BGC temperature cluster analysis
David Stappard & Jens Daniel Müller
04 December, 2023
Last updated: 2023-12-04
Checks: 7 0
Knit directory: bgc_argo_r_argodata/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f2b7887 | ds2n19 | 2023-12-04 | Cluster under surface extreme. |
| html | 31b8905 | ds2n19 | 2023-12-04 | Build site. |
| Rmd | 20e0381 | ds2n19 | 2023-12-04 | Cluster under surface extreme. |
| html | 2a330f3 | ds2n19 | 2023-12-04 | Build site. |
| Rmd | 1231773 | ds2n19 | 2023-12-04 | Cluster under surface extreme. |
| html | cd75bb7 | ds2n19 | 2023-12-04 | Build site. |
| Rmd | d1088e0 | ds2n19 | 2023-12-04 | Cluster under surface extreme. |
| html | a6b9ac3 | ds2n19 | 2023-12-04 | Build site. |
| Rmd | c3b9451 | ds2n19 | 2023-12-04 | Cluster under surface extreme. |
| html | bb6c7b2 | ds2n19 | 2023-12-03 | Build site. |
| Rmd | b83178b | ds2n19 | 2023-12-03 | Cluster under surface extreme. |
| html | cfa1850 | ds2n19 | 2023-12-03 | Build site. |
| Rmd | d93ff61 | ds2n19 | 2023-12-03 | Cluster under surface extreme. |
| html | 9d9ac38 | ds2n19 | 2023-12-01 | Build site. |
| Rmd | 27c706c | ds2n19 | 2023-12-01 | Cluster under surface extreme. |
| html | ba42bd5 | ds2n19 | 2023-11-30 | Build site. |
| Rmd | 964d84d | ds2n19 | 2023-11-30 | Cluster under surface extreme. |
| html | 1ad7f89 | ds2n19 | 2023-11-30 | Build site. |
| Rmd | 0a8a718 | ds2n19 | 2023-11-30 | Cluster under surface extreme. |
| html | 98f3220 | ds2n19 | 2023-11-30 | Build site. |
| Rmd | d9e17d4 | ds2n19 | 2023-11-30 | Cluster under surface extreme. |
| html | 77cc54e | ds2n19 | 2023-11-24 | Build site. |
| Rmd | b24f211 | ds2n19 | 2023-11-24 | Cluster under surface extreme. |
| Rmd | 59f5cc4 | ds2n19 | 2023-11-23 | Moved spatiotemporal analysis to use aligned profiles. |
| html | 80c16c2 | ds2n19 | 2023-11-15 | Build site. |
| Rmd | 3eba518 | ds2n19 | 2023-11-15 | Introduction of vertical alignment and cluster analysis to github website. |
Tasks
This markdown file carries out cluster analysis on previously created bgc temperature anomaly profiles.
The cluster_analysis_determine_k chunk is used to give an indication of an appropriate number of clusers and this can then set the option opt_num_clusters. Chunk cluster_analysis_cluster_details carried out the cluster analysis and the results are used in subsequent figures.
