Convert data to numeric by converting characters to factors and factors to
either numeric levels or dummy variables. The "counterpart" to convert
variables into factors is to_factor().
to_numeric(x, ...)# S3 method for data.frame
to_numeric(
x,
select = NULL,
exclude = NULL,
dummy_factors = FALSE,
preserve_levels = FALSE,
lowest = NULL,
append = FALSE,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
A data frame of numeric variables.
A data frame, factor or vector.
Arguments passed to or from other methods.
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. extract_column_names(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.
Transform factors to dummy factors (all factor levels as different columns filled with a binary 0-1 value).
Logical, only applies if x is a factor. If TRUE,
and x has numeric factor levels, these will be converted into the related
numeric values. If this is not possible, the converted numeric values will
start from 1 to number of levels.
Numeric, indicating the lowest (minimum) value when converting factors or character vectors to numeric values.
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.
Toggle warnings.
For most functions that have a select argument the complete input data
frame is returned, even when select only selects a range of variables.
However, for to_numeric(), factors might be converted into dummies,
thus, the number of variables of the returned data frame no longer match
the input data frame. Hence, when select is used, only those variables
(or their dummies) specified in select will be returned. Use append=TRUE
to also include the original variables in the returned data frame.
to_numeric(head(ToothGrowth))
to_numeric(head(ToothGrowth), dummy_factors = TRUE)
# factors
x <- as.factor(mtcars$gear)
to_numeric(x)
to_numeric(x, preserve_levels = TRUE)
# same as:
coerce_to_numeric(x)
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