Categorize cases based on external data and classification scheme
Source:R/categorize.R
categorize.RdThis is the main function of the package, which relies of a triad of objects:
(1) data with unit id:s and possible dates of interest;
(2) codedata for corresponding
units and with optional dates of interest and;
(3) a classification scheme (classcodes object; cc) with regular
expressions to identify and categorize relevant codes.
The function combines the three underlying steps performed by
codify(), classify() and index().
Relevant arguments are passed to those functions by
codify_args and cc_args.
Usage
categorize(x, ...)
# S3 method for data.frame
categorize(x, ...)
# S3 method for tbl_df
categorize(x, ...)
# S3 method for data.table
categorize(x, ..., codedata, id, code, codify_args = list())
# S3 method for codified
categorize(
x,
...,
cc,
index = NULL,
cc_args = list(),
check.names = TRUE,
.data_cols = NULL
)Arguments
- x
data set with mandatory character id column (identified by argument
id = "<col_name>"), and optionalDateof interest (identified by argumentdate = "<col_name>"). Alternatively, the output fromcodify()- ...
arguments passed between methods
- codedata
external code data with mandatory character id column (identified by
id = "<col_name>"), code column (identified by argumentcode = "<col_name>") and optionalDatecolumn (identified bycodify_args = list(code_date = "<col_name>")).- id
name of unique character id column found in both
xandcodedata. (where it must not be unique).- code
name of code column in
codedata.- codify_args
Lists of named arguments passed to
codify()- cc
classcodesobject (or name of a default object fromall_classcodes()).- index
Argument passed to
index(). A character vector of names of columns with index weights from the corresponding classcodes object (as supplied by theccargument). Seeattr(cc, "indices")for available options. Set toFALSEif no index should be calculated. IfNULL, the default, all available indices (fromattr(cc, "indices")) are provided.- cc_args
List with named arguments passed to
set_classcodes()- check.names
Column names are based on
cc$group, which might include spaces. Those names are changed to syntactically correct names bycheck.names = TRUE. Syntactically invalid, but grammatically correct names might be preferred for presentation of the data as achieved bycheck.names = FALSE. Alternatively, ifcategorizeis called repeatedly, longer informative names might be created bycc_args = list(tech_names = TRUE).- .data_cols
used internally
Value
Object of the same class as x with additional logical columns
indicating membership of groups identified by the
classcodes object (the cc argument).
Numeric indices are also included if requested by the index argument.
Examples
# For this example, 1 core would suffice:
old_threads <- data.table::getDTthreads()
data.table::setDTthreads(1)
# For some patient data (ex_people) and related hospital visit code data
# with ICD 10-codes (ex_icd10), add the Elixhauser comorbidity
# conditions based on all registered ICD10-codes
categorize(
x = ex_people,
codedata = ex_icd10,
cc = "elixhauser",
id = "name",
code = "icd10"
)
