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://r4epis.netlify.com
Installation
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") Accessing dictionaries
There are four MSF outbreak dictionaries available in {epidict} based on DHIS2 exports:
- Acute Jaundice Syndrome (often suspected to be Hepatitis E) (“AJS”)
- Cholera/Acute watery diarrhea (“Cholera”)
- Measles/Rubella (“Measles”)
- Meningitis (“Meningitis”)
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):
Click here for code examples
library("epidict")
msf_dict("AJS")
#> # A tibble: 68 × 8
#> data_element_uid data_elem…¹ data_…² data_…³ data_…⁴ data_…⁵ used_…⁶ options
#> <lgl> <chr> <chr> <chr> <chr> <chr> <chr> <list>
#> 1 NA egen_044_e… event_… Is the… TEXT <NA> HgdrO8… <tibble>
#> 2 NA egen_001_p… case_n… Anonym… TEXT <NA> <NA> <tibble>
#> 3 NA egen_004_d… date_o… Date p… DATE <NA> <NA> <tibble>
#> 4 NA egen_022_d… detect… How pa… TEXT <NA> BlfHX5… <tibble>
#> 5 NA egen_005_p… patien… Patien… TEXT <NA> YNeOOp… <tibble>
#> 6 NA egen_029_m… msf_in… How ex… TEXT <NA> PN5NWt… <tibble>
#> 7 NA egen_008_a… age_ye… Age of… INTEGE… <NA> <NA> <tibble>
#> 8 NA egen_009_a… age_mo… Age of… INTEGE… <NA> <NA> <tibble>
#> 9 NA egen_010_a… age_da… Age of… INTEGE… <NA> <NA> <tibble>
#> 10 NA egen_011_s… sex Sex of… TEXT <NA> orgc5Y… <tibble>
#> # … with 58 more rows, and abbreviated variable names ¹data_element_name,
#> # ²data_element_shortname, ³data_element_description,
#> # ⁴data_element_valuetype, ⁵data_element_formname, ⁶used_optionset_uid
#> # ℹ Use `print(n = ...)` to see more rows
msf_dict("Cholera")
#> # A tibble: 45 × 8
#> data_element_uid data_elem…¹ data_…² data_…³ data_…⁴ data_…⁵ used_…⁶ options
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <list>
#> 1 AafTlSwliVQ egen_001_p… case_n… Anonym… TEXT Case n… <NA> <tibble>
#> 2 OTGOtWBz39J egen_004_d… date_o… Date p… DATE Date o… <NA> <tibble>
#> 3 wnmMr2V3T3u egen_006_p… patien… Locati… ORGANI… Patien… <NA> <tibble>
#> 4 sbgqjeVwtb8 egen_008_a… age_ye… Age of… INTEGE… Age in… <NA> <tibble>
#> 5 eXYhovYyl61 egen_009_a… age_mo… Age of… INTEGE… Age in… <NA> <tibble>
#> 6 UrYJSk2Wp46 egen_010_a… age_da… Age of… INTEGE… Age in… <NA> <tibble>
#> 7 D1Ky5K7pFN6 egen_011_s… sex Sex of… TEXT Sex orgc5Y… <tibble>
#> 8 dTm5R53YYXC egen_012_p… pregna… Pregna… TEXT Pregna… IEjzG2… <tibble>
#> 9 FF7d81Zy0yQ egen_013_p… trimes… If pre… TEXT Trimes… QjGHFN… <tibble>
#> 10 vLAmA6Pmjip egen_014_p… foetus… If pre… TEXT Foetus… SR8Jtf… <tibble>
#> # … with 35 more rows, and abbreviated variable names ¹data_element_name,
#> # ²data_element_shortname, ³data_element_description,
#> # ⁴data_element_valuetype, ⁵data_element_formname, ⁶used_optionset_uid
#> # ℹ Use `print(n = ...)` to see more rows
msf_dict("Measles")
