Package 'humind'

Description

This function recodes the best type of phone accessible in the household and the coverage network type. It provides two main functions:

1. add_access_to_phone_best(): Recodes the best type of phone accessible in the household.

2. add_access_to_phone_coverage(): Recodes the coverage network type and the best type of phone owned in the household.

Usage

add_access_to_phone_best(
  df,
  access_to_phone = "etc_access_to_phone",
  none = "none",
  smartphone = "smartphone",
  feature_phone = "feature_phone",
  basic_phone = "basic_phone",
  dnk = "dnk",
  pnta = "pnta",
  sep = "/"
)

add_access_to_phone_coverage(
  df,
  coverage_network_type = "etc_coverage_network_type",
  coverage_none = "no_coverage",
  coverage_no_internet = c("only_sms", "voice_sms", "only_voice"),
  coverage_yes_internet = c("voice_sms_internet", "only_internet"),
  coverage_undefined = c("dnk", "pnta"),
  access_to_phone_best = "etc_access_to_phone_best",
  access_to_phone_none = "none",
  access_to_basic_phone = "basic_phone",
  access_to_feature_phone = "feature_phone",
  access_to_smartphone = "smartphone",
  access_to_undefined = "undefined"
)

Arguments

Value

A data frame with an additional column 'etc_access_to_phone_best' containing the recoded best type of phone accessible.

A data frame with an additional column 'etc_access_to_phone_coverage' containing the recoded coverage and phone access information.

Description

This function provides two main functionalities:

1. add_age_cat(): Adds age categories to a dataframe based on specified breaks.

2. add_age_18_cat(): Adds age categories and a dummy variable for below and above 18 years old.

Usage

add_age_cat(
  df,
  age_col,
  breaks = c(0, 18, 60, 120),
  labels = NULL,
  int_undefined = c(-999, 999),
  char_undefined = "undefined",
  new_colname = NULL
)

add_age_18_cat(
  df,
  age_col,
  int_undefined = c(-999, 999),
  char_undefined = "undefined",
  new_colname = NULL
)

Arguments

Value

• For add_age_cat(): A dataframe with an additional column containing the age categories.

• For add_age_18_cat(): A dataframe with two additional columns: one with categories (below_18, above_18) and one with a dummy variable (0 for below 18, 1 for above 18).

Description

This function recodes any barriers to accessing humanitarian assistance encountered from the barriers to accessing humanitarian assistance by type indicator.

Usage

add_barriers_assistance_any(
  df,
  barriers_assistance = "aap_barriers_assistance",
  none = "none",
  pnta = "pnta",
  dnk = "dnk"
)

Arguments

Value

The data frame with the new variable aap_barriers_assistance_any_barrier

Description

This function calculates an education sectoral composite score based on various education-related indicators. It computes scores for disrupted education and attendance/barriers, combines them into a total score, and determines if a household is in need or in severe need of educational assistance.

Prerequisite functions:

• add_loop_age_dummy.R

• add_loop_edu_access_d.R

• add_loop_edu_barrier_protection_d.R

• add_loop_edu_disrupted_d.R

• add_loop_edu_ind_age_corrected.R

Usage

add_comp_edu(
  df,
  schooling_age_n = "edu_schooling_age_n",
  no_access_n = "edu_no_access_n",
  barrier_protection_n = "edu_barrier_protection_n",
  attack_n = "edu_disrupted_attack_n",
  hazards_n = "edu_disrupted_hazards_n",
  displaced_n = "edu_disrupted_displaced_n",
  teacher_n = "edu_disrupted_teacher_n"
)

Arguments

Value

A data frame with additional columns:

• comp_edu_score_disrupted: Score for disrupted education (1-4).

• comp_edu_score_attendance: Score for attendance and barriers (1-4)

• comp_edu_score: Total education composite score (max of disrupted and attendance scores).

• comp_edu_in_need: Binary indicator for being in need of educational assistance.

• comp_edu_in_severe_need: Binary indicator for being in severe need of educational assistance.

Description

This function calculates a food security sectoral composite score based on the FCLCM phase. It assigns a score from 1 to 5 corresponding to the FCLCM phase, and determines if a household is in need or in severe need of food security assistance.

Prerequisite food security functions must be run before this function:

• add_fcs()

• add_hhs()

• add_rcsi()

• add_lcsi()

• add_fclcm_phase()

Usage

add_comp_foodsec(
  df,
  fclcm_phase = "fclcm_phase",
  phase1 = "Phase 1 FCLC",
  phase2 = "Phase 2 FCLC",
  phase3 = "Phase 3 FCLC",
  phase4 = "Phase 4 FCLC",
  phase5 = "Phase 5 FCLC"
)

Arguments

Value

A data frame with additional columns:

• comp_foodsec_score: Food security composite score (1-5)

• comp_foodsec_in_need: Binary indicator for being in need of food security assistance

• comp_foodsec_in_severe_need: Binary indicator for being in severe need of food security assistance

Description

This function calculates a health composite score based on healthcare access and needs. It considers whether individuals needed healthcare, whether they accessed it if needed, and optionally takes into account disability status. The function then determines if a household is in need or in severe need of health assistance based on the calculated score.

Prerequisite functions:

• add_loop_healthcare_needed_cat.R

Usage

add_comp_health(
  df,
  ind_healthcare_needed_no_n = "health_ind_healthcare_needed_no_n",
  ind_healthcare_needed_yes_unmet_n = "health_ind_healthcare_needed_yes_unmet_n",
  ind_healthcare_needed_yes_met_n = "health_ind_healthcare_needed_yes_met_n"
)

Arguments

Value

A data frame with additional columns:

• comp_health_score: Health composite score (1-4)

• comp_health_in_need: Binary indicator for being in need of health assistance

• comp_health_in_severe_need: Binary indicator for being in severe need of health assistance

Description

Assumes that per‑dimension severity scores have already been computed and added to df by earlier functions: add_prot_score_movement(), add_prot_score_practices(), and add_prot_score_rights(). This function performs only Step 4 of the 2025 Protection Composite workflow: it takes the maximum of those three existing columns to create comp_prot_score, then generates binary “in need” indicators.

Usage

add_comp_prot(df)

Arguments

Details

• Column checks via purrr::iwalk() ensure the three dimension scores exist.

• Computation uses pmax() (with na.rm = FALSE)

• Thresholds (3 for “in need”, 4 for “severe need”) are currently hard‑coded.

Value

The input df, with three new columns:

• comp_prot_score – overall protection severity (maximum of the three dimensions)

• comp_prot_in_need – binary (0/1): 1 if comp_prot_score >= 3, else 0

• comp_prot_in_severe_need – binary (0/1): 1 if comp_prot_score >= 4, else 0

Description

This function calculates the Shelter, NFI and HLP (SNFI) sectoral composite score based on shelter type, shelter issues, security of tenure, shelter damage, and functional disability scale (FDS) indicators. It also determines if a household is in need or in severe need based on the calculated score.

Prerequisite functions:

• add_shelter_issue_cat.R

• add_shelter_type_cat.R

• add_occupancy_cat.R

• add_fds_cannot_cat.R

• add_shelter_damage_cat.R

Usage

add_comp_snfi(
  df,
  shelter_type_cat = "snfi_shelter_type_cat",
  shelter_type_cat_none = "none",
  shelter_type_cat_inadequate = "inadequate",
  shelter_type_cat_adequate = "adequate",
  shelter_type_cat_undefined = "undefined",
  shelter_issue_cat = "snfi_shelter_issue_cat",
  shelter_issue_cat_1_to_3 = "1_to_3",
  shelter_issue_cat_4_to_7 = "4_to_7",
  shelter_issue_cat_8_to_11 = "8_to_11",
  shelter_issue_cat_none = "none",
  shelter_issue_cat_undefined = "undefined",
  shelter_issue_cat_other = "other",
  tenure_security_cat = "hlp_tenure_security",
  tenure_security_cat_high_risk = "high_risk",
  tenure_security_cat_medium_risk = "medium_risk",
  tenure_security_cat_low_risk = "low_risk",
  tenure_security_cat_undefined = "undefined",
  fds_cannot_cat = "snfi_fds_cannot_cat",
  fds_cannot_cat_4 = "4_tasks",
  fds_cannot_cat_2_to_3 = "2_to_3_tasks",
  fds_cannot_cat_1 = "1_task",
  fds_cannot_cat_none = "none",
  fds_cannot_cat_undefined = "undefined",
  shelter_damage_cat = "snfi_shelter_damage_cat",
  shelter_damage_cat_none = "none",
  shelter_damage_cat_damaged = "damaged",
  shelter_damage_cat_part = "part",
  shelter_damage_cat_total = "total",
  shelter_damage_cat_undefined = "undefined"
)

Arguments

Value

A data frame with added columns:

• comp_snfi_score_shelter_type_cat Score based on shelter type

• comp_snfi_score_shelter_issue_cat Score based on shelter issues

• comp_snfi_score_tenure_security_cat Score based on security of tenure status

• comp_snfi_score_fds_cannot_cat Score based on FDS

• comp_snfi_score_shelter_damage_cat Score based on shelter damage

• comp_snfi_score: Overall SNFI composite score

• comp_snfi_in_need: Indicator for being in need

• comp_snfi_in_severe_need: Indicator for being in severe need

Description

Calculates a comprehensive WASH (Water, Sanitation, and Hygiene) composite score for households across different settings (camp, urban, rural). The function generates sub-scores for water quantity, water quality, sanitation, and hygiene, and creates a dummy variable to indicate households in need of WASH assistance.