#> Classification based on: icd10
#> # A tibble: 100 × 40
#> name surgery congestive.heart.fai…¹ cardiac.arrhythmias valvular.disease
#> <chr> <date> <lgl> <lgl> <lgl>
#> 1 Chen,… 2023-02-28 FALSE FALSE FALSE
#> 2 Grave… 2022-11-20 FALSE FALSE FALSE
#> 3 Truji… 2022-11-07 FALSE FALSE FALSE
#> 4 Simps… 2023-02-09 FALSE FALSE FALSE
#> 5 Chin,… 2023-01-23 FALSE FALSE FALSE
#> 6 Le, C… 2022-08-27 FALSE FALSE FALSE
#> 7 Kang,… 2022-11-29 FALSE FALSE FALSE
#> 8 Shuem… 2022-08-28 FALSE FALSE FALSE
#> 9 Bouch… 2023-02-03 FALSE FALSE FALSE
#> 10 Le, S… 2023-01-08 FALSE FALSE FALSE
#> # ℹ 90 more rows
#> # ℹ abbreviated name: ¹congestive.heart.failure
#> # ℹ 35 more variables: pulmonary.circulation.disorder <lgl>,
#> # peripheral.vascular.disorder <lgl>, hypertension.uncomplicated <lgl>,
#> # hypertension.complicated <lgl>, paralysis <lgl>,
#> # other.neurological.disorders <lgl>, chronic.pulmonary.disease <lgl>,
#> # diabetes.uncomplicated <lgl>, diabetes.complicated <lgl>, …
# Add Charlson categories and two versions of a calculated index
# ("quan_original" and "quan_updated").
categorize(
x = ex_people,
codedata = ex_icd10,
cc = "charlson",
id = "name",
code = "icd10",
index = c("quan_original", "quan_updated")
)
#> Classification based on: icd10
#> # A tibble: 100 × 21
#> name surgery myocardial.infarction congestive.heart.failure
#> <chr> <date> <lgl> <lgl>
#> 1 Chen, Trevor 2023-02-28 FALSE FALSE
#> 2 Graves, Acineth 2022-11-20 FALSE FALSE
#> 3 Trujillo, Yanelly 2022-11-07 FALSE FALSE
#> 4 Simpson, Kenneth 2023-02-09 FALSE FALSE
#> 5 Chin, Nelson 2023-01-23 FALSE FALSE
#> 6 Le, Christina 2022-08-27 FALSE FALSE
#> 7 Kang, Xuan 2022-11-29 FALSE FALSE
#> 8 Shuemaker, Lauren 2022-08-28 FALSE FALSE
#> 9 Boucher, Teresa 2023-02-03 FALSE FALSE
#> 10 Le, Soraiya 2023-01-08 FALSE FALSE
#> # ℹ 90 more rows
#> # ℹ 17 more variables: peripheral.vascular.disease <lgl>,
#> # cerebrovascular.disease <lgl>, dementia <lgl>,
#> # chronic.pulmonary.disease <lgl>, rheumatic.disease <lgl>,
#> # peptic.ulcer.disease <lgl>, mild.liver.disease <lgl>,
#> # diabetes.without.complication <lgl>, hemiplegia.or.paraplegia <lgl>,
#> # renal.disease <lgl>, diabetes.complication <lgl>, malignancy <lgl>, …
# Only include recent hospital visits within 30 days before surgery,
categorize(
x = ex_people,
codedata = ex_icd10,
cc = "charlson",
id = "name",
code = "icd10",
index = c("quan_original", "quan_updated"),
codify_args = list(
date = "surgery",
days = c(-30, -1),
code_date = "admission"
)
)
