library(tidyverse)
library(skimr)Anemia in Women
Proposal
Data 1
Introduction and data
Identify the source of the data.
- The World Health Organization (WHO)
State when and how it was originally collected (by the original data curator, not necessarily how you found the data).
This data was collected by combining data from data sources where “blood hemoglobin was measured; the study reported anemia or mean hemoglobin for preschool-age children (6-59 months of age) or women of reproductive age (15 to 49 years of age); a probabilistic sampling method with a defined sampling frame was used and data were representative of at least three areas within a country; data were collected in or after 1995; data were from 194 Member States or 3 territories: Puerto Rico; Taiwan, China; West Bank and Gaza Strip; and standard, validated data collection techniques and laboratory methodologies were used. across the world.”
“We accessed data in two forms: 1) anonymized individual-level haemoglobin data when available, and 2) summary statistics, including mean hemoglobin and anaemia prevalences below specific thresholds. We used anaemia prevalences with any threshold in our statistical model described in Section 4, which accounts for the specific thresholds used to define anaemia when using the data.”
(https://cdn.who.int/media/docs/default-source/anaemia-in-women-and-children/hb-methods-for-gather.pdf?sfvrsn=da0fbb5f_11)
https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children
Write a brief description of the observations.
- The observations in the data set include a country, the year the data was collected, the age range of participants in the study, whether the study included pregnant women, the sample size, the mean level, and proportions of various ranges that the data fell into.
Research question
- A well formulated research question. (You may include more than one research question if you want to receive feedback on different ideas for your project. However, one per data set is required.)
- Do women from developing countries have higher rates of anemia than developed countries?
- A description of the research topic along with a concise statement of your hypotheses on this topic.
- I believe that women from developing countries will have higher rates of anemia in their country because there is less access to supplements to improve these levels and higher prevalence of a poor diet.
- Identify the types of variables in your research question. Categorical? Quantitative?
- Categorical variables includes the country’s name (member_state) and quantitative variables include the rates of anemia.
Glimpse of data
# add code here
library(readxl)
anaemia_estimates_inputdata_final <- read_excel(path = "data/anaemia-estimates_inputdata_final.xlsx",
sheet = "data")
anaemia_estimates_inputdata_final# A tibble: 1,067 × 23
iso3 member_state beginyear endyear adminlevel sex agerange
<chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
1 AFG Afghanistan 2002 2002 National 0 0-71
2 AFG Afghanistan 2002 2002 National 2 15-49
3 AFG Afghanistan 2004 2004 National 0 6-59
4 AFG Afghanistan 2004 2004 National 2 15-49
5 AFG Afghanistan 2013 2013 National 0 6-59
6 AFG Afghanistan 2013 2013 National 2 15-49
7 AFG Afghanistan 2013 2013 National 2 15-49
8 ALB Albania 2008 2009 National 0 6-59
9 ALB Albania 2008 2009 National 2 15-49
10 ALB Albania 2008 2009 National 2 17-43
# ℹ 1,057 more rows
# ℹ 16 more variables: altitude_adjusted <chr>, pregnancy <chr>,
# samplesize <chr>, mean <dbl>, below130 <dbl>, below120 <dbl>,
# below110 <dbl>, below115 <dbl>, below100 <dbl>, below90 <dbl>,
# below80 <dbl>, below70 <dbl>, is_microdata <dbl>, Reference1 <chr>,
# Reference2 <chr>, Reference3 <chr>glimpse(anaemia_estimates_inputdata_final)Rows: 1,067
Columns: 23
$ iso3 <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AL…
$ member_state <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghan…
$ beginyear <dbl> 2002, 2002, 2004, 2004, 2013, 2013, 2013, 2008, 2008…
$ endyear <dbl> 2002, 2002, 2004, 2004, 2013, 2013, 2013, 2009, 2009…
$ adminlevel <chr> "National", "National", "National", "National", "Nat…
$ sex <chr> "0", "2", "0", "2", "0", "2", "2", "0", "2", "2", "0…
$ agerange <chr> "0-71", "15-49", "6-59", "15-49", "6-59", "15-49", "…
$ altitude_adjusted <chr> "no", "no", "yes", "yes", "yes", "yes", "yes", "yes"…
$ pregnancy <chr> "non-pregnant", "all women", "non-pregnant", "non-pr…
$ samplesize <chr> "858", "916", "868", "1142", "909", "993", "144", "1…
$ mean <dbl> NA, NA, 112.0000, 128.0000, 110.1000, 121.3000, 117.…
$ below130 <dbl> NA, NA, NA, NA, NA, NA, NA, 0.8726373, 0.5368404, 0.…
$ below120 <dbl> NA, NA, NA, 0.2470000, NA, 0.4130000, 0.5280000, 0.4…
$ below110 <dbl> 0.28799999, 0.12899999, 0.37900001, NA, 0.44900000, …
$ below115 <dbl> NA, NA, NA, NA, NA, NA, NA, 0.3019797, 0.1091358, 0.…
$ below100 <dbl> NA, NA, NA, NA, NA, NA, NA, 0.06182195, 0.02641631, …
$ below90 <dbl> NA, NA, NA, NA, NA, NA, NA, 0.020917023, 0.012083710…
$ below80 <dbl> NA, NA, NA, NA, NA, 0.016000001, 0.021000000, 0.0060…
$ below70 <dbl> NA, NA, NA, NA, 0.0189999994, NA, 0.0000000000, 0.00…
$ is_microdata <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1…
$ Reference1 <chr> "National Malaria Prevalence Survey Afghanistan, Oct…
$ Reference2 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Reference3 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …Data 2
Introduction and data
Identify the source of the data.
- The source of this data is UNICEF, the WHO, and the World Bank Group, but I got this data set off of the UNICEF website.
State when and how it was originally collected (by the original data curator, not necessarily how you found the data).
- The data was collected from surveys and other primary sources.
Write a brief description of the observations.
- The observations include a country name, region, the country’s income level, the data source year, the national point estimate for childhod obesity, male point estimate childhood obesity, female point estimate for childhood obesity, urban, rural, and the point estimates for 0-5 months, 5-11 months, all the way to 59 months.
Research question
- A well formulated research question. (You may include more than one research question if you want to receive feedback on different ideas for your project. However, one per data set is required.)
- Do countries apart of the WHO low income group have higher childhood obesity rates than countries in a higher income group?
- A description of the research topic along with a concise statement of your hypotheses on this topic.
- I believe that childhood obesity is more prevalent in countries in a lower income group because they will more likely have access to lower quality, less nutritious food, experience food insecurity, and less access to food in general.
- Identify the types of variables in your research question. Categorical? Quantitative?
- Quantitative variables include the obesity rates and categorical variables include the countries and income group.