#> # A tibble: 52 × 8
#> data_element_uid data_elem…¹ data_…² data_…³ data_…⁴ data_…⁵ used_…⁶ options
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <list>
#> 1 DE_EGEN_001 egen_001_p… case_n… Anonym… TEXT Case n… <NA> <tibble>
#> 2 DE_EGEN_004 egen_004_d… date_o… Date p… DATE Date o… <NA> <tibble>
#> 3 DE_EGEN_005 egen_005_p… patien… Patien… TEXT Patien… YNeOOp… <tibble>
#> 4 DE_EGEN_006 egen_006_p… patien… Locati… ORGANI… Patien… <NA> <tibble>
#> 5 DE_EGEN_008 egen_008_a… age_ye… Age of… INTEGE… Age in… <NA> <tibble>
#> 6 DE_EGEN_009 egen_009_a… age_mo… Age of… INTEGE… Age in… <NA> <tibble>
#> 7 DE_EGEN_010 egen_010_a… age_da… Age of… INTEGE… Age in… <NA> <tibble>
#> 8 DE_EGEN_011 egen_011_s… sex Sex of… TEXT Sex orgc5Y… <tibble>
#> 9 DE_EGEN_012 egen_012_p… pregna… Pregna… TEXT Pregna… IEjzG2… <tibble>
#> 10 DE_EGEN_013 egen_013_p… trimes… If pre… TEXT Trimes… QjGHFN… <tibble>
#> # … with 42 more rows, and abbreviated variable names ¹data_element_name,
#> # ²data_element_shortname, ³data_element_description,
#> # ⁴data_element_valuetype, ⁵data_element_formname, ⁶used_optionset_uid
#> # ℹ Use `print(n = ...)` to see more rows
msf_dict("Meningitis")
#> # A tibble: 53 × 8
#> data_element_uid data_elem…¹ data_…² data_…³ data_…⁴ data_…⁵ used_…⁶ options
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <list>
#> 1 AafTlSwliVQ egen_001_p… case_n… Anonym… TEXT Case n… <NA> <tibble>
#> 2 OTGOtWBz39J egen_004_d… date_o… Date p… DATE Date o… <NA> <tibble>
#> 3 udXAcFEE1dl egen_005_p… patien… Patien… TEXT Patien… YNeOOp… <tibble>
#> 4 wnmMr2V3T3u egen_006_p… patien… Locati… ORGANI… Patien… <NA> <tibble>
#> 5 sbgqjeVwtb8 egen_008_a… age_ye… Age of… INTEGE… Age in… <NA> <tibble>
#> 6 eXYhovYyl61 egen_009_a… age_mo… Age of… INTEGE… Age in… <NA> <tibble>
#> 7 UrYJSk2Wp46 egen_010_a… age_da… Age of… INTEGE… Age in… <NA> <tibble>
#> 8 D1Ky5K7pFN6 egen_011_s… sex Sex of… TEXT Sex orgc5Y… <tibble>
#> 9 ADfNqpCL5kf egen_015_e… exit_s… Final … TEXT Exit s… hO9TET… <tibble>
#> 10 JZ8yqTow79G egen_016_d… date_o… Date p… DATE Exit d… <NA> <tibble>
#> # … with 43 more rows, and abbreviated variable names ¹data_element_name,
#> # ²data_element_shortname, ³data_element_description,
#> # ⁴data_element_valuetype, ⁵data_element_formname, ⁶used_optionset_uid
#> # ℹ Use `print(n = ...)` to see more rowsIn addition, there are four MSF survey dictionaries available:
- Retrospective mortality and access to care (“Mortality”)
- Malnutrition (“Nutrition”)
- Vaccination coverage long form (“vaccination_long”)
- Vaccination coverage short form (“vaccination_short”)
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 name. You can also read in your own Kobo (ODK) dictionaries by specifying tempalte = FALSE and then setting name = <path to your .xlsx>.
