Convert ICD data from wide to long format
Note the distinction between labelling existing data with any classes which
icd
provides, and actually converting the structure of the data.
wide_to_long(
x,
visit_name = get_visit_name(x),
icd_labels = NULL,
icd_name = "icd_code",
icd_regex = c("icd", "diag", "dx_", "dx")
)
data.frame
in wide format, i.e. one row per patient, and
multiple columns containing ICD codes, empty strings or NA.
The name of the column in the data frame which contains the
patient or visit identifier. Typically this is the visit identifier, since
patients come leave and enter hospital with different ICD-9 codes. It is a
character vector of length one. If left empty, or NULL
, then an
attempt is made to guess which field has the ID for the patient encounter
(not a patient ID, although this can of course be specified directly). The
guesses proceed until a single match is made. Data frames may be wide with
many matching fields, so to avoid false positives, anything but a single
match is rejected. If there are no successful guesses, and visit_id
was not specified, then the first column of the data frame is used.
vector of column names in which codes are found. If NULL,
all columns matching the regular expression icd_regex
will be
included.
The name of the column in the data.frame
which
contains the ICD codes. This is a character vector of length one. If it is
NULL
, icd9
will attempt to guess the column name, looking for
progressively less likely possibilities until it matches a single column.
Failing this, it will take the first column in the data frame. Specifying
the column using this argument avoids the guesswork.
vector of character strings containing a regular expression
to identify ICD-9 diagnosis columns to try (case-insensitive) in order.
Default is c("icd", "diag", "dx_", "dx")
data.frame
with visit_name column named the same as input, and
a column named by icd.name
containing all the non-NA and non-empty
codes found in the wide input data.
As is common with many data sets, key variables can be concentrated in one column or spread over several. Tools format of clinical and administrative hospital data, we can perform the conversion efficiently and accurately, while keeping some metadata about the codes intact, e.g. whether they are ICD-9 or ICD-10.
Long or wide format ICD data are all expected to be
in a data frame. The data.frame
itself does not carry any ICD
classes at the top level, even if it only contains one type of code;
whereas its constituent columns may have a class specified, e.g.
icd9
or icd10who
.
Reshaping data is a common task, and is made easier here by knowing
more about the underlying structure of the data. This function wraps the
reshape
function with specific behavior and checks
related to ICD codes. Empty strings and NA values will be dropped, and
everything else kept. No validation of the ICD codes is done.
Other ICD data conversion:
comorbid_df_to_mat()
,
comorbid_mat_to_df()
,
convert
,
decimal_to_short()
,
long_to_wide()
,
short_to_decimal()
# NOT RUN {
widedf <- data.frame(
visit_name = c("a", "b", "c"),
icd9_01 = c("441", "4424", "441"),
icd9_02 = c(NA, "443", NA)
)
wide_to_long(widedf)
# }
Run the code above in your browser using DataLab