qol - Quality of Life

Description

This package brings some quality of life concepts and functions inspired by 'SAS' to 'R'. The main goal is to make descriptive evaluations easier, so one can create bigger and more complex outputs in less time with less code. Introducing format containers with multilabels, a more powerful summarise, which is capable to output every possible combination of the provided grouping variables in one go, tabulation functions which can create any table in different styles and other more readable functions.

In addition it offers an error handling which often catches errors and just let's your code flow, even if there are small errors. You always get an understandable message which helps you to get rid of the problem.

The package builds on the incredibly fast data.table and collapse packages for maximum speed and on the wonderful openxlsx2 package for maximum style.

Imports

data.table, collapse

Minimal R Version

4.1.0 or higher

Functions

Creating formats: discrete_format(), interval_format().

Summarisation and tabulation: summarise_plus(), frequencies(), crosstabs(), any_table().

Creating a custom table style: excel_output_style(), modify_output_style(), number_format_style(), modify_number_formats().

Recoding: recode(), recode_multi().

Selecting: keep(), dropp().

If-statement: if.(), else_if.(), else.().

Monitoring: monitor_start(), monitor_end(), monitor_next(), monitor_plot().

Renaming: rename_pattern(), add_extension(), remove_stat_extension().

Generate dummy data: dummy_data()

Small helpers: inverse(), setcolorder_by_pattern(), running_number(), drop_type_vars(), fuse_variables().

Split data frame: split_by_var(), split_by_condition().

Other: args_to_char(), convert_numeric(), is_numeric()

Snippets

snippet splus
    summarise_plus(class      = c(var1, var2, ...),
                   values     = c(var1, var2, ...),
                   statistics = c("pct_group", "sum", "sum_wgt", "freq"),
                   formats    = list(var = format., ...),
                   weight     = weight_var,
                   nesting    = "deepest")

snippet if.
         if.(condition, var, value) |>
    else_if.(condition, var, value) |>
    else.   (           var, value)

snippet freq
    frequencies(variables = c(var1, var2, ...),
                formats   = list(var = "format.", ...),
                titles    = c(),
                footnotes = c(),
                weight    = weight_var)

snippet cross
    crosstabs(rows       = row_var,
              columns    = col_var,
              statistics = c("sum", "pct_row", "pct_column", "pct_total", "freq"),
              formats   = list(var = format., ...),
              titles    = c(),
              footnotes = c(),
              weight    = weight_var)

snippet any
    any_table(rows        = c("var1 + var2 + ...", "var1"),
              columns     = c("var3", "var3 + var4 + ..."),
              values.     = c("value_var1", "value_var2")
              statistics  = c("sum", "pct_group", "pct_value", "freq"),
              pct_group   = c("var1", "var2"),
              pct_value   = list(new_var = "numerator / denominator"),
              formats     = list(var = format., ...),
              titles      = c(),
              footnotes   = c(),
              var_labels  = list("var1" = "My label", ...),
              stat_labels = list("pct" = "
              box         = ""
              weight      = weight_var)

Author(s)

Tim Siebenmorgen

See Also

Useful links:

• https://github.com/s3rdia/qol

Add Extensions to Variable Names

Description

Renames variables in a data frame by adding the desired extensions to the original names. This can be useful if you want to use pre summarised data with any_table(), which needs the value variables to have the statistic extensions.

Usage

add_extension(data_frame, from, extensions, reuse = "none")

Arguments

Value

Returns a data frame with extended variable names.

Examples

# Example data frame
my_data <- dummy_data(1000)

# Add extensions to variable names
new_names1 <- my_data |> add_extension(5, c("sum", "pct"))
new_names2 <- my_data |> add_extension(5, c("sum", "pct"), reuse = "last")
new_names3 <- my_data |> add_extension(5, c("sum", "pct"), reuse = "alternate")

Compute Any Possible Table

Description

any_table() produces any possible descriptive table in 'Excel' format. Any number of variables can be nested and crossed. The output is an individually styled 'Excel' table.

Usage

any_table(
  data_frame,
  rows,
  columns,
  values,
  statistics = c("sum"),
  pct_group = c(),
  pct_value = list(),
  formats = list(),
  by = c(),
  weight = NULL,
  order_by = "stats",
  titles = c(),
  footnotes = c(),
  var_labels = list(),
  stat_labels = list(),
  box = "",
  workbook = NULL,
  style = excel_output_style(),
  output = "excel",
  pre_summed = FALSE,
  na.rm = FALSE,
  print = TRUE,
  monitor = FALSE
)

Arguments

Details

any_table() is based on the 'SAS' procedure Proc Tabulate, which provides efficient and readable ways to perform complex tabulations.

With this function you can combine any number of variables in any possible way, all at once. You just define which variables or variable combinations should end up in the table rows and columns with a simple syntax. Listing variables in a vector like c("var1", "var2", "var3",...) means to put variables below (in case of the row variables) or besides (in case of the column variables) each other. Nesting variables is as easy as putting a plus sign between them, e.g. c("var1 + var2", "var2" + "var3" + "var4", etc.). And of course you can combine both both versions.

The real highlight is, that this function not only creates all the desired variable combinations and exports them to an 'Excel' file, it prints a fully custom styled table to a workbook. Setting up a custom, reusable style is as easy as setting up options like: provide a color for the table header, set the font size for the row header, should borders be drawn for the table cells yes/no, and so on. Merging doubled header texts, happens automatically.

With this function you basically can fully concentrate on designing a table, instead of thinking hard about how to calculate where to put a border or to even manually prepare a designed workbook.

Value

Returns a list with the data table containing the results for the table and the formatted 'Excel' workbook.

See Also

Creating a custom table style: excel_output_style(), modify_output_style(), number_format_style(), modify_number_formats().

Creating formats: discrete_format() and interval_format().

Functions that can handle formats and styles: frequencies(), crosstabs().

