pH cluster analysis

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Last updated: 2023-12-14

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Knit directory: bgc_argo_r_argodata/

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Rmd	23a24d1	ds2n19	2023-12-05	Revised extreme identification. Adjusted profiles for cluster analysis based on max anomaly.
html	3212753	ds2n19	2023-12-04	Build site.
Rmd	f7ab3b5	ds2n19	2023-12-04	Cluster under surface extreme.
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html	80c16c2	ds2n19	2023-11-15	Build site.
Rmd	3eba518	ds2n19	2023-11-15	Introduction of vertical alignment and cluster analysis to github website.

---

Tasks

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

The cluster_analysis_determine_k chunk is used to give an indication of an appropriate number of clusters 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.

In addition the cluster analysis is repeated under under surface extremes conditions. Mean pH clusters are also presented with dissolved oxygen and chlorophyll a ocerlayed

Dependencies

pH_anomaly_va.rds - bgc preprocessed folder, created by pH_align_climatology

chla_bgc_va.rds - bgc preprocessed folder, created by chla_vertical_align

doxy_bgc_va.rds - bgc preprocessed folder, created by doxy_vertical_align

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

Set directories

location of pre-prepared data

Set options

Define options that are used to determine what analysis is done

# Options

# opt_analysis_type
# opt_analysis_type = 1 determine number of clusters to use
# opt_analysis_type = 2 do analysis based on identified number of clusters
opt_analysis_type <- 2

# opt_num_clusters
# How many clusters are used in the cluster analysis
opt_num_clusters_min <- c(8, 8, 4)
opt_num_clusters_max <- c(8, 8, 5)
# What is the max depth of each profile_range
opt_max_depth <- c(614, 1225, 1600)
# Which profile range is used
opt_profile_range <- 3

# opt_measure
# opt_measure = 1 analysis is done using pH
# opt_measure = 2 analysis is done using h_plus
# opt_measure_label, opt_xlim and opt_xbreaks are associated formatting
opt_measure <- 2
if (opt_measure == 1){
  opt_measure_label <- "pH anomaly"
  opt_measure_label_adjusted <- "adjusted pH anomaly"
  opt_xlim <- c(-0.08, 0.08)
  opt_xbreaks <- c(-0.08, -0.04, 0, 0.04, 0.08)
} else {
  opt_measure_label <- expression("[H]"^"+" ~ "anomaly")
  opt_measure_label_adjusted <- expression("adjusted [H]"^"+" ~ "anomaly")
  opt_xlim <- c(-2e-9, 2e-9)
  opt_xbreaks <- c(-2e-9, -1e-9, 0, 1e-9, 2e-9)
}

# adjusted to be in scale -1 to 1
opt_measure_label_adjusted <- expression("adjusted [H]"^"+" ~ "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"~")")
opt_chla_xlim <- c(-0.5, 2.0)
opt_chla_xbreaks <- c(-0.5, 0, 0.5, 1.0, 1.5, 2.0)

# oxygen formatting
opt_doxy_measure_label <- expression("dissolved oxygen ( µmol kg"^"-1"~")")
opt_doxy_xlim <- c(50, 350)
opt_doxy_xbreaks <- c(50, 100, 150, 200, 250, 300, 350)

# options relating to cluster analysis
opt_n_start <- 25
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
# Carried out for 1500m profiles
opt_profile_range = 3
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 pH) > 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 = ""))

Preperation

Prepare data for cluster analysis

# read data
pH_anomaly_va <- read_rds(file = paste0(path_argo_preprocessed, "/pH_anomaly_va.rds"))

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

# Select target variable
if (opt_measure == 1) {
  pH_anomaly_va_id <- pH_anomaly_va %>% 
    rename(
      anomaly = anomaly_pH
    )
} else {
  pH_anomaly_va_id <- pH_anomaly_va %>% 
    rename(
      anomaly = anomaly_h_plus
    )
}

pH_anomaly_va_id <- pH_anomaly_va_id %>% 
  select(
    file_id, depth, anomaly,
    year, month, lat, lon
  )

# wide table with each depth becoming a column
pH_anomaly_va_wide <- pH_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
pH_anomaly_va_wide <- pH_anomaly_va_wide %>% 
  drop_na()

# profile_id <- pH_anomaly_va_wide %>% select(file_id)

# Table for cluster analysis
# points <- pH_anomaly_va_wide %>%
#   select(-c(file_id))

points <- pH_anomaly_va_wide %>%
  column_to_rownames(var = "file_id")

