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Task

This markdown file reads previously created temperature climatology file and uses that as the definition of the depth levels that the Argo temperature data should be aligned to. As the climatology is based on pressure it is first aligned to standard depths. Previously created annual data files (yyyy_core_data_temp.rds), metadata (yyyy_core_metadata.rds) and fileid (core_fileid.rds) files are loaded from the core preprocessed folder.

Base data qc flags are checked to ensure that the float position, pressure measurements and temperature measurements are good. Pressure is used to derive the depth of each measurement. The temperature profile is checked to ensure that significant gaps (specified by the opt_gap_limit, opt_gap_min_depth and opt_gap_max_depth) do not exist. Profiles are assigned a profile_range field that identifies the depth 1 = 600 m, 2 = 1200 m and 3 = 1500 m.

The float temperature profiles are then aligned using the spline function to match the depth levels of the climatology resulting in data frame temp_core_va. An anomaly file is then created from the vertically aligned profiles and climatology.

Dependencies

yyyy_core_data_temp.rds, yyyy_core_metadata.rds, core_fileid.rds - core preprocessed folder created by load_argo_core.

temp_clim_va.rds - BGC preprocessed folder created by temp_align_climatology.

Outputs (Core preprocessed folder)

temp_core_va.rds – vertically aligned temperature profiles.

temp_clim_va.rds – climatology temperature profiles.

temp_anomaly_va.rds – anomaly temperature profiles.

Set directories

location of pre-prepared data

Set options

Define options that are used to determine profiles that we will us in the ongoing analysis

# Options

# opt_profile_depth_range
# The profile must have at least one temperature reading at a depth <= opt_profile_depth_range[1, ]
# The profile must have at least one temperature reading at a depth >= opt_profile_depth_range[2, ].
# In addition if the profile depth does not exceed the min(opt_profile_depth_range[2, ]) (i.e. 600) it will be removed.
profile_range <- c(1, 2, 3)
min_depth <- c(5.0, 5.0, 5.0)
max_depth <- c(600, 1200, 1500)
opt_profile_depth_range <- data.frame(profile_range, min_depth, max_depth)

# The profile should not have a gap greater that opt_gap_limit within the range defined by opt_gap_min_depth and opt_gap_max_depth
opt_gap_limit <- c(28, 55, 110)
opt_gap_min_depth <- c(0, 400, 1000)
opt_gap_max_depth <- c(400, 1000, 1500)

# year to be refreshed are set by opt_min_year and opt_max_year
opt_min_year = 2013
opt_max_year = 2023

# 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)

# opt_exclude_shallower
# This option will exclude depths from the climatology and subsequent vertically aligned data that are shallower than opt_exclude_shallower.
# e.g. set to 4.5 to ensure that the top depth of 0.0 m is excluded
# Set to 0.0 to ensure no depths are excluded.
opt_exclude_shallower <- 4.5

read climatology

Temperature climatology has been prepared during BGC analysis

temp_clim_va <- read_rds(file = paste0(path_argo_preprocessed, "/temp_clim_va.rds"))

target_depth_levels <- unique(temp_clim_va[c("depth")])
target_depth_levels <- target_depth_levels %>% select(target_depth = depth)

# Exclude 0.0 m depth from the climatology and target_depth_levels
target_depth_levels <- target_depth_levels %>% filter(target_depth > opt_exclude_shallower)

Prepare years

Read annual core temperature and metadata file. Validate profile against limits and align to target_depth_range

for (target_year in opt_min_year:opt_max_year) {

#target_year = 2023

# --------------------------------------------------------------------------------
# Read data
# --------------------------------------------------------------------------------

# base data and associated metadata
core_data <- read_rds(file = paste0(path_core_preprocessed, "/", target_year, "_core_data_temp.rds"))
core_metadata <- read_rds(file = paste0(path_core_preprocessed, "/", target_year, "_core_metadata.rds"))
core_fileid <- read_rds(file = paste0(path_core_preprocessed, "/core_fileid.rds"))