Glimpse of data
# add code here
library(readxl)
UNICEF_WHO_WB_Global_Expanded_Databases_Overweight_Dec_2022 <- read_excel("data/UNICEF_WHO_WB_Global_Expanded_Databases_Overweight_Dec_2022.xlsx",
sheet = "Trend", skip = 8)
glimpse(UNICEF_WHO_WB_Global_Expanded_Databases_Overweight_Dec_2022)Rows: 931
Columns: 803
$ UNICEF_Survey_ID <dbl> 1, 2, 3, 3076, 4, 5, 6, 7, 2606, 9, 10, …
$ ISO3Code <chr> "AFG", "AFG", "AFG", "AFG", "ALB", "ALB"…
$ CountryName <chr> "Afghanistan", "Afghanistan", "Afghanist…
$ UNICEF_Reporting_Sub_Region <chr> "SA", "SA", "SA", "SA", "EECA", "EECA", …
$ UNRegion <chr> "Asia", "Asia", "Asia", "Asia", "Europe"…
$ WHORegion <chr> "EMRO", "EMRO", "EMRO", "EMRO", "EURO", …
$ WB_Latest <chr> "Low Income", "Low Income", "Low Income"…
$ DataSourceYears <chr> "1997", "2004", "2013", "2018", "1996-98…
$ CMRS_year <dbl> 1997, 2004, 2013, 2018, 1997, 2000, 2005…
$ DataSourceTypeGlobal <chr> "MICS", "NNS", "NNS", "Other", "NNS", "M…
$ FullSourceTitle <chr> "Afghanistan 1997 multiple indicator bas…
$ LatestSource <chr> "Historical Trend", "Historical Trend", …
$ Estimate_Type <chr> "Adjusted", "External Reanalysis", "Rean…
$ National_r <dbl> 6.500000, 4.600000, 5.341543, 4.112625, …
$ National_ll <dbl> NA, NA, 4.748376, 3.484750, NA, 26.43551…
$ National_ul <dbl> NA, NA, 6.004138, 4.847946, NA, 33.96021…
$ National_weighted_N <dbl> 4846.000, 946.000, 4247295.000, 19692.41…
$ National_Footnote <chr> "cw 17", "w 18", "rs", "rs", "c", NA, NA…
$ male_r <dbl> NA, 6.300000, 4.877720, 4.026020, NA, 29…
$ male_ll <dbl> NA, NA, 4.279081, 3.307235, NA, 25.11148…
$ male_ul <dbl> NA, NA, 5.555248, 4.893119, NA, 34.14138…
$ male_weighted_N <dbl> NA, 510.0000, 2167205.3000, 10099.3080, …
$ male_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ female_r <dbl> NA, 2.600000, 5.824791, 4.203799, NA, 30…
$ female_ll <dbl> NA, NA, 5.0197797, 3.3498871, NA, 26.081…
$ female_ul <dbl> NA, NA, 6.749728, 5.263526, NA, 35.73515…
$ female_weighted_N <dbl> NA, 436.0000, 2080089.9000, 9593.1113, N…
$ female_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ urban_r <dbl> NA, NA, 3.338518, 4.174238, NA, 29.22605…
$ urban_ll <dbl> NA, NA, 2.459419, 3.211777, NA, 25.26097…
$ urban_ul <dbl> NA, NA, 4.517290, 5.409000, NA, 33.53426…
$ urban_weighted_N <dbl> NA, NA, 1013277.6000, 4477.0874, NA, 434…
$ urban_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ rural_r <dbl> NA, NA, 5.969129, 4.094495, NA, 30.59583…
$ rural_ll <dbl> NA, NA, 5.2738957, 3.3486671, NA, 25.330…
$ rural_ul <dbl> NA, NA, 6.749480, 4.997847, NA, 36.42130…
$ rural_weighted_N <dbl> NA, NA, 3234017.5000, 15215.3310, NA, 68…
$ rural_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ month_0_5_r <dbl> NA, NA, 8.684652, 9.696854, NA, 23.51956…
$ month_0_5_ll <dbl> NA, NA, 6.9367766, 7.7714591, NA, 15.355…
$ month_0_5_ul <dbl> NA, NA, 10.821731, 12.037010, NA, 34.267…
$ month_0_5_weighted_N <dbl> NA, NA, 479172.53000, 1902.75560, NA, 91…
$ month_0_5_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ month_6_11_r <dbl> NA, 1.900000, 5.210856, 5.136866, NA, 28…
$ month_6_11_ll <dbl> NA, NA, 3.9883125, 3.3756652, NA, 19.393…
$ month_6_11_ul <dbl> NA, NA, 6.781677, 7.743309, NA, 39.55723…
$ month_6_11_weighted_N <dbl> NA, 107.00000, 470237.34000, 1905.15890,…
$ month_6_11_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ month_12_23_r <dbl> NA, 4.3000002, 5.0334592, 3.0880234, NA,…
$ month_12_23_ll <dbl> NA, NA, 3.9956398, 2.2676706, NA, 25.385…
$ month_12_23_ul <dbl> NA, NA, 6.323086, 4.192416, NA, 38.85385…
$ month_12_23_weighted_N <dbl> NA, 189.0000, 851898.8100, 3785.3064, NA…
$ month_12_23_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ month_0_23_r <dbl> NA, NA, 6.051036, 5.258164, NA, 29.18906…
$ month_0_23_ll <dbl> NA, NA, 5.2406063, 4.4893394, NA, 24.203…
$ month_0_23_ul <dbl> NA, NA, 6.977566, 6.150177, NA, 34.73123…
$ month_0_23_weighted_N <dbl> NA, NA, 1801308.8000, 7593.2212, NA, 414…
$ month_0_23_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ month_24_35_r <dbl> NA, 3.000000, 4.978026, 3.699833, NA, 36…
$ month_24_35_ll <dbl> NA, NA, 4.1009269, 2.6765881, NA, 29.822…
$ month_24_35_ul <dbl> NA, NA, 6.030919, 5.093785, NA, 43.71833…
$ month_24_35_weighted_N <dbl> NA, 182.0000, 836250.0000, 3955.9377, NA…
$ month_24_35_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ month_36_47_r <dbl> NA, 5.600000, 5.109035, 3.697031, NA, 28…
$ month_36_47_ll <dbl> NA, NA, 4.159408, 2.703498, NA, 22.58336…
$ month_36_47_ul <dbl> NA, NA, 6.261305, 5.036785, NA, 34.86068…
$ month_36_47_weighted_N <dbl> NA, 227.0000, 821790.2500, 4209.2612, NA…
$ month_36_47_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ month_48_59_r <dbl> NA, 6.400000, 4.347883, 2.682182, NA, 26…
$ month_48_59_ll <dbl> NA, NA, 3.3467705, 1.7192675, NA, 20.047…
$ month_48_59_ul <dbl> NA, NA, 5.631009, 4.161562, NA, 35.17643…
$ month_48_59_weighted_N <dbl> NA, 241.0000, 787946.0600, 3670.1497, NA…
$ month_48_59_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ month_24_59_r <dbl> NA, NA, 4.819048, 3.383262, NA, 30.57963…
$ month_24_59_ll <dbl> NA, NA, 4.104224, 2.631692, NA, 26.47752…
$ month_24_59_ul <dbl> NA, NA, 5.651035, 4.339902, NA, 35.01459…
$ month_24_59_weighted_N <dbl> NA, NA, 2445986.3000, 11835.3490, NA, 70…
$ month_24_59_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Q1_r <dbl> NA, NA, NA, 4.486930, NA, NA, 24.932640,…
$ Q1_ll <dbl> NA, NA, NA, 3.159135, NA, NA, 17.532993,…
$ Q1_ul <dbl> NA, NA, NA, 6.336286, NA, NA, 34.161575,…
$ Q1_weighted_N <dbl> NA, NA, NA, 3576.3015, NA, NA, 242.1194,…
$ Q1_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Q2_r <dbl> NA, NA, NA, 4.670275, NA, NA, 20.335453,…
$ Q2_ll <dbl> NA, NA, NA, 3.371494, NA, NA, 14.784638,…
$ Q2_ul <dbl> NA, NA, NA, 6.436054, NA, NA, 27.302578,…
$ Q2_weighted_N <dbl> NA, NA, NA, 3767.8843, NA, NA, 229.6549,…
$ Q2_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Q3_r <dbl> NA, NA, NA, 4.078213, NA, NA, 26.438326,…
$ Q3_ll <dbl> NA, NA, NA, 3.229279, NA, NA, 20.034916,…
$ Q3_ul <dbl> NA, NA, NA, 5.138470, NA, NA, 34.017708,…
$ Q3_weighted_N <dbl> NA, NA, NA, 4178.4541, NA, NA, 196.4414,…
$ Q3_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Q4_r <dbl> NA, NA, NA, 3.264172, NA, NA, 27.741989,…
$ Q4_ll <dbl> NA, NA, NA, 2.544465, NA, NA, 21.161648,…
$ Q4_ul <dbl> NA, NA, NA, 4.178721, NA, NA, 35.448486,…
$ Q4_weighted_N <dbl> NA, NA, NA, 4315.4229, NA, NA, 224.7820,…