Additional functions that can handle styles: export_with_style()

Additional functions that can handle formats: summarise_plus(), recode(), recode_multi()

Examples

# Example data frame
my_data <- dummy_data(1000)
my_data[["person"]] <- 1

# 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)

education. <- discrete_format(
    "Total"            = c("low", "middle", "high"),
    "low education"    = "low",
    "middle education" = "middle",
    "high education"   = "high")

# Define style
my_style <- excel_output_style(column_widths = c(2, 15, 15, 15, 9))

# Define titles and footnotes. If you want to add hyperlinks you can do so by
# adding "link:" followed by the hyperlink to the main text.
titles <- c("This is title number 1 link: https://cran.r-project.org/",
            "This is title number 2",
            "This is title number 3")
footnotes <- c("This is footnote number 1",
               "This is footnote number 2",
               "This is footnote number 3 link: https://cran.r-project.org/")

# Output complex tables with different percentages
my_data |> any_table(rows       = c("sex + age", "sex", "age"),
                     columns    = c("year", "education + year"),
                     values     = weight,
                     statistics = c("sum", "pct_group"),
                     pct_group  = c("sex", "age", "education", "year"),
                     formats    = list(sex = sex., age = age.,
                                       education = education.),
                     style      = my_style,
                     na.rm      = TRUE)

# If you want to get a clearer vision of what the result table looks like, in terms
# of the row and column categories, you can write the code like this, to make out
# the variable crossings and see the order.
my_data |> any_table(columns = c(            "year", "education + year"),
                     rows    = c("sex + age",
                                 "sex",
                                 "age"),
                     values     = weight,
                     statistics = c("sum", "pct_group"),
                     pct_group  = c("sex", "age", "education", "year"),
                     formats    = list(sex = sex., age = age.,
                                       education = education.),
                     style      = my_style,
                     na.rm      = TRUE)

# Percentages based on value variables instead of categories
my_data |> any_table(rows       = c("age + year"),
                     columns    = c("sex"),
                     values     = c(probability, person),
                     statistics = c("pct_value", "sum", "freq"),
                     pct_value  = list(rate = "probability / person"),
                     weight     = weight,
                     formats    = list(sex = sex., age = age.),
                     style      = my_style,
                     na.rm      = TRUE)

# Customize the visual appearance by adding titles, footnotes and variable
# and statistic labels.
# Note: You don't have to describe every element. Sometimes a table can be more
# readable with less text. To completely remove a variable label just put in an
# empty text "" as label.
my_data |> any_table(rows        = c("age + year"),
                     columns     = c("sex"),
                     values      = weight,
                     statistics  = c("sum", "pct_group"),
                     order_by    = "interleaved",
                     formats     = list(sex = sex., age = age.),
                     titles      = titles,
                     footnotes   = footnotes,
                     var_labels  = list(age = "Age categories",
                                       sex = "", weight = ""),
                     stat_labels = list(pct = "%"),
                     style       = my_style,
                     na.rm       = TRUE)

# With individual styling
my_style <- my_style |> modify_output_style(header_back_color = "0077B6",
                                            font              = "Times New Roman")

my_data |> any_table(rows       = c("age + year"),
                     columns    = c("sex"),
                     values     = c(probability, person),
                     statistics = c("pct_value", "sum", "freq"),
                     pct_value  = list(rate = "probability / person"),
                     weight     = weight,
                     formats    = list(sex = sex., age = age.),
                     style      = my_style,
                     na.rm      = TRUE)

# Pass on workbook to create more sheets in the same file
my_style <- my_style |> modify_output_style(sheet_name = "age_sex")

result_list <- my_data |>
           any_table(rows       = c("age"),
                     columns    = c("sex"),
                     values     = weight,
                     statistics = c("sum"),
                     formats    = list(sex = sex., age = age.),
                     style      = my_style,
                     na.rm      = TRUE,
                     print      = FALSE)

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

my_data |> any_table(workbook   = result_list[["workbook"]],
                     rows       = c("education"),
                     columns    = c("year"),
                     values     = weight,
                     statistics = c("pct_group"),
                     formats    = list(education = education.),
                     style      = my_style,
                     na.rm      = TRUE)

# Output multiple complex tables by expressions of another variable.
# If you specify the sheet name as "by" in the output style, the sheet
# names are named by the variable expressions of the by-variable. Otherwise
# the given sheet named gets a running number.
my_style <- my_style |> modify_output_style(sheet_name = "by")

my_data |> any_table(rows       = c("sex", "age"),
                     columns    = c("education + year"),
                     values     = weight,
                     by         = state,
                     statistics = c("sum", "pct_group"),
                     pct_group  = c("education"),
                     formats    = list(sex = sex., age = age., state = state.,
                                       education = education.),
                     titles     = titles,
                     footnotes  = footnotes,
                     style      = my_style,
                     na.rm      = TRUE)

Convert Ellipsis to Character Vector

Description

When you define a function and want the user to be able to pass variable names without the need to have them stored in a vector c() or list() beforehand and without putting the names into quotation marks, you can convert this variable list passed as ... into a character vector.

Note: If the user passes a list of characters it is returned as given.

Usage

args_to_char(...)

Arguments

Value

Returns a character vector

Examples

# Example function
print_vnames <- function(...){
    var_names <- args_to_char(...)
    print(var_names)
}

print_vnames(age, sex, income, weight)
print_vnames("age", "sex", "income", "weight")

# You can also pass in a character vector, if you have stored variable names elsewhere
var_names <- c("age", "sex", "income", "weight")
print_vnames(var_names)

Check and Convert to Numeric

Description

is_numeric() checks whether all values of the given variable, that are not NA, are numerical.

convert_numeric() converts all given variables to numeric if possible. If a variable contains none numerical values (not including NAs), the variable will not be converted.