#Replace file with file_id
core_metadata <- full_join(core_metadata, core_fileid)
core_metadata <- core_metadata %>%
  select(-c(file))
core_data <- full_join(core_data, core_fileid)
core_data <- core_data %>%
  select(-c(file))

# Select relevant field from metadata ready to join to core_data
core_metadata_select <- core_metadata %>%
  filter (position_qc == 1) %>%
  select(file_id,
         date,
         lat,
         lon) %>%
  mutate(year = year(date),
         month = month(date),
         .after = date)

# we only want pressure and temperature data
# conditions 
# !is.na(depth) - pressure value must be present
# !is.na(temp_adjusted) - temperature value must be present
# pres_adjusted_qc %in% c(1, 8), temp_adjusted_qc %in% c(1, 8) and n_prof == 1 has been applied in load process
core_data_temp <- core_data %>%
  filter(!is.na(depth) & !is.na(temp_adjusted)) %>%
  select(file_id,
         depth,
         temp_adjusted)

# # join with metadata information and calculate depth field
# core_data_temp <-
#   inner_join(core_metadata_select %>% select(file_id, lat),
#              core_data_temp) %>%
#   select(-c(lat, pres_adjusted))

# # ensure we have a depth, and temp_adjusted for all rows in bgc_data_temp
# core_data_temp <- core_data_temp %>%
#   filter(!is.na(depth) & !is.na(temp_adjusted))


# clean up working tables
rm(core_data, core_metadata)
gc()

# --------------------------------------------------------------------------------
# Profile limits
# --------------------------------------------------------------------------------

# Apply the rules that are determined by options set in set_options.
# Profile must cover a set range and not contain gaps.

# Determine profile min and max depths
core_profile_limits <- core_data_temp %>%
  group_by(file_id) %>%
  summarise(
    min_depth = min(depth),
    max_depth = max(depth),
  ) %>%
  ungroup()

# The profile much match at least one of teh range criteria
force_min <- min(opt_profile_depth_range$min_depth)
force_max <- min(opt_profile_depth_range$max_depth)

# Apply profile min and max restrictions
core_apply_limits <- core_profile_limits %>%
  filter(
    min_depth <= force_min &
    max_depth >= force_max
    )

# Ensure working data set only contains profiles that have confrormed to the range test
core_data_temp <- right_join(core_data_temp,
                             core_apply_limits %>% select(file_id))

# Add profile type field and set all to 1.  
# All profile that meet the minimum requirements are profile_range = 1
core_data_temp <- core_data_temp %>%
  mutate(profile_range = 1)

for (i in 2:nrow(opt_profile_depth_range)) {

  #i = 3
  range_min <- opt_profile_depth_range[i,'min_depth']
  range_max <- opt_profile_depth_range[i,'max_depth']

  # Apply profile min and max restrictions
  core_apply_limits <- core_profile_limits %>%
    filter(min_depth <= range_min &
             max_depth >= range_max) %>%
    select(file_id) %>%
    mutate (profile_range = i)

  # Update profile range to i for these profiles
  # core_data_temp <- full_join(core_data_temp, core_apply_limits) %>%
  #                         filter(!is.na(min_depth))
  core_data_temp <-
    core_data_temp %>% rows_update(core_apply_limits, by = "file_id")

}

# Find the gaps within the profiles
profile_gaps <- full_join(core_data_temp,
                          opt_profile_depth_range) %>%
  filter(depth >= min_depth &
           depth <= max_depth) %>%
  select(file_id,
         depth) %>%
  arrange(file_id, depth) %>%
  group_by(file_id) %>%
  mutate(gap = depth - lag(depth, default = 0)) %>%
  ungroup()