$ Q4_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Q5_r <dbl> NA, NA, NA, 4.254687, NA, NA, 25.074383,…
$ Q5_ll <dbl> NA, NA, NA, 3.151943, NA, NA, 19.318176,…
$ Q5_ul <dbl> NA, NA, NA, 5.720449, NA, NA, 31.868288,…
$ Q5_weighted_N <dbl> NA, NA, NA, 3575.6462, NA, NA, 182.3377,…
$ Q5_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Bottom_80_r <dbl> NA, NA, NA, 4.089552, NA, NA, 24.788746,…
$ Bottom_80_ll <dbl> NA, NA, NA, 3.389277, NA, NA, 20.979603,…
$ Bottom_80_ul <dbl> NA, NA, NA, 4.927134, NA, NA, 29.035368,…
$ Bottom_80_weighted_N <dbl> NA, NA, NA, 15838.0630, NA, NA, 892.9977…
$ Bottom_80_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Top_20_r <dbl> NA, NA, NA, 4.254687, NA, NA, 25.074383,…
$ Top_20_ll <dbl> NA, NA, NA, 3.151943, NA, NA, 19.318176,…
$ Top_20_ul <dbl> NA, NA, NA, 5.720449, NA, NA, 31.868288,…
$ Top_20_weighted_N <dbl> NA, NA, NA, 3575.6462, NA, NA, 182.3377,…
$ Top_20_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Bottom_60_r <dbl> NA, NA, NA, 4.398670, NA, NA, 23.795301,…
$ Bottom_60_ll <dbl> NA, NA, NA, 3.517382, NA, NA, 19.428713,…
$ Bottom_60_ul <dbl> NA, NA, NA, 5.488207, NA, NA, 28.792572,…
$ Bottom_60_weighted_N <dbl> NA, NA, NA, 11522.6400, NA, NA, 668.2157…
$ Bottom_60_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Top_40_r <dbl> NA, NA, NA, 3.713000, NA, NA, 26.547241,…
$ Top_40_ll <dbl> NA, NA, NA, 3.016004, NA, NA, 21.936138,…
$ Top_40_ul <dbl> NA, NA, NA, 4.563491, NA, NA, 31.733604,…
$ Top_40_weighted_N <dbl> NA, NA, NA, 7891.0693, NA, NA, 407.1197,…
$ Top_40_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Bottom_40_r <dbl> NA, NA, NA, 4.580994, NA, NA, 22.694775,…
$ Bottom_40_ll <dbl> NA, NA, NA, 3.403478, NA, NA, 17.427719,…
$ Bottom_40_ul <dbl> NA, NA, NA, 6.140005, NA, NA, 28.994698,…
$ Bottom_40_weighted_N <dbl> NA, NA, NA, 7344.1860, NA, NA, 471.7743,…
$ Bottom_40_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Top_60_r <dbl> NA, NA, NA, 3.839436, NA, NA, 26.511795,…
$ Top_60_ll <dbl> NA, NA, NA, 3.217672, NA, NA, 22.472521,…
$ Top_60_ul <dbl> NA, NA, NA, 4.575666, NA, NA, 30.986912,…
$ Top_60_weighted_N <dbl> NA, NA, NA, 12069.5230, NA, NA, 603.5611…
$ Top_60_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Bottom_20_r <dbl> NA, NA, NA, 4.486930, NA, NA, 24.932640,…
$ Bottom_20_ll <dbl> NA, NA, NA, 3.159135, NA, NA, 17.532993,…
$ Bottom_20_ul <dbl> NA, NA, NA, 6.336286, NA, NA, 34.161575,…
$ Bottom_20_weighted_N <dbl> NA, NA, NA, 3576.3015, NA, NA, 242.1194,…
$ Bottom_20_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Top_80_r <dbl> NA, NA, NA, 4.037101, NA, NA, 24.809441,…
$ Top_80_ll <dbl> NA, NA, NA, 3.443670, NA, NA, 21.442814,…
$ Top_80_ul <dbl> NA, NA, NA, 4.727789, NA, NA, 28.512794,…
$ Top_80_weighted_N <dbl> NA, NA, NA, 15837.407, NA, NA, 833.216, …
$ Top_80_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ meduc_none_r <dbl> NA, NA, NA, 4.183402, NA, NA, NA, NA, 5.…
$ meduc_none_ll <dbl> NA, NA, NA, 3.452476, NA, NA, NA, NA, 1.…
$ meduc_none_ul <dbl> NA, NA, NA, 5.060963, NA, NA, NA, NA, 18…
$ meduc_none_weighted_N <dbl> NA, NA, NA, 16289.35600, NA, NA, NA, 13.…
$ meduc_none_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, "y", "z", …
$ meduc_primary_r <dbl> NA, NA, NA, 2.141115, NA, 28.809017, NA,…
$ meduc_primary_ll <dbl> NA, NA, NA, 1.309050, NA, 23.371481, NA,…
$ meduc_primary_ul <dbl> NA, NA, NA, 3.483392, NA, 34.934887, NA,…
$ meduc_primary_weighted_N <dbl> NA, NA, NA, 1341.238200, NA, 590.752010,…
$ meduc_primary_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ meduc_noneprimary_r <dbl> NA, NA, NA, 4.028037, NA, 28.809017, 18.…
$ meduc_noneprimary_ll <dbl> NA, NA, NA, 3.343358, NA, 23.371481, 8.6…
$ meduc_noneprimary_ul <dbl> NA, NA, NA, 4.845900, NA, 34.934887, 33.…
$ meduc_noneprimary_weighted_N <dbl> NA, NA, NA, 17630.594000, NA, 590.752010…
$ meduc_noneprimary_Footnote <chr> NA, NA, NA, "rs", NA, NA, "z", NA, NA, N…
$ meduc_second_r <dbl> NA, NA, NA, 6.074845, NA, 31.225658, NA,…
$ meduc_second_ll <dbl> NA, NA, NA, 3.155119, NA, 26.733294, NA,…
$ meduc_second_ul <dbl> NA, NA, NA, 11.379004, NA, 36.100964, NA…
$ meduc_second_weighted_N <dbl> NA, NA, NA, 657.1468, NA, 425.3177, NA, …
$ meduc_second_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ meduc_high_r <dbl> NA, NA, NA, 4.069086, NA, 32.500053, NA,…
$ meduc_high_ll <dbl> NA, NA, NA, 2.677547, NA, 24.286261, NA,…
$ meduc_high_ul <dbl> NA, NA, NA, 6.138204, NA, 41.952553, NA,…
$ meduc_high_weighted_N <dbl> NA, NA, NA, 1269.7307, NA, 101.4368, NA,…
$ meduc_high_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ meduc_secondhigh_r <dbl> NA, NA, NA, 4.753134, NA, 31.471067, 25.…
$ meduc_secondhigh_ll <dbl> NA, NA, NA, 3.241366, NA, 27.382851, 21.…
$ meduc_secondhigh_ul <dbl> NA, NA, NA, 6.919567, NA, 35.868168, 28.…
$ meduc_secondhigh_weighted_N <dbl> NA, NA, NA, 1926.8776, NA, 526.7545, 103…
$ meduc_secondhigh_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Q1_urban_r <dbl> NA, NA, NA, 3.016361, NA, NA, NA, NA, 10…
$ Q1_urban_ll <dbl> NA, NA, NA, 0.5583032, NA, NA, NA, NA, 6…
$ Q1_urban_ul <dbl> NA, NA, NA, 14.69710, NA, NA, NA, NA, 17…
$ Q1_urban_weighted_N <dbl> NA, NA, NA, 25.527323, NA, NA, 14.611031…
$ Q1_urban_Footnote <chr> NA, NA, NA, "rs", NA, NA, "y", "y", NA, …
$ Q1_rural_r <dbl> NA, NA, NA, 4.497502, NA, NA, 25.102837,…
$ Q1_rural_ll <dbl> NA, NA, NA, 3.161255, NA, NA, 17.342329,…
$ Q1_rural_ul <dbl> NA, NA, NA, 6.361470, NA, NA, 34.871025,…
$ Q1_rural_weighted_N <dbl> NA, NA, NA, 3550.7742, NA, NA, 227.5084,…
$ Q1_rural_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Q2_urban_r <dbl> NA, NA, NA, 3.049177, NA, NA, 14.350218,…
$ Q2_urban_ll <dbl> NA, NA, NA, 1.310740, NA, NA, 5.962076, …
$ Q2_urban_ul <dbl> NA, NA, NA, 6.931367, NA, NA, 30.688480,…
$ Q2_urban_weighted_N <dbl> NA, NA, NA, 53.87443, NA, NA, 29.56476, …
$ Q2_urban_Footnote <chr> NA, NA, NA, "rs", NA, NA, "z", "z", NA, …
$ Q2_rural_r <dbl> NA, NA, NA, 4.693790, NA, NA, 21.219812,…
$ Q2_rural_ll <dbl> NA, NA, NA, 3.378205, NA, NA, 15.116982,…
$ Q2_rural_ul <dbl> NA, NA, NA, 6.487310, NA, NA, 28.946226,…
$ Q2_rural_weighted_N <dbl> NA, NA, NA, 3714.0098, NA, NA, 200.0902,…
$ Q2_rural_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Q3_urban_r <dbl> NA, NA, NA, 7.412202, NA, NA, 32.646793,…
$ Q3_urban_ll <dbl> NA, NA, NA, 3.249572, NA, NA, 22.792301,…
$ Q3_urban_ul <dbl> NA, NA, NA, 16.023939, NA, NA, 44.316387…
