5 Data Collection and Processing | R cookbook for analysis with IMPACT

5.1 Testing the tool

5.1.1 Creating dummy data

xlsformfill has a function xlsform_fill that will create dataset based on your KOBO questionnaire. It takes 3 arguments: your questions, your choices and how many rows you want.

Notes: - All questions will be filled. - Skip logic are not implemented. - Constraints are not implemented. - Integers and text will be generated randomly

dummy_dataset <- xlsformfill::xlsform_fill(questions = questions, choices = choices, n = 300)

dummy_dataset %>% head(10)

start	end	deviceid	enum_office_city	enum_id	a1_informed_consent	a2_hh_representative_name	a3_consent_to_follow_up_calls	a3_1_phone	a4_are_you_hohh	a5_respond_behalf_hh	b4_gender	b5_age	b7_hohh_age	b8_hohh_sex	b9_hohh_marital_status	b10_hohh_vulnerability	b10_hohh_vulnerability/no	b10_hohh_vulnerability/older_person	b10_hohh_vulnerability/disability_not_including_chronic_illness	b10_hohh_vulnerability/unemployed	b10_hohh_vulnerability/veteran_of_war_ato	b10_hohh_vulnerability/single_parent	b10_hohh_vulnerability/family_with_3_or_more_children	b10_hohh_vulnerability/family_with_foster_children	b10_hohh_vulnerability/chronic_illness	b10_hohh_vulnerability/other_specify	b10_1_hohh_vulnerability_other	b11_hohh_chronic_illness	b11_hohh_chronic_illness/blood_pressure_diseases	b11_hohh_chronic_illness/cardiovascular_disease	b11_hohh_chronic_illness/diabetes_need_insulin	b11_hohh_chronic_illness/diabetes_does_not_need_insulin	b11_hohh_chronic_illness/chronic_respiratory_condition	b11_hohh_chronic_illness/musculoskeletal_system_and_joints	b11_hohh_chronic_illness/cancer	b11_hohh_chronic_illness/neurological	b11_hohh_chronic_illness/sensory_disorder	b11_hohh_chronic_illness/gastrointestinal_digestive_tract_incl_liver_gallbladder_pancreas_diseases	b11_hohh_chronic_illness/genitourinary_system_diseases	b11_hohh_chronic_illness/endocrine_system_thyroid_gland_and_other_diseases	b11_hohh_chronic_illness/other_specify	b11_1_hohh_chronic_illness_other	b12_situation_description	b12_1_situation_description_other	b13_why_hohh_unemployed	b13_1_why_hohh_unemployed_other	b14_sector_hohh_employed	b14_1_sector_hohh_employed_other	b15_hohh_income	b16_hohh_pension_eligible	b17_hohh_pension_receive	b18_hohh_benefit_eligible	b19_hohh_benefit_receive	b20_hohh_level_education	hohh_age_18	b21_num_additional_hh_members	c1_hh_memb_age	age_5_18	age_16_18	c2_hh_memb_sex	c3_hh_member_description	c4_hh_member_description_other	c5_hh_memb_income	age_5_18_number	age_16_18_number	d1_hh_displacement_status	d2_year_first_displaced	d2_1_year_first_displaced_other	e1_shelter_type	e1_1_shelter_type_other	e2_home_type	e2_1_home_type_other	e3_shelter_enclosure_issues	e3_shelter_enclosure_issues/no_issues	e3_shelter_enclosure_issues/lack_of_insulation_from_cold	e3_shelter_enclosure_issues/leaks_during_light_rain_snow	e3_shelter_enclosure_issues/leaks_during_heavy_rain_snow	e3_shelter_enclosure_issues/limited_ventilation_less_than_05m2_ventilation_in_each_room_including_kitchen	e3_shelter_enclosure_issues/presence_of_dirt_or_debris_removable	e3_shelter_enclosure_issues/presence_of_dirt_or_debris_nonremovable	e3_shelter_enclosure_issues/none_of_the_above	e3_shelter_enclosure_issues/other_specify	e3_shelter_enclosure_issues/dont_know	e3_1_shelter_enclosure_issues_other	e4_damaged_accomodation	e4_damaged_accomodation/none	e4_damaged_accomodation/opening_or_cracks_in_roof	e4_damaged_accomodation/roof_partially_collapsed	e4_damaged_accomodation/broken_or_cracked_windows	e4_damaged_accomodation/exterior_doors_broken_unable_to_shut_properly	e4_damaged_accomodation/exterior_doors_or_windows_missing	e4_damaged_accomodation/large_cracks_openings_in_most_walls	e4_damaged_accomodation/some_cracks_in_some_walls	e4_damaged_accomodation/some_walls_fully_collapsed	e4_damaged_accomodation/damaged_floors	e4_damaged_accomodation/severe_structural_damage_and_unsafe_for_living	e4_damaged_accomodation/total_structural_collapse	e4_damaged_accomodation/foundation_damaged_or_shifted	e4_damaged_accomodation/gas_water_or_sewage_system_damaged	e4_damaged_accomodation/electricity_supply_line_damaged_and_not_functional	e4_damaged_accomodation/other_specify_none_of_the_above	e4_damaged_accomodation/dont_know_prefer_not_to_say	e4_1_damaged_accomodation_other	e5_damaged_accomodation_severity	e6_ownership_documents	e7_rent_agreement	e8_ownership_documents_name	e9_cost_of_month_rent	e9_1_hh_have_ability_to_pay_rent	e10_gpc_eviction_risk	e11_items_do_not_have_per_hh	e11_items_do_not_have_per_hh/refigerator	e11_items_do_not_have_per_hh/functional_stove	e11_items_do_not_have_per_hh/functional_heater_movable	e11_items_do_not_have_per_hh/hh_has_all_items	e12_items_not_every_hh_member	e12_items_not_every_hh_member/mattress	e12_items_not_every_hh_member/bedsheets	e12_items_not_every_hh_member/towel	e12_items_not_every_hh_member/blanket	e12_items_not_every_hh_member/winter_jacket	e12_items_not_every_hh_member/warm_winter_boots	e12_items_not_every_hh_member/thick_socks	e12_items_not_every_hh_member/warm_gloves	e12_items_not_every_hh_member/warm_scarf	e12_items_not_every_hh_member/thermal_underwear	e12_items_not_every_hh_member/adult_warm_clothing	e12_items_not_every_hh_member/child_warm_clothing	e12_items_not_every_hh_member/each_member_has_all_items	e13_need_but_do_not_have	e13_need_but_do_not_have/adult_diapers	e13_need_but_do_not_have/child_diapers	e13_need_but_do_not_have/sanitary_pads	e13_need_but_do_not_have/none_needed	f1_utility_services_used	f1_utility_services_used/central_heating	f1_utility_services_used/centralised_piped_hot_water_supply	f1_utility_services_used/centralised_piped_cold_water_supply	f1_utility_services_used/electricity	f1_utility_services_used/gas	f1_utility_services_used/none	f2_spend_on_utilities	f2_1_why_if_spend_on_utilities_0	f3_spend_on_heating	f3_1_if_0_for_heating	f4_type_fuel_heating	f4_type_fuel_heating/central_heating	f4_type_fuel_heating/electricity	f4_type_fuel_heating/gas	f4_type_fuel_heating/kerosene	f4_type_fuel_heating/wood	f4_type_fuel_heating/coal	f4_type_fuel_heating/briquettes_not_coal	f4_type_fuel_heating/briquettes_coal	f4_type_fuel_heating/other_specify	f4_2_type_fuel_heating_other	f5_type_fuel_heating_amount	f6_how_many_wood_hh_consumed_last_winter	f7_how_many_briquettes_hh_consumed_last_winter	f7_1_how_many_briquettes_coal_hh_consumed_last_winter	f8_do_you_receive_state_subsidies_for_utilities_gas	f9_electricity_shortages	f10_gas_shortages	f11_dinking_water_source	f11_1_dinking_water_source_other	f12_drinking_water_treat	f12_drinking_water_treat/do_not_process_purify	f12_drinking_water_treat/cleaning_with_chemicals_chlorination	f12_drinking_water_treat/water_precipitation	f12_drinking_water_treat/filtering_the_water_pitcher_filter	f12_drinking_water_treat/filtering_the_water_reverse_osmosis_filter	f12_drinking_water_treat/boiling	f12_drinking_water_treat/percolation	f12_drinking_water_treat/other_specify	f12_drinking_water_treat/dont_know_ns	f12_drinking_water_treat/refuse_to_answer	f12_1_drinking_water_treat_other	f13_treatment_whynot	f13_1_treatment_whynot_other	f14_technical_water_source	f14_1_technical_water_source_other	f15_water_shortages	f16_duration_water_shortages	f17_store_drinking_water	f17_store_drinking_water/do_not_store_water	f17_store_drinking_water/plastic_bottles	f17_store_drinking_water/glass_bottles	f17_store_drinking_water/a_bucket_with_a_lid	f17_store_drinking_water/a_bucket_without_a_lid	f17_store_drinking_water/a_canister	f17_store_drinking_water/a_barrel_tank	f17_store_drinking_water/we_fill_in_the_bathtub	f17_store_drinking_water/other_specify	f17_store_drinking_water/dont_know_ns	f17_store_drinking_water/refuse_to_answer	f17_1_store_drinking_water_other	f18_water_sufficient	f18_water_sufficient/drinking	f18_water_sufficient/cooking	f18_water_sufficient/personal_hygiene	f18_water_sufficient/other_domestic_purposes	f18_water_sufficient/none_of_the_previous	f18_water_sufficient/dont_know_ns	f19_type_toilets	f19_1_type_toilets_other	f20_problms_pump_sewage	f21_method_garbage_dispose	g1_cereals_consumption	g2_roots_consumption	g3_vegetables_consumption	g4_fruits_consumption	g5_meat_consumption	g6_eggs_consumption	g7_pulses_consumption	g8_dairy_consumption	g9_oil_consumption	g10_sugar_consumption	g11_condiments_consumption	h1_nofood_noresources	h1_1_how_often_nofood_noresources	h2_sleep_hungry	h2_1_how_often_sleep_hungry	h3_day_and_night_hungry	h3_1_how_often_day_and_night_hungry	i1_coping_strategies	i2_spent_savings	i3_borrowed_food	i4_live_another_family	i5_sold_assets	i6_removed_children_from_school_kindergarten	i7_reduced_health_care	i8_reduced_education	i9_sold_house	i10_hh_migrated	i11_moved_for_work	i12_risk_job	j1_edu_facility_hh_access	j1_edu_facility_hh_access/none	j1_edu_facility_hh_access/kindergarten	j1_edu_facility_hh_access/school	j1_edu_facility_hh_access/boarding_school	j1_edu_facility_hh_access/specialized_school_for_children_with_disabilities	j1_edu_facility_hh_access/vocational_school	j1_1_edu_facility_not_access_why	j1_1_edu_facility_not_access_why/school_too_far_and_no_transport	j1_1_edu_facility_not_access_why/cannot_afford_supplies	j1_1_edu_facility_not_access_why/children_at_home_as_carer	j1_1_edu_facility_not_access_why/children_working_at_home	j1_1_edu_facility_not_access_why/children_in_paid_employment	j1_1_edu_facility_not_access_why/children_has_disability_that_school_does_not_have_infrastructure_for	j1_1_edu_facility_not_access_why/other_specify	j1_2_other_edu_facility_not_access_why	j2_precovid_number_children_school	j2_1_boys_5_12_precovid_attend	j2_2_girls_5_12_precovid_attend	j2_3_boys_13_15_precovid_attend	j2_4_girls_13_15_precovid_attend	j2_5_boys_16_18_precovid_attend	j2_6_girls_16_18_precovid_attend	j3_precovid_number_children_dropped	j3_1_boys_5_12_precovid_dropped	j3_2_girls_5_12_precovid_dropped	j3_3_boys_13_15_precovid_dropped	j3_4_girls_13_15_precovid_dropped	j3_5_boys_16_18_precovid_dropped	j3_6_girls_16_18_precovid_dropped	j4_number_children_follow_curriculum	j4_1_boys_5_12_follow_curriculum	j4_2_girls_5_12_follow_curriculum	j4_3_boys_13_15_follow_curriculum	j4_4_girls_13_15_follow_curriculum	j4_5_boys_16_18_follow_curriculum	j4_6_girls_16_18_follow_curriculum	j5_unable_attend_school_more_than_month	j5_1_boys_5_12_unable_attend_school_more_than_month	j5_2_girls_5_12_unable_attend_school_more_than_month	j5_3_boys_13_15_unable_attend_school_more_than_month	j5_4_girls_13_15_unable_attend_school_more_than_month	j5_5_boys_16_18_unable_attend_school_more_than_month	j5_6_girls_16_18_unable_attend_school_more_than_month	j6_reason_gap_education	j6_reason_gap_education/school_closed_due_to_security	j6_reason_gap_education/school_closed_other	j6_reason_gap_education/insufficient_teachers	j6_reason_gap_education/security_concerns_by_parent	j6_reason_gap_education/school_is_damaged	j6_reason_gap_education/health_issues	j6_reason_gap_education/family_personal_reasons	j6_reason_gap_education/too_expensive_school_fees_supplies_transport_etc	j6_reason_gap_education/transport_unavavailable	j6_reason_gap_education/a_child_contributes_to_hh_income	j6_reason_gap_education/other_specify	j6_reason_gap_education/refuse_to_answer	j6_1_reason_gap_education_other	j7_gap_education_length	j8_problems_school	j8_problems_school/no_problems	j8_problems_school/distance_to_school	j8_problems_school/quality_of_teaching_staff	j8_problems_school/quantity_of_teaching_staff	j8_problems_school/lack_of_pss	j8_problems_school/price_for_service	j8_problems_school/conditions_of_the_venue	j8_problems_school/overcrowded_classrooms	j8_problems_school/lack_of_medical_support	j8_problems_school/school_is_unsafe	j8_problems_school/problems_with_distance_learning_due_to_closure_of_school_for_covid_eg_bad_internet_connection	j8_problems_school/other_specify	j8_1_problems_school_other	j9_education_security_concerns_childs_commute_to_school	j9_education_security_concerns_childs_commute_to_school/none	j9_education_security_concerns_childs_commute_to_school/uxos	j9_education_security_concerns_childs_commute_to_school/shelling	j9_education_security_concerns_childs_commute_to_school/shooting	j9_education_security_concerns_childs_commute_to_school/military_presence	j9_education_security_concerns_childs_commute_to_school/danger_at_checkpoints	j9_education_security_concerns_childs_commute_to_school/wildanimals_stray_dogs	j9_education_security_concerns_childs_commute_to_school/other_specify	j9_1_education_security_concerns_childs_commute_to_school_other	j10_education_security_concerns_in_the_vicinity_of_facility	j10_education_security_concerns_in_the_vicinity_of_facility/none	j10_education_security_concerns_in_the_vicinity_of_facility/uxos_on_school_grounds	j10_education_security_concerns_in_the_vicinity_of_facility/shelling_in_the_vicinity_of_the_school	j10_education_security_concerns_in_the_vicinity_of_facility/shooting_in_the_vicinity_of_the_school	j10_education_security_concerns_in_the_vicinity_of_facility/military_presence_inside_of_schools	j10_education_security_concerns_in_the_vicinity_of_facility/military_presence_in_the_vicinity_of_schools	j10_education_security_concerns_in_the_vicinity_of_facility/other_specify	j10_1_education_security_concerns_in_the_vicinity_of_facility_other	j11_school_supplies_affordable	j12_children_under18_absent	j12_1_number_children_under18_absent	j12_2_why	l1_specialist_care_available	l1_specialist_care_available/obstetricgynaecological	l1_specialist_care_available/paediatrics	l1_specialist_care_available/gastroentrology	l1_specialist_care_available/mental_health_care	l1_specialist_care_available/ear_nose_throat_ent	l1_specialist_care_available/ambulance	l1_specialist_care_available/inpatient_facility	l1_specialist_care_available/outpatient_care	l1_specialist_care_available/laboratory	l1_specialist_care_available/x_ray	l1_specialist_care_available/ultrasound	l1_specialist_care_available/chest_photofluorography	l1_specialist_care_available/post_trauma_rehabilitation	l1_specialist_care_available/dont_know_ns	l1_specialist_care_available/none_of_listed	l2_HH_access_healthcare	l3_difficult_access_health	l4_which_difficult_access_health	l4_which_difficult_access_health/lack_of_facilities	l4_which_difficult_access_health/cost_of_medicine	l4_which_difficult_access_health/distance_to_facility	l4_which_difficult_access_health/cost_of_travel_to_facility	l4_which_difficult_access_health/lack_of_documents	l4_which_difficult_access_health/security_problems_in_travelling_to_facility	l4_which_difficult_access_health/cost_of_appointment	l4_which_difficult_access_health/lack_of_referral_system	l4_which_difficult_access_health/have_to_pass_through_entry_exit_checkpoint_for_medical_facility_in_the_ngca	l4_which_difficult_access_health/irregular_presence_of_doctors	l4_which_difficult_access_health/required_a_covid_test_prior_to_treatment	l4_which_difficult_access_health/unable_to_leave_the_house_due_to_covid19	l4_which_difficult_access_health/other_specify	l4_which_difficult_access_health/lack_of_doctors	l4_which_difficult_access_health/insufficient_qualifications_of_doctors	l4_which_difficult_access_health/long_queues	l4_1_which_difficult_access_health_other	l5_state_medication_aware	l6_state_medication_received	l7_pss	l8_trauma_rehab	l9_time_walk_health_facility	m1_probl_food_market_access	m2_probl3_food_market_access	m2_probl3_food_market_access/no_problems	m2_probl3_food_market_access/opening_hours_insufficient	m2_probl3_food_market_access/items_not_available	m2_probl3_food_market_access/items_too_expensive	m2_probl3_food_marke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other_specify	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	DJIAeghtZXqbzAbDpVqquUKEnMuMEGsSZzQmxOSwTsdMpOApOYEPahDXgEnMsFdTqpCdnpXUeLSelGMEhgnSblDXBolZbnB	21.22525	19.38395	20.65138	17.45102	yes	no_shortages	yes_infrequently_irregularly	public_well_or_boreholes_shared_access	ysmHaAU sAqJEnReXtQNbpTYFfoZcqGoGIwWiKyYIUqtPbcIuWXItgwaFzJEltraskiQVTpNMBf	filtering_the_water_pitcher_filter percolation refuse_to_answer	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	nGIceCbexFyXLqPQgjJffruVgleTWIcJgubLOyVUiBrfjwtNerDqqkwouLWdzVilTCLpmITtYgglDRGewHxnncTaPT	refuse_to_answer	GoLcZXfCsqdEiICoIGQEYbxZoqMaOpGQnzrAKFOrSCUGi KKjoYj ZbvNrwzgXjEUi	trucked_in_water_truck_with_a_tank_etc	ASrsunMYBBVtnaRmPDuSTBORRZzbbloyOWtFAVMgcNhutUpipyqomwfyRiDvHGTLxGWWvBJrRpAzySS	1_time_a_week	20	a_bucket_without_a_lid we_fill_in_the_bathtub other_specify	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	nlhHrSIIoWudSS BeWBsXRCMThuPvUwHQFISIYnrEIRSRatVtMZmJrflqKY cOhGIJzSTomqgCEooYtzNhITuqbUfSkApN	cooking personal_hygiene none_of_the_previous dont_know_ns	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	none_of_the_above	GqSFLADZJtSIvsNeJFlkfCgYecWiCTMHCkjYzrhEvFtpfh	not_applicable	we_burn_all_the_garbage	6	6	7	2	4	1	0	0	0	5	2	0_no	2_sometimes_3_10	1_yes	1_rarely_1_2	1_yes	3_often_10plus_times	no_was_not_needed	no_but_needed	yes	yes	yes	no_already_done_so_cant_do_it_anymore	no_but_needed	no_was_not_needed	no_was_not_needed	no_but_needed	yes	no_was_not_needed	none kindergarten school	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	school_too_far_and_no_transport cannot_afford_supplies children_working_at_home children_in_paid_employment	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	UdAjxUaAeXIeoJESkoiBCuQiMXrXDNBapR	21	17	18	22	22	18	18	21	20	21	16	23	24	20	21	19	20	19	22	24	17	dont_know_ns	21	24	23	23	22	19	security_concerns_by_parent school_is_damaged family_personal_reasons transport_unavavailable a_child_contributes_to_hh_income refuse_to_answer	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	iUEqbbwpMhgMTcUSdmLOnkeHFEhK	more_4_months	no_problems overcrowded_classrooms other_specify	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	hdWsDmgOQDOZTFrAHbbzDryKJvaKyNjnXdEbqIktfoLebURfISarZx	shelling shooting danger_at_checkpoints other_specify	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	SaszsWatZNZhVTKjfHyCHZwycjMTfPnRnxwXENqiNEWZdTM	uxos_on_school_grounds other_specify	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	pbeHBQDRru pS BTeyJNsJdoJsbzANYjxiiUdgHaWc msvVLRrKomVsypVHLMPaDDCaLb	refuse_to_answer	refuse_to_answer	21	jlfNCQXMvYNWMepkptymfPSafqvktiNoswQODNUkWJrNTrgJazwykuhjFYUdEEIlzDJJ	paediatrics mental_health_care ear_nose_throat_ent ambulance outpatient_care x_ray