Package index • cd2030.rmncah

Data Import and Setup

To load and prepare the initial data required for analysis.

load_data(): Load Processed Countdown 2030 Data from RDS or DTA File

load_excel_data(): Load and Clean Countdown 2030 Excel Data

load_un_estimates(): Load UN Estimates

load_un_mortality_data(): Load and Filter UN Mortality Estimates

load_wuenic_data(): Load WUENIC Immunization Data

load_survey_data(): Load and Process Survey Data

load_equity_data(): Load and Filter Equity Data

save_data(): Save Processed Countdown 2030 Data to File

save_dhis2_excel(): Save DHIS2 Data to an Excel Workbook

save_dhis2_master_data(): Create a Master Dataset from DHIS2 Data

get_dhis2_hfd(): Retrieve DHIS2 Health Facility Data

Data Quality Assessment

To check the completeness and consistency of data, ensuring its readiness for analysis.

Reporting Rate

calculate_average_reporting_rate(): Reporting Rate Summary by Administrative Level

calculate_district_reporting_rate(): District-Level Reporting Rates by Year

plot(<cd_average_reporting_rate>): Plot Reporting Rates for Subnational Units

plot(<cd_district_reporting_rate>): Plot District Reporting Rate Summary

tbl_sum(<cd_average_reporting_rate>): Summary for Average Reporting Rates by Year

tbl_sum(<cd_district_reporting_rate>): Summary for District-Level Reporting Rates by Year

Date Completeness

calculate_completeness_summary(): Calculate Percentage of Non-Missing Values by Year

calculate_district_completeness_summary(): Check for Missing Values by District and Year

plot(<cd_completeness_summary>): Plot Missing Summary

tbl_sum(<cd_completeness_summary>): Summary for cd_completeness_summary

tbl_sum(<cd_district_completeness_summary>): Summary for cd_district_completeness_summary

Consistency Checks

plot_comparison() plot_comparison_anc1_penta1() plot_comparison_penta1_penta3() plot_comparison_opv1_opv3(): Internal Consistency Plot Functions for Indicator Comparisons

plot_comparison(<cd_data>): Plot Comparison with Linear Fit and R-squared

Outlier detection

calculate_outliers_summary(): Calculate Outliers Summary by Year

calculate_district_outlier_summary(): Calculate District-Level Outliers Summary by Year

list_outlier_units(): Identify Outlier Units by Month for a Given Indicator

plot(<cd_outlier>): Plot Outlier Detection Summary

plot(<cd_outlier_list>): Plot Outlier Series for a Single Region and Indicator

tbl_sum(<cd_outliers_summary>): Summary for cd_outliers_summary

tbl_sum(<cd_district_outliers_summary>): Summary for cd_district_outliers_summary

Calculate Ratios

calculate_ratios_summary(): Calculate Yearly Indicator Ratios Summary with Expected Ratios

calculate_district_ratios_summary(): Calculate District Adequacy Summary

plot(<cd_ratios_summary>): Plot Ratios Summary for Indicator Ratios Summary Object

tbl_sum(<cd_ratios_summary>): Summary for cd_ratios_summary Object

tbl_sum(<cd_district_ratios_summary>): Summary for cd_district_ratios_summary Object

Overall Score

calculate_overall_score(): Calculate Overall Quality Score for Data Quality Metrics

Data Adjustment

To adjust for issues like incomplete reporting and extreme outliers, improving data quality

filter_out_years(): Filter Dataset by Removing Specified Years

generate_adjustment_values(): Generate Adjusted and Unadjusted Service Counts by Year

adjust_service_data(): Adjust Service Data for Coverage Analysis

plot(<cd_adjustment_values>): Plot Adjusted vs. Unadjusted Data for Health Indicators

tbl_sum(<cd_adjustment_values>): Summary for cd_adjustment_values Objects

Denominator Assessment

To determine suitable population-based denominators for calculating coverage rates

compute_indicator_numerator(): Compute Aggregated Numerators for Health Indicators

prepare_population_metrics(): Compute Population Metrics for DHIS-2 and UN Data

calculate_indicator_coverage(): Calculate Health Coverage Indicators

calculate_indicator_threshold_coverage(): Calculate Threshold Coverage for Health Indicators

