Package 'janitor'

Add underlying Ns to a tabyl displaying percentages.

Description

This function adds back the underlying Ns to a tabyl whose percentages were calculated using adorn_percentages(), to display the Ns and percentages together. You can also call it on a non-tabyl data.frame to which you wish to append Ns.

Usage

adorn_ns(
  dat,
  position = "rear",
  ns = attr(dat, "core"),
  format_func = function(x) {
     format(x, big.mark = ",")
 },
  ...
)

Arguments

dat	A data.frame of class tabyl that has had adorn_percentages and/or adorn_pct_formatting called on it. If given a list of data.frames, this function will apply itself to each data.frame in the list (designed for 3-way tabyl lists).
position	Should the N go in the front, or in the rear, of the percentage?
ns	The Ns to append. The default is the "core" attribute of the input tabyl dat, where the original Ns of a two-way tabyl are stored. However, if your Ns are stored somewhere else, or you need to customize them beyond what can be done with format_func, you can supply them here.
format_func	A formatting function to run on the Ns. Consider defining with base::format().
...	Columns to adorn. This takes a tidyselect specification. By default, all columns are adorned except for the first column and columns not of class numeric, but this allows you to manually specify which columns should be adorned, for use on a data.frame that does not result from a call to tabyl.

Value

A data.frame with Ns appended

Examples

mtcars %>%
  tabyl(am, cyl) %>%
  adorn_percentages("col") %>%
  adorn_pct_formatting() %>%
  adorn_ns(position = "front")

# Format the Ns with a custom format_func:
set.seed(1)
bigger_dat <- data.frame(
  sex = rep(c("m", "f"), 3000),
  age = round(runif(3000, 1, 102), 0)
)
bigger_dat$age_group <- cut(bigger_dat$age, quantile(bigger_dat$age, c(0, 1 / 3, 2 / 3, 1)))

bigger_dat %>%
  tabyl(age_group, sex, show_missing_levels = FALSE) %>%
  adorn_totals(c("row", "col")) %>%
  adorn_percentages("col") %>%
  adorn_pct_formatting(digits = 1) %>%
  adorn_ns(format_func = function(x) format(x, big.mark = ".", decimal.mark = ","))
# Control the columns to be adorned with the ... variable selection argument
# If using only the ... argument, you can use empty commas as shorthand
# to supply the default values to the preceding arguments:

cases <- data.frame(
  region = c("East", "West"),
  year = 2015,
  recovered = c(125, 87),
  died = c(13, 12)
)

cases %>%
 adorn_percentages("col",,recovered:died) %>%
 adorn_pct_formatting(,,,,,recovered:died) %>%
 adorn_ns(,,,recovered:died)

---

Format a data.frame of decimals as percentages.

Description

Numeric columns get multiplied by 100 and formatted as percentages according to user specifications. This function defaults to excluding the first column of the input data.frame, assuming that it contains a descriptive variable, but this can be overridden by specifying the columns to adorn in the ... argument. Non-numeric columns are always excluded.

The decimal separator character is the result of getOption("OutDec"), which is based on the user's locale. If the default behavior is undesirable, change this value ahead of calling the function, either by changing locale or with options(OutDec = ","). This aligns the decimal separator character with that used in base::print().

Usage

adorn_pct_formatting(
  dat,
  digits = 1,
  rounding = "half to even",
  affix_sign = TRUE,
  ...
)

Arguments

dat	a data.frame with decimal values, typically the result of a call to adorn_percentages on a tabyl. If given a list of data.frames, this function will apply itself to each data.frame in the list (designed for 3-way tabyl lists).
digits	how many digits should be displayed after the decimal point?
rounding	method to use for rounding - either "half to even", the base R default method, or "half up", where 14.5 rounds up to 15.
affix_sign	should the % sign be affixed to the end?
...	columns to adorn. This takes a tidyselect specification. By default, all numeric columns (besides the initial column, if numeric) are adorned, but this allows you to manually specify which columns should be adorned, for use on a data.frame that does not result from a call to tabyl.

