What clean_the_nest() does

clean_the_nest() is the foundational layer of the mudnester pipeline. It:

  • Renames your columns to the mudnester internal schema (standardised names like lettername1, dob, onset_date) so every downstream function knows what to expect
  • Validates date formats and raises a clear error if a date column is not Date class
  • Strips and lowercases name fields for linkage consistency
  • Derives blocking variables (block1, block2, block3) used by starling::murmuration()
  • Standardises Medicare numbers into 9-, 10-, and 11-digit variants
  • Derives age, length of stay, ICU outcome, and death outcome automatically when the required columns are present
  • Pivots long vaccination data to wide (one row per person) via lie_nest_flat = TRUE

Nothing downstream is trustworthy until this layer is structurally sound. Think of it as laying the mud — subsequent functions can only build on what is solid here.

The three data types

data_type is required. The three values map to different source systems in a Queensland public health context.

data_type Typical source Key mandatory dates
"cases" NNDSS, NoCS, EDIS linelists onset_date
"hospital" iPM, HBCIS, admitted patient collections admission_date
"vaccination" Australian Immunisation Register (AIR), local registers vax_date

Age and age categories are derived for "cases" and "hospital" when dob and onset_date / admission_date are supplied. They are not derived for "vaccination" (the AIR does not reliably carry age at time of vaccination).

Worked examples

Cases (notifiable disease linelist)

set.seed(1)
cases_raw <- data.frame(
  identity          = paste0("PT", 1:20),
  first_name        = sample(c("James","Sarah","Michael"), 20, TRUE),
  surname           = sample(c("Smith","Jones","Williams"), 20, TRUE),
  date_of_birth     = as.Date("1970-01-01") + sample(-5000:5000, 20),
  date_of_onset     = as.Date("2024-03-01") + sample(0:120, 20),
  disease_name      = sample(c("COVID-19","Influenza A","RSV"), 20, TRUE),
  gender            = sample(c("M","F"), 20, TRUE),
  postcode          = sample(c("4556","4557","4560"), 20, TRUE),
  medicare_no       = paste0(sample(2000:9999, 20), sample(10000:99999, 20)),
  indigenous_status = sample(c("Non-Indigenous","Aboriginal","Unknown"), 20, TRUE,
                              prob = c(0.85, 0.10, 0.05)),
  stringsAsFactors  = FALSE
)

df_cases <- clean_the_nest(
  cases_raw,
  data_type   = "cases",
  drop_eggs   = TRUE,
  id_var      = "identity",
  diagnosis   = "disease_name",
  lettername1 = "first_name",
  lettername2 = "surname",
  dob         = "date_of_birth",
  medicare    = "medicare_no",
  gender      = "gender",
  postcode    = "postcode",
  fn          = "indigenous_status",
  onset_date  = "date_of_onset"
)

head(df_cases[, c("lettername1","lettername2","dob","age","diagnosis","block1")], 4)
#>   lettername1 lettername2        dob  age   diagnosis      block1
#> 1       james    williams 1965-10-29 58.7    COVID-19 M 4556 1965
#> 2     michael       smith 1961-08-23 62.8 Influenza A F 4557 1961
#> 3       james       smith 1963-05-17 61.0    COVID-19 M 4556 1963
#> 4       sarah       smith 1960-07-01 63.9    COVID-19 M 4560 1960

Notice that lettername1 is lowercased, punctuation-stripped, and truncated to the first name token only. block1 is gender + postcode + birth_year — used as the primary blocking variable in starling::murmuration().

Hospital admissions

When admission_date and discharge_date are both supplied, length of stay (los, in days) is derived automatically. admission_outcome is a factor indicating whether the person was actually admitted.

set.seed(2)
hosp_raw <- data.frame(
  patient_id         = paste0("UR", 1:15),
  firstname          = sample(c("James","Sarah","Michael"), 15, TRUE),
  last_name          = sample(c("Smith","Jones"), 15, TRUE),
  birth_date         = as.Date("1945-01-01") + sample(0:10000, 15),
  date_of_admission  = as.Date("2024-06-01") + sample(0:180, 15),
  date_of_discharge  = as.Date("2024-06-15") + sample(0:180, 15),
  medicare_number    = paste0(sample(2000:9999, 15), sample(10000:99999, 15)),
  sex                = sample(c("M","F"), 15, TRUE),
  zip_codes          = sample(c("4556","4557"), 15, TRUE),
  icd_codes          = sample(c("J06.9","U07.1","J44.1"), 15, TRUE),
  stringsAsFactors   = FALSE
)