ultrasound post_trauma_rehabilitation none_of_listed	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	no	all_of_the_time	lack_of_facilities cost_of_medicine distance_to_facility cost_of_travel_to_facility lack_of_documents security_problems_in_travelling_to_facility lack_of_referral_system insufficient_qualifications_of_doctors	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	CKniIqxvvjTiXYzHjKXUZyNDQmVIsVUVpMFTacRsthLpjWWowjY	yes	yes	dont_know_ns	no	less_than_15_mins	no	items_not_available distance security other_specify	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	csuktdDzqvGoRhE	cereals_grains_bread_pasta vegetables_and_leaves_tomato_lettuce_spinach_carrot pulses_nuts_seeds_lentils_beans_nuts oil_and_fat_salo_butter_sunflower_oil sugar_or_sweets_cakes_chocolate_sugary_cold_drinks	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	hPFKYBgaSTLbnFcm	19	18	no	21	PQmRzltWDhCeUMsuzqInwrPzsWi gpdqBvBIdxMGBPObqozUzrzWAkFjOCvZH	22	dont_know	yes_in_our_possession	17	yes	dont_know	both	23	yes	refuse_to_answer	refuse_to_answer	dont_know	no	yes	2_i_am_aware_of_mines_but_it_doesnt_affect_my_life	movement_and_access_constraints	FALSE	TRUE	FALSE	FALSE	FALSE	pbyMoDJaWvkXbTRJwGsMNEzKiXoQdAQFQo djPkJZzwNRPAOffySlCPejuGncYCXpdRVoTnVQtIWgrZVfHFja vzvLxaiaBnhhw	administration_head_of_the_village) mine_clearance_ngo police to_relatives dont_know_ns	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE		loss_of_or_diminished_access_to_education loss_of_or_diminished_access_to_clean_water_and_sanitation sickness_of_household_members other_specify no_covid19_impacts_on_my_household restriction_in_movement breaking_up_with_family_or_relatives psychological_deterioration	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	GGqElBvsrWuT Z	not_leaving_the_house_at_all stopping_handshakes_or_physical_contact keeping_distance_from_people wearing_a_face_mask washing_hands_more_regularly keeping_surfaces_clean staying_away_from_animals dont_know	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	ZZRAPhFPOKoLJdBKp npt chqEgjAdloWBo PYCqixBb	no	no	refuse_to_answer	no	yes	no	yes	refuse_to_answer	yes	yes	refuse_to_answer	yes	dont_know_ns	dont_know_ns	dont_know_ns	dont_know_ns	food healthcare drinking_water hygiene_nfis_eg_soap_sanitary_pads_and_sanitation_services_eg_latrines psychosocial_support fuel_for_heating none other_specify house_repairs	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	ETPckfqdfCH XAy QUhVMxCJdHeaHknLpftmeofjIibhFBEAiWzNFtvLpxuMdTLwdcUZmQFdBAyzq kkYCLrEJaF W	dont_know_ns	dont_know_ns	no	dont_know_ns	dont_know_ns	dont_know_ns	dont_want_to_get_information how_to_get_water how_to_get_food how_to_get_shelter_accomodation_shelter_materials how_to_get_help_after_attack_or_harassment how_to_access_education how_to_provide_feedback_on_aid_you_are_receiving other	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	XZHfBnGPDCqDAyzenYCEdrWsJqcfuYWPBJpgoIFP	other	LAwnTTNIQxeFTfZMmdjkWwngJIwqzZRhEetAmMlPwjhWgYgOAQCYkuELbpdpdBUjfHU	telegram_viber_or_other_mobile_phone_based_platform	BHYjuYSIDxECthMCuHyMsBfmztqOxgRjEKdksNjgUUqNnLmKYP	do_not_want_to_receive_humanitarian_assistance inkind_food physical_cash cash_via_bank_transfer vouchers services_eg_healthcare_education_etc other_please_specify dont_know	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	KQKPBZZDvGNJWKXhaSGwAyCKNatVhKApAieBkzsHiQXkLGGc WLAxNPpdFmiG	no	no	mWFKpFzcIy NcNWIZLCdQPNiweswFofqwjDApjjBXK	UA44	UA14126	UKR1422485502	BO_1	no	c3fd519b-03d8-4d4147-8a8581-2eadb6831059
2023-08-16T10:53:02+0200	2023-08-16T09:48:51+0200	deviceID00000007	sievero	siev_006	yes	kCsTIOpmDMEZzxxiLqjpqvUOcahvqLuiBZ	no	IV eHLVwWUEZGJIRFkENMvSLCQHa	yes	no	female	20	21	female	widowed	no older_person unemployed family_with_3_or_more_children family_with_foster_children other_specify	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	IIhEt	diabetes_need_insulin diabetes_does_not_need_insulin musculoskeletal_system_and_joints cancer neurological genitourinary_system_diseases	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	JjZeLVFFzLeoOWaNCTWgzKSoASlaTiIJvXHnYNkbFLfUWPKjFMymUpLaRNbLfRoL	retired_but_still_working	kDD	pay_too_low	mFvgygPXVkPethjWFlaTvajwOeo	health_care	OxCElIqq nOkmHihLSemIBFhYibnSDDmTZZXWHHDDPJqzCpQbumlWkltcvsiauVstuoMjLPCuCwWbcnnuzQLtQ sWIk	19	no	yes	refuse_to_answer	refuse_to_answer	none	0	19	20	0	0	male	in_education_doesnt_work	rcP APUixICtDpSDGqPwkuiYDCStZTMWdJHIY	21	0	0	used_to_be_displaced_but_returned_and_has_no_status_of_idp	2014	VyUUQzYiDBvIoGmwEv TKm	tent	WFYSbfCRpULuoNdupTsHpzHrZPnsgbYmirEpHuPRquoOnNIqMrOtjzSq s	no_collective_centre	TiwkEOSEZacduNmIwPiRHjYKJbNgcZTPRYRzJ wUz lAJwFwyjygnYcxGZUkub	no_issues lack_of_insulation_from_cold leaks_during_heavy_rain_snow limited_ventilation_less_than_05m2_ventilation_in_each_room_including_kitchen presence_of_dirt_or_debris_removable presence_of_dirt_or_debris_nonremovable dont_know	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	dUZj	none broken_or_cracked_windows exterior_doors_broken_unable_to_shut_properly exterior_doors_or_windows_missing large_cracks_openings_in_most_walls damaged_floors severe_structural_damage_and_unsafe_for_living	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	cKkHqRILDIuddnfLtCtabtrBHMYeJFSqKhxlawo NaTjXKWfI FRponmoFQpzRYlcOOhhQrGNclEXRUScfaRJgksVAzWbPxcOu	yes	dont_know_ns	yes	no	17	yes	yes_after_3_months	refigerator	TRUE	FALSE	FALSE	FALSE	thick_socks warm_gloves child_warm_clothing	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	child_diapers sanitary_pads	FALSE	TRUE	TRUE	FALSE	central_heating centralised_piped_cold_water_supply none	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	18	FMrnmshnbUtYacOjpfdp rpqgkTeCsMZLBsPhWgpdMFDiawmNDbuGUcyGsooyGmbRwzCPWebJVQGWpiDFFJOj acUavLafo	21	tgilhVEZbHYbtDJIahkGMZOmWbuQrSKSYBF	central_heating electricity wood briquettes_coal	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	JoYvRUtEdE ZLOnSGnROCKbHkpakxUMubRbFGgdcqaALCnsckyTifLkmvytzzXzcduqmLOp WxNvoWxHIhxHlptxrnsjnBM hk	15.40725	17.15860	17.23396	19.69760	no	yes_infrequently_irregularly	yes_daily_every_day	personal_well	CiSmZwTXtjCOZubEUNPNyqwNlHvdVPvfACyxbNeqGLHuSZRRWeRDNsHjjMCWXDIqNCKYUcDLrRhAelePFMFZPO ByXeMYzV	cleaning_with_chemicals_chlorination filtering_the_water_reverse_osmosis_filter boiling dont_know_ns	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	KxGlssIxuLONeFLwwAGVdgDmzHnHRoXsdUhQGSRyoTQa	no_need	RSQhcftnofYLzaw	personal_well	CjlNZOHqaQTSasiuc URTUQnNKoKhwqpqX d qwCDgWMCffRHFJjkbkLYEuXQuTVH ZAKBpInwbqoNTYNHXfBiIeWJgTE	from_time_to_time_more_rarely_than_once_a_month	19	do_not_store_water glass_bottles a_bucket_with_a_lid a_canister we_fill_in_the_bathtub	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	eKlNQBpsZdOuYtDyngEgiAfpIufTjBuUKyZGDMISuMXoVqsGzUmsmosLBZowbfyK	drinking cooking personal_hygiene none_of_the_previous dont_know_ns	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	flush_toilet_central_sewerage	YkWYsUVWtqjJcZeAozHjGDxlvTediOiByduAXYMOBKAO mRmrkWFhshcWrCndSrBePZgBDfCZwL	dont_know	we_burn_a_part_of_the_garbage_and_the_other_part_is_thrown_down_the_pit_latrine	5	6	3	3	6	4	6	1	7	6	0	0_no	2_sometimes_3_10	1_yes	1_rarely_1_2	0_no	1_rarely_12	no_already_done_so_cant_do_it_anymore	no_already_done_so_cant_do_it_anymore	no_was_not_needed	yes	no_already_done_so_cant_do_it_anymore	no_but_needed	no_was_not_needed	no_but_needed	no_but_needed	yes	no_already_done_so_cant_do_it_anymore	no_was_not_needed	none vocational_school	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	school_too_far_and_no_transport children_working_at_home children_in_paid_employment children_has_disability_that_school_does_not_have_infrastructure_for	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	WTotpnqPsYxeWGgZJBUuKPGDcxVWxIRFPbWSZZrFQTNaPFaphpMlkkGQfbhIoGVASOsBSMQDK	19	17	18	21	21	22	20	18	20	20	21	17	21	24	21	18	20	20	19	19	20	yes	19	17	19	20	17	19	school_closed_due_to_security security_concerns_by_parent school_is_damaged family_personal_reasons refuse_to_answer	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	WtDXUCFoFxsyfwGGUPSKSoMdgTdEIaDqQCrB	2_4_months	no_problems overcrowded_classrooms other_specify	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	OLDD NxhYBNLrVkuXdToZAWixYozTqbZNAZxbPEzQZitxsyDzS	none uxos shelling military_presence	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	VcpXXwTLeRifkteAOxACh	shelling_in_the_vicinity_of_the_school shooting_in_the_vicinity_of_the_school military_presence_inside_of_schools military_presence_in_the_vicinity_of_schools	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	qvRbHJaCfactnUdSLbY yLiifXwijHbWjseoCkrvZtIKnUfDQljYYTFEan GUvJhRgfRNYKpXDYgNboAHyKUQhDAOzkDEEXW	yes	refuse_to_answer	18	OgJDGRjNmevpHZvrB	gastroentrology ambulance outpatient_care chest_photofluorography none_of_listed	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	yes	all_of_the_time	cost_of_medicine required_a_covid_test_prior_to_treatment other_specify long_queues	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	PYfjLuBOXjSVdEexuGahiUaRS mDKLqsn koukXqSSUqeHlPBTHRweENkfanzoIKDFbXtECdneoAJamp	yes	dont_know_ns	no	no	less_than_30_mins	no	no_problems opening_hours_insufficient items_not_available items_poor_quality distance security other_specify	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	cRmXUFibxqAlAO auEYYZlSpFzTBAUfbxLIjAuJgmdBMTqaGkuhktzzyUFK zkKDNOIQgMWYr	eggs pulses_nuts_seeds_lentils_beans_nuts other_specify	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	CdxLUaz kl FzUbbzxNvbCjWeNWz	23	18	no	20	nRfVkEYjUrxYEojIeRZgmjxRBlSxQqWPrkffQCoIuGlYNxtSpzW RHxuuLp osHbWv	20	no	dont_know	21	no	no_some_hh_members_are_missing_birth_certificates	refuse_to_answer	18	yes	dont_know_ns	no	yes	dont_know	dont_know	3_sometimes_i_need_to_change_my_behaviour_due_to_mines	fear_for_physical_safety movement_and_access_constraints psychological_safety	TRUE	TRUE	TRUE	FALSE	FALSE	hMrEewnfNmlwNvV AXBJ	administration_head_of_the_village) state_emergency_services mine_clearance_ngo police dont_know_ns	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	dkQpVGtR	diminished_lost_source_of_income loss_of_or_diminished_access_to_education sickness_of_household_members other_specify restriction_in_movement	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	LZYdwSZdSEruAcgOCRKGdiDNYCpWlRDeBdQyMeXRJqGPlijbDGLocXUocXLvVdXtVzSJFSdu	no_no_action_taken_cannot_select_with_any_other_option not_leaving_the_house_at_all reducing_movement_outside_the_house keeping_distance_from_people avoiding_public_places_and_gatherings avoiding_public_transport wearing_a_face_mask wearing_gloves washing_hands_more_regularly keeping_surfaces_clean other_specify	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	PiSIisZVMykMTwAHwufRx	yes_other_non_covid_related_reason	no	refuse_to_answer	no	refuse_to_answer	no	yes	no	yes	yes	yes	refuse_to_answer	yes	refuse_to_answer	yes	no	seeds_or_other_agricultural_inputs livelihoods_support_employment need_to_repay_debt education_for_children_under_18 fuel_for_heating none other_specify house_repairs	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	QtIqdthFOVcJRTfeVqApPSKiroFuoisrUYJwSCKmLnfLAaTjHTSmeiormgfX vkWDzPcvSnCcgiJyMbmfEnkBwcwSifA	no	dont_know_ns	yes	yes	refuse_to_answer	dont_know_ns	dont_want_to_get_information how_to_get_water how_to_provide_feedback_on_aid_you_are_receiving other	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	zORVNALPYHkuQrgfGufhT	friends_family_members	GebAZWeLcqcDMJRZgmAUBGPjakLKjNJiKTEaXbMOWNDYh QpVrbkdGqwyFSsWSZYjSfLUgCGOrVJVBhoRmAQgTDV	billboards_posters_leaflets	Gb jsKB RYlvCAlOaepXrBOEnNqleYlwSnsSIpNGZPPhLqzbHH mKohLRcluuYxuc	do_not_want_to_receive_humanitarian_assistance inkind_food inkind_nfis cash_via_prepaid_cards cash_via_mobile_money construction_materials	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	BEhzbVOAmfDiEeNaptlZrFKbGCCMnOyvThpJkeRSl PkcrxXBzkhqZvGyVysUauRLYyBJZUrukRWDFAUzAIwriFETxWsM	yes_soap_is_not_shown	yes	Szi	UA14	UA44238	UKR1421588801	GJ_5	no	2abf4e71-97ed-494347-a0ada9-2e165d8ca73b
2023-08-16T10:43:06+0200	2023-08-16T08:14:21+0200	deviceID00000003	sievero	slov_002	yes	pY OZlxHGgToZ gffjs jNWkXRblqbJ	no	thLAVIexzlwpyUcetUDYOdGvTDvyvHRVPjEXp AYQogYomVgFlPNSwbuawZuRqTmdXZYeTMVqCaEqoFtupmwp	yes	no	male	19	20	female	single	no veteran_of_war_ato family_with_3_or_more_children other_specify	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	AVcHbVeBPBYYfWoBpBJbfqVRlDUVUkYHYDbVObhznPCcnHPFHUYkigsWJqJWNxUsrLE rZIplzFcuAkLakXe	cardiovascular_disease diabetes_need_insulin chronic_respiratory_condition musculoskeletal_system_and_joints sensory_disorder gastrointestinal_digestive_tract_incl_liver_gallbladder_pancreas_diseases	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	fhLEsgndMOTlXUuWKBgYYTLraSdnJ trCP	retired_not_working	NTmwqkHcqFrRfakGHMVIHjSuMEnJjzvBGeqgHVwiEPiINXAGxDdafoYCprfTSGUsXjVXFOzkQIlAmwPXk	lack_of_relevant_vacancies_in_the_settlement	zVsFaYppxuOfCtAd 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refuse_to_answer	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	ojndsxfYGXudcexSBrQxBFNqJX ZGhFFSAGbEUtwYZnDxxmRmIRwBTtxTZuKEkddn	more_4_months	no_problems quantity_of_teaching_staff lack_of_medical_support school_is_unsafe other_specify	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	swbJcQde ADXXVbQykmBlPXGqRZrLtuEAgFnIahTkZFopHuTACCZzfgCXnJGqgPVYmhhLaejr	uxos shelling shooting danger_at_checkpoints wildanimals_stray_dogs	FALSE	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	xhgrxrlgvdkVRJBFeXDcwjvqloRqVHWua	shelling_in_the_vicinity_of_the_school shooting_in_the_vicinity_of_the_school	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	XbaRpLKZaUgjHNtBfRIAkQTyRMuafJpERbqxR tbfMistPXaBacBxoBSqXSAQnuQXTZSeVCeh	yes	refuse_to_answer	20	etACZk y hhkDGeSIiXMdGivgjlVVskJMrQmmzERfORwbD cGwVLHOeHzthzHbNyA	obstetricgynaecological mental_health_care ambulance 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other_specify	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	dmrSXsYgCMLfmDSaLIAbAxX	other_specify	MpsjSmfosSsGPWMKWEJnxbypQLBULJhwsghNsfRlKfHyEWdgFdxHwVAiKIfjzGlMdSgvEjZcvHOy	health_reasons	YuDyVSwaeJuWIXXdhtULmUdQIispoiGVTFmqvrpmEGcoqdOeKXHHroOVqhXYsDBDItrOVyi	other_specify	OYgsXzCirzqWUSykpvWDnSICPBBNRLJgcnkgeLJxeMmuzj	20	yes	refuse_to_answer	dont_know_ns	no	preschool	0	23	20	0	0	female	military_service	GtFszZoSrNcoguJc	20	0	0	refuse_to_answer	2014	ZqoQefmGqXTweJPaOFaAtkwmrqBSTzqgFGAGxSfMfwrwKxHbK VcoYdDjyUukDoKqNkvwuaFgje FShZaQvSHGdNkPSpozAZc	makeshift_shelter	xnIb	other_specify	blKQoCYqUDKtwnCZsRLPV	no_issues leaks_during_light_rain_snow leaks_during_heavy_rain_snow limited_ventilation_less_than_05m2_ventilation_in_each_room_including_kitchen presence_of_dirt_or_debris_removable presence_of_dirt_or_debris_nonremovable none_of_the_above dont_know	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	TRUE	eHrDSkrdQPil ejJBcwjWKASotM	opening_or_cracks_in_roof roof_partially_collapsed broken_or_cracked_windows some_cracks_in_some_walls damaged_floors severe_structural_damage_and_unsafe_for_living foundation_damaged_or_shifted gas_water_or_sewage_system_damaged electricity_supply_line_damaged_and_not_functional other_specify_none_of_the_above dont_know_prefer_not_to_say	FALSE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	HiGOnyW	yes	yes	yes	no	21	no	no	refigerator functional_heater_movable	TRUE	FALSE	TRUE	FALSE	towel blanket winter_jacket warm_winter_boots thick_socks child_warm_clothing each_member_has_all_items	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	adult_diapers child_diapers none_needed	TRUE	TRUE	FALSE	TRUE	central_heating centralised_piped_hot_water_supply electricity gas	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	18	eVLCWPPjWsobSFbMoPLRTDYYGTDohIliedlqjfFSVRWMZctzVoeGoaMhFteCakeXRtoSCcQZCp xVyeQqmVKBvGavDsZEG	19	SGrWkaqwbpDrU	electricity coal briquettes_coal 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dont_know_ns	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	UTFLBzwFAYOwLTJGXcsdmfjEQPGTAGGPhFJRGbJqEnV VKIURmgKxMFTaXhDXLgzpPbsy	cooking	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	bucket_toilet	fWXjM NDTAl MMOvzpfvlWVTlNlTiDvaMeerhQniOgDIodEneBtvMQVNvBMjyPRcVHIkFgTJcQiJQCBuAjVMYynYWqtb	yes	we_will_take_garbage_to_the_garbage_dump_by_ourselves	0	3	7	4	6	4	1	7	0	1	7	1_yes	3_often_10plus_times	0_no	1_rarely_1_2	1_yes	1_rarely_12	no_already_done_so_cant_do_it_anymore	yes	no_was_not_needed	yes	no_already_done_so_cant_do_it_anymore	no_was_not_needed	no_but_needed	no_but_needed	no_was_not_needed	yes	no_was_not_needed	no_already_done_so_cant_do_it_anymore		FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	school_too_far_and_no_transport cannot_afford_supplies children_working_at_home children_has_disability_that_school_does_not_have_infrastructure_for	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	NAmLkzOLNEZOMkvGXEnngoDPyqNiuQftQOEHbhlulmXmvrwXWXEHhvbOIfFio	17	18	20	19	17	17	23	17	20	21	21	17	18	22	18	21	19	20	20	21	18	dont_know_ns	22	21	21	18	17	19	school_closed_due_to_security health_issues too_expensive_school_fees_supplies_transport_etc transport_unavavailable a_child_contributes_to_hh_income other_specify refuse_to_answer	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	hdykpVFqyPyqpyjEXtnmAOGYTUqAXamGbDl GGwU zjCCMqFDOxIfzXsVpBgoQYAkZvureQuBsiZvKbcbJIgoY	refuse_to_answer	distance_to_school quantity_of_teaching_staff lack_of_pss price_for_service conditions_of_the_venue school_is_unsafe problems_with_distance_learning_due_to_closure_of_school_for_covid_eg_bad_internet_connection	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	HCbtAENBigTPXPOoDETMCaIA hpoE lZTNDBGWfrfNcVAMfLftTaAXRSCbIloxPttoMtfjjyzYscNEkchtfktdU	uxos shelling shooting 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items_poor_quality market_damaged	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	dKwymITPQwQlJUiArkaVnyVEAwflk BEzRHTRyFZCfcXDIXoSFuoENoFwzQWGQWAKFDriJmqltySHJexVywsyunCseLGWo	cereals_grains_bread_pasta vegetables_and_leaves_tomato_lettuce_spinach_carrot meat_or_fish_chicken_pork_beef_fish eggs dairy_products	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	oFyBIXMIxrVEABBApLgjurvBSAWkTKXdZnUlyHFiekRHEnnrsuWhprS zoi	17	21	no	18	nmwxLFZhTrNHaqBuidwCGsVgmbHleWYgzevgPcEhoMArAlxNEnXsbXzlfFJtOgfgkWVLKWLnJTjHFtFRRWUJxMeHMWuBgc	21	no	yes_in_our_possession	17	no	yes_we_all_have_birth_certificates_but_they_are_not_in_our_possession	refuse_to_answer	21	yes	dont_know_ns	no	yes	dont_know	no	1_not_at_all	has_affected_hh_budget other_specify	FALSE	FALSE	FALSE	TRUE	TRUE	GPM vgppbOxiiXTEiDEOwwpyMkWhYwDKvpmvEcnwIqzRpZPJYQTHKDBVEqrStXWTzHuDurQfNW	state_emergency_services army to_relatives	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	UAKTGabmjessiNKNjwRwDnOhHunprrdukRQzAImFqqEjln Cjticn	diminished_lost_source_of_income limited_access_to_food loss_of_or_diminished_access_to_clean_water_and_sanitation death_of_household_members other_specify restriction_in_movement breaking_up_with_family_or_relatives	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	QBHrNyTfzuzGcjxqPYpByjLjyi	no_no_action_taken_cannot_select_with_any_other_option not_leaving_the_house_at_all reducing_movement_outside_the_house stopping_handshakes_or_physical_contact keeping_distance_from_people avoiding_public_places_and_gatherings avoiding_public_transport wearing_a_face_mask wearing_gloves keeping_surfaces_clean other_specify dont_know	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	zxiDHvjVBqOoPdpSkpIYZxhSHERxanzAxMdaEgBzYyQgLbsxrwYWmplBemhllOKqPnUgCanqKrbsuut	no	refuse_to_answer	no	yes	refuse_to_answer	no	no	yes	yes	refuse_to_answer	refuse_to_answer	yes	dont_know_ns	yes	yes	dont_know_ns	food healthcare seeds_or_other_agricultural_inputs drinking_water hygiene_nfis_eg_soap_sanitary_pads_and_sanitation_services_eg_latrines need_to_repay_debt education_for_children_under_18 psychosocial_support fuel_for_heating none	FALSE	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	vTbqlmjjvfEBi	yes	no	dont_know_ns	refuse_to_answer	refuse_to_answer	no	how_to_register_for_aid how_to_get_water how_to_get_healthcare_medical_attention how_to_get_help_after_attack_or_harassment how_to_stay_safe_to_prevent_attack_harassment how_to_provide_feedback_on_aid_you_are_receiving	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	jSQvuRwkvxfshqFKjfDnxeKYEEzgZXFHGiBshPEthPQZBSlhwvzlJPzAmePcoSMHeJEYIjIco W	places_of_worship	REQfMdHYxvJ rQPtRnkXbiNC bdNBkPLWTrTfCEWKrUsxHJLOcrBxwrhW	television	LVZhjldUmsAf qVrlTbNNJUnSmGGazvnvqaHFEorKjjqSkiTBupcAbaRapiArS jedIIpPRalkTRbCcsodWRFPNqCZvkFBY	inkind_nfis physical_cash cash_via_bank_transfer cash_via_prepaid_cards cash_via_mobile_money vouchers services_eg_healthcare_education_etc other_please_specify dont_know	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	r fCyVJxDAEYtBqAfxHOcuCAvioigQQgoA	yes_soap_is_not_shown	refuse_to_answer	TJtTnDsWfWlfQsSIUexCsbSMVVOkyJpSSTDHqds	UA44	UA44129	UKR1421557200	LB_4	no	170e35f9-856b-4c4948-94989e-3c6abd29f701