$ Q3_urban_weighted_N <dbl> NA, NA, NA, 255.80826, NA, NA, 75.73734,…
$ Q3_urban_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Q3_rural_r <dbl> NA, NA, NA, 3.860793, NA, NA, 22.542744,…
$ Q3_rural_ll <dbl> NA, NA, NA, 3.027953, NA, NA, 14.748907,…
$ Q3_rural_ul <dbl> NA, NA, NA, 4.911104, NA, NA, 32.867275,…
$ Q3_rural_weighted_N <dbl> NA, NA, NA, 3922.64580, NA, NA, 120.7040…
$ Q3_rural_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Q4_urban_r <dbl> NA, NA, NA, 3.470106, NA, NA, 36.073513,…
$ Q4_urban_ll <dbl> NA, NA, NA, 2.360802, NA, NA, 26.038397,…
$ Q4_urban_ul <dbl> NA, NA, NA, 5.073569, NA, NA, 47.492714,…
$ Q4_urban_weighted_N <dbl> NA, NA, NA, 1427.3945, NA, NA, 110.3379,…
$ Q4_urban_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Q4_rural_r <dbl> NA, NA, NA, 3.1623905, NA, NA, 19.709400…
$ Q4_rural_ll <dbl> NA, NA, NA, 2.28423810, NA, NA, 12.66734…
$ Q4_rural_ul <dbl> NA, NA, NA, 4.363066, NA, NA, 29.350609,…
$ Q4_rural_weighted_N <dbl> NA, NA, NA, 2888.02830, NA, NA, 114.4441…
$ Q4_rural_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, "z", NA, N…
$ Q5_urban_r <dbl> NA, NA, NA, 4.244097, NA, NA, 28.082657,…
$ Q5_urban_ll <dbl> NA, NA, NA, 3.061449, NA, NA, 21.488605,…
$ Q5_urban_ul <dbl> NA, NA, NA, 5.856008, NA, NA, 35.778069,…
$ Q5_urban_weighted_N <dbl> NA, NA, NA, 2613.1265, NA, NA, 150.2905,…
$ Q5_urban_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Q5_rural_r <dbl> NA, NA, NA, 4.283437, NA, NA, 10.966565,…
$ Q5_rural_ll <dbl> NA, NA, NA, 2.165822, NA, NA, 4.542721, …
$ Q5_rural_ul <dbl> NA, NA, NA, 8.295963, NA, NA, 24.173899,…
$ Q5_rural_weighted_N <dbl> NA, NA, NA, 962.519900, NA, NA, 32.04713…
$ Q5_rural_Footnote <chr> NA, NA, NA, "rs", NA, NA, "z", "y", NA, …
$ Bottom_80_urban_r <dbl> NA, NA, NA, 4.022788, NA, NA, 31.281895,…
$ Bottom_80_urban_ll <dbl> NA, NA, NA, 2.858216, NA, NA, 24.131861,…
$ Bottom_80_urban_ul <dbl> NA, NA, NA, 5.634339, NA, NA, 39.448872,…
$ Bottom_80_urban_weighted_N <dbl> NA, NA, NA, 1762.6046, NA, NA, 230.2510,…
$ Bottom_80_urban_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Bottom_80_rural_r <dbl> NA, NA, NA, 4.097912, NA, NA, 22.532902,…
$ Bottom_80_rural_ll <dbl> NA, NA, NA, 3.332086, NA, NA, 18.147964,…
$ Bottom_80_rural_ul <dbl> NA, NA, NA, 5.030592, NA, NA, 27.619822,…
$ Bottom_80_rural_weighted_N <dbl> NA, NA, NA, 14075.4580, NA, NA, 662.7467…
$ Bottom_80_rural_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Top_20_urban_r <dbl> NA, NA, NA, 4.244097, NA, NA, 28.082657,…
$ Top_20_urban_ll <dbl> NA, NA, NA, 3.061449, NA, NA, 21.488605,…
$ Top_20_urban_ul <dbl> NA, NA, NA, 5.856008, NA, NA, 35.778069,…
$ Top_20_urban_weighted_N <dbl> NA, NA, NA, 2613.1265, NA, NA, 150.2905,…
$ Top_20_urban_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Top_20_rural_r <dbl> NA, NA, NA, 4.283437, NA, NA, 10.966565,…
$ Top_20_rural_ll <dbl> NA, NA, NA, 2.165822, NA, NA, 4.542721, …
$ Top_20_rural_ul <dbl> NA, NA, NA, 8.295963, NA, NA, 24.173899,…
$ Top_20_rural_weighted_N <dbl> NA, NA, NA, 962.519900, NA, NA, 32.04713…
$ Top_20_rural_Footnote <chr> NA, NA, NA, "rs", NA, NA, "z", "y", NA, …
$ Bottom_60_urban_r <dbl> NA, NA, NA, 6.376226, NA, NA, 26.872894,…
$ Bottom_60_urban_ll <dbl> NA, NA, NA, 3.059550, NA, NA, 19.069309,…
$ Bottom_60_urban_ul <dbl> NA, NA, NA, 12.813088, NA, NA, 36.432316…
$ Bottom_60_urban_weighted_N <dbl> NA, NA, NA, 335.2100, NA, NA, 119.9131, …
$ Bottom_60_urban_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Bottom_60_rural_r <dbl> NA, NA, NA, 4.339416, NA, NA, 23.122234,…
$ Bottom_60_rural_ll <dbl> NA, NA, NA, 3.442301, NA, NA, 18.148245,…
$ Bottom_60_rural_ul <dbl> NA, NA, NA, 5.457121, NA, NA, 28.976860,…
$ Bottom_60_rural_weighted_N <dbl> NA, NA, NA, 11187.4300, NA, NA, 548.3026…
$ Bottom_60_rural_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Top_40_urban_r <dbl> NA, NA, NA, 3.970669, NA, NA, 31.465611,…
$ Top_40_urban_ll <dbl> NA, NA, NA, 3.046003, NA, NA, 25.537010,…
$ Top_40_urban_ul <dbl> NA, NA, NA, 5.161091, NA, NA, 38.066952,…
$ Top_40_urban_weighted_N <dbl> NA, NA, NA, 4040.5210, NA, NA, 260.6284,…
$ Top_40_urban_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Top_40_rural_r <dbl> NA, NA, NA, 3.442618, NA, NA, 17.796776,…
$ Top_40_rural_ll <dbl> NA, NA, NA, 2.4478943, NA, NA, 11.868052…
$ Top_40_rural_ul <dbl> NA, NA, NA, 4.821578, NA, NA, 25.819536,…
$ Top_40_rural_weighted_N <dbl> NA, NA, NA, 3850.54830, NA, NA, 146.4912…
$ Top_40_rural_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, "z", NA, N…
$ Bottom_40_urban_r <dbl> NA, NA, NA, 3.038626, NA, NA, 16.973812,…
$ Bottom_40_urban_ll <dbl> NA, NA, NA, 1.376772, NA, NA, 9.213968, …
$ Bottom_40_urban_ul <dbl> NA, NA, NA, 6.572761, NA, NA, 29.169127,…
$ Bottom_40_urban_weighted_N <dbl> NA, NA, NA, 79.40176, NA, NA, 44.17578, …
$ Bottom_40_urban_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, "z", NA, N…
$ Bottom_40_rural_r <dbl> NA, NA, NA, 4.597851, NA, NA, 23.285814,…
$ Bottom_40_rural_ll <dbl> NA, NA, NA, 3.408615, NA, NA, 17.603952,…
$ Bottom_40_rural_ul <dbl> NA, NA, NA, 6.175475, NA, NA, 30.130938,…
$ Bottom_40_rural_weighted_N <dbl> NA, NA, NA, 7264.7842, NA, NA, 427.5985,…
$ Bottom_40_rural_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Top_60_urban_r <dbl> NA, NA, NA, 4.175582, NA, NA, 31.731569,…
$ Top_60_urban_ll <dbl> NA, NA, NA, 3.207554, NA, NA, 26.283968,…
$ Top_60_urban_ul <dbl> NA, NA, NA, 5.419400, NA, NA, 37.730347,…
$ Top_60_urban_weighted_N <dbl> NA, NA, NA, 4296.3291, NA, NA, 336.3657,…
$ Top_60_urban_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Top_60_rural_r <dbl> NA, NA, NA, 3.653645, NA, NA, 19.940741,…
$ Top_60_rural_ll <dbl> NA, NA, NA, 2.875887, NA, NA, 14.724467,…
$ Top_60_rural_ul <dbl> NA, NA, NA, 4.631709, NA, NA, 26.432144,…
$ Top_60_rural_weighted_N <dbl> NA, NA, NA, 7773.1943, NA, NA, 267.1953,…
$ Top_60_rural_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Bottom_20_urban_r <dbl> NA, NA, NA, 3.016361, NA, NA, NA, NA, 10…
$ Bottom_20_urban_ll <dbl> NA, NA, NA, 0.5583032, NA, NA, NA, NA, 6…
$ Bottom_20_urban_ul <dbl> NA, NA, NA, 14.69710, NA, NA, NA, NA, 17…
$ Bottom_20_urban_weighted_N <dbl> NA, NA, NA, 25.527323, NA, NA, 14.611031…
$ Bottom_20_urban_Footnote <chr> NA, NA, NA, "rs", NA, NA, "y", "y", NA, …
$ Bottom_20_rural_r <dbl> NA, NA, NA, 4.497502, NA, NA, 25.102837,…
$ Bottom_20_rural_ll <dbl> NA, NA, NA, 3.161255, NA, NA, 17.342329,…