df_hosp <- clean_the_nest(
  hosp_raw,
  data_type      = "hospital",
  drop_eggs      = TRUE,
  id_var         = "patient_id",
  lettername1    = "firstname",
  lettername2    = "last_name",
  dob            = "birth_date",
  medicare       = "medicare_number",
  gender         = "sex",
  postcode       = "zip_codes",
  icd_code       = "icd_codes",
  admission_date = "date_of_admission",
  discharge_date = "date_of_discharge"
)

df_hosp[, c("los","admission_outcome","icd_code")] |> head(4)
#>   los admission_outcome icd_code
#> 1 -18         Admission    J44.1
#> 2  75         Admission    J06.9
#> 3 156         Admission    J44.1
#> 4  46         Admission    U07.1

Vaccination data — long to wide

The Australian Immunisation Register exports one row per vaccination event. lie_nest_flat = TRUE pivots this to one row per person, with vax_date_1, vax_type_1, vax_date_2, vax_type_2, etc.

set.seed(3)
vax_raw <- data.frame(
  patient_id       = rep(paste0("VAX", 1:10), each = 2),
  firstname        = rep(c("Alice","Bob","Carol","Dan","Eve",
                            "Frank","Grace","Henry","Iris","Jack"), each = 2),
  last_name        = rep(c("Smith","Jones","Williams","Taylor","Brown",
                            "White","Black","Green","Blue","Red"), each = 2),
  birth_date       = rep(as.Date("1980-01-01") + sample(-2000:2000, 10), each = 2),
  gender           = rep(sample(c("M","F"), 10, TRUE), each = 2),
  postcode         = rep(sample(c("4556","4557"), 10, TRUE), each = 2),
  medicare_number  = rep(paste0(sample(2000:9999, 10), sample(10000:99999, 10)), each = 2),
  vaccine_delivered = rep(c("COVID-19 XBB.1.5","COVID-19 XBB.1.5"), 10),
  service_date     = c(rbind(
    as.Date("2024-01-15") + sample(0:30, 10),
    as.Date("2024-05-01") + sample(0:30, 10)
  )),
  stringsAsFactors = FALSE
)

df_vax <- clean_the_nest(
  vax_raw,
  data_type     = "vaccination",
  lie_nest_flat = TRUE,
  id_var        = "patient_id",
  lettername1   = "firstname",
  lettername2   = "last_name",
  dob           = "birth_date",
  medicare      = "medicare_number",
  gender        = "gender",
  postcode      = "postcode",
  vax_type      = "vaccine_delivered",
  vax_date      = "service_date"
)

df_vax[, c("id_var","vax_date_1","vax_type_1","vax_date_2","vax_type_2")] |> head(4)
#> # A tibble: 4 × 5
#>   id_var vax_date_1 vax_type_1       vax_date_2 vax_type_2      
#>   <chr>       <dbl> <chr>                 <dbl> <chr>           
#> 1 VAX1        19737 COVID-19 XBB.1.5      19847 COVID-19 XBB.1.5
#> 2 VAX10       19746 COVID-19 XBB.1.5      19870 COVID-19 XBB.1.5
#> 3 VAX2        19745 COVID-19 XBB.1.5      19857 COVID-19 XBB.1.5
#> 4 VAX3        19753 COVID-19 XBB.1.5      19852 COVID-19 XBB.1.5

Birth-cohort studies

In birth-cohort vaccine effectiveness studies (e.g. nirsevimab or Abrysvo effectiveness in infants), the date of birth is the cohort entry date. Pass the same column name to both dob and cohort_entry_date — no duplication needed.

df_cohort <- clean_the_nest(
  birth_cohort_data,
  data_type         = "cases",
  id_var            = "baby_id",
  lettername1       = "first_name",
  lettername2       = "last_name",
  dob               = "babys_date_of_birth",
  cohort_entry_date = "babys_date_of_birth",   # same column — aliased internally
  cohort_exit_date  = "end_of_followup_date",
  gender            = "sex"
)

Australian Medicare numbers

clean_the_nest() understands the full structure of Australian Medicare numbers and produces semantically named output columns for each component, rather than opaque digit-count names.

