Type:	Package
Title:	Working with Healthcare Databases
Version:	0.4.1
Description:	A system for identifying diseases or events from healthcare databases and preparing data for epidemiological studies. It includes capabilities not supported by 'SQL', such as matching strings by 'stringr' style regular expressions, and can compute comorbidity scores (Quan et al. (2005) <doi:10.1097/01.mlr.0000182534.19832.83>) directly on a database server. The implementation is based on 'dbplyr' with full 'tidyverse' compatibility.
License:	MIT + file LICENSE
Encoding:	UTF-8
Imports:	clock, data.table, dbplyr (≥ 2.5.0), dplyr (≥ 1.1.0), glue, lubridate, magrittr, purrr, rlang, stringr (≥ 1.5.0), tidyr
RoxygenNote:	7.3.2
URL:	https://github.com/KevinHzq/healthdb, https://kevinhzq.github.io/healthdb/
BugReports:	https://github.com/KevinHzq/healthdb/issues
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), withr, RSQLite
Config/testthat/edition:	3
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-04-05 00:26:44 UTC; kevin.hu
Author:	Kevin Hu [aut, cre, cph]
Maintainer:	Kevin Hu <kevin.hu@bccdc.ca>
Repository:	CRAN
Date/Publication:	2025-04-05 02:10:02 UTC

healthdb: Working with Healthcare Databases

Description

A system for identifying diseases or events from healthcare databases and preparing data for epidemiological studies. It includes capabilities not supported by 'SQL', such as matching strings by 'stringr' style regular expressions, and can compute comorbidity scores (Quan et al. (2005) doi:10.1097/01.mlr.0000182534.19832.83) directly on a database server. The implementation is based on 'dbplyr' with full 'tidyverse' compatibility.

Author(s)

Maintainer: Kevin Hu kevin.hu@bccdc.ca (ORCID) [copyright holder]

See Also

Useful links:

• https://github.com/KevinHzq/healthdb

• https://kevinhzq.github.io/healthdb/

• Report bugs at https://github.com/KevinHzq/healthdb/issues

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

Row-bind a list of data.frames or remote tables with variable selection and renaming

Description

Row bind a list of data.frames or remote tables with variable selection and renaming.

Usage

bind_source(data, ..., force_proceed = getOption("healthdb.force_proceed"))

Arguments

Value

A data.frame or remote table containing combined rows of the input list with variables specified by ...

Examples

df1 <- subset(iris, Species == "setosa")
df2 <- subset(iris, Species == "versicolor")
df3 <- subset(iris, Species == "virginica")

bind_source(list(df1, df2, df3),
  s_l = "Sepal.Length",
  s_w = "Sepal.Width",
  p_l_setosa = c("Petal.Length", NA, NA),
  p_l_virginica = c(NA, NA, "Petal.Length")
) %>%
head()

Build case definition function calls

Description

This function assembles function calls from the supplied functions and their required arguments, leaving the data argument empty for easy re-use of the definition calls with different data and batch execution (see execute_def() for detail). It is useful for defining multiple diseases/events across multiple sources.

Usage

build_def(def_lab, src_labs, def_fn = define_case, fn_args)

Arguments

Value

A tibble with a number of rows equal to the length of src_labs, containing the input arguments and the synthetic function call in the fn_call column.

Examples

sud_def <- build_def("SUD", # usually a disease name
  src_lab = c("src1", "src2"), # identify from multiple sources, e.g., hospitalization, ED visits.
  # functions that filter the data with some criteria,
  # including mean here for src2 as a trivial example
  # to show only valid arguments will be in the call
  def_fn = list(define_case, mean),
  fn_args = list(
    vars = list(starts_with("diagx"), "diagx_2"),
    match = "start", # "start" will be applied to all sources as length = 1
    vals = list(c("304"), c("305")),
    clnt_id = "clnt_id",
    # c() can be used in place of list
    # if this argument only takes one value for each source
    n_per_clnt = c(2, 3),
    x = list(1:10) # src2 with mean as def_fn will only accept this argument
  )
)

# the result is a tibble
sud_def

# the fn_call column stores the code that can be ran with execute_def
sud_def#fn_call

Group records no more than n days apart as episodes

Description

This function is useful for collapsing, e.g., medication dispensation or hospitalization, records into episodes if the records' dates are no more than n days gap apart. The length of the gap can be relaxed by another grouping variable.