calculate_threshold(): Calculate Dropout Coverage for Health Indicators Below a Threshold

calculate_derived_coverage(): Generate Coverage Data with Derived Denominators

plot_line_graph(): Plot Line Graph for Multiple Series with Dynamic Y-axis Scaling

plot_absolute_differences(): Plot DHIS2-Based Indicator Coverage with Different Denominators and Survey Coverage

plot(<cd_indicator_coverage>): Plot National Denominators and Coverage Indicators

plot(<cd_population_metrics>): Plot National Population or Births Metrics

plot(<cd_derived_coverage>): Plot Derived vs Traditional Coverage Over Time

tbl_sum(<cd_indicator_coverage>): Table Summary for National Coverage Estimates

tbl_sum(<cd_national_denominators>): Summarize National Denominators Data

Calculate Coverage

To determine the coverage of various indicators

calculate_coverage(): Combine Immunization Coverage Data

filter_coverage(): Filter and Reshape Coverage Data

plot(<cd_coverage>): Plot National Coverage Data

calculate_inequality(): Analyze Subnational Health Coverage Data with Inequality Metrics

filter_inequality(): Filter Subnational Inequality Metrics

plot(<cd_inequality>): Plot Subnational Health Coverage Analysis

get_mapping_data(): Get Mapping Data for Subnational Analysis

get_country_shapefile(): Retrieve Country Shapefile

plot(<cd_mapping>): Plot Subnational Mapping Data

equiplot(): Create Dot Plots for Equity Analysis

equiplot_area(): A Specialized Dot Plot for Area of Residence Analysis

equiplot_education(): A Specialized Dot Plot for Maternal Education Analysis

equiplot_wealth(): A Specialized Dot Plot for Wealth Quintile Analysis

Calculate Mortality Metrics

To calculate the quality of mortality data

create_mortality_summary(): Create Mortality Summary Object

create_mortality_ratios(): Extract Latest Mortality Ratios for UN Comparison

plot(<cd_mortality_rate>): Plot Mortality Rate Indicators

plot(<cd_mortality_ratio>): Plot Completeness of Facility Reporting Ratios

Calculate Health Utilization Metrics

To Calculate National and Sub-national metric of how services are Utilize

compute_service_utilization(): Compute Service Utilization Metrics

get_excel_version(): Format Service Utilization Data for Excel Export

plot(<cd_service_utilization>): Plot Service Utilization Indicators

Calculate Health System Metrics

calculate_health_system_metrics(): Calculate Health System Metrics

calculate_health_system_comparison(): Compare Health System Metrics Across Levels

plot(<cd_health_system_metric>): Plot Health Metrics for Admin 1 Units

plot(<cd_health_system_comparison>): Compare District vs Admin 1 Health Metrics

plot_national_health_metric(): Plot National Health System Metrics

Reporting

To document findings, generate summaries, and create exportable formats for sharing and presentation

dashboard(): Launch the Dashboard Application

generate_report(): Generate and Export Checks Report

Other Functions

add_outlier5std_column(): Add Outlier Flags Based on 5-MAD Bounds

add_mad_med_columns(): Add Median and MAD Columns for Indicators

add_missing_column(): Add Missing Value Flags to Health Indicators

get_all_indicators(): Get All Indicators

get_country_iso3(): Get Country ISO3 Code

get_country_name(): Get Country Name

get_indicator_groups(): Get Indicator Groups

get_indicator_group_names(): Get Indicator Group Names

get_named_indicators(): Get Named Indicator Vector

get_population_column(): Get Population Denominator Column Based on Indicator Only

get_indicator_without_opd_ipd(): Get Indicators excluding indicators without denominator

get_admin_columns(): Get Grouping Columns by Administrative Level

robust_max(): Robust Maximum Value Calculation

new_countdown(): Create Countdown 2030 Data Object

print(<cd_data>): Print Method for cd_data Class

with_cd_quiet() local_cd_quiet(): Execute Code in Quiet Mode

clean_error_message(): Clean and Validate Error Messages

CacheConnection: CacheConnection Class

init_CacheConnection(): Create a CacheConnection Object