$ Bottom_20_rural_ul <dbl> NA, NA, NA, 6.361470, NA, NA, 34.871025,…
$ Bottom_20_rural_weighted_N <dbl> NA, NA, NA, 3550.7742, NA, NA, 227.5084,…
$ Bottom_20_rural_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Top_80_urban_r <dbl> NA, NA, NA, 4.161632, NA, NA, 30.327272,…
$ Top_80_urban_ll <dbl> NA, NA, NA, 3.203606, NA, NA, 24.986862,…
$ Top_80_urban_ul <dbl> NA, NA, NA, 5.390199, NA, NA, 36.257389,…
$ Top_80_urban_weighted_N <dbl> NA, NA, NA, 4350.2036, NA, NA, 365.9305,…
$ Top_80_urban_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ Top_80_rural_r <dbl> NA, NA, NA, 3.989942, NA, NA, 20.488436,…
$ Top_80_rural_ll <dbl> NA, NA, NA, 3.275420, NA, NA, 16.505808,…
$ Top_80_rural_ul <dbl> NA, NA, NA, 4.852514, NA, NA, 25.142647,…
$ Top_80_rural_weighted_N <dbl> NA, NA, NA, 11487.2040, NA, NA, 467.2855…
$ Top_80_rural_Footnote <chr> NA, NA, NA, "rs", NA, NA, NA, NA, NA, NA…
$ male_month_0_5_r <dbl> NA, NA, 8.5055065, 9.2116747, NA, 22.412…
$ male_month_0_5_ll <dbl> NA, NA, 6.5303035, 6.7816892, NA, 11.544…
$ male_month_0_5_ul <dbl> NA, NA, 11.007784, 12.396573, NA, 38.999…
$ male_month_0_5_weighted_N <dbl> NA, NA, 251284.52000, 992.35248, NA, 38.…
$ male_month_0_5_Footnote <chr> NA, NA, "rs", "rs", NA, "z", NA, NA, NA,…
$ male_month_6_11_r <dbl> NA, 3.900000, 4.871327, 5.656235, NA, 28…
$ male_month_6_11_ll <dbl> NA, NA, 3.3524032, 3.2136645, NA, 17.391…
$ male_month_6_11_ul <dbl> NA, NA, 7.028406, 9.767940, NA, 42.44111…
$ male_month_6_11_weighted_N <dbl> NA, 54.00000, 237199.20000, 980.54602, N…
$ male_month_6_11_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, "z", NA,…
$ male_month_12_23_r <dbl> NA, 5.100000, 4.421356, 2.683141, NA, 25…
$ male_month_12_23_ll <dbl> NA, NA, 3.3203032, 1.8022888, NA, 16.616…
$ male_month_12_23_ul <dbl> NA, NA, 5.865379, 3.977065, NA, 36.69996…
$ male_month_12_23_weighted_N <dbl> NA, 100.0000, 426757.7800, 1948.9771, NA…
$ male_month_12_23_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ male_month_0_23_r <dbl> NA, NA, 5.659298, 5.078388, NA, 25.67195…
$ male_month_0_23_ll <dbl> NA, NA, 4.7636924, 4.0171652, NA, 19.523…
$ male_month_0_23_ul <dbl> NA, NA, 6.711419, 6.401260, NA, 32.96418…
$ male_month_0_23_weighted_N <dbl> NA, NA, 915241.5000, 3921.8755, NA, 224.…
$ male_month_0_23_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ male_month_24_35_r <dbl> NA, 3.400000, 4.433299, 3.126397, NA, 35…
$ male_month_24_35_ll <dbl> NA, NA, 3.4388163, 2.0211215, NA, 26.724…
$ male_month_24_35_ul <dbl> NA, NA, 5.698406, 4.806457, NA, 46.41336…
$ male_month_24_35_weighted_N <dbl> NA, 94.00000, 442162.44000, 2022.56100, …
$ male_month_24_35_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ male_month_36_47_r <dbl> NA, 8.299999, 5.406296, 3.897113, NA, 27…
$ male_month_36_47_ll <dbl> NA, NA, 4.254167, 2.648536, NA, 20.54607…
$ male_month_36_47_ul <dbl> NA, NA, 6.848133, 5.699835, NA, 36.00901…
$ male_month_36_47_weighted_N <dbl> NA, 126.0000, 405687.7200, 2080.4016, NA…
$ male_month_36_47_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ male_month_48_59_r <dbl> NA, 8.200000, 3.063221, 3.196704, NA, 34…
$ male_month_48_59_ll <dbl> NA, NA, 2.2232635, 1.9265977, NA, 23.388…
$ male_month_48_59_ul <dbl> NA, NA, 4.206864, 5.259224, NA, 47.79512…
$ male_month_48_59_weighted_N <dbl> NA, 136.00000, 404113.50000, 1974.63390,…
$ male_month_48_59_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ male_month_24_59_r <dbl> NA, NA, 4.306351, 3.413061, NA, 31.96515…
$ male_month_24_59_ll <dbl> NA, NA, 3.619783, 2.578099, NA, 26.52214…
$ male_month_24_59_ul <dbl> NA, NA, 5.116229, 4.505933, NA, 37.94830…
$ male_month_24_59_weighted_N <dbl> NA, NA, 1251963.6000, 6077.5967, NA, 331…
$ male_month_24_59_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ female_month_0_5_r <dbl> NA, NA, 8.882191, 10.225705, NA, 24.3213…
$ female_month_0_5_ll <dbl> NA, NA, 6.4039626, 7.4755983, NA, 13.492…
$ female_month_0_5_ul <dbl> NA, NA, 12.19450, 13.83623, NA, 39.83850…
$ female_month_0_5_weighted_N <dbl> NA, NA, 227888.02000, 910.40320, NA, 53.…
$ female_month_0_5_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ female_month_6_11_r <dbl> NA, 0.000000, 5.556447, 4.586079, NA, 28…
$ female_month_6_11_ll <dbl> NA, NA, 3.9235263, 2.8790376, NA, 15.008…
$ female_month_6_11_ul <dbl> NA, NA, 7.813697, 7.229914, NA, 47.89710…
$ female_month_6_11_weighted_N <dbl> NA, 53.00000, 233038.13000, 924.61292, N…
$ female_month_6_11_Footnote <chr> NA, "w 18", "rs", "rs", NA, "z", "z", "z…
$ female_month_12_23_r <dbl> NA, 3.4000001, 5.6478901, 3.5177431, NA,…
$ female_month_12_23_ll <dbl> NA, NA, 4.02236840, 2.21714430, NA, 29.9…
$ female_month_12_23_ul <dbl> NA, NA, 7.876410, 5.538076, NA, 48.92794…
$ female_month_12_23_weighted_N <dbl> NA, 89.00000, 425141.06000, 1836.32930, …
$ female_month_12_23_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ female_month_0_23_r <dbl> NA, NA, 6.4556718, 5.4502082, NA, 33.346…
$ female_month_0_23_ll <dbl> NA, NA, 5.3221302, 4.3953409, NA, 26.071…
$ female_month_0_23_ul <dbl> NA, NA, 7.810725, 6.740391, NA, 41.51165…
$ female_month_0_23_weighted_N <dbl> NA, NA, 886067.1900, 3671.3455, NA, 189.…
$ female_month_0_23_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ female_month_24_35_r <dbl> NA, 2.5000000, 5.5892048, 4.2997208, NA,…
$ female_month_24_35_ll <dbl> NA, NA, 4.2713461, 2.8200912, NA, 27.804…
$ female_month_24_35_ul <dbl> NA, NA, 7.282735, 6.503719, NA, 47.18011…
$ female_month_24_35_weighted_N <dbl> NA, 88.0000, 394087.5600, 1933.3767, NA,…
$ female_month_24_35_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ female_month_36_47_r <dbl> NA, 2.100000, 4.819213, 3.501502, NA, 28…
$ female_month_36_47_ll <dbl> NA, NA, 3.6329298, 2.0096257, NA, 21.339…
$ female_month_36_47_ul <dbl> NA, NA, 6.367267, 6.032713, NA, 37.98265…
$ female_month_36_47_weighted_N <dbl> NA, 101.0000, 416102.5600, 2128.8596, NA…
$ female_month_36_47_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ female_month_48_59_r <dbl> NA, 3.9000001, 5.7004228, 2.0829582, NA,…
$ female_month_48_59_ll <dbl> NA, NA, 4.0842037, 1.2565204, NA, 13.083…
$ female_month_48_59_ul <dbl> NA, NA, 7.903519, 3.434057, NA, 31.07550…
$ female_month_48_59_weighted_N <dbl> NA, 105.00000, 383832.56000, 1695.51590,…
$ female_month_48_59_Footnote <chr> NA, "w 18", "rs", "rs", NA, NA, NA, NA, …
$ female_month_24_59_r <dbl> NA, NA, 5.356624, 3.351807, NA, 29.34147…