Usage

collapse_episode(
  data,
  clnt_id,
  start_dt,
  end_dt = NULL,
  gap,
  overwrite = NULL,
  gap_overwrite = 99999,
  .dt_trans = data.table::as.IDate,
  ...
)

Arguments

Value

The original data.frame or remote table with new columns indicating episode grouping. The new variables include:

• epi_id: unique identifier of episodes across the whole data set

• epi_no: identifier of episodes within a client/group

• epi_seq: identifier of records within an episode

• epi_start/stop_dt: start and end dates corresponding to epi_id

Examples

# make toy data
df <- make_test_dat() %>%
dplyr::select(clnt_id, dates)

head(df)

# collapse records no more than 90 days apart
# end_dt could be absent then it is assumed to be the same as start_dt
collapse_episode(df, clnt_id, start_dt = dates, gap = 90)

Compute Elixhauser Comorbidity Index

Description

This function computes unweighted Elixhauser Comorbidity Index for both data.frame and remote table input. The ICD codes used to identify the 31 disease categories is from Quan et al. (2005).

Usage

compute_comorbidity(
  data,
  vars,
  icd_ver = c("ICD-10", "ICD-9-CM-3digits", "ICD-9-CM-5digits"),
  clnt_id,
  uid = NULL,
  sum_by = c("row", "clnt"),
  excl = NULL
)

Arguments

Details

List of disease categories - labels (in quote):

1. Congestive Heart Failure - "chf"

2. Cardiac Arrhythmia - "arrhy"

3. Valvular Disease - "vd"

4. Pulmonary Circulation Disorders - "pcd"

5. Peripheral Vascular Disorders - "pvd"

6. Hypertension Uncomplicated - "hptn_nc"

7. Hypertension complicated - "hptn_C"

8. Paralysis - "para"

9. Other Neurological Disorders - "Othnd"

10. Chronic Pulmonary Disease - "copd"

11. Diabetes Uncomplicated - "diab_nc"

12. Diabetes Complicated - "diab_c"

13. Hypothyroidism - "hptothy"

14. Renal Failure - "rf"

15. Liver Disease - "ld"

16. Peptic Ulcer Disease excluding bleeding - "pud_nb"

17. AIDS/HIV - "hiv"

18. Lymphoma - "lymp"

19. Metastatic Cancer - "mets"

20. Solid Tumor without Metastasis - "tumor"

21. Rheumatoid Arthritis/collagen - "rheum_a"

22. Coagulopathy - "coag"

23. Obesity - "obesity"

24. Weight Loss - "wl"

25. Fluid and Electrolyte Disorders - "fluid"

26. Blood Loss Anemia - "bla"

27. Deficiency Anemia - "da"

28. Alcohol Abuse - "alcohol"

29. Drug Abuse - "drug"

30. Psychoses - "psycho"

31. Depression - "dep"

Value

A data.frame or remote table with binary indicators for each categories as columns.

References

Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005;43(11):1130-1139.