# Ensure we do not have gaps in the profile that invalidate it 
for (i_gap in opt_gap_limit) {

  # The limits to be applied in that pass of for loop
  # i_gap <- opt_gap_limit[3]
  i_gap_min = opt_gap_min_depth[which(opt_gap_limit == i_gap)]
  i_gap_max = opt_gap_max_depth[which(opt_gap_limit == i_gap)]
  
  # Which gaps are greater than i_gap
  profile_gaps_remove <- profile_gaps %>%
    filter(gap > i_gap) %>%
    filter(depth >= i_gap_min & depth <= i_gap_max) %>%
    distinct(file_id) %>% 
    pull()
  
  # Remonve gap-containing profiles from working data set
  core_data_temp <- core_data_temp %>% 
    filter(!file_id %in% profile_gaps_remove)
}

# clean up working tables
rm(core_apply_limits, core_profile_limits, profile_gaps, profile_gaps_remove)
gc()

# create df that contains the observations prior to vertical alignment 
core_data_temp_full <- left_join(core_data_temp, core_metadata_select)

# --------------------------------------------------------------------------------
# vertical alignment
# --------------------------------------------------------------------------------

# We have a set of temperature profiles that match our criteria we now need to align that data set to match the 
# depth that are in target_depth_range, this will match the range of climatology values in temp_clim_va

# create unique combinations of file_id and profile ranges
profile_range_file_id <- 
  core_data_temp %>% 
  distinct(file_id, profile_range)


# select variable of interest and prepare target_depth field
core_data_temp_clean <- core_data_temp %>%
  select(-profile_range) %>%
  mutate(target_depth = depth, .after = depth)

rm(core_data_temp)
gc()

# create all possible combinations of location, month and depth levels for interpolation
target_depth_grid <-
  expand_grid(
    target_depth_levels,
    profile_range_file_id
  )

# Constrain target_depth_grid to profile depth range
target_depth_grid <-
  left_join(target_depth_grid, opt_profile_depth_range) %>%
  filter(target_depth <= max_depth)

target_depth_grid <- target_depth_grid %>%
  select(target_depth,
         file_id)

# extend temp depth vectors with target depths
core_data_temp_extended <-
  full_join(core_data_temp_clean, target_depth_grid) %>%
  arrange(file_id, target_depth)

rm(core_data_temp_clean)
gc()

# predict spline interpolation on adjusted depth grid for temp location and month
core_data_temp_interpolated <-
  core_data_temp_extended %>%
  group_by(file_id) %>%
  mutate(temp_spline = spline(target_depth, temp_adjusted,
                                method = "natural",
                                xout = target_depth)$y) %>%
  ungroup()

rm(core_data_temp_extended)
gc()

# subset interpolated values on target depth range
core_data_temp_interpolated_clean <- 
  inner_join(target_depth_levels, core_data_temp_interpolated)

rm(core_data_temp_interpolated)
gc()

# select columns and rename to initial names
core_data_temp_interpolated_clean <-
  core_data_temp_interpolated_clean %>%
  select(file_id,
         depth = target_depth,
         temp = temp_spline)

# merge with profile range
core_data_temp_interpolated_clean <-
  full_join(core_data_temp_interpolated_clean,
            profile_range_file_id)

# merge with meta data
core_data_temp_interpolated_clean <-
  left_join(core_data_temp_interpolated_clean,
            core_metadata_select)

# --------------------------------------------------------------------------------
# Create anomaly profiles
# --------------------------------------------------------------------------------

# Create anomaly profiles as observed - climatology

# Create core_temp_anomaly, but only where we have an climatology temperature
core_temp_anomaly <-
  inner_join(core_data_temp_interpolated_clean,
             temp_clim_va)

# Calculate the anomaly temperature
core_temp_anomaly <- core_temp_anomaly %>%
  mutate(anomaly = temp - clim_temp)

# -----------------------------------------------------------------------------
# Climatology check
# -----------------------------------------------------------------------------
# It is possible even though we have observational measures to full depth the climatology may not matach all depth.
# These profiles will be removed from both temp_core_va and temp_anomaly_va

