Variable types: rd vs ri

library(swereg)
#> swereg 26.5.20
#> https://papadopoulos-lab.github.io/swereg/
library(data.table)
#> 
#> Attaching package: 'data.table'
#> The following object is masked from 'package:base':
#> 
#>     %notin%

Introduction

In longitudinal registry data analysis with swereg, every derived variable is either row-dependent (can change value across rows for the same person) or row-independent (fixed per person). The distinction is so important that swereg uses short name prefixes to make it visible at the call site:

rd_ – row-dependent (also called “rowdep”). Variables that can change over time for a person.
ri_ – row-independent (also called “rowind”). Variables that are fixed per person.

Understanding this distinction is crucial when preparing analysis-ready datasets: many transformations convert rd_ variables into ri_ variables by capturing a value at a specific moment (“age at first diagnosis”, “year of death”).

The concept

Row-dependent (`rd_*`) variables

These variables can have different values across time periods for the same person:

Education level (rd_education): can improve over time
Income (rd_income_inflation_adjusted): changes annually
Had diagnosis this week (f20_diag): TRUE/FALSE depending on the specific week
Current age (rd_age_continuous): increases continuously
Civil status (rd_civil_status): can change on marriage, divorce, bereavement
Region of residence (rd_region): changes on relocation

Row-independent (`ri_*`) variables

These variables have the same value across all time periods for a person:

Age at first diagnosis (ri_age_first_dx): fixed once it happens
Year of first diagnosis (ri_isoyear_first_dx): fixed historical fact
Birth country (ri_birthcountry): never changes
Cohort classification (ri_register_tag): person’s role in the study (case, control, sibling, etc.)
Age at death (ri_age_death): fixed once death occurs
Sex at birth (ri_sex): constant

Why this matters

In longitudinal data, you often need to:

Identify when something first happened (first diagnosis, first prescription).
Capture characteristics at specific time points (education level when first diagnosed).
Create person-level summaries that don’t vary by time period.

Converting rd_ -> ri_ lets you create stable, person-level characteristics for downstream analysis – for example, to use as baseline covariates in a target trial emulation, where the enrollment time zero fixes which row’s rd_ values you care about.

Common transformation patterns

Pattern 1: First-occurrence transformations

The most common transformation finds the first time a condition is TRUE and extracts a value from that time point. This works for any condition – time-based, value-based, or complex combinations.

# Age at first F64 diagnosis restricted to AFAB individuals
make_rowind_first_occurrence(skeleton,
                             condition = "f64_diag == TRUE & ri_is_amab == FALSE",
                             value_var = "rd_age_continuous",
                             new_var   = "ri_age_first_f64_afab")

# Education level at the time of first diagnosis
make_rowind_first_occurrence(skeleton,
                             condition = "isoyearweek == ri_isoyearweek_first_dx",
                             value_var = "rd_education",
                             new_var   = "ri_education_at_first_dx")

Manual approach (for understanding)

make_rowind_first_occurrence() is a thin wrapper. The manual equivalent is:

skeleton[f64_diag == TRUE & ri_is_amab == FALSE, temp := rd_age_continuous]
skeleton[, ri_age_first_f64_afab := swereg::first_non_na(temp), by = .(id)]
skeleton[, temp := NULL]

The helper function just automates the three-line pattern and guarantees the temporary column is cleaned up.

Pattern 2: Simple renaming (already row-independent)

When a variable is already row-independent but needs consistent naming:

# If date of birth is truly the same for all rows (it should be)
data.table::setnames(skeleton, "dob", "ri_dob")

# If birth country was added via add_onetime() and is person-level
data.table::setnames(skeleton, "birth_country", "ri_birthcountry")

Important: only use this when the variable is genuinely the same across all rows for each person. If values differ, you have a data-integration problem to fix first, not a renaming one.

Practical example with fake data

# Create a small skeleton for demonstration
ids <- swereg::fake_demographics$lopnr[1:5]
skeleton <- create_skeleton(ids, "2020-01-01", "2020-03-31")

# Add demographic data (creates ri_ variables)
fake_demographics <- swereg::fake_demographics |>
  data.table::copy() |>
  swereg::make_lowercase_names(date_columns = "doddatum")
#> Found additional date columns not in date_columns: fodelseman. Consider adding them for automatic date parsing.
add_onetime(skeleton, fake_demographics, "lopnr")