2023-08-16T09:45:42+0200	2023-08-16T10:46:07+0200	deviceID00000006	sloviansk	siev_001	yes	zAAbmbq	yes	JlHDOYhvgh QUg HyJTfQrorlVMCErLGlgw	no	yes	female	21	20	male	married	disability_not_including_chronic_illness unemployed single_parent family_with_foster_children	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	mYvFpSzn avSoIvTaMcMIRLjBDsadhLeBwyaIgeHKnosNwvPOkLUxULvK HYZYpiqgJUvvWBR	blood_pressure_diseases cardiovascular_disease diabetes_need_insulin chronic_respiratory_condition musculoskeletal_system_and_joints gastrointestinal_digestive_tract_incl_liver_gallbladder_pancreas_diseases genitourinary_system_diseases endocrine_system_thyroid_gland_and_other_diseases	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	qkJPnFqGspDSGfHCrLfLzKacpOtZTbcgsgc nyRJFFsHWVdNNXoZUjuVWFkHjkqDKoNOrndVtLEvPKyoiXOHxTYqxkzIVpjeu	unemployed_not_looking	KLrzIYdSlppVFAxYCpxvXNVnHMrVhAMmOeEcXfscdHgTdqVlQRocbRq	health_reasons	cSRkehWUQlGWvioz	mines	gamhZkQeRHdmooemNVArXqAdfWXrGkxANQpxpBwpSNceGpx aKhmyAyuzCXfSimWCKJqDfKjwcxXzwrDZuGUhBLfeglUgAxWPL	21	no	yes	yes	no	complete_higher	0	21	17	1	1	female	doing_housework_looking_after_children_or_other_persons_unpaid	ciSyoDVJUGOnYdBKRAEsiZBAqKpRUGemTrgoXaRQQLIuOTjHOErxNzyNJeUkpKzLZrusYZJrmsDLJY	24	1	1	displaced_and_idp_status	2019	nmfHGuCmBeyrPLU QOhEDqNQehtlReVXkHaDjFXowQBqmFsoVjkagUB	tent	bImcoNdtwQdOZllSFQIoAZClqghZyYDOmeDQyvIRAcoobFMHgRPrmU	no_but_free_accommodation_paying_utilities	uxxetguigLv PLTLouv qjEysTdsiLPwTT	presence_of_dirt_or_debris_removable dont_know	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	TSPURVVihIdmtDpDFsmzUvOkTxMosNLCXzSJvpdaHitTbhjFlavDRuccUnXWpIYOQEeesvX hbEjrB iYMscs	opening_or_cracks_in_roof roof_partially_collapsed broken_or_cracked_windows exterior_doors_broken_unable_to_shut_properly large_cracks_openings_in_most_walls some_walls_fully_collapsed severe_structural_damage_and_unsafe_for_living total_structural_collapse foundation_damaged_or_shifted gas_water_or_sewage_system_damaged other_specify_none_of_the_above dont_know_prefer_not_to_say	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	RgcHwVSFvTgpvQQcgoU syVjXeDB bxYhpHLBbHMNPRnlhjrpjDXLNLvwye	yes	no	dont_know_ns	dont_know_ns	20	yes	yes_after_3_months	functional_stove functional_heater_movable hh_has_all_items	FALSE	TRUE	TRUE	TRUE	bedsheets towel thermal_underwear	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE		FALSE	FALSE	FALSE	FALSE	central_heating electricity none	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	20	VMxUHFztzlgpHOBbKKXYNZXCTiLwrxjVrQtit hNPniAmuVmLksYxbJTJqFtlGr	19	NSNVkuyUfmTnhGf fUDVKIjqUWPNtK sdxtdzslLPOybdkAKsmyMMO bKNTBIONxXrTBynBoOOBrT	wood briquettes_not_coal other_specify	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	saWRLiROIDIKSzoROkdzpkdKFSemcNOMRhlAnixhjbwfkfiZf	20.32529	19.38777	17.69224	15.47431	no	yes_infrequently_irregularly	yes_infrequently_irregularly	public_well_or_boreholes_shared_access	cYWSBBVhRMjLCBovRFOs EHZWYDph	do_not_process_purify filtering_the_water_pitcher_filter boiling other_specify	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	PkuWzDBwXIBaLEGJVPxZbHRzXU aSfjcYPHyE JoNv	refuse_to_answer	wzilYWfNR OOVvNDjybHTDQrmctKzEkRlmGSgzLCguOcvtKHbqEFCfhGemS EYllLRJcGXKPoHqnZlNF CsK DaYsecef	tap_water_centralized_water_supply_appropriate_for_drinking	YOCQfYcKMDSRiFUkLyysURnMehmcThKgHqhJcjzuhKzMWfmGurNPjlAJSBjXtT	dont_know_ns	19	do_not_store_water glass_bottles a_bucket_with_a_lid a_canister a_barrel_tank other_specify refuse_to_answer	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	KncSlhcwjDLduQQYR ATEhRmTUoQA ZQ mvQnNpGdJHHgXRouIHxNPFdWlIAzqTZEn cdzIDscpleKXcVxAbKuVyMY	drinking personal_hygiene dont_know_ns	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	pit_latrine_without_a_slab_or_platform	MwenG QaTEblMhHXadRIqUW YuIJTpABNULgOytrTCrhxscjsqRxYaQOFZMEFOLKOOygbPqIaDsEvh	not_applicable	we_use_services_of_a_special_service_which_regularly_takes_out_garbage	6	2	0	6	1	6	1	3	1	7	0	0_no	3_often_10plus_times	0_no	1_rarely_1_2	0_no	1_rarely_12	no_but_needed	no_but_needed	yes	no_already_done_so_cant_do_it_anymore	no_already_done_so_cant_do_it_anymore	no_but_needed	no_was_not_needed	no_already_done_so_cant_do_it_anymore	no_was_not_needed	yes	no_was_not_needed	no_but_needed	kindergarten boarding_school vocational_school	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	school_too_far_and_no_transport cannot_afford_supplies children_at_home_as_carer	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	tOEYBrtELZdFSiWZxZ RCghhaWnTgJbsbMRYZxuohskXIMwmKUQQIHopwkaTQIrBDgPeSkmTeOKtIUYK	21	19	19	21	18	19	21	17	17	22	18	20	18	22	22	17	22	21	19	20	22	refuse_to_answer	20	19	16	21	18	17	school_closed_due_to_security insufficient_teachers security_concerns_by_parent health_issues family_personal_reasons too_expensive_school_fees_supplies_transport_etc transport_unavavailable	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	RtzQUTDyqIynnrsNQTTukLRVrtrEgvZWTJbCjOvh mucspGhuleSFyVCgYbneUuo fwPHeNJHUO ulko	more_4_months	no_problems quality_of_teaching_staff quantity_of_teaching_staff lack_of_pss lack_of_medical_support other_specify	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	samAwiEATQQbHNaucPPRwowNGsldMnJuJHnFLtDB uLwMgdzjvuIKQsuwQqctAlCIGtSaODlbqiSgOcbBMEBMd	uxos wildanimals_stray_dogs other_specify	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	tSsyokI	none uxos_on_school_grounds shooting_in_the_vicinity_of_the_school military_presence_in_the_vicinity_of_schools other_specify	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	OHIKuNlSvyarXNCxYGwIOuIUJm xUjIpPQyOse YmreuzBcHWjRqaSbjipvzicLvdoyZSk	no	no	21	hFfKenChvPFxfxyPPJTSVktiCBmBicsGiqpNTwb	obstetricgynaecological mental_health_care ear_nose_throat_ent inpatient_facility laboratory x_ray chest_photofluorography post_trauma_rehabilitation dont_know_ns	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	no	dont_know_ns	lack_of_facilities distance_to_facility cost_of_travel_to_facility lack_of_documents security_problems_in_travelling_to_facility irregular_presence_of_doctors required_a_covid_test_prior_to_treatment other_specify long_queues	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	QraIX VB xiCCnJxzWtOrqibzruRnzsgJvJwXGxStFrymZqdVPHhVqYQrCEnoRHeTlHaHKcMWGeCTlTgDtfedty	no	yes	refuse_to_answer	refuse_to_answer	less_than_3h	yes	items_not_available items_too_expensive distance	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	gPCjFBoTgBRF	cereals_grains_bread_pasta roots_and_tubers_potato_onion_beet fruits_apple_orange_strawberry meat_or_fish_chicken_pork_beef_fish pulses_nuts_seeds_lentils_beans_nuts oil_and_fat_salo_butter_sunflower_oil sugar_or_sweets_cakes_chocolate_sugary_cold_drinks other_specify	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	tULryqwSCYBIubzqBO g	23	20	refuse_to_answer	19	LHkHOAkuDvGRMtaDxn	21	dont_want_to_say	dont_know	21	yes	yes_we_all_have_birth_certificates_but_they_are_not_in_our_possession	dont_know	21	yes	refuse_to_answer	yes	no	refuse_to_answer	no	3_sometimes_i_need_to_change_my_behaviour_due_to_mines	psychological_safety has_affected_hh_budget	FALSE	FALSE	TRUE	TRUE	FALSE	HTEEcDZQKaQzvdtbvWrwzoIeCRhxuwjcDVLbG l	state_emergency_services mine_clearance_ngo police army to_relatives	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	jPaxcSUlPePDhVqpb EcLITnvZUOLoMqWgWUhnTQxZDLVqGbaagzyeniPJXPXHPDetsCFyQqMuuUVSTJVZ	diminished_lost_source_of_income limited_access_to_food loss_of_or_severely_diminished_access_to_basic_services sickness_of_household_members other_specify no_covid19_impacts_on_my_household psychological_deterioration	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	GTWGcQYIZdaufPLKgT	no_no_action_taken_cannot_select_with_any_other_option stopping_handshakes_or_physical_contact keeping_distance_from_people avoiding_public_places_and_gatherings wearing_gloves washing_hands_more_regularly praying_to_god	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	CnuDkQSAyiLhwFXMqQycUoZibCjroWsmr	refuse_to_answer	no	refuse_to_answer	refuse_to_answer	refuse_to_answer	no	no	refuse_to_answer	refuse_to_answer	yes	yes	yes	yes	dont_know_ns	no	no	shelter_housing fuel_for_heating other_specify	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	XApYA Ku hylaoVMycDqBBjGie	dont_know_ns	yes	dont_know_ns	dont_know_ns	refuse_to_answer	no	dont_want_to_get_information how_to_register_for_aid how_to_get_water how_to_stay_safe_to_prevent_attack_harassment how_to_access_education	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	YoAYvxeyKuCHmi AbEgsCNiUcOnZVhwgWKH qjMM	community_leader	Bm bwobQdANqMLDWEn	social_media_twitter_facebook_etc	GSiqrOeqpdHLve PIaRkZtfU	physical_cash cash_via_bank_transfer cash_via_mobile_money vouchers services_eg_healthcare_education_etc other_please_specify prefer_not_to_answer fuel_for_heating construction_materials	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	kuoAkYXQbHYkfLHXrSYOzIguaJjgHUvOZNPzLpUeowZIbGiKecHeguUqWnN	no	yes	cNwKjkDKzowzXOkxxwDJnoJEwgSKxtWxgbxa jvShkcmzCO	UA14	UA14123	UKR1421588202	EA_1	no	5bd1af64-e731-454a42-9f9798-0dbef257a341
2023-08-16T10:25:37+0200	2023-08-16T09:50:00+0200	deviceID00000006	mariupol	mari_003	no	RDolWTIdRBL	no	DnqYRZaUveDhgc	yes	no	female	20	23	male	single	unemployed family_with_foster_children chronic_illness	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	eVnTXvbtqShySUhRKaaEHKml eREczPKaJkYhhqXtDBENCCRbXPEtEUgQnwoyGJfoK ArMbbyYyiqAsBWFmkKHJamn	cardiovascular_disease diabetes_need_insulin diabetes_does_not_need_insulin chronic_respiratory_condition neurological gastrointestinal_digestive_tract_incl_liver_gallbladder_pancreas_diseases endocrine_system_thyroid_gland_and_other_diseases	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	deG hfDzcZWpfseZNkKDkRybsZJYEfUNHSoCxoOgCAUWZRCfjecTrTWHCFcunyQRxIUIXmzi	paid_work	aYnFcLsMzGdzFBTOSZezOaYzSAJWew	lack_of_relevant_vacancies_in_the_settlement	DKjQboQYRQzDqDGUXirUTmmUuTpjSxVGiUEvno XFrQAecxTcqAUiVDsyOnpBKYZOeTcOUuFcmrfXkLaOCkbxzWhIZm	it	JkCfFyKqRl JLEDRMgTbPuizFqups	20	no	refuse_to_answer	yes	refuse_to_answer	basic_secondary	0	20	19	0	0	male	doing_housework_looking_after_children_or_other_persons_unpaid	CWRfXfCKfbk RvsrSQQmk BuvLRgBaGcJooPFGgyIsWncLJqGMpuPRI upctHOilsiZzRZttMxZcpFewYhIvowmF	17	0	0	displaced_and_idp_status	2014	HRaoKDjKhLsyroNMSMwVpWcCmmgpiRlhYFkrDCWwxIHAKpZgFY	collective_shelter	HNkScfGzZkVjezNdhCFeoV	other_specify	VffIKRrAVn BYFzqV GqacNVAcvnfGrprbvBjBpTZgZZG MvTRLfjZER	no_issues lack_of_insulation_from_cold presence_of_dirt_or_debris_removable presence_of_dirt_or_debris_nonremovable dont_know	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	vGzefIKH cAPI	opening_or_cracks_in_roof some_walls_fully_collapsed severe_structural_damage_and_unsafe_for_living gas_water_or_sewage_system_damaged electricity_supply_line_damaged_and_not_functional other_specify_none_of_the_above dont_know_prefer_not_to_say	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	DiLDM hzTThtWFdQuViUjmsbpvBCzkAcoSvffpHrshAuSUzKKw JbKDDIqfssK aUZYmRWawSrXQLWrNRkdvzpbny	dont_know_ns	no	dont_know_ns	dont_know_ns	18	yes	no	refigerator	TRUE	FALSE	FALSE	FALSE	bedsheets towel winter_jacket thermal_underwear adult_warm_clothing each_member_has_all_items	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	adult_diapers none_needed	TRUE	FALSE	FALSE	TRUE	centralised_piped_hot_water_supply centralised_piped_cold_water_supply gas	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	20	iVgcqwNffyKQP tysaRDkoK EhNfxZjRSPjnzZprismczniT	22	JpNZwEeARucjXOaDPvbxvquzqdRTfNH	central_heating electricity gas kerosene coal briquettes_not_coal	TRUE	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	bcgynnvTExCOWJTHgNsQAWYANgLkQMknVUQBFsWDNAi Ev	22.67407	21.27733	18.85252	18.88043	no	not_connected_to_this_service	yes_daily_every_day	trucked_in_water_truck_with_a_tank_etc	FTzfQbJEAZCFfUcgyvkkqOQkJiNtSpMJqQzHjDQN	do_not_process_purify water_precipitation filtering_the_water_reverse_osmosis_filter boiling percolation other_specify	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	HFvHczVqrrKZgCbSJODdHIqQovEyIyLNpfiIXlVrcPlUj vfIlIFwjG yWMSHFhRjNSLFyViGhUdqMXHePITkvTchiQvzxfZ	refuse_to_answer	AyI	personal_well	IsiqOWXZWiOqwAIAoeKIfFtMGPwBfdhBwXCHsEZQBEjJkJ	no_shortages	22	glass_bottles we_fill_in_the_bathtub other_specify refuse_to_answer	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	qrkHwUzMsRYRkEgvVfIdJpT VqgNHfHKbjtODfuAZUsMUzeNJylCHR	cooking personal_hygiene other_domestic_purposes	FALSE	TRUE	TRUE	TRUE	FALSE	FALSE	open_defecation	EwxNnuyfGxBOhgUsApQVmnyN	refuse_to_answer	we_sort_garbage_and_submit_it_for_recycling	0	0	7	2	7	7	6	3	3	7	6	1_yes	1_rarely_1_2	0_no	1_rarely_1_2	0_no	1_rarely_12	no_but_needed	no_already_done_so_cant_do_it_anymore	yes	no_but_needed	no_already_done_so_cant_do_it_anymore	no_was_not_needed	no_already_done_so_cant_do_it_anymore	no_but_needed	no_was_not_needed	no_but_needed	no_but_needed	yes	school boarding_school	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	children_at_home_as_carer other_specify	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	RejtGJwQohDYHbbSXQmcmDwO jQXYoVbcBnuArOdaDAafDtdZCnSMx PRTa	21	18	19	19	20	17	24	23	22	18	19	22	15	21	18	19	19	23	19	20	21	dont_know_ns	21	20	23	25	17	19	insufficient_teachers school_is_damaged transport_unavavailable a_child_contributes_to_hh_income refuse_to_answer	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	wCMQXyEKCWsedBbE sYdoueKHoUJKZBBWaVkIdbtO lTBOPN	2_4_months	no_problems lack_of_medical_support other_specify	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	leeZnUZOwJJedaYuynJjYUgsMMQrRZdtfoSnVyBPXIDJHVVDgkzoziQRkfze	shooting military_presence	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	cThsjjMEshekLwlgKPxgQjJEZQpDzoVqVqLQSQjvVNZUvVaRzjzImQUyrFxEIrKOmdj	none uxos_on_school_grounds shelling_in_the_vicinity_of_the_school military_presence_in_the_vicinity_of_schools	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	KRgIjDYhlMgxf	no	refuse_to_answer	17	MEbsdMIdnAbELxhwupfpoYeTBkqZwerEYEJqndLziGuRAoZBLtsoMQuQmksEKYZPJSAOOpbWWMIbhyHcZfWeTYuknjYqV	gastroentrology mental_health_care ear_nose_throat_ent ambulance laboratory x_ray ultrasound chest_photofluorography post_trauma_rehabilitation	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	refuse_to_answer	never	security_problems_in_travelling_to_facility irregular_presence_of_doctors	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	frLMsiwIeCxlVNDzygilqPgfTnWYZvwNFudlDP GNxESNpIfWm nuu	dont_know	refuse_to_answer	no	refuse_to_answer	less_than_3h	yes	no_problems items_poor_quality	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	owPWntLiBQqiFvnxYtGCOSIvlIFhNp	cereals_grains_bread_pasta roots_and_tubers_potato_onion_beet fruits_apple_orange_strawberry meat_or_fish_chicken_pork_beef_fish oil_and_fat_salo_butter_sunflower_oil sugar_or_sweets_cakes_chocolate_sugary_cold_drinks	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	UoOMTlFexfJVvEtHNsCEoaY crHjYKHoRVXXuuN Qp	22	21	yes	21	juLmUHndFngkmidRVyGXqrZGXkmzkHZwGkXEJKBdLBKTamUoaFSjeIyDAQIfGAlZ	17	dont_want_to_say	no_some_hh_members_are_missing_ids	17	no	yes_in_our_possession	both	20	yes	refuse_to_answer	refuse_to_answer	dont_know	yes	refuse_to_answer	5_they_severely_affect_my_everyday_life	fear_for_physical_safety	TRUE	FALSE	FALSE	FALSE	FALSE	XLDzpwCHCYrH UchIgrDGrmMcxFaewUIVAxED	state_emergency_services police other_specify dont_know_ns	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	nWTGiJhlVMODBccYCMPmARVFXoSJBvcbu zNllDnhYFvoTtoLmummVpJCeoWVgXKgrbdeZzQQZkVXavRxskUmMgMvjClkdvyXW	diminished_lost_source_of_income loss_of_or_severely_diminished_access_to_basic_services loss_of_or_diminished_access_to_clean_water_and_sanitation sickness_of_household_members no_covid19_impacts_on_my_household	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	gwBlquVTihwxEtuoytEWVlNQobZLhX	reducing_movement_outside_the_house stopping_handshakes_or_physical_contact avoiding_public_places_and_gatherings avoiding_public_transport wearing_a_face_mask keeping_surfaces_clean dont_know	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	LzvUXzmRkKfDEScHXTXaKeeKItfcUSbQhFxugtqeMlAUZTwgRCawBr V	yes_covid_related_reason	refuse_to_answer	refuse_to_answer	refuse_to_answer	refuse_to_answer	no	no	no	refuse_to_answer	no	no	no	no	yes	no	dont_know_ns	food livelihoods_support_employment drinking_water psychosocial_support fuel_for_heating none	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	FALSE		yes	refuse_to_answer	refuse_to_answer	dont_know_ns	dont_know_ns	refuse_to_answer	dont_want_to_get_information how_to_register_for_aid how_to_get_healthcare_medical_attention how_to_access_education how_to_find_work other	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	amTqkwvIJvFC	friends_family_members	wODuACSTNozz	sms	bteCSOvZezFKGaWh wLeHXfYCNkAhVklZwXAcCQWcKPTXzCplpFMknbhIbHGa	inkind_food cash_via_bank_transfer cash_via_prepaid_cards other_please_specify dont_know fuel_for_heating	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	WTlBVGlZhC pgvJTVUuFZo wQbQcSdwKOuLLDawRdwffGKXIOIbsTUfudMrJhp	yes_soap_is_not_shown	dont_know_ns	wcgsVTAViBHIAkCyTdKJBCwnBcnnhylsFmRJwOXdSeOAcelqtcypRCWwmEWOtKfsKbSfQsKasBCArfZvBxOUQFtjGjU	UA44	UA44129	UKR1425583101	DK_1	no	82dea9f1-719f-4a4d4e-b8beb4-f92450e3da87
