vignettes/in_union_women.md

In FPRgroup/FPEMcountry: fpemlocal

Estimating family planning indicators for in-union women

library(fpemlocal)

Introduction

In this vignette we obtain estimates for married women with package datasets. By default, functions utilize UNPD datasets.

1. Fit a one country model fit_fp_c
2. Calculate point estimates for indicators calc_fp_c
3. Plot the point estimates against the survey data plot_fp_c

1. Fit a one country model

First, find the UNPD country code under the variable division_numeric_code in the dataset divisions. See ?divisions for the metadata.

divisions %>%
  dplyr::filter(name_country == "Afghanistan")

## # A tibble: 1 x 13
##   division_numeri~ name_country name_region name_sub_region region_numeric_~
##              <dbl> <chr>        <chr>       <chr>                      <dbl>
## 1                4 Afghanistan  Asia        South-Central ~              935
## # ... with 8 more variables: sub_region_numeric_code <dbl>,
## #   is_developed_region <chr>, is_less_developed_region <chr>,
## #   is_least_developed_country <chr>, is_in_sub_saharan_africa <chr>,
## #   is_unmarried_sexual_activity <chr>, is_low_population <chr>,
## #   is_fp2020 <chr>

Fit the one-country family planning estimation model with the function fit_fp_c. First, supply the UNPD country code to the argument division_numeric_code. There are two versions of this model, one for in-union women and another for not-in-union women denoted "Y" and "N" respectively. Specify the model of your choice with the argument is_in_union. By default, the function fit_fp_c utilizes the UNPD contraceptive use survey dataset, contraceptive_use and filters the dataset based on the aforementioned function arguments. Lastly, specify the years of estimates to be returned. Note: year arguments will not filter the supplied survey data. All years of available survey data will be used.

fit <- fit_fp_c(
  division_numeric_code = 4,
  is_in_union = "Y",
  first_year = 1970,
  last_year = 2030
)

2. Calculate point estimates for indicators

Calculate point estimates for family planning indicators with the function calc_fp_c. Supply the fit object from fit_fp_c. By default, the function calc_fp_C utilizes the UNPD population dataset, population_counts to calculate count variables such as the number of women using modern contraceptives.

results <- calc_fp_c(fit)

A set of results here consist of the following family planning indicators

results$Y %>% names

##  [1] "contraceptive_use_any"                     
##  [2] "contraceptive_use_modern"                  
##  [3] "contraceptive_use_traditional"             
##  [4] "non_use"                                   
##  [5] "unmet_need_any"                            
##  [6] "unmet_need_modern"                         
##  [7] "demand"                                    
##  [8] "demand_modern"                             
##  [9] "demand_satisfied"                          
## [10] "demand_satisfied_modern"                   
## [11] "no_need"                                   
## [12] "contraceptive_use_any_population_counts"   
## [13] "contraceptive_use_modern_population_counts"
## [14] "traditional_cpr_population_counts"         
## [15] "non_use_population_counts"                 
## [16] "unmet_need_population_counts"              
## [17] "unmet_need_modern_population_counts"       
## [18] "demand_modern_population_counts"           
## [19] "demand_population_counts"                  
## [20] "demand_satisfied_population_counts"        
## [21] "demand_satisfied_modern_population_counts" 
## [22] "no_need_population_counts"