Usage

is_numeric(variable)

convert_numeric(data_frame, variables)

Arguments

Value

is_numeric() returns TRUE if all none NA values are numerical, otherwise FALSE.

convert_numeric() returns the same data frame with converted variables where possible.

Examples

# Check if vectors contain only numeric values
test_vector1 <- c(1, 2, 3, NA, 4, 5)
test_vector2 <- c(1, 2, "Hello", NA, 4, 5)

numeric_check1 <- is_numeric(test_vector1)
numeric_check2 <- is_numeric(test_vector2)

# Convert variables in a data frame to numeric where possible
test_df <- data.frame(var_a = c(1, 2, 3, NA, 4, 5),
                      var_b = c(1, 2, "Hello", NA, 4, 5))

convert_df <- test_df |> convert_numeric(c("var_a", "var_b"))

Display Cross Table of Two Variables

Description

crosstabs() produces a cross table of two variables. Statistics can be weighted sums, unweighted frequencies or different percentages.

Usage

crosstabs(
  data_frame,
  rows,
  columns,
  statistics = c("sum"),
  formats = c(),
  by = c(),
  weight = NULL,
  titles = c(),
  footnotes = c(),
  style = excel_output_style(),
  output = "console",
  na.rm = FALSE,
  print = TRUE,
  monitor = FALSE
)

Arguments

Details

crosstabs() is based on the 'SAS' procedure Proc Freq, which provides efficient and readable ways to perform cross tabulations.

To create a cross table you only need to provide a variable for the rows and columns. Nothing special about this. The real power comes into play, when you output your tables as a fully styled 'Excel' workbook. Setting up a custom, reusable style is as easy as setting up options like: provide a color for the table header, set the font size for the row header, should borders be drawn for the table cells yes/no, and so on.

You can not only output sums and frequencies, but also different percentages, all set up in separate, evenly designed tables. For just a quick overview, rather than fully designed tables, you can also just output the tables in ASCII style format.

Value

Returns a data tables containing the results for the cross table.

See Also

Creating a custom table style: excel_output_style(), modify_output_style(), number_format_style(), modify_number_formats().

Creating formats: discrete_format() and interval_format().

Functions that can handle formats and styles: frequencies(), any_table().

Additional functions that can handle styles: export_with_style()

Additional functions that can handle formats: summarise_plus(), recode(), recode_multi()

Examples

# Example data frame
my_data <- dummy_data(1000)

# Define titles and footnotes. If you want to add hyperlinks you can do so by
# adding "link:" followed by the hyperlink to the main text.
titles <- c("This is title number 1 link: https://cran.r-project.org/",
            "This is title number 2",
            "This is title number 3")
footnotes <- c("This is footnote number 1",
               "This is footnote number 2",
               "This is footnote number 3 link: https://cran.r-project.org/")

# Output cross tables
my_data |> crosstabs(age, sex)
my_data |> crosstabs(age, sex,
                     weight = "weight")

# Also works with characters
my_data |> crosstabs("age", "sex")
my_data |> crosstabs("age", "sex",
                     weight = "weight")

# Applying formats and titles
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)

my_data |> crosstabs(age, sex,
                     formats   = list(age = age., sex = sex.),
                     titles    = titles,
                     footnotes = footnotes)

# Split cross table by expressions of another variable
my_data |> crosstabs(age, sex, by = education)

# Compute different stats
my_data |> crosstabs(age, sex,
                     statistics = c("sum", "freq", "pct_row", "pct_column", "pct_total"))

# Get a list with two data tables for further usage
result_list <- my_data |> crosstabs(age, sex,
                                    formats = list(age = age., sex = sex.))

# Output in text file
my_data |> crosstabs(age, sex, output = "text")

# Output to Excel
my_data |> crosstabs(age, sex, output = "excel")

# With individual styling
my_style <- excel_output_style(header_back_color = "0077B6",
                               font              = "Times New Roman")

my_data |> crosstabs(age, sex, output = "excel", style = my_style)

Drop automatically generated Variables

Description

If summarise_plus() is used with the nested options "all" or "single", three variables are automatically generated: TYPE, TYPE_NR and DEPTH. With this functions these variables are dropped.

Usage

drop_type_vars(data_frame)

Arguments

Value

Returns a data frame without the variables TYPE, TYPE_NR and DEPTH.

Examples

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

# Example data frame
my_data <- dummy_data(1000)

# Call function
all_possible <- my_data |>
    summarise_plus(class      = c(year, sex),
                   values     = c(income, probability),
                   statistics = c("sum", "mean", "freq"),
                   formats    = list(sex = "sex."),
                   weight     = weight,
                   nesting    = "all",
                   na.rm      = TRUE) |>
    drop_type_vars()

Dummy Data

Description

The dummy data frame contains a few randomly generated variables like year, sex, age, income and weight to test out functionalities. It can be generated with the desired number of observations.

Usage

dummy_data(no_obs, monitor = FALSE)

Arguments

Value

Returns a dummy data table.

Examples

my_data <- dummy_data(1000)

Style for 'Excel' Table Outputs

Description

Set different options which define the visual output of 'Excel' tables produced by frequencies(), crosstabs() and any_table().

