📊 add birth rate monthly data

dag/demography.yml

@@ -122,6 +122,7 @@ steps:
     - data://garden/un/2022-07-11/un_wpp
   data://grapher/un/2024-03-14/un_wpp_most:
     - data://garden/un/2024-03-14/un_wpp_most
+
   ########################################################################
   # Life expectancy                                                      #
   ########################################################################
@@ -252,16 +253,20 @@ steps:
   data://grapher/demography/2024-12-03/fertility_rate:
     - data://garden/demography/2024-12-03/fertility_rate
 
-  # OMM: Mean Age at Birth -- HFD + UN WPP
-  # data://garden/demography/2024-12-03/mean_age_birth:
-  #   - data://garden/hmd/2024-11-19/hfd
-  #   - data://garden/un/2024-07-12/un_wpp
-  # data://grapher/demography/2024-12-03/mean_age_birth:
-  #   - data://garden/demography/2024-12-03/mean_age_birth
-
   # OMM: Birth rate -- HFD + UN WPP
   data://garden/demography/2024-12-03/birth_rate:
     - data://garden/hmd/2024-12-01/hmd
     - data://garden/un/2024-07-12/un_wpp
   data://grapher/demography/2024-12-03/birth_rate:
     - data://garden/demography/2024-12-03/birth_rate
+
+  # HMD country data
+  data://meadow/hmd/2024-12-03/hmd_country:
+    - snapshot://hmd/2024-12-01/hmd_country.zip
+
+  # HMD - Birth rate by month
+  data://garden/hmd/2024-12-03/hmd_country:
+    - data://meadow/hmd/2024-12-03/hmd_country
+    - data://garden/hmd/2024-12-01/hmd
+  data://grapher/hmd/2024-12-03/hmd_country:
+    - data://garden/hmd/2024-12-03/hmd_country

etl/steps/data/garden/hmd/2024-12-03/hmd_country.countries.json

@@ -0,0 +1,47 @@
+{
+    "AUS": "Australia",
+    "AUT": "Austria",
+    "BEL": "Belgium",
+    "BGR": "Bulgaria",
+    "BLR": "Belarus",
+    "CAN": "Canada",
+    "CHE": "Switzerland",
+    "CHL": "Chile",
+    "CZE": "Czechia",
+    "DNK": "Denmark",
+    "ESP": "Spain",
+    "EST": "Estonia",
+    "FIN": "Finland",
+    "GRC": "Greece",
+    "HKG": "Hong Kong",
+    "HRV": "Croatia",
+    "HUN": "Hungary",
+    "IRL": "Ireland",
+    "ISL": "Iceland",
+    "ISR": "Israel",
+    "ITA": "Italy",
+    "JPN": "Japan",
+    "KOR": "South Korea",
+    "LTU": "Lithuania",
+    "LUX": "Luxembourg",
+    "LVA": "Latvia",
+    "NLD": "Netherlands",
+    "NOR": "Norway",
+    "POL": "Poland",
+    "PRT": "Portugal",
+    "RUS": "Russia",
+    "SVK": "Slovakia",
+    "SVN": "Slovenia",
+    "SWE": "Sweden",
+    "UKR": "Ukraine",
+    "USA": "United States",
+    "DEUTE": "East Germany",
+    "DEUTNP": "Germany",
+    "DEUTW": "West Germany",
+    "FRATNP": "France",
+    "GBRTENW": "England and Wales",
+    "GBR_NIR": "Northern Ireland",
+    "GBR_NP": "United Kingdom",
+    "GBR_SCO": "Scotland",
+    "NZL_NP": "New Zealand"
+}

etl/steps/data/garden/hmd/2024-12-03/hmd_country.excluded_countries.json

@@ -0,0 +1,4 @@
+[
+    "FRACNP",
+    "GBRCENW"
+]

