The goal of {epidict} is to provide standardized data dictionaries for the MSF R4EPIs project. Learn more about R4EPIs at https://r4epis.netlify.com
Installation
You can install {epidict} from CRAN:
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 install {epidict} from the R4EPI repository: ``` r # install.packages("drat") drat::addRepo("R4EPI") install.packages("epidict") ``` You can also install the in-development version from GitHub using the {remotes} package (but there’s no guarantee that it will be stable): ``` r # install.packages("remotes") remotes::install_github("R4EPI/epidict") ```Accessing dictionaries
There are four MSF outbreak dictionaries available in {epidict} based on DHIS2 exports:
- Cholera/Acute watery diarrhea (“cholera”)
- Meningitis
- Measles/Rubella (“measles”)
- Acute Jaundice Syndrome (often suspected to be Hepatitis E) (“ajs”)
You can read more about the outbreak dictionaries at https://r4epis.netlify.com/outbreaks
The dictionary can be obtained via the msf_dict() function, which specifies a dictionary that describes recorded variables (data_element_shortname) in rows and their possible options (if categorical):
library("epidict")
msf_dict("Measles")
#> # A tibble: 52 x 8
#> data_element_uid data_element_na… data_element_sh… data_element_de…
#> <chr> <chr> <chr> <chr>
#> 1 DE_ESYM_012 esym_012_seizur… seizure_episodes Complication (S…
#> 2 DE_EGEN_013 egen_013_pregna… trimester If pregnant, tr…
#> 3 DE_ESYM_014 esym_014_croup croup Complication (C…
#> 4 DE_ESYM_002 esym_002_dehydr… dehydration_lev… Dehydration bas…
#> 5 DE_EGEN_030 egen_030_reside… residential_sta… Whether the pat…
#> 6 DE_EGEN_020 egen_020_previo… previously_vacc… Has the patient…
#> 7 DE_EGEN_062 egen_062_patien… patient_origin_… Name of village…
#> 8 DE_EGEN_010 egen_010_age_da… age_days Age of patient …
#> 9 DE_EGEN_029 egen_029_msf_in… msf_involvement How extensive i…
#> 10 DE_EGEN_053 egen_053_nutrit… nutrition_statu… What is the nut…
#> # … with 42 more rows, and 4 more variables:
#> # data_element_valuetype <chr>, data_element_formname <chr>,
#> # used_optionset_uid <chr>, options <list>
msf_dict("AJS")
#> # A tibble: 68 x 8
#> data_element_uid data_element_na… data_element_sh… data_element_de…
#> <lgl> <chr> <chr> <chr>
#> 1 NA egen_044_event_… event_file_type Is the event fi…
#> 2 NA egen_001_patien… case_number Anonymised pati…
#> 3 NA egen_004_date_o… date_of_consult… Date patient pr…
#> 4 NA egen_022_detect… detected_by How patient was…
#> 5 NA egen_005_patien… patient_facilit… Patient is pres…
#> 6 NA egen_029_msf_in… msf_involvement How extensive i…
#> 7 NA egen_008_age_ye… age_years Age of patient …
#> 8 NA egen_009_age_mo… age_months Age of patient …
#> 9 NA egen_010_age_da… age_days Age of patient …
#> 10 NA egen_011_sex sex Sex of patient …
#> # … with 58 more rows, and 4 more variables:
#> # data_element_valuetype <chr>, data_element_formname <chr>,
#> # used_optionset_uid <chr>, options <list>
msf_dict("Cholera")
#> # A tibble: 45 x 8
#> data_element_uid data_element_na… data_element_sh… data_element_de…
#> <chr> <chr> <chr> <chr>
#> 1 FF7d81Zy0yQ egen_013_pregna… trimester If pregnant, tr…
#> 2 ADfNqpCL5kf egen_015_exit_s… exit_status Final status of…
#> 3 wjCDTwXmtix egen_064_treatm… treatment_facil… Name of facilit…
#> 4 UUVnMdaBY5T esym_002_dehydr… dehydration_lev… Dehydration bas…
#> 5 BTZdJKpS3S5 egen_059_commen… comments_on_lab… Any additional …
#> 6 bpT8T341oQG egen_054_fluids… fluids_treatmen… What was the fl…
#> 7 FqPqIr5m0AQ egen_017_time_b… time_to_death Hours between p…
#> 8 epbKb1GczaS esym_003_malari… malaria_rdt_at_… Malaria rapid d…
#> 9 F04TM58HHsd egen_020_previo… previously_vacc… Has the patient…
