The goal of {epidict} is to provide standardized data dictionaries for use in epidemiological data analysis templates. Currently it supports standardised dictionaries from MSF OCA. This is a product of the R4EPIs project; learn more at https://r4epi.github.io/sitrep/
You can install {epidict} from CRAN:
install.packages("epidict")
Click here for alternative installation options
If there is a bugfix or feature that is not yet on CRAN, you can install it via the {drat} package:
You can also install the in-development version from GitHub using the {remotes} package (but there’s no guarantee that it will be stable):
# install.packages("remotes")
remotes::install_github("R4EPI/epidict") The dictionaries can be obtained via the msf_dict() function, which
specifies a variable and their possible options (if categorical).
There are MSF intersectional outbreak dictionaries available in {epidict} based on ODK exports.
There are MSF OCA outbreak dictionaries available in {epidict} based on DHIS2 exports. > You can read more about the outbreak dictionaries at https://r4epi.github.io/epidict/articles/Outbreaks.html
In addition, there are MSF survey dictionaries available based on ODK exports. > You can read more about the survey dictionaries at https://r4epi.github.io/epidict/articles/Surveys.html
You can also read in your own ODK dictionaries using read_dict().
The {epidict} package has a function for generating data that’s called
gen_data(), which takes three arguments: The dictionary, which column
describes the variable names, and how many rows are needed in the
output.
Click here for code examples
library("epidict")
gen_data("Measles", varnames = "data_element_shortname", numcases = 100, org = "MSF")
#> # A tibble: 100 × 52
#> case_number date_of_consultation_admis…¹ patient_facility_type patient_origin
#> <chr> <date> <fct> <chr>
#> 1 A1 2018-04-23 OP Village D
#> 2 A2 2018-02-23 OP Village C
#> 3 A3 2018-04-15 OP Village C
#> 4 A4 2018-04-30 OP Village A
#> 5 A5 2018-01-09 IP Village D
#> 6 A6 2018-03-14 OP Village D
#> 7 A7 2018-03-20 OP Village D
#> 8 A8 2018-03-23 OP Village A
#> 9 A9 2018-01-23 IP Village A
#> 10 A10 2018-03-19 OP Village D
#> # ℹ 90 more rows
#> # ℹ abbreviated name: ¹date_of_consultation_admission
#> # ℹ 48 more variables: age_years <int>, age_months <int>, age_days <int>,
#> # sex <fct>, pregnant <fct>, trimester <fct>,
#> # foetus_alive_at_admission <fct>, exit_status <fct>, date_of_exit <date>,
#> # time_to_death <fct>, pregnancy_outcome_at_exit <fct>,
#> # baby_born_with_complications <fct>, previously_vaccinated <fct>, …
gen_data("Vaccination_long", varnames = "name", numcases = 100, org = "MSF")
#> # A tibble: 100 × 120
#> start end today deviceid date team_number village_name village_other
#> <lgl> <lgl> <lgl> <lgl> <date> <lgl> <fct> <lgl>
#> 1 NA NA NA NA 2018-02-11 NA village_4 NA
#> 2 NA NA NA NA 2018-03-24 NA village_2 NA
#> 3 NA NA NA NA 2018-02-02 NA village_9 NA
#> 4 NA NA NA NA 2018-02-20 NA village_3 NA
#> 5 NA NA NA NA 2018-04-09 NA village_5 NA
#> 6 NA NA NA NA 2018-01-27 NA village_7 NA
#> 7 NA NA NA NA 2018-03-12 NA village_6 NA
#> 8 NA NA NA NA 2018-04-11 NA village_3 NA
#> 9 NA NA NA NA 2018-04-28 NA village_3 NA
#> 10 NA NA NA NA 2018-03-09 NA other NA
#> # ℹ 90 more rows
#> # ℹ 112 more variables: cluster_number <dbl>, household_number <int>,
#> # households_building <int>, random_hh <int>, consent <chr>,
#> # no_consent_reason <fct>, no_consent_other <lgl>, caretaker_relation <fct>,
#> # caretaker_other <lgl>, number_children <dbl>, child_number <chr>,
#> # sex <fct>, date_birth <date>, age_years <int>, age_months <int>,
#> # any_vaccine <fct>, vaccine_card <fct>, hf_records <fct>, …You can use the dictionaries to clean the data via the {matchmaker} package:
Click here for code examples
library("matchmaker")
library("dplyr")