# Anomaly max depth
core_temp_anomaly_depth_check <- core_temp_anomaly %>%
  group_by(file_id,
           profile_range) %>%
  summarise(min_pdepth = min(depth),
            max_pdepth = max(depth)) %>%
  ungroup()

# Add the required min depth and max depth
core_temp_anomaly_depth_check <-
  left_join(core_temp_anomaly_depth_check, opt_profile_depth_range)

# This profiles do not match the depth required by the profile_range
# min_depth_check <- min(target_depth_grid$target_depth)

# Please double check if the criterion should be
# each profile is checked against the min depth specified in min_depth
# max depth specified in max_depth.
remove_profiles <- core_temp_anomaly_depth_check %>%
  filter((max_pdepth < max_depth) | (min_pdepth > min_depth)) %>%
  distinct(file_id)

# remove from both core_data_temp_interpolated_clean and core_temp_anomaly
core_data_temp_interpolated_clean <-
  anti_join(core_data_temp_interpolated_clean, remove_profiles)

core_temp_anomaly <- anti_join(core_temp_anomaly, remove_profiles)

# --------------------------------------------------------------------------------
# Write files
# --------------------------------------------------------------------------------

# Write the climatology that maps onto depth levels, interpolated temperature profiles that map onto depth levels and resulting anomaly files.
core_data_temp_interpolated_clean %>%
  write_rds(file = paste0(path_core_preprocessed, "/", target_year, "_temp_core_va.rds"))

core_temp_anomaly %>%
  write_rds(file = paste0(path_core_preprocessed, "/", target_year, "_temp_anomaly_va.rds"))

core_data_temp_full %>%
  select(file_id, lat, lon, date, year, month, depth, temp_adjusted) %>%
  write_rds(file = paste0(path_core_preprocessed, "/", target_year, "_temp_core_observed.rds"))

rm(core_data_temp_interpolated_clean, core_temp_anomaly, core_data_temp_full)

}

Consolidate years

This process create three files in the path_argo_core_preprocessed directory that will be used for further analysis

# ------------------------------------------------------------------------------
# Process temperature file
# ------------------------------------------------------------------------------
consolidated_created = 0

for (target_year in opt_min_year:opt_max_year) {

  # read the yearly file based on target_year
  temp_core_va_yr <-
  read_rds(file = paste0(path_core_preprocessed, "/", target_year, "_temp_core_va.rds"))

  # Combine into a consolidated all years file
  if (consolidated_created == 0) {
    temp_core_va <- temp_core_va_yr
    consolidated_created = 1
  } else {
    temp_core_va <- rbind(temp_core_va, temp_core_va_yr)
  }
}

# write consolidated files  
temp_core_va %>%
  write_rds(file = paste0(path_core_preprocessed, "/temp_core_va.rds"))

# remove files to free space
rm(temp_core_va)
rm(temp_core_va_yr)
gc()
             used    (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells    3630535   193.9    7049188   376.5    5588982   298.5
Vcells 2429128466 18532.8 4273545849 32604.6 4273545849 32604.6
# ------------------------------------------------------------------------------
# Process anomaly file
# ------------------------------------------------------------------------------
consolidated_created = 0

for (target_year in opt_min_year:opt_max_year) {

  # read the yearly file based on target_year
  temp_anomaly_va_yr <-
  read_rds(file = paste0(path_core_preprocessed, "/", target_year, "_temp_anomaly_va.rds"))

  # Combine into a consolidated all years file
  if (consolidated_created == 0) {
    temp_anomaly_va <- temp_anomaly_va_yr
    consolidated_created = 1
  } else {
    temp_anomaly_va <- rbind(temp_anomaly_va, temp_anomaly_va_yr)
  }
}

# write consolidated files  
temp_anomaly_va %>%
  write_rds(file = paste0(path_core_preprocessed, "/temp_anomaly_va.rds"))

