R Notebook

Note: d1 is the name of my data. As you’ll see, it has 6149 rows of 221 variables. About half of the variables were created or edited significantly by me.

d1 has a row of data for every phone call we attempted. So a single child might be represented 4 times if we called them 3 times before the answered.

You’ll see that the last command includes a filter - only including those that completed the survey, and those that lived in the relevant areas.

The first two commands don’t include any PII.

d1 %>% select(!rchid:is_respondent) %>% glimpse()

Rows: 6,149
Columns: 207
$ SubmissionDate                <date> 2024-06-25, 2024-06-25, 2024-06-25, 2024-…
$ starttime                     <date> 2024-06-25, 2024-06-25, 2024-06-25, 2024-…
$ endtime                       <date> 2024-06-25, 2024-06-25, 2024-06-25, 2024-…
$ duration                      <dbl> 929, 78, 78, 149, 368, 223, 143, 281, 732,…
$ resp_sno                      <dbl> 1115, 1116, 1117, 1723, 1724, 1725, 1726, …
$ get_mother                    <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ get_mother_verification       <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ has_number                    <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ mother_number                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ speak_other                   <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ speak_other_name              <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ consent                       <fct> हो, NA, NA, NA, NA, NA, NA, NA, हो, NA, NA…
$ have_vaccination_card         <chr> "yes", NA, NA, NA, NA, NA, NA, NA, "yes", …
$ get_vacc_card                 <chr> "yes", NA, NA, NA, NA, NA, NA, NA, "yes", …
$ no_card_reason                <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ age                           <dbl> 30, NA, NA, NA, NA, NA, NA, NA, 33, NA, NA…
$ gender                        <chr> "female", NA, NA, NA, NA, NA, NA, NA, "mal…
$ religion                      <chr> "hindu", NA, NA, NA, NA, NA, NA, NA, "othe…
$ caste                         <fct> FC, NA, NA, NA, NA, NA, NA, NA, OBC, NA, N…
$ education                     <chr> "degree", NA, NA, NA, NA, NA, NA, NA, "deg…
$ literacy                      <chr> "literate", "not literate", "not literate"…
$ occupation                    <chr> "agri", NA, NA, NA, NA, NA, NA, NA, "agri"…
$ occupation_agri               <chr> "owns", NA, NA, NA, NA, NA, NA, NA, "owns"…
$ occupation_agri_2             <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ child_gender                  <chr> "female", NA, NA, NA, NA, NA, NA, NA, "mal…
$ dob_stated                    <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ child_age                     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ child_age_other               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ child_location                <chr> "always", NA, NA, NA, NA, NA, NA, NA, "alw…
$ child_migration_pattern       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ age_or_date                   <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ child_migration_1_calendar    <date> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ child_migration_2_calendar    <date> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ vacc_visits                   <fct> जन्मानंतर सहा पेक्षा जास्त वेळा गेलो, NA, NA, NA,…
$ remaining_vaccines            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ next_vaccination              <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ penta1_stated                 <fct> हो, NA, NA, NA, NA, NA, NA, NA, हो, NA, NA…
$ penta2_stated                 <fct> हो, NA, NA, NA, NA, NA, NA, NA, हो, NA, NA…
$ penta3_stated                 <fct> हो, NA, NA, NA, NA, NA, NA, NA, हो, NA, NA…
$ mr1_stated                    <fct> हो, NA, NA, NA, NA, NA, NA, NA, हो, NA, NA…
$ mr2_stated                    <fct> हो, NA, NA, NA, NA, NA, NA, NA, हो, NA, NA…
$ mobilised                     <dbl> 0, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, …
$ reminded                      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ reminded_asha                 <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ reminded_anm                  <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ reminded_aww                  <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ reminded_doc                  <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ reminded_fam                  <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ reminded_others               <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ reminded_7                    <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ reminded__9999                <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ checked_vaccine_status        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ checked_vaccine_status_asha   <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ checked_vaccine_status_anm    <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ checked_vaccine_status_aww    <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ checked_vaccine_status_doc    <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ checked_vaccine_status_fam    <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ checked_vaccine_status_others <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ checked_vaccine_status_7      <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ checked_vaccine_status__9999  <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ vaccine_info                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ vaccine_info_asha             <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ vaccine_info_anm              <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ vaccine_info_aww              <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ vaccine_info_doc              <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ vaccine_info_fam              <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ vaccine_info_others           <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ vaccine_info_7                <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ vaccine_info__9999            <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ amb_presence                  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA,…
$ know_asha                     <dbl> 1, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, …
$ asha_convo                    <chr> NA, NA, NA, NA, NA, NA, NA, NA, "this week…
$ convo_initiation              <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA,…
$ belief_one                    <fct> नक्कीच उद्याच्या लसीकरणासाठी जाईन, NA, NA, NA…
$ get_vacc_card_later           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ get_card_appointment          <date> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ get_card_pic                  <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ which_phone_pic               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ note_phone                    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ iscomplete                    <fct> हो, NA, NA, NA, NA, NA, NA, NA, हो, NA, ना…
$ callback                      <fct> हो, NA, NA, NA, NA, NA, NA, NA, हो, NA, ना…
$ surveyorcomment               <chr> "*****", NA, NA, NA, NA, NA, NA, NA, NA, N…
$ instanceID                    <chr> "uuid:85b919a9-3fe4-4a51-aa90-a97feb5544be…
$ formdef_version               <dbl> 2406251337, 2406251337, 2406251337, 240625…
$ penta1_date                   <date> 2022-07-14, NA, NA, NA, NA, NA, NA, NA, 2…
$ penta2_date                   <date> 2022-08-11, NA, NA, NA, NA, NA, NA, NA, 2…
$ penta3_date                   <date> 2022-10-10, NA, NA, NA, NA, NA, NA, NA, 2…
$ mr1_date                      <date> 2023-03-13, NA, NA, NA, NA, NA, NA, NA, 2…
$ mr2_date                      <date> 2023-11-13, NA, NA, NA, NA, NA, NA, NA, 2…
$ dob_rch                       <date> 2022-05-30, 2022-08-17, 2022-07-30, 2023-…
$ anm_name                      <chr> "Rupali  Dattatray Kumthekar(ID-120227)(Mo…
$ asha_name                     <chr> "Nilam Vinod Shirke(ID-72234)(MobNo.-97635…
$ registrationdate              <date> 2022-05-30, 2022-08-17, 2022-07-30, 2023-…
$ weight                        <dbl> 2.800, 2.800, 3.200, 2.400, 2.400, 2.100, …
$ bcg                           <date> 2022-06-01, 2022-08-19, 2022-08-02, 2023-…
$ opv0                          <date> 2022-06-01, 2022-08-19, 2022-08-02, 2023-…
$ penta1_rch                    <date> 2022-07-11, 2022-10-07, 2022-09-12, 2023-…
$ penta2_rch                    <date> 2022-08-08, 2022-11-05, 2022-11-11, 2023-…
$ penta3_rch                    <date> 2022-09-12, 2022-12-02, 2022-12-08, 2023-…
$ mr1_rch                       <date> 2023-03-13, 2023-06-21, 2023-08-05, NA, N…
