This functions recodes old values into new values and can be used to to recode numeric or character vectors, or factors.
recode_values(x, ...)# S3 method for numeric
recode_values(
x,
recode = NULL,
default = NULL,
preserve_na = TRUE,
verbose = TRUE,
...
)
# S3 method for data.frame
recode_values(
x,
select = NULL,
exclude = NULL,
recode = NULL,
default = NULL,
preserve_na = TRUE,
append = FALSE,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
change_code(x, ...)
x, where old values are replaced by new values.
A data frame, numeric or character vector, or factor.
not used.
A list of named vectors, which indicate the recode pairs.
The names of the list-elements (i.e. the left-hand side) represent the
new values, while the values of the list-elements indicate the original
(old) values that should be replaced. When recoding numeric vectors,
element names have to be surrounded in backticks. For example,
recode=list(`0`=1) would recode all 1 into 0 in a numeric
vector. See also 'Examples' and 'Details'.
Defines the default value for all values that have
no match in the recode-pairs. Note that, if preserve_na=FALSE, missing
values (NA) are also captured by the default argument, and thus will
also be recoded into the specified value. See 'Examples' and 'Details'.
Logical, if TRUE, NA (missing values) are preserved.
This overrides any other arguments, including default. Hence, if
preserve_na=TRUE, default will no longer convert NA into the specified
default value.
Toggle warnings.
Variables that will be included when performing the required tasks. Can be either
a variable specified as a literal variable name (e.g., column_name),
a string with the variable name (e.g., "column_name"), or a character
vector of variable names (e.g., c("col1", "col2", "col3")),
a formula with variable names (e.g., ~column_1 + column_2),
a vector of positive integers, giving the positions counting from the left
(e.g. 1 or c(1, 3, 5)),
a vector of negative integers, giving the positions counting from the
right (e.g., -1 or -1:-3),
one of the following select-helpers: starts_with(), ends_with(),
contains(), a range using : or regex(""). starts_with(),
ends_with(), and contains() accept several patterns, e.g
starts_with("Sep", "Petal").
or a function testing for logical conditions, e.g. is.numeric() (or
is.numeric), or any user-defined function that selects the variables
for which the function returns TRUE (like: foo <- function(x) mean(x) > 3),
ranges specified via literal variable names, select-helpers (except
regex()) and (user-defined) functions can be negated, i.e. return
non-matching elements, when prefixed with a -, e.g. -ends_with(""),
-is.numeric or -Sepal.Width:Petal.Length. Note: Negation means
that matches are excluded, and thus, the exclude argument can be
used alternatively. For instance, select=-ends_with("Length") (with
-) is equivalent to exclude=ends_with("Length") (no -). In case
negation should not work as expected, use the exclude argument instead.
If NULL, selects all columns. Patterns that found no matches are silently
ignored, e.g. find_columns(iris, select = c("Species", "Test")) will just
return "Species".
See select, however, column names matched by the pattern
from exclude will be excluded instead of selected. If NULL (the default),
excludes no columns.
Logical or string. If TRUE, recoded or converted variables
get new column names and are appended (column bind) to x, thus returning
both the original and the recoded variables. The new columns get a suffix,
based on the calling function: "_r" for recode functions, "_n" for
to_numeric(), "_f" for to_factor(), or "_s" for
slide(). If append=FALSE, original variables in x will be
overwritten by their recoded versions. If a character value, recoded
variables are appended with new column names (using the defined suffix) to
the original data frame.
Logical, if TRUE and when one of the select-helpers or
a regular expression is used in select, ignores lower/upper case in the
search pattern when matching against variable names.
Logical, if TRUE, the search pattern from select will be
treated as regular expression. When regex = TRUE, select must be a
character string (or a variable containing a character string) and is not
allowed to be one of the supported select-helpers or a character vector
of length > 1. regex = TRUE is comparable to using one of the two
select-helpers, select = contains("") or select = regex(""), however,
since the select-helpers may not work when called from inside other
functions (see 'Details'), this argument may be used as workaround.
For most functions that have a select argument (including this function),
the complete input data frame is returned, even when select only selects
a range of variables. That is, the function is only applied to those variables
that have a match in select, while all other variables remain unchanged.
In other words: for this function, select will not omit any non-included
variables, so that the returned data frame will include all variables
from the input data frame.
This section describes the pattern of the recode arguments, which also
provides some shortcuts, in particular when recoding numeric values.