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 do analysis to determine number of clusters to use
# opt_analysis_type = 2 do cluster analysis and further analysis on the identified clusters clusters
opt_analysis_type <- 2
# 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, 1)
opt_num_clusters_max <- c(8, 8, 8)
# What is the max depth of each profile_range
opt_max_depth <- c(600, 1000, 1500)
# Which profile range is used
opt_profile_range <- 3
# opt_measure_label, opt_xlim and opt_xbreaks are associated with formatting
opt_measure_label <- "temperature anomaly (°C)"
opt_xlim <- c(-4.5, 4.5)
opt_xbreaks <- c(-4, -2, 0, 2, 4)
# adjusted to be in scale -1 to 1
opt_measure_label_adjusted <- "adjusted temperature anomaly"
opt_xlim_adjusted <- c(-1, 1)
opt_xbreaks_adjusted <- c(-1.0, -0.5, 0, 0.5, 1.0)
# 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
# Carried out for 1500m profiles
opt_profile_range = 3
extreme_type <- c('L', 'N', 'H')
opt_num_clusters_ext_min <- c(1, 1, 1)
opt_num_clusters_ext_max <- c(6, 6, 6)
# 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_amolaly <- TRUEtheme_set(theme_bw())
map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))Cluster analysis
Preparation
Prepare data for cluster analysis
# read data
temp_anomaly_va <- read_rds(file = paste0(path_argo_preprocessed, "/temp_anomaly_va.rds"))
# select profile based on profile_range and he appropriate max depth
temp_anomaly_va <- temp_anomaly_va %>%
filter(profile_range == opt_profile_range & depth <= opt_max_depth[opt_profile_range])
# Simplified table ready to pivot
temp_anomaly_va_id <- temp_anomaly_va %>%
select(file_id,
depth,
anomaly,
year,
month,
lat,
lon)
# wide table with each depth becoming a column
temp_anomaly_va_wide <- temp_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
temp_anomaly_va_wide <- temp_anomaly_va_wide %>%
drop_na()
# Table for cluster analysis
points <- temp_anomaly_va_wide %>%
column_to_rownames(var = "file_id")
# normalisation?
if (opt_norm_amolaly) {
surf_anomaly <- abs(temp_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 <- temp_anomaly_va_id %>%
group_by(file_id) %>%
summarise(abs_sa = max(abs(anomaly))) %>%
ungroup() %>%
select (file_id, abs_sa)
temp_anomaly_va_id_normalised <- left_join(temp_anomaly_va_id, surf_anomaly)
#temp_anomaly_va_id_normalised <- temp_anomaly_va_id_normalised %>%
# mutate(anomaly = if_else(abs_sa > 1.0, anomaly/abs_sa, anomaly))
temp_anomaly_va_id_normalised <- temp_anomaly_va_id_normalised %>%
mutate(anomaly = anomaly/abs_sa)
# wide table with each depth becoming a column
temp_anomaly_va_wide <- temp_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
temp_anomaly_va_wide <- temp_anomaly_va_wide %>%
drop_na()
# Table for cluster analysis
points_normalised <- temp_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)) +
labs(
title = paste0('within ss by number of clusters: ', opt_max_depth[i_range], ' m profiles'),
x = 'number of clusters',
y = 'within ss'
)
}Cluster analysis
Based on all floats regardless of surface condition.
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 temp_anomaly_va
temp_anomaly_cluster <-
full_join(temp_anomaly_va_id, profile_id)
# Add profile_type field
temp_anomaly_cluster <- temp_anomaly_cluster %>%
mutate(profile_type = 'base')
# Check null clusters
temp_anomaly_cluster <- temp_anomaly_cluster %>%
filter(!is.na(cluster))
# Create table to be used for later analysis and Set the number of clusters field
if (!exists('temp_anomaly_cluster_all')) {
temp_anomaly_cluster_all <- temp_anomaly_cluster %>%
mutate(num_clusters = inum_clusters)
} else {
temp_anomaly_cluster_all <-
rbind(
temp_anomaly_cluster_all,
temp_anomaly_cluster %>%
mutate(num_clusters = inum_clusters)
)
}
} else if (iType == 2 & opt_norm_amolaly) {
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 temp_anomaly_va
temp_anomaly_cluster_norm <-
full_join(temp_anomaly_va_id_normalised %>% select(-c(abs_sa)) ,
profile_id)
# Add profile_type field
temp_anomaly_cluster_norm <- temp_anomaly_cluster_norm %>%
mutate(profile_type = 'adjusted')
# Check null clusters
temp_anomaly_cluster_norm <- temp_anomaly_cluster_norm %>%
filter(!is.na(cluster))
# Create table to be used for later analysis and Set the number of clusters field
if (!exists('temp_anomaly_cluster_all')) {