# normalisation?
if (opt_norm_anomaly) {
  
  surf_anomaly <- abs(pH_anomaly_va_id %>% 
    filter (depth == 5) %>%
    select (file_id, abs_sa = anomaly))
  
  # Get the maximum anomaly for each profile - the normalisation will then fit max to 1
  surf_anomaly <- pH_anomaly_va_id %>%
    group_by(file_id) %>%
    summarise(abs_sa = max(abs(anomaly))) %>%
    ungroup() %>%
    select (file_id, abs_sa)

  pH_anomaly_va_id_normalised <- left_join(pH_anomaly_va_id, surf_anomaly)
  
  #pH_anomaly_va_id_normalised <- pH_anomaly_va_id_normalised %>%
  #  mutate(anomaly = if_else(abs_sa > 1.0, anomaly/abs_sa, anomaly))
  pH_anomaly_va_id_normalised <- pH_anomaly_va_id_normalised %>%
    mutate(anomaly = anomaly/abs_sa)
    
  # wide table with each depth becoming a column
  pH_anomaly_va_wide <- pH_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
  pH_anomaly_va_wide <- pH_anomaly_va_wide %>% 
    drop_na()
  
  # Table for cluster analysis
  points_normalised <- pH_anomaly_va_wide %>%
    column_to_rownames(var = "file_id")

}

Number of clusters

if (opt_analysis_type == 1) {

  # cluster analysis - What k? try between 1 and opt_n_clusters clusters
  kclusts <- 
  tibble(k = 1:opt_n_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)
  )
  

  # cluster analysis data
  clusters <-
    kclusts %>%
    unnest(cols = c(tidied))
  
  assignments <-
    kclusts %>%
    unnest(cols = c(augmented))
  
  clusterings <-
    kclusts %>%
    unnest(cols = c(glanced))
  
  # What cluster works best?
  clusterings %>%
  ggplot(aes(k, tot.withinss)) +
    geom_line() +
    geom_point() +
    scale_x_continuous(breaks = c(2, 4, 6, 8, 10, 12, 14))

}

Cluster analysis

Cluster means

if (opt_analysis_type == 2) {
  
  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 pH_anomaly_va
        pH_anomaly_cluster <-
          full_join(pH_anomaly_va_id, profile_id)
        
        # Add profile_type field
        pH_anomaly_cluster <- pH_anomaly_cluster %>%
          mutate(profile_type = 'base')
        
        # Check null clusters
        pH_anomaly_cluster <- pH_anomaly_cluster %>%
          filter(!is.na(cluster))
        
        # Create table to be used for later analysis and Set the number of clusters field
        if (!exists('pH_anomaly_cluster_all')) {
          pH_anomaly_cluster_all <- pH_anomaly_cluster %>%
            mutate(num_clusters = inum_clusters)
        } else {
          pH_anomaly_cluster_all <-
            rbind(
              pH_anomaly_cluster_all,
              pH_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 pH_anomaly_va
        pH_anomaly_cluster_norm <-
          full_join(pH_anomaly_va_id_normalised %>% select(-c(abs_sa)) ,
                    profile_id)
        
        # Add profile_type field
        pH_anomaly_cluster_norm <- pH_anomaly_cluster_norm %>%
          mutate(profile_type = 'adjusted')
        
        # Check null clusters
        pH_anomaly_cluster_norm <- pH_anomaly_cluster_norm %>%
          filter(!is.na(cluster))
        
        # Create table to be used for later analysis and Set the number of clusters field
        if (!exists('pH_anomaly_cluster_all')) {
          pH_anomaly_cluster_all <- pH_anomaly_cluster_norm %>%
            mutate(num_clusters = inum_clusters)
        } else {
          pH_anomaly_cluster_all <-
            rbind(
              pH_anomaly_cluster_all,
              pH_anomaly_cluster_norm %>%
                mutate(num_clusters = inum_clusters)
            )
        }
        
      }
      
    }
  }
  
  # Plot cluster mean
  anomaly_cluster_mean <- pH_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 <- pH_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 <- pH_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 <- pH_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)
  
  # 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_cluster_details-1.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04
efa9686	ds2n19	2023-11-24
80c16c2	ds2n19	2023-11-15

[[2]]

Past versions of cluster_analysis_cluster_details-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

Adjusted profiles

if (opt_norm_anomaly) {
  
  # Repeat for adjusted 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_cluster_details_adj-1.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[2]]

Past versions of cluster_analysis_cluster_details_adj-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

Cluster mean by year

if (opt_analysis_type == 2) {

  # 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
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04
efa9686	ds2n19	2023-11-24

