Razón de ser
Although new pragmatic platforms (such as RedCap) currently exist, a
great deal of research data is still being collected directly in
excel, where it is easier to code variables in
a short form. For example, “birth date” is commonly coded
in a short form as “dob” instead of “Date of birth”, which
is the publication form. The same applies to the
values of variables, such as “F” and “M”, which are both
values for the “Gender” variable, and stand for “Female” and “Male”,
respectively.
Recoding variables and their values back to their publication form is
an inevitable task during statistical analysis and reporting
results.
The recode_vrs() function helps effortlessly transform
collected data into a publication-ready format using a user-supplied
data dictionary. Combining recode_vrs() with a
data dictionary ensures consistency in
recoding research terms across all analyses and publications as one
could easily forget how a variable or a term was labelled
in a previous analysis or publication. The recoded data can then be
further used to make figures, table one…etc.
Terminología
In the above introduction, we have referred to 4 terms:
Variable, such as “dob”: this is the
short form of “Date of birth” that is usually used in
excel sheets.
Variable label, such as “Date of birth”: this is the
publication form that we usually encounter in
publications.
Value, such as “F” and “M”, which are both values for the
“Gender” variable.
Value label, such as “Female” and “Male”, which are the
labels of the “Gender” values, “F” and “M”, respectively.
The inflammatory bowel disease (IBD) data dictionary
ibd_data_dict provided in the phdcocktail
package consists of 4 columns, one for each of the above-described
terms.
library(phdcocktail)
data(ibd_data_dict, package = "phdcocktail")
View(ibd_data_dict)
All 4 columns are required in order for recode_vrs() to
function as needed. Therefore, user-supplied data dictionaries
should logically have these columns!
Uso
When passing a data frame with raw data and a data dictionary to
recode_vrs(), the function will:
Search the data dictionary for variables labels
for all variables, and attach these to the
corresponding variables in the original data frame as “label
attributes”. these attributes can be recognized by
gtsummary::tbl_summary() or other functions for
printing.
Search the data dictionary for values labels
only for variables specified in the vrs
argument. These values will be “recoded” to their
corresponding labels.
If the factor argument is set to TRUE,
variables specified in the vrs argument will be converted
to ordered factors, and the order of the levels will be
inherited from the order of appearance of the values in the data
dictionary. These ordered factors are important to have the
desired display of values when passing the resulted data frame to
functions from ggplot2,
gtsummary…etc.
To see recode_vrs() in action, we will make table one
from the ibd_data1 available with the package:
Let’s first view this data frame…
data(ibd_data1, package = "phdcocktail")
View(ibd_data1)
We can see that variables and their values are stored in the
short form. We can make a table one using the data in its
current form, but it won’t be suitable to be published!
library(gtsummary)
theme_gtsummary_compact() # to make a compact table
ibd_data1 |>
tbl_summary(include = -"patientid") # we don't need patient IDs in our table
Now let’s recode this data frame using recode_vrs(), and
view the new, recoded data frame, which we name here as
ibd_data_recoded…
ibd_data_recoded <- recode_vrs(data = ibd_data1, data_dictionary = ibd_data_dict,
vrs = c("disease_location", "disease_behaviour", "gender"), factor = TRUE)
View(ibd_data_recoded)
We can notice three changes in the new data frame compared to the
original one:
Variables labels are now attached as “attributes” underneath
variables names for all variables for which a corresponding
variable label could be found in the supplied dictionary.
Values have been replaced by their labels
for variables specified in the vrs argument.
Variables specified in the vrs argument have been
converted to ordered factors.
Finally, let’s make table one from the new recoded data…
ibd_data_recoded |>
tbl_summary(include = -"patientid")
