Package {SporeLag}

Type:	Package
Title:	Lagged and Moving-Average Exposure Features for Aeroallergen Epidemiology
Version:	0.1.1
Description:	Deterministic, group-safe utilities that transform daily environmental exposure series (pollen and spore counts, with support for other time-varying exposures such as ozone and particulate matter) into analysis-ready lagged and moving-average exposure features. Functions validate temporal regularity, assign ISO 8601 weeks and configurable seasons, impute missing daily values transparently, and construct lagged and windowed exposures suitable for environmental epidemiology and public health analyses.
URL:	https://github.com/friveramariani/SporeLag
BugReports:	https://github.com/friveramariani/SporeLag/issues
License:	MIT + file LICENSE
Encoding:	UTF-8
Suggests:	dplyr, knitr, lubridate, rmarkdown, spelling, testthat (≥ 3.0.0)
Config/testthat/edition:	3
RoxygenNote:	8.0.0
Imports:	cli, rlang, stats
Depends:	R (≥ 4.1.0)
LazyData:	true
VignetteBuilder:	knitr
Language:	en-US
NeedsCompilation:	no
Packaged:	2026-07-24 00:02:53 UTC; felixrivera-mariani
Author:	Félix E. Rivera-Mariani [aut, cre]
Maintainer:	Félix E. Rivera-Mariani <felixrm@friveram.com>
Repository:	CRAN
Date/Publication:	2026-08-04 09:30:02 UTC

SporeLag: Lagged and Moving-Average Exposure Features for Aeroallergen Epidemiology

Description

Deterministic, group-safe utilities that transform daily environmental exposure series (pollen and spore counts, with support for other time-varying exposures such as ozone and particulate matter) into analysis-ready lagged and moving-average exposure features. Functions validate temporal regularity, assign ISO 8601 weeks and configurable seasons, impute missing daily values transparently, and construct lagged and windowed exposures suitable for environmental epidemiology and public health analyses.

Package conventions

All exported functions in SporeLag share a single contract. These are guarantees, not conventions of style, and downstream code may rely on them.

Input and output

A data.frame goes in; the same data.frame comes out with new columns appended. Input columns are never dropped, reordered, or modified in place.

Grouping

The by argument is always a character vector of column names. All operations are computed strictly within group. Values never carry across a group boundary.

Determinism

No randomness anywhere. Repeated calls on identical input return identical output.

Column naming

New columns follow ⁠{value}_lag{n}⁠, ⁠{value}_ma{k}⁠, and ⁠{value}_imputed⁠.

Temporal safety

Functions that index by time require a complete daily grid and raise an error otherwise, rather than silently shifting by row position. See complete_daily_grid().

Errors

Conditions are raised with a message class (for example "sporelag_error_gaps") so that callers and tests can handle them specifically.

Defaults are analytic decisions

Every default in this package changes what an exposure variable means. Moving-average alignment, window inclusivity, the minimum number of observations required, the season definition, and the treatment of missing days are all documented explicitly in the ⁠@details⁠ of each function. They are not cosmetic, and changing one changes the estimand.

Author(s)

Maintainer: Félix E. Rivera-Mariani felixrm@friveram.com (ORCID)

Authors:

• Félix E. Rivera-Mariani felixrm@friveram.com (ORCID)

See Also

Useful links:

• https://github.com/friveramariani/SporeLag

• Report bugs at https://github.com/friveramariani/SporeLag/issues

Apply time-indexed lags to an exposure series

Description

apply_lag() appends one column per requested lag, shifting the exposure series backwards in time within each group. A lag of n places the value observed n days earlier alongside the current day.

Usage

apply_lag(data, value, lags, date, by = character())

Arguments

Details

This function indexes by position, and therefore requires a complete daily grid. On a gapped series, "lag 1" would mean "the previous row", which may be one day earlier or thirty. The resulting exposure column looks entirely plausible, runs cleanly through a regression, and yields a silently misaligned lag–response estimate. apply_lag() therefore refuses to operate on a gapped grid and raises sporelag_error_gaps. Call complete_daily_grid() first. This is a deliberate design decision, not an oversight; see vignette("getting-started").

Documented defaults:

• Lags are computed strictly within each group defined by by. Values never carry across a group boundary — the first n days of each site's series are NA, not the tail of the previous site's series.

• lags = 0 is permitted and yields a copy of the same-day value. This exists so a uniform model matrix (⁠lag0, lag1, lag2, ...⁠) can be built without special-casing the contemporaneous term.

• Negative values are an error. A negative lag is a lead: it aligns an exposure with an outcome that preceded it. That is almost never intended, and when it is (e.g. a negative-control exposure), it should be written explicitly rather than arrived at by a sign slip.