$ mr2_rch                       <date> 2023-10-09, 2024-03-02, 2023-11-12, NA, N…
$ block_village.x               <chr> "Wai(18) Degaon  (37904)", "Wai(18) Kalang…
$ block_cat                     <chr> "Wai", "Wai", "Wai", "Wai", "Wai", "Khanda…
$ health_block_r                <chr> "Wai", "Wai", "Wai", "Koregaon", "Wai", "K…
$ block_village.y               <chr> "Wai Degaon  (37904)", "Wai Kalangwadi  (3…
$ category_rch                  <chr> "pre_treat_wai", "pre_treat_wai", "pre_tre…
$ dob_actual                    <date> 2022-05-30, 2022-08-17, 2022-07-30, 2023-…
$ penta1_diff                   <dbl> 3, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, …
$ dob_diff                      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ penta2_diff                   <dbl> 3, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, …
$ penta3_diff                   <dbl> 28, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA,…
$ mr1_diff                      <dbl> 0, NA, NA, NA, NA, NA, NA, NA, -2, NA, NA,…
$ mr2_diff                      <dbl> 35, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ big_dob                       <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ big_penta1                    <lgl> FALSE, NA, NA, NA, NA, NA, NA, NA, FALSE, …
$ big_penta2                    <lgl> FALSE, NA, NA, NA, NA, NA, NA, NA, FALSE, …
$ big_penta3                    <lgl> FALSE, NA, NA, NA, NA, NA, NA, NA, FALSE, …
$ big_mr1                       <lgl> FALSE, NA, NA, NA, NA, NA, NA, NA, FALSE, …
$ big_mr2                       <lgl> FALSE, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ big_count                     <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ age_diff                      <drtn> 757 days, 678 days, 696 days, 446 days, 7…
$ agereg                        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ age1                          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ age2                          <dbl> 757, 678, 696, 446, 744, 707, 474, 714, 69…
$ agesurv                       <dbl> 757, 678, 696, 446, 744, 707, 474, 714, 69…
$ prepost                       <chr> "pre", "pre", "pre", "post", "pre", "post"…
$ treatment                     <dbl> 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, …
$ edu2                          <fct> degree, NA, NA, NA, NA, NA, NA, NA, degree…
$ occupation_simp               <chr> "farms own land", NA, NA, NA, NA, NA, NA, …
$ occupation_simp2              <fct> farms own land, NA, NA, NA, NA, NA, NA, NA…
$ card                          <chr> "has & got card", NA, NA, NA, NA, NA, NA, …
$ penta1_improv                 <chr> "both", "RCH only", "RCH only", "RCH only"…
$ penta2_improv                 <chr> "both", "RCH only", "RCH only", "RCH only"…
$ penta3_improv                 <chr> "both", "RCH only", "RCH only", "RCH only"…
$ mr1_improv                    <chr> "both", "RCH only", "RCH only", "neither",…
$ mr2_improv                    <chr> "both", "RCH only", "RCH only", "neither",…
$ penta1_date_joint             <date> 2022-07-14, 2022-10-07, 2022-09-12, 2023-…
$ penta2_date_joint             <date> 2022-08-11, 2022-11-05, 2022-11-11, 2023-…
$ penta3_date_joint             <date> 2022-10-10, 2022-12-02, 2022-12-08, 2023-…
$ mr1_date_joint                <date> 2023-03-13, 2023-06-21, 2023-08-05, NA, N…
$ mr2_date_joint                <date> 2023-11-13, 2024-03-02, 2023-11-12, NA, N…
$ penta1_age                    <dbl> 45, NA, NA, NA, NA, NA, NA, NA, 66, NA, NA…
$ penta2_age                    <dbl> 73, NA, NA, NA, NA, NA, NA, NA, 94, NA, NA…
$ penta3_age                    <dbl> 133, NA, NA, NA, NA, NA, NA, NA, 122, NA, …
$ mr1_age                       <dbl> 287, NA, NA, NA, NA, NA, NA, NA, 365, NA, …
$ mr2_age                       <dbl> 532, NA, NA, NA, NA, NA, NA, NA, 521, NA, …
$ penta1_time                   <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ penta2_time                   <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ penta3_time                   <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ mr1_time                      <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ mr2_time                      <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ penta1_age_joint              <dbl> 45, 51, 44, 68, 50, NA, NA, 70, 66, 61, 47…
$ penta2_age_joint              <dbl> 73, 80, 104, 96, 82, NA, NA, 98, 94, 89, 7…
$ penta3_age_joint              <dbl> 133, 107, 131, 124, 114, NA, NA, 133, 122,…
$ mr1_age_joint                 <dbl> 287, 308, 371, NA, NA, NA, NA, NA, 365, 33…
$ mr2_age_joint                 <dbl> 532, 563, 470, NA, NA, NA, NA, NA, 521, NA…
$ penta1_time_joint             <dbl> 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, …
$ penta2_time_joint             <dbl> 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, …
$ penta3_time_joint             <dbl> 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, …
$ mr1_time_joint                <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, …
$ mr2_time_joint                <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, …
$ vacc_stated                   <dbl> 6, NA, NA, NA, NA, NA, NA, NA, 3, NA, NA, …
$ penta1_implied                <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ penta2_implied                <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ penta3_implied                <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ mr1_implied                   <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ mr2_implied                   <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ base_sub_penta1               <dbl> 100.00000, 100.00000, 66.66667, 100.00000,…
$ base_sub_penta2               <dbl> 100.00000, 100.00000, 66.66667, 100.00000,…
$ base_sub_penta3               <dbl> 100.00000, 100.00000, 66.66667, 50.00000, …
$ base_sub_mr1                  <dbl> 100.00000, 100.00000, 66.66667, 50.00000, …
$ base_sub_mr2                  <dbl> 100.00000, 100.00000, 66.66667, 0.00000, 1…
$ base_sub_penta1_joint         <dbl> 100.00000, 100.00000, 100.00000, 100.00000…
$ base_sub_penta2_joint         <dbl> 100.00000, 100.00000, 100.00000, 100.00000…
$ base_sub_penta3_joint         <dbl> 100.00000, 100.00000, 100.00000, 50.00000,…
$ base_sub_mr1_joint            <dbl> 100.00000, 100.00000, 100.00000, 50.00000,…
$ base_sub_mr2_joint            <dbl> 100.00000, 100.00000, 100.00000, 0.00000, …
$ base_phc_penta1               <dbl> 93.75000, 93.75000, 93.75000, 91.66667, 81…
$ base_phc_penta2               <dbl> 95.31250, 95.31250, 95.31250, 91.66667, 81…
$ base_phc_penta3               <dbl> 93.75000, 93.75000, 93.75000, 75.00000, 81…
$ base_phc_mr1                  <dbl> 84.37500, 84.37500, 84.37500, 72.22222, 72…
$ base_phc_mr2                  <dbl> 35.93750, 35.93750, 35.93750, 25.00000, 27…
$ base_phc_penta1_joint         <dbl> 95.31250, 95.31250, 95.31250, 91.66667, 10…
$ base_phc_penta2_joint         <dbl> 96.87500, 96.87500, 96.87500, 91.66667, 10…
$ base_phc_penta3_joint         <dbl> 95.31250, 95.31250, 95.31250, 75.00000, 10…
$ base_phc_mr1_joint            <dbl> 85.93750, 85.93750, 85.93750, 72.22222, 90…
$ base_phc_mr2_joint            <dbl> 37.50000, 37.50000, 37.50000, 27.77778, 27…
$ amb_recruit                   <date> 2023-10-04, 2023-10-04, 2023-10-04, 2023-…
$ amb_age                       <dbl> 492, 413, 431, 181, 479, 178, 209, 449, 42…
$ amb_treat_time                <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ numb_amb                      <dbl> 0, 3, 0, 0, 3, 0, 4, 0, 0, 0, 0, 0, 0, 3, …
$ amb_in_vill                   <dbl> 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, …
$ base_sub_penta1_implied       <dbl> 80.00000, 100.00000, 85.71429, 80.00000, 1…
$ base_sub_penta2_implied       <dbl> 80.00000, 100.00000, 85.71429, 80.00000, 1…
$ base_sub_penta3_implied       <dbl> 80.00000, 100.00000, 85.71429, 80.00000, 1…
$ base_sub_mr1_implied          <dbl> 80.00000, 100.00000, 85.71429, 80.00000, 1…
$ base_sub_mr2_implied          <dbl> 60.00000, 75.00000, 85.71429, 80.00000, 83…
$ base_phc_penta1_implied       <dbl> 88.59649, 88.59649, 88.59649, 82.27848, 94…
$ base_phc_penta2_implied       <dbl> 88.59649, 88.59649, 88.59649, 82.27848, 94…
$ base_phc_penta3_implied       <dbl> 88.59649, 88.59649, 88.59649, 82.27848, 94…
$ base_phc_mr1_implied          <dbl> 83.33333, 83.33333, 83.33333, 73.41772, 94…
$ base_phc_mr2_implied          <dbl> 49.12281, 49.12281, 49.12281, 45.56962, 63…
$ nom_surv                      <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ asha_convo_this_month         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA,…
$ belief_one_code               <dbl> 1, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, …
$ mobilised_others              <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ got_card_code                 <dbl> 1, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, …