dat <- gen_data(dictionary = "Cholera",
varnames = "data_element_shortname",
numcases = 20,
org = "MSF"
)
print(dat)
#> # A tibble: 20 × 45
#> case_number date_of_consultation_admiss…¹ patient_origin age_years age_months
#> <chr> <date> <chr> <int> <int>
#> 1 A1 2018-03-23 Village D 18 NA
#> 2 A2 2018-01-29 Village B 27 NA
#> 3 A3 2018-01-23 Village D 40 NA
#> 4 A4 2018-02-02 Village D 15 NA
#> 5 A5 2018-01-23 Village B 28 NA
#> 6 A6 2018-01-15 Village A 40 NA
#> 7 A7 2018-01-04 Village B 34 NA
#> 8 A8 2018-04-25 Village D 29 NA
#> 9 A9 2018-04-18 Village D 45 NA
#> 10 A10 2018-01-22 Village C 10 NA
#> 11 A11 2018-02-06 Village A 19 NA
#> 12 A12 2018-03-03 Village D 61 NA
#> 13 A13 2018-01-08 Village B 20 NA
#> 14 A14 2018-03-08 Village C 73 NA
#> 15 A15 2018-03-08 Village C 66 NA
#> 16 A16 2018-03-18 Village A 52 NA
#> 17 A17 2018-04-03 Village A 59 NA
#> 18 A18 2018-01-12 Village B 24 NA
#> 19 A19 2018-01-19 Village D 53 NA
#> 20 A20 2018-03-07 Village A 7 NA
#> # ℹ abbreviated name: ¹date_of_consultation_admission
#> # ℹ 40 more variables: age_days <int>, sex <fct>, pregnant <fct>,
#> # trimester <fct>, foetus_alive_at_admission <fct>, exit_status <fct>,
#> # date_of_exit <date>, time_to_death <fct>, pregnancy_outcome_at_exit <fct>,
#> # previously_vaccinated <fct>, previous_vaccine_doses_received <fct>,
#> # readmission <fct>, msf_involvement <fct>,
#> # cholera_treatment_facility_type <fct>, residential_status_brief <fct>, …
# We want the expanded dictionary, so we will select `compact = FALSE`
dict <- msf_dict(dictionary = "Cholera",
long = TRUE,
compact = FALSE,
tibble = TRUE
)
print(dict)
#> # A tibble: 182 × 11
#> data_element_uid data_element_name data_element_shortname
#> <chr> <chr> <chr>
#> 1 AafTlSwliVQ egen_001_patient_case_number case_number
#> 2 OTGOtWBz39J egen_004_date_of_consultation_admiss… date_of_consultation_…
#> 3 wnmMr2V3T3u egen_006_patient_origin patient_origin
#> 4 sbgqjeVwtb8 egen_008_age_years age_years
#> 5 eXYhovYyl61 egen_009_age_months age_months
#> 6 UrYJSk2Wp46 egen_010_age_days age_days
#> 7 D1Ky5K7pFN6 egen_011_sex sex
#> 8 D1Ky5K7pFN6 egen_011_sex sex
#> 9 D1Ky5K7pFN6 egen_011_sex sex
#> 10 dTm5R53YYXC egen_012_pregnancy_status pregnant
#> # ℹ 172 more rows
#> # ℹ 8 more variables: data_element_description <chr>,
#> # data_element_valuetype <chr>, data_element_formname <chr>,
#> # used_optionset_uid <chr>, option_code <chr>, option_name <chr>,
#> # option_uid <chr>, option_order_in_set <dbl>
# 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)
#> # A tibble: 20 × 45
#> case_number date_of_consultation_admiss…¹ patient_origin age_years age_months
#> <chr> <date> <chr> <int> <int>
#> 1 A1 2018-03-23 Village D 18 NA
#> 2 A2 2018-01-29 Village B 27 NA
#> 3 A3 2018-01-23 Village D 40 NA
#> 4 A4 2018-02-02 Village D 15 NA
#> 5 A5 2018-01-23 Village B 28 NA
#> 6 A6 2018-01-15 Village A 40 NA
#> 7 A7 2018-01-04 Village B 34 NA
#> 8 A8 2018-04-25 Village D 29 NA
#> 9 A9 2018-04-18 Village D 45 NA
#> 10 A10 2018-01-22 Village C 10 NA
#> 11 A11 2018-02-06 Village A 19 NA
#> 12 A12 2018-03-03 Village D 61 NA
#> 13 A13 2018-01-08 Village B 20 NA
#> 14 A14 2018-03-08 Village C 73 NA
#> 15 A15 2018-03-08 Village C 66 NA
#> 16 A16 2018-03-18 Village A 52 NA
#> 17 A17 2018-04-03 Village A 59 NA
#> 18 A18 2018-01-12 Village B 24 NA
#> 19 A19 2018-01-19 Village D 53 NA
#> 20 A20 2018-03-07 Village A 7 NA
#> # ℹ abbreviated name: ¹date_of_consultation_admission
#> # ℹ 40 more variables: age_days <int>, sex <fct>, pregnant <fct>,
#> # trimester <fct>, foetus_alive_at_admission <fct>, exit_status <fct>,
#> # date_of_exit <date>, time_to_death <fct>, pregnancy_outcome_at_exit <fct>,
#> # previously_vaccinated <fct>, previous_vaccine_doses_received <fct>,
#> # readmission <fct>, msf_involvement <fct>,
#> # cholera_treatment_facility_type <fct>, residential_status_brief <fct>, …