Value

a data.frame with formatted percentages

Examples

mtcars %>%
  tabyl(am, cyl) %>%
  adorn_percentages("col") %>%
  adorn_pct_formatting()

# Control the columns to be adorned with the ... variable selection argument
# If using only the ... argument, you can use empty commas as shorthand
# to supply the default values to the preceding arguments:

cases <- data.frame(
  region = c("East", "West"),
  year = 2015,
  recovered = c(125, 87),
  died = c(13, 12)
)

cases %>%
  adorn_percentages("col", , recovered:died) %>%
  adorn_pct_formatting(, , , recovered:died)

---

Convert a data.frame of counts to percentages.

Description

This function defaults to excluding the first column of the input data.frame, assuming that it contains a descriptive variable, but this can be overridden by specifying the columns to adorn in the ... argument.

Usage

adorn_percentages(dat, denominator = "row", na.rm = TRUE, ...)

Arguments

dat	A tabyl or other data.frame with a tabyl-like layout. If given a list of data.frames, this function will apply itself to each data.frame in the list (designed for 3-way tabyl lists).
denominator	The direction to use for calculating percentages. One of "row", "col", or "all".
na.rm	should missing values (including NaN) be omitted from the calculations?
...	columns to adorn. This takes a <tidy-select> specification. By default, all numeric columns (besides the initial column, if numeric) are adorned, but this allows you to manually specify which columns should be adorned, for use on a data.frame that does not result from a call to tabyl().

Value

A data.frame of percentages, expressed as numeric values between 0 and 1.

Examples

mtcars %>%
  tabyl(am, cyl) %>%
  adorn_percentages("col")

# calculates correctly even with totals column and/or row:
mtcars %>%
  tabyl(am, cyl) %>%
  adorn_totals("row") %>%
  adorn_percentages()

# Control the columns to be adorned with the ... variable selection argument
# If using only the ... argument, you can use empty commas as shorthand
# to supply the default values to the preceding arguments:

cases <- data.frame(
  region = c("East", "West"),
  year = 2015,
  recovered = c(125, 87),
  died = c(13, 12)
)

cases %>%
  adorn_percentages(, , recovered:died)

---

Round the numeric columns in a data.frame.

Description

Can run on any data.frame with at least one numeric column. This function defaults to excluding the first column of the input data.frame, assuming that it contains a descriptive variable, but this can be overridden by specifying the columns to round in the ... argument.

If you're formatting percentages, e.g., the result of adorn_percentages(), use adorn_pct_formatting() instead. This is a more flexible variant for ad-hoc usage. Compared to adorn_pct_formatting(), it does not multiply by 100 or pad the numbers with spaces for alignment in the results data.frame. This function retains the class of numeric input columns.

Usage

adorn_rounding(dat, digits = 1, rounding = "half to even", ...)

Arguments

dat	A tabyl or other data.frame with similar layout. If given a list of data.frames, this function will apply itself to each data.frame in the list (designed for 3-way tabyl lists).
digits	How many digits should be displayed after the decimal point?
rounding	Method to use for rounding - either "half to even" (the base R default method), or "half up", where 14.5 rounds up to 15.
...	Columns to adorn. This takes a tidyselect specification. By default, all numeric columns (besides the initial column, if numeric) are adorned, but this allows you to manually specify which columns should be adorned, for use on a data.frame that does not result from a call to tabyl.

Value

The data.frame with rounded numeric columns.

Examples

mtcars %>%
  tabyl(am, cyl) %>%
  adorn_percentages() %>%
  adorn_rounding(digits = 2, rounding = "half up")

# tolerates non-numeric columns:
library(dplyr)
mtcars %>%
  tabyl(am, cyl) %>%
  adorn_percentages("all") %>%
  mutate(dummy = "a") %>%
  adorn_rounding()

# Control the columns to be adorned with the ... variable selection argument
# If using only the ... argument, you can use empty commas as shorthand
# to supply the default values to the preceding arguments:
cases <- data.frame(
  region = c("East", "West"),
  year = 2015,
  recovered = c(125, 87),
  died = c(13, 12)
)

cases %>%
  adorn_percentages(, , ends_with("ed")) %>%
  adorn_rounding(, , all_of(c("recovered", "died")))

---

Add column name to the top of a two-way tabyl.

Description

This function adds the column variable name to the top of a tabyl for a complete display of information. This makes the tabyl prettier, but renders the data.frame less useful for further manipulation.