[[2]]

Past versions of cluster_mean_year-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

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
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[2]]

Past versions of cluster_mean_year_adj-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

Cluster climatology

# A nice short alternative, uses the package ggpmisc
anomaly_year_mean %>%
  ggplot(aes(x = year,
             y = anomaly_mean)) +
  stat_poly_line() +
  stat_poly_eq(use_label(c("eq", "R2","P", "n"))) +
  geom_point()

Cluster by year

count of each cluster by year

if (opt_analysis_type == 2) {

  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
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04
80c16c2	ds2n19	2023-11-15

[[2]]

Past versions of cluster_by_year-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

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 = 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
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[2]]

Past versions of cluster_by_year_adj-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

Cluster by month

count of each cluster by month of year

if (opt_analysis_type == 2) {

  # Determine profile count by cluster and year
  # Count the measurements
  cluster_by_year <- pH_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
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04
80c16c2	ds2n19	2023-11-15

[[2]]

Past versions of cluster_by_month-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

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
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[2]]

Past versions of cluster_by_month_adj-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

Cluster spatial

location of each cluster on map, spatial analysis

if (opt_analysis_type == 2) {

  # create figure
  pH_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 = c(-85,-30)) +
        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
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04
efa9686	ds2n19	2023-11-24

[[2]]

Past versions of spatial_cluster_01-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

Adjusted profiles

if (opt_norm_anomaly) {

  # create figure
  pH_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 = c(-85,-30)) +
        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
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[2]]

Past versions of spatial_cluster_01_adj-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

location of each cluster on separate maps, spatial analysis

if (opt_analysis_type == 2) {

  # create figure
map +
  geom_tile(data = pH_anomaly_cluster,
            aes(x = lon,
                y = lat,
                fill = cluster)) +
  lims(y = c(-85, -30)) +
  scale_fill_brewer(palette = 'Dark2') +
  facet_wrap( ~ cluster, ncol = 2) +
  labs(title = 'cluster spatial distribution')

}

Cluster spatial counts

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

if (opt_analysis_type == 2) {

  # Count profiles    
  cluster_by_location <- pH_anomaly_cluster_all %>%
    count(profile_type, num_clusters, file_id, lat, lon, cluster,
          name = "count_cluster")
  
  # 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),
                  aes(
                    x = lon,
                    y = lat,
                    fill = n
                  )) +
        lims(y = c(-85,-30)) +
        scale_fill_gradient(low = "blue",
                            high = "red",
                            trans = "log10") +
        facet_wrap(~ cluster, 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
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04
efa9686	ds2n19	2023-11-24

[[2]]

Past versions of spatial_cluster_03-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

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),
                  aes(
                    x = lon,
                    y = lat,
                    fill = n
                  )) +
        lims(y = c(-85,-30)) +
        scale_fill_gradient(low = "blue",
                            high = "red",
                            trans = "log10") +
        facet_wrap(~ cluster, 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
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[2]]

Past versions of spatial_cluster_03_adj-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

Cluster spatial year

location of each cluster on map, spatial analysis by year

if (opt_analysis_type == 2) {

  # create figure
  pH_anomaly_cluster %>%
    group_split(year) %>%
    map(
      ~ map +
        geom_tile(data = .x,
                  aes(
                    x = lon,
                    y = lat,
                    fill = cluster
                  )) +
        #lims(y = c(-85, -30))+
        scale_fill_brewer(palette = 'Dark2') +
        facet_wrap( ~ cluster, ncol = 2) +
        labs(title = paste0(
          'cluster spatial distribution ', unique(.x$year)
        ))
    )

}

pH and Chl-a

for each cluster identified by pH cluster analysis show alongside chl-a

if (opt_analysis_type == 2) {
  
  # Read chl-a data and link to cluster ID
  chla_bgc_va <- read_rds(file = paste0(path_argo_preprocessed, "/chla_bgc_va.rds"))
  chla_cluster <- right_join(chla_bgc_va, profile_id)

  # summarise by cluster
  chla_cluster_mean <- chla_cluster %>% 
      group_by(cluster, depth) %>% 
      summarise(chla_mean = mean(chla, na.rm = TRUE),
                chla_sd = sd(chla, na.rm = TRUE)) %>%
      ungroup()