The function performs detailed scoring based on multiple WASH-related variables, applying different scoring logic for camp, urban, and rural settings.

Prerequisite functions:

• add_hwise()

• add_sanitation_facility_cat()

• add_handwashing_facility_cat()

• add_drinking_water_source_cat()

Usage

add_comp_wash(
  df,
  setting = "setting",
  setting_camp = c("camp_formal", "camp_informal"),
  setting_urban = "urban",
  setting_rural = "rural",
  comp_wash_hwise_score = "comp_wash_score_water_quantity",
  drinking_water_quality_jmp_cat = "wash_drinking_water_quality_jmp_cat",
  drinking_water_quality_jmp_cat_surface_water = "surface_water",
  drinking_water_quality_jmp_cat_unimproved = "unimproved",
  drinking_water_quality_jmp_cat_limited = "limited",
  drinking_water_quality_jmp_cat_basic = "basic",
  drinking_water_quality_jmp_cat_safely_managed = "safely_managed",
  drinking_water_quality_jmp_cat_undefined = "undefined",
  sanitation_facility_jmp_cat = "wash_sanitation_facility_jmp_cat",
  sanitation_facility_jmp_cat_open_defecation = "open_defecation",
  sanitation_facility_jmp_cat_unimproved = "unimproved",
  sanitation_facility_jmp_cat_limited = "limited",
  sanitation_facility_jmp_cat_basic = "basic",
  sanitation_facility_jmp_cat_undefined = "undefined",
  sanitation_facility_cat = "wash_sanitation_facility_cat",
  sanitation_facility_cat_none = "none",
  sanitation_facility_cat_unimproved = "unimproved",
  sanitation_facility_cat_improved = "improved",
  sanitation_facility_cat_undefined = "undefined",
  sanitation_facility_n_ind = "wash_sharing_sanitation_facility_n_ind",
  sanitation_facility_n_ind_50_and_above = "50_and_above",
  sanitation_facility_n_ind_20_to_49 = "20_to_49",
  sanitation_facility_n_ind_19_and_below = "19_and_below",
  sharing_sanitation_facility_cat = "wash_sharing_sanitation_facility_cat",
  sharing_sanitation_facility_cat_shared = "shared",
  sharing_sanitation_facility_cat_not_shared = "not_shared",
  sharing_sanitation_facility_cat_not_applicable = "not_applicable",
  sharing_sanitation_facility_cat_undefined = "undefined",
  handwashing_facility_jmp_cat = "wash_handwashing_facility_jmp_cat",
  handwashing_facility_jmp_cat_no_facility = "no_facility",
  handwashing_facility_jmp_cat_limited = "limited",
  handwashing_facility_jmp_cat_basic = "basic",
  handwashing_facility_jmp_cat_undefined = "undefined"
)

Arguments

Value

A data frame with new columns:

• comp_wash_score_water_quantity: Numeric score for water quantity (1-5).

• comp_wash_score_water_quality: Numeric score for water quality (1-5).

• comp_wash_score_sanitation: Numeric score for sanitation (1-5).

• comp_wash_score_hygiene: Numeric score for hygiene (1-5).

• comp_wash_score: Overall WASH composite score (0-20).

• comp_wash_in_need: Binary indicator (0 or 1) for households in need of WASH assistance.

Description

This function recodes the water source and time to fetch water into a joint JMP (Joint Monitoring Programme) category.

Usage

add_drinking_water_source_cat(
  df,
  drinking_water_source = "wash_drinking_water_source",
  drinking_water_source_cat_improved = c("piped_dwelling", "piped_compound",
    "piped_neighbour", "tap", "borehole", "protected_well", "well_spring",
    "rainwater_collection", "tank_truck", "cart_tank", "kiosk", "bottled_water",
    "sachet_water"),
  drinking_water_source_cat_unimproved = c("unprotected_well", "unprotected_spring"),
  drinking_water_source_cat_surface_water = "surface_water",
  drinking_water_source_cat_undefined = c("dnk", "pnta", "other")
)

add_drinking_water_time_cat(
  df,
  drinking_water_time_yn = "wash_drinking_water_time_yn",
  water_on_premises = c("water_in_dwelling", "water_in_plot"),
  number_minutes = "number_minutes",
  dnk = "dnk",
  undefined = "pnta",
  drinking_water_time_int = "wash_drinking_water_time_int",
  max_minutes = 600,
  drinking_water_time_sl = "wash_drinking_water_time_sl",
  sl_under_30_min = c("5min_or_less", "5min_15min", "15min_30min"),
  sl_30min_1hr = "30min_1hr",
  sl_more_than_1hr = "more_than_1hr",
  sl_undefined = c("dnk", "pnta"),
  drinking_water_source = "wash_drinking_water_source",
  skipped_drinking_water_source_premises = "piped_dwelling",
  skipped_drinking_water_source_undefined = c("dnk", "pnta")
)

add_drinking_water_time_threshold_cat(
  df,
  drinking_water_time_30min_cat = "wash_drinking_water_time_cat",
  drinking_water_time_30min_cat_premises = "premises",
  drinking_water_time_30min_cat_under_30min = c("under_30_min"),
  drinking_water_time_30min_cat_above_30min = c("30min_1hr", "more_than_1hr"),
  drinking_water_time_30min_cat_undefined = "undefined"
)

add_drinking_water_quality_jmp_cat(
  df,
  drinking_water_source_cat = "wash_drinking_water_source_cat",
  drinking_water_source_cat_improved = "improved",
  drinking_water_source_cat_unimproved = "unimproved",
  drinking_water_source_cat_surface_water = "surface_water",
  drinking_water_source_cat_undefined = "undefined",
  drinking_water_time_30min_cat = "wash_drinking_water_time_30min_cat",
  drinking_water_time_30min_cat_premises = "premises",
  drinking_water_time_30min_cat_under_30min = "under_30min",
  drinking_water_time_30min_cat_above_30min = "above_30min",
  drinking_water_time_30min_cat_undefined = "undefined"
)

Arguments

Value

A data frame with a new column 'wash_drinking_water_quality_jmp_cat' containing:

• limited: Response indicating limited access to safe drinking water.

• basic: Response indicating basic access to safe drinking water.

• unimproved: Response indicating unimproved water sources.

• surface_water: Response indicating surface water sources.

• undefined: Response for undefined categories.

Description

This function ranks the top 3 most frequent expenditure types for each household based on the amount spent on various categories. It adds new columns to the input data frame indicating the top 3 expenditure types.

Prerequisite function:

• add_expenditure_type_zero_freq.R

Usage

add_expenditure_type_freq_rank(
  df,
  expenditure_freq_types = c("cm_expenditure_frequent_food",
    "cm_expenditure_frequent_rent", "cm_expenditure_frequent_water",
    "cm_expenditure_frequent_nfi", "cm_expenditure_frequent_utilities",
    "cm_expenditure_frequent_fuel", "cm_expenditure_frequent_transportation",
    "cm_expenditure_frequent_communication", "cm_expenditure_frequent_other"),
  id_col = "uuid"
)

Arguments

Value

A data frame with additional columns:

• cm_freq_expenditure_top1: The most frequent expenditure type.

• cm_freq_expenditure_top2: The second most frequent expenditure type.

• cm_freq_expenditure_top3: The third most frequent expenditure type.

Description

This function ranks the top 3 most infrequent expenditure types for each household based on the amount spent on various categories. It adds new columns to the input data frame indicating the top 3 infrequent expenditure types.

Prerequisite function:

• add_expenditure_type_zero_infreq.R

Usage

add_expenditure_type_infreq_rank(
  df,
  expenditure_infreq_types = c("cm_expenditure_infrequent_shelter",
    "cm_expenditure_infrequent_nfi", "cm_expenditure_infrequent_health",
    "cm_expenditure_infrequent_education", "cm_expenditure_infrequent_debt",
    "cm_expenditure_infrequent_clothing", "cm_expenditure_infrequent_other"),
  id_col = "uuid"
)

Arguments

Details

Rank Top 3 Infrequent Expenditure Types

Value

A data frame with additional columns:

• cm_infreq_expenditure_top1: The most infrequent expenditure type.

• cm_infreq_expenditure_top2: The second most infrequent expenditure type.

• cm_infreq_expenditure_top3: The third most infrequent expenditure type.

Description

This function calculates the proportion of each frequent expenditure type relative to the total frequent expenditure. It adds new columns to the input data frame representing these proportions.

