Demographic characteristics of Amazonian mothers
In this script, we compute the demographic characteristics of the population under study. The resulting table can be seen at section Show demographic characteristics. This output corresponds to Supplementary Table 1 of our paper.
Load packages, read data and source custom scripts
Paths are defined relative to the git repository location. Only birthweight data used for modelling is required.
library(dplyr)#>
#> Attaching package: 'dplyr'#> The following objects are masked from 'package:stats':
#>
#> filter, lag#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, unionpath_proj <- day2day::git_path()
path_data <- file.path(path_proj, "data")
path_processed <- file.path(path_data, "processed")
path_summarised <- file.path(path_data, "summarised")
source(file.path(path_proj, "src", "52-summary.R"))
bwdata_model <- fst::read_fst(file.path(path_processed, "bwdata_41_model.fst"))Socio-economic characteristics
socio_vars <- c("Maternal age (years)" = "age",
"Formal education (years)" = "study_years",
"Marital status" = "marital_status",
"Anetanal care (number of consultations)" = "consult_num",
"Birth Place" = "birth_place",
"Newborn ethnicity" = "born_race",
"Gestational age" = "gestation_weeks")
age_cuts <- seq(10, 40, 5)
n_ind <- nrow(bwdata_model)
bwdata_demo <- bwdata_model[socio_vars] %>%
mutate(age = cut(age, c(age_cuts, 65), right = FALSE, include.lowest = TRUE,
labels = paste(age_cuts, "to", c(age_cuts[-1] - 1, 65)))) %>%
purrr::map2_df(names(socio_vars), table_rel)
bwdata_demo <- within(bwdata_demo, Category[is.na(Category)] <- "missing")Save demography characteristics
readr::write_csv(bwdata_demo, file.path(path_summarised, "paper-data-mothers-demography.csv"))Show demographic characteristics
bwdata_demo$Percentage <- scales::label_percent(scale = 1)(bwdata_demo$Percentage)
knitr::kable(bwdata_demo, "html", table.attr = "class=\"table\"", align = "lcrr",
caption = "Demographic characteristics of Amazonian mothers")| Variable | Category | Count | Percentage |
|---|---|---|---|
| Maternal age (years) | 10 to 14 | 6605 | 2.270% |
| Maternal age (years) | 15 to 19 | 84566 | 29.010% |
| Maternal age (years) | 20 to 24 | 87070 | 29.870% |
| Maternal age (years) | 25 to 29 | 57028 | 19.570% |
| Maternal age (years) | 30 to 34 | 33355 | 11.440% |
| Maternal age (years) | 35 to 39 | 16689 | 5.730% |
| Maternal age (years) | 40 to 65 | 6166 | 2.120% |
| Formal education (years) | 0 | 17335 | 5.950% |
| Formal education (years) | 1 to 3 | 39376 | 13.510% |
| Formal education (years) | 4 to 7 | 99269 | 34.060% |
| Formal education (years) | 8 to 11 | 118492 | 40.650% |
| Formal education (years) | >= 12 | 14821 | 5.080% |
| Formal education (years) | missing | 2186 | 0.750% |
| Marital status | Single | 162607 | 55.790% |
| Marital status | Married | 36788 | 12.620% |
| Marital status | Widow | 295 | 0.100% |
| Marital status | Divorced | 386 | 0.130% |
| Marital status | Consensual union | 87528 | 30.030% |
| Marital status | missing | 3875 | 1.330% |
| Anetanal care (number of consultations) | 0 | 18734 | 6.430% |
| Anetanal care (number of consultations) | 1 to 3 | 63904 | 21.920% |
| Anetanal care (number of consultations) | 4 to 6 | 115889 | 39.760% |
| Anetanal care (number of consultations) | >= 7 | 90360 | 31.000% |
| Anetanal care (number of consultations) | missing | 2592 | 0.890% |
| Birth Place | Hospital | 240700 | 82.580% |
| Birth Place | Another health center | 1064 | 0.370% |
| Birth Place | Home | 44897 | 15.400% |
| Birth Place | Other | 2335 | 0.800% |
| Birth Place | missing | 2483 | 0.850% |
| Newborn ethnicity | Non-indigenous | 234228 | 80.360% |
| Newborn ethnicity | Indigenous | 55201 | 18.940% |
| Newborn ethnicity | missing | 2050 | 0.700% |
| Gestational age | >= 37 | 264970 | 90.910% |
| Gestational age | 32 to 36 | 23954 | 8.220% |
| Gestational age | 28 to 31 | 1888 | 0.650% |
| Gestational age | 22 to 27 | 667 | 0.230% |
Time to execute the task
Only useful when executed with Rscript.
proc.time()#> user system elapsed
#> 9.625 0.473 10.078