These function produces MSF OCA dictionaries based on DHIS2 (for outbreaks) and Kobo (for 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(
disease,
name = "MSF-outbreak-dict.xlsx",
tibble = TRUE,
compact = TRUE,
long = TRUE
)
msf_dict_survey(
disease,
name = "MSF-survey-dict.xlsx",
tibble = TRUE,
compact = TRUE,
long = TRUE,
template = TRUE
)Arguments
- disease
Specify which disease you would like to use.
msf_dict()supports "AJS", "Cholera", "Measles", "Meningitis"msf_dict_survey()supports "Mortality", "Nutrition", "Vaccination_long" and "Vaccination_short" (only used in surveys iftemplate = TRUE)
- name
the name of the dictionary stored in the package.
msf_dict_survey()supports Kobo dictionaries not stored within this package, to use these: specifynameas path to .xlsx file and set thetemplate = False
- tibble
Return data dictionary as a tidyverse tibble (default is TRUE)
- 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 withtidyr::unnest(). This only works iflong = TRUE.- long
If
TRUE(default), the returned data dictionary is in long format with each option getting one row. IfFALSE, then two data frames are returned, one with variables and the other with content options.@param template Only used for
msf_dict_survey(). IfTRUE(default) the returned data dictionary is a generic MSF OCA ERB pre-approved dictionary. IfFALSEallows you to read in your own Kobo dictionary by defining a path inname.- template
(for survey dictionaries): if
TRUEread in a generic dictionary based on the MSF OCA ERB pre-approved template. However you can also specify your own dictionary if this differs substantially, by settingtemplate = FALSEand defining a path inname.
See also
matchmaker::match_df() gen_data() msf_dict_survey()
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)
})
}
#> > dat <- gen_data(dictionary = "Cholera", varnames = "data_element_shortname",
#> + numcases = 20, org = "MSF")
#> > print(dat)
#> # A tibble: 20 × 45
#> case_number date_of_c…¹ patie…² age_y…³ age_m…⁴ age_d…⁵ sex pregn…⁶ trime…⁷
#> <chr> <date> <chr> <int> <int> <int> <fct> <fct> <fct>
#> 1 A1 2018-04-02 Villag… 82 NA NA F W NA
#> 2 A2 2018-01-17 Villag… 10 NA NA U NA NA
#> 3 A3 2018-01-16 Villag… 23 NA NA M NA NA
#> 4 A4 2018-03-02 Villag… 63 NA NA M NA NA
#> 5 A5 2018-04-27 Villag… 40 NA NA M NA NA
#> 6 A6 2018-01-31 Villag… 18 NA NA M NA NA
#> 7 A7 2018-02-25 Villag… 37 NA NA F Y 1
#> 8 A8 2018-02-05 Villag… 47 NA NA F NA NA
#> 9 A9 2018-03-20 Villag… 15 NA NA M NA NA
#> 10 A10 2018-01-06 Villag… 35 NA NA F NA NA
#> 11 A11 2018-04-08 Villag… 62 NA NA F W NA
#> 12 A12 2018-01-08 Villag… 56 NA NA U NA NA
#> 13 A13 2018-01-08 Villag… 26 NA NA F NA NA
#> 14 A14 2018-03-13 Villag… 16 NA NA M NA NA
#> 15 A15 2018-01-26 Villag… 61 NA NA M NA NA
#> 16 A16 2018-02-14 Villag… 44 NA NA M NA NA
#> 17 A17 2018-01-02 Villag… 59 NA NA U NA NA
#> 18 A18 2018-02-08 Villag… 74 NA NA U NA NA
#> 19 A19 2018-02-18 Villag… 7 NA NA F NA NA