Prerequisite function:

• add_expenditure_type_zero_freq.R

Usage

add_expenditure_type_prop_freq(
  df,
  cm_expenditure_frequent_food = "cm_expenditure_frequent_food",
  cm_expenditure_frequent_rent = "cm_expenditure_frequent_rent",
  cm_expenditure_frequent_water = "cm_expenditure_frequent_water",
  cm_expenditure_frequent_nfi = "cm_expenditure_frequent_nfi",
  cm_expenditure_frequent_utilities = "cm_expenditure_frequent_utilities",
  cm_expenditure_frequent_fuel = "cm_expenditure_frequent_fuel",
  cm_expenditure_frequent_transportation = "cm_expenditure_frequent_transportation",
  cm_expenditure_frequent_communication = "cm_expenditure_frequent_communication",
  cm_expenditure_frequent_other = "cm_expenditure_frequent_other"
)

Arguments

Value

A data frame with additional columns:

• cm_expenditure_frequent_total: The total frequent expenditure amount.

• cm_expenditure_frequent_food_prop: Proportion of food expenditure.

• cm_expenditure_frequent_rent_prop: Proportion of rent expenditure.

• cm_expenditure_frequent_water_prop: Proportion of water expenditure.

• cm_expenditure_frequent_nfi_prop: Proportion of non-food items expenditure.

• cm_expenditure_frequent_utilities_prop: Proportion of utilities expenditure.

• cm_expenditure_frequent_fuel_prop: Proportion of fuel expenditure.

• cm_expenditure_frequent_transportation_prop: Proportion of transportation expenditure.

• cm_expenditure_frequent_communication_prop: Proportion of communication expenditure.

• cm_expenditure_frequent_other_prop: Proportion of other frequent expenditures.

Description

This function calculates the proportion of each infrequent expenditure type relative to the total infrequent expenditure. It adds new columns to the input data frame representing these proportions.

Prerequisite function:

• add_expenditure_type_zero_infreq.R

Usage

add_expenditure_type_prop_infreq(
  df,
  cm_expenditure_infrequent_shelter = "cm_expenditure_infrequent_shelter",
  cm_expenditure_infrequent_nfi = "cm_expenditure_infrequent_nfi",
  cm_expenditure_infrequent_health = "cm_expenditure_infrequent_health",
  cm_expenditure_infrequent_education = "cm_expenditure_infrequent_education",
  cm_expenditure_infrequent_debt = "cm_expenditure_infrequent_debt",
  cm_expenditure_infrequent_clothing = "cm_expenditure_infrequent_clothing",
  cm_expenditure_infrequent_other = "cm_expenditure_infrequent_other"
)

Arguments

Value

A data frame with additional columns:

• cm_expenditure_infrequent_total: The total infrequent expenditure amount.

• cm_expenditure_infrequent_shelter_prop: Proportion of shelter maintenance or repair expenditure.

• cm_expenditure_infrequent_nfi_prop: Proportion of non-food household items expenditure.

• cm_expenditure_infrequent_health_prop: Proportion of health-related expenditures.

• cm_expenditure_infrequent_education_prop: Proportion of education-related expenditures.

• cm_expenditure_infrequent_debt_prop: Proportion of debt repayment expenditure.

• cm_expenditure_infrequent_clothing_prop: Proportion of clothing-related expenditure.

• cm_expenditure_infrequent_other_prop: Proportion of other infrequent expenditures.

Description

This function adds zero values to frequent expenditure types that were skipped in the survey. Frequent expenditures are defined as those with a 30-day recall period. The function also ensures that when no expenditure is reported, all expenditure types are set to zero.

Usage

add_expenditure_type_zero_freq(
  df,
  expenditure_freq = "cm_expenditure_frequent",
  none = "none",
  undefined = c("dnk", "pnta"),
  expenditure_freq_types = c("cm_expenditure_frequent_food",
    "cm_expenditure_frequent_rent", "cm_expenditure_frequent_water",
    "cm_expenditure_frequent_nfi", "cm_expenditure_frequent_utilities",
    "cm_expenditure_frequent_fuel", "cm_expenditure_frequent_transportation",
    "cm_expenditure_frequent_communication", "cm_expenditure_frequent_other")
)

Arguments

Value

A data frame with updated expenditure columns: columns specified in expenditure_freq_types are updated with zeros for skipped entries.

Description

This function adds zero values to infrequent expenditure types that were skipped in the survey. Infrequent expenditures are defined as those with a 6-month recall period. The function also ensures that when no expenditure is reported, all expenditure types are set to zero.

Usage

add_expenditure_type_zero_infreq(
  df,
  expenditure_infreq = "cm_expenditure_frequent",
  none = "none",
  undefined = c("dnk", "pnta"),
  expenditure_infreq_types = c("cm_expenditure_infrequent_shelter",
    "cm_expenditure_infrequent_nfi", "cm_expenditure_infrequent_health",
    "cm_expenditure_infrequent_education", "cm_expenditure_infrequent_debt",
    "cm_expenditure_infrequent_clothing", "cm_expenditure_infrequent_other")
)

Arguments

Value

A data frame with updated expenditure columns: columns specified in expenditure_infreq_types are updated with zeros for skipped entries.

Description

Calculating the Food Consumption-Livelihood Coping Matrix

Usage

add_fclcm_phase(
  .dataset,
  fc_phase_var = "fsl_fc_phase",
  fc_phase_1 = "Phase 1 FC",
  fc_phase_2 = "Phase 2 FC",
  fc_phase_3 = "Phase 3 FC",
  fc_phase_4 = "Phase 4 FC",
  fc_phase_5 = "Phase 5 FC",
  lcs_cat_var = "fsl_lcsi_cat",
  lcs_cat_none = "None",
  lcs_cat_stress = "Stress",
  lcs_cat_crisis = "Crisis",
  lcs_cat_emergency = "Emergency"
)

Arguments

Value

Returns a dataframe with a additional column for FCLC phase.

Examples

test_df <- data.frame(
  fsl_lcsi_cat = c("None", "Stress"),
  fsl_fc_phase = c("Phase 1 FC", "Phase 2 FC")
)
test_df |> add_fclcm_phase()

Description

Add the food consumption matrix to the dataset

Usage

add_fcm_phase(
  .dataset,
  fcs_column_name = "fsl_fcs_cat",
  rcsi_column_name = "fsl_rcsi_cat",
  hhs_column_name = "fsl_hhs_cat_ipc",
  hdds_column_name = "fsl_hdds_cat",
  fcs_categories_acceptable = "Acceptable",
  fcs_categories_poor = "Poor",
  fcs_categories_borderline = "Borderline",
  rcsi_categories_low = "No to Low",
  rcsi_categories_medium = "Medium",
  rcsi_categories_high = "High",
  hhs_categories_none = "None",
  hhs_categories_little = "Little",
  hhs_categories_moderate = "Moderate",
  hhs_categories_severe = "Severe",
  hhs_categories_very_severe = "Very Severe",
  hdds_categories_low = "Low",
  hdds_categories_medium = "Medium",
  hdds_categories_high = "High"
)

Arguments

Value

this function returns a dataframe with a column called fc_cell that includes values from 1 to 45 representing the Food Consumption Score Matrix and the fc_phase column that includes the different 5 phases of food consumption

Examples

test_data <- data.frame(
  fsl_fcs_cat = c("Acceptable", "Poor", "Borderline", "Acceptable"),
  fsl_rcsi_cat = c("No to Low", "Medium", "No to Low", "High"),
  fsl_hhs_cat = c("None", "Little", "Severe", "Very Severe"),
  fsl_hdds_cat = c("Low", "High", "Medium", "High")
)
add_fcm_phase(test_data,
  fcs_column_name = "fsl_fcs_cat",
  rcsi_column_name = "fsl_rcsi_cat",
  hhs_column_name = "fsl_hhs_cat",
  hdds_column_name = "fsl_hdds_cat",
  fcs_categories_acceptable = "Acceptable",
  fcs_categories_poor = "Poor",
  fcs_categories_borderline = "Borderline",
  rcsi_categories_low = "No to Low",
  rcsi_categories_medium = "Medium",
  rcsi_categories_high = "High",
  hhs_categories_none = "None",
  hhs_categories_little = "Little",
  hhs_categories_moderate = "Moderate",
  hhs_categories_severe = "Severe",
  hhs_categories_very_severe = "Very Severe",
  hdds_categories_low = "Low",
  hdds_categories_medium = "Medium",
  hdds_categories_high = "High"
)

Description

add_fcs

Usage

add_fcs(
  .dataset,
  cutoffs = c("normal", "alternative"),
  fsl_fcs_cereal = "fsl_fcs_cereal",
  fsl_fcs_legumes = "fsl_fcs_legumes",
  fsl_fcs_veg = "fsl_fcs_veg",
  fsl_fcs_fruit = "fsl_fcs_fruit",
  fsl_fcs_meat = "fsl_fcs_meat",
  fsl_fcs_dairy = "fsl_fcs_dairy",
  fsl_fcs_sugar = "fsl_fcs_sugar",
  fsl_fcs_oil = "fsl_fcs_oil"
)

Arguments

Value

the dataset with fsl_fcs_score and fsl_fcs_cat computed, as well as the 8 weighted food groups

Examples

df1 <- data.frame(
  fsl_fcs_cereal = c(1, 2, 3, 2, 5, 6, 7),
  fsl_fcs_legumes = c(3, 4, 5, 6, 1, 6, 5),
  fsl_fcs_veg = c(3, 2, 1, 6, 5, 4, 3),
  fsl_fcs_fruit = c(1, 4, 6, 2, 2, 2, 4),
  fsl_fcs_meat = c(5, 4, 3, 2, 7, 4, 5),
  fsl_fcs_dairy = c(1, 2, 6, 7, 3, 4, 2),
  fsl_fcs_sugar = c(1, 7, 6, 5, 2, 3, 4),
  fsl_fcs_oil = c(2, 3, 6, 5, 1, 7, 4)
)
add_fcs(.dataset = df1,
  cutoffs = "normal"
)

Description

This function adds categories for functional domestic space tasks based on various input parameters. It processes cooking, sleeping, storing, and lighting tasks, creating new columns with standardized categories and a summary of tasks that cannot be performed.