d1 %>% select(!rchid:is_respondent) %>% head()

d1 %>% summary()

 SubmissionDate         starttime             endtime              duration   
 Min.   :2024-06-25   Min.   :2024-06-25   Min.   :2024-06-25   Min.   :   7  
 1st Qu.:2024-07-04   1st Qu.:2024-07-04   1st Qu.:2024-07-04   1st Qu.:  48  
 Median :2024-07-15   Median :2024-07-12   Median :2024-07-12   Median :  77  
 Mean   :2024-07-12   Mean   :2024-07-11   Mean   :2024-07-12   Mean   : 228  
    resp_sno       rchid           mother_name        father_name       
 Min.   : 811   Length:6149        Length:6149        Length:6149       
 1st Qu.:1289   Class :character   Class :character   Class :character  
 Median :1786   Mode  :character   Mode  :character   Mode  :character  
 Mean   :1797                                                           
 enrollee_phonenumber village_surv          block           health_centre     
 Length:6149          Length:6149        Length:6149        Length:6149       
 Class :character     Class :character   Class :character   Class :character  
 Mode  :character     Mode  :character   Mode  :character   Mode  :character  
                                                                              
 health_subcentre   category_surv      surveyor_name     
 Length:6149        Length:6149        Length:6149       
 Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character  
                                                         
                              call_status     call_later        
 उचलला                              :1011   Min.   :1900-06-28  
 प्रतिसादकर्त्याने नंतर कॉल करण्यास सांगितल: 262   1st Qu.:2024-07-02  
 अवैध क्रमांक / चुकीचा क्रमांक            : 314   Median :2024-07-15  
 नंबर अगम्य/स्विच्ड ऑफ                  :1547   Mean   :2024-01-21  
 verify_respondent is_respondent      get_mother       get_mother_verification
 नाही: 152         Length:6149        नाही:  87   इतर [निर्दिष्ट करा]:   4      
 हो  : 853         Class :character   हो  :   4   father_name      :   0      
 NA's:5144         Mode  :character   NA's:6058   mother_name      :   0      
                                                  NA's             :6145      
 has_number  mother_number      speak_other speak_other_name   consent    
 नाही:  34   Length:6149        नाही:  24   Length:6149        नाही:  67  
 हो  :  57   Class :character   हो  :   9   Class :character   हो  : 704  
 NA's:6058   Mode  :character   NA's:6116   Mode  :character   NA's:5378  
                                                                          
 have_vaccination_card get_vacc_card      no_card_reason          age       
 Length:6149           Length:6149        Length:6149        Min.   :20.00  
 Class :character      Class :character   Class :character   1st Qu.:26.00  
 Mode  :character      Mode  :character   Mode  :character   Median :29.00  
                                                             Mean   :29.47  
    gender            religion           caste       education        
 Length:6149        Length:6149        OBC  : 130   Length:6149       
 Class :character   Class :character   FC   : 395   Class :character  
 Mode  :character   Mode  :character   SC/ST: 158   Mode  :character  
                                       NA's :5466                     
   literacy          occupation        occupation_agri        occupation_agri_2
 Length:6149        Length:6149        Length:6149        शेतकरी        :   0   
 Class :character   Class :character   Class :character   शेत मजूर       :   0   
 Mode  :character   Mode  :character   Mode  :character   उत्तर दिले नाही:   1   
                                                          NA's         :6148   
 child_gender                   dob_stated    child_age        
 Length:6149        माहिती नाही      :  52   Length:6149       
 Class :character   इतर [निर्दिष्ट करा]:  79   Class :character  
 Mode  :character   child_dob        :   0   Mode  :character  
                    NA's             :6018                     
 child_age_other    child_location     child_migration_pattern
 Length:6149        Length:6149        Length:6149            
 Class :character   Class :character   Class :character       
 Mode  :character   Mode  :character   Mode  :character       
                                                              