  # join pH anomaly with chl-a
  cluster_ph_chla <- full_join(anomaly_cluster_mean, chla_cluster_mean)
  
  # create figures
  cluster_ph_chla %>% 
    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_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
      facet_wrap(~cluster)+
      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)'
      )
    )
    
  cluster_ph_chla %>% 
    filter (profile_type == "base") %>%
    group_split(profile_type, num_clusters) %>%
    map(
    ~ ggplot(data = .x,)+
      geom_path(aes(x = chla_mean,
                    y = depth))+
      geom_ribbon(aes(xmax = chla_mean + chla_sd,
                      xmin = chla_mean - chla_sd,
                      y = depth),
                  alpha = 0.2)+
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
      facet_wrap(~cluster)+
      coord_cartesian(xlim = opt_chla_xlim)+
      scale_x_continuous(breaks = opt_chla_xbreaks)+
      labs(
        title = paste0(
          'Overall mean anomaly profiles by cluster \n',
          'type = ', unique(.x$profile_type), ', ', 
          'num clusters = ', unique(.x$num_clusters)
        ),
        x = opt_chla_measure_label,
        y = 'depth (m)'
      )
    )


  if (opt_measure == 1){
    chla_to_ph_factor <- 25
    chla_to_ph_offset <- 1
  } else {
    chla_to_ph_factor <- 1e9
    chla_to_ph_offset <- 1
  }
  chla_color <- "#69b3a2"
  
  cluster_ph_chla %>% 
    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 = (chla_mean - chla_to_ph_offset) / chla_to_ph_factor,
        y = depth
      ), color = chla_color) +
      geom_ribbon(
        aes(
          xmax = (chla_mean + chla_sd - chla_to_ph_offset) / chla_to_ph_factor,
          xmin = (chla_mean - chla_sd - chla_to_ph_offset) / chla_to_ph_factor,
          y = depth
        ),
        fill = chla_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))) +
      facet_wrap( ~ cluster,
                  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 =  ~ . * chla_to_ph_factor + chla_to_ph_offset,
          name = opt_chla_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 = chla_color),
            axis.text.x.top = element_text(color = chla_color))
    )

}

[[1]]

Past versions of ph_with_chla-1.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04
80c16c2	ds2n19	2023-11-15

[[2]]

Past versions of ph_with_chla-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

pH and oxygen

for each cluster identified by pH cluster analysis show alongside oxygen

if (opt_analysis_type == 2) {
  
  # Read chl-a data and link to cluster ID
  doxy_bgc_va <- read_rds(file = paste0(path_argo_preprocessed, "/doxy_bgc_va.rds"))
  doxy_cluster <- right_join(doxy_bgc_va, profile_id)

  # summarise by cluster
  doxy_cluster_mean <- doxy_cluster %>% 
      group_by(cluster, depth) %>% 
      summarise(doxy_mean = mean(doxy, na.rm = TRUE),
                doxy_sd = sd(doxy, na.rm = TRUE)) %>%
      ungroup()

  # join pH anomaly with chl-a
  cluster_ph_doxy <- full_join(anomaly_cluster_mean, doxy_cluster_mean)
  
  # create figure
  cluster_ph_doxy %>% 
    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(~cluster)+
      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)')
    )
    
  cluster_ph_doxy %>% 
    filter (profile_type == "base") %>%
    group_split(profile_type, num_clusters) %>%
    map(
    ~ ggplot(data = .x,)+
      geom_path(aes(x = doxy_mean,
                    y = depth))+
      geom_ribbon(aes(xmax = doxy_mean + doxy_sd,
                      xmin = doxy_mean - doxy_sd,
                      y = depth),
                  alpha = 0.2)+
      scale_y_reverse()+
      facet_wrap(~cluster)+
      coord_cartesian(xlim = opt_doxy_xlim)+
      scale_x_continuous(breaks = opt_doxy_xbreaks)+
      labs(        
        title = paste0(
          'Overall mean anomaly profiles by cluster \n',
          'type = ', unique(.x$profile_type), ', ', 
          'num clusters = ', unique(.x$num_clusters)
        ),
        x = opt_doxy_measure_label, 
        y = 'depth (m)')
    )


  if (opt_measure == 1){
    doxy_to_ph_factor <- 1875
    doxy_to_ph_offset <- 200
  } else {
    doxy_to_ph_factor <- 75e9
    doxy_to_ph_offset <- 200
  }
  doxy_color <- "#5B9BD5"
  
