b7b3b3	ds2n19	2023-12-18

[[2]]

Past versions of spatial_cluster_01-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
7b7b3b3	ds2n19	2023-12-18

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
7b7b3b3	ds2n19	2023-12-18

[[2]]

Past versions of spatial_cluster_01_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
7b7b3b3	ds2n19	2023-12-18

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
7b7b3b3	ds2n19	2023-12-18

[[2]]

Past versions of spatial_cluster_03-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
7b7b3b3	ds2n19	2023-12-18

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
7b7b3b3	ds2n19	2023-12-18

[[2]]

Past versions of spatial_cluster_03_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
7b7b3b3	ds2n19	2023-12-18

Under temp extreme

Cluster analysis of oxygen profiles under temperature extremes

if (opt_category == "bgc_doxy") {
  
  # ---------------------------------------------------------------------------------------------
  # 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)
  }

  # Under extreme analysis
  opt_extreme_analysis <- TRUE
  
}

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
9b78fd8	ds2n19	2023-12-20

[[2]]

Past versions of cluster_mean_ext-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[3]]

Past versions of cluster_mean_ext-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[4]]

Past versions of cluster_mean_ext-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[5]]

Past versions of cluster_mean_ext-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[6]]

Past versions of cluster_mean_ext-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

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
9b78fd8	ds2n19	2023-12-20

[[2]]

Past versions of cluster_mean_ext_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[3]]

Past versions of cluster_mean_ext_adj-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[4]]

Past versions of cluster_mean_ext_adj-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[5]]

Past versions of cluster_mean_ext_adj-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[6]]

Past versions of cluster_mean_ext_adj-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

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
9b78fd8	ds2n19	2023-12-20

[[2]]

Past versions of cluster_mean_year_ext-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[3]]

Past versions of cluster_mean_year_ext-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[4]]

Past versions of cluster_mean_year_ext-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[5]]

Past versions of cluster_mean_year_ext-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[6]]

Past versions of cluster_mean_year_ext-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

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
9b78fd8	ds2n19	2023-12-20

[[2]]

Past versions of cluster_mean_year_ext_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[3]]

Past versions of cluster_mean_year_ext_adj-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[4]]

Past versions of cluster_mean_year_ext_adj-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[5]]

Past versions of cluster_mean_year_ext_adj-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[6]]

Past versions of cluster_mean_year_ext_adj-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

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
9b78fd8	ds2n19	2023-12-20

[[2]]

Past versions of cluster_by_year_ext-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[3]]

Past versions of cluster_by_year_ext-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[4]]

Past versions of cluster_by_year_ext-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[5]]

Past versions of cluster_by_year_ext-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[6]]

Past versions of cluster_by_year_ext-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

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
9b78fd8	ds2n19	2023-12-20

[[2]]

Past versions of cluster_by_year_ext_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[3]]

Past versions of cluster_by_year_ext_adj-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[4]]

Past versions of cluster_by_year_ext_adj-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[5]]

Past versions of cluster_by_year_ext_adj-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[6]]

Past versions of cluster_by_year_ext_adj-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

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
9b78fd8	ds2n19	2023-12-20

[[2]]

Past versions of cluster_by_month_ext-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[3]]

Past versions of cluster_by_month_ext-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[4]]

Past versions of cluster_by_month_ext-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[5]]

Past versions of cluster_by_month_ext-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[6]]

Past versions of cluster_by_month_ext-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

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
9b78fd8	ds2n19	2023-12-20

[[2]]

Past versions of cluster_by_month_ext_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[3]]

Past versions of cluster_by_month_ext_adj-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[4]]

Past versions of cluster_by_month_ext_adj-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[5]]

Past versions of cluster_by_month_ext_adj-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[6]]

Past versions of cluster_by_month_ext_adj-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

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
9b78fd8	ds2n19	2023-12-20

[[2]]

Past versions of spatial_cluster_combined_ext-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[3]]

Past versions of spatial_cluster_combined_ext-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[4]]

