If - Else if - Else Statements

These functions make if statements more readable. Especially if an if block becomes bigger it can be hard to read with multiple nested if_else statements. With these new functions if blocks can be written like in other languages with a clear and simpler structure. In addition not only for one variable can a new value be assigned, but for multiple.

if.() always creates a new variable if the given variable name is not part of the given data frame. If there already is a variable with the given name, the existing values will be overwritten if the condition is TRUE.

If no new variable is provided, if.() will select observations by the given condition instead.

else_if.() only acts if there already is a variable with the given name. Only NA values will get new values if condition is TRUE. The existing values will not be overwritten.

else.() only acts if there already is a variable with the given name. Sets every remaining NA in given variable to the given value.

Usage

if.(data_frame, condition, ...)

else_if.(data_frame, condition, ...)

else.(data_frame, ...)

Arguments

data_frame: A data frame on which to apply an if statement.
condition: The condition on which a value should be passed to a variable.
...: The assignment of what should happen when condition becomes TRUE.

Value

Returns a data frame with conditionally computed variables.

Examples

# Example data frame
my_data <- dummy_data(1000)

# Call function
new_df <- my_data |>
         if.(age < 18,             age_group = "under 18") |>
    else_if.(age >= 18 & age < 65, age_group = "18 to under 65") |>
    else.   (                      age_group = "65 and older")

# Or with multiple variables
new_df <- my_data |>
         if.(age < 18,             age_group = "under 18"      , age_num = 1L) |>
    else_if.(age >= 18 & age < 65, age_group = "18 to under 65", age_num = 2L) |>
    else.   (                      age_group = "65 and older",   age_num = 3L)

# NOTE: As in other languages the following if blocks won't produce the same result.
#       if.() will overwrite existing values, while else_if.() will not.
state_df <- my_data |>
         if.(state == 1, state_a = "State 1") |>
    else_if.(state < 11, state_a = "West") |>
    else.   (            state_a = "East")

state_df <- state_df |>
      if.(state == 1, state_b = "State 1") |>
      if.(state < 11, state_b = "West") |>
    else.(            state_b = "East")

# Add multiple variables based on single conditions
multi_df <- my_data |>
         if.(age < 18 & education == "low",                  var1 = 1,
                                                             var2 = TRUE,
                                                             var3 = "Category 1") |>
    else_if.(age >= 18 & education %in% c("middle", "high"), var1 = 2,
                                                             var2 = FALSE,
                                                             var3 = "Category 2") |>
    else.   (                                                var1 = 3,
                                                             var2 = FALSE,
                                                             var3 = "Category 3")

# Use if.() as a do-over-loop. In this kind of loop all vectors will be
# advanced one iteration at a time in parallel.
money    <- c("income", "expenses", "balance", "probability")
new_vars <- c("var1", "var2", "var3", "var4")
result   <- c(1, 2, 3, 4)

do_over_df <- my_data |>
      if.(money > 0, new_vars = result) |>
    else.(           new_vars = 0)

# It is also possible to select character expressions based on whether they
# start/end with or contain a certain text.
text_select_df <- my_data |>
    if.(income_class == "01.:",  start    = 1) |>
    if.(income_class == ":more", end      = 1) |>
    if.(education    == ":i:",   contains = 1)

# Select observations by condition instead of generating new variable
subset_df <- my_data |> if.(sex == 1)

# Select all non NA observations by variable
subset_df <- my_data |> if.(sex)

# All these functions can be used in a do_if() situation and are aware of
# overarching conditions.
do_if_df <- my_data |>
    do_if(state < 11) |>
          if.(age < 18, new_var = 1) |>
        else.(          new_var = 2) |>
    else_do() |>
          if.(age < 18, new_var = 3) |>
        else.(          new_var = 4) |>
    end_do()

If - Else if - Else Statements

Usage

Arguments

Value

See also

Examples