#> 20 A20 2018-03-02 Villag… 42 NA NA F N NA
#> # … with 36 more variables: 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>,
#> # date_of_last_vaccination <date>, prescribed_zinc_supplement <fct>,
#> # prescribed_antibiotics <fct>, ors_consumed_litres <int>, …
#> > dict <- msf_dict(disease = "Cholera", long = TRUE, compact = FALSE, tibble = TRUE)
#> > print(dict)
#> # A tibble: 182 × 11
#> data_elemen…¹ data_…² data_…³ data_…⁴ data_…⁵ data_…⁶ used_…⁷ optio…⁸ optio…⁹
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 AafTlSwliVQ egen_0… case_n… Anonym… TEXT Case n… NA NA NA
#> 2 OTGOtWBz39J egen_0… date_o… Date p… DATE Date o… NA NA NA
#> 3 wnmMr2V3T3u egen_0… patien… Locati… ORGANI… Patien… NA NA NA
#> 4 sbgqjeVwtb8 egen_0… age_ye… Age of… INTEGE… Age in… NA NA NA
#> 5 eXYhovYyl61 egen_0… age_mo… Age of… INTEGE… Age in… NA NA NA
#> 6 UrYJSk2Wp46 egen_0… age_da… Age of… INTEGE… Age in… NA NA NA
#> 7 D1Ky5K7pFN6 egen_0… sex Sex of… TEXT Sex orgc5Y… M Male
#> 8 D1Ky5K7pFN6 egen_0… sex Sex of… TEXT Sex orgc5Y… F Female
#> 9 D1Ky5K7pFN6 egen_0… sex Sex of… TEXT Sex orgc5Y… U Unknow…
#> 10 dTm5R53YYXC egen_0… pregna… Pregna… TEXT Pregna… IEjzG2… N Not cu…
#> # … with 172 more rows, 2 more variables: option_uid <chr>,
#> # option_order_in_set <dbl>, and abbreviated variable names
#> # ¹data_element_uid, ²data_element_name, ³data_element_shortname,
#> # ⁴data_element_description, ⁵data_element_valuetype, ⁶data_element_formname,
#> # ⁷used_optionset_uid, ⁸option_code, ⁹option_name
#> > 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_c…¹ patie…² age_y…³ age_m…⁴ age_d…⁵ sex pregn…⁶ trime…⁷
#> <chr> <date> <chr> <int> <int> <int> <fct> <fct> <fct>
#> 1 A1 2018-04-02 Villag… 82 NA NA Fema… Was pr… NA
#> 2 A2 2018-01-17 Villag… 10 NA NA Unkn… Not ap… NA
#> 3 A3 2018-01-16 Villag… 23 NA NA Male Not ap… NA
#> 4 A4 2018-03-02 Villag… 63 NA NA Male Not ap… NA
#> 5 A5 2018-04-27 Villag… 40 NA NA Male Not ap… NA
#> 6 A6 2018-01-31 Villag… 18 NA NA Male Not ap… NA
#> 7 A7 2018-02-25 Villag… 37 NA NA Fema… Yes, c… 1st tr…
#> 8 A8 2018-02-05 Villag… 47 NA NA Fema… Not ap… NA
#> 9 A9 2018-03-20 Villag… 15 NA NA Male Not ap… NA
#> 10 A10 2018-01-06 Villag… 35 NA NA Fema… Not ap… NA
#> 11 A11 2018-04-08 Villag… 62 NA NA Fema… Was pr… NA
#> 12 A12 2018-01-08 Villag… 56 NA NA Unkn… Not ap… NA
#> 13 A13 2018-01-08 Villag… 26 NA NA Fema… Not ap… NA
#> 14 A14 2018-03-13 Villag… 16 NA NA Male Not ap… NA
#> 15 A15 2018-01-26 Villag… 61 NA NA Male Not ap… NA
#> 16 A16 2018-02-14 Villag… 44 NA NA Male Not ap… NA
#> 17 A17 2018-01-02 Villag… 59 NA NA Unkn… Not ap… NA
#> 18 A18 2018-02-08 Villag… 74 NA NA Unkn… Not ap… NA
#> 19 A19 2018-02-18 Villag… 7 NA NA Fema… Not ap… NA
#> 20 A20 2018-03-02 Villag… 42 NA NA Fema… Not cu… NA
#> # … with 36 more variables: 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>,
#> # date_of_last_vaccination <date>, prescribed_zinc_supplement <fct>,
#> # prescribed_antibiotics <fct>, ors_consumed_litres <int>, …