Usage

adorn_title(dat, placement = "top", row_name, col_name)

Arguments

dat	A data.frame of class tabyl or other data.frame with a tabyl-like layout. If given a list of data.frames, this function will apply itself to each data.frame in the list (designed for 3-way tabyl lists).
placement	The title placement, one of "top", or "combined". See Details for more information.
row_name	(optional) default behavior is to pull the row name from the attributes of the input tabyl object. If you wish to override that text, or if your input is not a tabyl, supply a string here.
col_name	(optional) default behavior is to pull the column_name from the attributes of the input tabyl object. If you wish to override that text, or if your input is not a tabyl, supply a string here.

Details

The placement argument indicates whether the column name should be added to the top of the tabyl in an otherwise-empty row "top" or appended to the already-present row name variable ("combined"). The formatting in the "top" option has the look of base R's table(); it also wipes out the other column names, making it hard to further use the data.frame besides formatting it for reporting. The "combined" option is more conservative in this regard.

Value

The input tabyl, augmented with the column title. Non-tabyl inputs that are of class tbl_df are downgraded to basic data.frames so that the title row prints correctly.

Examples

mtcars %>%
  tabyl(am, cyl) %>%
  adorn_title(placement = "top")

# Adding a title to a non-tabyl
library(tidyr)
library(dplyr)
mtcars %>%
  group_by(gear, am) %>%
  summarise(avg_mpg = mean(mpg), .groups = "drop") %>%
  pivot_wider(names_from = am, values_from = avg_mpg) %>%
  adorn_rounding() %>%
  adorn_title("top", row_name = "Gears", col_name = "Cylinders")

---

Append a totals row and/or column to a data.frame

Description

This function defaults to excluding the first column of the input data.frame, assuming that it contains a descriptive variable, but this can be overridden by specifying the columns to be totaled in the ... argument. Non-numeric columns are converted to character class and have a user-specified fill character inserted in the totals row.

Usage

adorn_totals(dat, where = "row", fill = "-", na.rm = TRUE, name = "Total", ...)

Arguments

dat	An input data.frame with at least one numeric column. If given a list of data.frames, this function will apply itself to each data.frame in the list (designed for 3-way tabyl lists).
where	One of "row", "col", or c("row", "col")
fill	If there are non-numeric columns, what should fill the bottom row of those columns? If a string, relevant columns will be coerced to character. If NA then column types are preserved.
na.rm	Should missing values (including NaN) be omitted from the calculations?
name	Name of the totals row and/or column. If both are created, and name is a single string, that name is applied to both. If both are created and name is a vector of length 2, the first element of the vector will be used as the row name (in column 1), and the second element will be used as the totals column name. Defaults to "Total".
...	Columns to total. This takes a tidyselect specification. By default, all numeric columns (besides the initial column, if numeric) are included in the totals, but this allows you to manually specify which columns should be included, for use on a data.frame that does not result from a call to tabyl.

Value

A data.frame augmented with a totals row, column, or both. The data.frame is now also of class tabyl and stores information about the attached totals and underlying data in the tabyl attributes.

Examples

mtcars %>%
  tabyl(am, cyl) %>%
  adorn_totals()

---

Add tabyl attributes to a data.frame

Description

A tabyl is a data.frame containing counts of a variable or co-occurrences of two variables (a.k.a., a contingency table or crosstab). This specialized kind of data.frame has attributes that enable adorn_ functions to be called for precise formatting and presentation of results. E.g., display results as a mix of percentages, Ns, add totals rows or columns, rounding options, in the style of Microsoft Excel PivotTable.

A tabyl can be the result of a call to janitor::tabyl(), in which case these attributes are added automatically. This function adds tabyl class attributes to a data.frame that isn't the result of a call to tabyl but meets the requirements of a two-way tabyl: 1) First column contains values of variable 1 2) Column names 2:n are the values of variable 2 3) Numeric values in columns 2:n are counts of the co-occurrences of the two variables.*

• = this is the ideal form of a tabyl, but janitor's adorn_ functions tolerate and ignore non-numeric columns in positions 2:n.

For instance, the result of dplyr::count() followed by tidyr::pivot_wider() can be treated as a tabyl.

The result of calling tabyl() on a single variable is a special class of one-way tabyl; this function only pertains to the two-way tabyl.