Examples

# make ICD-9 toy data
df <- data.frame(
  uid = 1:10, clnt_id = sample(1:3, 10, replace = TRUE),
  diagx_1 = c("193", "2780", "396", "4254", "4150", "401", "401", "0932", "5329", "2536"),
  diagx_2 = c(NA, NA, "72930", "V6542", "493", "405", "5880", "2409", "714", NA)
)

# compute Elixhauser Comorbidity Index by row
# uid is needed for by row calculation
# 3 categories were excluded in total_eci
compute_comorbidity(df,
  vars = starts_with("diagx"),
  icd_ver = "ICD-9-CM-5digits",
  clnt_id = clnt_id, uid = uid,
  excl = c("drug", "psycho", "dep")
)

# compute ECI by person
compute_comorbidity(df,
  vars = starts_with("diagx"),
  icd_ver = "ICD-9-CM-5digits",
  clnt_id = clnt_id,
  sum_by = "clnt"
)

Compute duration between two dates

Description

This function is meant to be for data frame input only and used with dplyr::mutate() to compute age or duration between two character or Date columns. If a vector of breaks is given, the output will be converted to factor with labels generated automatically.

Usage

compute_duration(
  from,
  to,
  lower_brks = NULL,
  unit = c("year", "day", "week", "month"),
  trans = FALSE,
  .transfn = lubridate::ymd,
  verbose = getOption("healthdb.verbose"),
  ...
)

Arguments

Value

A numeric or factor vector of the duration.

Examples

# toy data
n <- 5
df <- data.frame(id = 1:n,
start_dt = sample(seq(as.Date("1970-01-01"), as.Date("2000-12-31"), by = 1), size = n),
end_dt = sample(seq(as.Date("2001-01-01"), as.Date("2023-12-31"), by = 1), size = n))

# get age group at a cut-off
df %>% dplyr::mutate(
 age_grp = compute_duration(start_dt, "2023-01-01", lower_brks = c(0, 19, 25, 35, 45, 55))
)

# compute gaps between two dates in weeks
df %>% dplyr::mutate(
 gap_wks = compute_duration(start_dt, end_dt, unit = "week")
)

Cut the time period in one row into multiple rows by interval

Description

This function is for cutting time periods into segments, which could be useful for subsequent overlap joins. Each original period (per row) will be expanded to multiple rows by weeks, months, etc. Only data.frame input is accepted as the output size is greater than the input. Thus, remote tables should be collected before running this function for optimal performance.

Usage

cut_period(
  data,
  start,
  end,
  len,
  unit = c("day", "week", "month", "quarter", "year"),
  .dt_trans = NULL
)

Arguments

Value

Data frame that each row is now a segment of the period defined by c(start, end) in the original row. Original variables are retained and repeated for each segment plus new variables defining the segment interval.

Examples

# toy data
df <- data.frame(sample_id = 1, period_id = 1, start_date = "2015-01-01", end_date = "2019-12-31")

# divide period into segments (multiple rows per period)
df_seg <- cut_period(
  data = df, start = start_date, end = end_date,
  len = 1,
  unit = "year",
  .dt_trans = lubridate::ymd
)

# categorize segment_id as factor
df_seg$segment <- cut(df_seg$segment_id,
  breaks = c(0, 1, 2, Inf),
  labels = c("< 1 year", "1 - 2 years", "Remainder")
)

head(df_seg)

Identify diseases/events from administrative records

Description

This function is a composite of identify_row(), exclude(), restrict_n(), and restrict_date(). It is aimed to implement common case definitions in epidemiological studies using administrative database as a one-shot big query. The intended use case is for definitions in the form of, e.g., two or more physician visits with some diagnostic code at least 30 days apart within two years. The component functions mentioned above are chained in the following order if all arguments were supplied: identify_row(vals) %>% exclude(identify_row(excl_vals), by = clnt_id) %>% restrict_n() %>% restrict_date(). Only necessary steps in the chain will be ran if some arguments are missing, see the verbose output for what was done. Note that if date_var is supplied, n_per_clnt will be counted by distinct dates instead of number of records.

Usage

define_case(
  data,
  vars,
  match = "in",
  vals,
  clnt_id,
  n_per_clnt = 1,
  date_var = NULL,
  apart = NULL,
  within = NULL,
  uid = NULL,
  excl_vals = NULL,
  excl_args = NULL,
  keep = c("all", "first", "last"),
  if_all = FALSE,
  mode = c("flag", "filter"),
  force_collect = FALSE,
  verbose = getOption("healthdb.verbose"),
  ...
)

Arguments

Value

A subset of input data satisfied the specified case definition.