2023-08-16T10:00:51+0200	2023-08-16T08:25:39+0200	deviceID00000005	sievero	slov_008	yes	SsOsgzUmixrPvpmFMYuQustwpzalDBSFaXHGtqBfMEGnQVGJrPlciiXIL BuXWjS nQCbtkHgyeIpvxGIlnDDSt h	yes	btWyLTGRIfZEqvKSZDvWZgEqmVmwdoWmBRiOSvdI	yes	yes	male	20	20	female	single	older_person unemployed single_parent family_with_foster_children chronic_illness	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	qcHNEuDYuEWb bYbMgstcwOnlLUhyBcpGWBlSNLtIeNaL	cardiovascular_disease musculoskeletal_system_and_joints cancer genitourinary_system_diseases endocrine_system_thyroid_gland_and_other_diseases	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	FBESXRhejuoWMwTbXLXrAjlPdrdWVqAeJ bxOdKniJqkcZkYMxMHBcDYwnsMsxyRmQvAHcLzr	paid_work	CyjPNZCzzTboYLLHVDKYMkVMjwjxavoICYTRbZnOnNwoFHqcfLWCmlu hXPxO	personal_family_reasons	MsbUWdAgMYZHtGcE	education	QyJSUYHMXcdAIGijXQObc	19	dont_know_ns	refuse_to_answer	yes	refuse_to_answer	vocational	0	16	18	1	1	male	military_service	xxwEmPyInoQfMAQXriwrPrlMEAVSLWZUaWPCTqyhSElqcDmCvrGlOnCTIAVJJ GvrTNSnqtHyPmjlzYoNpTx	19	1	1	displaced_but_does_not_have_idp_status	other_specify	cpxJHPtxFeusGrGdHpwbyx lZFkxpUfV igvHWQlSCdlWIal fpswhQGSCR	other_specify	wpqEpFLVZDQCfUfmXZKvqFprMKRe	no_free_accommodation_free_utilities	rIIxIquVQwtHJnSSdiwXTST	no_issues lack_of_insulation_from_cold leaks_during_heavy_rain_snow presence_of_dirt_or_debris_removable presence_of_dirt_or_debris_nonremovable	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	CHLOWGtKGd JWiTzbMOrVN ooSOaCegGylnAJzpeqtbAJrUXgUBrdXyfruuEjsYyN Z	none roof_partially_collapsed broken_or_cracked_windows exterior_doors_broken_unable_to_shut_properly some_walls_fully_collapsed other_specify_none_of_the_above dont_know_prefer_not_to_say	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	HGeulsrrH	dont_know_ns	no	dont_know_ns	no	19	refuse_to_answer	yes_after_3_months	refigerator functional_stove functional_heater_movable	TRUE	TRUE	TRUE	FALSE	mattress towel winter_jacket warm_winter_boots warm_scarf thermal_underwear child_warm_clothing each_member_has_all_items	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	adult_diapers child_diapers none_needed	TRUE	TRUE	FALSE	TRUE	centralised_piped_hot_water_supply electricity gas	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	19	mTXcqEfsJOgAFvakoAzAuRJwbXZsSxtSoZwjUaJyAt	19	rszbUSkwiCdRsTuNNpEljuxfgYobKAVmPbEnCYOqYBIPVlD rZ NzNpadqIqwOtQmkmWMkQixcPpcvxWMHWRaO	electricity wood coal briquettes_coal other_specify	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	pBBWyndUpZqqEPhjvKuEWMDNMfkUAKFoZhpLBXRcuYwaEXLLGHdenjPbSPGwKlZDktqXRYzkuhStpZuOHfwXuUnsTxhhAavcY	18.06702	18.64185	16.69118	20.96493	yes	yes_infrequently_irregularly	yes_daily_every_day	drinking_water_from_water_kiosk_booth_with_water_for_bottling	hGsWEyJgwvWKOYAISUlXPLFqRGdaFwBVfieTEZK	do_not_process_purify cleaning_with_chemicals_chlorination water_precipitation filtering_the_water_reverse_osmosis_filter boiling dont_know_ns refuse_to_answer	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	SUhkIbzHkgAIwsNbJOMrfDDAYKxnRGjUIGwRbzoeYQOLuTsauVvpfehUdkumJNmgmHsfVJpvqatYSxqInRzmWvBhRWgRUukcDbG	dont_know_ns	ADdsj y bZyQvwHZTuILn JCVfCcNL jANegCeSvi	personal_well	EvpcOKogiJHgPsHVVsWoVaZMrPXEnxZOKScWovKGLpQCIr HOUfKtDlplqKZfJbNxifJYSjDpLvQD	no_shortages	19	a_bucket_with_a_lid a_bucket_without_a_lid a_canister we_fill_in_the_bathtub	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	rNRVcziZGYSIbUKFDCdgQceTBExyXrEYPZhFfg	drinking personal_hygiene dont_know_ns	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	pit_latrine_with_a_slab_and_platform	CooCZUZVX GkgopcVnXIoIrmxrtjgCUdqFNwJczvkFGccR DJywursqIKTLTG	yes	refuse_to_answer	5	1	0	7	0	2	0	4	3	3	5	0_no	3_often_10plus_times	1_yes	2_sometimes_3_10	1_yes	3_often_10plus_times	no_already_done_so_cant_do_it_anymore	no_already_done_so_cant_do_it_anymore	yes	no_but_needed	no_was_not_needed	no_but_needed	no_already_done_so_cant_do_it_anymore	no_was_not_needed	no_already_done_so_cant_do_it_anymore	no_but_needed	no_already_done_so_cant_do_it_anymore	no_already_done_so_cant_do_it_anymore	none kindergarten boarding_school vocational_school	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	school_too_far_and_no_transport children_at_home_as_carer children_in_paid_employment children_has_disability_that_school_does_not_have_infrastructure_for other_specify	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	AyQCtwNlVHYQIXvMZobAMMSLsZHUb HWkDdppOVZtCJKiGseJnpGbgpuSkBSPcZJdUBkBxvbHml	21	23	21	22	18	18	23	22	18	23	20	17	18	16	20	22	17	20	20	17	19	yes	20	18	23	16	18	17	school_closed_other school_is_damaged transport_unavavailable a_child_contributes_to_hh_income other_specify refuse_to_answer	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	PBlHEhMlLplmTYRVvkeDfAtPSEPdcYVWbKTe cLwQXHZv OwLZfvlc	1_2_months	price_for_service school_is_unsafe problems_with_distance_learning_due_to_closure_of_school_for_covid_eg_bad_internet_connection	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	LEuyRRkUQGiNlBKpkBRyNRUakliwPZMeLrLeCdsnYmeYVkLtKHgQEglJGlYpZXDCX MTYFCSaKYyy KddE	none shooting military_presence wildanimals_stray_dogs	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	sLstkKAlVTdqLTBNKboUZUmEllJwvqlPkfwCKWulsJYdyAayEnHEykMSMWyatxUjyXRLKeRyWCNrYVXtlDNHo	none shelling_in_the_vicinity_of_the_school military_presence_in_the_vicinity_of_schools other_specify	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	cobQBZNnRObWADbxJodFRMhvceiUNLuG yDsXmnzUXQzQ PQWFEqRsacN dhURQ LLicMLMjowtXPeSfGwqbSXDOdsOZFbEP	no	no	19	WQooQcCUbHwAXr kOjkfwCOTrtBsFumtmUJqslSbEeOePlWcEmUelioyXuvCYvXsGmlTGTqTZZaDwIvZKP	obstetricgynaecological ambulance inpatient_facility outpatient_care x_ray ultrasound none_of_listed	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	yes	often	lack_of_facilities distance_to_facility security_problems_in_travelling_to_facility cost_of_appointment lack_of_referral_system have_to_pass_through_entry_exit_checkpoint_for_medical_facility_in_the_ngca insufficient_qualifications_of_doctors	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	JisybKahRyCPSIgXBqdhetiobeH fvvOnhLEMjyZvaRxsgdj IWVqiixPoWQCuRodVYCPJoYtmxXrAzoXVmSkgmJliiKPUvev	yes	refuse_to_answer	no	yes	less_than_3h	no	items_not_available items_too_expensive market_damaged distance	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	pgDFqaXIYyRnSnHHxmeacFxYzOzYTWzdJzplkvEOjg	vegetables_and_leaves_tomato_lettuce_spinach_carrot fruits_apple_orange_strawberry eggs pulses_nuts_seeds_lentils_beans_nuts condiments_and_spices_tea_coffee_spices	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	wCQGlBTgeKDWKSAUrqoLnNTLQjBjnsYIXThzLwhCnxTTsXSWkksRcSwmAJEvoHDZbGDLJbVGq gRMiWBG	19	22	no	19	nCdiNdawTWYXkkJWWXpUzTGFZB	23	dont_want_to_say	yes_we_all_have_ids_but_they_are_not_in_our_possession	19	no	no_some_hh_members_are_missing_birth_certificates	refuse_to_answer	22	no	yes	no	no	no	dont_know	1_not_at_all	fear_for_physical_safety movement_and_access_constraints	TRUE	TRUE	FALSE	FALSE	FALSE	TjnkukS	state_emergency_services army to_relatives dont_know_ns	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	xmvAYHOkmfPWENjihmbgrFFBBWcVyvLyJSR I	diminished_lost_source_of_income loss_of_or_severely_diminished_access_to_basic_services loss_of_or_diminished_access_to_education loss_of_or_diminished_access_to_clean_water_and_sanitation sickness_of_household_members other_specify restriction_in_movement	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	GOhwPNUNbdiVtZpDxxdcuLyAcdKYWeRI	no_no_action_taken_cannot_select_with_any_other_option avoiding_public_places_and_gatherings wearing_a_face_mask washing_hands_more_regularly praying_to_god	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	i	no	refuse_to_answer	yes	no	refuse_to_answer	no	refuse_to_answer	yes	refuse_to_answer	no	yes	no	yes	refuse_to_answer	refuse_to_answer	refuse_to_answer	food seeds_or_other_agricultural_inputs livelihoods_support_employment drinking_water hygiene_nfis_eg_soap_sanitary_pads_and_sanitation_services_eg_latrines psychosocial_support none house_repairs	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	LXDNuuyGEhK	yes	refuse_to_answer	no	refuse_to_answer	no	refuse_to_answer	dont_want_to_get_information how_to_get_water how_to_get_food how_to_stay_safe_to_prevent_attack_harassment how_to_access_education how_to_find_work how_to_provide_feedback_on_aid_you_are_receiving other	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	xLxdoKAkRZqZiURdpcbzEBsTGtUkVkHYNwgAfxQlRopkukl oVFqiRmibTYHCbLBQhOaCbchCltb	aid_workers_from_local_ngos_or_civil_society_organisations	AL VNsxmhmYWGiDGKZiNgtdSjCSEhsJRjNKYrnpKArSRIUKuRgBCyKmUgfWuodA vrsesBjARJiPlLiCT	billboards_posters_leaflets	ciYGRDlXndEEILBOTfQqVSlLCQQZjDOEqGNydqoJYmBKLmbfKPCZT	do_not_want_to_receive_humanitarian_assistance inkind_food inkind_nfis physical_cash cash_via_prepaid_cards dont_know	TRUE	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	mecFYfmIIXVZVLPCubCEXZvXdzQQmqTVvnbDNSwVaCLKLlKwWoyEHw kNOmqNLILpoB UGUCTPOjRCwKHgmwLw	no	refuse_to_answer	DWNXjHaxubpcrzfY TtDArBwQjNisrXsXyxzj	UA44	UA44238	UKR4423180701	BD_3	no	b31c7654-ac65-464148-878681-072cfead9b53
2023-08-16T09:13:38+0200	2023-08-16T09:52:27+0200	deviceID00000003	sievero	slov_007	yes	CZIRcuDJvzQRzawZBVUGUokxTJ vGXYmwjSqN hxciw GyMAH tNntWnkksJbzlCekurDzvGHaXDsRIFptLpoULlFia	no	YurkpfICATSaepNuWwGLWEVWLOapAAEcMRhzdcMXoQlINgUWABXus	no	yes	female	16	18	female	separated_married_but_not_living_together	no family_with_3_or_more_children	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	IZvYbsaNhoMMjximXmkbMgpcyQDjjZEFlzLilIENkvZBtcYuzi	diabetes_does_not_need_insulin musculoskeletal_system_and_joints cancer sensory_disorder gastrointestinal_digestive_tract_incl_liver_gallbladder_pancreas_diseases	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	ClWlnUTKosVKeOenujYvL	permanently_sick_or_disabled_cant_work	dUJpEopZElJun gdYUFMLEGqkauqSAZNHUmgJkge	no_employment_opportunities_in_settlement	yVZeyNLwme	construction	hVfsqeQIeykJolqUmBBmBOwgXoQt	21	refuse_to_answer	no	no	yes	primary_education	1	16	18	1	1	male	retired_not_working	rFgmZXatqpojSlt VVNJouIQFsXalFPjjjZcFK MXmDx	21	1	1	refuse_to_answer	2016	piozjmEWvQRPFydTeDFjAWnRHXMKwKwzKhiicxkYEXilMLbKcGGndVhDxOaTU TKlVSsNvsh	solid_finished_house	icwboI kpPNuGIWRupYDMBTkzIlMmqzlYjZqjmhIMyjYmjCgaOcyGOmttYuTdpgNsctZGrmcvkwDzMNRNmauooPOUHbWMENXpahE	other_specify	AXcCLtqcrlSkuEYjSuhdTsayf	presence_of_dirt_or_debris_nonremovable none_of_the_above other_specify	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	NbKMdAEmnIShPYdFTGycOWNkAzAETzUzazPRVBfxOlEmfCCGuTxkh	none roof_partially_collapsed broken_or_cracked_windows large_cracks_openings_in_most_walls some_cracks_in_some_walls damaged_floors gas_water_or_sewage_system_damaged electricity_supply_line_damaged_and_not_functional other_specify_none_of_the_above	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	PvZMJM IWslPKYPsIgVctC	no	dont_know_ns	yes	yes	21	yes	yes_after_3_months	functional_stove functional_heater_movable	FALSE	TRUE	TRUE	FALSE	bedsheets towel warm_winter_boots warm_gloves adult_warm_clothing child_warm_clothing each_member_has_all_items	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	adult_diapers child_diapers sanitary_pads none_needed	TRUE	TRUE	TRUE	TRUE	electricity gas none	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	23	EUeRYTgrHqyIPlsMlEQywFhoJpTfAyFslH	17	uufdtUpQZLz lvmnRlhAYnIEEDepwkbQfhjgmnuDOpuGoYwPTByavcUsFxFXVLQtRXuJHeIsGgaIeVRQhBxQXDO	gas kerosene wood coal briquettes_coal	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	IKXrKteqFtIRjzixKvI YtVfxDNLsNQHKCIAgRpg tFOKBsJbufOthwHWXMHXUAORDtw	21.73913	21.28984	16.24886	22.00986	yes	yes_infrequently_irregularly	not_connected_to_this_service	drinking_water_from_water_kiosk_booth_with_water_for_bottling	jNnlNBJTJREGFpdSnkacYIQpTVddGokNYdlZyMMNJZlZeyM	filtering_the_water_reverse_osmosis_filter boiling other_specify dont_know_ns	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	yOF wWMqXOXnddNrOFvuDprfJRhBmVbS	we_cant_afford_necessary_means_filters_chemicals_for_chlorination	GI Durkf SpeLAPNwEFC	trucked_in_water_truck_with_a_tank_etc	AchjHV	1_time_a_week	17	glass_bottles a_bucket_without_a_lid other_specify dont_know_ns	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	dRxRojGUPNFxDnQHuabPAqpNqhxtCKKqkYMm	personal_hygiene none_of_the_previous	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	other_specify	PDmvsdeqIfyahITiMZuLkUGMXaEgkqywuxjGAXOJrziIrbZYhIbVwe	refuse_to_answer	we_sort_garbage_and_submit_it_for_recycling	5	1	2	6	0	5	7	5	1	0	5	0_no	1_rarely_1_2	0_no	1_rarely_1_2	1_yes	3_often_10plus_times	no_was_not_needed	no_was_not_needed	yes	yes	yes	no_already_done_so_cant_do_it_anymore	yes	no_but_needed	no_was_not_needed	yes	yes	no_already_done_so_cant_do_it_anymore	kindergarten vocational_school	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	children_at_home_as_carer children_working_at_home children_in_paid_employment other_specify	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	TRUE	LAnQJXLayFzILD kvzojXsNuideVdiwDXVABIdrrqpht	18	19	20	23	17	21	19	21	21	20	20	21	23	18	17	14	20	17	20	21	21	refuse_to_answer	16	22	19	18	15	24	school_closed_other security_concerns_by_parent school_is_damaged health_issues too_expensive_school_fees_supplies_transport_etc refuse_to_answer	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	mLWKRbghOVQPwcgQAujimEpjbqrdLpFyV A hnkhSLCEdRxpJyuYKeuvmjvcCqraPYVWGbiCdXXmVaKZgBdCLiZFxLXgEUHfa	2_4_months	quality_of_teaching_staff lack_of_pss conditions_of_the_venue lack_of_medical_support	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	y CYxZi	none uxos wildanimals_stray_dogs	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	QUAWMjlcEMZgJGMvtPDfbrvFNZxEcxbcKUCV RFmZctRJRoqFwmWmyDpBdlySADk	uxos_on_school_grounds shelling_in_the_vicinity_of_the_school other_specify	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	HFOSPrV	no	yes	20	OR Xmg ctmcODJMmloMZAiaqUFRxEPaTukVnYisWNGUFIfjtBhGnAoi	obstetricgynaecological paediatrics gastroentrology ear_nose_throat_ent ambulance inpatient_facility outpatient_care laboratory chest_photofluorography post_trauma_rehabilitation dont_know_ns	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	yes	often	lack_of_facilities distance_to_facility lack_of_documents have_to_pass_through_entry_exit_checkpoint_for_medical_facility_in_the_ngca required_a_covid_test_prior_to_treatment unable_to_leave_the_house_due_to_covid19 lack_of_doctors insufficient_qualifications_of_doctors	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	HxoXkKeytAepLXlDHvbudwiaA PXaBo	no	refuse_to_answer	no	dont_know_ns	less_than_3h	no	items_too_expensive market_damaged distance	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	fMhICdCEHRjcrjOI zOkdXzrfkzgaJkz qGNLtH dRzzmpeohWhGJInUR poRRUFLW	cereals_grains_bread_pasta meat_or_fish_chicken_pork_beef_fish pulses_nuts_seeds_lentils_beans_nuts oil_and_fat_salo_butter_sunflower_oil sugar_or_sweets_cakes_chocolate_sugary_cold_drinks condiments_and_spices_tea_coffee_spices	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	LfZsWn	18	20	dont_know_ns	22	kWLalcZsziwgGAI BetbVnpZXVULSbegwYWQYKwOYoioVBuyORHMHxHsDKrPDwDWuGDJaFqncKNN	21	dont_know	no_some_hh_members_are_missing_ids	22	yes	no_some_hh_members_are_missing_birth_certificates	government_of_ukraine	21	yes	refuse_to_answer	no	yes	dont_know	refuse_to_answer	3_sometimes_i_need_to_change_my_behaviour_due_to_mines	has_affected_hh_budget other_specify	FALSE	FALSE	FALSE	TRUE	TRUE	NmwqJQXagkkoxe	administration_head_of_the_village) state_emergency_services mine_clearance_ngo army	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	PaRwrGrfDHNwUUZUYbdCLfXJKPCkfCVrqzpHTklPivuZcseWfUWmBTKhOSgk DLMhoPgimtYZ	diminished_lost_source_of_income limited_access_to_food loss_of_or_diminished_access_to_education loss_of_or_diminished_access_to_clean_water_and_sanitation other_specify no_covid19_impacts_on_my_household	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	MovoMWPkhnsFcgULupqlVUUIoYWtKoqTperjZalMtrXiXfmffUtzXOAJvjRHWlIucVaBNgQJxqHUbeCyaSwmwNTQYm	stopping_handshakes_or_physical_contact keeping_distance_from_people avoiding_public_places_and_gatherings avoiding_public_transport wearing_gloves washing_hands_more_regularly keeping_surfaces_clean praying_to_god staying_away_from_animals	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	BovuuPMnQw JuTdtwrCiBGUwyvwiqBvDiL	refuse_to_answer	yes	refuse_to_answer	yes	no	no	refuse_to_answer	yes	yes	refuse_to_answer	refuse_to_answer	no	dont_know_ns	refuse_to_answer	no	no	shelter_housing food healthcare seeds_or_other_agricultural_inputs education_for_children_under_18 psychosocial_support	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	UkHHDqBUfnkvEdSYiyEhdCgSxAjOGsZYqq	yes	dont_know_ns	no	refuse_to_answer	no	refuse_to_answer	dont_want_to_get_information how_to_register_for_aid how_to_get_help_after_attack_or_harassment how_to_stay_safe_to_prevent_attack_harassment how_to_provide_feedback_on_aid_you_are_receiving other	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	igmUQRhjJgEoFpzvKKNLIWsFfPmJdMKXbSnGbdhiIwcCNcqCcSwzmt	places_of_worship	auagSVhcxYmWC	sms	FH RQItZ FGTtJtYTWSzzXLKCjXGPxsHePGDdQI NpEakFvLTwhDtaWiuIYBPhlYwb	inkind_nfis vouchers services_eg_healthcare_education_etc dont_know prefer_not_to_answer fuel_for_heating	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	SrrSgokSggiOndsXRZUQNqxMsUipAz KYGk	dont_know_refuse_to_answer	dont_know_ns	AvMRTRLsEGpxpQVxKOdEprGSAf	UA44	UA14138	UKR1421587701	BA_1	yes	e10ba53f-fbd9-4f484b-afa5a9-d0c39ba865ef