results$Y

## $contraceptive_use_any
## # A tibble: 488 x 3
##     year percentile  value
##    <int> <chr>       <dbl>
##  1  1970 mean       0.0167
##  2  1971 mean       0.0177
##  3  1972 mean       0.0189
##  4  1973 mean       0.0202
##  5  1974 mean       0.0217
##  6  1975 mean       0.0233
##  7  1976 mean       0.0251
##  8  1977 mean       0.0270
##  9  1978 mean       0.0290
## 10  1979 mean       0.0312
## # ... with 478 more rows
## 
## $contraceptive_use_modern
## # A tibble: 488 x 3
##     year percentile  value
##    <int> <chr>       <dbl>
##  1  1970 mean       0.0115
##  2  1971 mean       0.0123
##  3  1972 mean       0.0131
##  4  1973 mean       0.0141
##  5  1974 mean       0.0152
##  6  1975 mean       0.0164
##  7  1976 mean       0.0178
##  8  1977 mean       0.0192
##  9  1978 mean       0.0208
## 10  1979 mean       0.0225
## # ... with 478 more rows
## 
## $contraceptive_use_traditional
## # A tibble: 488 x 3
##     year percentile   value
##    <int> <chr>        <dbl>
##  1  1970 mean       0.00516
##  2  1971 mean       0.00543
##  3  1972 mean       0.00573
##  4  1973 mean       0.00607
##  5  1974 mean       0.00647
##  6  1975 mean       0.00689
##  7  1976 mean       0.00733
##  8  1977 mean       0.00779
##  9  1978 mean       0.00828
## 10  1979 mean       0.00875
## # ... with 478 more rows
## 
## $non_use
## # A tibble: 488 x 3
##     year percentile value
##    <int> <chr>      <dbl>
##  1  1970 mean       0.983
##  2  1971 mean       0.982
##  3  1972 mean       0.981
##  4  1973 mean       0.980
##  5  1974 mean       0.978
##  6  1975 mean       0.977
##  7  1976 mean       0.975
##  8  1977 mean       0.973
##  9  1978 mean       0.971
## 10  1979 mean       0.969
## # ... with 478 more rows
## 
## $unmet_need_any
## # A tibble: 488 x 3
##     year percentile value
##    <int> <chr>      <dbl>
##  1  1970 mean       0.289
##  2  1971 mean       0.289
##  3  1972 mean       0.289
##  4  1973 mean       0.288
##  5  1974 mean       0.287
##  6  1975 mean       0.288
##  7  1976 mean       0.287
##  8  1977 mean       0.286
##  9  1978 mean       0.285
## 10  1979 mean       0.285
## # ... with 478 more rows
## 
## $unmet_need_modern
## # A tibble: 488 x 3
##     year percentile value
##    <int> <chr>      <dbl>
##  1  1970 mean       0.294
##  2  1971 mean       0.294
##  3  1972 mean       0.294
##  4  1973 mean       0.294
##  5  1974 mean       0.294
##  6  1975 mean       0.294
##  7  1976 mean       0.294
##  8  1977 mean       0.294
##  9  1978 mean       0.293
## 10  1979 mean       0.293
## # ... with 478 more rows
## 
## $demand
## # A tibble: 488 x 3
##     year percentile value
##    <int> <chr>      <dbl>
##  1  1970 mean       0.306
##  2  1971 mean       0.306
##  3  1972 mean       0.307
##  4  1973 mean       0.308
##  5  1974 mean       0.309
##  6  1975 mean       0.311
##  7  1976 mean       0.312
##  8  1977 mean       0.313
##  9  1978 mean       0.314
## 10  1979 mean       0.316
## # ... with 478 more rows
## 
## $demand_modern
## # A tibble: 488 x 3
##     year percentile value
##    <int> <chr>      <dbl>
##  1  1970 mean       0.306
##  2  1971 mean       0.306
##  3  1972 mean       0.307
##  4  1973 mean       0.308
##  5  1974 mean       0.309
##  6  1975 mean       0.311
##  7  1976 mean       0.312
##  8  1977 mean       0.313
##  9  1978 mean       0.314
## 10  1979 mean       0.316
## # ... with 478 more rows
## 
## $demand_satisfied
## # A tibble: 488 x 3
##     year percentile  value
##    <int> <chr>       <dbl>
##  1  1970 mean       0.0567
##  2  1971 mean       0.0602
##  3  1972 mean       0.0639
##  4  1973 mean       0.0681
##  5  1974 mean       0.0730
##  6  1975 mean       0.0779
##  7  1976 mean       0.0834
##  8  1977 mean       0.0892
##  9  1978 mean       0.0954
## 10  1979 mean       0.102 
## # ... with 478 more rows
## 
## $demand_satisfied_modern
## # A tibble: 488 x 3
##     year percentile  value
##    <int> <chr>       <dbl>
##  1  1970 mean       0.0391
##  2  1971 mean       0.0417
##  3  1972 mean       0.0444
##  4  1973 mean       0.0476
##  5  1974 mean       0.0511
##  6  1975 mean       0.0548
##  7  1976 mean       0.0590
##  8  1977 mean       0.0634
##  9  1978 mean       0.0681
## 10  1979 mean       0.0732
## # ... with 478 more rows
## 
## $no_need
## # A tibble: 488 x 3
##     year percentile value
##    <int> <chr>      <dbl>
##  1  1970 mean       0.694
##  2  1971 mean       0.694
##  3  1972 mean       0.693
##  4  1973 mean       0.692
##  5  1974 mean       0.691
##  6  1975 mean       0.689
##  7  1976 mean       0.688
##  8  1977 mean       0.687
##  9  1978 mean       0.686
## 10  1979 mean       0.684
## # ... with 478 more rows
## 
## $contraceptive_use_any_population_counts
## # A tibble: 488 x 3
##     year percentile population_count
##    <dbl> <chr>                 <dbl>
##  1  1970 mean                 33874.
##  2  1971 mean                 36805.
##  3  1972 mean                 40118.
##  4  1973 mean                 43944.
##  5  1974 mean                 48159.
##  6  1975 mean                 52766.
##  7  1976 mean                 57402.
##  8  1977 mean                 62396.
##  9  1978 mean                 67540.
## 10  1979 mean                 72656.
## # ... with 478 more rows
## 
## $contraceptive_use_modern_population_counts
## # A tibble: 488 x 3
##     year percentile population_count
##    <dbl> <chr>                 <dbl>
##  1  1970 mean                 23397.