Single values
Single values either need to be wrapped in backticks (in case of numeric
values) or "as is" (for character or factor levels). Example:
recode=list(`0`=1,`1`=2) would recode 1 into 0, and 2 into 1.
For factors or character vectors, an example is:
recode=list(x="a",y="b") (recode "a" into "x" and "b" into "y").
Multiple values
Multiple values that should be recoded into a new value can be separated
with comma. Example: recode=list(`1`=c(1,4),`2`=c(2,3)) would recode the
values 1 and 4 into 1, and 2 and 3 into 2. It is also possible to define the
old values as a character string, like: recode=list(`1`="1,4",`2`="2,3")
For factors or character vectors, an example is:
recode=list(x=c("a","b"),y=c("c","d")).
Value range
Numeric value ranges can be defined using the :. Example:
recode=list(`1`=1:3,`2`=4:6) would recode all values from 1 to 3 into
1, and 4 to 6 into 2.
min and max
placeholder to use the minimum or maximum value of the
(numeric) variable. Useful, e.g., when recoding ranges of values.
Example: recode=list(`1`="min:10",`2`="11:max").
default values
The default argument defines the default value for all values that have
no match in the recode-pairs. For example,
recode=list(`1`=c(1,2),`2`=c(3,4)), default=9 would
recode values 1 and 2 into 1, 3 and 4 into 2, and all other values into 9.
If preserve_na is set to FALSE, NA (missing values) will also be
recoded into the specified default value.
Reversing and rescaling
See reverse() and rescale().
Functions to rename stuff: data_rename(), data_rename_rows(), data_addprefix(), data_addsuffix()
Functions to reorder or remove columns: data_reorder(), data_relocate(), data_remove()
Functions to reshape, pivot or rotate data frames: data_to_long(), data_to_wide(), data_rotate()
Functions to recode data: rescale(), reverse(), categorize(), recode_values(), slide()
Functions to standardize, normalize, rank-transform: center(), standardize(), normalize(), ranktransform(), winsorize()
Split and merge data frames: data_partition(), data_merge()
Functions to find or select columns: data_select(), data_find()
Functions to filter rows: data_match(), data_filter()
# numeric ----------
set.seed(123)
x <- sample(c(1:4, NA), 15, TRUE)
table(x, useNA = "always")
out <- recode_values(x, list(`0` = 1, `1` = 2:3, `2` = 4))
out
table(out, useNA = "always")
# to recode NA values, set preserve_na to FALSE
out <- recode_values(
x,
list(`0` = 1, `1` = 2:3, `2` = 4, `9` = NA),
preserve_na = FALSE
)
out
table(out, useNA = "always")
# preserve na ----------
out <- recode_values(x, list(`0` = 1, `1` = 2:3), default = 77)
out
table(out, useNA = "always")
# recode na into default ----------
out <- recode_values(
x,
list(`0` = 1, `1` = 2:3),
default = 77,
preserve_na = FALSE
)
out
table(out, useNA = "always")
# factors (character vectors are similar) ----------
set.seed(123)
x <- as.factor(sample(c("a", "b", "c"), 15, TRUE))
table(x)
out <- recode_values(x, list(x = "a", y = c("b", "c")))
out
table(out)
out <- recode_values(x, list(x = "a", y = "b", z = "c"))
out
table(out)
out <- recode_values(x, list(y = "b,c"), default = 77)
# same as
# recode_values(x, list(y = c("b", "c")), default = 77)
out
table(out)
# data frames ----------
set.seed(123)
d <- data.frame(
x = sample(c(1:4, NA), 12, TRUE),
y = as.factor(sample(c("a", "b", "c"), 12, TRUE)),
stringsAsFactors = FALSE
)
recode_values(
d,
recode = list(`0` = 1, `1` = 2:3, `2` = 4, x = "a", y = c("b", "c")),
append = TRUE
)
# switch recode pattern to "old=new" ----------
options(data_recode_pattern = "old=new")
# numeric
set.seed(123)
x <- sample(c(1:4, NA), 15, TRUE)
table(x, useNA = "always")
out <- recode_values(x, list(`1` = 0, `2:3` = 1, `4` = 2))
table(out, useNA = "always")
# factors (character vectors are similar)
set.seed(123)
x <- as.factor(sample(c("a", "b", "c"), 15, TRUE))
table(x)
out <- recode_values(x, list(a = "x", `b, c` = "y"))
table(out)
# reset options
options(data_recode_pattern = NULL)
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