Usage

excel_output_style(
  file = NULL,
  sheet_name = "Table",
  font = "Arial",
  column_widths = "auto",
  row_heights = "auto",
  title_heights = NULL,
  header_heights = NULL,
  table_heights = NULL,
  footnote_heights = NULL,
  start_row = 2,
  start_column = 2,
  freeze_col_header = FALSE,
  freeze_row_header = FALSE,
  filters = TRUE,
  grid_lines = TRUE,
  header_back_color = "FFFFFF",
  header_font_color = "000000",
  header_font_size = 10,
  header_font_bold = TRUE,
  header_alignment = "center",
  header_wrap = "1",
  header_indent = 0,
  header_borders = TRUE,
  header_border_color = "000000",
  cat_col_back_color = "FFFFFF",
  cat_col_font_color = "000000",
  cat_col_font_size = 10,
  cat_col_font_bold = FALSE,
  cat_col_alignment = "left",
  cat_col_wrap = "1",
  cat_col_indent = 1,
  cat_col_borders = TRUE,
  cat_col_border_color = "000000",
  table_back_color = "FFFFFF",
  table_font_color = "000000",
  table_font_size = 10,
  table_font_bold = FALSE,
  table_alignment = "right",
  table_indent = 1,
  table_borders = FALSE,
  table_border_color = "000000",
  box_back_color = "FFFFFF",
  box_font_color = "000000",
  box_font_size = 10,
  box_font_bold = TRUE,
  box_alignment = "center",
  box_wrap = "1",
  box_indent = 0,
  box_borders = TRUE,
  box_border_color = "000000",
  number_formats = number_format_style(),
  title_font_color = "000000",
  title_font_size = 10,
  title_font_bold = TRUE,
  title_alignment = "left",
  footnote_font_color = "000000",
  footnote_font_size = 8,
  footnote_font_bold = FALSE,
  footnote_alignment = "left",
  na_symbol = "."
)

Arguments

Details

excel_output_style() is based on the Output Delivery System (ODS) in 'SAS', which provides efficient and readable ways to set up different table styles.

With the output style you have full control over the table design. There is no need to think about calculating the right place to input a background color or a border of a certain type and how to do this in a loop for multiple cells. Just input colors, borders, font styles, etc. for the different table parts and everything else is handled by the functions capable of using styles.

The concept basically is: design over complex calculations.

Value

Returns a list of named style options.

See Also

Creating a custom table style: modify_output_style(), number_format_style(), modify_number_formats().

Functions that can handle styles: frequencies(), crosstabs(), any_table(), export_with_style()

Examples

# For default values
excel_style <- excel_output_style()

# Set specific options, the rest will be set to default values
excel_style <- excel_output_style(font       = "Calibri",
                                  sheet_name = "My_Output")

# For cells with no background color pass an empty string
excel_style <- excel_output_style(table_back_color = "")

Export Data Frame With Style

Description

export_with_style() prints a data frame as an individually styled 'Excel' table. Titles, footnotes and labels for variable names can optionally be added.

Usage

export_with_style(
  data_frame,
  titles = c(),
  footnotes = c(),
  var_labels = list(),
  workbook = NULL,
  style = excel_output_style(),
  output = "excel",
  print = TRUE,
  monitor = FALSE
)

Arguments

Details

export_with_style() is based on the 'SAS' procedure Proc Print, which outputs the data frame as is into a styled table.

Value

Returns a formatted 'Excel' workbook.

See Also

Creating a custom table style: excel_output_style(), modify_output_style(), number_format_style(), modify_number_formats().

Functions that can handle styles: frequencies(), crosstabs(), any_table().

Examples

# Example data frame
my_data <- dummy_data(1000)

# Define style
my_style <- excel_output_style(column_widths = c(2, 15, 15, 15, 9))

# Define titles and footnotes. If you want to add hyperlinks you can do so by
# adding "link:" followed by the hyperlink to the main text.
titles <- c("This is title number 1 link: https://cran.r-project.org/",
            "This is title number 2",
            "This is title number 3")
footnotes <- c("This is footnote number 1",
               "This is footnote number 2",
               "This is footnote number 3 link: https://cran.r-project.org/")

# Print styled data frame
my_data |> export_with_style(titles    = titles,
                             footnotes = footnotes,
                             style     = my_style)

# Retrieve formatted workbook for further usage
wb <- my_data |>
    export_with_style(titles    = titles,
                      footnotes = footnotes,
                      style     = my_style)

Create Format Container

Description

Create a format container which stores discrete or interval values with corresponding labels that can be applied by using summarise_plus().

Create a format container independent from any data frame. Define which values should be recoded into which new categories, if the format is applied to a variable in a data frame. It is possible to assign a single value to multiple new categories to create a multilabel. It is recommended to let format names end with a dot to make them stand out.

Usage

discrete_format(...)

interval_format(...)

Arguments

Details

The concept of having formats as molds or stencils to put the data through, is inspired by 'SAS' formats. In 'SAS' formats are defined with the procedure Proc Formats, which is adapted with discrete_format() and interval_format(). Here you can define, which values should be transferred into which result categories. This is completely detached from the data your working with.

The great thing about this is, that one can not only label and recode values, but one can also define so called multilabels. Meaning, one original value can be transferred into multiple result categories.

A cell in a data frame can only hold one distinct value, which is normally a good thing. But let's say you want to convert single ages into age categories. The age "3" for example could go into the category "under 6", but also in "under 12", "under 18" and "total". Normally you would compute additional variables, which hold the different categorizations, or you could also double up the observations for each category. Both ways would just bloat up the data frame and cost additional memory, particularly if you work with big data sets.

With these format containers, you just keep a small reference of original values and result categories. Formats and data find their way together only just before computing the results, meaning the original data frame can be passed into a function capable of handling formats (see below), without any data transformation beforehand. You just tell the function which format should be applied to which variable. That's it. The function handles the rest and outputs all the desired categories.

This method is very memory efficient, readable and user friendly for creating larger and more complex outputs at the same time.

Value

Returns a data table which contains the values/value ranges with the corresponding labels

See Also

Functions that can handle formats: summarise_plus(), frequencies(), crosstabs(), any_table(), recode_multi().

