match_df {matchmaker}

Check and clean spelling or codes of multiple variables in a data frame

Description

This function allows you to clean your data according to pre-defined rules encapsulated in either a data frame or list of data frames. It has application for addressing mis-spellings and recoding variables (e.g. from electronic survey data).

Usage

match_df(
  x = data.frame(),
  dictionary = list(),
  from = 1,
  to = 2,
  by = 3,
  order = NULL,
  warn = FALSE
)

Arguments

x

a character or factor vector

dictionary

a data frame or named list of data frames with at least two columns defining the word list to be used. If this is a data frame, a third column must be present to split the dictionary by column in x (see by).

from

a column name or position defining words or keys to be replaced

to

a column name or position defining replacement values

by

character or integer. If dictionary is a data frame, then this column in defines the columns in x corresponding to each section of the dictionary data frame. This defaults to 3, indicating the third column is to be used.

order

a character the column to be used for sorting the values in each data frame. If the incoming variables are factors, this determines how the resulting factors will be sorted.

warn

if TRUE, warnings and errors from match_vec() will be shown as a single warning. Defaults to FALSE, which shows nothing.

Details

By default, this applies the function match_vec() to all columns specified by the column names listed in by, or, if a global dictionary is used, this includes all character and factor columns as well.

by column

Spelling variables within dictionary represent keys that you want to match to column names in x (the data set). These are expected to match exactly with the exception of two reserved keywords that starts with a full stop:

• .regex [pattern]: any column whose name is matched by ⁠[pattern]⁠. The ⁠[pattern]⁠ should be an unquoted, valid, PERL-flavored regular expression.

• .global: any column (see Section Global dictionary)

Global dictionary

A global dictionary is a set of definitions applied to all valid columns of x indiscriminantly.

• .global keyword in by: If you want to apply a set of definitions to all valid columns in addition to specified columns, then you can include a .global group in the by column 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 = no will not override that. See Example.

• by = NULL: If you want your data frame to be applied to all character/factor columns indiscriminantly, then setting by = NULL will use that dictionary globally.

Value

a data frame with re-defined data based on the dictionary

Author(s)

Zhian N. Kamvar

Patrick Barks

See Also

match_vec(), which this function wraps.

Examples

# Read in dictionary and coded date examples --------------------

dict <- read.csv(matchmaker_example("spelling-dictionary.csv"),
  stringsAsFactors = FALSE)
dat <- read.csv(matchmaker_example("coded-data.csv"),
  stringsAsFactors = FALSE)
dat$date <- as.Date(dat$date)

# Clean spelling based on dictionary -----------------------------

dict # show the dict
head(dat) # show the data

res1 <- match_df(dat,
  dictionary = dict,
  from = "options",
  to = "values",
  by = "grp")
head(res1)

# Show warnings/errors from each column --------------------------
# Internally, the `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
# warn = TRUE option:

res1 <- match_df(dat,
  dictionary = dict,
  from = "options",
  to = "values",
  by = "grp",
  warn = TRUE)
head(res1)


# You can ensure the order of the factors are correct by specifying
# a column that defines order.

dat[] <- lapply(dat, as.factor)
as.list(head(dat))
res2 <- match_df(dat,
  dictionary = dict,
  from = "options",
  to = "values",
  by = "grp",
  order = "orders")
head(res2)
as.list(head(res2))