epidict (version 0.3.0)

msf_dict: MSF data dictionaries and dummy datasets

Description

These function produce MSF dictionaries based on DHIS2 (for OCA outbreaks) and ODK (for intersectional outbreaks and surveys) data sets defining the data element name, code, short names, types, and key/value pairs for translating the codes into human-readable format.

Usage

msf_dict(dictionary, tibble = TRUE, long = TRUE, compact = TRUE, clean = TRUE)

Value

A data frame (tibble) containing the specified MSF data dictionary. If long = TRUE, each variable-option pair is represented as a row. If compact = TRUE, the options are nested as a data frame column named "options". If long = FALSE, a list is returned with two data frames: dictionary and options.

Arguments

dictionary

Specify which dictionary you would like to use.

• MSF OCA outbreaks include: "AJS", "Cholera", "Measles", "Meningitis"

• MSF intersectional outbreaks include: "AJS_intersectional", "Cholera_intersectional", "Diphtheria_intersectional", "Measles_intersectional", "Meningitis_intersectional"

• MSF OCA surveys include "Mortality", "Nutrition", "Vaccination_long", "Vaccination_short" and "ebs"

tibble

If TRUE (default), return data dictionary as a tidyverse tibble otherwise will return a list.

long

If TRUE (default), the returned data dictionary is in long format with each option getting one row. If FALSE, then two data frames are returned, one with variables and the other with content options.

compact

If TRUE (default), then a nested data frame is returned where each row represents a single variable and a nested data frame column called "options", which can be expanded with tidyr::unnest(). This only works if long = TRUE.

clean

If TRUE (default), then will clean variable names and variable options. This will set text to lower snake case and remove any accents.

See Also

read_dict() gen_data() matchmaker::match_df()

Examples

if (require("dplyr") & require("matchmaker")) {
  withAutoprint({
    # You will often want to use MSF dictionaries to translate codes to human-
    # readable variables. Here, we generate a data set of 20 cases:
    dat <- gen_data(
      dictionary = "Cholera",
      varnames = "data_element_shortname",
      numcases = 20,
      org = "MSF"
    )
    print(dat)

    # We want the expanded dictionary, so we will select `compact = FALSE`
    dict <- msf_dict(dictionary = "Cholera", long = TRUE, compact = FALSE, tibble = TRUE)
    print(dict)

    # Now we can use matchmaker to filter the data:
    dat_clean <- matchmaker::match_df(dat, dict,
      from = "option_code",
      to = "option_name",
      by = "data_element_shortname",
      order = "option_order_in_set"
    )
    print(dat_clean)
  })
}