• Row order is preserved. Columns are appended.

Value

data with one column appended per lag, named ⁠{value}_lag{n}⁠. Row order and all input columns are unchanged.

Errors

sporelag_error_gaps

The daily grid is incomplete within at least one group. Call complete_daily_grid() first.

sporelag_error_duplicate_dates

A date appears more than once within a group.

sporelag_error_bad_input

Bad types; missing columns; value is not numeric; lags contains negative or non-whole numbers; an output column name already exists.

See Also

complete_daily_grid(), build_moving_average()

Examples

df <- data.frame(
  site  = rep(c("A", "B"), each = 4),
  date  = rep(seq(as.Date("2024-05-01"), by = "day", length.out = 4), 2),
  count = c(10, 20, 30, 40, 100, 200, 300, 400)
)

apply_lag(df, value = "count", lags = 0:2, date = "date", by = "site")

Assign ISO 8601 week and year

Description

assign_iso_week() appends the ISO 8601 week number and ISO year for each date. It is a row-wise mapping: no grouping, no temporal indexing, no requirement that the grid be complete.

Usage

assign_iso_week(data, date, week_col = "iso_week", year_col = "iso_year")

Arguments

Details

The ISO year is not the calendar year, and this matters. Late-December and early-January dates routinely belong to an ISO week owned by the adjacent year:

Consequently, week number alone is not a valid grouping key. Grouping a multi-year series by iso_week pools week 1 of every year together, and pools the days either side of a New Year boundary into different weeks than the calendar suggests. Always group by c(iso_year, iso_week). Downstream aggregation functions in SporeLag require both columns for this reason.

ISO weeks are computed in base R via the nearest-Thursday rule rather than strftime()'s ⁠%V⁠/⁠%G⁠ format codes, whose behaviour has historically varied by platform. Output is therefore identical on every operating system.

Value

data with two integer columns appended: week_col (1–53) and year_col. Row order and all input columns are unchanged.

Errors

sporelag_error_bad_input

data, date, week_col, or year_col do not match the documented types; date is not a Date column; or an output column name already exists in data.

sporelag_error_missing_date

date contains NA.

See Also

assign_season()

Examples

df <- data.frame(
  date = as.Date(c("2019-12-30", "2020-12-31", "2021-01-01")),
  count = c(1, 2, 3)
)
assign_iso_week(df, date = "date")

Assign a season label

Description

assign_season() appends a season label to each date. The definition is an explicit argument with no silent default behaviour, because "season" means materially different things in different literatures.

Usage

assign_season(
  data,
  date,
  definition = c("meteorological", "astronomical", "custom"),
  hemisphere = c("northern", "southern"),
  breaks = NULL,
  season_col = "season"
)

Arguments

Details

definition = "meteorological" (the default) uses whole calendar months — Northern Hemisphere: Winter = Dec–Feb, Spring = Mar–May, Summer = Jun–Aug, Autumn = Sep–Nov. Boundaries are exact and leap-year stable.

definition = "astronomical" uses fixed conventional dates (Mar 20, Jun 21, Sep 22, Dec 21). True equinoxes and solstices drift by up to a day or two across years and depend on time zone; computing them exactly requires an ephemeris, which is out of scope for a zero-dependency package. If your analysis is sensitive to a one-day boundary shift, do not use this option — supply exact dates via definition = "custom".

definition = "custom" takes breaks: a named character vector of "MM-DD" start dates, where the names are the season labels. Each season runs from its start date until the day before the next, wrapping the year boundary. This is the option to use for pollen seasons, which are taxon- and region-specific and bear no necessary relation to the calendar.

Epidemiologic caution. In aeroallergen research, "season" usually means pollen season — a taxon-specific exposure window, often defined by a cumulative-count threshold, that varies by region and by year. It is not the astronomical or meteorological calendar. Using a calendar season as a proxy for a pollen season will misclassify exposure windows. The default here is a calendar convenience, not an exposure definition; state which you used in any analysis.

The returned column is an ordered factor whose levels follow the chronological cycle, so its behaviour in a model matrix is predictable. The reference level is the first level; set it explicitly with stats::relevel() if your model requires a different baseline.

Value

data with an ordered factor column appended. Row order and all input columns are unchanged.

Errors

sporelag_error_bad_input

Bad types; date is not a Date column; season_col already exists; breaks missing, unnamed, duplicated, or not in "MM-DD" form.

sporelag_error_missing_date

date contains NA.

