qol 1.3.3: Yet another ‘if’ and some other additions

R
Update
Author

qol Blog

Published

July 16, 2026

Version 1.3.3 has a new function and some new functionalities in its backpack. The full release notes can be seen here.

Yet another ‘if’

This time the new ifelse_multi is able to evaluate multiple nested if-else statements in one go. But that is not all, because the function comes with a twist: Conditions can be written in SAS like syntax. The conditions therefore have to be passed as characters which enables them to be parsed and translated before evaluation.

This twist brings multiple new ways to write conditions, like:

  • Ranges: Instead of “age >= 15 & age < 65” you can write “15 <= age < 65”.
  • You can write “and/or” instead of “&/|”.
  • The “in” (or “not in”) keyword can be used the SAS way, e.g.: “age in (10 20 30 40 50)”
  • Select strings starting with (:text), ending with (text:) or containing (:text:) a specific text.
  • Use SAS makro variables as placeholders with the &.
  • Check for NA values with “== .” or “!= .”.

The if. and else_if. functions have been updated to also take in the new character style conditions.

# Example data frame
my_data <- dummy_data(100)

# Simple ifelse statement
my_data[["under18"]]    <- my_data |> ifelse_multi(" age < 18 " = 1,       else. = 0)
my_data[["middle_age"]] <- my_data |> ifelse_multi(" 15 <= age < 65 " = 1, else. = 0)

my_data[["age_gr"]] <- my_data |>
    ifelse_multi(" age < 18 "       = "under 18",
                 " 18 <= age < 25 " = "18 to under 25",
                 " 25 <= age < 50 " = "25 to under 50",
                 " 50 <= age < 65 " = "50 to under 65",
                 " 65 <= age      " = "65 and more")

# With overarching do_if condition
my_data[["age_gr_edu"]] <- my_data |>
    ifelse_multi(do_if = " education in ('middle' 'high') ",
                     " age < 18 "       = "under 18",
                     " 18 <= age < 25 " = "18 to under 25",
                     " 25 <= age < 50 " = "25 to under 50",
                     " 50 <= age < 65 " = "50 to under 65",
                     " 65 <= age      " = "65 and more")

# And/or translation
my_data[["and"]] <- my_data |> ifelse_multi(" age > 65 and sex = 1 " = 1,
                                            " age > 65 and sex = 2 " = 2,
                                            else. = 0)

my_data[["or"]] <- my_data |> ifelse_multi(" age > 65 or sex = 1 " = 1,
                                           " age > 65 or sex = 2 " = 2,
                                           else. = 0)

# "in" translation
my_data[["in"]] <- my_data |> ifelse_multi(" age in (1 10 25 65 90) " = 1, else. = 0)

# Colon translation: start/ends with and contains
my_data[["start"]]    <- my_data |> ifelse_multi(" education == 'lo:' "  = 1, else. = 0)
my_data[["end"]]      <- my_data |> ifelse_multi(" education == ':le' "  = 1, else. = 0)
my_data[["contains"]] <- my_data |> ifelse_multi(" education == ':ig:' " = 1, else. = 0)

# Macro variables can be integrated in any place
variable     <- "age"
age_to_check <- 18
value_to_set <- "under 18"

my_data[["macro"]] <- my_data |>
    ifelse_multi(" &variable < &age_to_check " = "&value_to_set",
                 else.                         = "other")

# NA translation
my_data[["NA"]]    <- my_data |> ifelse_multi(" age == .       " = 1, else. = 0)
my_data[["notNA"]] <- my_data |> ifelse_multi(" education != . " = 1, else. = 0)

# Pass in existing variable values
my_data[["income_mix"]] <- my_data |>
    ifelse_multi(" age < 50 " = income, else. = expenses)

any_table received even more possibilities

The function now has the built in possibility to compute. new individual calculated variables before tabulation. Which means the function now summarises the data, then allows the compute. in between and then tabulates. If you ever had to pre summarise and calculate variables before tabulation, these extra steps are now potentially gone.
To be able to order the newly generated variables the order_by parameter can now take in a vector of variable names, to have full control over every column.

There is another new functionality: It is now possible to specify per variable which statistics should be output. Instead of passing a vector of statistics into the statistics parameter, it can now also be a named list of variable names, where the list entry names are the statistics and the list elements the variable names or vectors of variable names. This allows you to select what you want before tabulation, so there is no need to throw out the stuff you don’t need afterwards. This is also possible with summarise_plus.

And one last thing: Variable combinations in rows, columns and types can now also take in variable combinations inside the brackets like “state + (age, sex + education, first_person)”. But therefore the “,” between entries inside the brackets is now mandatory. For summarise_plus this now also works in the types parameter.

# Example data frame
my_data <- dummy_data(1000)

# Formats
age. <- discrete_format(
    "Total"          = 0:100,
    "under 18"       = 0:17,
    "18 to under 25" = 18:24,
    "25 to under 55" = 25:54,
    "55 to under 65" = 55:64,
    "65 and older"   = 65:100)

sex. <- discrete_format(
    "Total"  = 1:2,
    "Male"   = 1,
    "Female" = 2)

# Individual calculations
my_data |> any_table(rows       = c("age + year"),
                     columns    = "sex",
                     values     = "probability",
                     statistics = c("sum", "sum_wgt"),
                     compute    = list(percent_pct = probability_sum * 100 / sum_wgt,
                                       square_pct  = percent_pct ^ 2),
                     weight     = weight,
                     formats    = list(sex = sex., age = age.),
                     na.rm      = TRUE)

# Select specific statistics for specific variables
my_data |> any_table(rows       = "sex",
                     columns    = "year",
                     statistics = list("sum"       = c(weight, income),
                                       "pct_group" = balance),
                     formats    = list(sex = sex.))

# You can also select repeating combinations faster like this
combi_types <- my_data |>
    summarise_plus(class      = c(year, sex, age, education),
                   values     = weight,
                   statistics = "sum",
                   formats    = list(sex = sex.,
                                     age = age.),
                   types      = "year + sex + (age, age + education, education)",
                   nesting    = "all",
                   na.rm      = TRUE)

# Select specific statistics for specific variables
specific_stats <- my_data |>
    summarise_plus(class      = c(year, sex),
                   statistics = list("sum"       = c(weight, income),
                                     "mean"      = expenses,
                                     "pct_group" = balance),
                   formats    = list(sex = sex.),
                   na.rm      = TRUE)

Macro variables in filepaths

Just a quick one: SAS like macro variables can now be used inside all filepath and -name parameters within this package.

# Example data frame
my_data <- dummy_data(100)

# Variable used as macro variables
YEAR <- 2026
FILE <- "Sales &YEAR..xlsx"

# Add them in the filepaths and export
my_style <- excel_output_style(save_path = "C:/&YEAR/", file = FILE)

my_data |> export_with_style(style = my_style)

New background

One note about a change in default behaviour: Every Excel output now receives an all white background, instead of unfilled. To make this work efficiently the minimum Version of openxlsx2 has been raised to 1.28, which optimizes this process.