Examples

sample_size <- 30
df <- data.frame(
  clnt_id = rep(1:3, each = 10),
  service_dt = sample(seq(as.Date("2020-01-01"), as.Date("2020-01-31"), by = 1),
    size = sample_size, replace = TRUE
  ),
  diagx = sample(letters, size = sample_size, replace = TRUE),
  diagx_1 = sample(c(NA, letters), size = sample_size, replace = TRUE),
  diagx_2 = sample(c(NA, letters), size = sample_size, replace = TRUE)
)

# define from one source
define_case(df,
  vars = starts_with("diagx"), "in", vals = letters[1:4],
  clnt_id = clnt_id, date_var = service_dt,
  excl_args = list(if_all = TRUE),
  # remove non-case
  mode = "filter",
  # keeping the first record
  keep = "first"
)

# multiple sources with purrr::pmap
# arguments with length = 1 will be recycle to match the number of sources
# wrap expressions/unquoted variables with bquote(),
# or rlang:exprs() to prevent immediate evaluation,
# or just use quoted variable names
purrr::pmap(
  list(
    data = list(df, df),
    vars = rlang::exprs(starts_with("diagx")),
    match = c("in", "start"),
    vals = list(letters[1:4], letters[5:10]),
    clnt_id = list(bquote(clnt_id)), n_per_clnt = c(2, 3),
    date_var = "service_dt",
    excl_vals = list(letters[11:13], letters[14:16]),
    excl_args = list(list(if_all = TRUE), list(if_all = FALSE))
  ),
  define_case
)

Remove rows based on conditions or another data set

Description

This function combines dplyr::anti_join(), and negation of dplyr::filter(). When a second data set is supplied through the excl argument, anti join would be performed; otherwise, data would be filtered with the expression given via the condition argument, and the filter result would in turn be removed using dplyr::setdiff().

Usage

exclude(
  data,
  excl = NULL,
  by = NULL,
  condition = NULL,
  verbose = getOption("healthdb.verbose"),
  report_on = NULL,
  ...
)

Arguments

Value

A data frame or remote table that is a subset of data.

Examples

# exclude with condition
cyl_not_4 <- exclude(mtcars, condition = cyl == 4, report_on = cyl)

# exclude with another data
exclude(mtcars, cyl_not_4, dplyr::join_by(cyl), report_on = cyl)

Execute parameterized case definitions

Description

This function executes the function calls stored in the output tibble from [build_def()] with data objects supplied through a named list and returns the results as a list. It is intended to facilitate re-use of pre-defined calls with different data.

Usage

execute_def(
  def,
  with_data,
  bind = FALSE,
  force_proceed = getOption("healthdb.force_proceed")
)

Arguments

Value

A single (if bind = TRUE) or a list of data.frames or remote tables.

See Also

[bind_sources()] for binding the output with convenient renaming features.

Examples

# toy data
sample_size <- 30
df <- data.frame(
  clnt_id = rep(1:3, each = 10),
  service_dt = sample(seq(as.Date("2020-01-01"), as.Date("2020-01-31"), by = 1),
    size = sample_size, replace = TRUE
  ),
  diagx = sample(letters, size = sample_size, replace = TRUE),
  diagx_1 = sample(c(NA, letters), size = sample_size, replace = TRUE),
  diagx_2 = sample(c(NA, letters), size = sample_size, replace = TRUE)
)

# make df a database table
db <- dbplyr::tbl_memdb(df)

# use build_def to make a toy definition
sud_def <- build_def("SUD", # usually a disease name
  src_lab = c("src1", "src2"), # identify from multiple sources, e.g., hospitalization, ED visits.
  # functions that filter the data with some criteria
  def_fn = define_case,
  fn_args = list(
    vars = starts_with("diagx"),
    match = "start", # "start" will be applied to all sources as length = 1
    vals = list(c("304"), c("305")),
    clnt_id = "clnt_id", # list()/c() could be omitted for single element
    # c() can be used in place of list
    # if this argument only takes one value for each source
    n_per_clnt = c(2, 3)
  )
)