See Also

assign_iso_week()

Examples

df <- data.frame(date = as.Date(c("2024-01-15", "2024-04-15", "2024-07-15")))

assign_season(df, date = "date")
assign_season(df, date = "date", hemisphere = "southern")

# A taxon-specific pollen season
assign_season(
  df,
  date = "date",
  definition = "custom",
  breaks = c(Dormant = "11-01", Tree = "02-15", Grass = "05-01",
             Ragweed = "08-15")
)

Build moving-average exposure windows

Description

build_moving_average() appends one column per requested window, containing the mean exposure over that window, computed within each group.

Usage

build_moving_average(
  data,
  value,
  window,
  date,
  by = character(),
  align = c("right", "center"),
  min_obs = window
)

Arguments

Details

Like apply_lag(), this function indexes by position and therefore requires a complete daily grid; it raises sporelag_error_gaps otherwise. Call complete_daily_grid() first.

Documented defaults — each of these changes what the variable means:

align = "right" (default)

The window is trailing and inclusive of the current day: window = 3 averages today and the two preceding days. This is the alignment appropriate for exposure→outcome inference.

align = "center"

The window is symmetric around the current day and therefore incorporates future exposure. If the outcome is measured on the current day, a centred window conditions on information that did not exist at the time — it can induce apparent associations with no causal interpretation. It is provided for smoothing and descriptive display, not for exposure construction. Requires an odd window.

Edge windows are NA

A window that extends past the start (or, when centred, either end) of a group's series is incomplete by construction and returns NA. The first window - 1 days of each trailing series are NA. They are not back-filled with a shorter average, because a "7-day mean" computed from 2 days is not a 7-day mean.

min_obs = window (default)

The minimum number of non-missing observations required within an otherwise complete window. At the default, any NA in the window yields NA. Lowering it (e.g. min_obs = 5 with window = 7) tolerates missing days and averages over those present — which shrinks variance and can bias a lag–response estimate toward the null. Lower it deliberately, and report that you did.

Value

data with one double column appended per window, named ⁠{value}_ma{k}⁠. Row order and all input columns are unchanged.

Errors

sporelag_error_gaps

The daily grid is incomplete within at least one group.

sporelag_error_bad_input

Bad types; value is not numeric; window is not a positive whole number; min_obs exceeds window; align = "center" with an even window; an output column already exists.

See Also

complete_daily_grid(), apply_lag()

Examples

df <- data.frame(
  site  = rep(c("A", "B"), each = 5),
  date  = rep(seq(as.Date("2024-05-01"), by = "day", length.out = 5), 2),
  count = c(10, 20, 30, 40, 50, 100, 200, 300, 400, 500)
)

build_moving_average(df, value = "count", window = 3, date = "date",
                     by = "site")

Build a complete daily grid

Description

complete_daily_grid() fills gaps in a daily time series so that every calendar day between the minimum and maximum date (within each group) is represented by exactly one row. Newly inserted rows carry NA for every column except date and the columns named in by.

Usage

complete_daily_grid(data, date, by = character())

Arguments

Details

Functions in SporeLag that index by time (lags, moving averages) assume the day-to-day step is exactly one day with no repeats and no gaps within a group. Rather than silently shifting values by row position when that assumption fails, those functions raise a classed error. This function is the documented mechanism for producing that guarantee before calling them.

complete_daily_grid() never guesses at missing values: inserted rows get NA, and it is the caller's responsibility to decide whether and how to impute them.

Value

A data.frame with the same columns as data, in the same order, with one row per calendar day between min(date) and max(date) within each group. Rows are sorted by by, then date. No columns are added or removed; this function only inserts rows.

Errors

sporelag_error_bad_input

data, date, or by do not match the documented types, or name columns not present in data.

sporelag_error_duplicate_dates

The same date appears more than once within a single group. A grid cannot be built unambiguously, so the function errors instead of silently choosing a row.

Examples

df <- data.frame(
  site = c("A", "A", "A", "B", "B"),
  date = as.Date(c(
    "2024-01-01", "2024-01-02", "2024-01-04",
    "2024-01-01", "2024-01-03"
  )),
  count = c(1, 2, 4, 10, 30)
)
complete_daily_grid(df, date = "date", by = "site")

Impute missing daily values with the ISO-week mean

Description

impute_weekly_mean() fills missing daily exposure values with the mean of the observed values in the same ISO week, within group. It appends the completed series and a flag marking which values were imputed. The input column is never modified.

Usage

impute_weekly_mean(
  data,
  value,
  by = character(),
  week_col = "iso_week",
  year_col = "iso_year",
  min_obs = 1L
)

Arguments

Details

Requires the ISO week and year columns produced by assign_iso_week(). Typically it also follows complete_daily_grid(), since a day that is absent from the data entirely cannot be imputed — it must first exist as a row with an NA value.

