Title:	Epidemiology Data Dictionaries and Random Data Generators
Version:	0.1.0
Description:	The 'R4EPIs' project https://r4epi.github.io/sitrep/ seeks to provide a set of standardized tools for analysis of outbreak and survey data in humanitarian aid settings. This package currently provides standardized data dictionaries from Medecins Sans Frontieres Operational Centre Amsterdam for outbreak scenarios (Acute Jaundice Syndrome, Cholera, Diphtheria, Measles, Meningitis) and surveys (Retrospective mortality and access to care, Malnutrition, Vaccination coverage and Event Based Surveillance) - as described in the following https://scienceportal.msf.org/assets/standardised-mortality-surveys?utm_source=chatgpt.com. In addition, a data generator from these dictionaries is provided. It is also possible to read in any Open Data Kit format data dictionary.
License:	GPL-3
URL:	https://github.com/R4EPI/epidict/, https://r4epi.github.io/epidict/
Imports:	clipr, dplyr, readxl, rlang, stats, tibble, tidyr, utils
Suggests:	covr, DT, knitr, matchmaker, rmarkdown, testthat (≥ 2.1.0), withr
VignetteBuilder:	knitr
Encoding:	UTF-8
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-11-10 13:13:32 UTC; spina
Author:	Alexander Spina [aut, cre], Zhian N. Kamvar [aut], Lukas Richter [aut], Patrick Keating [aut], Annick Lenglet [ctb], Applied Epi Incorporated [cph], Medecins Sans Frontieres Operational Centre Amsterdam [fnd]
Maintainer:	Alexander Spina <aspina@appliedepi.org>
Repository:	CRAN
Date/Publication:	2025-11-13 18:50:07 UTC

epidict: Epidemiology Data Dictionaries and Random Data Generators

Description

The 'R4EPIs' project https://r4epi.github.io/sitrep/ seeks to provide a set of standardized tools for analysis of outbreak and survey data in humanitarian aid settings. This package currently provides standardized data dictionaries from Medecins Sans Frontieres Operational Centre Amsterdam for outbreak scenarios (Acute Jaundice Syndrome, Cholera, Diphtheria, Measles, Meningitis) and surveys (Retrospective mortality and access to care, Malnutrition, Vaccination coverage and Event Based Surveillance) - as described in the following https://scienceportal.msf.org/assets/standardised-mortality-surveys?utm_source=chatgpt.com. In addition, a data generator from these dictionaries is provided. It is also possible to read in any Open Data Kit format data dictionary.

Author(s)

Maintainer: Alexander Spina aspina@appliedepi.org (ORCID)

Authors:

• Zhian N. Kamvar zkamvar@gmail.com (ORCID)

• Lukas Richter

• Patrick Keating

Other contributors:

• Annick Lenglet [contributor]

• Applied Epi Incorporated [copyright holder]

• Medecins Sans Frontieres Operational Centre Amsterdam [funder]

See Also

Useful links:

• https://github.com/R4EPI/epidict/

• https://r4epi.github.io/epidict/

Dictionary-based helper for aligning your data to variables used in a script

Description

Dictionary-based helper for aligning your data to variables used in a script

Usage

dict_rename_helper(
  dictionary,
  varnames,
  varnames_type,
  rmd,
  copy_to_clipboard = TRUE
)

Arguments

Value

A dplyr command used to rename columns in your data frame according to the dictionary

See Also

read_dict()

Generate random linelist or survey data

Description

Based on a dictionary generator like msf_dict(), this function will generate a randomized dataset based on values defined in the dictionaries. The randomized dataset produced should mimic an excel export from DHIS2 or ODK.

Usage

gen_data(dictionary, varnames = "name", numcases = 300, org = "MSF")

Arguments

Value

a data frame with cases in rows and variables in columns. The number of columns will vary from dictionary to dictionary, so please use the dictionary functions to generate a corresponding dictionary.

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)

  })
}

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)

Arguments

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.

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)
  })
}

Data dictionaries

Description

These function read dictionaries in ODK and DHIS2 formats, and reformats them for dataset recoding into human-readable format.

Usage

read_dict(path, sheet, format, tibble = TRUE, long = TRUE, compact = TRUE)

Arguments

Value

If long = TRUE, returns a tibble of the merged dictionary and value options. If long = FALSE, returns a list with elements dictionary and options. If compact = TRUE, options are nested as a column of data frames under "options".