  cluster_ph_doxy %>%
    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 = (doxy_mean - doxy_to_ph_offset)  / doxy_to_ph_factor,
        y = depth
      ), color = doxy_color) +
      geom_ribbon(
        aes(
          xmax = (doxy_mean + doxy_sd - doxy_to_ph_offset) / doxy_to_ph_factor,
          xmin = (doxy_mean - doxy_sd - doxy_to_ph_offset) / doxy_to_ph_factor,
          y = depth
        ),
        fill = doxy_color,
        alpha = 0.2
      ) +
      geom_vline(xintercept = 0) +
      scale_y_reverse() +
      facet_wrap( ~ cluster,
                  strip.position = "right") +
      coord_cartesian(xlim = opt_xlim) +
      scale_x_continuous(
        # First axis
        name = opt_measure_label,
        breaks = opt_xbreaks,
        # First axis
        sec.axis = sec_axis(
          trans =  ~ . * doxy_to_ph_factor + doxy_to_ph_offset,
          name = opt_doxy_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 = doxy_color),
            axis.text.x.top = element_text(color = doxy_color))
    )
  
}

[[1]]

Past versions of ph_with_oxygen-1.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04
80c16c2	ds2n19	2023-11-15

[[2]]

Past versions of ph_with_oxygen-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

Cluster by surface Extreme

# ---------------------------------------------------------------------------------------------
# read data
# ---------------------------------------------------------------------------------------------
# load previously created OceanSODA extreme data. date, position and nature of extreme
if (opt_extreme_determination == 1){
  pH_extreme <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_pH_anomaly_field_01.rds")) %>%
    select(lon, lat, date, ph_extreme)
} else if (opt_extreme_determination == 2){
  pH_extreme <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_pH_anomaly_field_02.rds")) %>%
    select(lon, lat, date, ph_extreme)
}

# -------------------------------------------------------------------------------------------------------------
# pH - review incidences of extremes based on method.
# -------------------------------------------------------------------------------------------------------------
# 
#   pH_extreme_info_01 <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_SST_anomaly_field_01.rds")) %>%
#     select(lon, lat, date, pH_extreme)
# 
# pH_extreme_info_01 <- pH_extreme_info_01 %>%
#   group_by(pH_extreme, date) %>%
#   summarise(n = n()) %>%
#   ungroup()
# 
# pH_extreme_info_01 %>%
#   filter (pH_extreme %in% c('H', 'L') & date >= '2013-01-01') %>%
#   ggplot(aes(
#            x = date,
#            y = n,
#            col = pH_extreme
#          )) +
#   geom_point() +
#   geom_line() +
#   lims(y = c(0,3200)) +
#   labs(
#     x = 'date',
#     y = 'number of extreme pixels',
#     col = 'extreme type',
#     title = paste0(
#       'Count of extreme pixels - single trend'
#     )
#   )
# 
# 
# pH_extreme_info_02 <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_SST_anomaly_field_02.rds")) %>%
#     select(lon, lat, date, pH_extreme)
# 
# pH_extreme_info_02 <- pH_extreme_info_02 %>%
#   group_by(pH_extreme, date) %>%
#   summarise(n = n()) %>%
#   ungroup()
# 
# pH_extreme_info_02 %>%
#   filter (pH_extreme %in% c('H', 'L') & date >= '2013-01-01') %>%
#   ggplot(aes(
#            x = date,
#            y = n,
#            col = pH_extreme
#          )) +
#   geom_point() +
#   geom_line() +
#   lims(y = c(0,3200)) +
#   labs(
#     x = 'date',
#     y = 'number of extreme pixels',
#     col = 'extreme type',
#     title = paste0(
#       'Count of extreme pixels - monthly trends'
#     )
#   )
#   
# -------------------------------------------------------------------------------------------------------------
  
# read data
pH_anomaly_va <- read_rds(file = paste0(path_argo_preprocessed, "/pH_anomaly_va.rds")) %>%
  mutate(date = ymd(format(date, "%Y-%m-15")))

# Add the OceanSODA extreme condition
pH_anomaly_va <- left_join(pH_anomaly_va, pH_extreme)

# If pH_extreme is NA set it to N
pH_anomaly_va <- pH_anomaly_va %>% replace_na(list(ph_extreme = 'N'))
pH_anomaly_va <- pH_anomaly_va %>% mutate (profile_type = 'base')

if (opt_measure == 1){
  pH_anomaly_va <- pH_anomaly_va %>%
    mutate(anomaly = anomaly_pH)
} else {
  pH_anomaly_va <- pH_anomaly_va %>%
    mutate(anomaly = anomaly_h_plus)
}

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

  # Determine surface anomaly for each profile
  # surf_anomaly <- abs(pH_anomaly_va_norm %>% 
  #   filter (depth == 5) %>%
  #   select (file_id, abs_sa = anomaly))