# remove files to free space
rm(temp_anomaly_va)
rm(temp_anomaly_va_yr)
gc()
             used    (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells    3630807   194.0    7049188   376.5    5588982   298.5
Vcells 2429128928 18532.8 5128335018 39126.1 4273545849 32604.6
# ------------------------------------------------------------------------------
# Process observed file
# ------------------------------------------------------------------------------
consolidated_created = 0

for (target_year in opt_min_year:opt_max_year) {

  # read the yearly file based on target_year
  temp_core_observed_yr <-
  read_rds(file = paste0(path_core_preprocessed, "/", target_year, "_temp_core_observed.rds"))

  # Combine into a consolidated all years file
  if (consolidated_created == 0) {
    temp_core_observed <- temp_core_observed_yr
    consolidated_created = 1
  } else {
    temp_core_observed <- rbind(temp_core_observed, temp_core_observed_yr)
  }
}

# write consolidated files  
temp_core_observed %>%
  write_rds(file = paste0(path_core_preprocessed, "/temp_core_observed.rds"))

# remove files to free space
rm(temp_core_observed)
rm(temp_core_observed_yr)
gc()
             used    (Mb)  gc trigger    (Mb)    max used     (Mb)
Ncells    3630900   194.0     7049188   376.5     5588982    298.5
Vcells 2429129015 18532.8 12826199373 97856.2 16032749216 122320.2

read files

Read files that were previously created ready for analysis

# read files
temp_core_va <- read_rds(file = paste0(path_core_preprocessed, "/temp_core_va.rds"))

temp_clim_va <- read_rds(file = paste0(path_argo_preprocessed, "/temp_clim_va.rds"))

temp_anomaly_va <- read_rds(file = paste0(path_core_preprocessed, "/temp_anomaly_va.rds"))

Temperature anomaly

Details of mean anomaly over analysis period

max_depth_1 <- opt_profile_depth_range[1, "max_depth"]
max_depth_2 <- opt_profile_depth_range[2, "max_depth"]
max_depth_3 <- opt_profile_depth_range[3, "max_depth"]

# Profiles to 600m
anomaly_overall_mean_1 <- temp_anomaly_va %>% 
  filter(profile_range %in% c(1, 2, 3) & depth <= max_depth_1) %>%
  group_by(depth) %>% 
  summarise(temp_count = n(),
            temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(anomaly, na.rm = TRUE))

anomaly_year_mean_1 <- temp_anomaly_va %>% 
  filter(profile_range %in% c(1, 2, 3) & depth <= max_depth_1) %>%
  group_by(year, depth) %>% 
  summarise(temp_count = n(),
            temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(anomaly, na.rm = TRUE))

# Profiles to 1200m
anomaly_overall_mean_2 <- temp_anomaly_va %>% 
  filter(profile_range %in% c(2, 3) & depth <= max_depth_2) %>%
  group_by(depth) %>% 
  summarise(temp_count = n(),
            temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(anomaly, na.rm = TRUE))

anomaly_year_mean_2 <- temp_anomaly_va %>% 
  filter(profile_range %in% c(2, 3) & depth <= max_depth_2) %>%
  group_by(year, depth) %>% 
  summarise(temp_count = n(),
            temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(anomaly, na.rm = TRUE))

# Profiles to 1500m
anomaly_overall_mean_3 <- temp_anomaly_va %>% 
  filter(profile_range %in% c(3) & depth <= max_depth_3) %>%
  group_by(depth) %>% 
  summarise(temp_count = n(),
            temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(anomaly, na.rm = TRUE))

anomaly_year_mean_3 <- temp_anomaly_va %>% 
  filter(profile_range %in% c(3) & depth <= max_depth_3) %>%
  group_by(year, depth) %>% 
  summarise(temp_count = n(),
            temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(anomaly, na.rm = TRUE))

