R/ffd_opt_dtgch_aorig.R
df_opt_dtgch_aorig.RdData from INCAP and CEBU, used in various projects, http://fanwangecon.github.io, note po stands for project optimal choice, to differentiate this from the files in REconTools
data(df_opt_dtgch_aorig)csv
"Early life height and weight production functions with endogenous energy and protein inputs", September 2016, 65-81, Economics & Human Biology, 22, Esteban Puentes, Fan Wang, Jere R. Behrman, Flavio Cunha, John Hoddinott, John A. Maluccio, Linda S. Adair, Judith Borja and Reynaldo Martorell
data(df_opt_dtgch_aorig)
var_wgt <- df_opt_dtgch_aorig$wgt
head(df_opt_dtgch_aorig, 10)
#> # A tibble: 10 x 15
#> S.country vil.id indi.id sex svymthRound momEdu wealthIdx hgt wgt hgt0
#> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Cebu 1 1 Male 0 5.3 6.3 45 985. 44.2
#> 2 Cebu 1 1 Male 2 5.3 6.3 54.6 3679. 44.2
#> 3 Cebu 1 1 Male 4 5.3 6.3 58.8 7954. 44.2
#> 4 Cebu 1 1 Male 6 5.3 6.3 65.6 6386. 44.2
#> 5 Cebu 1 1 Male 8 5.3 6.3 67.4 6039. 44.2
#> 6 Cebu 1 1 Male 10 5.3 6.3 70.4 8892. 44.2
#> 7 Cebu 1 1 Male 12 5.3 6.3 70.8 7325. 44.2
#> 8 Cebu 1 1 Male 14 5.3 6.3 NA NA 44.2
#> 9 Cebu 1 1 Male 16 5.3 6.3 NA NA 44.2
#> 10 Cebu 1 1 Male 18 5.3 6.3 NA NA 44.2
#> # ... with 5 more variables: wgt0 <dbl>, prot <dbl>, cal <dbl>, p.A.prot <dbl>,
#> # p.A.nProt <dbl>
var_wgt[1:10]
#> [1] 985.4 3678.8 7954.4 6386.3 6038.7 8892.4 7325.1 NA NA NA