  # Get the maximum anomaly for each profile - the normalisation will then fit max to 1
  surf_anomaly <- pH_anomaly_va %>%
    group_by(file_id) %>%
    summarise(abs_sa = max(abs(anomaly))) %>%
    ungroup() %>%
    select (file_id, abs_sa)
  
  pH_anomaly_va_norm <- left_join(pH_anomaly_va_norm, surf_anomaly)
    
  # Carry out the adjustment
  #pH_anomaly_va_norm <- pH_anomaly_va_norm %>%
  #  mutate(anomaly = if_else(abs_sa > 1.0, anomaly/abs_sa, anomaly))
  if (opt_measure == 1){
    pH_anomaly_va_norm <- pH_anomaly_va_norm %>%
      mutate(anomaly = anomaly_pH/abs_sa)
  } else {
    pH_anomaly_va_norm <- pH_anomaly_va_norm %>%
      mutate(anomaly = anomaly_h_plus/abs_sa)
  }
  
  #remove the surface anomaly field
  pH_anomaly_va_norm <- pH_anomaly_va_norm %>% select(-c(abs_sa))
  
  # Append to base profiles
  pH_anomaly_va <- rbind(pH_anomaly_va, pH_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
    pH_anomaly_va_id <- pH_anomaly_va %>% 
      filter(profile_range == opt_profile_range & depth <= opt_max_depth[opt_profile_range] & ph_extreme == extreme_type[i] & profile_type == sel_profile_type)
    
    # Simplified table ready to pivot
    pH_anomaly_va_id <- pH_anomaly_va_id %>%
      select(file_id,
             depth,
             anomaly,
             year, 
             month, 
             lat, 
             lon)
    
    # wide table with each depth becoming a column
    pH_anomaly_va_wide <- pH_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
    pH_anomaly_va_wide <- pH_anomaly_va_wide %>% 
      drop_na()
    
    # Table for cluster analysis
    points <- pH_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 pH_anomaly_va
      pH_anomaly_cluster <- full_join(pH_anomaly_va_id, profile_id)
      
      # Plot cluster mean
      pH_anomaly_cluster <- pH_anomaly_cluster %>% 
        filter(!is.na(cluster))
      
      # cluster mean
      anomaly_cluster_mean <- pH_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 <- pH_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 <- pH_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(
            pH_extreme_order = i,
            pH_extreme = extreme_type[i],
            num_clusters = inum_clusters,
            profile_type = sel_profile_type
          )
        anomaly_cluster_mean_year_ext <-
          anomaly_cluster_mean_year %>% mutate(
            pH_extreme_order = i,
            pH_extreme = extreme_type[i],
            num_clusters = inum_clusters,
            profile_type = sel_profile_type
          )
        anomaly_year_mean_ext <-
          anomaly_year_mean %>% mutate(
            pH_extreme_order = i,
            pH_extreme = extreme_type[i],
            num_clusters = inum_clusters,
            profile_type = sel_profile_type
          )
        pH_anomaly_cluster_ext <-
          pH_anomaly_cluster %>% mutate(
            pH_extreme_order = i,
            pH_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(
              pH_extreme_order = i,
              pH_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(
              pH_extreme_order = i,
              pH_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(
              pH_extreme_order = i,
              pH_extreme = extreme_type[i],
              num_clusters = inum_clusters,
              profile_type = sel_profile_type
            )
          )
        pH_anomaly_cluster_ext <-
          rbind(
            pH_anomaly_cluster_ext,
            pH_anomaly_cluster_ext <-
              pH_anomaly_cluster %>% mutate(
                pH_extreme_order = i,
                pH_extreme = extreme_type[i],
                num_clusters = inum_clusters,
                profile_type = sel_profile_type
              )
          )
      }
  
    }
    
  }
  
}

Cluster means

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

#cluster_by_year %>% filter (num_clusters == 5 & pH_extreme == 'N')

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

# total of each type of cluster
cluster_count <- cluster_by_year %>%
  group_by(profile_type, num_clusters, pH_extreme, pH_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, pH_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$profile_type), ', ', 
          'surface extreme: ', unique(.x$pH_extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        ),
        x = opt_measure_label,
        y = 'depth (m)'
      )
  )

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Adjusted profiles

if (opt_norm_anomaly) {

  # create figure of cluster mean profiles
  anomaly_cluster_mean_ext %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters, pH_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$profile_type), ', ', 
            'surface extreme: ', unique(.x$pH_extreme), ', ', 
            'number clusters: ', unique(.x$num_clusters)
          ),
          x = opt_measure_label_adjusted,
          y = 'depth (m)'
        )
    )