#> 10 CfRNuWWuTr5 egen_058_choler… cholera_pcr_res… Was a PCR test …
#> # … with 35 more rows, and 4 more variables:
#> # data_element_valuetype <chr>, data_element_formname <chr>,
#> # used_optionset_uid <chr>, options <list>
msf_dict("Meningitis")
#> # A tibble: 53 x 8
#> data_element_uid data_element_na… data_element_sh… data_element_de…
#> <chr> <chr> <chr> <chr>
#> 1 Ow8ss6T1tT5 esym_028_seizur… seizures_at_adm… Presenting symp…
#> 2 CEEr9lcwKVG esym_036_kernig… kernigs_brudzin… Presenting symp…
#> 3 tIe59Htd2Ba esym_012_seizur… seizure_episodes Complication (S…
#> 4 Sjms36Aj6bT esym_023_febril… febrile_coma Complication (F…
#> 5 BTZdJKpS3S5 egen_059_commen… comments_on_lab… Any additional …
#> 6 wHnNEMRrS5A esym_025_chills… chills Presenting symp…
#> 7 T3Gn8mTYu0c esym_026_stiff_… stiff_neck Presenting symp…
#> 8 JCexRYsmjmS eobr_006_ti_sam… ti_result_organ… TI sample resul…
#> 9 myL5BYzy6zW esym_034_coma_a… coma Presenting symp…
#> 10 AhllpMovCeu egen_062_patien… patient_origin_… Name of village…
#> # … with 43 more rows, and 4 more variables:
#> # data_element_valuetype <chr>, data_element_formname <chr>,
#> # used_optionset_uid <chr>, options <list>In addition, there are three MSF survey dictionaries available:
- Retrospective mortality and access to care (“mortality”)
- Malnutrition (“nutrition”)
- Vaccination Coverage (“vaccination”)
You can read more about the survey dictionaries at https://r4epis.netlify.com/surveys
These are accessible via msf_dict_survey() where the variables are in column_name and are often labeled by question number.
msf_dict_survey("Mortality")
#> # A tibble: 128 x 7
#> level column_name description data_element_va… option_code option_name
#> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 0 q65_iq4 What house… INTEGER_POSITIVE <NA> <NA>
#> 2 0 q66_locati… GPS locati… LONG_TEXT <NA> <NA>
#> 3 0 health_dis… Health Dis… LONG_TEXT <NA> <NA>
#> 4 0 village Village LONG_TEXT <NA> <NA>
#> 5 0 cluster_nu… Cluster INTEGER_POSITIVE 0 <NA>
#> 6 0 q28_cq1 Head of Ho… TEXT 0 Yes
#> 7 0 q28_cq1 Head of Ho… TEXT 1 No
#> 8 0 q45_cq2 Head of Ho… TEXT 0 Yes
#> 9 0 q45_cq2 Head of Ho… TEXT 1 No
#> 10 0 q49_cq3 Head of Ho… TEXT 0 Yes
#> # … with 118 more rows, and 1 more variable: option_order_in_set <int>
msf_dict_survey("Nutrition")
#> # A tibble: 29 x 7
#> level column_name description data_element_va… option_code option_name
#> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 0 health_dis… Health Dis… LONG_TEXT <NA> <NA>
#> 2 0 village Village LONG_TEXT <NA> <NA>
#> 3 0 cluster_nu… Cluster INTEGER_POSITIVE 0 <NA>
#> 4 0 household_… Number of … INTEGER_POSITIVE 0 <NA>
#> 5 0 q28_cq1 Head of Ho… TEXT 0 Yes
#> 6 0 q28_cq1 Head of Ho… TEXT 1 No
#> 7 0 q45_cq2 Head of Ho… TEXT 0 Yes
#> 8 0 q45_cq2 Head of Ho… TEXT 1 No
#> 9 0 q49_cq3 Head of Ho… TEXT 0 Yes
#> 10 0 q49_cq3 Head of Ho… TEXT 1 No
#> # … with 19 more rows, and 1 more variable: option_order_in_set <int>
msf_dict_survey("Vaccination")
#> # A tibble: 113 x 7
#> level column_name description data_element_va… option_code option_name
#> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 0 q77_what_i… What is th… INTEGER_POSITIVE <NA> <NA>
#> 2 0 health_dis… Health Dis… LONG_TEXT <NA> <NA>
#> 3 0 village Village LONG_TEXT <NA> <NA>
#> 4 0 q14_hh_no What house… INTEGER_POSITIVE <NA> <NA>
#> 5 0 q15_home Are any oc… TEXT 1 Yes
#> 6 0 q15_home Are any oc… TEXT 0 No
#> 7 0 q16_occupa… How old is… INTEGER_POSITIVE <NA> <NA>
#> 8 0 q58_consent Does the o… TEXT 1 Yes
#> 9 0 q58_consent Does the o… TEXT 0 No
#> 10 0 q75_careta… What is th… TEXT 1 Mother
#> # … with 103 more rows, and 1 more variable: option_order_in_set <int>Generating data
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.
