This update doesn’t implement any completely new functions, but focuses on refining existing ones. The full release notes can be seen here.

Renamed functions

compute() and recode() have been renamed and now have a “.” at the end (compute.() and recode.()) to prevent masking errors in combination with dplyr. This means existing code will break if these functions where used.

printify update

The printify functions have been updated:

set_no_color(): Suppresses the color codes so that messages can be printed clean. The option is auto controlled on load via the system variable NO_COLOR but can also be set individually by this function. Console output in e.g. RStudio vs. output to a logging system should be handled automatically right now. Thanks goes to @TroyHernandez who pointed this out.
set_up_custom_message(): Waiting symbols as well as the color of the time stamps can now be customized.
print_step(): Now has a new in_place parameter, which prints the message on the same line as before, instead of in the next line. This can e.g. be used inside loops as follows.

new_in_place_steps <- function(){
    print_start_message()
    
    print_step("MAJOR", "Let's get started...")
    
    for (i in seq_len(10)){
        print_step("Minor", "This is in place step [i] of 10", i = i, in_place = TRUE)
        Sys.sleep(0.25)
    }
    
    print_step("MAJOR", "Loop has ended")
    
    print_closing()
}


new_in_place_steps()

Tabulation workflow

any_table() and export_with_style(): If the whole result list from these functions is passed for the workbook parameter, the functions now are able to extract the actual workbook from the list and run without error. Additionally if a list is passed, which is not a result list containing the workbook, the functions error and abort execution.

# First tabulation
result_list <- my_data |>
           any_table(rows  = "age",
                     style = my_style,
                     print = FALSE)

my_style <- my_style |> modify_output_style(sheet_name = "edu_year")

# Pass on workbook
my_data |> any_table(workbook = result_list[["workbook"]],
                     rows     = "education",
                     columns  = "year",
                     style    = my_style,
                     na.rm    = TRUE)

# Now one can also just do
my_data |> any_table(workbook   = result_list, # Pass the whole result list
                     rows     = "education",
                     columns  = "year",
                     style    = my_style,
                     na.rm    = TRUE)

any_table(), frequencies(), crosstabs(): If ‘csv’ is specified as extension in the file name set in the global options or the style parameter the result table will then be exported as ‘csv’. Otherwise the actual workbook will be exported as xlsx as normal.

my_data |> any_table(rows  = "age",
                     style = excel_output_style(save_path = "C:/", file = "MyFile.csv"),
                     print = FALSE)

New way to transpose data

transpose_plus() can now in a wide to long transposition not only put results below each other, but also side by side.

# Example 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)

# Example data frame
my_data <- dummy_data(1000)

# Transpose from long to wide and use a multilabel to generate additional categories
long_to_wide <- my_data |>
    transpose_plus(preserve = c(year, age),
                   pivot    = "sex",
                   values   = c(income, weight),
                   formats  = list(sex = sex., age = age.),
                   weight   = weight,
                   na.rm    = TRUE) |>
    rename_multi("income_Total"  = "Total",
                 "income_Male"   = "Male",
                 "income_Female" = "Female")

# Transpose back from wide to long but this time put results side by side.
# To do that every list entry has to have the same name. The values parameter
# is then used to give the new value variables a name. For the expressions of
# the new categorical variable the variable names from the first pivot list
# entry are used.
wide_to_long <- long_to_wide |>
    transpose_plus(preserve = c(year, age),
                   values   = c(income, weight),
                   pivot    = list(sex = c("Total", "Male", "Female"),
                                   sex = c("weight_Total", "weight_Male", "weight_Female")))

if.() can now explicitly delete

If the new delete keyword is passed instead of a variable assignment, the provided condition deletes observations instead of keeping them.

subset_df <- my_data |> if.(sex == 1, delete)

# Is the same as
subset_df <- my_data |> if.(sex != 1)