Examples

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)

education. <- discrete_format(
    "Total"            = c("low", "middle", "high"),
    "low education"    = "low",
    "middle education" = "middle",
    "high education"   = "high")

income. <- interval_format(
    "Total"              = 0:99999,
    "below 500"          = 0:499,
    "500 to under 1000"  = 500:999,
    "1000 to under 2000" = 1000:1999,
    "2000 and more"      = 2000:99999)

state. <- discrete_format(
    "Germany"                       = 1:16,
    "Schleswig-Holstein"            = 1,
    "Hamburg"                       = 2,
    "Lower Saxony"                  = 3,
    "Bremen"                        = 4,
    "North Rhine-Westphalia"        = 5,
    "Hesse"                         = 6,
    "Rhineland-Palatinate"          = 7,
    "Baden-Württemberg"             = 8,
    "Bavaria"                       = 9,
    "Saarland"                      = 10,
    "West"                          = 1:10,
    "Berlin"                        = 11,
    "Brandenburg"                   = 12,
    "Mecklenburg-Western Pomerania" = 13,
    "Saxony"                        = 14,
    "Saxony-Anhalt"                 = 15,
    "Thuringia"                     = 16,
    "East"                          = 11:16)

Display Frequency Tables of Single Variables

Description

frequencies() produces two kinds of tables for a quick overview of single variables. The first table is for a broader overview and contains mean, sd, min, max, freq and missings. The second table is the actual frequency table which shows the weighted sums, percentages and unweighted frequencies per expression.

Usage

frequencies(
  data_frame,
  variables,
  formats = c(),
  by = c(),
  weight = NULL,
  titles = c(),
  footnotes = c(),
  style = excel_output_style(),
  output = "console",
  na.rm = FALSE,
  print = TRUE,
  monitor = FALSE
)

Arguments

Details

frequencies() is based on the 'SAS' procedure Proc Freq, which provides efficient and readable ways to output frequency tables.

To create a frequency table you only need to provide a single variable. Nothing special about this. The real power comes into play, when you output your tables as a fully styled 'Excel' workbook. Setting up a custom, reusable style is as easy as setting up options like: provide a color for the table header, set the font size for the row header, should borders be drawn for the table cells yes/no, and so on.

You also can provide multiple single variables to generate multiple, evenly designed tables, all at once. For just a quick overview, rather than fully designed tables, you can also just output the tables in ASCII style format.

Value

Returns a list of two data tables containing the results for the frequency tables.

See Also

Creating a custom table style: excel_output_style(), modify_output_style(), number_format_style(), modify_number_formats().

Creating formats: discrete_format() and interval_format().

Functions that can handle formats and styles: crosstabs(), any_table().

Additional functions that can handle styles: export_with_style()

Additional functions that can handle formats: summarise_plus(), recode(), recode_multi()

Examples

# Example data frame
my_data <- dummy_data(1000)

# Define titles and footnotes. If you want to add hyperlinks you can do so by
# adding "link:" followed by the hyperlink to the main text.
titles <- c("This is title number 1 link: https://cran.r-project.org/",
            "This is title number 2",
            "This is title number 3")
footnotes <- c("This is footnote number 1",
               "This is footnote number 2",
               "This is footnote number 3 link: https://cran.r-project.org/")

# Output frequency tables
my_data |> frequency(sex)
my_data |> frequency(c(age, education),
                     weight = weight)

# Also works with characters
my_data |> frequency("sex")
my_data |> frequency(c("age", "education"),
                     weight = "weight")

# Applying formats and titles
sex. <- discrete_format(
    "Total"  = 1:2,
    "Male"   = 1,
    "Female" = 2)

my_data |> frequency(sex, formats(sex = sex.),
                     titles    = titles,
                     footnotes = footnotes)

# Split frequencies by expressions of another variable
my_data |> frequency(sex, by = education)

# Get a list with two data tables for further usage
result_list <- my_data |> frequency(sex, formats(sex = sex.))

# Output in text file
my_data |> frequency(sex, output = "text")

# Output to Excel
my_data |> frequency(sex, output = "excel")

# With individual styling
my_style <- excel_output_style(header_back_color = "0077B6",
                               font              = "Times New Roman")

my_data |> frequency(sex, output = "excel", style = my_style)

Fuse Multiple Variables

Description

When you have a situation where you have multiple variables with different NA values that happen to be in different places (where one variable has a value the other is NA and vice versa) you can fuse these together to a single variable.

Usage

fuse_variables(
  data_frame,
  new_variable_name,
  variables_to_fuse,
  drop_original_vars = TRUE
)

Arguments

Value

Returns a data frame without the variables TYPE, TYPE_NR and DEPTH.

Examples

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

# Example data frame
my_data <- dummy_data(1000)

# Call function
all_possible <- my_data |>
    summarise_plus(class      = c(year, sex),
                   values     = c(income, probability),
                   statistics = c("sum", "mean", "freq"),
                   formats    = list(sex = "sex."),
                   weight     = weight,
                   nesting    = "all",
                   na.rm      = TRUE)

all_possible <- all_possible[DEPTH <= 1] |>
    fuse_variables("fusion", c("year", "sex"))

# NOTE: You can generally use this function to fuse variables. What is done in
#       multiple steps above can be achieved by just using nested = "single" in
#       summarise_plus.
single <- my_data |>
    summarise_plus(class      = c(year, sex),
                   values     = c(income, probability),
                   statistics = c("sum", "mean", "freq"),
                   formats    = list(sex = "sex."),
                   weight     = weight,
                   nesting    = "single",
                   na.rm      = TRUE)

Converts Numbers into 'Excel' Ranges

Description

Converts a column number into the according letter to form a cell reference like it is used in 'Excel' (e.g "A1"). Also can compute a range from cell to cell (e.g. "A1:BY22").

Usage

get_excel_range(
  row = NULL,
  column = NULL,
  from_row = NULL,
  from_column = NULL,
  to_row = NULL,
  to_column = NULL
)

Arguments

Value

Returns a character with an 'Excel' range.

Examples

single_cell <- get_excel_range(row = 1, column = 6)
range       <- get_excel_range(from_row = 1, from_column = 6,
                                 to_row = 5,   to_column = 35)

If - Else if - Else Statements

Description

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

Value

Returns a data frame with conditionally computed variables. If assigned values are of different types a character variable will be returned.