Usage

as_tabyl(dat, axes = 2, row_var_name = NULL, col_var_name = NULL)

Arguments

dat	a data.frame with variable values in the first column and numeric values in all other columns.
axes	is this a two_way tabyl or a one_way tabyl? If this function is being called by a user, this should probably be "2". One-way tabyls are created by tabyl but are a special case.
row_var_name	(optional) the name of the variable in the row dimension; used by adorn_title().
col_var_name	(optional) the name of the variable in the column dimension; used by adorn_title().

Value

Returns the same data.frame, but with the additional class of "tabyl" and the attribute "core".

Examples

as_tabyl(mtcars)

---

Apply stats::chisq.test() to a two-way tabyl

Description

This generic function overrides stats::chisq.test. If the passed table is a two-way tabyl, it runs it through janitor::chisq.test.tabyl, otherwise it just calls stats::chisq.test().

Usage

chisq.test(x, ...)

## Default S3 method:
chisq.test(x, y = NULL, ...)

## S3 method for class 'tabyl'
chisq.test(x, tabyl_results = TRUE, ...)

Arguments

x	a two-way tabyl, a numeric vector or a factor
...	other parameters passed to stats::chisq.test()
y	if x is a vector, must be another vector or factor of the same length
tabyl_results	If TRUE and x is a tabyl object, also return observed, expected, residuals and stdres as tabyl.

Value

The result is the same as the one of stats::chisq.test(). If tabyl_results is TRUE, the returned tables observed, expected, residuals and stdres are converted to tabyls.

Examples

tab <- tabyl(mtcars, gear, cyl)
chisq.test(tab)
chisq.test(tab)$residuals

---

Cleans names of an object (usually a data.frame).

Description

Resulting names are unique and consist only of the ⁠_⁠ character, numbers, and letters. Capitalization preferences can be specified using the case parameter.

Accented characters are transliterated to ASCII. For example, an "o" with a German umlaut over it becomes "o", and the Spanish character "enye" becomes "n".

This function takes and returns a data.frame, for ease of piping with ⁠%>%⁠. For the underlying function that works on a character vector of names, see make_clean_names(). clean_names relies on the versatile function snakecase::to_any_case(), which accepts many arguments. See that function's documentation for ideas on getting the most out of clean_names. A few examples are included below.

A common issue is that the micro/mu symbol is replaced by "m" instead of "u". The replacement with "m" is more correct when doing Greek-to-ASCII transliteration but less correct when doing scientific data-to-ASCII transliteration. A warning will be generated if the "m" replacement occurs. To replace with "u", please add the argument replace=janitor:::mu_to_u which is a character vector mapping all known mu or micro Unicode code points (characters) to "u".

Usage

clean_names(dat, ...)

## Default S3 method:
clean_names(dat, ...)

## S3 method for class 'sf'
clean_names(dat, ...)

## S3 method for class 'tbl_graph'
clean_names(dat, ...)

## S3 method for class 'tbl_lazy'
clean_names(dat, ...)

Arguments

dat

The input data.frame.

...

Arguments passed on to make_clean_names case The desired target case (default is "snake") will be passed to snakecase::to_any_case() with the exception of "old_janitor", which exists only to support legacy code (it preserves the behavior of clean_names() prior to addition of the "case" argument (janitor versions <= 0.3.1). "old_janitor" is not intended for new code. See snakecase::to_any_case() for a wide variety of supported cases, including "sentence" and "title" case. replace A named character vector where the name is replaced by the value. ascii Convert the names to ASCII (TRUE, default) or not (FALSE). use_make_names Should make.names() be applied to ensure that the output is usable as a name without quoting? (Avoiding make.names() ensures that the output is locale-independent but quoting may be required.) allow_dupes Allow duplicates in the returned names (TRUE) or not (FALSE, the default). sep_in (short for separator input) if character, is interpreted as a regular expression (wrapped internally into stringr::regex()). The default value is a regular expression that matches any sequence of non-alphanumeric values. All matches will be replaced by underscores (additionally to "_" and " ", for which this is always true, even if NULL is supplied). These underscores are used internally to split the strings into substrings and specify the word boundaries. parsing_option An integer that will determine the parsing_option. 1: "RRRStudio" -> "RRR_Studio" 2: "RRRStudio" -> "RRRS_tudio" 3: "RRRStudio" -> "RRRSStudio". This will become for example "Rrrstudio" when we convert to lower camel case. -1, -2, -3: These parsing_options's will suppress the conversion after non-alphanumeric values. 0: no parsing transliterations A character vector (if not NULL). The entries of this argument need to be elements of stringi::stri_trans_list() (like "Latin-ASCII", which is often useful) or names of lookup tables (currently only "german" is supported). In the order of the entries the letters of the input string will be transliterated via stringi::stri_trans_general() or replaced via the matches of the lookup table. When named character elements are supplied as part of 'transliterations', anything that matches the names is replaced by the corresponding value. You should use this feature with care in case of case = "parsed", case = "internal_parsing" and case = "none", since for upper case letters, which have transliterations/replacements of length 2, the second letter will be transliterated to lowercase, for example Oe, Ae, Ss, which might not always be what is intended. In this case you can make usage of the option to supply named elements and specify the transliterations yourself. numerals A character specifying the alignment of numerals ("middle", left, right, asis or tight). I.e. numerals = "left" ensures that no output separator is in front of a digit.