2023-08-16T10:06:26+0200	2023-08-16T09:25:19+0200	deviceID00000008	sloviansk	mari_004	no	KyayDKvzMPSaxhmFSnDcMaeZQyXplDUHblotzapkeczCxGY XsunuRSSuozhAOuVbZAreByRpmMbm cVszgCZFOUYhywUMUCEVs	no	oUSgDLboQGUVBljVbOSnUE	yes	no	male	20	21	female	divorced	disability_not_including_chronic_illness veteran_of_war_ato single_parent family_with_foster_children other_specify	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	WqZfUJulUXioSucDBCBzPksWgMdiycdbvPUgcSkcqluSCQxJjrioXFVwfGMapAC	cardiovascular_disease diabetes_does_not_need_insulin musculoskeletal_system_and_joints sensory_disorder	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	nOwzPdsukbMiikfVUK PAKlEpEztA lGHtCCpIAFt uKvSqrUEcmuzKvdKHjnJrUzDnHaBCPzmgAZzQerBu	paid_work	cAtigFLFkDwowSvLHNQMXiyBSWA	no_employment_opportunities_in_settlement	eJkWuXjYtcomvLmtgSXWRsGsXKzHqjnbiT	agriculture	L	20	dont_know_ns	no	refuse_to_answer	refuse_to_answer	postgraduate	0	17	22	0	0	female	permanently_sick_or_disabled_cant_work	LLBFfbVtBDVioWBmiXNhdmMNcbMJphFbxbKppwTULHTXncPDWpeQEmuKbJYAMfPAD	17	0	0	used_to_be_displaced_but_returned_and_has_a_status_of_idp	2014	srBoPoHmakwMjnBlsARNzKOUwHMDxVheZVXNdATVZdApyjZerNPHlSijDSAfCcVEegyhKrJlvRE	makeshift_shelter	csKweBnejqnfdNujzmZ	no_but_free_accommodation_paying_utilities	bnCJXzriCUqWCrAtFkNOyWfEYoeWsm GdlswiwllwGLNAHTLqAXZCX	no_issues leaks_during_light_rain_snow presence_of_dirt_or_debris_removable presence_of_dirt_or_debris_nonremovable dont_know	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	iqtlmoRViRVbjK wYATFBjYMLcFEeSeZkPNYpphfpHrcTi SecNQwPmPoLnBKBNGVQhMhBqzfpQrIwulwFUBuCdsHWFbaYkEV	none roof_partially_collapsed exterior_doors_broken_unable_to_shut_properly total_structural_collapse electricity_supply_line_damaged_and_not_functional	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	nvD IkZJRdDIPufvemezSSErwOtwRovXhxaJWPqJghY	yes	dont_know_ns	no	dont_know_ns	17	no	yes_within_the_next_3_months	functional_stove hh_has_all_items	FALSE	TRUE	FALSE	TRUE	bedsheets blanket winter_jacket adult_warm_clothing child_warm_clothing each_member_has_all_items	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	sanitary_pads	FALSE	FALSE	TRUE	FALSE	centralised_piped_cold_water_supply none	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	20	JnklUrhEhyaQeJxuIoplFnFXnPQjWdwELntPJHNCWyIYJlhnOMr	20	ejzyKpfKoxKGdKmEEAzWATbngNB DMXbbgQGQYZwPoUSHCFXIfVLlfqIMScytlIPm	central_heating gas kerosene wood briquettes_coal other_specify	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	UybksebG TcrLGMktwduOVT	16.64820	18.02282	20.38760	22.04191	yes	yes_weekly_min_once_per_week	not_connected_to_this_service	tap_drinking_water_centralized_water_supply	iP	do_not_process_purify boiling refuse_to_answer	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	EbNHyBfpdIWBluXePUTcWaSNnhYRcMUpSUZNyL vItrn	dont_know_ns	uzDetwz VhkXbcQsWRqbTOGUtwORhCvbLXLY sqG	water_from_water_kiosk_booth_with_water_for_bottling	HFdQUFpvGgQ hFPCRZaKSoqsueaCEVEdCHhvMMiIAXIhBRYWgt mnTMuFiVkvdEJgODIiHBz	once_a_month	23	plastic_bottles a_bucket_with_a_lid a_canister a_barrel_tank	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	chGezQBqEkGGYPvtVJHhpwFoIulPfWiWBltEWqwNqV rAVFAhvSKcmmwbmtCpwrIPghthFnxdbWWbHezSFJaardyTMZfrJBcAHWV	drinking cooking personal_hygiene dont_know_ns	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	bucket_toilet	jWLRHeeXZocFMvH	no	we_burn_a_part_of_the_garbage_and_the_other_part_is_thrown_down_the_pit_latrine	4	5	6	6	4	7	2	5	6	1	7	1_yes	1_rarely_1_2	1_yes	2_sometimes_3_10	0_no	2_sometimes_3_10	no_was_not_needed	no_was_not_needed	no_was_not_needed	no_but_needed	yes	yes	no_already_done_so_cant_do_it_anymore	no_was_not_needed	no_but_needed	no_already_done_so_cant_do_it_anymore	no_but_needed	no_was_not_needed	kindergarten vocational_school	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	children_at_home_as_carer children_in_paid_employment other_specify	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	TserMkAMdCmbtEPHrtVJPH EpYqCJVYvBSXeVOQhbgNnrKJSxjbsXjUEDjGLrRNvQtDTklyqDOjaiUIWgohKKmcOMOTQGZ	21	20	21	21	20	21	21	18	20	17	16	17	25	21	23	19	18	21	21	19	23	refuse_to_answer	19	17	19	20	20	15	school_closed_due_to_security school_is_damaged health_issues family_personal_reasons too_expensive_school_fees_supplies_transport_etc other_specify	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	IugTguYlFnQBIJRkRzLIrQeWTA lASJpZMeLoIwHLOWAg	refuse_to_answer	quantity_of_teaching_staff price_for_service conditions_of_the_venue overcrowded_classrooms lack_of_medical_support other_specify	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	KyfgugoCQdQkoe IEiLlRjhzirDekspZAhWlf iBMnTKzyRrNehzOXCyTdGYBtjdoQJNuFK DNhnVxWxouu	none shooting danger_at_checkpoints	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	zbDjTstsylzNCH bjFNCWXgZYvcNDxUltNKbJRBzztwUKLaRaIQ qiDiDgSXcvulXfkpPunez	none uxos_on_school_grounds military_presence_inside_of_schools military_presence_in_the_vicinity_of_schools	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	jxeiTyIeFCDmLffsUCqdbABvEeSJWzzXhyfzZUoy	yes	refuse_to_answer	23	sxdsfOWFjuWVnwMPEtvXKHOMFCEspROHGFFWKuAsUzMgzxm	mental_health_care inpatient_facility laboratory x_ray	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	yes	sometimes	lack_of_facilities distance_to_facility unable_to_leave_the_house_due_to_covid19 other_specify lack_of_doctors long_queues	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	TRUE	xcUexuzkeLHFywyMVsuHDEyYeUkNmGOSPhdSGesBdzqLqAdqrbvzXKYda	no	no	no	no	less_than_1h	yes	no_problems items_too_expensive items_poor_quality distance security	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	PuaTMqawdXbmmakizTLuCOesSyYliIpfmDirFIZxbVWgxrXYPoGGngUkYMEING cKl	roots_and_tubers_potato_onion_beet meat_or_fish_chicken_pork_beef_fish eggs oil_and_fat_salo_butter_sunflower_oil sugar_or_sweets_cakes_chocolate_sugary_cold_drinks	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	KWkBpxSFN hflnQaivzswQckKjZVKNzyznsGseCrhOmtDMXZQfD vlWFgtpsydvfblPHxrieAzHCVGWgOBjaIvKrJRVadtykW	20	22	no	20	IziiJCbuEdhkIbIqVMrupHrlZZbxqmlKiWxJqypkhVbZUWkxwLzXhjZOvzi IhxkYkhoxCcGTobBMvlVZoByQXgrrd	21	yes	yes_we_all_have_ids_but_they_are_not_in_our_possession	21	no	yes_we_all_have_birth_certificates_but_they_are_not_in_our_possession	government_of_ukraine	19	yes	refuse_to_answer	refuse_to_answer	yes	yes	yes	1_not_at_all	movement_and_access_constraints psychological_safety	FALSE	TRUE	TRUE	FALSE	FALSE	rAQXmRAmeIGZZSNyRAvelcANZwRFIAlRKHNdfj	mine_clearance_ngo army to_relatives other_specify	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	hHvfOMIYfVyxviWVFutpdPsolWuslwYZdZqqIfoYVNilidPuIZanJcUnwjuevMkaEsJeSwVcQI	diminished_lost_source_of_income limited_access_to_food loss_of_or_severely_diminished_access_to_basic_services loss_of_or_diminished_access_to_education loss_of_or_diminished_access_to_clean_water_and_sanitation sickness_of_household_members death_of_household_members restriction_in_movement	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	ryTgSJopHA	not_leaving_the_house_at_all stopping_handshakes_or_physical_contact keeping_distance_from_people avoiding_public_transport wearing_a_face_mask wearing_gloves washing_hands_more_regularly praying_to_god other_specify dont_know	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	NPSfRokBYOBLUTwOnfyfhtxHgaOjGHJXabeokaYWDVHkZujHYwqnXfngHNQDIrIDfqKhdCzKAGrxULd	yes_covid_related_reason	yes	yes	yes	no	yes	yes	no	no	yes	refuse_to_answer	yes	refuse_to_answer	refuse_to_answer	no	yes	food healthcare seeds_or_other_agricultural_inputs drinking_water hygiene_nfis_eg_soap_sanitary_pads_and_sanitation_services_eg_latrines education_for_children_under_18 other_specify	FALSE	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	TVUhROkSEbtymUphYZFsvvmvQknsCKkdzin xneDZaBifTroAK	dont_know_ns	yes	refuse_to_answer	no	yes	dont_know_ns	dont_want_to_get_information how_to_get_food how_to_get_healthcare_medical_attention how_to_stay_safe_to_prevent_attack_harassment	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	bQXTPNphm WOYxoTxyDdniXNVzBtkeslrLWLglOLZbWXbnDlmlbNKCYMaSbrMfpKt	government_officials		newspapers	Gd	do_not_want_to_receive_humanitarian_assistance inkind_food inkind_nfis physical_cash services_eg_healthcare_education_etc other_please_specify construction_materials	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	RsVlxNaOOFkDjMGhoTWcxQnVJEnicKQlfp KZkCLIFqTjMRvmmVveARGkTmReIznuygMqfmEGHMiIfEoLitYKVrj	dont_know_refuse_to_answer	yes	IxgZDmjqeZYsyxlQEfIXlzNXxZhyxrWnBkfbBvLcvwNxhTSNIwvYYmfOoMVwcFSvQXfUFtSZGwddKHtcaEKURT	UA14	UA14217	UKR1421556600	JP_2	no	12738f49-3b9c-4e4449-939d9e-dcb58192764e
2023-08-16T09:29:31+0200	2023-08-16T10:02:19+0200	deviceID00000010	sievero	slov_004	no	XPYnWZYkbFSGCLHtNmHazWipOBirFnXIoyEAEhgITUWbEEjZguqbBqTAwAqmcZ	yes	NnTVHB	yes	no	female	17	21	female	divorced	no older_person family_with_3_or_more_children chronic_illness other_specify	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	PaiLLqPHJirvQcwvsZD	diabetes_need_insulin diabetes_does_not_need_insulin chronic_respiratory_condition cancer sensory_disorder gastrointestinal_digestive_tract_incl_liver_gallbladder_pancreas_diseases endocrine_system_thyroid_gland_and_other_diseases other_specify	FALSE	FALSE	TRUE	TRUE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	amfSwvXazsJTUPhTHFmmMbWYt wElkaGcwiJsGkCR	retired_not_working	ETMtLrgWCOTG NSZeh LqT	closing_the_enterprise	DXcYqGtJhGrJHHImPR XnFEkPGqORVbXRUcAVBZnCFMPXtOhLOctmGnyotATDIELUDkgLdbVEYflfLCOyyDaXZdxIEPkdlc	agriculture	frOm ZDlCGYTcJythUpGtJCrgnTCEKFfPUAPoL	18	no	refuse_to_answer	refuse_to_answer	yes	primary_education	0	24	19	0	0	female	in_paid_work_employee_self_employed_working_for_family_business	zkeaxtNyLBOgmKbTxAAAQZHlR dBaRMRESDBpeZdhTxGtGsHDgYssEKeoNjGgfMqXHUevolDsVYhlaVIgBTdpc	19	0	0	partially_displaced_and_have_idp_status	2016	LQkDiZKdabKJkL	solid_finished_house	KjOytAdIWpcFnVYZvUEKwJnbCDBThqrykbBxxubvSvUhOpEsCaIVXHqQJcUSwrQM	no_collective_centre	becfOabxHqiXWemEdDfjtGCCqwkIoLDpmmEGIpAGVCedyuphLesguuToteCUa	leaks_during_light_rain_snow presence_of_dirt_or_debris_removable dont_know	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	TRUE	SuUrL nLOwjhDdaEkewcaxRTgIag	none roof_partially_collapsed broken_or_cracked_windows exterior_doors_or_windows_missing large_cracks_openings_in_most_walls some_cracks_in_some_walls some_walls_fully_collapsed foundation_damaged_or_shifted electricity_supply_line_damaged_and_not_functional	TRUE	FALSE	TRUE	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	kSavMdsJyTljirScjInkdKfrdQHdBNQqUGncuhmfTvpNkoZpSu KFRSZofIcvTHugojZQIFoHIf kBdpztbLksNLeaTQS	yes	no	dont_know_ns	yes	23	refuse_to_answer	yes_after_3_months	refigerator functional_stove hh_has_all_items	TRUE	TRUE	FALSE	TRUE	winter_jacket thick_socks warm_gloves adult_warm_clothing child_warm_clothing each_member_has_all_items	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	adult_diapers	TRUE	FALSE	FALSE	FALSE	central_heating gas	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	18	VsptVWLOnAlPrQdSozFWqw ZHxHEnQNRBrVMzIbGWHSsYxaILteOwoTm	16	JFStxhkdIuyJFiKBPNRmG	central_heating gas kerosene wood briquettes_coal other_specify	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	FALSE	TRUE	TRUE	fcxb OJUMBcn	20.31742	24.42986	21.73002	19.48783	refuse_to_answer	yes_daily_every_day	dont_know_ns	personal_well	tTSoNceJcazeGzpQd	do_not_process_purify water_precipitation filtering_the_water_pitcher_filter filtering_the_water_reverse_osmosis_filter dont_know_ns	TRUE	FALSE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	GjYOciNdcHnQaBINSAvMApIsJfIRluKd CGcWTlgsjruUkiJMgodwuRLVBhXhAU	dont_know_ns	rGJsnlZPTJTxXMybsokbwNJMwChrWFNzrNtORKYLA	bottled_water_water_purchased_in_bottles	iUlWqidWiAjJidNxZyy UFLCJFnPzWfvauGEmGWsYEbpmZSHNkqREBsXgpynhqybKbcqcZoFyTR oRzwSitOrEDnTFcQKPPTV	every_day	20	do_not_store_water glass_bottles a_bucket_with_a_lid a_bucket_without_a_lid a_canister we_fill_in_the_bathtub other_specify	TRUE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	TUVWviqFeGmVjTpqskSmedoYUzYxFTJAgIUufgCMbOHMDcouWvWBXEp	drinking	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	pit_latrine_without_a_slab_or_platform	soNfXiPHrZjYObqqkg OrtGOkHnBWvKMwnkGlKET kyMlbkMiHdSlfdjefcodDufT BTnOWISfqNr	yes	refuse_to_answer	5	0	6	7	7	0	2	6	1	7	5	0_no	3_often_10plus_times	1_yes	2_sometimes_3_10	0_no	2_sometimes_3_10	no_but_needed	no_was_not_needed	yes	no_already_done_so_cant_do_it_anymore	no_but_needed	no_already_done_so_cant_do_it_anymore	no_was_not_needed	no_already_done_so_cant_do_it_anymore	no_but_needed	no_but_needed	no_was_not_needed	no_but_needed	boarding_school specialized_school_for_children_with_disabilities vocational_school	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	children_at_home_as_carer children_in_paid_employment children_has_disability_that_school_does_not_have_infrastructure_for	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	mXqRaFFPIPuGPBuOiQWzGmVMLsjEZJCRPqcVqtHnAlrddoh	20	18	17	22	22	18	18	22	18	21	20	21	20	20	16	23	18	21	18	18	19	yes	21	22	15	20	21	17	school_closed_other insufficient_teachers security_concerns_by_parent too_expensive_school_fees_supplies_transport_etc a_child_contributes_to_hh_income refuse_to_answer	FALSE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	imhwevZMNzKVH	1_2_months	no_problems quantity_of_teaching_staff price_for_service conditions_of_the_venue problems_with_distance_learning_due_to_closure_of_school_for_covid_eg_bad_internet_connection other_specify	TRUE	FALSE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	wMxlkkUdMwVjFGLieUPrYbjhUJPcaBngbwluuozYdwc	none shelling military_presence	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	eRhRJmcAthnrxWzbDAEfYshkXtbJTBAEeEShUCmjvFtbuTFslBrsodGmNKsOTgYbjlcupL	none uxos_on_school_grounds shooting_in_the_vicinity_of_the_school military_presence_inside_of_schools	TRUE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	IPYVgwcoXwtYJJihGgMyyVQxCgOnwPDFTBRgDZAmDDXes	yes	no	20	MusJfUwyvBDSLLT gLeXPPbUVKdRMuVireL eHPwpShPS	obstetricgynaecological ambulance outpatient_care x_ray post_trauma_rehabilitation dont_know_ns none_of_listed	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	no	rarely	cost_of_medicine distance_to_facility cost_of_appointment lack_of_referral_system have_to_pass_through_entry_exit_checkpoint_for_medical_facility_in_the_ngca irregular_presence_of_doctors lack_of_doctors insufficient_qualifications_of_doctors	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	jpCyVDBAFheAVHzfJjNeKRFTCUOwfbLeKi uKhHhkdxrgVmFhQJwsakKFywtUYkVFADyWfuBI	no	yes	refuse_to_answer	refuse_to_answer	less_than_15_mins	yes	no_problems items_not_available items_poor_quality distance security other_specify	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	TRUE	ShftXFihwRYVTHPryWSkWMAZnNfMMSwmVogLGujgELHZrwjFxGRZvgSjsTGORRGtnoyrtsusQlcbWjUWxP	meat_or_fish_chicken_pork_beef_fish pulses_nuts_seeds_lentils_beans_nuts oil_and_fat_salo_butter_sunflower_oil condiments_and_spices_tea_coffee_spices	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	FALSE	bYmXNNtPDFdIdjQrbDuSC	21	20	dont_know_ns	19	rhimMyps gYk	24	dont_want_to_say	no_some_hh_members_are_missing_ids	17	no	yes_in_our_possession	authorities_in_non_government_controlled_area	19	no	refuse_to_answer	yes	yes	refuse_to_answer	yes	2_i_am_aware_of_mines_but_it_doesnt_affect_my_life	fear_for_physical_safety has_affected_hh_budget other_specify	TRUE	FALSE	FALSE	TRUE	TRUE	jHpWuwZjtgKN PabJmzIUQyuOkMllCfmlQIfKniWZPtubrlLuUn	administration_head_of_the_village) army other_specify	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	MhtsSYnkRqtKsmbWKfOMldEgxEimuXzeYoPDXlxbMelXlHbeFHOMBRYuzqMwjJjczIzHRRurzAeTlfYLWQdzVDh	limited_access_to_food loss_of_or_severely_diminished_access_to_basic_services loss_of_or_diminished_access_to_clean_water_and_sanitation other_specify no_covid19_impacts_on_my_household restriction_in_movement breaking_up_with_family_or_relatives psychological_deterioration	FALSE	TRUE	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	XKKlfSSECfaiXMctGosAwSqfITDFFWvRgaCLzKfwYdzPPL ZXJGIYzLbLzr LCuIKqkHtClxb BHDoTALHWvixgr	not_leaving_the_house_at_all stopping_handshakes_or_physical_contact keeping_distance_from_people wearing_gloves keeping_surfaces_clean staying_away_from_animals other_specify	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	TRUE	TRUE	FALSE	KdvIZotMl	yes_covid_related_reason	refuse_to_answer	refuse_to_answer	refuse_to_answer	refuse_to_answer	refuse_to_answer	yes	refuse_to_answer	yes	yes	no	no	refuse_to_answer	refuse_to_answer	no	yes	food healthcare seeds_or_other_agricultural_inputs livelihoods_support_employment drinking_water hygiene_nfis_eg_soap_sanitary_pads_and_sanitation_services_eg_latrines fuel_for_heating none other_specify house_repairs	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	QzZyfwbrWddxHAwotiYcYSCARMGniPQezrSIy	yes	refuse_to_answer	dont_know_ns	refuse_to_answer	no	yes	how_to_stay_safe_to_prevent_attack_harassment how_to_access_education other	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	mQutfUcmDGGPuaSKEvBGsWYwGtEoEMAHBWCjlvDpYOfqeEsEyFvmpgesNTJFWHzFJHkgQDaFoJlAviXyzfn	other	CSDAdOGIBBoCYLcVODHudkoaTCcKTlzXKKpAPPfafFUVqxPUkihvMKWCKqqZntvKRNsQccLfZAKdzkZjfJUSHVySu aniZtDXpnn	film_or_theatre	vmZKtmAyqEiIaWwFpcHH	inkind_nfis cash_via_prepaid_cards cash_via_mobile_money vouchers services_eg_healthcare_education_etc other_please_specify dont_know	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	TRUE	TRUE	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	JTmelNcYRRqItfpuqjRZiNqCEFwykyQhL ExLWoXAiQkVDWvslD Ze	yes_soap_is_shown	refuse_to_answer	vtTIBuKnmRzsRTXMvVYlJlocWXujIdlLUDJPpLKRBknyRS	UA14	UA14123	UKR1423385404	AK_3	no	e93b068c-7069-424c40-808f88-fae429b73cd5