# All years anomaly
anomaly_overall_mean_1 %>% 
  ggplot()+
  geom_path(aes(x = temp_anomaly_mean,
                y = depth))+
  geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
                  xmin = temp_anomaly_mean - temp_anomaly_sd,
                  y = depth),
              alpha = 0.2)+
  geom_vline(xintercept = 0)+
  scale_y_reverse() +
  coord_cartesian(xlim = opt_xlim) +
  scale_x_continuous(breaks = opt_xbreaks) +
  labs(
    title = paste0('Overall mean anomaly profiles to ', max_depth_1, 'm'),
    x = opt_measure_label,
    y = 'depth (m)'
  )

Version Author Date
80c16c2 ds2n19 2023-11-15 
anomaly_overall_mean_2 %>% 
  ggplot()+
  geom_path(aes(x = temp_anomaly_mean,
                y = depth))+
  geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
                  xmin = temp_anomaly_mean - temp_anomaly_sd,
                  y = depth),
              alpha = 0.2)+
  geom_vline(xintercept = 0)+
  scale_y_reverse() +
  coord_cartesian(xlim = opt_xlim) +
  scale_x_continuous(breaks = opt_xbreaks) +
  labs(
    title = paste0('Overall mean anomaly profiles to ', max_depth_2, 'm'),
    x = opt_measure_label,
    y = 'depth (m)'
  )

Version Author Date
80c16c2 ds2n19 2023-11-15 
anomaly_overall_mean_3 %>% 
  ggplot()+
  geom_path(aes(x = temp_anomaly_mean,
                y = depth))+
  geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
                  xmin = temp_anomaly_mean - temp_anomaly_sd,
                  y = depth),
              alpha = 0.2)+
  geom_vline(xintercept = 0)+
  scale_y_reverse() +
  coord_cartesian(xlim = opt_xlim) +
  scale_x_continuous(breaks = opt_xbreaks) +
  labs(
    title = paste0('Overall mean anomaly profiles to ', max_depth_3, 'm'),
    x = opt_measure_label,
    y = 'depth (m)'
  )

Version Author Date
80c16c2 ds2n19 2023-11-15 
# yearly anomaly
anomaly_year_mean_1 %>% 
  ggplot()+
  geom_path(aes(x = temp_anomaly_mean,
                y = depth))+
  geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
                  xmin = temp_anomaly_mean - temp_anomaly_sd,
                  y = depth),
              alpha = 0.2)+
  geom_vline(xintercept = 0)+
  scale_y_reverse() +
  facet_wrap(~year)+
  coord_cartesian(xlim = opt_xlim) +
  scale_x_continuous(breaks = opt_xbreaks) +
  labs(
    title = paste0('Yearly mean anomaly profiles to ', max_depth_1, 'm'),
    x = opt_measure_label,
    y = 'depth (m)'
  )

Version Author Date
80c16c2 ds2n19 2023-11-15 
anomaly_year_mean_2 %>% 
  ggplot()+
  geom_path(aes(x = temp_anomaly_mean,
                y = depth))+
  geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
                  xmin = temp_anomaly_mean - temp_anomaly_sd,
                  y = depth),
              alpha = 0.2)+
  geom_vline(xintercept = 0)+
  scale_y_reverse() +
  facet_wrap(~year)+
  coord_cartesian(xlim = opt_xlim) +
  scale_x_continuous(breaks = opt_xbreaks) +
  labs(
    title = paste0('Yearly mean anomaly profiles to ', max_depth_2, 'm'),
    x = opt_measure_label,
    y = 'depth (m)'
  )