Click here for code examples
msf_dict_survey("Mortality")
#> # A tibble: 174 × 15
#> type name short…¹ label…² label…³ hint_…⁴ hint_…⁵ default relev…⁶ appea…⁷
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 start start start Start … <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 end end end End Ti… <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 today today today Date o… <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 device… devi… device… Phone … <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 date date Date o… Date Date <NA> <NA> today() <NA> <NA>
#> 6 integer team… Team n… Team n… Numéro… <NA> <NA> <NA> <NA> numbers
#> 7 village vill… Villag… Villag… Nom du… <NA> <NA> <NA> <NA> <NA>
#> 8 text vill… Other … Specif… Autre,… <NA> <NA> <NA> ${vill… <NA>
#> 9 integer clus… Cluste… Cluste… Numéro… <NA> <NA> <NA> <NA> numbers
#> 10 integer hous… Househ… Househ… Numéro… <NA> <NA> <NA> <NA> numbers
#> # … with 164 more rows, 5 more variables: constraint <chr>, repeat_count <chr>,
#> # calculation <chr>, value_type <chr>, options <list>, and abbreviated
#> # variable names ¹short_name, ²label_english, ³label_french, ⁴hint_english,
#> # ⁵hint_french, ⁶relevant, ⁷appearance
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
msf_dict_survey("Nutrition")
#> # A tibble: 27 × 15
#> type name short…¹ label…² label…³ hint_…⁴ hint_…⁵ repea…⁶ relev…⁷ calcu…⁸
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <lgl>
#> 1 start start <NA> Start … <NA> <NA> <NA> <NA> <NA> NA
#> 2 end end <NA> End Ti… <NA> <NA> <NA> <NA> <NA> NA
#> 3 today today <NA> Date o… <NA> <NA> <NA> <NA> <NA> NA
#> 4 device… devi… <NA> Phone … <NA> <NA> <NA> <NA> <NA> NA
#> 5 date date Date Date <NA> <NA> <NA> <NA> <NA> NA
#> 6 integer team… Team n… Team n… <NA> <NA> <NA> <NA> <NA> NA
#> 7 village vill… Villag… Villag… Nom du… <NA> <NA> <NA> <NA> NA
#> 8 text vill… Other … Specif… Précis… <NA> <NA> <NA> ${vill… NA
#> 9 geopoi… vill… Villag… Villag… Locali… <NA> <NA> <NA> <NA> NA
#> 10 integer clus… Cluste… Cluste… Numéro… <NA> <NA> <NA> <NA> NA
#> # … with 17 more rows, 5 more variables: constraint <chr>, appearance <chr>,
#> # default <chr>, value_type <chr>, options <list>, and abbreviated variable
#> # names ¹short_name, ²label_english, ³label_french, ⁴hint_english,
#> # ⁵hint_french, ⁶repeat_count, ⁷relevant, ⁸calculation
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
msf_dict_survey("Vaccination_long")
#> # A tibble: 106 × 15
#> type name short…¹ label…² label…³ hint_…⁴ hint_…⁵ default relev…⁶ appea…⁷
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 start start <NA> Start … Start … <NA> <NA> <NA> <NA> <NA>
#> 2 end end <NA> End Ti… End Ti… <NA> <NA> <NA> <NA> <NA>
#> 3 today today <NA> Date o… Date o… <NA> <NA> <NA> <NA> <NA>
#> 4 device… devi… <NA> Phone … Phone … <NA> <NA> <NA> <NA> <NA>
#> 5 date date Date Date Date <NA> <NA> today() <NA> <NA>
#> 6 integer team… Team n… Team n… Numéro… <NA> <NA> <NA> <NA> <NA>
#> 7 village vill… Villag… Villag… Nom du… <NA> <NA> <NA> <NA> <NA>
#> 8 text vill… Other … Specif… Veuill… <NA> <NA> <NA> ${vill… <NA>
#> 9 integer clus… Cluste… Cluste… Numéro… <NA> <NA> <NA> <NA> numbers
