This function provides an interface for forcats::fct_recode(), forcats::fct_explicit_na(), and forcats::fct_relevel() in such a way that a data wordlist can be imported from a data frame.

clean_spelling(
x = character(),
wordlist = data.frame(),
from = 1,
to = 2,
quiet = FALSE,
warn_default = TRUE,
anchor_regex = TRUE
)

## Arguments

x a character or factor vector a matrix or data frame defining mis-spelled words or keys in one column (from) and replacement values (to) in another column. There are keywords that can be appended to the from column for addressing default values and missing data. a column name or position defining words or keys to be replaced a column name or position defining replacement values a logical indicating if warnings should be issued if no replacement is made; if FALSE, these warnings will be disabled a logical. When a .default keyword is set and warn_default = TRUE, a warning will be issued listing the variables that were changed to the default value. This can be used to update your wordlist. a logical. When TRUE (default), any regex within the keywork

## Value

a vector of the same type as x with mis-spelled labels cleaned. Note that factors will be arranged by the order presented in the data wordlist; other levels will appear afterwards.

## Details

### Keys (from column)

The from column of the wordlist will contain the keys that you want to match in your current data set. These are expected to match exactly with the exception of three reserved keywords that start with a full stop:

• .regex [pattern]: will replace anything matching [pattern]. This is executed before any other replacements are made. The [pattern] should be an unquoted, valid, PERL-flavored regular expression. Any whitespace padding the regular expression is discarded.

• .missing: replaces any missing values (see NOTE)

• .default: replaces ALL values that are not defined in the wordlist and are not missing.

### Values (second column)

The values will replace their respective keys exactly as they are presented.

There is currently one recognised keyword that can be placed in the to column of your wordlist:

• .na: Replace keys with missing data. When used in combination with the .missing keyword (in column 1), it can allow you to differentiate between explicit and implicit missing data.

## Note

If there are any missing values in the from column (keys), then they are automatically converted to the character "NA" with a warning. If you want to target missing data with your wordlist, use the .missing keyword. The .regex keyword uses gsub() with the perl = TRUE option for replacement.

matchmaker::match_vec(), which this function wraps and matchmaker::match_df() for an implementation that acts across multiple variables in a data frame.

## Examples


corrections <- data.frame(
bad = c("foubar", "foobr", "fubar", "unknown", ".missing"),
good = c("foobar", "foobar", "foobar", ".na", "missing"),
stringsAsFactors = FALSE
)
#> 1   foubar  foobar
#> 2    foobr  foobar
#> 3    fubar  foobar
#> 4  unknown     .na
#> 5 .missing missing
# create some fake data
my_data <- c(letters[1:5], sample(corrections$bad[-5], 10, replace = TRUE)) my_data[sample(6:15, 2)] <- NA # with missing elements clean_spelling(my_data, corrections)#> [1] "a" "b" "c" "d" "e" "missing" "foobar" #> [8] NA "foobar" "foobar" NA "foobar" "foobar" "missing" #> [15] "foobar" # You can use regular expressions to simplify your list corrections <- data.frame( bad = c(".regex f[ou][^m].+?r$", "unknown", ".missing"),
good = c("foobar",                ".na",     "missing"),
stringsAsFactors = FALSE
)

# You can also set a default value
corrections_with_default <- rbind(corrections, c(bad = ".default", good = "unknown"))
#> 1 .regex f[ou][^m].+?r\$  foobar
#> 2               unknown     .na
#> 3              .missing missing
#> 4              .default unknown
# a warning will be issued about the data that were converted
clean_spelling(my_data, corrections_with_default)#> Warning: 'a', 'b', 'c', 'd', 'e' were changed to the default value ('unknown')#>  [1] "unknown" "unknown" "unknown" "unknown" "unknown" "missing" "foobar"
#>  [8] NA        "foobar"  "foobar"  NA        "foobar"  "foobar"  "missing"
#> [15] "foobar"
# use the warn_default = FALSE, if you are absolutely sure you don't want it.
clean_spelling(my_data, corrections_with_default, warn_default = FALSE)#>  [1] "unknown" "unknown" "unknown" "unknown" "unknown" "missing" "foobar"
#>  [8] NA        "foobar"  "foobar"  NA        "foobar"  "foobar"  "missing"
#> [15] "foobar"
# The function will give you a warning if the wordlist does not
# match the data
clean_spelling(letters, corrections)#>  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
#> [20] "t" "u" "v" "w" "x" "y" "z"
# The can be used for translating survey output

words <- data.frame(
option_code = c(".regex ^[yY][eE]?[sS]?",
".regex ^[nN][oO]?",
".regex ^[uU][nN]?[kK]?",
".missing"),
option_name = c("Yes", "No", ".na", "Missing"),
stringsAsFactors = FALSE
)
clean_spelling(c("Y", "Y", NA, "No", "U", "UNK", "N"), words)#> [1] "Yes"     "Yes"     "Missing" "No"      NA        NA        "No"