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")

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

Get Variable Names which are not Part of the Given Vector

Description

If you have stored variable names inside a character vector, this function gives you the inverse variable name vector.

Usage

inverse(data_frame, var_names)

Arguments

Value

Returns the inverse vector of variable names compared to the given vector.

Examples

# Example data frame
my_data <- dummy_data(1000)

# Get variable names
var_names <- c("year", "age", "sex")
other_names <- my_data |> inverse(var_names)

# Can also be used to just get all variable names
all_names <- my_data |> inverse(NULL)
all_names <- my_data |> inverse(character(0))

Keep and Drop Variables Inside a Data Frame

Description

keep() enables you to put in a vector of variable names which then are kept inside the given data frame. All other variables are dropped.

dropp() enables you to put in a vector of variable names which then are dropped from the given data frame. All other variables are kept.

Usage

keep(data_frame, ..., order_vars = FALSE)

dropp(data_frame, ...)

Arguments

Value

Returns a reduced data table.

Examples

# Example data frame
my_data <- dummy_data(1000)

# Call function
new_dt1 <- my_data |> keep(year, age, sex)
new_dt2 <- my_data |> keep(weight, income, education, sex, order_vars = TRUE)
new_dt3 <- my_data |> dropp(year, age, sex)

# Also works with characters
new_dt4 <- my_data |> keep("year", "age", "sex")
new_dt5 <- my_data |> dropp("year", "age", "sex")

# Or variable names stored as a character vector
var_names <- c("age", "sex", "income", "weight")

new_dt6 <- my_data |> keep(var_names)
new_dt7 <- my_data |> dropp(var_names)

Modify Number Formats Used by any_table()

Description

Modify previously created number formats with number_format_style().

Usage

modify_number_formats(formats_to_modify, ...)

Arguments

Details

modify_number_formats() is based on 'SAS' number formats and the Output Delivery System (ODS), which provides efficient and readable ways to set up different table styles.

With the number format style you have full control over formatting numbers according to the different statistics. There is no need to think about calculating the right place to input the number formats and how to do this in a loop for multiple cells. Just input the different number formats and decimals for the different statistics and everything else is handled by the functions capable of using number styles.

The concept basically is: design over complex calculations.

Value

Returns a modified list of number format options.

See Also

Creating a custom table style: excel_output_style(), modify_output_style(), number_format_style().

Functions that can handle styles: frequencies(), crosstabs(), any_table(), export_with_style().

Examples

# For default values
format_list <- number_format_style(pct_excel    = "0.00000000",
                                   pct_decimals = 8)

# Set specific options, the rest will be kept as is
format_list <- format_list |> modify_number_formats(sum_excel = "#,###,##0.000")

# IMPORTANT: Don't forget to add individual formats to an excel style, otherwise
# they won't come into affect.
excel_style <- excel_output_style(number_formats = format_list)

Modify Style for 'Excel' Table Outputs

Description

Modify a previously created style with excel_output_style().

Usage

modify_output_style(style_to_modify, ...)

Arguments

Details

modify_output_style() is based on the Output Delivery System (ODS) in 'SAS', which provides efficient and readable ways to set up different table styles.

With the output style you have full control over the table design. There is no need to think about calculating the right place to input a background color or a border of a certain type and how to do this in a loop for multiple cells. Just input colors, borders, font styles, etc. for the different table parts and everything else is handled by the functions capable of using styles.

The concept basically is: design over complex calculations.

Value

Returns a modified list of named style options.

See Also

Creating a custom table style: excel_output_style(), number_format_style(), modify_number_formats().

Functions that can handle styles: frequencies(), crosstabs(), any_table(), export_with_style()

Examples

# For default values
excel_style <- excel_output_style()

# Set specific options, the rest will be kept as is
excel_style <- excel_style |> modify_output_style(sheet_name      = "Sheet",
                                                  title_font_bold = FALSE)

# For cells with no background color pass an empty string
excel_style <- excel_style |> modify_output_style(table_back_color = "")

Monitor Time Consumption

Description

The monitor functions offer a simple way to keep track of timings and visualize them in charts. If used throughout a longer syntax it is useful to identify bottlenecks or just get a better feeling which passages take more time than others.

monitor_start() starts a new timing and adds this as an observation to the monitoring data table. Pass NULL as monitor_df if you call the function for the first time to create a new monitoring data table.

monitor_end() ends the current timing and calculates corresponding delta.

monitor_next() ends the current timing and calculates corresponding delta. In addition directly starts a new timing for a new section.

monitor_plot() outputs two charts to visualize the saved delta times.

Usage

monitor_start(monitor_df, section, group = "Total")

monitor_end(monitor_df)

monitor_next(monitor_df, section, group = "Total")

monitor_plot(monitor_df, by = "section", draw_plot = TRUE)

Arguments

Value

Returns a small data table with section-, group-names and corresponding delta times.

Examples

# Example data frame
monitor_df <- NULL |> monitor_start("Generate data frame", "Preparation")

my_data <- dummy_data(1000)

# Formats
monitor_df <- monitor_df |> monitor_next("Create formats", "Preparation")

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" = 55:65,
    "65 and older"   = 65:100)

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

# Evaluations
monitor_df <- monitor_df |> monitor_next("Nested summarise", "Summarise")

all_nested <- my_data |>
    summarise_plus(class      = c(year, sex, age),
                   values     = income,
                   statistics = c("sum", "pct_group", "pct_total", "sum_wgt", "freq"),
                   formats    = list(sex = "sex.", age = "age."),
                   weight     = weight,
                   nesting    = "deepest",
                   na.rm      = TRUE)

monitor_df <- monitor_df |> monitor_next("All summarise", "Summarise")

all_possible <- my_data |>
    summarise_plus(class      = c(year, sex, age),
                   values     = c(probability),
                   statistics = c("sum", "p1", "p99", "min", "max", "freq", "freq_g0"),
                   formats    = list(sex    = "sex.",
                                     age    = "age."),
                   weight     = weight,
                   nesting    = "all",
                   na.rm      = TRUE)

monitor_df <- monitor_df |> monitor_end()

# For detailed plot
monitor_df |> monitor_plot()

# For summarised plot
monitor_df |> monitor_plot(by = "group")

# NOTE: The more complex functions in this package have a detailed monitoring
#       integrated which can be viewed by setting the argument 'monitor' to TRUE.