Usage

add_fds_cannot_cat(
  df,
  fds_cooking = "snfi_fds_cooking",
  fds_cooking_can = "yes",
  fds_cooking_cannot = "no",
  fds_cooking_no_need = "no_need",
  fds_cooking_undefined = "pnta",
  fds_sleeping = "snfi_fds_sleeping",
  fds_sleeping_can = "yes",
  fds_sleeping_cannot = "no",
  fds_sleeping_undefined = "pnta",
  fds_storing = "snfi_fds_storing",
  fds_storing_cannot = "no",
  fds_storing_can = "yes",
  fds_storing_undefined = "pnta",
  lighting_source = "energy_lighting_source",
  lighting_source_none = "none",
  lighting_source_undefined = c("pnta", "dnk")
)

Arguments

Value

A data frame with additional columns:

• snfi_fds_cooking: Standardized categories for cooking tasks

• snfi_fds_sleeping: Standardized categories for sleeping tasks

• snfi_fds_storing: Standardized categories for storing tasks

• energy_lighting_source: Standardized categories for lighting source

• snfi_fds_cannot_n: Number of tasks that cannot be performed

• snfi_fds_cannot_cat: Categorized number of tasks that cannot be performed

Description

This function categorizes handwashing facilities into "Basic," "Limited," or "No facility" based on survey data. It evaluates various parameters related to the availability of handwashing facilities and the presence of soap and water.

Usage

add_handwashing_facility_cat(
  df,
  survey_modality = "survey_modality",
  survey_modality_in_person = c("in_person"),
  survey_modality_remote = c("remote"),
  facility = "wash_handwashing_facility",
  facility_yes = c("available_fixed_in_dwelling", "available_fixed_in_plot",
    "available_mobile"),
  facility_no = "none",
  facility_no_permission = "no_permission",
  facility_undefined = "other",
  facility_observed_water = "wash_handwashing_facility_observed_water_yn",
  facility_observed_water_yes = "water_available",
  facility_observed_water_no = c("water_not_available"),
  facility_observed_soap = "wash_soap_observed_yn",
  facility_observed_soap_yes = "soap_available",
  facility_observed_soap_no = "soap_not_available",
  facility_observed_soap_undefined = c("other", "pnta"),
  facility_reported = "wash_handwashing_facility_reported",
  facility_reported_yes = c("fixed_dwelling", "fixed_yard", "mobile"),
  facility_reported_no = c("none"),
  facility_reported_undefined = c("other", "dnk", "pnta"),
  facility_reported_water = "wash_handwashing_facility_water_reported_yn",
  facility_reported_water_yes = "yes",
  facility_reported_water_no = c("no"),
  facility_reported_water_undefined = c("dnk", "pnta"),
  facility_reported_soap = "wash_soap_reported_yn",
  facility_reported_soap_yes = "yes",
  facility_reported_soap_no = c("no"),
  facility_reported_soap_undefined = c("dnk", "pnta"),
  soap_type_observed = "wash_soap_observed_type",
  soap_type_observed_yes = c("soap", "detergent"),
  soap_type_observed_no = c("ash_mud_sand"),
  soap_type_observed_undefined = c("dnk", "pnta", "other"),
  soap_type_reported = "wash_soap_reported_type",
  soap_type_reported_yes = c("soap", "detergent"),
  soap_type_reported_no = c("ash_mud_sand"),
  soap_type_reported_undefined = c("dnk", "pnta", "other")
)

Arguments

Details

The function checks for the presence and validity of all required columns and values in the input data frame. It ensures that all parameters are documented and used in the logic. The categorization follows JMP (Joint Monitoring Programme) standards for handwashing facilities.

Value

A data frame with an additional column 'wash_handwashing_facility_jmp_cat': Categorized handwashing facilities: "Basic," "Limited," or "No Facility."

Description

Add the household hunger scale to the dataset

Usage

add_hhs(
  .dataset,
  fsl_hhs_nofoodhh = "fsl_hhs_nofoodhh",
  fsl_hhs_nofoodhh_freq = "fsl_hhs_nofoodhh_freq",
  fsl_hhs_sleephungry = "fsl_hhs_sleephungry",
  fsl_hhs_sleephungry_freq = "fsl_hhs_sleephungry_freq",
  fsl_hhs_alldaynight = "fsl_hhs_alldaynight",
  fsl_hhs_alldaynight_freq = "fsl_hhs_alldaynight_freq",
  yes_answer = "yes",
  no_answer = "no",
  rarely_answer = "rarely",
  sometimes_answer = "sometimes",
  often_answer = "often"
)

Arguments

Value

It returns the dataframe with 12 extras columns: recoded hhs questions, score for the 3 sets of questions (from 0 to 2), the HHS score (from 0 to 6), the HHS category and the HHS IPC category

Examples

{
  input_data <- data.frame(
    fsl_hhs_nofoodhh = c("no", "yes", "no", "no", "no"),
    fsl_hhs_nofoodhh_freq = c(NA_character_, "rarely",
     NA_character_, NA_character_, NA_character_),
    fsl_hhs_sleephungry = c("no", "no", "yes", "no", "no"),
    fsl_hhs_sleephungry_freq = c(NA_character_, NA_character_,
     "often", NA_character_, NA_character_),
    fsl_hhs_alldaynight = c("no", "no", "yes", "yes", "yes"),
    fsl_hhs_alldaynight_freq = c(NA_character_, NA_character_,
     "often", "rarely", "sometimes")
  )

  add_hhs(
    .dataset = input_data,
    fsl_hhs_nofoodhh = "fsl_hhs_nofoodhh",
    fsl_hhs_nofoodhh_freq = "fsl_hhs_nofoodhh_freq",
    fsl_hhs_sleephungry = "fsl_hhs_sleephungry",
    fsl_hhs_sleephungry_freq = "fsl_hhs_sleephungry_freq",
    fsl_hhs_alldaynight = "fsl_hhs_alldaynight",
    fsl_hhs_alldaynight_freq = "fsl_hhs_alldaynight_freq",
    yes_answer = "yes",
    no_answer = "no",
    rarely_answer = "rarely",
    sometimes_answer = "sometimes",
    often_answer = "often"
  )
}

Description

This function updates the head of household (HoH) gender and age information based on whether the respondent is the HoH or not. If the respondent is the HoH, their gender and age are used; otherwise, the existing HoH information is retained.

Usage

add_hoh_final(
  df,
  resp_hoh_yn = "resp_hoh_yn",
  yes = "yes",
  no = "no",
  hoh_gender = "hoh_gender",
  hoh_age = "hoh_age",
  resp_gender = "resp_gender",
  resp_age = "resp_age"
)

Arguments

Value

A modified dataframe with updated columns:

• hoh_gender: Updated head of household gender.

• hoh_age: Updated head of household age.

Description

Computes the total hwise4_score (0–12) from the four HWISE survey items (water quantity), and maps it to a WASH component score for water quantity (comp_wash_score_water_quantity, 1–5). The four items are internally recoded to numeric scores but are only exposed in the output (as ⁠_score⁠-suffixed columns) when .keep_recoded = TRUE.

Usage

add_hwise(
  .df,
  wash_hwise_drink = "wash_hwise_drink",
  wash_hwise_hands = "wash_hwise_hands",
  wash_hwise_plans = "wash_hwise_plans",
  wash_hwise_worry = "wash_hwise_worry",
  hwise_never = "never",
  hwise_rarely = "rarely",
  hwise_sometimes = "sometimes",
  hwise_often = "often",
  hwise_always = "always",
  hwise_dnk = "dnk",
  hwise_pnta = "pnta",
  .keep_recoded = FALSE
)

Arguments

Value

.df with new columns:

• The four HWISE columns recoded to numeric scores (0–3, NA for dnk/pnta), suffixed with ⁠_score⁠ (only kept in the output if .keep_recoded = TRUE).

• hwise4_score: row sum of the four recoded columns (0–12).

• comp_wash_score_water_quantity: WASH severity score (1–5): 1 = score 0–3, 2 = 4–6, 3 = 7–8, 4 = 9–10, 5 = 11+.

See Also

add_comp_wash()

Examples

df <- dplyr::tibble(
  wash_hwise_drink = c("never",     "sometimes", "often"),
  wash_hwise_hands = c("rarely",    "often",     "always"),
  wash_hwise_plans = c("sometimes", "often",     "always"),
  wash_hwise_worry = c("never",     "sometimes", "always")
)

result <- add_hwise(df)
dplyr::select(result, hwise4_score, comp_wash_score_water_quantity)

# keep the per-item recoded scores (ending in `_score`)
result_recoded <- add_hwise(df, .keep_recoded = TRUE)
dplyr::select(result_recoded, dplyr::matches("_score$"))

Description

This function calculates the proportion of each income source relative to the total income. It also computes the total income from all sources.