etl/steps/data/garden/hmd/2024-12-03/hmd_country.meta.yml

@@ -0,0 +1,74 @@
+# NOTE: To learn more about the fields, hover over their names.
+definitions:
+  common:
+    presentation:
+      topic_tags:
+        - Fertility Rate
+
+# Learn more about the available fields:
+# http://docs.owid.io/projects/etl/architecture/metadata/reference/
+dataset:
+  title: Birth rate by month (HMD)
+  update_period_days: 365
+
+tables:
+  birth_rate:
+    variables:
+      birth_rate:
+        title: Birth rate (monthly)
+        unit: births per 1,000 people
+        description_short: |-
+          The total number of births per 1,000 people in a given month.
+        display:
+          name: |-
+            Birth rate
+
+      birth_rate_per_day:
+        title: Daily birth Rate (average in month)
+        unit: births per 1,000 people
+        description_short: |-
+          The average daily number of births, per 1,000 people, calculated monthly.
+        display:
+          name: |-
+            Birth rate, per day
+
+  birth_rate_month:
+    variables:
+      birth_rate:
+        title: Birth rate (monthly) - << month >>
+        unit: births per 1,000 people
+        description_short: |-
+          The total number of births per 1,000 people in <<month>>.
+        display:
+          name: |-
+            Birth rate
+
+      birth_rate_per_day:
+        title: Daily birth rate (average in month) - << month >>
+        unit: births per 1,000 people
+        description_short: |-
+          The average daily number of births, per 1,000 people, calculated for <<month>>.
+        display:
+          name: |-
+            Birth rate, per day
+
+  birth_rate_month_max:
+    variables:
+      month_max:
+        title: Month ordinal with the peak daily birth rate
+        unit: ""
+        description_short: |-
+          Number corresponding to the month with the highest daily birth rate.
+      month_max_name:
+        title: Month name with the peak daily birth rate
+        unit: ""
+        description_short: |-
+          Month with the highest daily birth rate.
+      birth_rate_per_day_max:
+        title: Peak daily birth rate
+        unit: births per 1,000 people
+        description_short: |-
+          The highest average daily number of births, per 1,000 people, recorded in the given year.
+        display:
+          name: |-
+            Maximum birth rate, per day