Number Formats Used by any_table()

Description

Set individual number formats for the different statistics in tables produced with any_table().

Usage

number_format_style(
  pct_excel = "0.0",
  freq_excel = "#,###,##0",
  freq.g0_excel = "#,###,##0",
  sum_excel = "#,###,##0",
  sum.wgt_excel = "#,###,##0",
  mean_excel = "#,###,##0",
  median_excel = "#,###,##0",
  mode_excel = "#,###,##0",
  min_excel = "#,###,##0",
  max_excel = "#,###,##0",
  sd_excel = "#,###,##0.000",
  variance_excel = "#,###,##0.000",
  first_excel = "#,###,##0",
  last_excel = "#,###,##0",
  p_excel = "#,###,##0",
  missing_excel = "#,###,##0",
  pct_decimals = 1,
  freq_decimals = 0,
  freq.g0_decimals = 0,
  sum_decimals = 3,
  sum.wgt_decimals = 3,
  mean_decimals = 2,
  median_decimals = 2,
  mode_decimals = 2,
  min_decimals = 2,
  max_decimals = 2,
  sd_decimals = 3,
  variance_decimals = 3,
  first_decimals = 0,
  last_decimals = 0,
  p_decimals = 2,
  missing_decimals = 0
)

Arguments

Details

number_format_style() is based on 'SAS' number formats and the Output Delivery System (ODS), which provides efficient and readable ways to set up different table styles.

With the number format style you have full control over formatting numbers according to the different statistics. There is no need to think about calculating the right place to input the number formats and how to do this in a loop for multiple cells. Just input the different number formats and decimals for the different statistics and everything else is handled by the functions capable of using number styles.

The concept basically is: design over complex calculations.

Value

Returns a list of named number format options.

See Also

Creating a custom table style: excel_output_style(), modify_output_style(), modify_number_formats().

Functions that can handle styles: frequencies(), crosstabs(), any_table(), export_with_style()

Examples

# For default values
format_list <- number_format_style()

# Set specific options, the rest will be set to default values
format_list <- number_format_style(pct_excel    = "0.00000000",
                                   pct_decimals = 8)

# IMPORTANT: Don't forget to add individual formats to an excel style, otherwise
# they won't come into affect.
excel_style <- excel_output_style(number_formats = format_list)

Recode New Variables With Formats

Description

Instead of writing multiple if-clauses to recode values into a new variable, you can use formats to recode a variable into a new one.

Usage

recode(data_frame, new_var, ...)

recode_multi(data_frame, ...)

Arguments

Details

recode() is based on the 'SAS' function put(), which provides an efficient and readable way, to generate new variables with the help of formats.

When creating a format you can basically write code like you think: This new category consists of these original values. And after that you just apply these new categories to the original values to create a new variable. No need for multiple if_else statements.

Value

Returns a data frame with the newly recoded variable.

See Also

Creating formats: discrete_format() and interval_format().

Functions that also make use of formats: frequencies(), crosstabs(), any_table().

Examples

# Example formats
age. <- discrete_format(
    "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)

# Example data frame
my_data <- dummy_data(1000)

# Call function
my_data <- my_data |> recode("age_group1", age = age.)

# Formats can also be passed as characters
my_data <- my_data |> recode("age_group2", age = "age.")

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

income. <- interval_format(
    "Total"              = 0:99999,
    "below 500"          = 0:499,
    "500 to under 1000"  = 500:999,
    "1000 to under 2000" = 1000:1999,
    "2000 and more"      = 2000:99999)

multi_data <- my_data |> recode_multi(sex = sex., income = income.)

Replace Statistic From Variable Names

Description

Remove the statistic name from variable names, so that they get back their old names without extension.

Usage

remove_stat_extension(data_frame, statistics)

Arguments

Value

Returns a data frame with renamed variables.

Examples

# Example data frame
my_data <- dummy_data(1000)

# Summarise data
all_nested <- my_data |>
    summarise_plus(class      = c(year, sex),
                   values     = c(weight, income),
                   statistics = c("sum", "pct_group", "pct_total", "sum_wgt", "freq"),
                   weight     = weight,
                   nesting    = "deepest",
                   na.rm      = TRUE)

# Remove statistic extension
new_names <- all_nested |> remove_stat_extension("sum")

Replace Patterns Inside Variable Names

Description

Replace a certain pattern inside a variable name with a new one. This can be used if there are multiple different variable names which have a pattern in common (e.g. all end in "_sum" but start different), so that there don't have to be multiple rename variable calls.

Usage

rename_pattern(data_frame, old_pattern, new_pattern)

Arguments

Value

Returns a data frame with renamed variables.

Examples

# Example data frame
my_data <- dummy_data(1000)

# Summarise data
all_nested <- my_data |>
    summarise_plus(class      = c(year, sex),
                   values     = c(weight, income),
                   statistics = c("sum", "pct_group", "pct_total", "sum_wgt", "freq"),
                   weight     = weight,
                   nesting    = "deepest",
                   na.rm      = TRUE)

# Rename variables by repacing patterns
new_names <- all_nested |>
    rename_pattern("pct", "percent") |>
    rename_pattern("_sum", "")

Compute Running Numbers

Description

Compute running numbers in a data frame. Without specifying a by variable results in the row number. With by variable computes the running number within each group of expressions.