Prerequisite function:

• add_income_source_zero_to_sl.R

Usage

add_income_source_prop(
  df,
  income_souce_salaried_n = "cm_income_source_salaried_n",
  income_source_casual_n = "cm_income_source_casual_n",
  income_source_own_business_n = "cm_income_source_own_business_n",
  income_source_own_production_n = "cm_income_source_own_production_n",
  income_source_social_benefits_n = "cm_income_source_social_benefits_n",
  income_source_rent_n = "cm_income_source_rent_n",
  income_source_remittances_n = "cm_income_source_remittances_n",
  income_source_assistance_n = "cm_income_source_assistance_n",
  income_source_support_friends_n = NULL,
  income_source_donation_n = NULL,
  income_source_other_n = "cm_income_source_other_n"
)

Arguments

Value

A data frame with additional columns:

• cm_income_total: Total income from all sources.

• *_prop: Proportion of each income source relative to total income (e.g., cm_income_source_salaried_n_prop).

Details on loans

Loans (income_source_support_friends_n and income_source_donation_n) are considered to be a cash influx. Yet they do not count as a formal income source. While it is good practice to collect these figures as part of this module, they should not be included in the total income calculation. This is why their default value here is NULL.

Description

This function categorizes income sources, counts them by type, and ranks the top 3 income sources. It also adds categories for emergency, unstable, stable, and other income sources.

Prerequisite function:

• add_income_source_zero_to_sl.R

Usage

add_income_source_rank(
  df,
  emergency = c("cm_income_source_assistance_n", "cm_income_source_support_friends_n",
    "cm_income_source_donation_n"),
  unstable = c("cm_income_source_casual_n", "cm_income_source_social_benefits_n",
    "cm_income_source_rent_n", "cm_income_source_remittances_n"),
  stable = c("cm_income_source_salaried_n", "cm_income_source_own_business_n",
    "cm_income_source_own_production_n"),
  other = "cm_income_source_other_n",
  id_col = "uuid"
)

Arguments

Value

A data frame with additional columns:

• cm_income_source_emergency_n: Count of emergency income sources.

• cm_income_source_unstable_n: Count of unstable income sources.

• cm_income_source_stable_n: Count of stable income sources.

• cm_income_source_other_n: Count of other income sources.

• cm_income_source_top1: Top income source.

• cm_income_source_top2: Second top income source.

• cm_income_source_top3: Third top income source.

Description

This function adds zero to income source columns when the main income source was skipped or undefined. It also ensures that all income sources are zero when the main income source is "none".

Usage

add_income_source_zero_to_sl(
  df,
  income_source = "cm_income_source",
  none = "none",
  undefined = c("dnk", "pnta"),
  income_sources = c("cm_income_source_salaried_n", "cm_income_source_casual_n",
    "cm_income_source_own_business_n", "cm_income_source_own_production_n",
    "cm_income_source_social_benefits_n", "cm_income_source_rent_n",
    "cm_income_source_remittances_n", "cm_income_source_assistance_n",
    "cm_income_source_support_friends_n", "cm_income_source_donation_n",
    "cm_income_source_other_n")
)

Arguments

Value

A data frame with updated income source columns:

• income_sources: All specified income source columns are updated to zero when the main income source is skipped, undefined, or none.

Description

Function to calculate Livelihood Coping Strategy Index (LCSI)

Usage

add_lcsi(
  .dataset,
  fsl_lcsi_stress1 = "fsl_lcsi_stress1",
  fsl_lcsi_stress2 = "fsl_lcsi_stress2",
  fsl_lcsi_stress3 = "fsl_lcsi_stress3",
  fsl_lcsi_stress4 = "fsl_lcsi_stress4",
  fsl_lcsi_crisis1 = "fsl_lcsi_crisis1",
  fsl_lcsi_crisis2 = "fsl_lcsi_crisis2",
  fsl_lcsi_crisis3 = "fsl_lcsi_crisis3",
  fsl_lcsi_emergency1 = "fsl_lcsi_emergency1",
  fsl_lcsi_emergency2 = "fsl_lcsi_emergency2",
  fsl_lcsi_emergency3 = "fsl_lcsi_emergency3",
  yes_val = "yes",
  no_val = "no_had_no_need",
  exhausted_val = "no_exhausted",
  not_applicable_val = "not_applicable"
)

Arguments

Value

Returns a dataframe with added columns for LCSI indicators.

• fsl_lcsi_x_yes : 1 means one of the of the x strategies was used (yes_val)

• fsl_lcsi_x_exhaust: 1 means one of the x strategies was exhausted and could not be used (exhausted_val)

• fsl_lcsi_x: 1 means one of the x strategies was if either used (yes_val) or exhausted (exhausted_val) Where x is stress, crisis or emergency

• fsl_lcsi_cat_yes : the highest category between the fsl_lcsi_x_yes

• fsl_lcsi_cat_exhaust: the highest category between the fsl_lcsi_x_exhaust

• fsl_lcsi_cat: the highest category between the fsl_lcsi_x

Examples

{
  input_data1 <- data.frame(fsl_lcsi_stress1 = c("No", "No", "Exhausted", "Not Applicable", "No"),
  fsl_lcsi_stress2 = c("No", "Yes", "Not Applicable", "No", "No"),
  fsl_lcsi_stress3 = c("Not Applicable", "Not Applicable", "Yes", "No", "No"),
  fsl_lcsi_stress4 = c("Not Applicable", "No", "Yes", "Yes", "No"),
  fsl_lcsi_crisis1 = c("No", "Not Applicable", "Yes", "Exhausted", "No"),
  fsl_lcsi_crisis2 = c("No", "No", "No", "No", "No"),
  fsl_lcsi_crisis3 = c("No", "No", "Yes", "Not Applicable", "No"),
  fsl_lcsi_emergency1 = c("No", "Not Applicable", "Not Applicable", "No", "No"),
  fsl_lcsi_emergency2 = c("No", "Not Applicable", "Yes", "Not Applicable", "No"),
  fsl_lcsi_emergency3 = c("Not Applicable", "No", "Not Applicable", "No", "Exhausted"))

add_lcsi(.dataset = input_data1,
yes_val = "Yes",
no_val = "No",
exhausted_val = "Exhausted",
not_applicable_val = "Not Applicable")

}

Description

This set of functions adds dummy variables for age classes and gender in the demographics loop, and optionally sums these dummies to the main dataset. It includes four main functions: add_loop_age_dummy(), add_loop_age_dummy_to_main(), add_loop_age_gender_dummy(), and add_loop_age_gender_dummy_to_main().

Usage

add_loop_age_dummy(
  loop,
  ind_age = "ind_age",
  lb = 5,
  ub = 18,
  new_colname = NULL
)

add_loop_age_dummy_to_main(
  main,
  loop,
  ind_age_dummy = "ind_age_5_18",
  id_col_main = "uuid",
  id_col_loop = "uuid",
  new_colname = NULL
)

add_loop_age_gender_dummy(
  loop,
  ind_age = "ind_age",
  lb = 5,
  ub = 18,
  ind_gender = "ind_gender",
  gender = "female",
  new_colname = NULL
)

add_loop_age_gender_dummy_to_main(
  main,
  loop,
  ind_age_gender_dummy = "ind_age_female_5_18",
  id_col_main = "uuid",
  id_col_loop = "uuid",
  new_colname = NULL
)

Arguments

Value

Depending on the function used:

• add_loop_age_dummy: Returns the loop data frame with an additional column for the age dummy variable.

• add_loop_age_dummy_to_main: Returns the main data frame with an additional column for the sum of age dummy variables.

• add_loop_age_gender_dummy: Returns the loop data frame with an additional column for the age-gender dummy variable.

• add_loop_age_gender_dummy_to_main: Returns the main data frame with an additional column for the sum of age-gender dummy variables.

Description

This function adds dummy variables to an individual-level dataframe for all school-aged children. It creates a variable (edu_ind_access_d) that indicates whether the child accessed school (1 if accessed, 0 if explicitly not accessed) and another variable (edu_ind_no_access_d) that indicates no access to school.

Prerequisite function:

• add_loop_edu_ind_age_corrected.R

Usage

add_loop_edu_access_d(
  loop,
  ind_access = "edu_access",
  yes = "yes",
  no = "no",
  pnta = "pnta",
  dnk = "dnk",
  ind_schooling_age_d = "edu_ind_age_schooling"
)

add_loop_edu_access_d_to_main(
  main,
  loop,
  ind_access_d = "edu_ind_access_d",
  ind_no_access_d = "edu_ind_no_access_d",
  id_col_main = "uuid",
  id_col_loop = "uuid"
)

Arguments

Details

pnta (prefer not to answer) and dnk (don't know) responses are coded as NA, not as "not accessed"

Value

A data frame with additional columns:

• edu_ind_access_d: Dummy variable indicating access to education (1 if accessed, 0 if explicitly not accessed, NA if not school age or pnta/dnk).