temp_anomaly_cluster_all <- temp_anomaly_cluster_norm %>%
mutate(num_clusters = inum_clusters)
} else {
temp_anomaly_cluster_all <-
rbind(
temp_anomaly_cluster_all,
temp_anomaly_cluster_norm %>%
mutate(num_clusters = inum_clusters)
)
}
}
}
}
# Plot cluster mean
anomaly_cluster_mean <- temp_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 <- temp_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 <- temp_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 <- temp_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]]
| Version | Author | Date |
|---|---|---|
| 31b8905 | ds2n19 | 2023-12-04 |
| 2a330f3 | ds2n19 | 2023-12-04 |
| cd75bb7 | ds2n19 | 2023-12-04 |
| a6b9ac3 | ds2n19 | 2023-12-04 |
| bb6c7b2 | ds2n19 | 2023-12-03 |
| cfa1850 | ds2n19 | 2023-12-03 |
| 9d9ac38 | ds2n19 | 2023-12-01 |
| ba42bd5 | ds2n19 | 2023-11-30 |
| 98f3220 | ds2n19 | 2023-11-30 |
| 77cc54e | ds2n19 | 2023-11-24 |
| 80c16c2 | ds2n19 | 2023-11-15 |
[[2]]
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[[6]]
[[7]]
[[8]]
Adjusted profiles
if (opt_norm_amolaly) {
# 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]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
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]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
Adjusted profiles
if (opt_norm_amolaly) {
# 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]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
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]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
Adjusted profiles
if (opt_norm_amolaly) {
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]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
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 <- temp_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]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
Adjusted profiles
if (opt_norm_amolaly) {
# 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]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
Cluster spatial
location of each cluster on map, spatial analysis
if (opt_analysis_type == 2) {
# create figure
temp_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]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
Adjusted profiles
if (opt_norm_amolaly) {
# create figure
temp_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]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
location of each cluster on separate maps, spatial analysis
if (opt_analysis_type == 2) {
# create figure
map +
geom_tile(data = temp_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')
}count of measurements for each cluster on separate maps, spatial analysis
if (opt_analysis_type == 2) {
# Count profiles
cluster_by_location <- temp_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_viridis_c(
option = "cividis",
direction = -1,
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]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
Adjusted profiles
if (opt_norm_amolaly) {
# 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_viridis_c(
option = "cividis",
direction = -1,
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]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
Cluster spatial year
location of each cluster on map, spatial analysis by year
if (opt_analysis_type == 2) {
# create figure
temp_anomaly_cluster_all %>%
group_split(profile_type, num_clusters, 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$year), '\n',
'type = ', unique(.x$profile_type), ', ',
'num clusters = ', unique(.x$num_clusters)
)
)
)
}Cluster by surface Extreme
# ---------------------------------------------------------------------------------------------
# read data
# ---------------------------------------------------------------------------------------------
# load previously created OceanSODA extreme data. date, position and nature of extreme
if (opt_extreme_determination == 1){
temp_extreme <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_SST_anomaly_field_01.rds")) %>%
select(lon, lat, date, temp_extreme)
} else if (opt_extreme_determination == 2){
temp_extreme <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_SST_anomaly_field_02.rds")) %>%
select(lon, lat, date, temp_extreme)