Usage

running_number(data_frame, var_name = "run_nr", by = NULL)

Arguments

Value

Returns the data frame with a new variable containing a running number.

Examples

# Example data frame
my_data <- dummy_data(1000)

# Get row numbers
my_data <- my_data |> running_number()
my_data <- my_data |> running_number("row_number")

# Running number per variable expression
my_data <- my_data |> running_number(by = year)

Order Columns by Variable Name Patterns

Description

Order variables in a data frame based on a pattern rather than whole variable names. E.g. grab every variable that contains "sum" in it's name and order them together so that they appear next to each other.

Usage

setcolorder_by_pattern(data_frame, pattern)

Arguments

Value

Returns a reordered data frame with the ordered variables at the end.

Examples

# Example data frame
my_data <- dummy_data(1000)

# Summarise data
all_nested <- my_data |>
    summarise_plus(class      = c(year, sex),
                   values     = c(weight, income),
                   statistics = c("sum", "pct_group", "pct_total", "sum_wgt", "freq"),
                   weight     = weight,
                   nesting    = "deepest",
                   na.rm      = TRUE)

# Set a different column order
new_order <- all_nested |> setcolorder_by_pattern(c("pct", "freq", "sum"))

Split Data Frame by Variable Expressions or Condition

Description

Split up a data frame based on variable expressions or on conditions to receive multiple smaller data frames.

Usage

split_by_var(data_frame, variable)

split_by_condition(data_frame, ..., inverse = FALSE)

Arguments

Details

split_by() is based on the explicit Output from 'SAS'. With the Output function one can - among other things - explicitly tell 'SAS' which observation to output into which data set. Which enables the user to output one observation into one or multiple data sets.

Instead of subsetting the same data frame multiple times manually, you can subset it multiple times at once with this function.

Value

split_by_var(): Returns a list of data frames split by variable expressions. The lists names are the variable expressions.

split_by_condition(): Returns a list of data frames split conditionally. The lists names are the conditions.

Examples

# Example data frame
my_data <- dummy_data(1000)

# Split by variable expressions
split_var_df <- my_data |> split_by_var(sex)

# Split by conditions
split_cond_df <- my_data |> split_by_condition(sex == 1 & age <  18,
                                               sex == 2 & age >= 18)

# Split by condition with inverse group
split_inv_df <- my_data |> split_by_condition(sex == 1, inverse = TRUE)

Fast and Powerful yet Simple to Use Summarise

Description

summarise_plus() creates a new aggregated data table with the desired grouping. It can output only the deepest nested combination of the grouping variables (default) or you can also output every possible combination of the grouping variables at once, with just one small change. Besides the normal summary functions like sum, mean or median, you can also calculate their respective weighted version by just setting a weight variable.

Usage

summarise_plus(
  data_frame,
  class = NULL,
  values,
  statistics = c("sum", "freq"),
  formats = c(),
  types = c(),
  weight = NULL,
  nesting = "deepest",
  merge_back = FALSE,
  na.rm = FALSE,
  monitor = FALSE,
  notes = TRUE
)

Arguments

Details

summarise_plus() is based on the 'SAS' procedure Proc Summary, which provides efficient and readable ways to perform complex aggregations.

Normally you would compute new categorical variables beforehand - probably even in different forms, if you wanted to have different categorizations - and bloat up the data set. After all this recoding footwork you could finally use multiple summaries to compute all the stats you need to then put them back together. With this function this is no more necessary.

In summarise_plus() you put in the original data frame and let the recoding happen via format containers. This is very efficient, since new variables and categories are only created just before the summarise happens.

Additionally you can specify whether you only want to produce the all nested version of all group variables or whether you want to produce every possible combination in one go. All with a single option.

The function is optimized to always take the fastest route, depending on the options specified.

Value

Returns a summarised data table.

See Also

Creating formats: discrete_format() and interval_format().

Functions that also make use of formats: frequencies(), crosstabs(), any_table(), recode(), recode_multi().

Examples

# 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)

income. <- interval_format(
    "Total"              = 0:99999,
    "below 500"          = 0:499,
    "500 to under 1000"  = 500:999,
    "1000 to under 2000" = 1000:1999,
    "2000 and more"      = 2000:99999)

# Example data frame
my_data <- dummy_data(1000)

# Call function
all_nested <- my_data |>
    summarise_plus(class      = c(year, sex, age),
                   values     = income,
                   statistics = c("sum", "pct_group", "pct_total", "sum_wgt", "freq"),
                   formats    = list(sex = sex., age = age.),
                   weight     = weight,
                   nesting    = "deepest",
                   na.rm      = TRUE)

all_possible <- my_data |>
    summarise_plus(class      = c(year, sex, age, income),
                   values     = c(probability),
                   statistics = c("sum", "p1", "p99", "min", "max", "freq", "freq_g0"),
                   formats    = list(sex    = sex.,
                                     age    = age.,
                                     income = income.),
                   weight     = weight,
                   nesting    = "all",
                   na.rm      = TRUE)

# Formats can also be passed as characters
single <- my_data |>
    summarise_plus(class      = c(year, age, sex),
                   values     = weight,
                   statistics = c("sum", "mean"),
                   formats    = list(sex = "sex.", age = "age."),
                   nesting    = "single")

merge_back <- my_data |>
    summarise_plus(class      = c(year, age, sex),
                   values     = weight,
                   statistics = c("sum", "mean"),
                   nesting    = "deepest",
                   merge_back = TRUE)

certain_types <- my_data |>
    summarise_plus(class      = c(year, sex, age),
                   values     = c(probability),
                   statistics = c("sum", "mean", "freq"),
                   formats    = list(sex = sex.,
                                     age = age.),
                   types      = c("year", "year + age", "age + sex"),
                   weight     = weight,
                   nesting    = "all",
                   na.rm      = TRUE)