$ female_month_24_59_ll <dbl> NA, NA, 4.3683147, 2.4520578, NA, 24.066…
$ female_month_24_59_ul <dbl> NA, NA, 6.553213, 4.566253, NA, 35.23654…
$ female_month_24_59_weighted_N <dbl> NA, NA, 1194022.6000, 5757.7524, NA, 371…
$ female_month_24_59_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Region_1_r <dbl> NA, NA, 0.4912166, 1.8721323, NA, 28.420…
$ Region_1_ll <dbl> NA, NA, 0.1097427, 0.8823794, NA, 22.979…
$ Region_1_ul <dbl> NA, NA, 2.169916, 3.928080, NA, 34.57156…
$ Region_1_weighted_N <dbl> NA, NA, 119804.59000, 440.54514, NA, 435…
$ Region_1_Region <chr> NA, NA, "Badakhshan", "Badakhshan", NA, …
$ Region_1_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Region_2_r <dbl> NA, NA, 5.985583, 8.996562, NA, 34.51955…
$ Region_2_ll <dbl> NA, NA, 4.276843, 5.977078, NA, 27.98906…
$ Region_2_ul <dbl> NA, NA, 8.317697, 13.325240, NA, 41.6915…
$ Region_2_weighted_N <dbl> NA, NA, 101226.07000, 437.38016, NA, 338…
$ Region_2_Region <chr> NA, NA, "Badghis", "Badghis", NA, "North…
$ Region_2_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Region_3_r <dbl> NA, NA, 2.4987860, 4.2150745, NA, 27.754…
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$ Region_5_weighted_N <dbl> NA, NA, 94735.86700, 219.10912, NA, NA, …
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$ Region_7_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
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$ Region_8_ul <dbl> NA, NA, 7.654221, 8.617937, NA, NA, NA, …
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$ Region_8_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
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$ Region_9_weighted_N <dbl> NA, NA, 220675.92000, 1110.95950, NA, NA…
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$ Region_10_weighted_N <dbl> NA, NA, 95516.77300, 812.21686, NA, NA, …
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$ Region_11_weighted_N <dbl> NA, NA, 124237.9700, 599.8754, NA, NA, N…
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$ Region_11_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
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$ Region_12_ll <dbl> NA, NA, 2.0814207, 2.0281396, NA, NA, NA…
$ Region_12_ul <dbl> NA, NA, 5.479564, 5.201447, NA, NA, NA, …
$ Region_12_weighted_N <dbl> NA, NA, 307903.3100, 1529.5945, NA, NA, …
$ Region_12_Region <chr> NA, NA, "Herat", "Herat", NA, NA, NA, NA…
$ Region_12_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
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$ Region_13_ll <dbl> NA, NA, 1.0461119, 3.9191496, NA, NA, NA…
$ Region_13_ul <dbl> NA, NA, 5.460087, 7.843442, NA, NA, NA, …
$ Region_13_weighted_N <dbl> NA, NA, 74561.31300, 470.53961, NA, NA, …
$ Region_13_Region <chr> NA, NA, "Jawzjan", "Jawzjan", NA, NA, NA…
$ Region_13_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
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$ Region_14_ll <dbl> NA, NA, 1.9159281, 3.2670979, NA, NA, NA…
$ Region_14_ul <dbl> NA, NA, 5.357854, 8.046988, NA, NA, NA, …
$ Region_14_weighted_N <dbl> NA, NA, 568960.06000, 2758.30540, NA, NA…
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$ Region_14_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
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$ Region_15_ll <dbl> NA, NA, 12.86497400, 2.70449470, NA, NA,…
$ Region_15_ul <dbl> NA, NA, 19.504080, 5.009379, NA, NA, NA,…
$ Region_15_weighted_N <dbl> NA, NA, 1.232709e+05, 6.330644e+02, NA, …
$ Region_15_Region <chr> NA, NA, "Kandahar", "Kandahar", NA, NA, …
$ Region_15_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Region_16_r <dbl> NA, NA, 0.5958103, 3.5266902, NA, NA, NA…
$ Region_16_ll <dbl> NA, NA, 0.2164124, 2.0165422, NA, NA, NA…
$ Region_16_ul <dbl> NA, NA, 1.629478, 6.097382, NA, NA, NA, …
$ Region_16_weighted_N <dbl> NA, NA, 70764.14100, 116.97514, NA, NA, …
$ Region_16_Region <chr> NA, NA, "Kapisa", "Kapisa", NA, NA, NA, …
$ Region_16_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Region_17_r <dbl> NA, NA, 10.8531970, 2.9167147, NA, NA, N…
$ Region_17_ll <dbl> NA, NA, 8.2017393, 1.3342413, NA, NA, NA…
$ Region_17_ul <dbl> NA, NA, 14.228955, 6.257050, NA, NA, NA,…
$ Region_17_weighted_N <dbl> NA, NA, 97846.4690, 1282.1185, NA, NA, N…
$ Region_17_Region <chr> NA, NA, "Khost", "Khost", NA, NA, NA, NA…
$ Region_17_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Region_18_r <dbl> NA, NA, 3.359285, 1.325326, NA, NA, NA, …
$ Region_18_ll <dbl> NA, NA, 1.5834458, 0.5624594, NA, NA, NA…
$ Region_18_ul <dbl> NA, NA, 6.985375, 3.090713, NA, NA, NA, …
$ Region_18_weighted_N <dbl> NA, NA, 97491.8670, 440.1858, NA, NA, NA…
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$ Region_18_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Region_19_r <dbl> NA, NA, 2.222583, 5.491089, NA, NA, NA, …
$ Region_19_ll <dbl> NA, NA, 1.248130, 3.744985, NA, NA, NA, …
$ Region_19_ul <dbl> NA, NA, 3.927563, 7.983791, NA, NA, NA, …
$ Region_19_weighted_N <dbl> NA, NA, 189084.9400, 250.8977, NA, NA, N…
$ Region_19_Region <chr> NA, NA, "Kunduz", "Kunduz", NA, NA, NA, …
$ Region_19_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Region_20_r <dbl> NA, NA, 1.379198, 3.136400, NA, NA, NA, …
$ Region_20_ll <dbl> NA, NA, 0.5559726, 2.0453527, NA, NA, NA…
$ Region_20_ul <dbl> NA, NA, 3.379938, 4.781035, NA, NA, NA, …
$ Region_20_weighted_N <dbl> NA, NA, 79824.6560, 540.0895, NA, NA, NA…
$ Region_20_Region <chr> NA, NA, "Laghman", "Laghman", NA, NA, NA…
$ Region_20_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Region_21_r <dbl> NA, NA, 1.423644, 4.057110, NA, NA, NA, …
$ Region_21_ll <dbl> NA, NA, 0.6496059, 2.2715847, NA, NA, NA…
$ Region_21_ul <dbl> NA, NA, 3.091291, 7.143519, NA, NA, NA, …
$ Region_21_weighted_N <dbl> NA, NA, 61827.9410, 238.6688, NA, NA, NA…
$ Region_21_Region <chr> NA, NA, "Logar", "Logar", NA, NA, NA, NA…
$ Region_21_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Region_22_r <dbl> NA, NA, 7.112528, 7.024985, NA, NA, NA, …
$ Region_22_ll <dbl> NA, NA, 4.220549, 3.556373, NA, NA, NA, …
$ Region_22_ul <dbl> NA, NA, 11.743173, 13.406348, NA, NA, NA…
$ Region_22_weighted_N <dbl> NA, NA, 265117.970, 2060.897, NA, NA, NA…
$ Region_22_Region <chr> NA, NA, "Nangarhar", "Nangarhar", NA, NA…
$ Region_22_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Region_23_r <dbl> NA, NA, 2.192575, 1.628730, NA, NA, NA, …