#> 10 integer hous… Househ… Househ… Numéro… <NA> <NA> <NA> <NA> <NA>
#> # … with 96 more rows, 5 more variables: repeat_count <chr>, constraint <chr>,
#> # calculation <chr>, value_type <chr>, options <list>, and abbreviated
#> # variable names ¹short_name, ²label_english, ³label_french, ⁴hint_english,
#> # ⁵hint_french, ⁶relevant, ⁷appearance
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
msf_dict_survey("Vaccination_short")
#> # A tibble: 38 × 16
#> type name short…¹ label…² label…³ hint_…⁴ hint_…⁵ default relev…⁶ appea…⁷
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 start start <NA> Start … Start … <NA> <NA> <NA> <NA> <NA>
#> 2 end end <NA> End Ti… End Ti… <NA> <NA> <NA> <NA> <NA>
#> 3 today today <NA> Date o… Date o… <NA> <NA> <NA> <NA> <NA>
#> 4 device… devi… <NA> Phone … Phone … <NA> <NA> <NA> <NA> <NA>
#> 5 date date Date Date Date <NA> <NA> .today… <NA> <NA>
#> 6 integer team… Team n… Team n… Numéro… <NA> <NA> <NA> <NA> <NA>
#> 7 village vill… Villag… Villag… Nom du… <NA> <NA> <NA> <NA> <NA>
#> 8 text vill… Other … Specif… Veuill… <NA> <NA> <NA> ${vill… <NA>
#> 9 integer clus… Cluste… Cluste… Numéro… <NA> <NA> <NA> <NA> numbers
#> 10 integer hous… Househ… Househ… Numéro… <NA> <NA> <NA> <NA> <NA>
#> # … with 28 more rows, 6 more variables: repeat_count <chr>, constraint <chr>,
#> # calculation <chr>, hxl <chr>, value_type <chr>, options <list>, and
#> # abbreviated variable names ¹short_name, ²label_english, ³label_french,
#> # ⁴hint_english, ⁵hint_french, ⁶relevant, ⁷appearance
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesGenerating 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.
Click here for code examples
gen_data("Measles", varnames = "data_element_shortname", numcases = 100, org = "MSF")
#> # A tibble: 100 × 52
#> case_number date_of_c…¹ patie…² patie…³ age_y…⁴ age_m…⁵ age_d…⁶ sex pregn…⁷
#> <chr> <date> <fct> <chr> <int> <int> <int> <fct> <fct>
#> 1 A1 2018-04-05 IP Villag… 38 NA NA M NA
#> 2 A2 2018-01-07 OP Villag… 77 NA NA M NA
#> 3 A3 2018-03-18 IP Villag… 13 NA NA F NA
#> 4 A4 2018-02-16 IP Villag… 28 NA NA F Y
#> 5 A5 2018-03-25 IP Villag… NA NA 9 U NA
#> 6 A6 2018-03-16 IP Villag… 24 NA NA F Y
#> 7 A7 2018-04-09 OP Villag… 86 NA NA M NA
#> 8 A8 2018-04-08 IP Villag… 8 NA NA U NA
#> 9 A9 2018-03-23 OP Villag… 60 NA NA M NA
#> 10 A10 2018-04-23 OP Villag… 21 NA NA U NA
#> # … with 90 more rows, 43 more variables: 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>,
#> # previous_vaccine_doses_received <fct>, detected_by <fct>,
#> # msf_involvement <fct>, residential_status <fct>,
#> # residential_status_brief <fct>, date_of_last_vaccination <date>, …
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
gen_data("Vaccination_long", varnames = "name", numcases = 100, org = "MSF")
#> # A tibble: 100 × 120
#> start end today deviceid date team_…¹ villa…² villa…³ clust…⁴ house…⁵
#> <lgl> <lgl> <lgl> <lgl> <date> <lgl> <fct> <lgl> <dbl> <int>
#> 1 NA NA NA NA 2018-02-18 NA villag… NA 1 1
#> 2 NA NA NA NA 2018-02-13 NA villag… NA 7 2
#> 3 NA NA NA NA 2018-02-21 NA villag… NA 6 1
#> 4 NA NA NA NA 2018-02-24 NA other NA 11 1
#> 5 NA NA NA NA 2018-03-21 NA villag… NA 3 1