# Add diagnosis data (creates rd_ variables — the diag column
# varies week-by-week)
fake_diagnoses <- swereg::fake_diagnoses |>
  data.table::copy() |>
  swereg::make_lowercase_names(date_columns = "indatum")
#> Found additional date columns not in date_columns: utdatum. Consider adding them for automatic date parsing.
add_diagnoses(skeleton, fake_diagnoses, "lopnr",
              codes = list(f64_diag = "^F64"))

head(skeleton[id == ids[1]], 8)
#>       id isoyear isoyearweek is_isoyear isoyearweeksun personyears fodelseman
#>    <int>   <int>      <char>     <lgcl>         <Date>       <num>     <char>
#> 1:     1    1900     1900-**       TRUE     1900-07-01           1       1959
#> 2:     1    1901     1901-**       TRUE     1901-06-30           1       1959
#> 3:     1    1902     1902-**       TRUE     1902-06-29           1       1959
#> 4:     1    1903     1903-**       TRUE     1903-06-28           1       1959
#> 5:     1    1904     1904-**       TRUE     1904-07-03           1       1959
#> 6:     1    1905     1905-**       TRUE     1905-07-02           1       1959
#> 7:     1    1906     1906-**       TRUE     1906-07-01           1       1959
#> 8:     1    1907     1907-**       TRUE     1907-06-30           1       1959
#>    doddatum f64_diag
#>      <Date>   <lgcl>
#> 1:     <NA>    FALSE
#> 2:     <NA>    FALSE
#> 3:     <NA>    FALSE
#> 4:     <NA>    FALSE
#> 5:     <NA>    FALSE
#> 6:     <NA>    FALSE
#> 7:     <NA>    FALSE
#> 8:     <NA>    FALSE

Now create ri_ variables from the row-dependent f64_diag:

# ISO year of first F64 diagnosis
make_rowind_first_occurrence(skeleton,
                             condition = "f64_diag == TRUE",
                             value_var = "isoyear",
                             new_var   = "ri_isoyear_first_f64")

# ISO year-week of first F64 diagnosis
make_rowind_first_occurrence(skeleton,
                             condition = "f64_diag == TRUE",
                             value_var = "isoyearweek",
                             new_var   = "ri_isoyearweek_first_f64")

head(skeleton[id == ids[1], .(id, isoyear, isoyearweek, f64_diag,
                              ri_isoyear_first_f64,
                              ri_isoyearweek_first_f64)], 8)
#>       id isoyear isoyearweek f64_diag ri_isoyear_first_f64
#>    <int>   <int>      <char>   <lgcl>                <int>
#> 1:     1    1900     1900-**    FALSE                   NA
#> 2:     1    1901     1901-**    FALSE                   NA
#> 3:     1    1902     1902-**    FALSE                   NA
#> 4:     1    1903     1903-**    FALSE                   NA
#> 5:     1    1904     1904-**    FALSE                   NA
#> 6:     1    1905     1905-**    FALSE                   NA
#> 7:     1    1906     1906-**    FALSE                   NA
#> 8:     1    1907     1907-**    FALSE                   NA
#>    ri_isoyearweek_first_f64
#>                      <char>
#> 1:                     <NA>
#> 2:                     <NA>
#> 3:                     <NA>
#> 4:                     <NA>
#> 5:                     <NA>
#> 6:                     <NA>
#> 7:                     <NA>
#> 8:                     <NA>

Notice how f64_diag varies by week (rd_ character even though the actual column name here is just f64_diag because it came from a code-registry entry) while ri_isoyear_first_f64 and ri_isoyearweek_first_f64 are constant for each person.

Best practices

1. Use prefixes consistently

rd_ for time-varying variables
ri_ for time-invariant variables

Code-registry columns (ov_*, sv_*, os_*, osd_*, rx_*, op_*, etc.) are de-facto row-dependent but keep their source prefix for clarity; you don’t need to prefix them a second time with rd_.

2. Always clean up temporary variables

When using manual transformations, always remove temp variables:

skeleton[condition == TRUE, temp := value]
skeleton[, new_ri_var := first_non_na(temp), by = .(id)]
skeleton[, temp := NULL]  # critical

3. Use helper functions when possible

make_rowind_first_occurrence() handles temp variable management automatically and reduces errors.

4. Validate your transformations

Always check that your ri_ variables are actually row-independent:

# Should return 1 for every person -- all rows have the same value
skeleton[, .(unique_values = uniqueN(ri_isoyear_first_f64)), by = .(id)]

How this fits into the swereg pipeline

In the three-phase RegistryStudy$process_skeletons() pipeline (see vignette("skeleton-pipeline")), the rd_ -> ri_ conversion happens inside phase 3 (randvars) steps. A typical pattern:

Phase 1 – framework produces the base time grid, including rd_age_continuous (derived from isoyearweeksun - dob).
Phase 2 – codes produces the code-derived columns like os_f20, osd_i21_to_i24, rx_n05a.
Phase 3 – randvars produces both:
- rd_* columns from LISA (annual demographics).
- ri_* columns from first-occurrence transformations.

The ri_* columns are then the baseline covariates consumed by target trial emulation in vignette("tte-workflow").

Conclusion

The rd_ / ri_ distinction is the scaffolding that makes person-week skeletons usable as TTE inputs: the ri_ columns become your stable per-person baseline characteristics, and the rd_ columns are the time-varying surface the enrollment procedure slices at candidate time-zeros. make_rowind_first_occurrence() is the workhorse that bridges the two.