• edu_ind_no_access_d: Dummy variable indicating no access to education (1 if explicitly not accessed, 0 if accessed, NA if not school age or pnta/dnk).

Description

This function adds a dummy variable for child protection barriers to education in individual-level data and aggregates it to household-level data.

Prerequisite function:

• add_loop_edu_ind_age_corrected.R

Usage

add_loop_edu_barrier_protection_d(
  loop,
  barriers = "edu_barrier",
  protection_issues = c("protection_at_school", "protection_travel_school",
    "child_work_home", "child_work_outside", "child_armed_group", "child_marriage",
    "child_pregnancy", "ban", "enroll_lack_documentation", "discrimination"),
  ind_schooling_age_d = "edu_ind_age_schooling",
  barriers_undefined = c("dnk", "pnta", "other"),
  non_protection_issues = c("costs", "lack_interest", "not_priority",
    "lack_accessible_school", "lack_classrooms", "lack_wash_facilities", "school_closed",
    "lack_teacher", "curriculum_not_useful", "child_health", "language",
    "enroll_displacement", "child_too_young", "child_graduated")
)

add_loop_edu_barrier_protection_d_to_main(
  main,
  loop,
  ind_barrier_protection_d = "edu_ind_barrier_protection_d",
  id_col_main = "uuid",
  id_col_loop = "uuid"
)

Arguments

Value

A data frame with an additional column:

• edu_ind_barrier_protection_d: Dummy variable indicating if a school-aged child faces a protection barrier (1) or not (0).

A data frame with an additional column:

Description

This function adds education disruption binaries to individual-level data and summarizes them at the household level. It includes two main functions: add_loop_edu_disrupted_d() for individual-level data and add_loop_edu_disrupted_d_to_main() for household-level data.

Prerequisite function:

• add_loop_edu_ind_age_corrected.R

Usage

add_loop_edu_disrupted_d(
  df,
  attack = "edu_disrupted_attack",
  hazards = "edu_disrupted_hazards",
  displaced = "edu_disrupted_displaced",
  teacher = "edu_disrupted_teacher",
  levels = c("yes", "no", "dnk", "pnta"),
  ind_schooling_age_d = "edu_ind_age_schooling"
)

add_loop_edu_disrupted_d_to_main(
  main,
  loop,
  attack_d = "edu_disrupted_attack_d",
  hazards_d = "edu_disrupted_hazards_d",
  displaced_d = "edu_disrupted_displaced_d",
  teacher_d = "edu_disrupted_teacher_d",
  id_col_main = "uuid",
  id_col_loop = "uuid"
)

Arguments

Value

A data frame with additional columns:

• *_d: Binary columns for each disruption type (e.g., edu_disrupted_attack_d).

A data frame with additional columns:

• edu_disrupted_*_n: Count of individuals experiencing each type of education disruption (e.g., edu_disrupted_hazards_n).

Description

Add a correct schooling age to the loop

Usage

add_loop_edu_ind_age_corrected(
  loop,
  main,
  id_col_loop = "uuid",
  id_col_main = "uuid",
  survey_start_date = "start",
  school_year_start_month = 9,
  ind_age = "ind_age",
  month = NULL,
  schooling_start_age = 5,
  schooling_end_age = 17
)

add_loop_edu_ind_schooling_age_d_to_main(
  main,
  loop,
  ind_schooling_age_d = "edu_ind_age_schooling",
  id_col_main = "uuid",
  id_col_loop = "uuid"
)

Arguments

Value

2 new columns: "edu_ind_age_corrected" with the corrected individual age, and a dummy variable edu_ind_age_schooling

Description

This function adds healthcare need categories to individual-level data, including disability information if provided. It creates dummy variables for different healthcare need scenarios and optionally includes disability-specific indicators.

This function aggregates individual-level healthcare need data to the household level, including disability-specific information if provided.

Usage

add_loop_healthcare_needed_cat(
  loop,
  ind_healthcare_needed = "health_ind_healthcare_needed",
  ind_healthcare_needed_no = "no",
  ind_healthcare_needed_yes = "yes",
  ind_healthcare_needed_dnk = "dnk",
  ind_healthcare_needed_pnta = "pnta",
  ind_healthcare_received = "health_ind_healthcare_received",
  ind_healthcare_received_no = "no",
  ind_healthcare_received_yes = "yes",
  ind_healthcare_received_dnk = "dnk",
  ind_healthcare_received_pnta = "pnta",
  ind_age = "ind_age"
)

add_loop_healthcare_needed_cat_to_main(
  main,
  loop,
  ind_healthcare_needed_no = "health_ind_healthcare_needed_no",
  ind_healthcare_needed_yes_unmet = "health_ind_healthcare_needed_yes_unmet",
  ind_healthcare_needed_yes_met = "health_ind_healthcare_needed_yes_met",
  id_col_main = "uuid",
  id_col_loop = "uuid"
)

Arguments

Value

A data frame with additional columns:

• health_ind_healthcare_needed_d: Dummy variable for healthcare needed.

• health_ind_healthcare_received_d: Dummy variable for healthcare received.

• health_ind_healthcare_needed_cat: Categorized healthcare need: "no_need", "yes_unmet_need", or "yes_met_need".

• health_ind_healthcare_needed_no: Dummy variable for no healthcare need.

• health_ind_healthcare_needed_yes_unmet: Dummy variable for unmet healthcare need.

• health_ind_healthcare_needed_yes_met: Dummy variable for met healthcare need.

A data frame with additional columns:

• health_ind_healthcare_needed_no_n: Count of individuals not needing healthcare.

• health_ind_healthcare_needed_yes_unmet_n: Count of individuals with unmet healthcare needs.

• health_ind_healthcare_needed_yes_met_n: Count of individuals with met healthcare needs.

Description

This function prepares dummy variables for each WG-SS component (vision, hearing, mobility, cognition, self-care, communication) and their levels (no difficulty, some difficulty, a lot of difficulty, cannot do at all). It combines them into sum scores for each level and creates disability binary cut-offs variables.

Usage

add_loop_wgq_ss(
  loop,
  ind_age = "ind_age",
  vision = "wgq_vision",
  hearing = "wgq_hearing",
  mobility = "wgq_mobility",
  cognition = "wgq_cognition",
  self_care = "wgq_self_care",
  communication = "wgq_communication",
  no_difficulty = "no_difficulty",
  some_difficulty = "some_difficulty",
  lot_of_difficulty = "a_lot_of_difficulty",
  cannot_do = "cannot_do_at_all",
  undefined = c("dnk", "pnta")
)

add_loop_wgq_ss_to_main(
  main,
  loop,
  wgq_dis_4 = "wgq_dis_4",
  wgq_dis_3 = "wgq_dis_3",
  wgq_dis_2 = "wgq_dis_2",
  wgq_dis_1 = "wgq_dis_1",
  ind_age_above_5 = "ind_age_above_5",
  id_col_main = "uuid",
  id_col_loop = "uuid"
)

Arguments

Value

A data frame with additional columns:

• *_cannot_do_d: Binary indicators for "cannot do" level for each component.

• *_lot_of_difficulty_d: Binary indicators for "a lot of difficulty" level for each component.

• *_some_difficulty_d: Binary indicators for "some difficulty" level for each component.

• *_no_difficulty_d: Binary indicators for "no difficulty" level for each component.

• wgq_cannot_do_n: Sum of "cannot do" indicators across all components.

• wgq_lot_of_difficulty_n: Sum of "a lot of difficulty" indicators across all components.

• wgq_some_difficulty_n: Sum of "some difficulty" indicators across all components.

• wgq_no_difficulty_n: Sum of "no difficulty" indicators across all components.

• wgq_cannot_do_d: Binary indicator for any "cannot do" across all components.

• wgq_lot_of_difficulty_d: Binary indicator for any "a lot of difficulty" across all components.

• wgq_some_difficulty_d: Binary indicator for any "some difficulty" across all components.

• wgq_no_difficulty_d: Binary indicator for any "no difficulty" across all components.

• wgq_dis_4: Disability cut-off 4: any domain coded as "cannot do at all".

• wgq_dis_3: Disability cut-off 3: any domain coded as "a lot of difficulty" or "cannot do at all".

• wgq_dis_2: Disability cut-off 2: at least 2 domains coded as "some difficulty" or any domain coded as "a lot of difficulty" or "cannot do at all".

• wgq_dis_1: Disability cut-off 1: at least one domain coded as "some difficulty", "a lot of difficulty", or "cannot do at all".

Description

This function calculates the MSNI score, determines if households are in need or severe need, counts the number of sectoral needs, and creates a sectoral needs profile.