# save the definition for re-use
# saveRDS(sud_def, file = some_path)

sud_def %>% execute_def(with_data = list(src1 = df, src2 = db), force_proceed = TRUE)

Get variables from multiple tables with common ID columns

Description

This function fetches variables from different tables that linked by common IDs. It calls dplyr::left_join() multiple times with various source tables (y argument of the join) to gather variables. It is not meant to replace left_join() but simplify syntax for the situation that you started off a table of study sample and wanted to gather covariates from different sources linked by common client IDs, which is often the case when working with healthcare databases. Caution: this function is intended for one-to-one joins only because it could be problematic when we do not know which source caused a one-to-many join and changed the number of rows. For data.frame input, an error will be given when one-to-many joins were detected. However, such checking could be an expensive operation on remote source. Therefore, for database input, the result will not be checked.

Usage

fetch_var(data, keys, linkage, ...)

Arguments

Value

A data.frame or remote table containing all original columns of x and new variables matched from other tables based on the specified linkage.

Examples

# make toy data
size <- 30
n <- 10
df1 <- data.frame(
  id = sample(1:n, size = size, replace = TRUE),
  service_dt = sample(seq(as.Date("2020-01-01"), as.Date("2022-01-31"), by = 1),
    size = size
  )
) %>%
  dplyr::mutate(year = lubridate::year(service_dt))
df2 <- data.frame(
  id = rep(1:n, size / n), year = rep(2020:2022, each = n),
  status_1 = sample(0:1, size = size, replace = TRUE),
  status_2 = sample(0:1, size = size, replace = TRUE)
)
df3 <- data.frame(id = 1:n, sex = sample(c("F", "M"), size = n, replace = TRUE))

# simple joins
# note that for left_join(df1, df2), boths keys have to be used,
# otherwise, error as the relation would not be one-to-one
fetch_var(df1,
  keys = c(id, year),
  linkage = list(
    df2 ~ starts_with("s"), # match both keys without '|'
    df3 ~ sex | id
  ) # match by id only; otherwise failed because df3 has no year
)

# example if some y is remote
# make df2 as database table
db2 <- dbplyr::tbl_memdb(df2)

fetch_var(df1,
  keys = c(id, year),
  linkage = list(
    db2 ~ starts_with("s"),
    df3 ~ sex | id
  ),
  copy = TRUE # pass to left_join for forced collection of remote table
)

Identify rows with a match

Description

Filter rows which values satisfy the specified conditions. The functionality is identical to dplyr::filter() combined with dplyr::if_any() or dplyr::if_all(), but it used the 'data.table' package vignette("datatable-intro", package = "data.table") for data.frame method, and has regular regular expression support for remote database tables. The motivation is to take away some pain when working with databases which often do not support regular expression and 'LIKE' operator with multiple string patterns.

Usage

identify_row(
  data,
  vars,
  match = c("in", "start", "regex", "like", "between", "glue_sql"),
  vals,
  if_all = FALSE,
  verbose = getOption("healthdb.verbose"),
  query_only = TRUE,
  ...
)

Arguments

Value

A data.frame or tbl_sql object depending on the input.

Examples

#applying to data.frame; both sepal length and width in range 3-5
identify_row(iris, starts_with("Sepal"), "between", c(3, 5), if_all = TRUE)

#applying to remote table; species starts with se or ends with ca
iris_db <- dbplyr::memdb_frame(iris)
identify_row(iris_db, Species, "like", c("se%", "%ca"))

#using glue_sql to write the WHERE clause
#use {`vars`} to refer to the variables selected by vars
#supply additional values required in the query through '...'
#note that if you use LIKE here, you cannot supply multiple patterns in what
identify_row(iris_db, Species, "glue_sql",
 "{`vars`} LIKE {what}",
  what = "se%")