$ Region_23_ll <dbl> NA, NA, 1.2366475, 0.9332596, NA, NA, NA…
$ Region_23_ul <dbl> NA, NA, 3.858564, 2.827676, NA, NA, NA, …
$ Region_23_weighted_N <dbl> NA, NA, 28620.6890, 136.8131, NA, NA, NA…
$ Region_23_Region <chr> NA, NA, "Nimroz", "Nimroz", NA, NA, NA, …
$ Region_23_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
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$ Region_24_ll <dbl> NA, NA, 5.0349312, 0.8205962, NA, NA, NA…
$ Region_24_ul <dbl> NA, NA, 12.20742, 4.30106, NA, NA, NA, N…
$ Region_24_weighted_N <dbl> NA, NA, 24722.4900, 153.6022, NA, NA, NA…
$ Region_24_Region <chr> NA, NA, "Nooristan", "Nooristan", NA, NA…
$ Region_24_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
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$ Region_25_ll <dbl> NA, NA, 4.3296690, 0.5887564, NA, NA, NA…
$ Region_25_ul <dbl> NA, NA, 11.157026, 2.170492, NA, NA, NA,…
$ Region_25_weighted_N <dbl> NA, NA, 84778.89100, 73.47184, NA, NA, N…
$ Region_25_Region <chr> NA, NA, "Paktika", "Panjsher", NA, NA, N…
$ Region_25_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
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$ Region_26_ll <dbl> NA, NA, 8.4596910, 0.5469708, NA, NA, NA…
$ Region_26_ul <dbl> NA, NA, 16.643904, 2.405434, NA, NA, NA,…
$ Region_26_weighted_N <dbl> NA, NA, 106806.7600, 313.6852, NA, NA, N…
$ Region_26_Region <chr> NA, NA, "Paktya", "Parwan", NA, NA, NA, …
$ Region_26_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Region_27_r <dbl> NA, NA, 2.517616, 2.252430, NA, NA, NA, …
$ Region_27_ll <dbl> NA, NA, 1.658753, 1.031345, NA, NA, NA, …
$ Region_27_ul <dbl> NA, NA, 3.803976, 4.848424, NA, NA, NA, …
$ Region_27_weighted_N <dbl> NA, NA, 33174.6410, 414.9645, NA, NA, NA…
$ Region_27_Region <chr> NA, NA, "Panjsher", "Samangan", NA, NA, …
$ Region_27_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
$ Region_28_r <dbl> NA, NA, 1.837873, 1.840677, NA, NA, NA, …
$ Region_28_ll <dbl> NA, NA, 0.9632634, 0.7444244, NA, NA, NA…
$ Region_28_ul <dbl> NA, NA, 3.478705, 4.478449, NA, NA, NA, …
$ Region_28_weighted_N <dbl> NA, NA, 106812.0500, 275.4312, NA, NA, N…
$ Region_28_Region <chr> NA, NA, "Parwan", "Sar_e_pul", NA, NA, N…
$ Region_28_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
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$ Region_29_ll <dbl> NA, NA, 2.437654, 2.642336, NA, NA, NA, …
$ Region_29_ul <dbl> NA, NA, 6.171064, 6.989895, NA, NA, NA, …
$ Region_29_weighted_N <dbl> NA, NA, 58478.0700, 748.1838, NA, NA, NA…
$ Region_29_Region <chr> NA, NA, "Samangan", "Takhar", NA, NA, NA…
$ Region_29_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
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$ Region_30_ll <dbl> NA, NA, 2.469252, 8.687517, NA, NA, NA, …
$ Region_30_ul <dbl> NA, NA, 5.464337, 13.656277, NA, NA, NA,…
$ Region_30_weighted_N <dbl> NA, NA, 100744.6500, 387.5995, NA, NA, N…
$ Region_30_Region <chr> NA, NA, "Sar-E-Pul", "Urozgan", NA, NA, …
$ Region_30_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
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$ Region_31_ll <dbl> NA, NA, 1.477589, 1.682606, NA, NA, NA, …
$ Region_31_ul <dbl> NA, NA, 3.425221, 4.486599, NA, NA, NA, …
$ Region_31_weighted_N <dbl> NA, NA, 173699.2200, 325.7985, NA, NA, N…
$ Region_31_Region <chr> NA, NA, "Takhar", "Wardak", NA, NA, NA, …
$ Region_31_Footnote <chr> NA, NA, "rs", "rs", NA, NA, NA, NA, NA, …
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$ Region_32_ll <dbl> NA, NA, 5.086329, NA, NA, NA, NA, NA, NA…
$ Region_32_ul <dbl> NA, NA, 9.037983, NA, NA, NA, NA, NA, NA…
$ Region_32_weighted_N <dbl> NA, NA, 52258.74, NA, NA, NA, NA, NA, NA…
$ Region_32_Region <chr> NA, NA, "Urozgan", NA, NA, NA, NA, NA, N…
$ Region_32_Footnote <chr> NA, NA, "rs", NA, NA, NA, NA, NA, NA, NA…
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$ Region_33_ll <dbl> NA, NA, 4.393306, NA, NA, NA, NA, NA, NA…
$ Region_33_ul <dbl> NA, NA, 10.9747, NA, NA, NA, NA, NA, NA,…
$ Region_33_weighted_N <dbl> NA, NA, 88039.67, NA, NA, NA, NA, NA, NA…
$ Region_33_Region <chr> NA, NA, "Wardak", NA, NA, NA, NA, NA, NA…
$ Region_33_Footnote <chr> NA, NA, "rs", NA, NA, NA, NA, NA, NA, NA…
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$ Region_34_ll <dbl> NA, NA, 9.172741, NA, NA, NA, NA, NA, NA…
$ Region_34_ul <dbl> NA, NA, 16.2564, NA, NA, NA, NA, NA, NA,…
$ Region_34_weighted_N <dbl> NA, NA, 39410.15, NA, NA, NA, NA, NA, NA…
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$ Region_34_Footnote <chr> NA, NA, "rs", NA, NA, NA, NA, NA, NA, NA…
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$ Region_35_ll <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_35_ul <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_35_weighted_N <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_35_Region <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_35_Footnote <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
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$ Region_36_ll <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_36_ul <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_36_weighted_N <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_36_Region <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_36_Footnote <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
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$ Region_37_ll <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_37_ul <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_37_weighted_N <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_37_Region <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_37_Footnote <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
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$ Region_38_ll <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_38_ul <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_38_weighted_N <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_38_Region <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_38_Footnote <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
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$ Region_39_ll <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_39_ul <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_39_weighted_N <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_39_Region <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_39_Footnote <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