Details

clean_names() is intended to be used on data.frames and data.frame-like objects. For this reason there are methods to support using clean_names() on sf and tbl_graph (from tidygraph) objects as well as on database connections through dbplyr. For cleaning other named objects like named lists and vectors, use make_clean_names().

Value

A data.frame with clean names.

See Also

Other Set names: find_header(), mu_to_u, row_to_names()

Examples

# --- Simple Usage ---
x <- data.frame(caseID = 1, DOB = 2, Other = 3)
clean_names(x)

# or pipe in the input data.frame:
x %>%
  clean_names()

# if you prefer camelCase variable names:
x %>%
  clean_names(., "lower_camel")

# (not run) run clean_names after reading in a spreadsheet:
# library(readxl)
# read_excel("messy_excel_file.xlsx") %>%
#   clean_names()

# --- Taking advantage of the underlying snakecase::to_any_case arguments ---

# Restore column names to Title Case, e.g., for plotting
mtcars %>%
  clean_names(case = "title")

# Tell clean_names to leave certain abbreviations untouched:
x %>%
  clean_names(case = "upper_camel", abbreviations = c("ID", "DOB"))

---

Compare data frames columns before merging

Description

Generate a comparison of data.frames (or similar objects) that indicates if they will successfully bind together by rows.

Usage

compare_df_cols(
  ...,
  return = c("all", "match", "mismatch"),
  bind_method = c("bind_rows", "rbind"),
  strict_description = FALSE
)

Arguments

...	A combination of data.frames, tibbles, and lists of data.frames/tibbles. The values may optionally be named arguments; if named, the output column will be the name; if not named, the output column will be the data.frame name (see examples section).
return	Should a summary of "all" columns be returned, only return "match"ing columns, or only "mismatch"ing columns?
bind_method	What method of binding should be used to determine matches? With "bind_rows", columns missing from a data.frame would be considered a match (as in dplyr::bind_rows(); with "rbind", columns missing from a data.frame would be considered a mismatch (as in base::rbind().
strict_description	Passed to describe_class. Also, see the Details section.

Details

Due to the returned "column_name" column, no input data.frame may be named "column_name".

The strict_description argument is most typically used to understand if factor levels match or are bindable. Factors are typically bindable, but the behavior of what happens when they bind differs based on the binding method ("bind_rows" or "rbind"). Even when strict_description is FALSE, data.frames may still bind because some classes (like factors and characters) can bind even if they appear to differ.

Value

A data.frame with a column named "column_name" with a value named after the input data.frames' column names, and then one column per data.frame (named after the input data.frame). If more than one input has the same column name, the column naming will have suffixes defined by sequential use of base::merge() and may differ from expected naming. The rows within the data.frame-named columns are descriptions of the classes of the data within the columns (generated by describe_class).

See Also

Other data frame type comparison: compare_df_cols_same(), describe_class()

Examples

compare_df_cols(data.frame(A = 1), data.frame(B = 2))
# user-defined names
compare_df_cols(dfA = data.frame(A = 1), dfB = data.frame(B = 2))
# a combination of list and data.frame input
compare_df_cols(listA = list(dfA = data.frame(A = 1), dfB = data.frame(B = 2)), data.frame(A = 3))

---

Do the the data.frames have the same columns & types?