}
# -------------------------------------------------------------------------------------------------------------
# Temp - review incidences of extremes based on method.
# -------------------------------------------------------------------------------------------------------------
#
# temp_extreme_info_01 <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_SST_anomaly_field_01.rds")) %>%
# select(lon, lat, date, temp_extreme)
#
# temp_extreme_info_01 <- temp_extreme_info_01 %>%
# group_by(temp_extreme, date) %>%
# summarise(n = n()) %>%
# ungroup()
#
# temp_extreme_info_01 %>%
# filter (temp_extreme %in% c('H', 'L') & date >= '2013-01-01') %>%
# ggplot(aes(
# x = date,
# y = n,
# col = temp_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'
# )
# )
#
#
# temp_extreme_info_02 <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_SST_anomaly_field_02.rds")) %>%
# select(lon, lat, date, temp_extreme)
#
# temp_extreme_info_02 <- temp_extreme_info_02 %>%
# group_by(temp_extreme, date) %>%
# summarise(n = n()) %>%
# ungroup()
#
# temp_extreme_info_02 %>%
# filter (temp_extreme %in% c('H', 'L') & date >= '2013-01-01') %>%
# ggplot(aes(
# x = date,
# y = n,
# col = temp_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
temp_anomaly_va <- read_rds(file = paste0(path_argo_preprocessed, "/temp_anomaly_va.rds")) %>%
mutate(date = ymd(format(date, "%Y-%m-15")))
# Add the OceanSODA extreme condition
temp_anomaly_va <- left_join(temp_anomaly_va, temp_extreme)
# If temp_extreme is NA set it to N
temp_anomaly_va <- temp_anomaly_va %>% replace_na(list(temp_extreme = 'N'))
temp_anomaly_va <- temp_anomaly_va %>% mutate (profile_type = 'base')
# Create a replica data set with profile_type = adjusted
if (opt_norm_amolaly){
# mark as adjusted
temp_anomaly_va_norm <- temp_anomaly_va %>% mutate (profile_type = 'adjusted')
# Determine surface anomaly for each profile
# surf_anomaly <- abs(temp_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 <- temp_anomaly_va_id %>%
group_by(file_id) %>%
summarise(abs_sa = max(abs(anomaly))) %>%
ungroup() %>%
select (file_id, abs_sa)
temp_anomaly_va_norm <- left_join(temp_anomaly_va_norm, surf_anomaly)
# Carry out the adjustment
#temp_anomaly_va_norm <- temp_anomaly_va_norm %>%
# mutate(anomaly = if_else(abs_sa > 1.0, anomaly/abs_sa, anomaly))
temp_anomaly_va_norm <- temp_anomaly_va_norm %>%
mutate(anomaly = anomaly/abs_sa)
#remove the surface anomaly field
temp_anomaly_va_norm <- temp_anomaly_va_norm %>% select(-c(abs_sa))
# Append to base profiles
temp_anomaly_va <- rbind(temp_anomaly_va, temp_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
temp_anomaly_va_id <- temp_anomaly_va %>%
filter(profile_range == opt_profile_range & depth <= opt_max_depth[opt_profile_range] & temp_extreme == extreme_type[i] & profile_type == sel_profile_type)
# Simplified table ready to pivot
temp_anomaly_va_id <- temp_anomaly_va_id %>%
select(file_id,
depth,
anomaly,
year,
month,
lat,
lon)
# wide table with each depth becoming a column
temp_anomaly_va_wide <- temp_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
temp_anomaly_va_wide <- temp_anomaly_va_wide %>%
drop_na()
# Table for cluster analysis
points <- temp_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 temp_anomaly_va
temp_anomaly_cluster <- full_join(temp_anomaly_va_id, profile_id)
# Plot cluster mean
temp_anomaly_cluster <- temp_anomaly_cluster %>%
filter(!is.na(cluster))
# cluster mean
anomaly_cluster_mean <- temp_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 <- temp_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 <- temp_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(
temp_extreme_order = i,
