This is the second step of codify() %>% classify() %>% index().
Hence, the function takes a codified data set and classify each case based on
relevant codes as identified by the classification scheme provided by a
classcodes object.
Usage
classify(codified, cc, ..., cc_args = list())
# Default S3 method
classify(codified, cc, ..., cc_args = list())
# S3 method for class 'codified'
classify(codified, ...)
# S3 method for class 'data.frame'
classify(codified, ...)
# S3 method for class 'data.table'
classify(codified, cc, ..., id, code, cc_args = list())Arguments
- codified
output from
codify()- cc
classcodesobject (or name of a default object fromall_classcodes()).- ...
arguments passed between methods
- cc_args
List with named arguments passed to
set_classcodes()- code, id
name of code/id columns (in
codified).
Value
Object of class "classified". Inheriting from a Boolean matrix with
one row for each element/row of codified
and columns for each class with corresponding class names (according to the
classcodes object). Note, however, that print.classified() preview
this output as a tibble.
See also
as.data.frame.classified(), as.data.table.classified() and
as.matrix.classified(), print.classified()
Other verbs:
categorize(),
codify(),
index_fun
Examples
# classify.default() ------------------------------------------------------
# Classify individual ICD10-codes by Elixhauser
classify(c("C80", "I20", "unvalid_code"), "elixhauser")
#> Classification based on: icd10
#>
#> The printed data is of class: classified, matrix.
#> It has 3 row(s).
#> It is here previewed as a tibble
#> Use `print(x, n = NULL)` to print as is (or use `n` to specify the number of rows to preview)!
#>
#> # A tibble: 3 × 31
#> `congestive heart failure` `cardiac arrhythmias` `valvular disease`
#> <lgl> <lgl> <lgl>
#> 1 FALSE FALSE FALSE
#> 2 FALSE FALSE FALSE
#> 3 FALSE FALSE FALSE
#> # ℹ 28 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>,
#> # hypothyroidism <lgl>, `renal failure` <lgl>, `liver disease` <lgl>,
#> # `peptic ulcer disease` <lgl>, `AIDS/HIV` <lgl>, lymphoma <lgl>, …
# classify.codified() -----------------------------------------------------
# Prepare some codified data with ICD-10 codes during 1 year (365 days)
# before surgery
x <-
codify(
ex_people,
ex_icd10,
id = "name",
code = "icd10",
date = "surgery",
days = c(-365, 0),
code_date = "admission"
)
# Classify those patients by the Charlson and Elixhasuer comorbidity indices
classify(x, "charlson") # classcodes object by name ...
#> Warning: 'classify()' does not preserve row order ('categorize()' does!)
#> Classification based on: icd10
#>
#> The printed data is of class: classified, matrix.
#> It has 100 row(s).
#> It is here previewed as a tibble
#> Use `print(x, n = NULL)` to print as is (or use `n` to specify the number of rows to preview)!
#>
#> # A tibble: 10 × 17
#> `myocardial infarction` `congestive heart failure` peripheral vascular dise…¹
#> <lgl> <lgl> <lgl>
#> 1 FALSE FALSE FALSE
#> 2 FALSE FALSE FALSE
#> 3 FALSE FALSE FALSE
#> 4 FALSE FALSE FALSE
#> 5 FALSE FALSE FALSE
#> 6 FALSE FALSE FALSE
#> 7 FALSE FALSE FALSE
#> 8 FALSE FALSE FALSE
#> 9 FALSE FALSE FALSE
#> 10 FALSE FALSE FALSE
#> # ℹ abbreviated name: ¹`peripheral vascular disease`
#> # ℹ 14 more variables: `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>,
#> # `moderate or severe liver disease` <lgl>, `metastatic solid tumor` <lgl>, …
classify(x, coder::elixhauser) # ... or by the object itself
#> Warning: 'classify()' does not preserve row order ('categorize()' does!)
#> Classification based on: icd10
#>
#> The printed data is of class: classified, matrix.
#> It has 100 row(s).
#> It is here previewed as a tibble
#> Use `print(x, n = NULL)` to print as is (or use `n` to specify the number of rows to preview)!