gen_data("Measles", varnames = "data_element_shortname", numcases = 100, org = "MSF")
#> # A tibble: 100 x 52
#> seizure_episodes trimester croup dehydration_lev… residential_sta…
#> <fct> <fct> <fct> <fct> <fct>
#> 1 0 <NA> 1 UN 4
#> 2 1 1 1 UN 2
#> 3 1 <NA> 0 SE 2
#> 4 1 <NA> 0 UN 4
#> 5 1 <NA> 0 UN 2
#> 6 1 <NA> 0 SE 2
#> 7 0 <NA> 0 SE 2
#> 8 1 <NA> 1 SO 5
#> 9 1 <NA> 1 NO 4
#> 10 1 2 0 NO 5
#> # … with 90 more rows, and 47 more variables:
#> # previously_vaccinated <fct>, patient_origin_free_text <chr>,
#> # age_days <int>, msf_involvement <fct>,
#> # nutrition_status_at_admission <fct>, fever <fct>, sex <fct>,
#> # patient_origin <chr>, pregnancy_outcome_at_exit <fct>,
#> # prescribed_vitamin_a <fct>, date_of_exit <date>,
#> # date_of_consultation_admission <date>, event_file_type <fct>,
#> # residential_status_brief <fct>, other_eye_complications <fct>,
#> # treatment_facility_site <int>, prescribed_antibiotics <fct>,
#> # treatment_location <chr>, date_of_onset <date>, exit_status <fct>,
#> # treatment_facility_name <lgl>, severe_oral_lesions <fct>,
#> # candidiasis <fct>, time_to_death <fct>,
#> # malaria_rdt_at_admission <fct>, patient_facility_type <fct>,
#> # previous_vaccine_doses_received <fct>, age_years <int>,
#> # arrival_date_in_area_if_3m <date>, severity_of_illness <fct>,
#> # age_months <int>, date_of_last_vaccination <date>,
#> # baby_born_with_complications <fct>, case_number <chr>,
#> # xerophthalmia <fct>, cough <fct>, contact_history <fct>,
#> # detected_by <fct>, delivery_event <fct>, encephalitis <fct>,
#> # nasal_discharge <fct>, acute_otitis_media <fct>, pregnant <fct>,
#> # foetus_alive_at_admission <fct>, maculopapular_rash <fct>,
#> # pneumonia <fct>, late_complications <fct>
gen_data("Vaccination", varnames = "column_name", numcases = 100, org = "MSF")
#> # A tibble: 100 x 41
#> q77_what_is_the… health_district village q14_hh_no q15_home
#> <dbl> <chr> <chr> <int> <fct>
#> 1 1 District A Villag… 1 No
#> 2 3 District B Villag… 3 Yes
#> 3 3 District B Villag… 1 Yes
#> 4 4 District B Villag… 3 Yes
#> 5 1 District A Villag… 4 Yes
#> 6 2 District A Villag… 3 No
#> 7 4 District B Villag… 3 Yes
#> 8 1 District A Villag… 1 Yes
#> 9 4 District B Villag… 1 No
#> 10 1 District A Villag… 2 No
#> # … with 90 more rows, and 36 more variables: q16_occupant_age <lgl>,
#> # q58_consent <fct>, q75_caretaker <fct>, q76_caretaker_other <lgl>,
#> # q65_consent_no_reason <fct>, q66_consent_no_reason_other <lgl>,
#> # q5_sex <fct>, q10_age_yr <int>, q55_age_mth <int>,
#> # q2_vaccine_9months <fct>, q20_routine_vaccine_age_yr <lgl>,
#> # q56_routine_vaccine_age_mth <lgl>, q21_routine_location <fct>,
#> # q72_routine_location_other <lgl>, q7_no_routine_vacc_r <fct>,
#> # q14_no_routine_vacc_other_r <lgl>, q17_vaccine_mass <fct>,