#add * after a vector
identify_row(iris_db, Species, "glue_sql",
 "{`vars`} IN ({what*})",
 what = c("setosa", "virginica"))

Interpret if any n elements drawn from a date vector could be some days apart within some time span

Description

Given a vector of dates x, interpret if there could be at least one set of n elements taken from x satisfy that adjacent elements in the set are at least certain days apart AND the dates in the set are within the specified time span. When identifying events/diseases from administrative data, definitions often require, e.g., n diagnoses that are at least some days apart within some years. This function is intended for such use and optimized to avoid looping through all n-size combinations in x. This function does not work with remote table input.

Usage

if_date(
  x,
  n,
  apart = NULL,
  within = NULL,
  detail = FALSE,
  align = c("left", "right"),
  dup.rm = TRUE,
  ...
)

Arguments

Value

Single or a vector of logical for whether there is any draw from x satisfied the conditions

See Also

restrict_date()

Examples

dates_of_records <- sample(seq(as.Date("2015-01-01"), as.Date("2021-12-31"), 7), 10)

# whether there is any 3 records at least 30 days apart within 2 years
if_date(dates_of_records, n = 3, apart = 30, within = 365 * 2)

# specified either apart or within or both
if_date(dates_of_records, n = 2, within = 365)

Find value corresponding to the input vector using a look-up table

Description

Find value corresponding to the input vector using a look-up table

Usage

lookup(x, link, lu, verbose = getOption("healthdb.verbose"))

Arguments

Value

A vector of matched values.

Examples

df <- data.frame(drug_code = 1:10)
lu <- data.frame(drug_id = 1:20, drug_code = as.character(1:10), drug_name = sample(letters, 20))

df %>% dplyr::mutate(
  drug_nm = lookup(drug_code, drug_id ~ drug_name, lu),
  # this will work as lu also has drug_code column
  drug_nm = lookup(drug_code, ~ drug_name, lu)
)

Make test data

Description

Make a toy data set for testing and demo. This is for internal use purpose and not intended to be called by users.

Usage

make_test_dat(
  vals_kept = c("304", "305", 3040:3049, 3050:3059),
  noise_val = "999",
  IDs = 1:50,
  date_range = seq(as.Date("2015-01-01"), as.Date("2020-12-31"), by = 1),
  nrows = 100,
  n_any = 50,
  n_all = 10,
  seed = NULL,
  answer_id = NULL,
  type = c("data.frame", "database")
)

Arguments

Value

A data.frame or remote table from 'dbplyr'.

Examples

make_test_dat() %>% head()

Pool qualified clients from results of multiple definitions

Description

This function filters and pools, i.e., row bind, qualified clients/groups from different source with an option to summarize by client. Unlike bind_source(), no need to supply variable names; the function will guess what should be included and their names from the supplied definition from build_def(). Whether a client is qualified relies on the flag variables set by define_case(). Therefore, this function is intended to be use only with the built-in define_case() as def_fn in build_def().

Usage

pool_case(
  data,
  def,
  output_lvl = c("raw", "clnt"),
  include_src = c("all", "has_valid", "n_per_clnt"),
  ...
)

Arguments

Value

A data.frame or remote table with clients that satisfied the predefined case definition. Columns started with "raw_in_" are source-specific counts of raw records, and columns started with "valid_in_" are the number of valid entries (or the number of flags) in each source.