temp_extreme = extreme_type[i],
num_clusters = inum_clusters,
profile_type = sel_profile_type
)
anomaly_cluster_mean_year_ext <-
anomaly_cluster_mean_year %>% mutate(
temp_extreme_order = i,
temp_extreme = extreme_type[i],
num_clusters = inum_clusters,
profile_type = sel_profile_type
)
anomaly_year_mean_ext <-
anomaly_year_mean %>% mutate(
temp_extreme_order = i,
temp_extreme = extreme_type[i],
num_clusters = inum_clusters,
profile_type = sel_profile_type
)
temp_anomaly_cluster_ext <-
temp_anomaly_cluster %>% mutate(
temp_extreme_order = i,
temp_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(
temp_extreme_order = i,
temp_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(
temp_extreme_order = i,
temp_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(
temp_extreme_order = i,
temp_extreme = extreme_type[i],
num_clusters = inum_clusters,
profile_type = sel_profile_type
)
)
temp_anomaly_cluster_ext <-
rbind(
temp_anomaly_cluster_ext,
temp_anomaly_cluster_ext <-
temp_anomaly_cluster %>% mutate(
temp_extreme_order = i,
temp_extreme = extreme_type[i],
num_clusters = inum_clusters,
profile_type = sel_profile_type
)
)
}
}
}
}Cluster mean
# Determine profile count by cluster and year
# Count the measurements
cluster_by_year <- temp_anomaly_cluster_ext %>%
count(profile_type, num_clusters, temp_extreme, temp_extreme_order, file_id, cluster, year,
name = "count_cluster")
#cluster_by_year %>% filter (num_clusters == 5 & temp_extreme == 'N')
# Convert to profiles
cluster_by_year <- cluster_by_year %>%
count(profile_type, num_clusters, temp_extreme, temp_extreme_order, cluster, year,
name = "count_cluster")
# total of each type of cluster
cluster_count <- cluster_by_year %>%
group_by(profile_type, num_clusters, temp_extreme, temp_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, temp_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$temp_extreme), ', ',
'number clusters: ', unique(.x$num_clusters)
),
x = opt_measure_label,
y = 'depth (m)'
)
)[[1]]
[[2]]
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[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
[[13]]
[[14]]
[[15]]
[[16]]
[[17]]
[[18]]
Adjusted profiles
if (opt_norm_amolaly) {
# create figure of cluster mean profiles
anomaly_cluster_mean_ext %>%
filter (profile_type == "adjusted") %>%
group_split(profile_type, num_clusters, temp_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$profile_type), ', ',
'surface extreme: ', unique(.x$temp_extreme), ', ',
'number clusters: ', unique(.x$num_clusters)
),
x = opt_measure_label_adjusted,
y = 'depth (m)'
)
)
}[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
[[13]]
[[14]]
[[15]]
[[16]]
[[17]]
[[18]]
Clusters mean by year
# cluster means by year
anomaly_cluster_mean_year_ext %>%
filter (profile_type == "base") %>%
mutate(year = as.factor(year)) %>%
group_split(profile_type, num_clusters, temp_extreme_order) %>%
map(
~ ggplot(data = .x,) +
geom_path(aes(
x = anomaly_mean,
y = depth,
col = year
)) +
geom_vline(xintercept = 0) +
scale_y_reverse() +
facet_wrap(~ cluster) +
coord_cartesian(xlim = opt_xlim) +
scale_x_continuous(breaks = opt_xbreaks) +
scale_color_viridis_d() +
labs(
title = paste0(
'Overall mean anomaly profiles by cluster by year \n',
'profile type: ', unique(.x$profile_type), ', ',
'surface extreme: ', unique(.x$temp_extreme), ', ',
'number clusters: ', unique(.x$num_clusters)
),
x = opt_measure_label,
y = 'depth (m)'
)
)[[1]]
[[2]]
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[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
[[13]]
[[14]]
[[15]]
[[16]]
[[17]]
[[18]]
Adjusted profiles
if (opt_norm_amolaly) {