#>
#> # A tibble: 10 × 31
#> `congestive heart failure` `cardiac arrhythmias` `valvular disease`
#> <lgl> <lgl> <lgl>
#> 1 FALSE FALSE FALSE
#> 2 FALSE FALSE FALSE
#> 3 FALSE FALSE FALSE
#> 4 FALSE FALSE FALSE
#> 5 FALSE FALSE FALSE
#> 6 FALSE FALSE FALSE
#> 7 FALSE FALSE FALSE
#> 8 FALSE FALSE FALSE
#> 9 FALSE FALSE FALSE
#> 10 NA NA NA
#> # ℹ 28 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>,
#> # hypothyroidism <lgl>, `renal failure` <lgl>, `liver disease` <lgl>,
#> # `peptic ulcer disease` <lgl>, `AIDS/HIV` <lgl>, lymphoma <lgl>, …
# -- start/stop --
# Assume that a prefix "ICD-10 = " is used for all codes and that some
# additional numbers are added to the end
x$icd10 <- paste0("ICD-10 = ", x$icd10)
# Set start = FALSE to identify codes which are not necessarily found in the
# beginning of the string
classify(x, "charlson", cc_args = list(start = FALSE))
#> Warning: 'classify()' does not preserve row order ('categorize()' does!)
#> Classification based on: icd10
#>
#> The printed data is of class: classified, matrix.
#> It has 100 row(s).
#> It is here previewed as a tibble
#> Use `print(x, n = NULL)` to print as is (or use `n` to specify the number of rows to preview)!
#>
#> # A tibble: 10 × 17
#> `myocardial infarction` `congestive heart failure` peripheral vascular dise…¹
#> <lgl> <lgl> <lgl>
#> 1 FALSE FALSE FALSE
#> 2 FALSE FALSE FALSE
#> 3 FALSE FALSE FALSE
#> 4 FALSE FALSE FALSE
#> 5 FALSE FALSE FALSE
#> 6 FALSE FALSE FALSE
#> 7 FALSE FALSE FALSE
#> 8 FALSE FALSE FALSE
#> 9 FALSE FALSE FALSE
#> 10 FALSE FALSE FALSE
#> # ℹ abbreviated name: ¹`peripheral vascular disease`
#> # ℹ 14 more variables: `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>,
#> # `moderate or severe liver disease` <lgl>, `metastatic solid tumor` <lgl>, …
# -- regex --
# Use a different version of Charlson (as formulated by regular expressions
# according to the Royal College of Surgeons (RCS) by passing arguments to
# `set_classcodes()` using the `cc_args` argument
y <-
classify(
x,
"charlson",
cc_args = list(regex = "icd10_rcs")
)
#> Warning: 'classify()' does not preserve row order ('categorize()' does!)
# -- tech_names --
# Assume that we want to compare the results using the default ICD-10
# formulations (from Quan et al. 2005) and the RCS version and that the result
# should be put into the same data frame. We can use `tech_names = TRUE`
# to distinguish variables with otherwise similar names
cc <- list(tech_names = TRUE) # Prepare sommon settings
compare <-
merge(
classify(x, "charlson", cc_args = cc),
classify(x, "charlson", cc_args = c(cc, regex = "icd10_rcs"))
)
#> Warning: 'classify()' does not preserve row order ('categorize()' does!)
#> Classification based on: icd10
#> Warning: 'classify()' does not preserve row order ('categorize()' does!)