Examples

# toy data
df1 <- make_test_dat()
df2 <- make_test_dat()

# use build_def to make a toy definition
sud_def <- build_def("SUD", # usually a disease name
  src_lab = c("src1", "src2"), # identify from multiple sources, e.g., hospitalization, ED visits.
  # functions that filter the data with some criteria
  def_fn = define_case,
  fn_args = list(
    vars = starts_with("diagx"),
    match = "start", # "start" will be applied to all sources as length = 1
    vals = list(c("304"), c("305")),
    clnt_id = "clnt_id", # list()/c() could be omitted for single element
    # c() can be used in place of list
    # if this argument only takes one value for each source
    n_per_clnt = c(2, 3)
  )
)

# save the definition for re-use
# saveRDS(sud_def, file = some_path)

# execute definition
sud_by_src <- sud_def %>% execute_def(with_data = list(src1 = df1, src2 = df2))

# pool results from src1 and src2 together at client level
pool_case(sud_by_src, sud_def, output_lvl = "clnt")

Report number of distinct value in a column across data frames

Description

This function is intended to mimic dplyr::n_distinct() for multiple inputs. It is useful to report the number of clients through out a series of inclusion or exclusion steps. An use case could be getting the Ns for the sample definition flowchart in an epidemiological study. It is also useful for inline reporting of Ns in a Rmarkdown document.

Usage

report_n(..., on, force_proceed = getOption("healthdb.force_proceed"))

Arguments

Value

A sequence of the number of distinct on for each data frames

Examples

# some exclusions
iris_1 <- subset(iris, Petal.Length > 1)
iris_2 <- subset(iris, Petal.Length > 2)

# get n at each operation
n <- report_n(iris, iris_1, iris_2, on = Species)
n

# get the difference at each step
diff(n)
# data in a list
iris_list <- list(iris_1, iris_2)
report_n(rlang::splice(iris_list), on = Species)
# if you loaded tidyverse, this will also work
# report_n(!!!iris_list, on = Species)

Remove or flag groups failed to meet conditions based on dates

Description

For each client or group, interpret if they have n records that are at least certain days apart AND within a specified time span. When identifying events/diseases from administrative data, definitions often require, e.g., n diagnoses that are at least some days apart within some years. This function is intended for such use and optimized to avoid looping through all n-size combinations of dates per client.

Usage

restrict_date(
  data,
  clnt_id,
  date_var,
  n,
  apart = NULL,
  within = NULL,
  uid = NULL,
  mode = c("flag", "filter"),
  flag_at = c("left", "right"),
  dup.rm = TRUE,
  force_collect = FALSE,
  verbose = getOption("healthdb.verbose"),
  check_missing = FALSE,
  ...
)

Arguments

Value

A subset of input data satisfied the dates requirement, or raw input data with an new flag column.

See Also

if_date()

Examples

sample_size <- 30
df <- data.frame(
  clnt_id = sample(1:sample_size, sample_size, replace = TRUE),
  service_dt = sample(seq(as.Date("2020-01-01"), as.Date("2020-01-31"), by = 1),
    size = sample_size, replace = TRUE
  ),
  diagx = sample(letters, size = sample_size, replace = TRUE),
  diagx_1 = sample(c(NA, letters), size = sample_size, replace = TRUE),
  diagx_2 = sample(c(NA, letters), size = sample_size, replace = TRUE)
)

# Keep clients with 2 records that were 1 week apart within 1 month
restrict_date(df, clnt_id, service_dt, n = 2, apart = 7, within = 30)

Remove or flag groups with n less than some number

Description

Remove or flags groups or clients that have less than some number of rows or some number of distinct values in a variable. For example, it can be used to remove clients that had less than n visits to some service on different dates from some administrative records. It offers filtering with dplyr::n_distinct() functionality for database input.

Usage

restrict_n(
  data,
  clnt_id,
  n_per_clnt,
  count_by = NULL,
  mode = c("flag", "filter"),
  verbose = getOption("healthdb.verbose")
)

Arguments

Value

A subset of input data satisfied the group size requirement, or raw input data with an new flag column.

See Also

dplyr::n(), dplyr::n_distinct()

Examples

# flag cyl groups with less than 8 cars
restrict_n(mtcars, clnt_id = cyl, n_per_clnt = 8, mode = "flag") %>%
head()

#remove cyl groups with less than 2 types of gear boxes
restrict_n(mtcars, clnt_id = cyl, n_per_clnt = 3, count_by = gear, mode = "filter")