# cluster means by year
anomaly_cluster_mean_year_ext %>%
filter (profile_type == "adjusted") %>%
mutate(year = as.factor(year)) %>%
group_split(profile_type, num_clusters, temp_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$profile_type), ', ',
'surface extreme: ', unique(.x$temp_extreme), ', ',
'number clusters: ', unique(.x$num_clusters)
),
x = opt_measure_label_adjusted,
y = 'depth (m)'
)
)
}[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
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[[13]]
[[14]]
[[15]]
[[16]]
[[17]]
[[18]]
Cluster by year
count of each cluster by year
# Determine profile count by extreme and cluster and year
# Count the measurements
cluster_by_year <- temp_anomaly_cluster_ext %>%
count(file_id, profile_type, num_clusters, temp_extreme_order, temp_extreme, cluster, year,
name = "count_cluster")
# Convert to profiles
cluster_by_year <- cluster_by_year %>%
count(profile_type, num_clusters, temp_extreme_order, temp_extreme, cluster, year,
name = "count_cluster")
year_min <- min(cluster_by_year$year)
year_max <- max(cluster_by_year$year)
# create figure
cluster_by_year %>%
filter (profile_type == "base") %>%
group_split(profile_type, num_clusters, temp_extreme_order) %>%
map(
~ ggplot(
data = .x,
aes(
x = year,
y = count_cluster,
col = cluster,
group = cluster
)
) +
geom_point() +
geom_line() +
scale_x_continuous(breaks = seq(year_min, year_max, 2)) +
scale_color_brewer(palette = 'Dark2') +
labs(
x = 'year',
y = 'number of profiles',
col = 'cluster',
title = paste0(
'Count of profiles by year and cluster \n',
'profile type: ', unique(.x$profile_type), ', ',
'surface extreme: ', unique(.x$temp_extreme), ', ',
'number clusters: ', unique(.x$num_clusters)
)
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
[[13]]
[[14]]
[[15]]
[[16]]
[[17]]
[[18]]
Adjusted profiles
if (opt_norm_amolaly) {
# create figure
cluster_by_year %>%
filter (profile_type == "adjusted") %>%
group_split(profile_type, num_clusters, temp_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$profile_type), ', ',
'surface extreme: ', unique(.x$temp_extreme), ', ',
'number clusters: ', unique(.x$num_clusters)
)
)
)
}[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
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[[13]]
[[14]]
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[[16]]
[[17]]
[[18]]
Cluster by month
count of each cluster by month of year
# Determine profile count by cluster and year
# Count the measurements
cluster_by_year <- temp_anomaly_cluster_ext %>%
count(file_id, profile_type, num_clusters, temp_extreme_order, temp_extreme, cluster, month,
name = "count_cluster")
# Convert to profiles
cluster_by_year <- cluster_by_year %>%
count(profile_type, num_clusters, temp_extreme_order, temp_extreme, cluster, month,
name = "count_cluster")
# create figure
cluster_by_year %>%
filter (profile_type == "base") %>%
group_split(profile_type, num_clusters, temp_extreme_order) %>%
map(
~ ggplot(
data = .x,
aes(
x = month,
y = count_cluster,
col = cluster,
group = cluster
)
) +
geom_point() +
geom_line() +
scale_x_continuous(breaks = seq(1, 12, 2)) +
scale_color_brewer(palette = 'Dark2') +
labs(
x = 'month',
y = 'number of profiles',
col = 'cluster',
title = paste0(
'Count of profiles by month and cluster \n',
'profile type: ', unique(.x$profile_type), ', ',
'surface extreme: ', unique(.x$temp_extreme), ', ',
'number clusters: ', unique(.x$num_clusters)
)
)
)[[1]]
[[2]]
[[3]]
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[[7]]
[[8]]
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[[10]]
[[11]]
[[12]]
[[13]]
[[14]]
[[15]]
[[16]]
[[17]]
[[18]]
Adjusted profiles
if (opt_norm_amolaly) {