etl/steps/data/garden/hmd/2024-12-03/hmd_country.py

@@ -0,0 +1,160 @@
+"""Load a meadow dataset and create a garden dataset."""
+
+import calendar
+
+import numpy as np
+import pandas as pd
+
+from etl.data_helpers import geo
+from etl.data_helpers.misc import interpolate_table
+from etl.helpers import PathFinder, create_dataset
+
+# Get paths and naming conventions for current step.
+paths = PathFinder(__file__)
+
+
+def run(dest_dir: str) -> None:
+    #
+    # Load inputs.
+    #
+    # Load meadow dataset.
+    ds_meadow = paths.load_dataset("hmd_country")
+    ds_hmd = paths.load_dataset("hmd")
+
+    # Read table from meadow dataset.
+    tb_month = ds_meadow.read("monthly")
+    tb_pop = ds_hmd.read("population")
+
+    #
+    # Process data.
+    #
+    tb_month_long, tb_month_dimensions, tb_month_max = make_monthly_tables(tb_month, tb_pop)
+    tables = [
+        tb_month_long.format(["country", "date"], short_name="birth_rate"),
+        tb_month_dimensions.format(["country", "year", "month"], short_name="birth_rate_month"),
+        tb_month_max.format(["country", "year"], short_name="birth_rate_month_max"),
+    ]
+
+    #
+    # Save outputs.
+    #
+    # Create a new garden dataset with the same metadata as the meadow dataset.
+    ds_garden = create_dataset(
+        dest_dir,
+        tables=tables,
+        check_variables_metadata=True,
+        default_metadata=ds_meadow.metadata,
+    )
+
+    # Save changes in the new garden dataset.
+    ds_garden.save()
+
+
+def make_monthly_tables(tb, tb_pop):
+    ## Discard unknown/total values
+    tb = tb.loc[~tb["month"].isin(["TOT", "UNK"])]
+    tb["month"] = tb["month"].astype(int)
+    ## Create date column. TODO: check what day of the month to assign
+    tb["date"] = pd.to_datetime(tb[["year", "month"]].assign(day=1))
+    # Harmonize country names
+    tb = geo.harmonize_countries(
+        df=tb,
+        countries_file=paths.country_mapping_path,
+        excluded_countries_file=paths.excluded_countries_path,
+        warn_on_unknown_excluded_countries=False,
+    )
+
+    # Add population to monthly birth data table
+    tb = add_population_column(tb, tb_pop)
+
+    # Estimate metrics
+    tb = estimate_metrics(tb)
+
+    # Sort rows
+    tb = tb.sort_values(["country", "date", "date"])
+
+    # Classic time-series, with date-values
+    tb_long = tb[["country", "date", "birth_rate", "birth_rate_per_day"]]
+
+    # Month as a dimension
+    tb_dimensions = tb[["country", "year", "month", "birth_rate", "birth_rate_per_day"]]
+    tb_dimensions["month"] = tb_dimensions["month"].apply(lambda x: calendar.month_name[x])
+
+    # For each year, ID of the month with highest birth rate per day
+    tb_month_max = tb.loc[
+        tb.groupby(["country", "year"])["birth_rate_per_day"].idxmax(),
+        ["country", "year", "month", "birth_rate_per_day"],
+    ].rename(columns={"month": "month_max", "birth_rate_per_day": "birth_rate_per_day_max"})
+    tb_month_max["month_max_name"] = tb_month_max["month_max"].apply(lambda x: calendar.month_name[x])
+
+    return tb_long, tb_dimensions, tb_month_max
+
+
+def clean_table(tb):
+    """Filter rows, harmonize country names, add date column."""
+    # Filter unwanted month categories, set dtype
+    tb = tb.loc[~tb["month"].isin(["TOT", "UNK"])]
+    tb["month"] = tb["month"].astype(int)
+    ## Create date column. TODO: check what day of the month to assign
+    tb["date"] = pd.to_datetime(tb[["year", "month"]].assign(day=1))
+    # Harmonize country names
+    tb = geo.harmonize_countries(
+        df=tb,
+        countries_file=paths.directory / (paths.short_name + "_month.countries.json"),
+        excluded_countries_file=paths.excluded_countries_path,
+        warn_on_unknown_excluded_countries=False,
+    )
+
+    return tb
+
+
+def add_population_column(tb, tb_pop):
+    """Add population column to main table for each date."""
+    # Prepare population table
+    tb_pop = _prepare_population_table(tb_pop)
+    # Merge population table with main table
+    tb = tb.merge(tb_pop, on=["country", "date"], how="left")
+    tb = tb.sort_values(["country", "date"])
+    # Interpolate to get monthly population estimates
+    tb_ = interpolate_table(
+        tb[["country", "date", "population"]],
+        entity_col="country",
+        time_col="date",
+        time_mode="none",
+    )
+    tb = tb.drop(columns="population").merge(tb_, on=["country", "date"], how="left")
+
+    return tb
+
+
+def _prepare_population_table(tb):
+    """Prepare population table to merge with main table.
+
+    Original table is given in years, but we need it in days! We use linear interpolation for that.
+    """
+    tb_aux = tb.loc[(tb["sex"] == "total") & ~(tb["age"].str.contains("-")), ["country", "year", "population"]]
+    tb_aux = tb_aux.groupby(["country", "year"], as_index=False)["population"].sum()
+    ## Assign a day to population. TODO: Check if this is true
+    tb_aux["date"] = pd.to_datetime(tb_aux["year"].astype(str) + "-01-01")
+    tb_aux = tb_aux.drop(columns="year")
+
+    return tb_aux
+
+
+def estimate_metrics(tb):
+    """Estimate metrics: birth rate and birth rate per day."""
+    # Get days in month
+    tb["days_in_month"] = tb.apply(lambda row: calendar.monthrange(row["year"], row["month"])[1], axis=1)
+    # Estimate rates
+    tb["birth_rate"] = tb["births"] / tb["population"] * 1_000
+    tb["birth_rate_per_day"] = tb["birth_rate"] / tb["days_in_month"] * 1_000
+    # Check
+    assert tb[["birth_rate", "birth_rate_per_day"]].notna().all().all()
+    # Replace INF values with NAs
+    tb[["birth_rate", "birth_rate_per_day"]] = tb[["birth_rate", "birth_rate_per_day"]].replace(
+        [np.inf, -np.inf], pd.NA
+    )
+    # Drop NAs
+    tb = tb.dropna(subset=["birth_rate", "birth_rate_per_day"])
+
+    return tb

etl/steps/data/grapher/hmd/2024-12-03/hmd_country.py

@@ -0,0 +1,28 @@
+"""Load a garden dataset and create a grapher dataset."""
+
+from etl.helpers import PathFinder, create_dataset
+
+# Get paths and naming conventions for current step.
+paths = PathFinder(__file__)
+
+
+def run(dest_dir: str) -> None:
+    #
+    # Load inputs.
+    #
+    # Load garden dataset.
+    ds_garden = paths.load_dataset("hmd_country")
+
+    # Read table from garden dataset.
+    tables = list(ds_garden)
+
+    #
+    # Save outputs.
+    #
+    # Create a new grapher dataset with the same metadata as the garden dataset.
+    ds_grapher = create_dataset(
+        dest_dir, tables=tables, check_variables_metadata=True, default_metadata=ds_garden.metadata
+    )
+
+    # Save changes in the new grapher dataset.
+    ds_grapher.save()