Past versions of spatial_cluster_combined_ext-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[5]]

Past versions of spatial_cluster_combined_ext-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[6]]

Past versions of spatial_cluster_combined_ext-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

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
9b78fd8	ds2n19	2023-12-20

[[2]]

Past versions of spatial_cluster_combined_ext_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[3]]

Past versions of spatial_cluster_combined_ext_adj-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[4]]

Past versions of spatial_cluster_combined_ext_adj-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[5]]

Past versions of spatial_cluster_combined_ext_adj-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[6]]

Past versions of spatial_cluster_combined_ext_adj-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

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
9b78fd8	ds2n19	2023-12-20

[[2]]

Past versions of spatial_cluster_count_ext-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[3]]

Past versions of spatial_cluster_count_ext-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[4]]

Past versions of spatial_cluster_count_ext-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[5]]

Past versions of spatial_cluster_count_ext-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[6]]

Past versions of spatial_cluster_count_ext-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

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
9b78fd8	ds2n19	2023-12-20

[[2]]

Past versions of spatial_cluster_count_ext_adj-2.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[3]]

Past versions of spatial_cluster_count_ext_adj-3.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[4]]

Past versions of spatial_cluster_count_ext_adj-4.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[5]]

Past versions of spatial_cluster_count_ext_adj-5.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

[[6]]

Past versions of spatial_cluster_count_ext_adj-6.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
9b78fd8	ds2n19	2023-12-20

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   
[29] workflowr_1.7.0   

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   furrr_0.3.1         listenv_0.8.0      
 [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      dbplyr_2.2.1        compiler_4.2.2     
 [22] httr_1.4.4          backports_1.4.1     assertthat_0.2.1   
 [25] Matrix_1.5-3        fastmap_1.1.0       gargle_1.2.1       
 [28] cli_3.6.1           later_1.3.0         tweenr_2.0.2       
 [31] htmltools_0.5.3     tools_4.2.2         gtable_0.3.1       
 [34] glue_1.6.2          Rcpp_1.0.10         cellranger_1.1.0   
 [37] jquerylib_0.1.4     RNetCDF_2.6-1       DiceDesign_1.9     
 [40] vctrs_0.6.4         iterators_1.0.14    timeDate_4021.106  
 [43] xfun_0.35           gower_1.0.0         globals_0.16.2     
 [46] ps_1.7.2            rvest_1.0.3         lifecycle_1.0.3    
 [49] googlesheets4_1.0.1 future_1.29.0       getPass_0.2-2      
 [52] MASS_7.3-58.1       ipred_0.9-13        hms_1.1.2          
 [55] promises_1.2.0.1    parallel_4.2.2      RColorBrewer_1.1-3 
 [58] yaml_2.3.6          sass_0.4.4          rpart_4.1.19       
 [61] stringi_1.7.8       highr_0.9           foreach_1.5.2      
 [64] lhs_1.1.6           hardhat_1.3.0       lava_1.7.0         
 [67] rlang_1.1.1         pkgconfig_2.0.3     evaluate_0.18      
 [70] lattice_0.20-45     labeling_0.4.2      processx_3.8.0     
 [73] tidyselect_1.2.0    here_1.0.1          parallelly_1.32.1  
 [76] magrittr_2.0.3      R6_2.5.1            generics_0.1.3     
 [79] DBI_1.1.3           pillar_1.9.0        haven_2.5.1        
 [82] whisker_0.4         withr_2.5.0         survival_3.4-0     
 [85] nnet_7.3-18         future.apply_1.10.0 modelr_0.1.10      
 [88] crayon_1.5.2        utf8_1.2.2          tzdb_0.3.0         
 [91] rmarkdown_2.18      grid_4.2.2          readxl_1.4.1       
 [94] callr_3.7.3         git2r_0.30.1        reprex_2.0.2       
 [97] digest_0.6.30       httpuv_1.6.6        GPfit_1.0-8        
[100] munsell_0.5.0       viridisLite_0.4.1   bslib_0.4.1