Description

Check whether a set of data.frames are row-bindable. Calls compare_df_cols() and returns TRUE if there are no mis-matching rows.

Usage

compare_df_cols_same(
  ...,
  bind_method = c("bind_rows", "rbind"),
  verbose = TRUE
)

Arguments

...	A combination of data.frames, tibbles, and lists of data.frames/tibbles. The values may optionally be named arguments; if named, the output column will be the name; if not named, the output column will be the data.frame name (see examples section).
bind_method	What method of binding should be used to determine matches? With "bind_rows", columns missing from a data.frame would be considered a match (as in dplyr::bind_rows(); with "rbind", columns missing from a data.frame would be considered a mismatch (as in base::rbind().
verbose	Print the mismatching columns if binding will fail.

Value

TRUE if row binding will succeed or FALSE if it will fail.

See Also

Other data frame type comparison: compare_df_cols(), describe_class()

Examples

compare_df_cols_same(data.frame(A = 1), data.frame(A = 2))
compare_df_cols_same(data.frame(A = 1), data.frame(B = 2))
compare_df_cols_same(data.frame(A = 1), data.frame(B = 2), verbose = FALSE)
compare_df_cols_same(data.frame(A = 1), data.frame(B = 2), bind_method = "rbind")

---

Parse dates from many formats

Description

Convert many date and date-time (POSIXct) formats as may be received from Microsoft Excel.

Usage

convert_to_date(
  x,
  ...,
  character_fun = lubridate::ymd,
  string_conversion_failure = c("error", "warning")
)

convert_to_datetime(
  x,
  ...,
  tz = "UTC",
  character_fun = lubridate::ymd_hms,
  string_conversion_failure = c("error", "warning")
)

Arguments

x	The object to convert
...	Passed to further methods. Eventually may be passed to excel_numeric_to_date(), base::as.POSIXct(), or base::as.Date().
character_fun	A function to convert non-numeric-looking, non-NA values in x to POSIXct objects.
string_conversion_failure	If a character value fails to parse into the desired class and instead returns NA, should the function return the result with a warning or throw an error?
tz	The timezone for POSIXct output, unless an object is POSIXt already. Ignored for Date output.

Details

Character conversion checks if it matches something that looks like a Microsoft Excel numeric date, converts those to numeric, and then runs convert_to_datetime_helper() on those numbers. Then, character to Date or POSIXct conversion occurs via character_fun(x, ...) or character_fun(x, tz=tz, ...), respectively.

Value

POSIXct objects for convert_to_datetime() or Date objects for convert_to_date().

See Also

Other date-time cleaning: excel_numeric_to_date(), excel_time_to_numeric(), sas_numeric_to_date()

Examples

convert_to_date("2009-07-06")
convert_to_date(40000)
convert_to_date("40000.1")
# Mixed date source data can be provided.
convert_to_date(c("2020-02-29", "40000.1"))
convert_to_datetime(
  c("2009-07-06", "40000.1", "40000", NA),
  character_fun = lubridate::ymd_h, truncated = 1, tz = "UTC"
)

---

Describe the class(es) of an object

Description

Describe the class(es) of an object

Usage

describe_class(x, strict_description = TRUE)

## S3 method for class 'factor'
describe_class(x, strict_description = TRUE)

## Default S3 method:
describe_class(x, strict_description = TRUE)

Arguments

x	The object to describe
strict_description	Should differing factor levels be treated as differences for the purposes of identifying mismatches? strict_description = TRUE is stricter and factors with different levels will be treated as different classes. FALSE is more lenient: for class comparison purposes, the variable is just a "factor".

Details

For package developers, an S3 generic method can be written for describe_class() for custom classes that may need more definition than the default method. This function is called by compare_df_cols().

Value

A character scalar describing the class(es) of an object where if the scalar will match, columns in a data.frame (or similar object) should bind together without issue.

Methods (by class)

• describe_class(factor): Describe factors with their levels and if they are ordered.

• describe_class(default): List all classes of an object.

See Also

Other data frame type comparison: compare_df_cols(), compare_df_cols_same()

Examples

describe_class(1)
describe_class(factor("A"))
describe_class(ordered(c("A", "B")))
describe_class(ordered(c("A", "B")), strict_description = FALSE)

---

Convert dates encoded as serial numbers to Date class.