#> 6 NA NA NA NA 2018-04-06 NA villag… NA 7 1
#> 7 NA NA NA NA 2018-02-09 NA villag… NA 3 2
#> 8 NA NA NA NA 2018-04-29 NA villag… NA 4 3
#> 9 NA NA NA NA 2018-04-15 NA villag… NA 8 1
#> 10 NA NA NA NA 2018-03-26 NA villag… NA 8 3
#> # … with 90 more rows, 110 more variables: 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>, health_facility <lgl>, date_records_checked <date>,
#> # injection_upper_arm <fct>, scar_present <fct>, poliodrop_woc <fct>, …
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namesCleaning data with the dictionaries
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_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 2018-03-08 Villag… 8 NA NA M NA <NA>
#> 2 A2 2018-04-09 Villag… 9 NA NA U NA <NA>
#> 3 A3 2018-02-21 Villag… 8 NA NA M NA <NA>
#> 4 A4 2018-04-24 Villag… 3 NA NA U NA <NA>
#> 5 A5 2018-04-07 Villag… 52 NA NA M NA <NA>
#> 6 A6 2018-01-30 Villag… 24 NA NA M NA <NA>
#> 7 A7 2018-02-21 Villag… 57 NA NA U NA <NA>
#> 8 A8 2018-01-10 Villag… 12 NA NA F NA <NA>
#> 9 A9 2018-01-11 Villag… NA NA 3 U NA <NA>
#> 10 A10 2018-04-02 Villag… 7 NA NA F Y 2
#> 11 A11 2018-04-10 Villag… 12 NA NA F W <NA>
#> 12 A12 2018-02-19 Villag… 23 NA NA M NA <NA>
#> 13 A13 2018-02-12 Villag… NA 8 NA F N <NA>
#> 14 A14 2018-01-31 Villag… 66 NA NA U NA <NA>
#> 15 A15 2018-01-11 Villag… 7 NA NA U NA <NA>
#> 16 A16 2018-04-22 Villag… 25 NA NA M NA <NA>
#> 17 A17 2018-01-10 Villag… 27 NA NA F NA <NA>
#> 18 A18 2018-01-18 Villag… 16 NA NA F N <NA>
#> 19 A19 2018-03-05 Villag… 9 NA NA F NA <NA>
#> 20 A20 2018-02-13 Villag… 8 NA NA M NA <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>, …
#> # ℹ Use `colnames()` to see all variable names
# 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 × 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
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
# 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_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 2018-03-08 Villag… 8 NA NA Male Not ap… <NA>
#> 2 A2 2018-04-09 Villag… 9 NA NA Unkn… Not ap… <NA>
#> 3 A3 2018-02-21 Villag… 8 NA NA Male Not ap… <NA>
#> 4 A4 2018-04-24 Villag… 3 NA NA Unkn… Not ap… <NA>
#> 5 A5 2018-04-07 Villag… 52 NA NA Male Not ap… <NA>
#> 6 A6 2018-01-30 Villag… 24 NA NA Male Not ap… <NA>
#> 7 A7 2018-02-21 Villag… 57 NA NA Unkn… Not ap… <NA>
#> 8 A8 2018-01-10 Villag… 12 NA NA Fema… Not ap… <NA>
#> 9 A9 2018-01-11 Villag… NA NA 3 Unkn… Not ap… <NA>
#> 10 A10 2018-04-02 Villag… 7 NA NA Fema… Yes, c… 2nd tr…
#> 11 A11 2018-04-10 Villag… 12 NA NA Fema… Was pr… <NA>
#> 12 A12 2018-02-19 Villag… 23 NA NA Male Not ap… <NA>
#> 13 A13 2018-02-12 Villag… NA 8 NA Fema… Not cu… <NA>
#> 14 A14 2018-01-31 Villag… 66 NA NA Unkn… Not ap… <NA>
#> 15 A15 2018-01-11 Villag… 7 NA NA Unkn… Not ap… <NA>
#> 16 A16 2018-04-22 Villag… 25 NA NA Male Not ap… <NA>
#> 17 A17 2018-01-10 Villag… 27 NA NA Fema… Not ap… <NA>
#> 18 A18 2018-01-18 Villag… 16 NA NA Fema… Not cu… <NA>
#> 19 A19 2018-03-05 Villag… 9 NA NA Fema… Not ap… <NA>
#> 20 A20 2018-02-13 Villag… 8 NA NA Male Not ap… <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>, …
#> # ℹ Use `colnames()` to see all variable names