prepare a DHS dataset for analysis

Usage

prep_dhs_sib_histories(
  df,
  varmap = sibhist_varmap_dhs6,
  add_maternal = FALSE,
  keep_missing = FALSE,
  keep_varmap_only = FALSE,
  verbose = TRUE
)

Arguments

df: the raw DHS dataset (indvidual recode)
varmap: see Details
add_maternal: should maternal/pregnancy-related death info be added? (Default: FALSE)
keep_missing: should we keep reported sibs that are missing sex or survival status?
keep_varmap_only: should we only keep ego variables mentioned in the varmap? (Default: FALSE)
verbose: report detailed summaries?

Value

a list; see Details

Details

Note that if the dataframe does not have a column called 'sex', then one will be added, and we will assume respondents are all female (sex='f'). If you want to avoid this, only pass in a dataframe after adding the 'sex' column.

varmap should be a dataframe with columns

orig.varname (the raw variable name)
new.varname (the new variable name)
sibvar (a 0/1 column, with 1 meaning this is a sibling variable and 0 meaning an ego variable)

Each row of varmap describes a variable to rename from the original dataset.

For respondents, you should be sure to include

survey (the survey id, usually a country code plus one digit)
caseid (the respondent id)
wwgt (the sampling weight for women)
psu (the primary sampling unit)
doi (the date of the interview)

For siblings, you should be sure to include

sib.death.date (the date of the sibling's death)
sib.alive (whether or not the sib is alive)
sib.sex (the sex of the sibling, coded 'f' or 'm').

The default varmap is sibhist_varmap_dhs6, which is included with the package.

Returns a list whose entries include

ego.dat - dataset with information about the survey respondents
sib.dat - dataset with information about the reported siblings
summ - a one-row tibble with a summary of the data

Examples

  # TODO - write example code