# create figure
cluster_by_year %>%
filter (profile_type == "adjusted") %>%
group_split(profile_type, num_clusters, temp_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$profile_type), ', ',
'surface extreme: ', unique(.x$temp_extreme), ', ',
'number clusters: ', unique(.x$num_clusters)
)
)
)
}[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
[[13]]
[[14]]
[[15]]
[[16]]
[[17]]
[[18]]
Cluster spatial
location of each cluster on map, spatial analysis
# create figure combined
temp_anomaly_cluster_ext %>%
filter (profile_type == "base") %>%
group_split(profile_type, num_clusters, temp_extreme_order) %>%
map(
~ map +
geom_tile(data = .x,
aes(
x = lon,
y = lat,
fill = cluster
)) +
lims(y = c(-85,-30)) +
scale_fill_brewer(palette = 'Dark2') +
labs(
title = paste0(
'cluster spatial distribution \n',
'profile type: ', unique(.x$profile_type), ', ',
'surface extreme: ', unique(.x$temp_extreme), ', ',
'number clusters: ', unique(.x$num_clusters)
)
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
[[13]]
[[14]]
[[15]]
[[16]]
[[17]]
[[18]]
Adjusted profiles
if (opt_norm_amolaly) {
# create figure combined
temp_anomaly_cluster_ext %>%
filter (profile_type == "adjusted") %>%
group_split(profile_type, num_clusters, temp_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$profile_type), ', ',
'surface extreme: ', unique(.x$temp_extreme), ', ',
'number clusters: ', unique(.x$num_clusters)
)
)
)
}[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
[[13]]
[[14]]
[[15]]
[[16]]
[[17]]
[[18]]
Spatial by cluster
location of each cluster on map, spatial analysis
# create figure by cluster
temp_anomaly_cluster_ext %>%
group_split(profile_type, num_clusters, temp_extreme_order) %>%
map(
~ map +
geom_tile(data = .x,
aes(
x = lon,
y = lat,
fill = cluster
)) +
lims(y = c(-85,-30)) +
scale_fill_brewer(palette = 'Dark2') +
facet_wrap( ~ cluster, ncol = 2) +
labs(
title = paste0(
'cluster spatial distribution \n',
'profile type: ', unique(.x$profile_type), ', ',
'surface extreme: ', unique(.x$temp_extreme), ', ',
'number clusters: ', unique(.x$num_clusters)
)
)
)Spatial count
location of each cluster on map, spatial analysis
# Count profiles
cluster_by_location <- temp_anomaly_cluster_ext %>%
count(profile_type, num_clusters, temp_extreme_order, temp_extreme, file_id, lat, lon, cluster,
name = "count_cluster")
# create figure
cluster_by_location %>%
filter (profile_type == "base") %>%
group_split(profile_type, num_clusters, temp_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_viridis_c(
option = "cividis",
direction = -1,
trans = "log10"
) +
facet_wrap(~ cluster, ncol = 2) +
labs(
title = paste0(
'cluster spatial distribution \n',
'profile type: ', unique(.x$profile_type), ', ',
'surface extreme: ', unique(.x$temp_extreme), ', ',
'number clusters: ', unique(.x$num_clusters)
)
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
[[13]]
[[14]]
[[15]]
[[16]]
[[17]]
[[18]]
Adjusted profiles
if (opt_norm_amolaly) {
cluster_by_location %>%
filter (profile_type == "adjusted") %>%
group_split(profile_type, num_clusters, temp_extreme_order) %>%
map(
~ map +
geom_tile(data = .x %>%
count(lat, lon, cluster),
aes(
x = lon,
y = lat,
fill = n
)) +
lims(y = c(-85,-30)) +
scale_fill_viridis_c(
option = "cividis",
direction = -1,
trans = "log10"
) +
facet_wrap(~ cluster, ncol = 2) +
labs(
title = paste0(
'cluster spatial distribution \n',
'profile type: ', unique(.x$profile_type), ', ',
'surface extreme: ', unique(.x$temp_extreme), ', ',
'number clusters: ', unique(.x$num_clusters)
)
)
)
} [[1]]
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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