}

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Clusters mean by year

# cluster means by year
anomaly_cluster_mean_year_ext %>%
  filter (profile_type == "base") %>%
  mutate(year = as.factor(year)) %>%
  group_split(profile_type, num_clusters, pH_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$profile_type), ', ', 
          'surface extreme: ', unique(.x$pH_extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        ),
        x = opt_measure_label,
        y = 'depth (m)'
      )
  )

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Adjusted profiles

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, pH_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$profile_type), ', ', 
            'surface extreme: ', unique(.x$pH_extreme), ', ', 
            'number clusters: ', unique(.x$num_clusters)
          ),
          x = opt_measure_label_adjusted,
          y = 'depth (m)'
        )
    )

}

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Past versions of cluster_mean_year_ext_adj-6.png

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3212753	ds2n19	2023-12-04

Cluster by year

count of each cluster by year

# Determine profile count by extreme and cluster and year
# Count the measurements
cluster_by_year <- pH_anomaly_cluster_ext %>% 
  count(file_id, profile_type, num_clusters, pH_extreme_order, pH_extreme, cluster, year,
        name = "count_cluster")
# Convert to profiles
cluster_by_year <- cluster_by_year %>% 
  count(profile_type, num_clusters, pH_extreme_order, pH_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, pH_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$profile_type), ', ', 
          'surface extreme: ', unique(.x$pH_extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        )
      )
  )

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Past versions of cluster_by_year_ext-1.png

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Adjusted profiles

if (opt_norm_anomaly) {

  # create figure
  cluster_by_year %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters, pH_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$profile_type), ', ', 
            'surface extreme: ', unique(.x$pH_extreme), ', ', 
            'number clusters: ', unique(.x$num_clusters)
          )
        )
    )
    
}

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Past versions of cluster_by_year_ext_adj-1.png

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Past versions of cluster_by_year_ext_adj-6.png

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3212753	ds2n19	2023-12-04

Cluster by month

count of each cluster by month of year

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

# create figure
cluster_by_year %>%
  filter (profile_type == "base") %>%
  group_split(profile_type, num_clusters, pH_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$profile_type), ', ', 
          'surface extreme: ', unique(.x$pH_extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        )
      )
  )

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3212753	ds2n19	2023-12-04

Adjusted profiles

if (opt_norm_anomaly) {

  # create figure
  cluster_by_year %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters, pH_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$profile_type), ', ', 
            'surface extreme: ', unique(.x$pH_extreme), ', ', 
            'number clusters: ', unique(.x$num_clusters)
          )
        )
    )
  
}

[[1]]

Past versions of cluster_by_month_ext_adj-1.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[2]]

Past versions of cluster_by_month_ext_adj-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[3]]

Past versions of cluster_by_month_ext_adj-3.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[4]]

Past versions of cluster_by_month_ext_adj-4.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[5]]

Past versions of cluster_by_month_ext_adj-5.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[6]]

Past versions of cluster_by_month_ext_adj-6.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

Cluster spatial

location of each cluster on map, spatial analysis

# create figure combined
pH_anomaly_cluster_ext %>%
  filter (profile_type == "base") %>%
  group_split(profile_type, num_clusters, pH_extreme_order) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(
                  x = lon,
                  y = lat,
                  fill = cluster
                )) +
      lims(y = c(-85,-30)) +
      scale_fill_brewer(palette = 'Dark2') +
      labs(
        title = paste0(
          'cluster spatial distribution \n',
          'profile type: ', unique(.x$profile_type), ', ', 
          'surface extreme: ', unique(.x$pH_extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        )
      )
  )

[[1]]

Past versions of spatial_cluster_combined_ext-1.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[2]]

Past versions of spatial_cluster_combined_ext-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[3]]

Past versions of spatial_cluster_combined_ext-3.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[4]]

Past versions of spatial_cluster_combined_ext-4.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[5]]

Past versions of spatial_cluster_combined_ext-5.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[6]]

Past versions of spatial_cluster_combined_ext-6.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

Adjusted profiles

if (opt_norm_anomaly) {

  # create figure combined
  pH_anomaly_cluster_ext %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters, pH_extreme_order) %>%
    map(
      ~ map +
        geom_tile(data = .x,
                  aes(
                    x = lon,
                    y = lat,
                    fill = cluster
                  )) +
        lims(y = c(-85,-30)) +
        scale_fill_brewer(palette = 'Dark2') +
        labs(
          title = paste0(
            'cluster spatial distribution \n',
            'profile type: ', unique(.x$profile_type), ', ', 
            'surface extreme: ', unique(.x$pH_extreme), ', ', 
            'number clusters: ', unique(.x$num_clusters)
          )
        )
    )