Prerequisite functions:

• add_comp_edu.R

• add_comp_foodsec.R

• add_comp_health.R

• add_comp_prot.R

• add_comp_snfi.R

• add_comp_wash.R

Usage

add_msni(
  df,
  comp_foodsec_score = "comp_foodsec_score",
  comp_snfi_score = "comp_snfi_score",
  comp_wash_score = "comp_wash_score",
  comp_prot_score = "comp_prot_score",
  comp_health_score = "comp_health_score",
  comp_edu_score = "comp_edu_score",
  comp_foodsec_in_need = "comp_foodsec_in_need",
  comp_snfi_in_need = "comp_snfi_in_need",
  comp_wash_in_need = "comp_wash_in_need",
  comp_prot_in_need = "comp_prot_in_need",
  comp_health_in_need = "comp_health_in_need",
  comp_edu_in_need = "comp_edu_in_need",
  comp_foodsec_in_severe_need = "comp_foodsec_in_severe_need",
  comp_snfi_in_severe_need = "comp_snfi_in_severe_need",
  comp_wash_in_severe_need = "comp_wash_in_severe_need",
  comp_prot_in_severe_need = "comp_prot_in_severe_need",
  comp_edu_in_severe_need = "comp_edu_in_severe_need"
)

Arguments

Value

A data frame with 7 new columns:

• msni_score: The Multi-Sectoral Needs Index score.

• msni_in_need: Binary indicator for households in need.

• msni_in_severe_need: Binary indicator for households in severe need.

• sector_in_need_n: Number of sectoral needs identified.

• sector_in_severe_need_n: Number of sectoral severe needs identified.

• sector_needs_profile: Profile of sectoral needs identified (NA if no sectoral need is identified).

• sector_severe_needs_profile: Profile of sectoral severe needs identified (NA if no sectoral severe need is identified).

Description

This function categorizes occupancy arrangements and eviction risk into high, medium, or low risk, and then creates a new column for overall tenure security, taking the maximum risk from both.

Usage

add_occupancy_cat(
  df,
  occupancy = "hlp_occupancy",
  occupancy_high_risk = c("no_agreement"),
  occupancy_medium_risk = c("rented", "hosted_free"),
  occupancy_low_risk = c("ownership"),
  occupancy_undefined = c("dnk", "pnta", "other"),
  eviction = "hlp_risk_eviction",
  eviction_high_risk = "yes",
  eviction_low_risk = "no",
  eviction_undefined = c("dnk", "pnta")
)

Arguments

Value

A data frame with additional columns:

• hlp_occupancy_cat: Categorized occupancy arrangement: "high_risk", "medium_risk", "low_risk", or "undefined".

• hlp_eviction_cat: Categorized eviction risk: "high_risk", "low_risk", or "undefined".

• hlp_tenure_security: Maximum risk between hlp_occupancy_cat and hlp_eviction_cat.

Description

add_prot_score_movement

Usage

add_prot_score_movement(
  df,
  sep = "/",
  prot_needs_3_movement = "prot_needs_3_movement",
  no_changes_feel_unsafe = "no_changes_feel_unsafe",
  no_safety_concerns = "no_safety_concerns",
  women_girls_avoid_places = "women_girls_avoid_places",
  men_avoid_places = "men_avoid_places",
  boys_avoid_places = "boys_avoid_places",
  women_girls_avoid_night = "women_girls_avoid_night",
  men_avoid_night = "men_avoid_night",
  boys_avoid_night = "boys_avoid_night",
  girls_boys_avoid_school = "girls_boys_avoid_school",
  different_routes = "different_routes",
  avoid_markets = "avoid_markets",
  avoid_public_offices = "avoid_public_offices",
  avoid_fields = "avoid_fields",
  women_girls_boys_avoid_firewood = "women_girls_boys_avoid_firewood",
  women_girls_boys_avoid_places = "women_girls_boys_avoid_places",
  other_safety_measures = "other_safety_measures",
  dnk = "dnk",
  pnta = "pnta",
  men_avoid_places_weight = 1,
  men_avoid_night_weight = 1,
  .keep_weighted = FALSE
)

Arguments

Value

data frame with additional columns:

• comp_prot_score_prot_needs_3

• comp_prot_score_movement

Description

Creates weighted scores for two multi-choice questions about threats affecting household members’ ability to engage in activities and social interactions. Then computes an overall severity category (1–4) from the combined scores.

Usage

add_prot_score_practices(
  df,
  sep = "/",
  prot_needs_2_activities = "prot_needs_2_activities",
  yes_work = "yes_work",
  yes_livelihood = "yes_livelihood",
  yes_safety = "yes_safety",
  yes_farm = "yes_farm",
  yes_water = "yes_water",
  yes_other_activities = "yes_other_activities",
  yes_free_choices = "yes_free_choices",
  prot_needs_2_social = "prot_needs_2_social",
  yes_visiting_family = "yes_visiting_family",
  yes_visiting_friends = "yes_visiting_friends",
  yes_community_events = "yes_community_events",
  yes_joining_groups = "yes_joining_groups",
  yes_other_social = "yes_other_social",
  yes_child_recreation = "yes_child_recreation",
  yes_decision_making = "yes_decision_making",
  no = "no",
  dnk = "dnk",
  pnta = "pnta",
  .keep_weighted = FALSE
)

Arguments

Value

The input data frame with three new composite-score columns:

• comp_prot_score_prot_needs_2_activities: weighted sum of activity-related options.

• comp_prot_score_prot_needs_2_social: weighted sum of social-related options.

• comp_prot_score_practices: overall severity (1–4) based on combined score.

Plus optional weighted columns (suffix "_w") if .keep_weighted = TRUE.

Description

Computes weighted sums for two questions about household difficulties accessing services and justice/legal resources, then derives an overall severity category (1–4).

Usage

add_prot_score_rights(
  df,
  sep = "/",
  prot_needs_1_services = "prot_needs_1_services",
  yes_healthcare = "yes_healthcare",
  yes_schools = "yes_schools",
  yes_therapeutic_services = "yes_therapeutic_services",
  yes_edu_facilities = "yes_edu_facilities",
  yes_social_services = "yes_social_services",
  yes_gov_services = "yes_gov_services",
  yes_other_services = "yes_other_services",
  prot_needs_1_justice = "prot_needs_1_justice",
  yes_identity_documents = "yes_identity_documents",
  yes_counselling_legal = "yes_counselling_legal",
  yes_property_docs = "yes_property_docs",
  yes_birth_certificates = "yes_birth_certificates",
  yes_gov_services_justice = "yes_gov_services",
  yes_other_services_justice = "yes_other_services",
  none = "none",
  no = "no",
  dnk = "dnk",
  pnta = "pnta",
  .keep_weighted = FALSE
)

Arguments

Value

Input data frame with three new composite-score columns:

• comp_prot_score_prot_needs_1_services: weighted sum of services options.

• comp_prot_score_prot_needs_1_justice: weighted sum of justice options.

• comp_prot_score_rights: overall severity (1–4) based on combined score. Plus optional ⁠_w⁠ columns if .keep_weighted = TRUE.

Description

This function calculates weighted income quantiles and classifies households according to these. It can handle multiple income variables and applies weighted or unweighted quantiles based on the input.

Usage

add_quantile_interval(
  df,
  vars,
  cut_offs = c(0, 0.2, 0.4, 0.6, 0.8, 1),
  weight = NULL
)

Arguments

Value

A dataframe with additional columns:

• *_qtl: For each input variable, a new column is added with the quantile interval classification. The naming format is 'original_variable_name'_qtl.

Description

Add indicator for reduced Household CSI Score(rcsi)

Usage

add_rcsi(
  .dataset,
  fsl_rcsi_lessquality = "fsl_rcsi_lessquality",
  fsl_rcsi_borrow = "fsl_rcsi_borrow",
  fsl_rcsi_mealsize = "fsl_rcsi_mealsize",
  fsl_rcsi_mealadult = "fsl_rcsi_mealadult",
  fsl_rcsi_mealnb = "fsl_rcsi_mealnb"
)

Arguments

Value

A dataset with 7 additional columns: 5 weighted strategy columns (⁠rcsi_*_weighted⁠), fsl_rcsi_score, and fsl_rcsi_cat.

Examples

test_data <- data.frame(
  fsl_rcsi_lessquality = c(1, 2, 3, 1),
  fsl_rcsi_borrow = c(0, 0, 3, 0),
  fsl_rcsi_mealsize = c(4, 2, 6, 1),
  fsl_rcsi_mealadult = c(4, 3, 5, 0),
  fsl_rcsi_mealnb = c(2, 5, NA_integer_, 1)
)
add_rcsi(test_data)

Description

This function calculates and adds a new column for received assistance based on two input columns: assistance received in the last 12 months and the recall period of the assistance.

Usage

add_received_assistance(
  df,
  received_assistance_12m = "aap_received_assistance_12m",
  yes = "yes",
  no = "no",
  undefined = c("dnk", "pnta"),
  received_assistance_date = "aap_received_assistance_date",
  date_past_30d = "past_30d",
  date_1_3_months = "1_3_months",
  date_4_6_months = "4_6_months",
  date_7_12_months = "7_12_months",
  date_undefined = c("dnk", "pnta")
)

Arguments

Value

A data frame with an additional column:

• aap_received_assistance: Categorized received assistance: "undefined", "no", "past_30d", "1_3_months", "4_6_months", or "7_12_months".

