When using the
match_df() function, you would construct the dictionary same
as you would above, with two extra columns that specify the column name in the
data frame and the order the resulting values should be (if the column is a
match_vec(), all the same keywords apply, but now there are also two
keywords for the columns:
.regex [pattern]: any column whose name is matched by [pattern]. The [pattern] should be an unquoted, valid, PERL-flavored regular expression. This will match any column that is named with a given pattern. This would commonly be used for recoding results from columns that all start with the same pattern:
.global: defines rules for any column that is a character or factor and any column named in the dictionary. If you want to apply a set of definitions to all valid columns in addition to specified columns, then you can include a
.globalgroup in the
bycolumn of your ‘dictionary’ data frame. This is useful for setting up a dictionary of common spelling errors. NOTE: specific variable definitions will override global defintions. For example: if you have a column for cardinal directions and a definiton for
N = North, then the global variable
N = nowill not override that.
Before you use regex, you should be aware of three special symbols that will help anchor your words and prevent any unintended matching.
^) should be placed at the beginning of a pattern to show that it's the beginning of the word. For example,
labwill match both
^labwill only match
$) should be placed at the end of a pattern to show that it's the end of a word. For example,
datewill match both
date$will only match
.) matches any character. Because it's common in column names imported by R, it's a good idea to wrap it in square brackets (
[.]) to tell R that you actually mean a dot. For example,
^lab[.]r$will only match
The best strategy is to use at least one anchor to prevent it greedily selecting columns to match.
In our example from the top, there are three columns that all start with
lab_result_, so we use the
.regex ^lab_result keyword:
# view the lab_result columns: print(labs <- grep("^lab_result_", names(dat), value = TRUE)) str(dat[labs]) # show the lab_result part of the dictionary: print(dict[grep("^[.]regex", dict$grp), ]) # clean the data and compare the result cleaned <- match_df(dat, dict, from = "options", to = "values", by = "grp", order = "orders" ) str(cleaned[labs])
.globalto clean up all character/factor columns
We've actually seen the
.global keyword in use already. Let's take one more
look at the results from above:
# show the lab_result part of the dictionary: print(dict[grep("^[.]regex", dict$grp), ]) # show the original data str(dat[labs]) # show the modified data str(cleaned[labs])
Notice above how there are rules for "high", "norm", and "inc", but not for "unk", which was turned into "unknown"? This is because of the global keywords:
print(dict[grep("^[.](regex|global)", dict$grp), ])
The "unk" keyword was defined in our global dictionary and has been used to translate "unk" to "unknown".
Of course, be very careful with this one.
match_vec() function can be quite noisy with warnings for
various reasons. Thus, by default, the
match_df() function will keep these
quiet, but you can have them printed to your console if you use the
cleaned <- match_df(dat, dict, from = "options", to = "values", by = "grp", order = "orders", warn = TRUE )
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