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$ Region_40_ll <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_40_ul <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_40_weighted_N <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_40_Region <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_40_Footnote <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
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$ Region_41_ll <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_41_ul <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_41_weighted_N <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_41_Region <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_41_Footnote <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
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$ Region_42_ll <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_42_ul <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Region_42_weighted_N <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
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$ Region_70_Footnote <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …UNICEF_WHO_WB_Global_Expanded_Databases_Overweight_Dec_2022# A tibble: 931 × 803
UNICEF_Survey_ID ISO3Code CountryName UNICEF_Reporting_Sub_Region UNRegion
<dbl> <chr> <chr> <chr> <chr>
1 1 AFG Afghanistan SA Asia
2 2 AFG Afghanistan SA Asia
3 3 AFG Afghanistan SA Asia
4 3076 AFG Afghanistan SA Asia
5 4 ALB Albania EECA Europe
6 5 ALB Albania EECA Europe
7 6 ALB Albania EECA Europe
8 7 ALB Albania EECA Europe
9 2606 ALB Albania EECA Europe
10 9 DZA Algeria MENA Africa
# ℹ 921 more rows
# ℹ 798 more variables: WHORegion <chr>, WB_Latest <chr>,
# DataSourceYears <chr>, CMRS_year <dbl>, DataSourceTypeGlobal <chr>,
# FullSourceTitle <chr>, LatestSource <chr>, Estimate_Type <chr>,
# National_r <dbl>, National_ll <dbl>, National_ul <dbl>,
# National_weighted_N <dbl>, National_Footnote <chr>, male_r <dbl>,
# male_ll <dbl>, male_ul <dbl>, male_weighted_N <dbl>, male_Footnote <chr>, …Data 3
Introduction and data
Identify the source of the data.
- The source of the data is from UNICEF.
State when and how it was originally collected (by the original data curator, not necessarily how you found the data).
- The data was originally collected by 2022 Member States who report to WHO and UNICEF up until July 15th, 2022.
Write a brief description of the observations.
- Each observation in the data set includes the country’s three-letter code, the English versions of the survey name, the survey name as given during production, starting year of data collection, ending year of data collection, age cohort according to the nationally recommended administration, abbreviated name for vaccine type, evidence for immunization coverage, card seen, validity of evidence, estimated scheduled vaccination age, scheduled vaccination age reported during the interview, immunization rates for the country, and sample size.
Research question
- A well formulated research question. (You may include more than one research question if you want to receive feedback on different ideas for your project. However, one per data set is required.)
- What is the correlation between the status of a country and its immunization rates?
- A description of the research topic along with a concise statement of your hypotheses on this topic.
What is the correlation between the status of a country and its immunization rates?
- This research question seeks to find the relationship between different countries that are reported in the data and their immunization rates. We hypothesize that countries with lower income countries have larger setbacks and thus lower immunization rates compared to countries with higher income.
- Identify the types of variables in your research question. Categorical? Quantitative?
- Variable types in the research question are categorical (country, wealth status) and quantitative (immunization rates).
Glimpse of data
# add code here
library(readxl)
survey_data_wuenic2021rev <- read_excel("data/survey_data_wuenic2021rev.xls",
skip = 1)
survey_data_wuenic2021rev# A tibble: 39,926 × 15
ISO3 surveyNameEnglish surveyNameProduction collectBegin collectEnd
<chr> <chr> <chr> <chr> <chr>
1 AFG Paktika Immunization Cove… Paktika Immunizatio… 1990-98 1990-98
2 AFG Paktika Immunization Cove… Paktika Immunizatio… 1990-98 1990-98
3 AFG The 1997 Afghanistan Mult… The 1997 Afghanista… 1997-01 1997-04
4 AFG The 1997 Afghanistan Mult… The 1997 Afghanista… 1997-01 1997-04
5 AFG The 1997 Afghanistan Mult… The 1997 Afghanista… 1997-01 1997-04
6 AFG The 1997 Afghanistan Mult… The 1997 Afghanista… 1997-01 1997-04
7 AFG The 1997 Afghanistan Mult… The 1997 Afghanista… 1997-01 1997-04
8 AFG The 1997 Afghanistan Mult… The 1997 Afghanista… 1997-01 1997-04
9 AFG EPI Coverage Situation in… EPI Coverage Situat… 1999-07 1999-07
10 AFG EPI Coverage Situation in… EPI Coverage Situat… 1999-07 1999-07
# ℹ 39,916 more rows
# ℹ 10 more variables: cohortYear <dbl>, vaccine <chr>, evidence <chr>,
# cardsSeen <dbl>, validity <chr>, ageVaccination <chr>, ageInterview <chr>,
# sex <chr>, coverage <dbl>, Sample_Size <dbl>glimpse(survey_data_wuenic2021rev)Rows: 39,926
Columns: 15
$ ISO3 <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", …
$ surveyNameEnglish <chr> "Paktika Immunization Coverage, Afghanistan 1990"…
$ surveyNameProduction <chr> "Paktika Immunization Coverage, Afghanistan 1990"…
$ collectBegin <chr> "1990-98", "1990-98", "1997-01", "1997-01", "1997…
$ collectEnd <chr> "1990-98", "1990-98", "1997-04", "1997-04", "1997…
$ cohortYear <dbl> 1989, 1989, 1996, 1996, 1996, 1996, 1996, 1996, 1…
$ vaccine <chr> "TT1", "TT2+", "BCG", "DTP1", "DTP3", "MCV1", "Po…
$ evidence <chr> "Card", "Card", "History", "History", "History", …
$ cardsSeen <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 48.3, 48.3, 48.3,…
$ validity <chr> "crude", "crude", "crude", "crude", "crude", "cru…
$ ageVaccination <chr> NA, NA, "0-35 m", "0-35 m", "0-35 m", "0-23 m", "…
$ ageInterview <chr> "Women 15-45", "Women 15-45", "6-35 m", "6-35 m",…
$ sex <chr> "both", "both", "both", "both", "both", "both", "…
$ coverage <dbl> 85.5, 72.6, 36.5, 24.5, 6.0, 45.5, 32.7, 7.5, 40.…
$ Sample_Size <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1681, 1681, 1681,…