##  2  1971 mean                 25524.
##  3  1972 mean                 27940.
##  4  1973 mean                 30731.
##  5  1974 mean                 33776.
##  6  1975 mean                 37172.
##  7  1976 mean                 40624.
##  8  1977 mean                 44396.
##  9  1978 mean                 48289.
## 10  1979 mean                 52296.
## # ... with 478 more rows
## 
## $traditional_cpr_population_counts
## # A tibble: 488 x 3
##     year percentile population_count
##    <dbl> <chr>                 <dbl>
##  1  1970 mean                 10477.
##  2  1971 mean                 11280.
##  3  1972 mean                 12178.
##  4  1973 mean                 13213.
##  5  1974 mean                 14383.
##  6  1975 mean                 15594.
##  7  1976 mean                 16778.
##  8  1977 mean                 18001.
##  9  1978 mean                 19252.
## 10  1979 mean                 20361.
## # ... with 478 more rows
## 
## $non_use_population_counts
## # A tibble: 488 x 3
##     year percentile population_count
##    <dbl> <chr>                 <dbl>
##  1  1970 mean               1996653.
##  2  1971 mean               2039541.
##  3  1972 mean               2086216.
##  4  1973 mean               2134179.
##  5  1974 mean               2173508.
##  6  1975 mean               2209605.
##  7  1976 mean               2231035.
##  8  1977 mean               2248702.
##  9  1978 mean               2257880.
## 10  1979 mean               2253341.
## # ... with 478 more rows
## 
## $unmet_need_population_counts
## # A tibble: 488 x 3
##     year percentile population_count
##    <dbl> <chr>                 <dbl>
##  1  1970 mean                586592.
##  2  1971 mean                599189.
##  3  1972 mean                613508.
##  4  1973 mean                627183.
##  5  1974 mean                638195.
##  6  1975 mean                650507.
##  7  1976 mean                656152.
##  8  1977 mean                661565.
##  9  1978 mean                663199.
## 10  1979 mean                662239.
## # ... with 478 more rows
## 
## $unmet_need_modern_population_counts
## # A tibble: 488 x 3
##     year percentile population_count
##    <dbl> <chr>                 <dbl>
##  1  1970 mean                597069.
##  2  1971 mean                610469.
##  3  1972 mean                625686.
##  4  1973 mean                640397.
##  5  1974 mean                652578.
##  6  1975 mean                666100.
##  7  1976 mean                672930.
##  8  1977 mean                679566.
##  9  1978 mean                682451.
## 10  1979 mean                682599.
## # ... with 478 more rows
## 
## $demand_modern_population_counts
## # A tibble: 488 x 3
##     year percentile population_count
##    <dbl> <chr>                 <dbl>
##  1  1970 mean                620466.
##  2  1971 mean                635993.
##  3  1972 mean                653626.
##  4  1973 mean                671128.
##  5  1974 mean                686354.
##  6  1975 mean                703272.
##  7  1976 mean                713554.
##  8  1977 mean                723961.
##  9  1978 mean                730739.
## 10  1979 mean                734895.
## # ... with 478 more rows
## 
## $demand_population_counts
## # A tibble: 488 x 3
##     year percentile population_count
##    <dbl> <chr>                 <dbl>
##  1  1970 mean                620466.
##  2  1971 mean                635993.
##  3  1972 mean                653626.
##  4  1973 mean                671128.
##  5  1974 mean                686354.
##  6  1975 mean                703272.
##  7  1976 mean                713554.
##  8  1977 mean                723961.
##  9  1978 mean                730739.
## 10  1979 mean                734895.
## # ... with 478 more rows
## 
## $demand_satisfied_population_counts
## # A tibble: 488 x 3
##     year percentile population_count
##    <dbl> <chr>                 <dbl>
##  1  1970 mean                115177.
##  2  1971 mean                124947.
##  3  1972 mean                135808.
##  4  1973 mean                148405.
##  5  1974 mean                162164.
##  6  1975 mean                176291.
##  7  1976 mean                190896.
##  8  1977 mean                206163.
##  9  1978 mean                221742.
## 10  1979 mean                236795.
## # ... with 478 more rows
## 
## $demand_satisfied_modern_population_counts
## # A tibble: 488 x 3
##     year percentile population_count
##    <dbl> <chr>                 <dbl>
##  1  1970 mean                 79396.
##  2  1971 mean                 86530.
##  3  1972 mean                 94456.
##  4  1973 mean                103659.
##  5  1974 mean                113530.
##  6  1975 mean                123985.
##  7  1976 mean                134914.
##  8  1977 mean                146460.
##  9  1978 mean                158346.
## 10  1979 mean                170322.
## # ... with 478 more rows
## 
## $no_need_population_counts
## # A tibble: 488 x 3
##     year percentile population_count
##    <dbl> <chr>                 <dbl>
##  1  1970 mean               1410061.
##  2  1971 mean               1440353.
##  3  1972 mean               1472708.
##  4  1973 mean               1506995.
##  5  1974 mean               1535313.
##  6  1975 mean               1559099.
##  7  1976 mean               1574883.
##  8  1977 mean               1587137.
##  9  1978 mean               1594681.
## 10  1979 mean               1591102.
## # ... with 478 more rows