The canonical pipeline is:

data |>
  complete_daily_grid(date = "date", by = "site") |>
  assign_iso_week(date = "date") |>
  impute_weekly_mean(value = "count", by = "site")

Grouping is by c(by, year_col, week_col) — never by week alone. ISO week 1 recurs every year, and the ISO year of a late-December date is often the following calendar year. Grouping on week number alone would pool observations from different years into a single mean. See assign_iso_week().

Documented defaults:

min_obs = 1

The minimum number of observed days required in a week before its mean is used. At the default, a single observed day is enough to fill the other six. Raise it (e.g. min_obs = 4) if you are unwilling to extrapolate a week from one observation; weeks below the threshold are left NA and flagged FALSE.

Weeks with no observations stay NA

Nothing is carried across a week boundary. There is no interpolation between weeks, no last-observation-carried-forward, and no global mean fallback.

Output is double

Even when value is an integer count. A mean is not a count, and rounding an imputed 12.5 to 12 or 13 would fabricate precision. If your model needs integers, round explicitly and say so.

Epidemiologic caution. Weekly-mean imputation replaces day-to-day variation with a constant, which shrinks the variance of the exposure. In a lag–response model, an exposure with attenuated variance typically produces effect estimates biased toward the null, and standard errors that do not reflect the true uncertainty (the imputed values are treated as though observed). The ⁠{value}_imputed_flag⁠ column exists so that you can quantify this: report the proportion imputed, and re-run the analysis on complete cases as a sensitivity check. This function makes imputation convenient; it does not make it innocuous.

Value

data with two columns appended:

⁠{value}_imputed⁠

double. The completed series.

⁠{value}_imputed_flag⁠

logical. TRUE where a value was filled.

Row order and all input columns, including value, are unchanged.

Errors

sporelag_error_missing_col

week_col or year_col is absent. Call assign_iso_week() first.

sporelag_error_bad_input

Bad types; value is not numeric; min_obs is not a positive whole number; an output column already exists.

See Also

assign_iso_week(), complete_daily_grid()

Examples

df <- data.frame(
  site  = rep("A", 7),
  date  = seq(as.Date("2024-05-06"), by = "day", length.out = 7),  # Mon-Sun
  count = c(10, NA, 30, NA, 50, 60, NA)
)

df |>
  assign_iso_week(date = "date") |>
  impute_weekly_mean(value = "count", by = "site")

Synthetic daily pollen counts from two monitoring sites

Description

A synthetic dataset of daily airborne pollen concentrations for a single spring tree-pollen season at two hypothetical monitoring stations. It exists to demonstrate and test SporeLag and is used throughout vignette("getting-started").

Usage

pollen_demo

Format

A data frame with three columns:

site

character. Monitoring station: "North" or "South".

date

Date. Calendar day, spanning 2024-02-15 to 2024-06-30.

count

integer. Pollen concentration in grains per cubic metre. NA where sampling failed on a day that was otherwise monitored.

Details

The data deliberately contain both of the defects SporeLag is designed to handle, because they are different problems and are frequently confused:

Gaps

Calendar days with no row at all, representing periods when the sampler was offline: 2024-03-18 to 2024-03-22 at North, and 2024-04-01 to 2024-04-03 at South. A gap is invisible to is.na() — the row simply is not there. Gaps must be closed with complete_daily_grid() before any lag or moving average is computed, or the shift will be by row position rather than by time.

Missing values

Days that are present but whose count is NA, representing failed samples on otherwise-monitored days (roughly 6% of days at each site). These are candidates for impute_weekly_mean().

The South gap falls at that site's seasonal peak — the point at which losing data does the most damage to an exposure estimate, and where a naive positional lag would silently produce the largest misalignment.

Counts were drawn from a negative binomial distribution around a Gaussian seasonal curve, so they are overdispersed in the way real aeroallergen counts are. The generating script is data-raw/make_pollen_demo.R and is fully deterministic.

Source

Simulated. These are not real surveillance data and must not be used for any substantive inference about pollen exposure. See data-raw/make_pollen_demo.R for the generating code.

See Also

complete_daily_grid(), impute_weekly_mean()

Examples

str(pollen_demo)

# Gaps are absent rows, not NA values -- is.na() cannot see them:
table(pollen_demo$site)          # neither site has a full 137 days
sum(is.na(pollen_demo$count))    # the NAs are a separate problem

# The canonical pipeline:
pollen_demo |>
  complete_daily_grid(date = "date", by = "site") |>
  assign_iso_week(date = "date") |>
  impute_weekly_mean(value = "count", by = "site") |>
  apply_lag(value = "count_imputed", lags = 0:2, date = "date", by = "site") |>
  head()