names(compare) # long but informative and distinguishable column names
#> [1] "name"
#> [2] "charlson_icd10_myocardial_infarction"
#> [3] "charlson_icd10_congestive_heart_failure"
#> [4] "charlson_icd10_peripheral_vascular_disease"
#> [5] "charlson_icd10_cerebrovascular_disease"
#> [6] "charlson_icd10_dementia"
#> [7] "charlson_icd10_chronic_pulmonary_disease"
#> [8] "charlson_icd10_rheumatic_disease"
#> [9] "charlson_icd10_peptic_ulcer_disease"
#> [10] "charlson_icd10_mild_liver_disease"
#> [11] "charlson_icd10_diabetes_without_complication"
#> [12] "charlson_icd10_hemiplegia_or_paraplegia"
#> [13] "charlson_icd10_renal_disease"
#> [14] "charlson_icd10_diabetes_complication"
#> [15] "charlson_icd10_malignancy"
#> [16] "charlson_icd10_moderate_or_severe_liver_disease"
#> [17] "charlson_icd10_metastatic_solid_tumor"
#> [18] "charlson_icd10_aids_hiv"
#> [19] "charlson_icd10_rcs_myocardial_infarction"
#> [20] "charlson_icd10_rcs_congestive_heart_failure"
#> [21] "charlson_icd10_rcs_peripheral_vascular_disease"
#> [22] "charlson_icd10_rcs_cerebrovascular_disease"
#> [23] "charlson_icd10_rcs_dementia"
#> [24] "charlson_icd10_rcs_chronic_pulmonary_disease"
#> [25] "charlson_icd10_rcs_rheumatic_disease"
#> [26] "charlson_icd10_rcs_hemiplegia_or_paraplegia"
#> [27] "charlson_icd10_rcs_renal_disease"
#> [28] "charlson_icd10_rcs_diabetes_complication"
#> [29] "charlson_icd10_rcs_malignancy"
#> [30] "charlson_icd10_rcs_moderate_or_severe_liver_disease"
#> [31] "charlson_icd10_rcs_metastatic_solid_tumor"
#> [32] "charlson_icd10_rcs_aids_hiv"
# classify.data.frame() / classify.data.table() ------------------------
# Assume that `x` is a data.frame/data.table without additional attributes
# from `codify()` ...
xdf <- as.data.frame(x)
xdt <- data.table::as.data.table(x)
# ... then the `id` and `code` columns must be specified explicitly
classify(xdf, "charlson", id = "name", code = "icd10")
#> Warning: 'classify()' does not preserve row order ('categorize()' does!)
#> Classification based on: icd10
#>
#> The printed data is of class: classified, matrix.
#> It has 100 row(s).
#> It is here previewed as a tibble
#> Use `print(x, n = NULL)` to print as is (or use `n` to specify the number of rows to preview)!
#>
#> # A tibble: 10 × 17
#> `myocardial infarction` `congestive heart failure` peripheral vascular dise…¹
#> <lgl> <lgl> <lgl>
#> 1 FALSE FALSE FALSE
#> 2 FALSE FALSE FALSE
#> 3 FALSE FALSE FALSE
#> 4 FALSE FALSE FALSE
#> 5 FALSE FALSE FALSE
#> 6 FALSE FALSE FALSE
#> 7 FALSE FALSE FALSE
#> 8 FALSE FALSE FALSE
#> 9 FALSE FALSE FALSE
#> 10 FALSE FALSE FALSE
#> # ℹ abbreviated name: ¹`peripheral vascular disease`
#> # ℹ 14 more variables: `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>,
#> # `moderate or severe liver disease` <lgl>, `metastatic solid tumor` <lgl>, …
classify(xdt, "charlson", id = "name", code = "icd10")
#> Warning: 'classify()' does not preserve row order ('categorize()' does!)
#> Classification based on: icd10
#>
#> The printed data is of class: classified, matrix.
#> It has 100 row(s).
#> It is here previewed as a tibble
#> Use `print(x, n = NULL)` to print as is (or use `n` to specify the number of rows to preview)!
#>
#> # A tibble: 10 × 17
#> `myocardial infarction` `congestive heart failure` peripheral vascular dise…¹
#> <lgl> <lgl> <lgl>
#> 1 FALSE FALSE FALSE
#> 2 FALSE FALSE FALSE
#> 3 FALSE FALSE FALSE
#> 4 FALSE FALSE FALSE
#> 5 FALSE FALSE FALSE
#> 6 FALSE FALSE FALSE
#> 7 FALSE FALSE FALSE
#> 8 FALSE FALSE FALSE
#> 9 FALSE FALSE FALSE
#> 10 FALSE FALSE FALSE
#> # ℹ abbreviated name: ¹`peripheral vascular disease`
#> # ℹ 14 more variables: `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>,
#> # `moderate or severe liver disease` <lgl>, `metastatic solid tumor` <lgl>, …