             age_or_date   child_migration_1_calendar child_migration_2_calendar
 बाळाचे वय          : 394   Min.   :2023-08-01         Min.   :2023-07-01        
 कॅलेंडर महिना आणि वर्ष:   2   1st Qu.:2023-08-01         1st Qu.:2023-07-01        
 NA's              :5753   Median :2023-08-01         Median :2023-07-01        
                           Mean   :2023-08-01         Mean   :2023-07-01        
                        vacc_visits   remaining_vaccines
 जन्मानंतर पाच वेळा गेलो          : 226   Min.   :0.000     
 जन्मानंतर चार वेळा गेलो          : 161   1st Qu.:0.000     
 जन्मानंतर सहा पेक्षा जास्त वेळा गेलो:  79   Median :1.000     
 माहीत नाही                   :  65   Mean   :1.333     
                             next_vaccination penta1_stated penta2_stated
 माहीत नाही                          :   9    नाही:   9     नाही:   1    
 या महिन्यात किंवा पुढच्या महिन्यात       :   2    हो  : 317     हो  : 314    
 3-5 महिन्यांनी                        :   4    NA's:5823     NA's:5834    
 6 महिने किंवा अधिक                    :   0                               
 penta3_stated mr1_stated  mr2_stated    mobilised       reminded        
 नाही:   0     नाही:   9   नाही: 152   Min.   :0.000   Length:6149       
 हो  : 314     हो  : 305   हो  : 151   1st Qu.:0.000   Class :character  
 NA's:5835     NA's:5835   NA's:5846   Median :0.000   Mode  :character  
                                       Mean   :0.472                     
 reminded_asha reminded_anm reminded_aww reminded_doc reminded_fam
 No  :  12     No  : 241    No  : 162    No  : 247    No  : 238   
 Yes : 263     Yes :  34    Yes : 113    Yes :  28    Yes :  37   
 NA's:5874     NA's:5874    NA's:5874    NA's:5874    NA's:5874   
                                                                  
 reminded_others reminded_7  reminded__9999 checked_vaccine_status
 No  : 263       No  : 275   No  : 275      Length:6149           
 Yes :  12       Yes :   0   Yes :   0      Class :character      
 NA's:5874       NA's:5874   NA's:5874      Mode  :character      
                                                                  
 checked_vaccine_status_asha checked_vaccine_status_anm
 No  :  46                   No  : 234                 
 Yes : 229                   Yes :  41                 
 NA's:5874                   NA's:5874                 
                                                       
 checked_vaccine_status_aww checked_vaccine_status_doc checked_vaccine_status_fam
 No  : 155                  No  : 228                  No  : 231                 
 Yes : 120                  Yes :  47                  Yes :  44                 
 NA's:5874                  NA's:5874                  NA's:5874                 
                                                                                 
 checked_vaccine_status_others checked_vaccine_status_7
 No  : 258                     No  : 266               
 Yes :  17                     Yes :   9               
 NA's:5874                     NA's:5874               
                                                       
 checked_vaccine_status__9999 vaccine_info       vaccine_info_asha
 No  : 275                    Length:6149        No  :  70        
 Yes :   0                    Class :character   Yes : 205        
 NA's:5874                    Mode  :character   NA's:5874        
                                                                  
 vaccine_info_anm vaccine_info_aww vaccine_info_doc vaccine_info_fam
 No  : 222        No  : 186        No  : 191        No  : 252       
 Yes :  53        Yes :  89        Yes :  84        Yes :  23       
 NA's:5874        NA's:5874        NA's:5874        NA's:5874       
                                                                    
 vaccine_info_others vaccine_info_7 vaccine_info__9999  amb_presence  
 No  : 265           No  : 261      No  : 265          Min.   :0.000  
 Yes :  10           Yes :  14      Yes :  10          1st Qu.:0.000  
 NA's:5874           NA's:5874      NA's:5874          Median :0.000  
                                                       Mean   :0.121  
   know_asha      asha_convo        convo_initiation
 Min.   :0.000   Length:6149        Min.   :0.000   
 1st Qu.:1.000   Class :character   1st Qu.:0.000   
 Median :1.000   Mode  :character   Median :0.000   
 Mean   :0.962                      Mean   :0.142   
                                 belief_one   get_vacc_card_later
 नक्कीच 2 आठवड्यांनंतरच्या लसीकरणासाठी जाईन:  50   Length:6149        
 बहुधा 2 आठवड्यांनंतरच्या लसीकरणासाठी जाईन :  22   Class :character   
 खत्री नाही                            :  19   Mode  :character   
 बहुधा उद्याच्या लसीकरणासाठीजाईन         :  27                      
 get_card_appointment get_card_pic which_phone_pic     note_phone       
 Min.   :2023-05-16   नाही:  49    Length:6149        Length:6149       
 1st Qu.:2024-07-02   हो  : 276    Class :character   Class :character  
 Median :2024-07-15   NA's:5824    Mode  :character   Mode  :character  
 Mean   :2024-07-06                                                     
 iscomplete  callback    surveyorcomment     instanceID       
 नाही: 146   नाही: 356   Length:6149        Length:6149       
 हो  : 865   हो  : 424   Class :character   Class :character  
 NA's:5138   NA's:5369   Mode  :character   Mode  :character  
                                                              
 formdef_version       penta1_date          penta2_date        
 Min.   :2406251337   Min.   :2019-09-12   Min.   :2019-11-14  
 1st Qu.:2407021004   1st Qu.:2022-10-03   1st Qu.:2022-11-11  
 Median :2407091817   Median :2023-03-13   Median :2023-04-17  
 Mean   :2406931489   Mean   :2023-01-18   Mean   :2023-02-27  
  penta3_date            mr1_date             mr2_date         
 Min.   :2022-02-15   Min.   :2022-04-04   Min.   :2022-04-20  
 1st Qu.:2022-12-19   1st Qu.:2023-05-31   1st Qu.:2023-12-08  
 Median :2023-05-24   Median :2023-10-22   Median :2024-03-08  
 Mean   :2023-04-14   Mean   :2023-09-20   Mean   :2024-01-25  
    dob_rch             anm_name          asha_name         registrationdate    
 Min.   :2021-10-15   Length:6149        Length:6149        Min.   :2021-10-15  
 1st Qu.:2022-07-02   Class :character   Class :character   1st Qu.:2022-07-02  
 Median :2022-10-03   Mode  :character   Mode  :character   Median :2022-10-03  
 Mean   :2022-10-03                                         Mean   :2022-10-03  
     weight           bcg                  opv0              penta1_rch        
 Min.   :1.000   Min.   :2021-10-15   Min.   :2021-10-15   Min.   :2021-12-01  
 1st Qu.:2.500   1st Qu.:2022-07-06   1st Qu.:2022-07-06   1st Qu.:2022-08-23  
 Median :2.800   Median :2022-10-07   Median :2022-10-04   Median :2022-11-04  
 Mean   :2.797   Mean   :2022-10-11   Mean   :2022-10-08   Mean   :2022-11-28  
   penta2_rch           penta3_rch            mr1_rch          
 Min.   :2022-01-03   Min.   :2022-01-12   Min.   :2022-07-19  
 1st Qu.:2022-10-03   1st Qu.:2022-11-01   1st Qu.:2023-04-10  
 Median :2022-12-08   Median :2023-01-11   Median :2023-06-19  
 Mean   :2022-12-31   Mean   :2023-02-02   Mean   :2023-07-30  
    mr2_rch           block_village.x     block_cat         health_block_r    
 Min.   :2023-02-11   Length:6149        Length:6149        Length:6149       
 1st Qu.:2023-11-12   Class :character   Class :character   Class :character  
 Median :2023-12-14   Mode  :character   Mode  :character   Mode  :character  
 Mean   :2024-01-02                                                           
 block_village.y    category_rch         dob_actual          penta1_diff      
 Length:6149        Length:6149        Min.   :2017-02-25   Min.   :-847.000  
 Class :character   Class :character   1st Qu.:2022-07-02   1st Qu.:  -4.500  
 Mode  :character   Mode  :character   Median :2022-10-03   Median :   0.000  
                                       Mean   :2022-10-03   Mean   :  -1.292  
    dob_diff           penta2_diff        penta3_diff         mr1_diff      
 Min.   :-2273.0000   Min.   :-819.000   Min.   :-300.00   Min.   :-359.00  
 1st Qu.:    0.0000   1st Qu.:   0.000   1st Qu.:   0.00   1st Qu.: -18.00  
 Median :    0.0000   Median :   0.000   Median :   0.00   Median :   0.00  
 Mean   :   -0.2012   Mean   :   5.236   Mean   :  15.56   Mean   : -16.96  
    mr2_diff       big_dob        big_penta1      big_penta2      big_penta3     
 Min.   :-149.0   Mode :logical   Mode :logical   Mode :logical   Mode :logical  
 1st Qu.:   0.0   FALSE:6131      FALSE:259       FALSE:258       FALSE:244      
 Median :   0.0   TRUE :18        TRUE :8         TRUE :5         TRUE :9        
 Mean   :   8.6                   NA's :5882      NA's :5886      NA's :5896     
  big_mr1         big_mr2          big_count         age_diff       
 Mode :logical   Mode :logical   Min.   :0.00000   Length:6149      
 FALSE:175       FALSE:24        1st Qu.:0.00000   Class :difftime  
 TRUE :22        TRUE :1         Median :0.00000   Mode  :numeric   
 NA's :5952      NA's :6124      Mean   :0.01025                    
     agereg               age1              age2           agesurv      
 Min.   :-670.0000   Min.   :  0.000   Min.   :   0.0   Min.   :   8.0  
 1st Qu.:   0.0000   1st Qu.:  0.000   1st Qu.: 490.0   1st Qu.: 498.0  
 Median :   0.0000   Median :  0.000   Median : 647.0   Median : 652.0  
 Mean   :   0.4915   Mean   :  4.916   Mean   : 636.1   Mean   : 648.4  
   prepost            treatment                            edu2     
 Length:6149        Min.   :0.0000   matriculation w/o degree: 226  
 Class :character   1st Qu.:0.0000   degree                  : 407  
 Mode  :character   Median :0.0000   secondary or less       :  64  
                    Mean   :0.4041   NA's                    :5452  
 occupation_simp                 occupation_simp2     card          
 Length:6149        farms own land       : 301    Length:6149       
 Class :character   corporate or gov     : 183    Class :character  
 Mode  :character   landless laborer     : 116    Mode  :character  
                    small_business__other:  96                      
 penta1_improv      penta2_improv      penta3_improv       mr1_improv       
 Length:6149        Length:6149        Length:6149        Length:6149       
 Class :character   Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character  
                                                                            
