pH cluster analysis
David Stappard & Jens Daniel Müller
15 November, 2023
Last updated: 2023-11-15
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Knit directory: bgc_argo_r_argodata/
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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 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.
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 <- 7
# 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_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_xlim <- c(-2e-9, 2e-9)
opt_xbreaks <- c(-2e-9, -1e-9, 0, 1e-9, 2e-9)
}
# 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 clusterstheme_set(theme_bw())
map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))Cluster analysis
Preperation
Prepare data for cluster analysis
# read data
pH_anomaly_va <- read_rds(file = paste0(path_argo_preprocessed, "/pH_anomaly_va.rds"))
# select just 1500m profiles
pH_anomaly_va <- pH_anomaly_va %>%
filter(profile_range == 3)
# 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")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
if (opt_analysis_type == 2) {
# caution: set.seed works only for one execution of the clustering
# How sensitive are our results to the acutal seed?
set.seed(1)
kclusts <-
tibble(k = opt_num_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)
# Note the null cluster results relate to the profiles that were removed
# from pH_anomaly_va_wide by the dron_na() function above.
pH_anomaly_cluster <- pH_anomaly_cluster %>%
filter(!is.na(cluster))
# Plot 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 ()
# create figure
anomaly_cluster_mean %>%
ggplot() +
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'),
x = opt_measure_label,
y = 'depth (m)'
)
anomaly_cluster_mean_year %>%
mutate(year = as.factor(year)) %>%
ggplot() +
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'),
x = opt_measure_label,
y = 'depth (m)'
)
}
Cluster climatology
# bf_coefs <-
# summary(lm(anomaly_year_mean$anomaly_mean ~ anomaly_year_mean$year))$coefficients
#
# line_eq <-
# paste0(
# "Y = ",
# formatC(bf_coefs[2, 1], format = "e", digits = 2),
# "X ",
# formatC(
# bf_coefs[1, 1],
# format = "e",
# digits = 2,
# flag = "+"
# )
# )
#
# # annual difference from climatology
# anomaly_year_mean %>%
# ggplot(aes(x = year,
# y = anomaly_mean)) +
# geom_point(col = 'red', size = 2) +
# geom_smooth(
# method = lm,
# se = FALSE,
# col = 'blue',
# linetype = 'dashed'
# ) +
# labs(
# title = paste0('Overall variation from climatology (', line_eq, ')'),
# x = 'year',
# y = opt_measure_label
# )
# A nice short alternative, uses the package ggpmisc
anomaly_year_mean %>%
ggplot(aes(x = year,
y = anomaly_mean)) +
stat_poly_line() +
stat_poly_eq(use_label(c("eq", "R2","P", "n"))) +
geom_point()
Cluster by year
count of each cluster by year
if (opt_analysis_type == 2) {
# Determine profile count by cluster and year
cluster_by_year <- pH_anomaly_cluster %>%
count(cluster, year,
name = "count_cluster")
year_min <- min(cluster_by_year$year)
year_max <- max(cluster_by_year$year)
# create figure
cluster_by_year %>%
ggplot(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 = 'Count of profiles by year and cluster')
}
Cluster by month
count of each cluster by month of year
if (opt_analysis_type == 2) {
# Determine profile count by cluster and year
cluster_by_year <- pH_anomaly_cluster %>%
count(cluster, month,
name = "count_cluster")
# create figure
cluster_by_year %>%
ggplot(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 = 'Count of profiles by month and cluster')
}
Cluster spatial
location of each cluster on map, 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')+
labs(title = 'cluster spatial distribution')
# 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')
# create figure
map +
geom_tile(data = pH_anomaly_cluster %>%
count(lat, lon, cluster),
aes(x = lon,
y = lat,
fill = n)) +
lims(y = c(-85, -30)) +
scale_fill_viridis_c(option = "cividis", direction = -1, trans = "log10") +
facet_wrap( ~ cluster, ncol = 2) +
labs(title = 'cluster spatial distribution')
}
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)
))
)
}[[1]]
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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 %>%
ggplot()+
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'), x = opt_measure_label, y = 'depth (m)')
cluster_ph_chla %>%
ggplot()+
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 profiles by cluster'), 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 %>%
ggplot() +
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 profiles by cluster'),
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))
}
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 %>%
ggplot()+
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'), x = opt_measure_label, y = 'depth (m)')
cluster_ph_doxy %>%
ggplot()+
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 profiles by cluster'), 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 %>%
ggplot() +
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 profiles by cluster'),
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))
}
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.4
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 confintr_1.0.2
[19] knitr_1.41 polyclip_1.10-4 polynom_1.4-1
[22] jsonlite_1.8.3 workflowr_1.7.0 dbplyr_2.2.1
[25] compiler_4.2.2 httr_1.4.4 backports_1.4.1
[28] assertthat_0.2.1 Matrix_1.5-3 fastmap_1.1.0
[31] gargle_1.2.1 cli_3.6.1 later_1.3.0
[34] tweenr_2.0.2 htmltools_0.5.3 quantreg_5.94
[37] tools_4.2.2 gtable_0.3.1 glue_1.6.2
[40] Rcpp_1.0.10 cellranger_1.1.0 jquerylib_0.1.4
[43] RNetCDF_2.6-1 DiceDesign_1.9 vctrs_0.6.4
[46] iterators_1.0.14 timeDate_4021.106 xfun_0.35
[49] gower_1.0.0 globals_0.16.2 rvest_1.0.3
[52] lifecycle_1.0.3 googlesheets4_1.0.1 future_1.29.0
[55] MASS_7.3-58.1 ipred_0.9-13 hms_1.1.2
[58] promises_1.2.0.1 parallel_4.2.2 SparseM_1.81
[61] RColorBrewer_1.1-3 yaml_2.3.6 sass_0.4.4
[64] rpart_4.1.19 stringi_1.7.8 highr_0.9
[67] foreach_1.5.2 lhs_1.1.6 hardhat_1.3.0
[70] lava_1.7.0 rlang_1.1.1 pkgconfig_2.0.3
[73] evaluate_0.18 lattice_0.20-45 labeling_0.4.2
[76] tidyselect_1.2.0 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