5.2 Download data

5.2.1 Downloading Audit files using audit_URL

Required Libraries: httr

“download_audit_files” will download audit files using the endpoint that is included in your data set, it’s available in column called “audit_URL”. The function will create a folder named by the uuid of each form/interview, and write the audit.csv inside it. The advantage of using the API over downloading manually from server is that you can download the audit files of each form that has recently been submitted to the server separately, rather than downloading cumulatively the whole audit files each time. One the top of that, you can benefit from the advantage of automation.

download_audit_files <- function(df, uuid_column = "_uuid", audit_dir, usr, pass){
  if (!"httr" %in% installed.packages()) 
    stop("The package is httr is required!")
  
  if (is.na(audit_dir) || audit_dir == "") 
    stop("The path for storing audit files can't be empty!")
  
  if (is.na(usr) || usr == "") 
    stop("Username can't be empty!")
  
  if (is.na(pass) || pass == "") 
    stop("Password can't be empty!")
  
  # checking if the output directory is already available
  if (!dir.exists(audit_dir)) {
    dir.create(audit_dir)
    if (dir.exists(audit_dir)) {
      cat("Attention: The audit file directory was created in", audit_dir,"\n")
    }
  }
  
  # checking if creating output directory was successful
  if (!dir.exists(audit_dir))
    stop("download_audit_fils was not able to create the output directory!")
  # checking if uuid column exists in data set
  if (!uuid_column %in% names(df))
    stop("The column ", uuid_column, " is not available in data set.")
  # checking if column audit_URL exists in data set
  if (!uuid_column %in% names(df))
    stop("The column ", uuid_column, " is not available in data set.")
  if (!"audit_URL" %in% names(df))
    stop("Error: the column audit_URL is not available in data set.")
  
  # getting the list of uuids that are already downloaded
  available_audits <- dir(audit_dir)
  
  # excluding uuids that their audit files are already downloaded
  df <- df[!df[[uuid_column]] %in% available_audits,]
  
  audits_endpoint_link <- df[["audit_URL"]]
  names(audits_endpoint_link) <- df[[uuid_column]]
  audits_endpoint_link <- na.omit(audits_endpoint_link)
  
  if (length(audits_endpoint_link) > 0) {
    # iterating over each audit endpoint from data
    for (i in 1:length(audits_endpoint_link)) {
      uuid = names(audits_endpoint_link[i])
      endpoint_link_i <- audits_endpoint_link[i]
      cat("Downloading audit file for", uuid, "\n")
      
      # requesting data
      audit_file <- content(GET(endpoint_link_i,
                                authenticate(usr, pass),
                                timeout(1000),
                                progress()), "text", encoding = "UTF-8")
      
      if (!is.na(audit_file)) {
        if (length(audit_file) > 2) {
          dir.create(paste0(audit_dir, "/", uuid), showWarnings = F)
          write.csv(audit_file, paste0(audit_dir, "/", uuid, "/audit.csv"), row.names = F)
        }else if(!audit_file == "Attachment not found"){
          if (grepl("[eventnodestartend]", audit_file)) {
            dir.create(paste0(audit_dir, "/", uuid), showWarnings = F)
            write.table(audit_file, paste0(audit_dir, "/", uuid, "/audit.csv"), row.names = F, col.names = FALSE, quote = F)
          } else{
            cat("Error: Downloading audit was unsucessful!\n")
          }
        }
      } else{
        cat("Error: Downloading audit was unsucessful!\n")
      }
    }
  } else{
    cat("Attention: All audit files for given data set is downloaded!")
  }
}

5.2.2 Download audit files using form name

Required Libraries: httr, jsonlite, dplyr, stringr

“download_audit_files_2” is a different version of “download_audit_files” module that will help you to download audit files using the form id directly from KOBO API. The rest of it’s specifications best matches the download_audit_files.

download_audit_files_2 <- function(form_id, output_dir, user, pass) {
  if (!"stringr" %in% installed.packages()) 
    stop("Could not find the package stringr!")
  
  if (!"dplyr" %in% installed.packages()) 
    stop("Could not find the package dplyr!")
  
  if (!"httr" %in% installed.packages()) 
    stop("Could not find the package httr!")
  
  if (!"jsonlite" %in% installed.packages()) 
    stop("Could not find the package jsonlite!")
  
  if (is.na(output_dir) || output_dir == "") 
    stop("The path for storing audit files can't be empty!")
  
  if (is.na(user) || user == "") 
    stop("Username can't be empty!")
  
  if (is.na(pass) || pass == "") 
    stop("Password can't be empty!")
  
  require(httr)
  require(dplyr)
  require(jsonlite)
  require(stringr)
  
  # checking if the output directory is already available
  if (!dir.exists(output_dir)) {
    dir.create(output_dir)
    if (dir.exists(output_dir)) {
      cat("Attention: The audit file directory was created in", output_dir,"\n")
    }
  }
  
  # checking if creating output directory was successful
  if (!dir.exists(output_dir))
    stop("download_audit_fils was not able to create the output directory!")
  
  # listing audit files that are already available
  available_audits <- dir(output_dir)
  
  # getting form url
  form_url <- GET("https://kc.humanitarianresponse.info/api/v1/data", authenticate(user, pass), timeout(1000)) %>% 
    content(., "text", encoding = "UTF-8") %>%
    fromJSON(., flatten = T) %>% 
    filter(id_string == form_id) %>% 
    select(url) %>% 
    unlist()
  
  if (length(form_url) > 0) {
    
    # getting attachment links of uuids that their audit is already downloaded
    form_data <- GET(form_url, authenticate(user, pass), timeout(1000)) %>% 
      content(., "text", encoding = "UTF-8") %>%
      fromJSON(., flatten = T) %>% 
      as.data.frame() %>% 
      filter(!`_uuid` %in% available_audits)
    
    attachment_url <- form_data[["_attachments"]]
    audit_file_links <- unlist(attachment_url)[names(unlist(attachment_url)) == "download_large_url"] %>% unname()
    
    # uuids <- form_data[["_uuid"]][lapply(attachment_url, length) %>% unlist() != 0]
    # uuids <- str_extract(audit_file_links, "(?<=%2F)[a-z-0-9]*(?=%2Faudit.csv$)")
    # names(audit_file_links) <- uuids
    
    download_audit <- function(audit_link, user, pass, output_dir, uuid) {
      uuid <- str_extract(audit_link, "(?<=%2F)[a-z-0-9]*(?=%2Faudit.csv$)")
      audit_file <- GET(audit_link, authenticate(user, pass), timeout(1000), progress()) %>% 
        content(., "text", encoding = "UTF-8")
      
      cat("Downloading audit file for", uuid, "\n")
      dir.create(paste0(output_dir, "/", uuid))
      output_file_name <- paste0(output_dir, "/", uuid,"/audit.csv")
      # write.csv(audit_file, output_file_name)
      
      if (!is.na(audit_file)) {
        if (length(audit_file) > 2) {
          write.csv(audit_file, output_file_name, row.names = F)
        }else if(!audit_file == "Attachment not found"){
          if (grepl("[eventnodestartend]", audit_file)) {
            write.table(audit_file, output_file_name, row.names = F, col.names = FALSE, quote = F)
          }
        }
      }
    }
    
    downloaded_uuids <- sapply(audit_file_links, download_audit, user = user, pass = pass, output_dir = output_dir)
  } else{
    cat("Attention: All audit files for given form is downloaded!")
  }
}

5.3 Data collection follow-up

5.3.1 checking surveys against the sampling frame

It is important to ensure that number of interviews that have been collected in each sampling unit corresponds with the initial sampling frame. The best choice will be to check it on daily basis during the data collection and follow-up with the Field Team in case there were not enough interviews (or some extra surveys) in certain locations. But it is also a good practice to make a final check to understand the overall difference between the sampling frame and collected data.

First let’s open initial sampling frame generated by the Probability sampling tool

sampling_frame <- read.csv("inputs/sampling_frame20200701-132150.csv")

Now we should create summary table that will show number of interviews actually collected for each primary sampling unit (in our case it was settlement). Keep in mind that you should use correct settlement codes that were verified during the data collection. In case you are not sure that settlement codes in the dataset are correct you should cross-check them with GPS-coordinates using Spatial verification checks

samples_collected <- main_dataset %>%
                    group_by(r3_current_settlement)%>%
                    count()

The next step will be left join of samples collected dataset to the sampling frame and finding the difference in collected surveys for each settlement.

sampling_frame <- sampling_frame %>%
  select(Survey, strata_id, adm4Pcode, adm4NameLat) %>%
  left_join(samples_collected, by = c("adm4Pcode" = "r3_current_settlement"))%>%
  tidyr::replace_na(list(n = 0))%>%
  mutate(sample_difference = n - Survey)%>%
  arrange(sample_difference)

sampling_frame %>% 
      head(10) %>%
      kable() %>% 
      kable_styling(font_size=12)

Survey	strata_id	adm4Pcode	adm4NameLat	n	sample_difference
1	5km_rural	UKR1421586403	Tarasivka	0	-1
56	20km_urban	UKR1410200000	Avdiivka	56	0
9	20km_urban	UKR1420955200	Zaitseve	9	0
5	5km_rural	UKR1423384001	Kostiantynivka	5	0
3	5km_rural	UKR1422783201	Mykhailivka	3	0
7	5km_rural	UKR1421581401	Dmytrivka	7	0
6	5km_rural	UKR4423180701	Bakhmutivka	6	0
4	5km_rural	UKR4424881001	Velyka Chernihivka	4	0
8	5km_rural	UKR1420980501	Berestove	8	0
1	5km_rural	UKR1420982001	Volodymyrivka	1	0

As we can see from the table above, there was only one settlement where the target number of the interviews was not collected. And considering that it’s only one survey it will not affect our results and we can proceed with other checks. After checking how a number of collected surveys corresponds with the sampling frame for each primary sampling unit it will be also good to make the same check on strata level. To do that we can use summarise function from dplyr library

strata_level_check <- sampling_frame %>%
                      group_by(strata_id)%>%
                      summarise(Survey_Planned = sum(Survey),
                                Survey_Collected = sum(n))%>%
                      mutate(Difference = Survey_Collected - Survey_Planned)%>%
                      arrange(desc(Difference))

strata_level_check %>% 
      kable() %>% 
      kable_styling(font_size=12)

strata_id	Survey_Planned	Survey_Collected	Difference
5km_rural	403	407	4
20km_rural	399	402	3
20km_urban	404	404	0
5km_urban	404	404	0

After this check, we can see that for two strata we collected the exact number of interviews that were initially planned. And for two strata there is slight overachievement in 4 and 3 surveys.

5.4 Data falsification

5.4.1 Check for time (in the dataset)

Required Libraries: “dplyr” and “lubridate”

“time_check” will return the elapsed time for each interview based on it’s start and end columns also classifies if it’s “too short”, “too long”, or “okay”. As an example, we will use a dummy data set to apply the function to it. The function also needs a time_min (the minimum time in minutes that an interview should take to be completed) and a time_max (the maximum time in minutes that an interview should take to be completed) parameters.

library(dplyr)
library(lubridate)

# Creating a dummy data set
start <- c("2020-12-01T09:40:05.750+04:30","2020-12-01T09:40:18.709+04:30","2020-12-01T09:45:40.879+04:30","2020-12-01T09:46:28.328+05:00")
end <- c("2020-12-01T09:44:44.438+04:30","2020-12-01T10:01:27.890+04:30","2020-12-01T10:17:44.021+04:30","2020-12-01T10:18:32.717+05:00")

my_df <- data.frame(start, end)

# Initializing variables
time_min <- 8
time_max <- 30

# declaring the function
time_check <- function(df, time_min, time_max){
  df <- df %>% mutate(interview_duration = difftime(as.POSIXct(ymd_hms(end)), as.POSIXct(ymd_hms(start)), units = "mins"),
                      CHECK_interview_duration = case_when(
                        interview_duration < time_min ~ "Too short",
                        interview_duration > time_max ~ "Too long",
                        TRUE ~ "Okay"
                      )
  )
  return(df)
}

# Applying the function to data frame
processed_df <- time_check(my_df, time_min, time_max)

processed_df

start	end	interview_duration	CHECK_interview_duration
2020-12-01T09:40:05.750+04:30	2020-12-01T09:44:44.438+04:30	4.64480 mins	Too short
2020-12-01T09:40:18.709+04:30	2020-12-01T10:01:27.890+04:30	21.15302 mins	Okay
2020-12-01T09:45:40.879+04:30	2020-12-01T10:17:44.021+04:30	32.05237 mins	Too long
2020-12-01T09:46:28.328+05:00	2020-12-01T10:18:32.717+05:00	32.07315 mins	Too long

5.4.2 Check for time (audit files)

Required Libraries: “dplyr”, “lubridate”, and “expss”

“time_check_audit” will calculate the interview duration using the audit files. And, if the audit file for that particular uuid is not found in the audit directory (where you paste the audit files), it will calculate it using start and end time columns in the data set. Audit files should be stored inside a folder (to avoid redefining its name while calling the function, call it “audit_files”) in the project folder.

library(dplyr)
library(lubridate)
library(expss)

# reading files.
main_dataset <- read.csv("inputs/UKR2007_MSNA20_HH_dataset_main_rcop.csv", na.strings = "")

# Initializing variables
time_min <- 8
time_max <- 30


# declaring the function
time_check_audit <- function(df_raw, x_uuid="_uuid", time_min, time_max, audit_dir_path = "./audit_files/", today = "today"){
  if (!any(duplicated(df_raw[[x_uuid]]))) {
    # Audit Checks
    # audit_dir_path < -audit_dir_path
    uuids <- dir(audit_dir_path)
    dfl <- list()
    all_uuids <- length(uuids)

    for(i in 1: length(uuids)){
      df <- read.csv(paste0(audit_dir_path, uuids[i], "/audit.csv"))
      df <- df %>% filter(node != "")
      duration_ms <- sum(df$end - df$start)
      duration_secs <-duration_ms/1000
      duration_minutes <- round(duration_secs/60,1)
      dfl[[i]] <- data.frame(uuid = uuids[i], duration_ms=duration_ms, 
                             durations_secs=duration_secs, duration_minutes = duration_minutes)
      cat("\014","Running audit: ", round((i/all_uuids) * 100,0),"%\n", sep = "")
    }
    duration_df <- do.call("rbind", dfl)
    duration_df <- dplyr::rename(duration_df, `_uuid` = uuid)
    
    #time check based on start end time
    df_str_audit_all <- df_raw %>% 
      mutate(start_end = difftime(as.POSIXct(ymd_hms(end)), as.POSIXct(ymd_hms(start)), units = "mins")) 
    
    #creating a binding column with same name.
    df_str_audit_all$`_uuid` <- df_str_audit_all[[x_uuid]]

    # Join Audit checks and main data set
    df_str_audit_all <- df_str_audit_all %>%
      left_join(select(duration_df, `_uuid`, duration_minutes), by =  "_uuid")
    
    # Checking time duration with audit file, if not available, from dataset start/end.
    df_str_audit_all <- df_str_audit_all %>%
      mutate(interview_duration = if_na(duration_minutes, start_end),
             CHECK_interview_duration = case_when(
               interview_duration < time_min ~ "Too short",
               interview_duration > time_max ~ "Too long",
               TRUE ~ "Okay")
      ) %>% select( -c(duration_minutes,start_end))
    
    return(df_str_audit_all)
  }else{
    stop("Error: df_raw has duplicate in uuid column, resolve the duplication to proceed!")
  }
}