  mr2_improv        penta1_date_joint    penta2_date_joint   
 Length:6149        Min.   :2019-09-12   Min.   :2019-11-14  
 Class :character   1st Qu.:2022-08-29   1st Qu.:2022-10-03  
 Mode  :character   Median :2022-11-08   Median :2022-12-09  
                    Mean   :2022-11-28   Mean   :2023-01-01  
 penta3_date_joint    mr1_date_joint       mr2_date_joint         penta1_age     
 Min.   :2022-01-12   Min.   :2022-04-04   Min.   :2022-04-20   Min.   :-320.00  
 1st Qu.:2022-11-01   1st Qu.:2023-04-10   1st Qu.:2023-11-13   1st Qu.:  49.00  
 Median :2023-01-13   Median :2023-06-20   Median :2023-12-25   Median :  57.00  
 Mean   :2023-02-04   Mean   :2023-07-31   Mean   :2024-01-05   Mean   :  59.42  
   penta2_age       penta3_age      mr1_age          mr2_age     
 Min.   :-281.0   Min.   :-253   Min.   : -92.0   Min.   :111.0  
 1st Qu.:  83.0   1st Qu.: 117   1st Qu.: 282.0   1st Qu.:503.0  
 Median :  91.0   Median : 128   Median : 294.0   Median :525.0  
 Mean   : 100.3   Mean   : 141   Mean   : 305.4   Mean   :522.5  
  penta1_time       penta2_time       penta3_time         mr1_time      
 Min.   :0.00000   Min.   :0.00000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.00000   Median :0.00000   Median :0.00000   Median :0.00000  
 Mean   :0.04846   Mean   :0.04798   Mean   :0.04521   Mean   :0.04326  
    mr2_time       penta1_age_joint  penta2_age_joint  penta3_age_joint
 Min.   :0.00000   Min.   :-621.00   Min.   :-336.00   Min.   :-289.0  
 1st Qu.:0.00000   1st Qu.:  48.00   1st Qu.:  80.00   1st Qu.: 111.0  
 Median :0.00000   Median :  56.00   Median :  89.00   Median : 121.0  
 Mean   :0.01854   Mean   :  59.38   Mean   :  92.46   Mean   : 126.1  
 mr1_age_joint    mr2_age_joint   penta1_time_joint penta2_time_joint
 Min.   :-107.0   Min.   :111.0   Min.   :0.0000    Min.   :0.0000   
 1st Qu.: 282.0   1st Qu.:497.0   1st Qu.:1.0000    1st Qu.:1.0000   
 Median : 294.0   Median :517.0   Median :1.0000    Median :1.0000   
 Mean   : 313.2   Mean   :531.7   Mean   :0.8172    Mean   :0.7827   
 penta3_time_joint mr1_time_joint   mr2_time_joint    vacc_stated   
 Min.   :0.0000    Min.   :0.0000   Min.   :0.0000   Min.   :0.000  
 1st Qu.:1.0000    1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:4.000  
 Median :1.0000    Median :1.0000   Median :0.0000   Median :5.000  
 Mean   :0.7552    Mean   :0.5209   Mean   :0.1321   Mean   :4.594  
 penta1_implied    penta2_implied    penta3_implied     mr1_implied     
 Min.   :0.00000   Min.   :0.00000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.00000   Median :0.00000   Median :0.00000   Median :0.00000  
 Mean   :0.08343   Mean   :0.08294   Mean   :0.08164   Mean   :0.07578  
  mr2_implied     base_sub_penta1  base_sub_penta2  base_sub_penta3 
 Min.   :0.0000   Min.   :  0.00   Min.   : 50.00   Min.   : 50.00  
 1st Qu.:0.0000   1st Qu.:100.00   1st Qu.:100.00   1st Qu.: 75.00  
 Median :0.0000   Median :100.00   Median :100.00   Median :100.00  
 Mean   :0.0496   Mean   : 93.54   Mean   : 94.52   Mean   : 87.64  
  base_sub_mr1     base_sub_mr2    base_sub_penta1_joint base_sub_penta2_joint
 Min.   :  0.00   Min.   :  0.00   Min.   :  0.00        Min.   : 50.00       
 1st Qu.: 75.00   1st Qu.: 20.00   1st Qu.:100.00        1st Qu.:100.00       
 Median :100.00   Median : 50.00   Median :100.00        Median :100.00       
 Mean   : 85.96   Mean   : 53.07   Mean   : 94.79        Mean   : 95.83       
 base_sub_penta3_joint base_sub_mr1_joint base_sub_mr2_joint base_phc_penta1 
 Min.   : 50.00        Min.   :  0.00     Min.   :  0.00     Min.   : 60.00  
 1st Qu.: 77.50        1st Qu.: 90.00     1st Qu.: 30.00     1st Qu.: 87.50  
 Median :100.00        Median :100.00     Median : 66.67     Median : 93.75  
 Mean   : 89.42        Mean   : 88.49     Mean   : 64.72     Mean   : 91.79  
 base_phc_penta2  base_phc_penta3   base_phc_mr1     base_phc_mr2  
 Min.   : 66.67   Min.   : 70.00   Min.   : 50.00   Min.   : 0.00  
 1st Qu.: 91.67   1st Qu.: 75.00   1st Qu.: 72.22   1st Qu.:20.00  
 Median : 92.86   Median : 85.00   Median : 84.38   Median :35.29  
 Mean   : 91.31   Mean   : 84.89   Mean   : 81.38   Mean   :36.59  
 base_phc_penta1_joint base_phc_penta2_joint base_phc_penta3_joint
 Min.   : 60.00        Min.   : 66.67        Min.   : 75.00       
 1st Qu.: 91.67        1st Qu.: 91.67        1st Qu.: 78.57       
 Median : 95.00        Median : 92.86        Median : 90.00       
 Mean   : 93.28        Mean   : 93.03        Mean   : 87.62       
 base_phc_mr1_joint base_phc_mr2_joint  amb_recruit            amb_age      
 Min.   : 50.00     Min.   : 0.00      Min.   :2023-01-13   Min.   :-529.0  
 1st Qu.: 75.00     1st Qu.:27.78      1st Qu.:2023-01-13   1st Qu.: 160.0  
 Median : 85.94     Median :37.50      Median :2023-10-04   Median : 232.0  
 Mean   : 84.69     Mean   :41.10      Mean   :2023-07-06   Mean   : 276.6  
 amb_treat_time      numb_amb     amb_in_vill     base_sub_penta1_implied
 Min.   :0.0000   Min.   :0.00   Min.   :0.0000   Min.   :  0.00         
 1st Qu.:1.0000   1st Qu.:0.00   1st Qu.:0.0000   1st Qu.: 75.00         
 Median :1.0000   Median :0.00   Median :0.0000   Median : 83.33         
 Mean   :0.9724   Mean   :1.07   Mean   :0.2571   Mean   : 84.75         
 base_sub_penta2_implied base_sub_penta3_implied base_sub_mr1_implied
 Min.   :  0.00          Min.   :  0.00          Min.   :  0.00      
 1st Qu.: 75.00          1st Qu.: 75.00          1st Qu.: 66.67      
 Median : 80.00          Median : 80.00          Median : 80.00      
 Mean   : 83.58          Mean   : 82.93          Mean   : 76.98      
 base_sub_mr2_implied base_phc_penta1_implied base_phc_penta2_implied
 Min.   :  0.00       Min.   : 76.47          Min.   : 76.47         
 1st Qu.: 50.00       1st Qu.: 82.28          1st Qu.: 82.28         
 Median : 66.67       Median : 86.79          Median : 86.54         
 Mean   : 67.45       Mean   : 85.71          Mean   : 85.24         
 base_phc_penta3_implied base_phc_mr1_implied base_phc_mr2_implied
 Min.   : 73.53          Min.   : 69.49       Min.   :  0.00      
 1st Qu.: 81.13          1st Qu.: 73.42       1st Qu.: 44.44      
 Median : 84.21          Median : 77.36       Median : 49.12      
 Mean   : 83.83          Mean   : 78.03       Mean   : 51.73      
    nom_surv       asha_convo_this_month belief_one_code mobilised_others
 Min.   :0.00000   Min.   :0.000         Min.   :0.000   Min.   :0.000   
 1st Qu.:0.00000   1st Qu.:0.000         1st Qu.:1.000   1st Qu.:0.000   
 Median :0.00000   Median :0.000         Median :1.000   Median :0.000   
 Mean   :0.07953   Mean   :0.373         Mean   :0.792   Mean   :0.109   
 got_card_code  
 Min.   :0.000  
 1st Qu.:0.000  
 Median :1.000  
 Mean   :0.543  
 [ reached getOption("max.print") -- omitted 3 rows ]