Description

Converts numbers like 42370 into date values like 2016-01-01.

Defaults to the modern Excel date encoding system. However, Excel for Mac 2008 and earlier Mac versions of Excel used a different date system. To determine what platform to specify: if the date 2016-01-01 is represented by the number 42370 in your spreadsheet, it's the modern system. If it's 40908, it's the old Mac system. More on date encoding systems at http://support.office.com/en-us/article/Date-calculations-in-Excel-e7fe7167-48a9-4b96-bb53-5612a800b487.

A list of all timezones is available from base::OlsonNames(), and the current timezone is available from base::Sys.timezone().

If your input data has a mix of Excel numeric dates and actual dates, see the more powerful functions convert_to_date() and convert_to_datetime().

Usage

excel_numeric_to_date(
  date_num,
  date_system = "modern",
  include_time = FALSE,
  round_seconds = TRUE,
  tz = Sys.timezone()
)

Arguments

date_num	numeric vector of serial numbers to convert.
date_system	the date system, either "modern" or "mac pre-2011".
include_time	Include the time (hours, minutes, seconds) in the output? (See details)
round_seconds	Round the seconds to an integer (only has an effect when include_time is TRUE)?
tz	Time zone, used when include_time = TRUE (see details for more information on timezones).

Details

When using include_time=TRUE, days with leap seconds will not be accurately handled as they do not appear to be accurately handled by Windows (as described in https://support.microsoft.com/en-us/help/2722715/support-for-the-leap-second).

Value

Returns a vector of class Date if include_time is FALSE. Returns a vector of class POSIXlt if include_time is TRUE.

See Also

excel_time_to_numeric()

Other date-time cleaning: convert_to_date(), excel_time_to_numeric(), sas_numeric_to_date()

Examples

excel_numeric_to_date(40000)
excel_numeric_to_date(40000.5) # No time is included
excel_numeric_to_date(40000.5, include_time = TRUE) # Time is included
excel_numeric_to_date(40000.521, include_time = TRUE) # Time is included
excel_numeric_to_date(40000.521,
  include_time = TRUE,
  round_seconds = FALSE
) # Time with fractional seconds is included

---

Convert a time that may be inconsistently or inconveniently formatted from Microsoft Excel to a numeric number of seconds between 0 and 86400.

Description

Convert a time that may be inconsistently or inconveniently formatted from Microsoft Excel to a numeric number of seconds between 0 and 86400.

Usage

excel_time_to_numeric(time_value, round_seconds = TRUE)

Arguments

time_value	A vector of values to convert (see Details)
round_seconds	Should the output number of seconds be rounded to an integer?

Details

time_value may be one of the following formats:

• numericThe input must be a value from 0 to 1 (exclusive of 1); this value is returned as-is.

• POSIXlt or POSIXctThe input must be on the day 1899-12-31 (any other day will cause an error). The time of day is extracted and converted to a fraction of a day.

• characterAny of the following (or a mixture of the choices):

  • A character string that is a number between 0 and 1 (exclusive of 1). This value will be converted like a numeric value.

  • A character string that looks like a date on 1899-12-31 (specifically, it must start with "1899-12-31 "), converted like a POSIXct object as described above.

  • A character string that looks like a time. Choices are 12-hour time as hour, minute, and optionally second followed by "am" or "pm" (case insensitive) or 24-hour time when hour, minute, optionally second, and no "am" or "pm" is included.

Value

A vector of numbers >= 0 and <86400

See Also

excel_numeric_to_date()

Other date-time cleaning: convert_to_date(), excel_numeric_to_date(), sas_numeric_to_date()

---

Find the header row in a data.frame

Description

Find the header row in a data.frame

Usage

find_header(dat, ...)

Arguments

dat	The input data.frame
...	See details

Details

If ... is missing, then the first row with no missing values is used.

When searching for a specified value or value within a column, the first row with a match will be returned, regardless of the completeness of the rest of that row. If ... has a single character argument, then the first column is searched for that value. If ... has a named numeric argument, then the column whose position number matches the value of that argument is searched for the name (see the last example below). If more than one row is found matching a value that is searched for, the number of the first matching row will be returned (with a warning).