# Applying the function to data frame
processed_df <- time_check_audit(main_dataset, 
                                 x_uuid = "X_uuid",
                                 time_min, time_max, 
                                 audit_dir_path = "inputs/reach_global/attachments/dc4b0f40bf934293aedd3f31ff43f6d1/")

processed_df %>%
  select(`_uuid`, interview_duration, CHECK_interview_duration) %>% 
  head(10)

_uuid	interview_duration	CHECK_interview_duration
e88800ab-c514-4074-b938-b618ec874255	14.3	Okay
15aa02bd-ce32-4cd3-afcb-6d590db3baa4	14.8	Okay
1a8f5939-fc1e-43f8-ac75-86fb6de3ae4c	14.4	Okay
ed87c6df-d78d-47bc-92dc-27d478073a53	18.2	Okay
df76de17-ffda-4f11-9cb7-6342f3d49940	15.6	Okay
e6f50033-ec7a-4016-850d-8be2c0302324	24.3	Okay
c9f30357-05bc-4228-8223-e1143b5293c8	48.4	Too long
2d5b8a48-52fa-45a6-b776-fa32242abf0d	19.5	Okay
022a6393-6d31-4958-926c-b5955aa4e215	18.5	Okay
98bb4c67-2bda-4848-9b19-b960a8506157	22.9	Okay

5.4.3 Check for time - calculating the elapsed time between each interview

Required Libraries: lubridate

“time_btwn_ints” will calculate the elapsed time between the ending time of the first interview of an enumerator and the start time of its second interview, and the process applies for all interviews of each enumerator. It needs a location identifier as a parameter to check if the elapsed time is matching with the threshold (given as a parameter) in the same location or not. Also it will be checked if the elapsed time matches the given threshold for interviews in different locations by the same enumerator.

library(lubridate)

# Creating a dummy data set
start <-  c("2020-12-01T09:40:05.750+04:30",
            "2020-12-01T09:45:18.709+04:30",
            "2020-12-01T09:45:40.879+04:30",
            "2020-12-01T10:02:40.879+04:30",
            "2020-12-01T10:25:28.32+04:30")
end <-  c("2020-12-01T09:44:44.438+04:30",
          "2020-12-01T10:01:27.890+04:30",
          "2020-12-01T10:17:44.021+04:30",
          "2020-12-01T10:05:40.879+04:30",
          "2020-12-01T10:38:32.717+04:30")
device_id <- c("000215",
               "000215",
               "000216",
               "000215",
               "000216")
village <- c("Village A",
             "Village A",
             "Village B",
             "Village A",
             "Village C")

my_df <- data.frame(start, end, device_id, village)

# declaring the function
time_btwn_ints <- function(df, device_id, start_col = "start", end_col = "end", 
                           village_col, same_village_threshold = 3, diff_village_threshold = 5){
  checked_df <- df
  
  # sort by device_id and start_col
  checked_df <- checked_df[order(checked_df[[start_col]]), ]
  checked_df <- checked_df[order(checked_df[[device_id]]), ]
  
  # For each row starting from the second row:
  # 1) calculate the time between the end of the (r-1) survey and the start of the (r) survey
  # 2) insert the eight check-message based on the calculated time and the village
  issue.same.village <- paste0("The elapsed time between two interviews in the same village is less than ",same_village_threshold, " minutes")
  issue.diff.village <- paste0("The elapsed time between two interviews in different villages is less than ", diff_village_threshold, " minutes")
  checked_df$check <- "OK"
  checked_df$gap_between_ints <- NA
  for (r in 2:nrow(checked_df)){
    if (as.character(checked_df[r, device_id])==as.character(checked_df[r-1, device_id])){
      checked_df$gap_between_ints[r] <- difftime(as.POSIXct(ymd_hms(checked_df[r, start_col])),
                                                 as.POSIXct(ymd_hms(checked_df[r-1, end_col])),
                                                 units = "mins")
      
      if (as.character(checked_df[r, village_col])==as.character(checked_df[r-1, village_col])){
        if (checked_df$gap_between_ints[r] < same_village_threshold) checked_df[r, "check"] <- issue.same.village
      } else{
        if (checked_df$gap_between_ints[r] < diff_village_threshold) checked_df[r, "check"] <- issue.diff.village
      }
    }
  }
  
  return(checked_df)
}

# Applying the function to data frame
processed_df <- time_btwn_ints(df = my_df, device_id = "device_id",village_col = "village", same_village_threshold = 2,diff_village_threshold = 10)

processed_df

	start	end	device_id	village	check	gap_between_ints
1	2020-12-01T09:40:05.750+04:30	2020-12-01T09:44:44.438+04:30	000215	Village A	OK	NA
2	2020-12-01T09:45:18.709+04:30	2020-12-01T10:01:27.890+04:30	000215	Village A	The elapsed time between two interviews in the same village is less than 2 minutes	0.5711833
4	2020-12-01T10:02:40.879+04:30	2020-12-01T10:05:40.879+04:30	000215	Village A	The elapsed time between two interviews in the same village is less than 2 minutes	1.2164833
3	2020-12-01T09:45:40.879+04:30	2020-12-01T10:17:44.021+04:30	000216	Village B	OK	NA
5	2020-12-01T10:25:28.32+04:30	2020-12-01T10:38:32.717+04:30	000216	Village C	The elapsed time between two interviews in different villages is less than 10 minutes	7.7383167

5.4.4 Silouhette analysis

Custom script (using ???)

5.4.5 Check for duplicates

5.4.5.1 cleaninginspectoR - find_duplicates

find_duplicates will take the dataset and a column name to look for duplicates as arguments.

cleaninginspectoR::find_duplicates(main_dataset, duplicate.column.name = "X_uuid")

## [1] index      value      variable   has_issue  issue_type
## <0 rows> (or 0-length row.names)

dummy_dataset[301, ] <- dummy_dataset[300, ]
cleaninginspectoR::find_duplicates(dummy_dataset, duplicate.column.name = "uuid")

##   index                                    value variable has_issue
## 1   301 91e2b6c3-3124-4f4341-afa7a5-78dcaebf6139     uuid      TRUE
##          issue_type
## 1 duplicate in uuid

cleaninginspectoR::find_duplicates(dummy_dataset, duplicate.column.name = "start")

##   index                    value variable has_issue         issue_type
## 1   202 2023-08-16T08:19:48+0200    start      TRUE duplicate in start
## 2   204 2023-08-16T10:00:50+0200    start      TRUE duplicate in start
## 3   235 2023-08-16T08:29:20+0200    start      TRUE duplicate in start
## 4   247 2023-08-16T09:46:06+0200    start      TRUE duplicate in start
## 5   265 2023-08-16T08:56:56+0200    start      TRUE duplicate in start
## 6   293 2023-08-16T10:38:17+0200    start      TRUE duplicate in start
## 7   301 2023-08-16T10:12:09+0200    start      TRUE duplicate in start

5.4.5.2 base - duplicated

If you are looking in duplicates value in several columns (first and second name, names and ID number,etc.), you can use the duplicated.

dummy_test <- data.frame(col_a = c("a", "a", "c"), col_b = c("b", "b", "f"))
dummy_test

##   col_a col_b
## 1     a     b
## 2     a     b
## 3     c     f

Rows 1 and 2 are duplications.

duplicated(dummy_test)

## [1] FALSE  TRUE FALSE

find_duplicates() and duplicated() functions will return position or value only of one duplicated record. But after identification of the duplicate, it will be good to check how many of such duplicated records in the dataset and check if they have any other duplicated columns. Based on your investigation, you will need to delete one or several records. e.g. Enumerator submitted the first survey by mistake and after some time submitted a corrected survey with the same id (in case we allow for the enumerator to select the id of the enterprise or sample). In such a way, find_duplicates() will identify the second survey but we will need to delete the first one.

5.4.5.3 Find most similar surveys

The function find_similar_surveys() compares each survey with each other survey in the dataset and finds the most similar one, i.e., the one with the lowest number of different answers. The function uses the gower matrix to make the comparison more efficient. The function returns a dataframe with the same number of rows (all surveys) and a few additional columns indicating the ID of the most similar survey and how many columns are different.

Depending on the size of the questionnaire and on the data collection methodology, we can set a maximum threshold on the number of differences and follow up on all the surveys that have a matching survey with a lower number of differences than the threshold. For example, in the MSNA in Syria, we used a maximum threshold of 7 differences.

In the version below, the function uses only the data from the main sheet of the survey. If the tools includes loop(s), it makes sense to add a few relevant columns from the loops to the main dataframe so that they are also used in the search of the most similar survey. For example, for an HH survey with a loop for the HH composition, one can add to the main dataframe one column with the concatenation of the genders of the HH components and one column with the concatenation of the ages of the HH components (from the loop).

find_similar_surveys <- function(raw.data, tool.survey, uuid="_uuid"){
  # 1) store UUIDs
  uuids <- raw.data[[uuid]]
  
  # 2) convert all columns to character and tolower
  raw.data <- mutate_all(raw.data, as.character)
  raw.data <- mutate_all(raw.data, tolower)
  
  # 3) remove columns that are naturally different in each survey:
  # - columns of type = "start", "end", "text" (for the other responses), etc.
  # - columns starting with "_"
  # - option columns for the select multiple -> keeping only the concatenation column
  types_to_remove <- c("start", "end", "today", "deviceid", "date", "geopoint", "audit", 
                       "note", "calculate", "text")
  cols_to_keep <- data.frame(column=colnames(raw.data)) %>% 
    left_join(select(tool.survey, name, type), by=c("column"="name")) %>% 
    filter(!(type %in% types_to_remove) & !str_starts(column, "_") & !str_detect(column, "/"))
  raw.data <- raw.data[, all_of(cols$column)]
  
  # 4) remove columns with all NA; convert remaining NA to "NA"; convert all columns to factor
  raw.data <- raw.data[, colSums(is.na(raw.data))<nrow(raw.data)]
  raw.data[is.na(raw.data)] <- "NA"
  raw.data <- raw.data %>% mutate_if(is.character, factor)
  error.message <- "NAs detected, remove them before proceeding (it can happen when converting to factor)"
  if (sum(is.na(raw.data))>0) stop(error.message)
  
  # 5) calculate gower distance
  gower_dist <- daisy(raw.data, metric="gower", warnBin=F, warnAsym=F, warnConst=F)
  gower_mat <- as.matrix(gower_dist)
  
  # 6) convert distance to number of differences and determine closest matching survey
  r <- unlist(lapply(1:nrow(raw.data), function(i) sort(gower_mat[i,]*ncol(raw.data))[2]))
  
  # 7) add relevant columns
  raw.data[["num_cols_not_NA"]] <- rowSums(raw.data!="NA")
  raw.data[[uuid]] <- uuids
  raw.data[["_id_most_similar_survey"]] <- uuids[as.numeric(names(r))]
  raw.data[["number_different_columns"]] <- as.numeric(r)
  raw.data <- raw.data %>% arrange(number_different_columns, uuid)
  
  return(raw.data)
}

5.5 Data checks

5.5.1 Check for outliers

There are 2 commons ways to detect outliers :

Using the range of 3 standards deviations from the mean.
Using the range of 1.5 inter quartile from the 1st and 3rd quartile.

Outliers can exist but it is important to check them.

5.5.1.1 cleaninginspectoR - find_outliers

The function find_outliers will use the rule of the 3 standards deviations from the mean for normal values and log values.

cleaning_log <- cleaninginspectoR::find_outliers(main_dataset)

cleaning_log %>% head(20)

index	value	variable	has_issue	issue_type
26	30000.0	b15_hohh_income	TRUE	log normal distribution outlier
117	210.0	b15_hohh_income	TRUE	log normal distribution outlier
225	400.0	b15_hohh_income	TRUE	log normal distribution outlier
384	300.0	b15_hohh_income	TRUE	log normal distribution outlier
517	300.0	b15_hohh_income	TRUE	log normal distribution outlier
710	24000.0	b15_hohh_income	TRUE	log normal distribution outlier
742	400.0	b15_hohh_income	TRUE	log normal distribution outlier
1126	50000.0	b15_hohh_income	TRUE	log normal distribution outlier
1384	400.0	b15_hohh_income	TRUE	log normal distribution outlier
1399	200.0	b15_hohh_income	TRUE	log normal distribution outlier
1417	400.0	b15_hohh_income	TRUE	log normal distribution outlier
5	20.0	f2_spend_on_utilities	TRUE	log normal distribution outlier
89	5000.0	f2_spend_on_utilities	TRUE	log normal distribution outlier
272	20.0	f2_spend_on_utilities	TRUE	log normal distribution outlier
918	20.0	f2_spend_on_utilities	TRUE	log normal distribution outlier
141	4800.0	f3_spend_on_heating	TRUE	normal distribution outlier
48	0.5	f5_type_fuel_heating_amount	TRUE	log normal distribution outlier
357	0.5	f5_type_fuel_heating_amount	TRUE	log normal distribution outlier
161	90.0	f16_duration_water_shortages	TRUE	log normal distribution outlier
1392	90.0	f16_duration_water_shortages	TRUE	log normal distribution outlier

5.5.2 Check others

5.5.2.1 cleaninginspectoR - find_other_responses

find_other_responses will look for all columns with “other”, “Other”, “autre”, “Autre”, and return their values.

Notes: - If your other* questions do not have those 4 strings in their names, the function will not pick it. *

cleaning_log <- cleaninginspectoR::find_other_responses(main_dataset)

cleaning_log %>% head(10)

index	value	variable	has_issue	issue_type
NA	“Child of war” status /// instances: 1	b10_1_hohh_vulnerability_other	TRUE	‘other’ response. may need recoding.
NA	“Child of war” status, “Veteran of labour” status /// instances: 1	b10_1_hohh_vulnerability_other	TRUE	‘other’ response. may need recoding.
NA	A carer for a disabled child /// instances: 1	b10_1_hohh_vulnerability_other	TRUE	‘other’ response. may need recoding.
NA	A carer for a disabled person /// instances: 1	b10_1_hohh_vulnerability_other	TRUE	‘other’ response. may need recoding.
NA	Chernobyl liquidator /// instances: 3	b10_1_hohh_vulnerability_other	TRUE	‘other’ response. may need recoding.
NA	Child custody /// instances: 1	b10_1_hohh_vulnerability_other	TRUE	‘other’ response. may need recoding.
NA	Deprived orphan /// instances: 1	b10_1_hohh_vulnerability_other	TRUE	‘other’ response. may need recoding.
NA	Family with a child with disability /// instances: 1	b10_1_hohh_vulnerability_other	TRUE	‘other’ response. may need recoding.
NA	Has a status of the victim of human trafficing /// instances: 1	b10_1_hohh_vulnerability_other	TRUE	‘other’ response. may need recoding.
NA	Husband has disability group 3 /// instances: 1	b10_1_hohh_vulnerability_other	TRUE	‘other’ response. may need recoding.

5.5.2.2 base + dplyr

This example will take all the text type from the questionnaire, filter for the ones that are in the dataset and return all the values.

oth <- questions$name[questions$type == "text"]
oth <- oth[oth %in% names(main_dataset)]
oth_log <- oth %>% 
  lapply(function(x) {
    main_dataset %>% 
      select("X_uuid", !!sym(x)) %>% 
      filter(!is.na(!!sym(x))) %>%
      as.data.frame() %>% 
      mutate(col_names = x) %>%
      rename(other_text = !!sym(x)) %>%
      arrange(other_text)}) %>% 
  do.call(rbind,.)

oth_log %>% head(20)

X_uuid	other_text	col_names
54e2b9b9-f3c4-477c-bd49-778183cae303	“Child of war” status	b10_1_hohh_vulnerability_other
7b8e753b-1aa6-493a-81c8-9c7caee74613	“Child of war” status, “Veteran of labour” status	b10_1_hohh_vulnerability_other
8e2a77cc-a81a-4319-8595-02dae93f0bd3	A carer for a disabled child	b10_1_hohh_vulnerability_other
2c08b929-63f0-42bc-80d1-ce87a0373787	A carer for a disabled person	b10_1_hohh_vulnerability_other
b8e0bb37-b2eb-439e-bc5b-03c12c87c7f6	Chernobyl liquidator	b10_1_hohh_vulnerability_other
0250339d-7a9c-4cd1-a72d-2fb5f9ec8439	Chernobyl liquidator	b10_1_hohh_vulnerability_other
83f40ea5-6882-4e46-824e-fcc7f734cc78	Chernobyl liquidator	b10_1_hohh_vulnerability_other
1113ae1b-80a3-41ce-951f-74ea18719bde	Child custody	b10_1_hohh_vulnerability_other
64285d7a-51fc-4256-b089-1a13d4521e97	Deprived orphan	b10_1_hohh_vulnerability_other
5ddae31b-90d1-4c96-a991-1958ff14719c	Family with a child with disability	b10_1_hohh_vulnerability_other
69615c4d-8fac-404e-bb45-fafccd498d92	Has a status of the victim of human trafficing	b10_1_hohh_vulnerability_other
2d5b8a48-52fa-45a6-b776-fa32242abf0d	Husband has disability group 3	b10_1_hohh_vulnerability_other
aad51ad7-7ea6-4589-bf06-ee8b53781303	Low-income family	b10_1_hohh_vulnerability_other
1d21d712-f98d-4cbc-af52-710f535ef2bd	Low-income family	b10_1_hohh_vulnerability_other
acbb66b9-31c5-44f7-84d4-53553aaa2f8c	Low-income family	b10_1_hohh_vulnerability_other
075ec034-4681-4d59-bce7-403dffb3c03c	Low-income family	b10_1_hohh_vulnerability_other
a4e91a4d-c176-43b2-ab2f-1953a1780bec	Own apartment destroyed, rented housing	b10_1_hohh_vulnerability_other
b98ef6c0-39df-4383-96e7-5f569f6fdc33	Pensioner because takes care of the sun, who has born disability (currently 33 y.o., 2 level of disability, lives in another HH)	b10_1_hohh_vulnerability_other
2b275831-e4d6-4b9b-8192-c58bcc13bcba	Raises a nephew	b10_1_hohh_vulnerability_other
956f170d-2bf0-40bf-848e-f8129aecd0fd	Was injured during the conflict	b10_1_hohh_vulnerability_other

Please note that it takes the values as they are. You may want to trim and remove caps or any other regex work if you want better summary

This other example looks at the frequency of a given other option, it could be used to see if some should be recoded as options directly.

oth_prop <- oth %>% 
  lapply(function(x) {
    main_dataset %>% 
      group_by(!!sym(x)) %>% 
      tally(sort = T) %>% 
      rename(other_text = !!sym(x)) %>% 
      filter(!is.na(other_text)) %>%
      mutate(col_names = x, 
             prop = n/nrow(main_dataset))
    }) %>% 
  do.call(rbind,.)

oth_prop %>% head(20)

other_text	n	col_names	prop
Low-income family	4	b10_1_hohh_vulnerability_other	0.0024737
Chernobyl liquidator	3	b10_1_hohh_vulnerability_other	0.0018553
“Child of war” status	1	b10_1_hohh_vulnerability_other	0.0006184
“Child of war” status, “Veteran of labour” status	1	b10_1_hohh_vulnerability_other	0.0006184
A carer for a disabled child	1	b10_1_hohh_vulnerability_other	0.0006184
A carer for a disabled person	1	b10_1_hohh_vulnerability_other	0.0006184
Child custody	1	b10_1_hohh_vulnerability_other	0.0006184
Deprived orphan	1	b10_1_hohh_vulnerability_other	0.0006184
Family with a child with disability	1	b10_1_hohh_vulnerability_other	0.0006184
Has a status of the victim of human trafficing	1	b10_1_hohh_vulnerability_other	0.0006184
Husband has disability group 3	1	b10_1_hohh_vulnerability_other	0.0006184
Own apartment destroyed, rented housing	1	b10_1_hohh_vulnerability_other	0.0006184
Pensioner because takes care of the sun, who has born disability (currently 33 y.o., 2 level of disability, lives in another HH)	1	b10_1_hohh_vulnerability_other	0.0006184
Raises a nephew	1	b10_1_hohh_vulnerability_other	0.0006184
Was injured during the conflict	1	b10_1_hohh_vulnerability_other	0.0006184
Psoriasis	3	b11_1_hohh_chronic_illness_other	0.0018553
Blood pressure	2	b11_1_hohh_chronic_illness_other	0.0012369
Allergy	1	b11_1_hohh_chronic_illness_other	0.0006184
Aseptic necrosis, deforming arthritus	1	b11_1_hohh_chronic_illness_other	0.0006184
Asthma	1	b11_1_hohh_chronic_illness_other	0.0006184

5.5.3 Issue log Function

this function will help the generation of standard cleaning log file that holds issues that need clarification. first we need to subset or filter data based on the check list, then that is the log_sheet function will help us to transform that subsetted data into standard cleaning log format.

log_sheet <- function(data, question.name, issue, action){
  cleaning_logbook <- data.frame("uuid" = as.character(),
                                 "question.name" = as.character(),
                                 "issue" = as.character(),
                                 "feedback" = as.character(),
                                 "action" = as.character(),
                                 "old.value" = as.character(),
                                 "new.value" = as.character(),
                                 stringsAsFactors = F)
  if(nrow(data) > 0){
    for(a in 1:nrow(data)) {
      cleaning_logbook <- cleaning_logbook %>% 
        add_row(
          tibble_row(
            uuid = as.character(data[a, "X_uuid"]),
            question.name = as.character(question.name),
            issue = as.character(issue),
            feedback = as.character(""),
            action = as.character(action),
            old.value = as.character(data[a, question.name]),
            new.value = as.character("")
            
          )
        )
    }
  }
  return(cleaning_logbook)
}

Now, lets use the function to log an issue where consent to calls follow-up is no and log them into standard cleaning log format

issue_file <- main_dataset %>% 
  filter(a3_consent_to_follow_up_calls == "no") %>% 
  log_sheet(question.name = "a3_consent_to_follow_up_calls", 
            issue = "flagging consent followup calls with no response as an example",
            action = "flag")

5.5.4 Check for logical check

hum hum hum ?? Custom script (using dplyr?)

