codify {coder}

Codify case data with external code data (within specified time frames)

Description

This is the first step of codify() %>% classify() %>% index(). The function combines case data from one data set with related code data from a second source, possibly limited to codes valid at certain time points relative to case dates.

Usage

codify(x, codedata, ..., id, code, date = NULL, code_date = NULL, days = NULL)

## S3 method for class 'data.frame'
codify(x, ..., id, date = NULL, days = NULL)

## S3 method for class 'data.table'
codify(
  x,
  codedata,
  ...,
  id,
  code,
  date = NULL,
  code_date = NULL,
  days = NULL,
  alnum = FALSE,
  .copy = NA
)

## S3 method for class 'codified'
print(x, ..., n = 10)

Arguments

x

data set with mandatory character id column (identified by argument id = "<col_name>"), and optional Date of interest (identified by argument date = "<col_name>"). Alternatively, the output from codify()

codedata

additional data with columns including case id (character), code and an optional date (Date) for each code. An optional column condition might distinguish codes/dates with certain characteristics (see example).

...

arguments passed between methods

id, code, date, code_date

column names with case id (character from x and codedata), code (from x) and optional date (Date from x) and code_date (Date from codedata).

days

numeric vector of length two with lower and upper bound for range of relevant days relative to date. See "Relevant period".

alnum

Should codes be cleaned from all non alphanumeric characters?

.copy

Should the object be copied internally by data.table::copy()? NA (by default) means that objects smaller than 1 GB are copied. If the size is larger, the argument must be set explicitly. Set TRUE to make copies regardless of object size. This is recommended if enough RAM is available. If set to FALSE, calculations might be carried out but the object will be changed by reference. IMPORTANT! This might lead to undesired consequences and should only be used if absolutely necessary!

n

number of rows to preview as tibble. The output is technically a data.table::data.table, which might be an unusual format to look at. Use n = NULL to print the object as is.

Value

Object of class codified (inheriting from data.table::data.table). Essentially x with additional columns: ⁠code, code_date⁠: left joined from codedata or NA if no match within period. in_period: Boolean indicator if the case had at least one code within the specified period.

The output has one row for each combination of "id" from x and "code" from codedata. Rows from x might be repeated accordingly.

Relevant period

Some examples for argument days:

• c(-365, -1): window of one year prior to the date column of x. Useful for patient comorbidity.

• c(1, 30): window of 30 days after date. Useful for adverse events after a surgical procedure.

• c(-Inf, Inf): no limitation on non-missing dates.

• NULL: no time limitation at all.

See Also

Other verbs: categorize(), classify(), index_fun

Examples

# Codify all patients from `ex_people` with their ICD-10 codes from `ex_icd10`
x <- codify(ex_people, ex_icd10, id = "name", code = "icd10")
x

# Only consider codes if recorded at hospital admissions within one year prior
# to surgery
codify(
  ex_people,
  ex_icd10,
  id        = "name",
  code      = "icd10",
  date      = "surgery",
  code_date = "admission",
  days      = c(-365, 0)   # admission during one year before surgery
)

# Only consider codes if recorded after surgery
codify(
  ex_people,
  ex_icd10,
  id        = "name",
  code      = "icd10",
  date      = "surgery",
  code_date = "admission",
  days      = c(1, Inf)     # admission any time after surgery
)


# Dirty code data ---------------------------------------------------------

# Assume that codes contain unwanted "dirty" characters
# Those could for example be a dot used by ICD-10 (i.e. X12.3 instead of X123)
dirt <- c(strsplit(c("!#%&/()=?`,.-_"), split = ""), recursive = TRUE)
rdirt <- function(x) sample(x, nrow(ex_icd10), replace = TRUE)
sub <- function(i) substr(ex_icd10$icd10, i, i)
ex_icd10$icd10 <-
  paste0(
    rdirt(dirt), sub(1),
    rdirt(dirt), sub(2),
    rdirt(dirt), sub(3),
    rdirt(dirt), sub(4),
    rdirt(dirt), sub(5)
  )
head(ex_icd10)

# Use `alnum = TRUE` to ignore non alphanumeric characters
codify(ex_people, ex_icd10, id = "name", code = "icd10", alnum = TRUE)



# Big data ----------------------------------------------------------------

# If `data` or `codedata` are large compared to available
# Random Access Memory (RAM) it might not be possible to make internal copies
# of those objects. Setting `.copy = FALSE` might help to overcome such problems

# If no copies are made internally, however, the input objects (if data tables)
# would change in the global environment
x2 <- data.table::as.data.table(ex_icd10)
head(x2) # Look at the "icd10" column (with dirty data)

# Use `alnum = TRUE` combined with `.copy = FALSE`
codify(ex_people, x2, id = "name", code = "icd10", alnum = TRUE, .copy = FALSE)

# Even though no explicit assignment was specified
# (neither for the output of codify(), nor to explicitly alter `x2`,
# the `x2` object has changed (look at the "icd10" column!):
head(x2)

# Hence, the `.copy` argument should only be used if necessary
# and if so, with caution!


# print.codify() ----------------------------------------------------------

x # Preview first 10 rows as a tibble
print(x, n = 20) # Preview first 20 rows as a tibble
print(x, n = NULL) # Print as data.table (ignoring the 'classified' class)