#> Classification based on: icd10
#> # A tibble: 100 × 21
#> name surgery myocardial.infarction congestive.heart.failure
#> <chr> <date> <lgl> <lgl>
#> 1 Chen, Trevor 2023-02-28 FALSE FALSE
#> 2 Graves, Acineth 2022-11-20 NA NA
#> 3 Trujillo, Yanelly 2022-11-07 NA NA
#> 4 Simpson, Kenneth 2023-02-09 FALSE FALSE
#> 5 Chin, Nelson 2023-01-23 FALSE FALSE
#> 6 Le, Christina 2022-08-27 FALSE FALSE
#> 7 Kang, Xuan 2022-11-29 FALSE FALSE
#> 8 Shuemaker, Lauren 2022-08-28 FALSE FALSE
#> 9 Boucher, Teresa 2023-02-03 NA NA
#> 10 Le, Soraiya 2023-01-08 FALSE FALSE
#> # ℹ 90 more rows
#> # ℹ 17 more variables: peripheral.vascular.disease <lgl>,
#> # cerebrovascular.disease <lgl>, dementia <lgl>,
#> # chronic.pulmonary.disease <lgl>, rheumatic.disease <lgl>,
#> # peptic.ulcer.disease <lgl>, mild.liver.disease <lgl>,
#> # diabetes.without.complication <lgl>, hemiplegia.or.paraplegia <lgl>,
#> # renal.disease <lgl>, diabetes.complication <lgl>, malignancy <lgl>, …
# Multiple versions -------------------------------------------------------
# We can compare categorization by according to Quan et al. (2005); "icd10",
# and Armitage et al. (2010); "icd10_rcs" (see `?charlson`)
# Note the use of `tech_names = TRUE` to distinguish the column names from the
# two versions.
# We first specify some common settings ...
ind <- c("quan_original", "quan_updated")
cd <- list(date = "surgery", days = c(-30, -1), code_date = "admission")
# ... we then categorize once with "icd10" as the default regular expression ...
categorize(
x = ex_people,
codedata = ex_icd10,
cc = "charlson",
id = "name",
code = "icd10",
index = ind,
codify_args = cd,
cc_args = list(tech_names = TRUE)
) %>%
# .. and once more with `regex = "icd10_rcs"`
categorize(
codedata = ex_icd10,
cc = "charlson",
id = "name",
code = "icd10",
index = ind,
codify_args = cd,
cc_args = list(regex = "icd10_rcs", tech_names = TRUE)
)
#> Classification based on: icd10
#> # A tibble: 100 × 37
#> name surgery charlson_icd10_myocardi…¹ charlson_icd10_conge…²
#> <chr> <date> <lgl> <lgl>
#> 1 Chen, Trevor 2023-02-28 FALSE FALSE
#> 2 Graves, Acineth 2022-11-20 NA NA
#> 3 Trujillo, Yanelly 2022-11-07 NA NA
#> 4 Simpson, Kenneth 2023-02-09 FALSE FALSE
#> 5 Chin, Nelson 2023-01-23 FALSE FALSE
#> 6 Le, Christina 2022-08-27 FALSE FALSE
#> 7 Kang, Xuan 2022-11-29 FALSE FALSE
#> 8 Shuemaker, Lauren 2022-08-28 FALSE FALSE
#> 9 Boucher, Teresa 2023-02-03 NA NA
#> 10 Le, Soraiya 2023-01-08 FALSE FALSE
#> # ℹ 90 more rows
#> # ℹ abbreviated names: ¹charlson_icd10_myocardial_infarction,
#> # ²charlson_icd10_congestive_heart_failure
#> # ℹ 33 more variables: charlson_icd10_peripheral_vascular_disease <lgl>,
#> # charlson_icd10_cerebrovascular_disease <lgl>,
#> # charlson_icd10_dementia <lgl>,
#> # charlson_icd10_chronic_pulmonary_disease <lgl>, …
# column names ------------------------------------------------------------
# Default column names are based on row names from corresponding classcodes
# object but are modified to be syntactically correct.
default <-
categorize(ex_people, codedata = ex_icd10, cc = "elixhauser",
id = "name", code = "icd10")
#> Classification based on: icd10
# Set `check.names = FALSE` to retain original names:
original <-
categorize(
ex_people, codedata = ex_icd10, cc = "elixhauser",
id = "name", code = "icd10",
check.names = FALSE
)
#> Classification based on: icd10
# Or use `tech_names = TRUE` for informative but long names (use case above)
tech <-
categorize(ex_people, codedata = ex_icd10, cc = "elixhauser",
id = "name", code = "icd10",
cc_args = list(tech_names = TRUE)
)
#> Classification based on: icd10
# Compare
tibble::tibble(names(default), names(original), names(tech))
#> # A tibble: 40 × 3
#> `names(default)` `names(original)` `names(tech)`
#> <chr> <chr> <chr>
#> 1 name name name
#> 2 surgery surgery surgery
#> 3 congestive.heart.failure congestive heart failure elixhauser_icd…
#> 4 cardiac.arrhythmias cardiac arrhythmias elixhauser_icd…
#> 5 valvular.disease valvular disease elixhauser_icd…
#> 6 pulmonary.circulation.disorder pulmonary circulation disorder elixhauser_icd…
#> 7 peripheral.vascular.disorder peripheral vascular disorder elixhauser_icd…
#> 8 hypertension.uncomplicated hypertension uncomplicated elixhauser_icd…
#> 9 hypertension.complicated hypertension complicated elixhauser_icd…
#> 10 paralysis paralysis elixhauser_icd…
#> # ℹ 30 more rows
# Go back to original number of threads
data.table::setDTthreads(old_threads)