Any value that is arbitrary set (from an informed source e.g. an informal setttlement cannot be lower than 15 households). This type of outliers could also be considered as logical checks.

5.6 cleaninginspectoR - inspect_all

cleanninginspectoR has a function inspect inspect_all that will look for outliers, others responses that may need recoding, duplicated uuid and possible sensitive columns. It takes as arguments the dataset and the uuid column name.

cleaning_log <- cleaninginspectoR::inspect_all(main_dataset, "X_uuid")

cleaning_log %>% head(20)

index	value	variable	has_issue	issue_type
NA	NA	a3_1_phone	TRUE	Potentially sensitive information. Please ensure all PII is removed
NA	NA	r5_gps_work	TRUE	Potentially sensitive information. Please ensure all PII is removed
NA	NA	r6_gpslocation	TRUE	Potentially sensitive information. Please ensure all PII is removed
NA	NA	X_r6_gpslocation_latitude	TRUE	Potentially sensitive information. Please ensure all PII is removed
NA	NA	X_r6_gpslocation_longitude	TRUE	Potentially sensitive information. Please ensure all PII is removed
NA	NA	X_r6_gpslocation_altitude	TRUE	Potentially sensitive information. Please ensure all PII is removed
NA	NA	X_r6_gpslocation_precision	TRUE	Potentially sensitive information. Please ensure all PII is removed
26	30000	b15_hohh_income	TRUE	log normal distribution outlier
117	210	b15_hohh_income	TRUE	log normal distribution outlier
225	400	b15_hohh_income	TRUE	log normal distribution outlier
384	300	b15_hohh_income	TRUE	log normal distribution outlier
517	300	b15_hohh_income	TRUE	log normal distribution outlier
710	24000	b15_hohh_income	TRUE	log normal distribution outlier
742	400	b15_hohh_income	TRUE	log normal distribution outlier
1126	50000	b15_hohh_income	TRUE	log normal distribution outlier
1384	400	b15_hohh_income	TRUE	log normal distribution outlier
1399	200	b15_hohh_income	TRUE	log normal distribution outlier
1417	400	b15_hohh_income	TRUE	log normal distribution outlier
5	20	f2_spend_on_utilities	TRUE	log normal distribution outlier
89	5000	f2_spend_on_utilities	TRUE	log normal distribution outlier

5.7 Data cleaning

5.7.1 Re-generate text column for select multiple questions

Description: During data cleaning (when recoding the other options for example), there is a high chance that the dummies and text columns become inconsistent. That can cause issues when analyzing the data if both the dummies and text columns are used. To avoid that, the following function can be used to regenerate and update the text columns based on the dummies columns.

Usage: 1- Create a list of the questions to regenerate:

select_multiple_questions = c("b10_hohh_vulnerability","f17_store_drinking_water","b11_hohh_chronic_illness")

2- Define the function

generate_from_binaries <- function(data,select_multiple_questions) {
  
  do.call("cbind",map(select_multiple_questions,function(question_name,data) {
    
    df <- data %>% select(grep(paste0("^",question_name,"\\."), names(data))) ## Subseting the dataset to include only dummies related to one question
    df <- mutate_all(df,as.numeric) ## Making sure the dummies columns are numerical
    colnames(df) <- gsub(paste0(question_name,"\\."),"",colnames(df)) ## Keeping only the options names in the colnames
    df <- map2_df(df, names(df), ~  replace(.x, .x==1, .y) %>% replace(. == 0, NA)) ## Replacing a cell with a value of '1' with its respective column name and '0' with NA
    df %>%
      unite(!!sym(question_name),names(df),remove=TRUE,na.rm=TRUE,sep=" ") %>% ## concatenate the columns using " " as a seperator 
      as.data.frame() %>% ## Convert to dataframe
      mutate_all(list(~na_if(.,""))) ## replace empty string with NA
  },data)) 
  
}

3- Call the function the generate the columns and replace directely in the data set using replace_columns

main_dataset <- replace_columns(main_dataset, generate_from_binaries(main_dataset,select_multiple_questions))

5.7.2 Clean data base on cleaning log

Required Libraries: Base R Packages

The function “incorporate_logs” applies cleaning log on raw data. It gets cleaning log in a format that must contain 5 mandatory columns including uuid, question.name, old.value, new.value, and changed. as result it would return the cleaned data frame, master cleaning log (logs that are both applied and not applied on data), the version of cleaning log that was applied on raw data, a report of duplicate logs, and logs that their question name or uuid is not available in raw data frame.

# Creating a dummy data set
city_name <- c("kabul", "new dlehi", "peshawar","new york")
population <- c(4430000, 21750000, 1970000, 8419000)
uuid <- c("eae001", "eae002", "eae003","eae004")

my_df <- data.frame(city_name, population, uuid)

# Creating a dummy cleaning log
old.value <- c("kabul", 4430000, "europe","kabul")
question.name <- c("city_name", "population", "continent","city_name")
new.value <- c("moscow",11920000,"asia","moscow")
uuid <- c("eae001","eae001","eae001","eae001")
changed <- c("yes","yes","yes","yes")

cleaning_log <- data.frame(uuid,question.name, old.value, new.value, changed)

### declaring the function
incorporate_logs = function(raw_df, cleaning_log, df_group_seprator = "/", uuid_col = "_uuid"){
  error <- "Error!
Execution was haulted due to one of the following issues:
  - Cleaning log is empty
  - There is no changes in data (in cleaning log changed property for all logs is set to 'NO')
  - One/morethan one of the (uuid, question.name, old.value, new.value, and changed) columns are missing or column names are misspelled
"
  if (sum(grepl("uuid|question.name|old.value|new.value|changed", names(cleaning_log)))==5) {
    `%nin%` = Negate(`%in%`)
    # changing the group seprator (operator) from "/" to "."
    names(raw_df) <- gsub(df_group_seprator,".",names(raw_df))
    cleaning_log$question.name <- gsub(df_group_seprator,".", cleaning_log$question.name)
    
    # subsetting logs that their question is not (available) in dataset
    logs_not_in_rawdf <- cleaning_log[cleaning_log$question.name %nin% names(raw_df) | cleaning_log$uuid %nin% raw_df[[uuid_col]], ]
    logs_not_in_rawdf <- logs_not_in_rawdf[logs_not_in_rawdf$changed %in% c("yes","Yes"),]
    
    # subsetting logs that their question exist in raw data frame and its new value is changed
    cleaning_log.changed <- cleaning_log[cleaning_log$question.name %in% names(raw_df) & cleaning_log$uuid %in% raw_df[[uuid_col]], ]
    cleaning_log.changed <- cleaning_log.changed[cleaning_log.changed$changed %in% c("yes","Yes"),]
    
    # capturing duplicate logs
    cleaning_log$unique_key <- paste(cleaning_log$uuid, cleaning_log$question.name, sep = "_")
    duplicate_logs <- cleaning_log[(duplicated(cleaning_log$unique_key) | duplicated(cleaning_log$unique_key, fromLast = T)),]
    
    # cleaning master cleaning log
    cleaning_log <- cleaning_log[cleaning_log$uuid %nin% logs_not_in_rawdf$uuid | cleaning_log$question.name %nin% logs_not_in_rawdf$question.name,]
    cleaning_log <- cleaning_log[!is.na(cleaning_log$question.name), ]
    cleaning_log <- cleaning_log[!is.na(cleaning_log$uuid), ]
    
    raw_df_valid <- raw_df
    if (nrow(cleaning_log.changed)>0) {
      # Apply cleaning log on raw data
      for (rowi in 1:nrow(cleaning_log.changed)){
        uuid_i <- cleaning_log.changed$uuid[rowi]
        var_i <- cleaning_log.changed$question.name[rowi]
        old_i <- cleaning_log.changed$old.value[rowi]
        new_i <- cleaning_log.changed$new.value[rowi]
        if(class(raw_df_valid[[var_i]]) == "character"){
          new_i <- as.character(new_i)
        }else if(class(raw_df_valid[[var_i]]) == "numeric"){
          new_i <- as.numeric(new_i)
        }else if(class(raw_df_valid[[var_i]]) == "logical"){
          new_i <- as.integer(new_i)
        }else if(class(raw_df_valid[[var_i]]) == "integer"){
          new_i <- as.integer(new_i)
        }
        # Find the variable according to the row of the cleaning log
        raw_df_valid[raw_df_valid[[uuid_col]] == uuid_i, var_i] <- new_i
        print(paste(rowi,"uuid:", uuid_i, "Old value:", old_i, "changed to", new_i, "for", var_i))
      }
      return(list(cleaned_df = raw_df_valid, cleaning_log.applied = cleaning_log.changed, logs_not_in_rawDF = logs_not_in_rawdf, duplicate_logs = duplicate_logs, master_cleaning_log = cleaning_log))
    }else{
      cat(error)
      return(list(cleaned_df = raw_df_valid, cleaning_log.applied = cleaning_log.changed,logs_not_in_rawdf = logs_not_in_rawdf))
    }
  }else{
    cat(error)
  }
}

### Applying the function to data frame
incorprated_logs <- incorporate_logs(my_df, cleaning_log, uuid_col = "uuid")

## [1] "1 uuid: eae001 Old value: kabul changed to moscow for city_name"
## [1] "2 uuid: eae001 Old value: 4430000 changed to 11920000 for population"
## [1] "3 uuid: eae001 Old value: kabul changed to moscow for city_name"

cleaned_data <- incorprated_logs$cleaned_df
master_cleaning_log <- incorprated_logs$master_cleaning_log
logs_not_in_rawDf <- incorprated_logs$logs_not_in_rawDF
cleaning_log.applied <- incorprated_logs$cleaning_log.applied
duplicate_log <- incorprated_logs$duplicate_logs

cleaned_data

city_name	population	uuid
moscow	11920000	eae001
new dlehi	21750000	eae002
peshawar	1970000	eae003
new york	8419000	eae004

master_cleaning_log

	uuid	question.name	old.value	new.value	changed	unique_key
1	eae001	city_name	kabul	moscow	yes	eae001_city_name
2	eae001	population	4430000	11920000	yes	eae001_population
4	eae001	city_name	kabul	moscow	yes	eae001_city_name

logs_not_in_rawDf

	uuid	question.name	old.value	new.value	changed
3	eae001	continent	europe	asia	yes

cleaning_log.applied

	uuid	question.name	old.value	new.value	changed
1	eae001	city_name	kabul	moscow	yes
2	eae001	population	4430000	11920000	yes
4	eae001	city_name	kabul	moscow	yes

duplicate_log

	uuid	question.name	old.value	new.value	changed	unique_key
1	eae001	city_name	kabul	moscow	yes	eae001_city_name
4	eae001	city_name	kabul	moscow	yes	eae001_city_name

5.7.3 Check cleaning log, raw dataset and clean dataset

dplyr waldo arsenal

5.8 Data cleaning - miscellaneous

5.8.1 Check for data sanity check if the data follow ODK format (select_one, select_multiple, xxx, choices)

To be created

5.8.2 Turns label to xml

Custom script (using ???)

5.9 Translation using Microsoft API

These functions will allow you to find specific vectors that needs to be translated in your data and automatically translate them using the Microsoft Azure Translator API.

API instances are setup at mission request and a normal procurement process applies. Once purchased, HQ hands over the API to the mission to configure and manage.

Find Responses

This function takes raw dataframe and find all the responses for a given set of questions.

find.responses <- function(data, questions, values_to="response.uk"){
  #' Look up a raw Kobo dataframe to find all responses to a given set of questions.
  #'
  #' The set of  `questions` needs to contain all columns for translation which will be used to look up `data`.
  #' The input `data` needs to contain a column "uuid"
  #' The vector containing found responses is stored in column specified by parameter `values_to`.
  #'
  #' Be warned: all responses will be converted to character.sou
  #' @param values_to Name of the column in which found responses will be stored.
  #' @returns A dataframe containing columns "uuid", "question.name", and the column specified by `values_to`. Additionally, "loop_index" if `is.loop` is TRUE.
  #' @example
  #' q.db <- data.frame(name = c("age", "occupation"))
  #' raw.data <- data.frame(age = c(21,32), occupation = c("cook", "train conductor"), uuid = c("abc","def"))
  #' find.responses(raw.data, q.db, "responses")
  
  # list of choices for each list_name (from TOOL_CHOICES)
  list.choices <- tool.choices %>% filter(!is.na(list_name)) %>% group_by(list_name) %>%
    mutate(choices=paste(name, collapse=";\n"),
           choices.label=paste(!!sym(label_colname), collapse=";\n")) %>%
    summarise(choices=choices[1], choices.label=choices.label[1])
  
  # list of choices for each question
  select.questions <- tool.survey %>%
    rename(q.label=label_colname) %>%
    select(type, name, q.label) %>%
    mutate(q.type=as.character(lapply(type, function(x) return(str_split(x, " ")[[1]][1]))),
           list_name=as.character(lapply(type, function(q_type){
  #' finds the choice list for a question basing on its type
  q_type.1 <- str_split(q_type, " ")[[1]]
  if (length(q_type.1)==1) return(NA)
  else return(q_type.1[2])
}))) %>%
    filter(list_name!="NA" & list_name!="group" & list_name!="repeat") %>%
    left_join(list.choices, by="list_name") %>%
    filter(!is.na(choices))
  # create a data frame with the questions selected
  question.db <- tool.survey %>% filter(name %in% questions) %>%
    rename(label=label_colname) %>%
    select("name", "label", "relevant") %>%
    mutate(ref.name=as.character(lapply(relevant, function(x) {
      x.1 <- str_split(x, "\\{")[[1]][2]
      return(str_split(x.1,"\\}")[[1]][1])
    }))) %>%
    left_join(select(select.questions, "name", "q.type", "q.label", "list_name", "choices", "choices.label"),
              by=c("ref.name"="name")) %>%
    rename(ref.label=q.label, ref.type=q.type) %>%
    mutate(full.label=paste0(ref.label, " - ", label)) %>%
    select(name, ref.name, full.label, ref.type, choices, choices.label)
  if(nrow(question.db) == 0){
      warning("questions is empty - returning an empty dataframe.")
      return(data.frame())
  }

  if(nrow(data) == 0){
      warning("data is empty - returning an empty dataframe.")
      return(data.frame())
  }

  responses <- data %>%
      select(c("uuid", any_of(question.db$name))) %>%
      pivot_longer(cols = any_of(question.db$name),
                   names_to="question.name", values_to=values_to,
                   values_transform = as.character) %>%
      filter(!is.na(!!sym(values_to))) %>%
      select(uuid, question.name, !!sym(values_to))

  responses.j <- responses %>%
      left_join(question.db, by=c("question.name"="name")) %>% dplyr::rename(name="question.name") %>%
      left_join(select(data, uuid), by="uuid") 
  return(responses.j)
}

Translate Responses

This function translate a vector from a given dataframe.

It will return the same dataframe as the find.responses function above but with a new column containing the translation. Also, it will return a logframe csv file in the same directory of the running script.

This function will require the remote package translateREACH. This package can be installed by:

install.packages(“devtools”) devtools::install_github(“REACH-WoU-Regional/translateR”)

translate.responses <- function(responses, values_from = "response.uk", language_codes = 'uk', target_lang = "en", microsoft.api.key = NULL, threshold = 200000){

  #' Translate a vector from a given dataframe.
  #'
  #' The provided dataframe `responses` must contain the column `values_from` which will be used as input vector for the translation.
  #' Also outputs informative logs to file named "translate_info.csv". Specify the target language using `target_lang` parameter
  #'
  #' Warning: If more than one source language code is provided, the entire translation WILL BE REPEATED. You are advised against that,
  #' because we do not want to hit our monthly limits for the API.
  #'
  #' @param respones Dataframe containing a column which shall be translated.
  #' @param values_from Name of the column from `responses` which shall be translated.
  #' @param language_codes Character vector of two-letter language codes. The input vector will be translated from both of these languages.
  #' @param target_lang Input vector will be translated into this language.
  #' @param microsoft.api.key Character input of the microsoft.api.key.
  #' @param threshold Input threshold to interrupt the user if the number of characters is exceeding 200,000 by default. 
  #' @returns The same dataframe as `responses`, but with a new column, containing the translation.
  #' The column will be named according to the given source and target languages. By default, the output will be stored in column named 'response.en.from.uk'
  
  if(is.null(microsoft.api.key)) stop("Please provide a microsoft.api.key while calling the function.")
  info_df <- data.frame()
  responses_batch <- data.frame()
  temp_resp_whole <- data.frame()
  start_time <- Sys.time()

  # extract unique responses from the source dataframe
  responses <- responses %>% mutate(resp_lower = str_to_lower(!!sym(values_from)))
   
  input_vec <- responses %>% distinct(resp_lower) %>% pull(resp_lower)
  # cleaning up html leftovers:
  input_vec <- gsub("&#39;", "'", input_vec)
  # counts characters which will be translated
  char_counter <- sum(str_length(input_vec))
  # TODO: pause here, print the char_counter, and ask the user if the translation should go ahead
  if (char_counter > threshold){
    yes_no <- svDialogs::dlgInput(paste0("The number of characters exceeds ", threshold, ". Please enter [YES] if you would like to proceed or [NO] to kill:"), "YES or NO")$res
  } else{
    yes_no <- "YES"
  }
  batching <- svDialogs::dlgInput(paste0("How many batches would you like to split your translation (",char_counter," characters)? (please only integer)"), 0)$res
  batching <- as.numeric(batching)
  if(yes_no == "YES"){
    if(length(input_vec) > 0){
      for (code in language_codes) {
        col_name <- paste0("response.",target_lang, ".from.",code)
        temp_resp <- tibble(input_vec)
        temp_resp[[col_name]] <- NA
        temp_resp <-  temp_resp[sample(1:nrow(temp_resp)),]
        ## create batches
        temp_resp_batches <- split(temp_resp, factor(sort(rank(row.names(temp_resp))%%batching)))
        progress.bar.title <- as.character(Sys.time())
        pb <- tcltk::tkProgressBar(progress.bar.title, "Number of batches executed", 0, batching, 0, width = 600)
        prog <- 1
        for (temp_resp_batch in temp_resp_batches){
          tcltk::setTkProgressBar(pb, prog, progress.bar.title, paste0("Number of batches executed: ", prog, " of ", batching,"\n",length(temp_resp_batch$input_vec)," responses will be translated from ",code," to ",target_lang, "\nThis means ",sum(str_length(temp_resp_batch$input_vec))," utf-8 characters."))
          prog <- prog + 1
          # actual translation:
          result_vec <- NULL
          result_vec <- try(translateR::translate(content.vec = temp_resp_batch$input_vec,
                              microsoft.api.key = microsoft.api.key,
                              microsoft.api.region = "switzerlandnorth",
                              source.lang = code, target.lang = target_lang))
          if(inherits(result_vec,"try-error")) break
          # checking the results
          info_df <- rbind(info_df, data.frame(## DEBUGG IT HERE
            "input_responses_num" = length(temp_resp_batch$input_vec),
            "translated_characters_num" = sum(str_length(temp_resp_batch$input_vec)),
            "language_from" = code,
            "result_num" = length(result_vec),
            "time_elapsed" = as.numeric(Sys.time() - start_time),
            "date"=Sys.Date(),
            "status"=NA))
          if(is.null(result_vec)){
            warning("Error while translating responses: result_vec is NULL\n")
            info_df$status <- "error"
          }else{
            temp_resp_batch[[col_name]] <- gsub("&#39;", "'", result_vec)
            if(length(result_vec) == length(temp_resp_batch$input_vec)){ 
              info_df$status <- "success"
              # bind the translated and source dfs
              temp_resp_whole <- rbind(temp_resp_whole,temp_resp_batch)
            }else{
              info_df$status <- "partial success"
            }
          }
        }
        close(pb)
        if("partial success" %in% info_df$status){
          svDialogs::msgBox("translate.responses: finished - PARTIAL SUCCESS?")
        } else{
          svDialogs::msgBox("translate.responses: finished - SUCCESS")
        }
        responses <- responses %>% left_join(temp_resp_whole, by = c("resp_lower" = "input_vec")) 
      }
    }else{
      warning("Nothing to be translated")
    }
  }
  # dump info about the results of translation
  log_filename <- "translate_info.csv"
  if(file.exists(log_filename)) write.table(info_df, file = log_filename, append = T, row.names = F, col.names = F, sep = ',')
  else write.table(info_df, file = log_filename, row.names = F, col.names = T, sep = ',')

  responses <- responses %>% select(-resp_lower)
  return(responses)
}

Example:

other_columns <- c("b12_1_situation_description_other")

responses <- find.responses(main_dataset,other_columns,values_to = "response.en")
responses.j <- translate.responses(responses, language_codes = "fr")