Compute New Variables

Compute new variables without having to write the name of the data frame multiple times. It can handle all kinds of operations like simple value assignment or calculations, but also can make use of other functions.

In addition it can be used in a conditional do_if() block.

Usage

compute.(data_frame, ..., monitor = .qol_options[["monitor"]])

Arguments

data_frame

A data frame in which to compute new variables.

...

The calculations that should be executed.

monitor

FALSE by default. If TRUE, outputs two charts to visualize the functions time consumption.

Value

Returns a data frame with newly computed variables.

Details

The loop you can use within compute.() is based on the 'SAS' do-over-loop. This type of loop iterates over every vector that appears in the loop in parallel. Means that in the first iteration all the first vector elements are used, in the second iteration all second elements of every vector, and so on. With this loop you don't have the need to construct an outer loop, but can directly pass in different vectors and let the function handle the loop inside.

See also

The following functions can make use of the do_if() filter variables:

Conditions: if.(), else_if.(), else.()

Filter Data Frame: where.()

Create new Variables: compute.()

Examples

# Example data frame
my_data <- dummy_data(1000)

# Simple assignment
assign_df <- my_data |> compute.(new_var1 = 1,
                                new_var2 = "Hello")

# Simple calculation
sum_df <- my_data |> compute.(new_sum = age + sex)

# Using functions
mean_df <- my_data |> compute.(new_mean = collapse::fmean(age))

# Using qol functions
qol_df <- my_data |> compute.(row_sum = row_calculation("sum", state, age, sex))

# Use compute.() as a do-over-loop. In this kind of loop all vectors will be
# advanced one iteration at a time in parallel.
new_vars <- c("var1", "var2", "var3")
money    <- c("income", "expenses", "balance")
multi    <- c(1, 2, 3)

do_over_df <- my_data |> compute.(new_vars = money * multi)

# You can also do all at once
all_df <- my_data |> compute.(new_var1 = 1,
                             new_var2 = "Hello",
                             new_sum  = age + sex,
                             new_mean = mean(age),
                             row_sum  = row_calculation("sum", state, age, sex),
                             new_vars = multi * money)

# compute.() can be used in a do_if() situation and is aware of overarching
# conditions.
age_west. <- discrete_format("under 18"     = 0:17,
                             "18 and older" = 18:100)

age_east. <- discrete_format("under 65"     = 0:64,
                             "65 and older" = 65:100)

do_if_df <- my_data |>
    do_if(state < 11) |>
        compute.(region    = "West",
                 age_group = recode.(age = age_west.)) |>
    else_do() |>
        compute.(region    = "East",
                 age_group = recode.(age = age_east.)) |>
    end_do()