Version Author Date
80c16c2 ds2n19 2023-11-15 
anomaly_year_mean_3 %>% 
  ggplot()+
  geom_path(aes(x = temp_anomaly_mean,
                y = depth))+
  geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
                  xmin = temp_anomaly_mean - temp_anomaly_sd,
                  y = depth),
              alpha = 0.2)+
  geom_vline(xintercept = 0)+
  scale_y_reverse() +
  facet_wrap(~year)+
  coord_cartesian(xlim = opt_xlim) +
  scale_x_continuous(breaks = opt_xbreaks) +
  labs(
    title = paste0('Yearly mean anomaly profiles to ', max_depth_3, 'm'),
    x = opt_measure_label,
    y = 'depth (m)'
  )

Version Author Date
80c16c2 ds2n19 2023-11-15 
#rm(anomaly_overall_mean)

Profile counts

Details of the number of profiles and to which depths over the analysis period

temp_histogram <- temp_core_va %>%
  group_by(year, profile_range = as.character(profile_range)) %>%
  summarise(num_profiles = n_distinct(file_id)) %>%
  ungroup()

temp_histogram %>%
  ggplot() +
  geom_bar(
    aes(
      x = year,
      y = num_profiles,
      fill = profile_range,
      group = profile_range
    ),
    position = "stack",
    stat = "identity"
  ) +
  scale_fill_viridis_d() +
  labs(title = "temperature profiles per year and profile range",
       x = "year",
       y = "profile count",
       fill = "profile range")

Version Author Date
80c16c2 ds2n19 2023-11-15 

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] ggforce_0.4.1    gsw_1.1-1        gridExtra_2.3    lubridate_1.9.0 
 [5] timechange_0.1.1 argodata_0.1.0   forcats_0.5.2    stringr_1.5.0   
 [9] dplyr_1.1.3      purrr_1.0.2      readr_2.1.3      tidyr_1.3.0     
[13] tibble_3.2.1     ggplot2_3.4.4    tidyverse_1.3.2 

loaded via a namespace (and not attached):
 [1] httr_1.4.4          sass_0.4.4          viridisLite_0.4.1  
 [4] jsonlite_1.8.3      modelr_0.1.10       bslib_0.4.1        
 [7] assertthat_0.2.1    highr_0.9           googlesheets4_1.0.1
[10] cellranger_1.1.0    yaml_2.3.6          pillar_1.9.0       
[13] backports_1.4.1     glue_1.6.2          digest_0.6.30      
[16] promises_1.2.0.1    polyclip_1.10-4     rvest_1.0.3        
[19] colorspace_2.0-3    htmltools_0.5.3     httpuv_1.6.6       
[22] pkgconfig_2.0.3     broom_1.0.5         haven_2.5.1        
[25] scales_1.2.1        tweenr_2.0.2        whisker_0.4        
[28] later_1.3.0         tzdb_0.3.0          git2r_0.30.1       
[31] googledrive_2.0.0   generics_0.1.3      farver_2.1.1       
[34] ellipsis_0.3.2      cachem_1.0.6        withr_2.5.0        
[37] cli_3.6.1           magrittr_2.0.3      crayon_1.5.2       
[40] readxl_1.4.1        evaluate_0.18       fs_1.5.2           
[43] fansi_1.0.3         MASS_7.3-58.1       xml2_1.3.3         
[46] tools_4.2.2         hms_1.1.2           gargle_1.2.1       
[49] lifecycle_1.0.3     munsell_0.5.0       reprex_2.0.2       
[52] compiler_4.2.2      jquerylib_0.1.4     RNetCDF_2.6-1      
[55] rlang_1.1.1         grid_4.2.2          rstudioapi_0.15.0  
[58] labeling_0.4.2      rmarkdown_2.18      gtable_0.3.1       
[61] DBI_1.1.3           R6_2.5.1            knitr_1.41         
[64] fastmap_1.1.0       utf8_1.2.2          workflowr_1.7.0    
[67] rprojroot_2.0.3     stringi_1.7.8       Rcpp_1.0.10        
[70] vctrs_0.6.4         dbplyr_2.2.1        tidyselect_1.2.0   
[73] xfun_0.35