}

[[1]]

Past versions of spatial_cluster_combined_ext_adj-1.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[2]]

Past versions of spatial_cluster_combined_ext_adj-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[3]]

Past versions of spatial_cluster_combined_ext_adj-3.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[4]]

Past versions of spatial_cluster_combined_ext_adj-4.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[5]]

Past versions of spatial_cluster_combined_ext_adj-5.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[6]]

Past versions of spatial_cluster_combined_ext_adj-6.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

Spatial by cluster location of each cluster on map, spatial analysis

# create figure by cluster
pH_anomaly_cluster_ext %>%
  group_split(profile_type, num_clusters, pH_extreme_order) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(
                  x = lon,
                  y = lat,
                  fill = cluster
                )) +
      lims(y = c(-85,-30)) +
      scale_fill_brewer(palette = 'Dark2') +
      facet_wrap( ~ cluster, ncol = 2) +
      labs(
        title = paste0(
          'cluster spatial distribution \n',
          'profile type: ', unique(.x$profile_type), ', ', 
          'surface extreme: ', unique(.x$pH_extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        )
      )
  )

Cluster spatial counts

location of each cluster on map, spatial analysis

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


# create figure
cluster_by_location %>%
  filter (profile_type == "base") %>%
  group_split(profile_type, num_clusters, pH_extreme_order) %>%
  map(
    ~ map +
      geom_tile(data = .x %>%
                  count(lat, lon, cluster),
                aes(
                  x = lon,
                  y = lat,
                  fill = n
                )) +
      lims(y = c(-85,-30)) +
      scale_fill_gradient(low = "blue",
                          high = "red",
                          trans = "log10") +
      facet_wrap(~ cluster, ncol = 2) +
      labs(
        title = paste0(
          'cluster spatial distribution \n',
          'profile type: ', unique(.x$profile_type), ', ', 
          'surface extreme: ', unique(.x$pH_extreme), ', ', 
          'number clusters: ', unique(.x$num_clusters)
        )
      )
  )

[[1]]

Past versions of spatial_cluster_count_ext-1.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[2]]

Past versions of spatial_cluster_count_ext-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[3]]

Past versions of spatial_cluster_count_ext-3.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[4]]

Past versions of spatial_cluster_count_ext-4.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[5]]

Past versions of spatial_cluster_count_ext-5.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[6]]

Past versions of spatial_cluster_count_ext-6.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

Adjusted profiles

if (opt_norm_anomaly) {

  cluster_by_location %>%
    filter (profile_type == "adjusted") %>%
    group_split(profile_type, num_clusters, pH_extreme_order) %>%
    map(
      ~ map +
        geom_tile(data = .x %>%
                    count(lat, lon, cluster),
                  aes(
                    x = lon,
                    y = lat,
                    fill = n
                  )) +
        lims(y = c(-85,-30)) +
        scale_fill_gradient(low = "blue",
                            high = "red",
                            trans = "log10") +
        facet_wrap(~ cluster, ncol = 2) +
        labs(
          title = paste0(
            'cluster spatial distribution \n',
            'profile type: ', unique(.x$profile_type), ', ', 
            'surface extreme: ', unique(.x$pH_extreme), ', ', 
            'number clusters: ', unique(.x$num_clusters)
          )
        )
    )
    
}    

[[1]]

Past versions of spatial_cluster_count_ext_adj-1.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[2]]

Past versions of spatial_cluster_count_ext_adj-2.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[3]]

Past versions of spatial_cluster_count_ext_adj-3.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[4]]

Past versions of spatial_cluster_count_ext_adj-4.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[5]]

Past versions of spatial_cluster_count_ext_adj-5.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

[[6]]

Past versions of spatial_cluster_count_ext_adj-6.png

Version	Author	Date
087d6fe	ds2n19	2023-12-07
3212753	ds2n19	2023-12-04

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] parallelly_1.32.1   magrittr_2.0.3      R6_2.5.1           
 [79] generics_0.1.3      DBI_1.1.3           pillar_1.9.0       
 [82] haven_2.5.1         whisker_0.4         withr_2.5.0        
 [85] survival_3.4-0      nnet_7.3-18         future.apply_1.10.0
 [88] modelr_0.1.10       crayon_1.5.2        utf8_1.2.2         
 [91] tzdb_0.3.0          rmarkdown_2.18      grid_4.2.2         
 [94] readxl_1.4.1        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