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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_modify.R
\name{data_modify}
\alias{data_modify}
\alias{data_modify.data.frame}
\title{Create new variables in a data frame}
\usage{
data_modify(data, ...)
\method{data_modify}{data.frame}(data, ..., .if = NULL, .at = NULL, .modify = NULL)
}
\arguments{
\item{data}{A data frame}
\item{...}{One or more expressions that define the new variable name and the
values or recoding of those new variables. These expressions can be one of:
\itemize{
\item A sequence of named, literal expressions, where the left-hand side refers
to the name of the new variable, while the right-hand side represent the
values of the new variable. Example: \code{Sepal.Width = center(Sepal.Width)}.
\item A sequence of string values, representing expressions.
\item A variable that contains a string representation of the expression. Example:
\if{html}{\out{<div class="sourceCode r">}}\preformatted{a <- "2 * Sepal.Width"
data_modify(iris, a)
}\if{html}{\out{</div>}}
\item A character vector of expressions. Example:
\code{c("SW_double = 2 * Sepal.Width", "SW_fraction = SW_double / 10")}. This
type of expression cannot be mixed with other expressions, i.e. if a
character vector is provided, you may not add further elements to \code{...}.
\item Using \code{NULL} as right-hand side removes a variable from the data frame.
Example: \code{Petal.Width = NULL}.
\item For data frames (including grouped ones), the function \code{n()} can be used to count the
number of observations and thereby, for instance, create index values by
using \code{id = 1:n()} or \code{id = 3:(n()+2)} and similar.
}
Note that newly created variables can be used in subsequent expressions,
including \code{.at} or \code{.if}. See also 'Examples'.}
\item{.if}{A function that returns \code{TRUE} for columns in the data frame where
\code{.if} applies. This argument is used in combination with the \code{.modify} argument.
Note that only one of \code{.at} or \code{.if} can be provided, but not both at the same
time. Newly created variables in \code{...} can also be selected, see 'Examples'.}
\item{.at}{A character vector of variable names that should be modified. This
argument is used in combination with the \code{.modify} argument. Note that only one
of \code{.at} or \code{.if} can be provided, but not both at the same time. Newly created
variables in \code{...} can also be selected, see 'Examples'.}
\item{.modify}{A function that modifies the variables defined in \code{.at} or \code{.if}.
This argument is used in combination with either the \code{.at} or the \code{.if} argument.
Note that the modified variable (i.e. the result from \code{.modify}) must be either
of length 1 or of same length as the input variable.}
}
\description{
Create new variables or modify existing variables in a data frame. Unlike \code{base::transform()}, \code{data_modify()}
can be used on grouped data frames, and newly created variables can be directly
used.
}
\note{
\code{data_modify()} can also be used inside functions. However, it is
recommended to pass the recode-expression as character vector or list of
characters.
}
\examples{
data(efc)
new_efc <- data_modify(
efc,
c12hour_c = center(c12hour),
c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE),
c12hour_z2 = standardize(c12hour)
)
head(new_efc)
# using strings instead of literal expressions
new_efc <- data_modify(
efc,
"c12hour_c = center(c12hour)",
"c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE)",
"c12hour_z2 = standardize(c12hour)"
)
head(new_efc)
# using character strings, provided as variable
stand <- "c12hour_c / sd(c12hour, na.rm = TRUE)"
new_efc <- data_modify(
efc,
c12hour_c = center(c12hour),
c12hour_z = stand
)
head(new_efc)
# providing expressions as character vector
new_exp <- c(
"c12hour_c = center(c12hour)",
"c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE)"
)
new_efc <- data_modify(efc, new_exp)
head(new_efc)
# attributes - in this case, value and variable labels - are preserved
str(new_efc)
# overwrite existing variable, remove old variable
out <- data_modify(iris, Petal.Length = 1 / Sepal.Length, Sepal.Length = NULL)
head(out)
# works on grouped data
grouped_efc <- data_group(efc, "c172code")
new_efc <- data_modify(
grouped_efc,
c12hour_c = center(c12hour),
c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE),
c12hour_z2 = standardize(c12hour),
id = 1:n()
)
head(new_efc)
# works from inside functions
foo <- function(data, z) {
head(data_modify(data, z))
}
foo(iris, "var_a = Sepal.Width / 10")
new_exp <- c("SW_double = 2 * Sepal.Width", "SW_fraction = SW_double / 10")
foo(iris, new_exp)
# modify at specific positions or if condition is met
d <- iris[1:5, ]
data_modify(d, .at = "Species", .modify = as.numeric)
data_modify(d, .if = is.factor, .modify = as.numeric)
# can be combined with dots
data_modify(d, new_length = Petal.Length * 2, .at = "Species", .modify = as.numeric)
# new variables used in `.at` or `.if`
data_modify(
d,
new_length = Petal.Length * 2,
.at = c("Petal.Length", "new_length"),
.modify = round
)
# combine "extract_column_names()" and ".at" argument
out <- data_modify(
d,
.at = extract_column_names(d, select = starts_with("Sepal")),
.modify = as.factor
)
# "Sepal.Length" and "Sepal.Width" are now factors
str(out)
}
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