data_modify: Create new variables in a data frame
In datawizard: Easy Data Wrangling and Statistical Transformations

data_modify

R Documentation

Create new variables in a data frame

Description

Create new variables or modify existing variables in a data frame. Unlike base::transform(), data_modify() can be used on grouped data frames, and newly created variables can be directly used.

Usage

data_modify(data, ...)

## S3 method for class 'data.frame'
data_modify(data, ..., .if = NULL, .at = NULL, .modify = NULL)

Arguments

`data`	A data frame
`...`	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: 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: `Sepal.Width = center(Sepal.Width)`. A vector of length 1 (which will be recycled to match the number of rows in the data), or of same length as the data. A variable that contains a value to be used. Example: a <- "abc" data_modify(iris, var_abc = a) # var_abc contains "abc" An expression can also be provided as string and wrapped in `as_expr()`. Example: data_modify(iris, as_expr("Sepal.Width = center(Sepal.Width)")) # or a <- "center(Sepal.Width)" data_modify(iris, Sepal.Width = as_expr(a)) # or a <- "Sepal.Width = center(Sepal.Width)" data_modify(iris, as_expr(a)) Note that `as_expr()` is no real function, which cannot be used outside of `data_modify()`, and hence it is not exported nor documented. Rather, it is only used for internally processing expressions. Using `NULL` as right-hand side removes a variable from the data frame. Example: `Petal.Width = NULL`. For data frames (including grouped ones), the function `n()` can be used to count the number of observations and thereby, for instance, create index values by using `id = 1:n()` or `id = 3:(n()+2)` and similar. Note that, like `as_expr()`, `n()` is also no true function and cannot be used outside of `data_modify()`. Note that newly created variables can be used in subsequent expressions, including `.at` or `.if`. See also 'Examples'.
`.if`	A function that returns `TRUE` for columns in the data frame where `.if` applies. This argument is used in combination with the `.modify` argument. Note that only one of `.at` or `.if` can be provided, but not both at the same time. Newly created variables in `...` can also be selected, see 'Examples'.
`.at`	A character vector of variable names that should be modified. This argument is used in combination with the `.modify` argument. Note that only one of `.at` or `.if` can be provided, but not both at the same time. Newly created variables in `...` can also be selected, see 'Examples'.
`.modify`	A function that modifies the variables defined in `.at` or `.if`. This argument is used in combination with either the `.at` or the `.if` argument. Note that the modified variable (i.e. the result from `.modify`) must be either of length 1 or of same length as the input variable.

Note

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,
  as_expr("c12hour_c = center(c12hour)"),
  as_expr("c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE)"),
  as_expr("c12hour_z2 = standardize(c12hour)")
)
head(new_efc)

# using a character vector, provided a variable
xpr <- c(
  "c12hour_c = center(c12hour)",
  "c12hour_z = c12hour_c / sd(c12hour, na.rm = TRUE)",
  "c12hour_z2 = standardize(c12hour)"
)
new_efc <- data_modify(efc, as_expr(xpr))
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 = as_expr(stand)
)
head(new_efc)

# attributes - in this case, value and variable labels - are preserved
str(new_efc)

# using `paste()` to build a string-expression
to_standardize <- c("Petal.Length", "Sepal.Length")
out <- data_modify(
  iris,
  as_expr(
    paste0(to_standardize, "_stand = standardize(", to_standardize, ")")
  )
)
head(out)

# 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
foo1 <- function(data, ...) {
  head(data_modify(data, ...))
}
foo1(iris, SW_fraction = Sepal.Width / 10)
# or
foo1(iris, as_expr("SW_fraction = Sepal.Width / 10"))

# also with string arguments, using `as_expr()`
foo2 <- function(data, modification) {
  head(data_modify(data, as_expr(modification)))
}
foo2(iris, "SW_fraction = Sepal.Width / 10")

# 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)

datawizard documentation built on June 8, 2025, 12:47 p.m.