Convert data to numeric

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().

Usage

to_numeric(x, ...)

# S3 method for class '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,
  ...
)

Arguments

x

A data frame, factor or vector.

...

Arguments passed to or from other methods.

select

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"), a character vector of variable names (e.g., c("col1", "col2", "col3")), or a character vector of variable names including ranges specified via : (e.g., c("col1:col3", "col5")),
for some functions, like data_select() or data_rename(), select can be a named character vector. In this case, the names are used to rename the columns in the output data frame. See 'Details' in the related functions to see where this option applies.
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"). regex() can be used to define regular expression patterns.
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".

exclude

See select, however, column names matched by the pattern from exclude will be excluded instead of selected. If NULL (the default), excludes no columns.

dummy_factors

Transform factors to dummy factors (all factor levels as different columns filled with a binary 0-1 value).

preserve_levels

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.

lowest

Numeric, indicating the lowest (minimum) value when converting factors or character vectors to numeric values.

append

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.

ignore_case

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.

regex

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.

verbose

Toggle warnings.

Value

A data frame of numeric variables.

Note

When factors should be converted into multiple "binary" dummies, i.e. each factor level is converted into a separate column filled with a binary 0-1 value, set dummy_factors = TRUE. If you want to preserve the original factor levels (in case these represent numeric values), use preserve_levels = TRUE.

Selection of variables - `select` argument

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.

Examples