#> # q28_no_campaign_vacc_r <fct>, q29_no_campaign_vacc_other_r <lgl>,
#> # q18_campaign_vacc_age_yr <lgl>, q57_campaign_vacc_age_mth <lgl>,
#> # q25_campaign_vacc_location <fct>, q69_campaign_vacc_loc_oth <lgl>,
#> # q32_vaccine_sia <fct>, q41_vaccine_sia_age_yr <lgl>,
#> # q58_vaccine_sia_age_mth <lgl>, q42_vaccine_sia_location <fct>,
#> # q70_vac_sia_loc_oth <lgl>, q38_no_sia_vacc_reason <fct>,
#> # q39_no_sia_vacc_other_r <lgl>, q47_disease_diagnosis <fct>,
#> # q48_diagnosis_age_yr <lgl>, q71_diagnosis_age_mth <lgl>,
#> # eligible <int>, interviewed <dbl>, fact_0_id <int>Cleaning data with the dictionaries
You can use the dictionaries to clean the data via the {matchmaker} package:
library("matchmaker")
library("dplyr")
dat <- gen_data(dictionary = "Cholera",
varnames = "data_element_shortname",
numcases = 20,
org = "MSF"
)
print(dat)
#> # A tibble: 20 x 45
#> trimester exit_status treatment_facil… dehydration_lev…
#> <fct> <fct> <lgl> <fct>
#> 1 <NA> TR NA NO
#> 2 3 DD NA NO
#> 3 <NA> DH NA UN
#> 4 <NA> LA NA UN
#> 5 1 DD NA SO
#> 6 <NA> LA NA SO
#> 7 <NA> DD NA UN
#> 8 <NA> LA NA NO
#> 9 <NA> LA NA SE
#> 10 3 DH NA UN
#> 11 <NA> DD NA UN
#> 12 <NA> AD NA SO
#> 13 <NA> DD NA SO
#> 14 <NA> DOA NA NO
#> 15 <NA> LA NA SO
#> 16 <NA> DOA NA UN
#> 17 <NA> DOA NA NO
#> 18 <NA> TR NA UN
#> 19 <NA> DOA NA NO
#> 20 <NA> DD NA UN
#> # … with 41 more variables: comments_on_lab_results <lgl>,
#> # fluids_treatment_plan <fct>, time_to_death <fct>,
#> # malaria_rdt_at_admission <fct>, previously_vaccinated <fct>,
#> # cholera_pcr_result <fct>, iv_fluids_received_litres <int>,
#> # patient_origin_free_text <chr>,
#> # previous_vaccine_doses_received <fct>, age_years <int>,
#> # arrival_date_in_area_if_3m <date>, age_days <int>,
#> # msf_involvement <fct>, date_lab_sample_taken <date>,
#> # readmission <fct>, age_months <int>, date_of_last_vaccination <date>,
#> # case_number <chr>, sex <fct>, patient_origin <chr>,
#> # prescribed_zinc_supplement <fct>, delivery_event <fct>,
#> # pregnancy_outcome_at_exit <fct>, date_of_exit <date>,
#> # date_of_consultation_admission <date>, oedema <fct>,
#> # event_file_type <fct>, residential_status_brief <fct>,
#> # pregnant <fct>, cholera_rdt_result <fct>,
#> # dehydration_severity_during_stay <fct>, cholera_culture_result <fct>,
#> # foetus_alive_at_admission <fct>, treatment_facility_site <int>,
#> # prescribed_antibiotics <fct>, cholera_referred_to <fct>,
#> # ors_consumed_litres <int>, treatment_location <chr>,
#> # cholera_referred_from <fct>, cholera_treatment_facility_type <fct>,
#> # date_of_onset <date>
# We want the expanded dictionary, so we will select `compact = FALSE`
dict <- msf_dict(disease = "Cholera",
long = TRUE,
compact = FALSE,
tibble = TRUE
)
print(dict)
#> # A tibble: 182 x 11
