ph (h plus) cluster analysis

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Last updated: 2024-01-01

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Knit directory: bgc_argo_r_argodata/analysis/

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File	Version	Author	Date	Message
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Rmd	867586f	ds2n19	2023-12-19	combined cluster analysis.

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Tasks

This markdown file carries out cluster analysis on previously created bgc ph 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.

ph is displayed on pH scale

Dependencies

pH_anomaly_va.rds - bgc preprocessed folder, created by ph_align_climatology

OceanSODA_pH_anomaly_field_01.rds (or _02.rds) - bgc preprocessed folder, created by extreme_pH

Set directories

location of pre-prepared data

Category options

What category of data is the cluster analysis related to

# opt_category
# bgc_temp_withph based on bgc_temp dataset but must also have ph profile.
# bgc_ph_hplus based on bgc_ph dataset with analysis carried out on h plus anomalies
#opt_category <- "bgc_temp"
#opt_category <- "bgc_temp_withph"
#opt_category <- "bgc_ph_h_plus"
#opt_category <- "bgc_ph_ph"
#opt_category <- "bgc_doxy"
opt_category <- "bgc_ph_ph"

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_ph_ph") {

  # ---------------------------------------------------------------------------------------------
  # 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,-30) # SO
  
  # ---------------------------------------------------------------------------------------------
  # read data - ph with analysis based on hplus
  # ---------------------------------------------------------------------------------------------
  # read data, applying geographical limits and standardize field names.
  anomaly_va <-
    read_rds(file = paste0(path_argo_preprocessed, "/pH_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 = pH,
           clim_measure = clim_pH,
           anomaly = anomaly_pH
           )

  # ---------------------------------------------------------------------------------------------
  # 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_pH_anomaly_field_01.rds")) %>%
    select(lon, lat, date, extreme_flag = ph_extreme)
  } else if (opt_extreme_determination == 2){
  extreme_data <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_pH_anomaly_field_02.rds")) %>%
    select(lon, lat, date, extreme_flag = ph_extreme)
  }

  # ---------------------------------------------------------------------------------------------
  # Associated data restrictions and formatting
  # ---------------------------------------------------------------------------------------------
  # What is the max depth of each profile_range
  opt_max_depth <- c(614, 1225, 1600)
    
  # opt_measure_label, opt_xlim and opt_xbreaks are associated with formatting
  opt_measure_label <- "pH anomaly"
  opt_xlim <- c(-0.08, 0.08)
  opt_xbreaks <- c(-0.08, -0.04, 0, 0.04, 0.08)
  
  # adjusted to be in scale -1 to 1
  opt_measure_label_adjusted <- "adjusted pH anomaly"
  opt_xlim_adjusted <- c(-1, 1)
  opt_xbreaks_adjusted <- c(-1.0, -0.5, 0, 0.5, 1.0)

  # Chl-a formatting
  opt_chla_measure_label <- expression("chlorophyll a ( mg m"^"-3"~")")

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

  # nitrate formatting
  opt_nitrate_measure_label <- expression("nitrate anomaly ( µmol kg"^"-1"~")")
    
  # 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

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665d290	ds2n19	2023-12-19

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Past versions of cluster_analysis_means-2.png

Version	Author	Date
665d290	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

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[[2]]

Past versions of cluster_analysis_means_adj-2.png

Version	Author	Date
665d290	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

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665d290	ds2n19	2023-12-19

[[2]]

Past versions of cluster_mean_year-2.png

Version	Author	Date
665d290	ds2n19	2023-12-19

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

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665d290	ds2n19	2023-12-19

[[2]]

Past versions of cluster_mean_year_adj-2.png

Version	Author	Date
665d290	ds2n19	2023-12-19

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

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[[2]]

Past versions of cluster_by_year-2.png

Version	Author	Date
665d290	ds2n19	2023-12-19

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
665d290	ds2n19	2023-12-19

[[2]]

Past versions of cluster_by_year_adj-2.png

Version	Author	Date
665d290	ds2n19	2023-12-19

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
665d290	ds2n19	2023-12-19

[[2]]

Past versions of cluster_by_month-2.png

Version	Author	Date
665d290	ds2n19	2023-12-19

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
665d290	ds2n19	2023-12-19

[[2]]

Past versions of cluster_by_month_adj-2.png

Version	Author	Date
665d290	ds2n19	2023-12-19

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
665d290	ds2n19	2023-12-19

[[2]]

Past versions of spatial_cluster_01-2.png

Version	Author	Date
665d290	ds2n19	2023-12-19

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
665d290	ds2n19	2023-12-19

[[2]]

Past versions of spatial_cluster_01_adj-2.png

Version	Author	Date
665d290	ds2n19	2023-12-19

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
665d290	ds2n19	2023-12-19

[[2]]

Past versions of spatial_cluster_03-2.png

Version	Author	Date
665d290	ds2n19	2023-12-19

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
665d290	ds2n19	2023-12-19

[[2]]

Past versions of spatial_cluster_03_adj-2.png

Version	Author	Date
665d290	ds2n19	2023-12-19

Overlay chlorophyll a

for each cluster identified show with second attribute overlayed

# Read chl-a data'
overlay_va <- read_rds(file = paste0(path_argo_preprocessed, "/chla_bgc_va.rds")) %>%
  select (file_id,
          date,
          year,
          month,
          lat,
          lon,
          profile_range,
          measure_depth = depth,
          measure = chla)

if (opt_category == "bgc_ph_ph"){
  overlay_factor <- 25
  overlay_offset <- 1
  overlay_factor_adjusted <- 1
  overlay_offset_adjusted <- 0.5
} else if (opt_category == "bgc_ph_h_plus"){
  overlay_factor <- 1e9
  overlay_offset <- 1
  overlay_factor_adjusted <- 1
  overlay_offset_adjusted <- 0.5
}

overlay_color <- "#69b3a2"
opt_overlay_measure_label <- opt_chla_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_chla_display-1.png