Structure

A complete Medicare number has up to 11 characters:

Component Digits Column Description
Account identifier 1–8 medicare08 Unique household/account number. First digit is always 2–6.
Checksum 9 medicare09 Mathematically derived from digits 1–8; used to validate the number. clean_the_nest() validates this automatically.
Card issue number 10 medicare10 Increments each time a new card is issued (lost, expired, family member added). Never 0.
IRN 11 medicare_irn Individual Reference Number — the digit printed left of a person’s name. Primary cardholder = 1, partner = 2, children = 3, 4, etc. Up to 9 people share a card.

Two additional columns are always produced: medicare_clean (spaces removed, as supplied) and medicare_valid (logical — TRUE if the checksum is mathematically correct).

Which column to use for linkage?

Linkage purpose Use
Person-level linkage (cases ↔︎ AIR) medicare09 (stable across card reissues and family members)
Card-specific linkage (e.g. AIR dose records) medicare10
Household-level blocking medicare08
Distinguishing individuals on the same card medicare_irn 
mc_data <- data.frame(
  patient_id = c("PT001", "PT002", "PT003"),
  # PT001: valid 10-digit number (no IRN appended)
  # PT002: valid 11-digit number (IRN = 1)
  # PT003: invalid checksum (digit 9 is 7, should be 3)
  mcare = c("2428778132", "24287781321", "2428778172"),
  stringsAsFactors = FALSE
)

# suppressWarnings() here because PT003 has an invalid checksum —
# that's exactly what we want to demonstrate.
df_mc <- suppressWarnings(suppressMessages(
  clean_the_nest(mc_data, data_type = "cases",
                 id_var = "patient_id", medicare = "mcare")
))

df_mc[, c("id_var", "medicare08", "medicare09", "medicare10",
           "medicare_irn", "medicare_valid")]
#>   id_var medicare08 medicare09 medicare10 medicare_irn medicare_valid
#> 1  PT001   24287781  242877813 2428778132         <NA>           TRUE
#> 2  PT002   24287781  242877813 2428778132            1           TRUE
#> 3  PT003   24287781  242877817 2428778172         <NA>          FALSE

Notice that PT003 has medicare_valid = FALSE — clean_the_nest() warns you about invalid numbers before linkage, because an invalid Medicare number will never match in starling::murmuration(). In real data, investigate these rows: a common cause is the IRN being stored as part of the 10-digit number rather than separately.

The drop_eggs argument

drop_eggs = TRUE retains only the columns needed for linkage and downstream analysis. This produces a lean, manageable dataset for early-stage work. Use keep_vars to retain additional columns you need alongside the defaults.

# Without drop_eggs — all original columns plus derived ones
df_full <- clean_the_nest(
  cases_raw, data_type = "cases",
  id_var = "identity", lettername1 = "first_name", lettername2 = "surname",
  dob = "date_of_birth", onset_date = "date_of_onset"
)
ncol(df_full)
#> [1] 19

# With drop_eggs — only the linkage and analysis essentials
df_lean <- clean_the_nest(
  cases_raw, data_type = "cases", drop_eggs = TRUE,
  id_var = "identity", lettername1 = "first_name", lettername2 = "surname",
  dob = "date_of_birth", onset_date = "date_of_onset",
  keep_vars = "disease_name"   # retain this one extra
)
ncol(df_lean)
#> [1] 15
names(df_lean)
#>  [1] "lettername1"                 "lettername2"                
#>  [3] "lettername1_lettername2"     "lettername1_lettername2_dob"
#>  [5] "age"                         "medicare_no"                
#>  [7] "onset_date"                  "block1"                     
#>  [9] "block2"                      "block3"                     
#> [11] "id_var"                      "dob"                        
#> [13] "postcode"                    "gender"                     
#> [15] "disease_name"

What comes next

Once your data is cleaned:

  • preening() — assign age bands using one of ~50 named schemes (see vignette("preening"))
  • plumage() — detect chronic comorbidities from ICD-10-AM coding (see vignette("plumage")). For hospital datasets, this is typically the next step after clean_the_nest() — the standardised icd_code column produced here is the direct input to plumage(icd_column = "icd_code").
  • roost() — aggregate to a time unit for an epi curve (see vignette("roost"))
  • starling::murmuration() — probabilistic record linkage across the cleaned datasets
  • molting() — de-identify before sharing (see vignette("molting"))