#> data_element_uid data_element_na… data_element_sh… data_element_de…
#> <chr> <chr> <chr> <chr>
#> 1 FF7d81Zy0yQ egen_013_pregna… trimester If pregnant, tr…
#> 2 FF7d81Zy0yQ egen_013_pregna… trimester If pregnant, tr…
#> 3 FF7d81Zy0yQ egen_013_pregna… trimester If pregnant, tr…
#> 4 ADfNqpCL5kf egen_015_exit_s… exit_status Final status of…
#> 5 ADfNqpCL5kf egen_015_exit_s… exit_status Final status of…
#> 6 ADfNqpCL5kf egen_015_exit_s… exit_status Final status of…
#> 7 ADfNqpCL5kf egen_015_exit_s… exit_status Final status of…
#> 8 ADfNqpCL5kf egen_015_exit_s… exit_status Final status of…
#> 9 ADfNqpCL5kf egen_015_exit_s… exit_status Final status of…
#> 10 wjCDTwXmtix egen_064_treatm… treatment_facil… Name of facilit…
#> # … with 172 more rows, and 7 more variables:
#> # 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 x 45
#> trimester exit_status treatment_facil… dehydration_lev…
#> <fct> <fct> <lgl> <fct>
#> 1 <NA> Transferre… NA None
#> 2 3rd trim… Dead in fa… NA None
#> 3 <NA> Discharged… NA Unknown
#> 4 <NA> Left again… NA Unknown
#> 5 1st trim… Dead in fa… NA Some
#> 6 <NA> Left again… NA Some
#> 7 <NA> Dead in fa… NA Unknown
#> 8 <NA> Left again… NA None
#> 9 <NA> Left again… NA Severe
#> 10 3rd trim… Discharged… NA Unknown
#> 11 <NA> Dead in fa… NA Unknown
#> 12 <NA> Transferre… NA Some
#> 13 <NA> Dead in fa… NA Some
#> 14 <NA> Dead on ar… NA None
#> 15 <NA> Left again… NA Some
#> 16 <NA> Dead on ar… NA Unknown
#> 17 <NA> Dead on ar… NA None
#> 18 <NA> Transferre… NA Unknown
#> 19 <NA> Dead on ar… NA None
#> 20 <NA> Dead in fa… NA Unknown
#> # … with 41 more variables: comments_on_lab_results <lgl>,
#> # fluids_treatment_plan <fct>, time_to_death <fct>,
#> # malaria_rdt_at_admission <fct>, previously_vaccinated <fct>,
#> # cholera_pcr_result <fct>, iv_fluids_received_litres <int>,
#> # patient_origin_free_text <chr>,
#> # previous_vaccine_doses_received <fct>, age_years <int>,
#> # arrival_date_in_area_if_3m <date>, age_days <int>,
#> # msf_involvement <fct>, date_lab_sample_taken <date>,
#> # readmission <fct>, age_months <int>, date_of_last_vaccination <date>,
#> # case_number <chr>, sex <fct>, patient_origin <chr>,
#> # prescribed_zinc_supplement <fct>, delivery_event <fct>,
#> # pregnancy_outcome_at_exit <fct>, date_of_exit <date>,
#> # date_of_consultation_admission <date>, oedema <fct>,
#> # event_file_type <fct>, residential_status_brief <fct>,
#> # pregnant <fct>, cholera_rdt_result <fct>,
#> # dehydration_severity_during_stay <fct>, cholera_culture_result <fct>,
#> # foetus_alive_at_admission <fct>, treatment_facility_site <chr>,
#> # prescribed_antibiotics <fct>, cholera_referred_to <fct>,
#> # ors_consumed_litres <int>, treatment_location <chr>,
#> # cholera_referred_from <fct>, cholera_treatment_facility_type <fct>,
#> # date_of_onset <date>