d1 %>% str()

tibble [6,149 × 221] (S3: tbl_df/tbl/data.frame)
 $ SubmissionDate               : Date[1:6149], format: "2024-06-25" "2024-06-25" ...
 $ starttime                    : Date[1:6149], format: "2024-06-25" "2024-06-25" ...
 $ endtime                      : Date[1:6149], format: "2024-06-25" "2024-06-25" ...
 $ duration                     : num [1:6149] 929 78 78 149 368 223 143 281 732 102 ...
  ..- attr(*, "label")= chr "Duration"
  ..- attr(*, "format.spss")= chr "F15.0"
 $ resp_sno                     : num [1:6149] 1115 1116 1117 1723 1724 ...
  ..- attr(*, "label")= chr "कृपया तुम्ही ज्या प्रतिसादकर्त्याला कॉल करू इच्छिता त्याचा sno प्रविष्ट करा"
  ..- attr(*, "format.spss")= chr "F15.0"
 $ rchid                        : chr [1:6149] "227023765057" "227024432924" "227024332530" "227027110279" ...
  ..- attr(*, "label")= chr "rchid"
  ..- attr(*, "format.spss")= chr "A12"
 $ mother_name                  : chr [1:6149] "Priyanka Sunil Ithape(127024696803)" "Priyanka Vinod Pawar(127025092576)" "Gayatri Sunil Gaikwad(127025395656)" "Priyanka Krushnath Hanmant Jagtap(127026654806)" ...
  ..- attr(*, "label")= chr "mother_name"
  ..- attr(*, "format.spss")= chr "A52"
 $ father_name                  : chr [1:6149] "Sunil Prakash Ithape" "Vinod Pawar" "Sunil GAIKWAD" "Krushnath Hanmant Jagtap" ...
  ..- attr(*, "label")= chr "father_name"
  ..- attr(*, "format.spss")= chr "A33"
 $ enrollee_phonenumber         : chr [1:6149] "7798814577" "9075128510" "8999762668" "9373826648" ...
  ..- attr(*, "label")= chr "enrollee_phonenumber"
  ..- attr(*, "format.spss")= chr "A10"
 $ village_surv                 : chr [1:6149] "Degaon (37904)" "Kalangwadi (37918)" "Virmade (37898)" "Randullabad (38359)" ...
  ..- attr(*, "label")= chr "village"
  ..- attr(*, "format.spss")= chr "A28"
 $ block                        : chr [1:6149] "Wai(18)" "Wai(18)" "Wai(18)" "Koregaon(22)" ...
  ..- attr(*, "label")= chr "block"
  ..- attr(*, "format.spss")= chr "A12"
 $ health_centre                : chr [1:6149] "Bhuinj(76)" "Bhuinj(76)" "Bhuinj(76)" "Wathar Station(26)" ...
  ..- attr(*, "label")= chr "health_centre"
  ..- attr(*, "format.spss")= chr "A18"
 $ health_subcentre             : chr [1:6149] "Degaon(184)" "Kikali(185)" "Udatare(183)" "Karanjkhop(152)" ...
  ..- attr(*, "label")= chr "health_subcentre"
  ..- attr(*, "format.spss")= chr "A21"
 $ category_surv                : chr [1:6149] "pre_treat_wai" "pre_treat_wai" "pre_treat_wai" "post_treat_wai" ...
  ..- attr(*, "label")= chr "category"
  ..- attr(*, "format.spss")= chr "A19"
 $ surveyor_name                : chr [1:6149] "Vishal Ghuge" "Vishal Ghuge" "Vishal Ghuge" "Sandhya Salve" ...
  ..- attr(*, "label")= chr "तुमचे नाव निवडा"
  ..- attr(*, "format.spss")= chr "A16"
 $ call_status                  : Factor w/ 6 levels "उचलला","प्रतिसादकर्त्याने नंतर कॉल करण्यास सांगितल",..: 1 6 4 6 2 3 6 2 1 6 ...
  ..- attr(*, "label")= chr "कृपया कॉल स्थिती निवडा"
 $ call_later                   : Date[1:6149], format: NA NA ...
 $ verify_respondent            : Factor w/ 2 levels "नाही","हो": 2 NA NA NA NA NA NA NA 2 NA ...
 $ is_respondent                : chr [1:6149] "Priyanka Sunil Ithape(127024696803)" NA NA NA ...
  ..- attr(*, "label")= chr "मी ${mother_name}/${father_name} यांच्याशी बोलत आहे का?"
  ..- attr(*, "format.spss")= chr "A48"
 $ get_mother                   : Factor w/ 2 levels "नाही","हो": NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "label")= chr "मी ${mother_name} किंवा ${father_name} यांच्याशी बोलू शकतो/शकते का?"
 $ get_mother_verification      : Factor w/ 3 levels "इतर [निर्दिष्ट करा]",..: NA NA NA NA NA NA NA NA NA NA ...
 $ has_number                   : Factor w/ 2 levels "नाही","हो": NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "label")= chr "कृपया तुम्ही मला ${mother_name} किंवा ${father_name} चा नंबर देऊ शकाल का?"
 $ mother_number                : chr [1:6149] NA NA NA NA ...
  ..- attr(*, "label")= chr "कृपया मोबाईल नंबर ची नोंद करा:"
  ..- attr(*, "format.spss")= chr "A10"
 $ speak_other                  : Factor w/ 2 levels "नाही","हो": NA NA NA NA NA NA NA NA NA NA ...
 $ speak_other_name             : chr [1:6149] NA NA NA NA ...
  ..- attr(*, "label")= chr "कृपया तुमचे नाव सांगाल का?"
  ..- attr(*, "format.spss")= chr "A27"
 $ consent                      : Factor w/ 2 levels "नाही","हो": 2 NA NA NA NA NA NA NA 2 NA ...
 $ have_vaccination_card        : chr [1:6149] "yes" NA NA NA ...
 $ get_vacc_card                : chr [1:6149] "yes" NA NA NA ...
 $ no_card_reason               : chr [1:6149] NA NA NA NA ...
 $ age                          : num [1:6149] 30 NA NA NA NA NA NA NA 33 NA ...
  ..- attr(*, "label")= chr "कृपया तुमचे वय सांगू शकाल का? (पूर्ण वर्षांच्या संख्येत)"
  ..- attr(*, "format.spss")= chr "F15.0"
 $ gender                       : chr [1:6149] "female" NA NA NA ...
 $ religion                     : chr [1:6149] "hindu" NA NA NA ...
 $ caste                        : Factor w/ 3 levels "OBC","FC","SC/ST": 2 NA NA NA NA NA NA NA 1 NA ...
 $ education                    : chr [1:6149] "degree" NA NA NA ...
 $ literacy                     : chr [1:6149] "literate" "not literate" "not literate" "not literate" ...
 $ occupation                   : chr [1:6149] "agri" NA NA NA ...
 $ occupation_agri              : chr [1:6149] "owns" NA NA NA ...
 $ occupation_agri_2            : Factor w/ 3 levels "शेतकरी","शेत मजूर",..: NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "label")= chr "कृपया विचारा की ते शेतकरी आहेत की शेतमजूर म्हणून काम करतात?"
 $ child_gender                 : chr [1:6149] "female" NA NA NA ...
 $ dob_stated                   : Factor w/ 3 levels "माहिती नाही",..: NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "label")= chr "कृपया मला तुमच्या बाळाची जन्मतारीख सांगू शकाल का?"
 $ child_age                    : chr [1:6149] NA NA NA NA ...
  ..- attr(*, "label")= chr "child_age"
  ..- attr(*, "format.spss")= chr "A5"
 $ child_age_other              : chr [1:6149] NA NA NA NA ...
  ..- attr(*, "label")= chr "child_age_other"
  ..- attr(*, "format.spss")= chr "A4"
 $ child_location               : chr [1:6149] "always" NA NA NA ...
 $ child_migration_pattern      : chr [1:6149] NA NA NA NA ...
 $ age_or_date                  : Factor w/ 2 levels "बाळाचे वय","कॅलेंडर महिना आणि वर्ष": NA NA NA NA NA NA NA NA NA NA ...
