| Title: | Tools for Creating Time-Varying Datasets |
| Version: | 2.0.2 |
| Date: | 2024-02-16 |
| Description: | Create a time-varying dataset using features, exposure, and look back specifications. |
| Suggests: | knitr, tibble, rmarkdown, testthat (≥ 3.0.0) |
| Imports: | lubridate, dplyr (≥ 1.1.1), magrittr, rlang |
| Depends: | R (≥ 3.6.0) |
| VignetteBuilder: | knitr |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| RoxygenNote: | 7.2.3 |
| LazyData: | true |
| Encoding: | UTF-8 |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2024-02-16 19:30:56 UTC; m144326 |
| Author: | Ethan Heinzen [aut, cre], Patrick Wilson [ctb], Brendan Broderick [ctb], Peter Martin [ctb] |
| Maintainer: | Ethan Heinzen <heinzen.ethan@mayo.edu> |
| Repository: | CRAN |
| Date/Publication: | 2024-02-16 20:10:03 UTC |
Objects exported from other packages
Description
These objects are imported from other packages. Follow the links below to see their documentation.
- magrittr
Create a time-varying dataset
Description
Create a time-varying dataset
Usage
time_varying(
x,
specs,
exposure,
...,
grid.only = FALSE,
time_units = c("days", "seconds"),
id = "pat_id",
sort = NA,
n_cores = as.numeric(Sys.getenv("SLURM_CPUS_PER_TASK", 1))
)
check_tv_data(x, time_units, id, sort)
check_tv_exposure(x, expected_ids, time_units, id, ..., check_overlap = TRUE)
check_tv_specs(specs, expected_features = NULL)
Arguments
x |
A data.frame with four columns: <id>, "feature", "datetime", "value" |
specs |
a data.frame with four columns: "feature", "use_for_grid", "lookback_start", "lookback_end", "aggregation". See details below. |
exposure |
a data.frame with (at least) three columns: <id>, "exposure_start", "exposure_stop" |
... |
Other arguments. Currently just passes |
grid.only |
Should just the grid be computed and returned? Useful only for debugging |
time_units |
What time units should be used? Seconds or days |
id |
The id to use. Default is "pat_id" |
sort |
Logical, indicating whether to sort the data before performing the analysis. By default ( |
n_cores |
Number of cores to use. If slurm is being used, it checks the |
expected_ids |
A vector of expected ids based on the data. |
check_overlap |
Should overlap be checked among exposure rows? A potentially costly operation, so you can opt out of it if you're really sure. |
expected_features |
A vector of expected features based on the data. |
Details
The defaults for specs are to use everything for the grid creation, and to set lookback_start=0, with a message in both cases.
Currently supported aggregation functions include counting ("count" or "n"), last-value-carried forward ("last value" or "lvcf"),
any/none ("any" or "binary"), time since ("time since" or "ts"), min/max/mean, and the special "event" (for which look backs are ignored).
The look back window begins at row_start - lookback_end and ends at row_start - lookback_start. Passing NA to either look back
changes the corresponding window boundary to exposure_start.
Value
A data.frame, with one row per grid value and one column per feature specification (plus grid columns).
Examples
data(tv_example)
time_varying(tv_example$data, tv_example$specs, tv_example$exposure,
time_units = "days", id = "mcn")
Time-varying aggregation functions
Description
Time-varying aggregation functions
Usage
tv_count(value, ...)
tv_any(value, ...)
tv_lvcf(value, datetime, ...)
tv_ts(datetime, current_time, ...)
tv_min(value, ...)
tv_max(value, ...)
tv_mean(value, ...)
tv_median(value, ...)
tv_sum(value, ...)
Arguments
value |
A vector of values |
... |
Other arguments (not used at this time) |
datetime |
A datetime |
current_time |
The current grid row's time |
Value
A scalar, indicating the corresponding aggregation over value or datetime.
Example data for time-varying
Description
Example data for time-varying
Usage
tv_example
Format
A list
- data
The data
- specs
The specs