Based on a dictionary generator like msf_dict() or msf_dict_survey(),
this function will generate a randomized data set based on values defined in
the dictionaries. The randomized dataset produced should mimic an excel
export from DHIS2 for outbreaks and a Kobo export for surveys.
Arguments
- dictionary
Specify which dictionary you would like to use.
- varnames
Specify name of column that contains variable names. If
dictionaryis a survey,varnamesneeds to be "name"`.- numcases
Specify the number of cases you want (default is 300)
- org
the organization the dictionary belongs to. Currently, only MSF exists. In the future, dictionaries from WHO and other organizations may become available.
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(disease = "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)
})
}
#> Loading required package: dplyr
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
#> Loading required package: matchmaker
#> > 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 2018-02-23 Village A 51 NA
#> 2 A2 2018-04-05 Village A 16 NA
#> 3 A3 2018-03-18 Village D 5 NA
#> 4 A4 2018-04-10 Village C 3 NA
#> 5 A5 2018-01-08 Village A 20 NA
#> 6 A6 2018-03-20 Village B 35 NA
#> 7 A7 2018-02-23 Village D 43 NA
#> 8 A8 2018-04-12 Village D 45 NA
#> 9 A9 2018-01-27 Village A 21 NA
#> 10 A10 2018-02-12 Village C 5 NA
#> 11 A11 2018-03-14 Village B 5 NA
#> 12 A12 2018-03-12 Village B 53 NA
#> 13 A13 2018-03-12 Village B 8 NA
#> 14 A14 2018-02-13 Village A 22 NA
#> 15 A15 2018-02-19 Village B 75 NA
#> 16 A16 2018-03-25 Village D 49 NA
#> 17 A17 2018-04-26 Village D 40 NA
#> 18 A18 2018-01-07 Village D 18 NA
#> 19 A19 2018-04-20 Village B 81 NA
#> 20 A20 2018-04-15 Village B 5 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>, …
#> > dict <- msf_dict(disease = "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>
#> > 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 2018-02-23 Village A 51 NA
#> 2 A2 2018-04-05 Village A 16 NA
#> 3 A3 2018-03-18 Village D 5 NA
#> 4 A4 2018-04-10 Village C 3 NA
#> 5 A5 2018-01-08 Village A 20 NA
#> 6 A6 2018-03-20 Village B 35 NA
#> 7 A7 2018-02-23 Village D 43 NA
#> 8 A8 2018-04-12 Village D 45 NA
#> 9 A9 2018-01-27 Village A 21 NA
#> 10 A10 2018-02-12 Village C 5 NA
#> 11 A11 2018-03-14 Village B 5 NA
#> 12 A12 2018-03-12 Village B 53 NA
#> 13 A13 2018-03-12 Village B 8 NA
#> 14 A14 2018-02-13 Village A 22 NA
#> 15 A15 2018-02-19 Village B 75 NA
#> 16 A16 2018-03-25 Village D 49 NA
#> 17 A17 2018-04-26 Village D 40 NA
#> 18 A18 2018-01-07 Village D 18 NA
#> 19 A19 2018-04-20 Village B 81 NA
#> 20 A20 2018-04-15 Village B 5 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>, …