Description

This set of functions classifies sanitation facilities according to various criteria. It includes functions to categorize sanitation facility types, sharing status, number of individuals sharing, and JMP (Joint Monitoring Programme) classification.

add_sharing_sanitation_facility_cat: Recode sharing status of sanitation facilities. Maps user responses to shared, not_shared, or not_applicable when the sanitation facility was skipped.

add_sharing_sanitation_facility_n_ind: Calculate the number of individuals sharing a sanitation facility per household. Uses household size and survey weights to estimate and then categorizes counts into predefined bands.

add_sanitation_facility_jmp_cat: Combine sanitation facility type and sharing status to derive a JMP classification (e.g., basic, limited, unimproved, open_defecation, undefined).

Usage

add_sanitation_facility_cat(
  df,
  sanitation_facility = "wash_sanitation_facility",
  improved = c("flush_piped_sewer", "flush_septic_tank", "flush_pit_latrine",
    "flush_dnk_where", "pit_latrine_slab", "twin_pit_latrine_slab",
    "ventilated_pit_latrine_slab", "container", "compost"),
  unimproved = c("flush_open_drain", "pit_latrine_wo_slab", "bucket", "hanging_toilet",
    "twin_pit_latrine_wo_slab", "other_container"),
  none = "none",
  undefined = c("other", "dnk", "pnta")
)

add_sharing_sanitation_facility_cat(
  df,
  sharing_sanitation_facility = "wash_sanitation_facility_sharing_yn",
  yes = "yes",
  no = "no",
  undefined = c("dnk", "pnta"),
  sanitation_facility = "wash_sanitation_facility",
  skipped_sanitation_facility = c("none")
)

add_sharing_sanitation_facility_n_ind(
  df,
  sharing_sanitation_facility_cat = "wash_sharing_sanitation_facility_cat",
  sharing_sanitation_facility_cat_shared = "shared",
  sharing_sanitation_facility_cat_not_shared = "not_shared",
  sharing_sanitation_facility_cat_not_applicable = "not_applicable",
  sharing_sanitation_facility_cat_undefined = "undefined",
  sanitation_facility_sharing_n = "wash_sanitation_facility_sharing_n",
  hh_size = "hh_size",
  weight = "weight"
)

add_sanitation_facility_jmp_cat(
  df,
  sanitation_facility_cat = "wash_sanitation_facility_cat",
  sanitation_facility_cat_improved = "improved",
  sanitation_facility_cat_unimproved = "unimproved",
  sanitation_facility_cat_none = "none",
  sanitation_facility_cat_undefined = "undefined",
  sharing_sanitation_facility_cat = "wash_sharing_sanitation_facility_cat",
  sharing_sanitation_facility_cat_shared = "shared",
  sharing_sanitation_facility_cat_not_shared = "not_shared",
  sharing_sanitation_facility_cat_not_applicable = "not_applicable",
  sharing_sanitation_facility_cat_undefined = "undefined"
)

Arguments

Value

A data frame with an additional column:

• wash_sanitation_facility_cat: Categorized sanitation facility: "none", "unimproved", "improved", or "undefined".

A data frame with an additional column:

• wash_sharing_sanitation_facility_cat: Categorized sharing status: "shared", "not_shared", or "not_applicable".

A data frame with additional columns:

• wash_sanitation_facility_sharing_n_calc: Estimated number of individuals sharing the sanitation facility.

• wash_sharing_sanitation_n_ind: Categorized number of individuals sharing a sanitation facility.

A data frame with an additional column:

• wash_sanitization_jmp_cat: Categorized JMP classification based on both type and sharing status.

Description

This function categorizes the shelter damage category based on provided criteria.

Usage

add_shelter_damage_cat(
  df,
  sep = "/",
  snfi_shelter_damage = "snfi_shelter_damage",
  none = "none",
  minor = "minor_roof",
  major = "major_roof",
  damage_windows_doors = "windows_doors",
  damage_floors = "floors",
  damage_walls = "walls",
  total_collapse = "total_collapse",
  other = "other",
  dnk = "dnk",
  pnta = "pnta"
)

Arguments

Value

A data frame with an additional column:

• snfi_shelter_damage_cat: Categorized shelter damages: "none", "damaged", "part", "total", or "undefined".

Description

This function calculates the number of shelter issues and categorizes them based on predefined thresholds. It also handles undefined and other responses.

Usage

add_shelter_issue_cat(
  df,
  shelter_issue = "snfi_shelter_issue",
  none = "none",
  issues = c("lack_privacy", "lack_space", "temperature", "ventilation", "vectors",
    "no_natural_light", "leak", "lock", "lack_lighting", "difficulty_move",
    "lack_space_laundry"),
  undefined = c("dnk", "pnta"),
  other = c("other"),
  sep = "/"
)

Arguments

Value

A data frame with additional columns:

• snfi_shelter_issue_n: Count of shelter issues.

• snfi_shelter_issue_cat: Categorized shelter issues: "none", "undefined", "other", "1_to_3", "4_to_7", or "8_to_11".

Description

This function combines both shelter types questions and recodes the type of shelter into categories such as "none", "inadequate", "adequate", or "undefined".

Usage

add_shelter_type_cat(
  df,
  shelter_type = "snfi_shelter_type",
  sl_none = "none",
  sl_collective_center = "collective_center",
  sl_undefined = "pnta",
  shelter_type_individual = "snfi_shelter_type_individual",
  adequate = c("house", "apartment"),
  inadequate = c("makeshift", "unfinished_building", "tent"),
  undefined = c("pnta", "other", "dnk")
)

Arguments

Value

A data frame with an additional column:

• snfi_shelter_type_cat: Categorized shelter type: "none", "inadequate", "adequate", or "undefined".

Description

Stop statement values are not numeric

Usage

are_cols_numeric(df, cols)

Arguments

Value

A stop statement

Description

Stop statement values are not in range

Usage

are_values_in_range(df, cols, lower = 0, upper = 7)

Arguments

Value

A stop statement

Description

Stop statement values are not in set

Usage

are_values_in_set(
  df,
  cols,
  set,
  main_message = "All values must be in the following set: "
)

Arguments

Value

A stop statement

Description

This function creates dummy variables for each input column, setting 1 if the value is above zero, 0 if it's zero, and NA if it's below zero. It then sums these dummy variables to create a count.

Usage

count_if_above_zero(df, vars, new_colname)

Arguments

Value

A data frame with additional columns:

• "*_d": Dummy variables for each input column, where 'star' is the original column name.

• new_colname: A new column containing the count of values above zero across the specified variables.

Description

Before joining loop-derived columns into main, drop any column names main already shares with loop (e.g. from a prior run), except the two id columns used for the join.

Usage

drop_shared_loop_cols(main, loop, id_col_main, id_col_loop)

Arguments

Value

main, with shared non-id columns dropped

Description

Dummy individual-level education data modeled on the 2026 MSNA guidance, used to illustrate the humind sector-by-sector MSNI pipeline in vignette("msni-workflow").

Usage

humind_edu_ind

Format

A data frame with 26,630 rows and 8 variables:

_submission__uuid

Join key back to humind_main's ⁠_uuid⁠.

edu_ind_age

Individual's age, in years.

edu_access

Whether the individual is currently attending education.

edu_barrier

Barriers to education access.

edu_disrupted_attack, edu_disrupted_hazards, edu_disrupted_displaced, edu_disrupted_teacher

Reasons education was disrupted.

Source

Dummy data modeled on the 2026 MSNA guidance, for demonstration purposes only.

Description

Dummy individual-level health data modeled on the 2026 MSNA guidance, used to illustrate the humind sector-by-sector MSNI pipeline in vignette("msni-workflow").

Usage

humind_health_ind

Format

A data frame with 30,300 rows and 3 variables:

_submission__uuid

Join key back to humind_main's ⁠_uuid⁠.

health_ind_healthcare_needed

Whether the individual needed healthcare.

health_ind_healthcare_received

Whether the individual received the healthcare they needed.

Source

Dummy data modeled on the 2026 MSNA guidance, for demonstration purposes only.

Description

Dummy household-level data modeled on the 2026 MSNA guidance, used to illustrate the humind sector-by-sector MSNI pipeline in vignette("msni-workflow").

Usage

humind_main

Format

A data frame with 5,408 rows and 148 variables, grouped by sector and prefix:

• ⁠_uuid⁠: household unique identifier; join key for humind_health_ind and humind_edu_ind (via ⁠_submission__uuid⁠).

• ⁠fsl_*⁠: food security items (FCS, HHS, rCSI, LCSI incl. ⁠_host⁠/⁠_camp⁠ variants) – see add_fcs(), add_hhs(), add_rcsi(), add_lcsi().

• ⁠wash_*⁠: WASH items (HWISE, drinking water, sanitation, handwashing) – see add_hwise(), add_sanitation_facility_cat().

• ⁠snfi_*⁠, ⁠hlp_*⁠: shelter, damage, occupancy – see add_shelter_type_cat(), add_occupancy_cat().

• ⁠prot_needs_*⁠: protection movement/practices/rights items – see add_prot_score_movement().

• admin1: geography, for context only; unused by any ⁠add_*()⁠.

Source

Dummy data modeled on the 2026 MSNA guidance, for demonstration purposes only.

Description

Stop statement "If not in colnames" with colnames

Usage

if_not_in_stop(df, cols, df_name, arg = NULL)

Arguments