 $ child_migration_1_calendar   : Date[1:6149], format: NA NA ...
 $ child_migration_2_calendar   : Date[1:6149], format: NA NA ...
 $ vacc_visits                  : Factor w/ 9 levels "माहीत नाही","कधीच नाही",..: 9 NA NA NA NA NA NA NA 6 NA ...
 $ remaining_vaccines           : num [1:6149] NA NA NA NA NA NA NA NA NA NA ...
 $ next_vaccination             : Factor w/ 5 levels "माहीत नाही","या महिन्यात किंवा पुढच्या महिन्यात",..: NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "label")= chr "तुमच्या बाळाचे पुढील लसीकरण कधी होणार आहे हे तुम्हाला माहीत आहे का?"
 $ penta1_stated                : Factor w/ 2 levels "नाही","हो": 2 NA NA NA NA NA NA NA 2 NA ...
 $ penta2_stated                : Factor w/ 2 levels "नाही","हो": 2 NA NA NA NA NA NA NA 2 NA ...
 $ penta3_stated                : Factor w/ 2 levels "नाही","हो": 2 NA NA NA NA NA NA NA 2 NA ...
 $ mr1_stated                   : Factor w/ 2 levels "नाही","हो": 2 NA NA NA NA NA NA NA 2 NA ...
 $ mr2_stated                   : Factor w/ 2 levels "नाही","हो": 2 NA NA NA NA NA NA NA 2 NA ...
 $ mobilised                    : num [1:6149] 0 NA NA NA NA NA NA NA 0 NA ...
 $ reminded                     : chr [1:6149] NA NA NA NA ...
  ..- attr(*, "label")= chr "खालीलपैकी कोणी-कोणी तुम्हाला तुमच्या बाळाचे लसीकरण करण्याची आठवण करून दिली आहे?"
  ..- attr(*, "format.spss")= chr "A9"
 $ reminded_asha                : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "label")= chr "खालीलपैकी कोणी-कोणी तुम्हाला तुमच्या बाळाचे लसीकरण करण्याची आठवण करून दिली आहे?: आशा ताई?"
 $ reminded_anm                 : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "label")= chr "खालीलपैकी कोणी-कोणी तुम्हाला तुमच्या बाळाचे लसीकरण करण्याची आठवण करून दिली आहे?: ANM?"
 $ reminded_aww                 : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ reminded_doc                 : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ reminded_fam                 : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ reminded_others              : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ reminded_7                   : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "label")= chr "खालीलपैकी कोणी-कोणी तुम्हाला तुमच्या बाळाचे लसीकरण करण्याची आठवण करून दिली आहे?: कोणी नाही?"
 $ reminded__9999               : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "label")= chr "खालीलपैकी कोणी-कोणी तुम्हाला तुमच्या बाळाचे लसीकरण करण्याची आठवण करून दिली आहे?: माहीत नाही?"
 $ checked_vaccine_status       : chr [1:6149] NA NA NA NA ...
  ..- attr(*, "label")= chr "खालीलपैकी कोणी-कोणी तुमच्या बाळाचे लसीकरण झाले की नाही याची चौकशी किंवा पडताळणी केली आहे?"
  ..- attr(*, "format.spss")= chr "A9"
 $ checked_vaccine_status_asha  : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ checked_vaccine_status_anm   : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "label")= chr "खालीलपैकी कोणी-कोणी तुमच्या बाळाचे लसीकरण झाले की नाही याची चौकशी किंवा पडताळणी केली आहे?: ANM?"
 $ checked_vaccine_status_aww   : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ checked_vaccine_status_doc   : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ checked_vaccine_status_fam   : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ checked_vaccine_status_others: Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ checked_vaccine_status_7     : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ checked_vaccine_status__9999 : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ vaccine_info                 : chr [1:6149] NA NA NA NA ...
  ..- attr(*, "format.spss")= chr "A11"
 $ vaccine_info_asha            : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ vaccine_info_anm             : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ vaccine_info_aww             : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ vaccine_info_doc             : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ vaccine_info_fam             : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ vaccine_info_others          : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ vaccine_info_7               : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ vaccine_info__9999           : Factor w/ 2 levels "No","Yes": NA NA NA NA NA NA NA NA NA NA ...
 $ amb_presence                 : num [1:6149] NA NA NA NA NA NA NA NA 0 NA ...
 $ know_asha                    : num [1:6149] 1 NA NA NA NA NA NA NA 1 NA ...
 $ asha_convo                   : chr [1:6149] NA NA NA NA ...
 $ convo_initiation             : num [1:6149] NA NA NA NA NA NA NA NA 0 NA ...
 $ belief_one                   : Factor w/ 5 levels "नक्कीच 2 आठवड्यांनंतरच्या लसीकरणासाठी जाईन",..: 5 NA NA NA NA NA NA NA 5 NA ...
 $ get_vacc_card_later          : chr [1:6149] NA NA NA NA ...
 $ get_card_appointment         : Date[1:6149], format: NA NA ...
 $ get_card_pic                 : Factor w/ 2 levels "नाही","हो": NA NA NA NA NA NA NA NA NA NA ...
 $ which_phone_pic              : chr [1:6149] NA NA NA NA ...
  ..- attr(*, "format.spss")= chr "A10"
 $ note_phone                   : chr [1:6149] NA NA NA NA ...
  ..- attr(*, "label")= chr "कृपया फोन नंबरची नोंद करा."
  ..- attr(*, "format.spss")= chr "A16"
 $ iscomplete                   : Factor w/ 2 levels "नाही","हो": 2 NA NA NA NA NA NA NA 2 NA ...
  ..- attr(*, "label")= chr "सर्वेक्षकाचे निरीक्षण [प्रतिसादकर्त्याला वाचू नका]: तुम्ही हे सर्वेक्षण पूर्ण करू शकलात का?"
 $ callback                     : Factor w/ 2 levels "नाही","हो": 2 NA NA NA NA NA NA NA 2 NA ...
  ..- attr(*, "label")= chr "या प्रतिसादकर्त्याला इतर काही सर्वेक्षणासाठी पुन्हा कॉल केला पाहिजे का?"
 $ surveyorcomment              : chr [1:6149] "*****" NA NA NA ...
  ..- attr(*, "format.spss")= chr "A255"
 $ instanceID                   : chr [1:6149] "uuid:85b919a9-3fe4-4a51-aa90-a97feb5544be" "uuid:2b56af35-067e-42d4-939b-1d824969b3ff" "uuid:b419131c-7b4e-4d15-b4f4-4e13783832c1" "uuid:c1dd931f-3430-4c9e-9da8-208f6e877d62" ...
  ..- attr(*, "label")= chr "instanceID"
  ..- attr(*, "format.spss")= chr "A41"
 $ formdef_version              : num [1:6149] 2406251337 2406251337 2406251337 2406251337 2406251337 ...
  ..- attr(*, "label")= chr "formdef_version"
  ..- attr(*, "format.spss")= chr "F15.0"
 $ penta1_date                  : Date[1:6149], format: "2022-07-14" NA ...
  [list output truncated]