Version	Author	Date
665d290	ds2n19	2023-12-19

[[2]]

Past versions of overlay_chla_display-2.png

Version	Author	Date
665d290	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_chla_display_adj-1.png

Version	Author	Date
665d290	ds2n19	2023-12-19

[[2]]

Past versions of overlay_chla_display_adj-2.png

Version	Author	Date
665d290	ds2n19	2023-12-19

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
665d290	ds2n19	2023-12-19

[[2]]

Past versions of overlay_doxy_display-2.png

Version	Author	Date
665d290	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
665d290	ds2n19	2023-12-19

[[2]]

Past versions of overlay_doxy_display_adj-2.png

Version	Author	Date
665d290	ds2n19	2023-12-19

Overlay nitrate anomaly

for each cluster identified show with second attribute overlayed

# Read nitrate data
overlay_va <- read_rds(file = paste0(path_argo_preprocessed, "/nitrate_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 <- 75
  overlay_offset <- 0
  overlay_factor_adjusted <- 6
  overlay_offset_adjusted <- 0
} else if (opt_category == "bgc_ph_h_plus"){
  overlay_factor <- 3e9
  overlay_offset <- 0
  overlay_factor_adjusted <- 6
  overlay_offset_adjusted <- 0
}

overlay_color <- "#ED7D31"
opt_overlay_measure_label <- opt_nitrate_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_nitrate_display-1.png

Version	Author	Date
665d290	ds2n19	2023-12-19

[[2]]

Past versions of overlay_nitrate_display-2.png

Version	Author	Date
665d290	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_nitrate_display_adj-1.png

Version	Author	Date
665d290	ds2n19	2023-12-19

[[2]]

Past versions of overlay_nitrate_display_adj-2.png

Version	Author	Date
665d290	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)'
      )
  )
}

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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)'
        )
    )

}
}

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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)'
      )
  )
}

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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)'
        )
    )

}
}

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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)
        )
      )
  )
}

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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)
          )
        )
    )
    
}
}

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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)
        )
      )
  )
}

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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)
          )
        )
    )
  
}
}

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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)
        )
      )
  )
}

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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)
          )
        )
    )

}
}

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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)
        )
      )
  )
}

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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)
          )
        )
    )
    
}    
}

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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))
    )

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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))
    )

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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   ggpmisc_0.5.4-1    ggpp_0.5.5         ggforce_0.4.1     
[17] gsw_1.1-1          gridExtra_2.3      lubridate_1.9.0    timechange_0.1.1  
[21] argodata_0.1.0     forcats_0.5.2      stringr_1.5.0      dplyr_1.1.3       
[25] purrr_1.0.2        readr_2.1.3        tidyr_1.3.0        tibble_3.2.1      
[29] 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   furrr_0.3.1         listenv_0.8.0      
 [10] farver_2.1.1        MatrixModels_0.5-1  prodlim_2019.11.13 
 [13] fansi_1.0.3         xml2_1.3.3          codetools_0.2-18   
 [16] splines_4.2.2       cachem_1.0.6        knitr_1.41         
 [19] polyclip_1.10-4     polynom_1.4-1       jsonlite_1.8.3     
 [22] workflowr_1.7.0     dbplyr_2.2.1        compiler_4.2.2     
 [25] httr_1.4.4          backports_1.4.1     assertthat_0.2.1   
 [28] Matrix_1.5-3        fastmap_1.1.0       gargle_1.2.1       
 [31] cli_3.6.1           later_1.3.0         tweenr_2.0.2       
 [34] htmltools_0.5.3     quantreg_5.94       tools_4.2.2        
 [37] gtable_0.3.1        glue_1.6.2          Rcpp_1.0.10        
 [40] cellranger_1.1.0    jquerylib_0.1.4     RNetCDF_2.6-1      
 [43] DiceDesign_1.9      vctrs_0.6.4         iterators_1.0.14   
 [46] timeDate_4021.106   xfun_0.35           gower_1.0.0        
 [49] globals_0.16.2      rvest_1.0.3         lifecycle_1.0.3    
 [52] googlesheets4_1.0.1 future_1.29.0       MASS_7.3-58.1      
 [55] ipred_0.9-13        hms_1.1.2           promises_1.2.0.1   
 [58] parallel_4.2.2      SparseM_1.81        RColorBrewer_1.1-3 
 [61] yaml_2.3.6          sass_0.4.4          rpart_4.1.19       
 [64] stringi_1.7.8       highr_0.9           foreach_1.5.2      
 [67] lhs_1.1.6           hardhat_1.3.0       lava_1.7.0         
 [70] rlang_1.1.1         pkgconfig_2.0.3     evaluate_0.18      
 [73] lattice_0.20-45     labeling_0.4.2      tidyselect_1.2.0   
 [76] here_1.0.1          parallelly_1.32.1   magrittr_2.0.3     
 [79] R6_2.5.1            generics_0.1.3      DBI_1.1.3          
 [82] pillar_1.9.0        haven_2.5.1         whisker_0.4        
 [85] withr_2.5.0         survival_3.4-0      nnet_7.3-18        
 [88] future.apply_1.10.0 modelr_0.1.10       crayon_1.5.2       
 [91] utf8_1.2.2          tzdb_0.3.0          rmarkdown_2.18     
 [94] grid_4.2.2          readxl_1.4.1        git2r_0.30.1       
 [97] reprex_2.0.2        digest_0.6.30       httpuv_1.6.6       
[100] GPfit_1.0-8         munsell_0.5.0       viridisLite_0.4.1  
[103] bslib_0.4.1