The point estimates for each indicator are long-format tibbles. Let’s take a look at the tibble for the indicator contraceptive_use_modern

results$Y$contraceptive_use_modern

## # A tibble: 488 x 3
##     year percentile  value
##    <int> <chr>       <dbl>
##  1  1970 mean       0.0115
##  2  1971 mean       0.0123
##  3  1972 mean       0.0131
##  4  1973 mean       0.0141
##  5  1974 mean       0.0152
##  6  1975 mean       0.0164
##  7  1976 mean       0.0178
##  8  1977 mean       0.0192
##  9  1978 mean       0.0208
## 10  1979 mean       0.0225
## # ... with 478 more rows

3. Plot estimates and survey data

fpemlocal also includes a function named plot_fp_c to plot the calculated point estimates against the survey data. The arguments to this function are, the fit object from step 1, the results from step 2, and a vector of indicator names. The vector of indicator names corresponds to the names which appear in the results from step 2.

plot_fp_c(
  fit,
  results,
  indicators = c(
    "unmet_need_any",
    "contraceptive_use_modern",
    "contraceptive_use_traditional",
    "contraceptive_use_any"
    )
  )

## $Y
## $Y$unmet_need_any

## 
## $Y$contraceptive_use_modern

## 
## $Y$contraceptive_use_traditional

## 
## $Y$contraceptive_use_any

FPRgroup/FPEMcountry documentation built on April 24, 2023, 4:32 p.m.