The Next Section uses the “skimr” package, creating multiple outputs all summarizing the data.

#devtools::install_github("ropensci/skimr")
#install.packages("skimr")
 skimr::skim(d1)

── Data Summary ────────────────────────
                           Values
Name                       d1    
Number of rows             6149  
Number of columns          221   
_______________________          
Column type frequency:           
  character                51    
  Date                     28    
  difftime                 1     
  factor                   48    
  logical                  6     
  numeric                  87    
________________________         
Group variables            None  

NA
NA

This section below only includes completed surveys of relevant children

 d1 %>% 
  filter(child_location == "always" | child_location == "moved") %>% ## only children living in relevant vilage 
  filter(iscomplete == "हो") %>% skimr::skim()

Warning: There was 1 warning in `dplyr::summarize()`.
ℹ In argument: `dplyr::across(tidyselect::any_of(variable_names),
  mangled_skimmers$funs)`.
ℹ In group 0: .
Caused by warning:
! There were 2 warnings in `dplyr::summarize()`.
The first warning was:
ℹ In argument: `dplyr::across(tidyselect::any_of(variable_names),
  mangled_skimmers$funs)`.
Caused by warning in `min.default()`:
! no non-missing arguments to min; returning Inf
ℹ Run ]8;;ide:run:dplyr::last_dplyr_warnings()dplyr::last_dplyr_warnings()]8;; to see the 1 remaining warning.

── Data Summary ────────────────────────
                           Values    
Name                       Piped data
Number of rows             579       
Number of columns          221       
_______________________              
Column type frequency:               
  character                51        
  Date                     28        
  difftime                 1         
  factor                   48        
  logical                  6         
  numeric                  87        
________________________             
Group variables            None