Value

The row number for the header row

See Also

Other Set names: clean_names(), mu_to_u, row_to_names()

Examples

# the first row
find_header(data.frame(A = "B"))
# the second row
find_header(data.frame(A = c(NA, "B")))
# the second row since the first has an empty value
find_header(data.frame(A = c(NA, "B"), B = c("C", "D")))
# The third row because the second column was searched for the text "E"
find_header(data.frame(A = c(NA, "B", "C", "D"), B = c("C", "D", "E", "F")), "E" = 2)

---

Apply stats::fisher.test() to a two-way tabyl

Description

This generic function overrides stats::fisher.test(). If the passed table is a two-way tabyl, it runs it through janitor::fisher.test.tabyl, otherwise it just calls stats::fisher.test().

Usage

fisher.test(x, ...)

## Default S3 method:
fisher.test(x, y = NULL, ...)

## S3 method for class 'tabyl'
fisher.test(x, ...)

Arguments

x	A two-way tabyl, a numeric vector or a factor
...	Parameters passed to stats::fisher.test()
y	if x is a vector, must be another vector or factor of the same length

Value

The same as the one of stats::fisher.test().

Examples

tab <- tabyl(mtcars, gear, cyl)
fisher.test(tab)

---

Get rows of a data.frame with identical values for the specified variables.

Description

For hunting duplicate records during data cleaning. Specify the data.frame and the variable combination to search for duplicates and get back the duplicated rows.

Usage

get_dupes(dat, ...)

Arguments

dat	The input data.frame.
...	Unquoted variable names to search for duplicates. This takes a tidyselect specification.

Value

A data.frame with the full records where the specified variables have duplicated values, as well as a variable dupe_count showing the number of rows sharing that combination of duplicated values. If the input data.frame was of class tbl_df, the output is as well.

Examples

get_dupes(mtcars, mpg, hp)

# or called with the magrittr pipe %>% :
mtcars %>% get_dupes(wt)

# You can use tidyselect helpers to specify variables:
mtcars %>% get_dupes(-c(wt, qsec))
mtcars %>% get_dupes(starts_with("cy"))

---

Find the list of columns that have a 1:1 mapping to each other

Description

Find the list of columns that have a 1:1 mapping to each other

Usage

get_one_to_one(dat)

Arguments

dat	A data.frame or similar object

Value

A list with one element for each group of columns that map identically to each other.

Examples

foo <- data.frame(
  Lab_Test_Long = c("Cholesterol, LDL", "Cholesterol, LDL", "Glucose"),
  Lab_Test_Short = c("CLDL", "CLDL", "GLUC"),
  LOINC = c(12345, 12345, 54321),
  Person = c("Sam", "Bill", "Sam"),
  stringsAsFactors = FALSE
)
get_one_to_one(foo)

---

Cleans a vector of text, typically containing the names of an object.

Description

Resulting strings are unique and consist only of the ⁠_⁠ character, numbers, and letters. By default, the resulting strings will only consist of ASCII characters, but non-ASCII (e.g. Unicode) may be allowed by setting ascii = FALSE. Capitalization preferences can be specified using the case parameter.

For use on the names of a data.frame, e.g., in a ⁠%>%⁠ pipeline, call the convenience function clean_names().

When ascii = TRUE (the default), accented characters are transliterated to ASCII. For example, an "o" with a German umlaut over it becomes "o", and the Spanish character "enye" becomes "n".

The order of operations is: make replacements, (optional) ASCII conversion, remove initial spaces and punctuation, apply base::make.names(), apply ⁠snakecase::to_any_case(()⁠, and add numeric suffixes to resolve any duplicated names.

This function relies on snakecase::to_any_case() and can take advantage of its versatility. For instance, an abbreviation like "ID" can have its capitalization preserved by passing the argument abbreviations = "ID". See the documentation for snakecase::to_any_case() for more about how to use its features.

On some systems, not all transliterators to ASCII are available. If this is the case on your system, all available transliterators will be used, and a warning will be issued once per session indicating that results may be different when run on a different system. That warning can be disabled with options(janitor_warn_transliterators=FALSE).

If the objective of your call to make_clean_names() is only to translate to ASCII, try the following instead: stringi::stri_trans_general(x, id="Any-Latin;Greek-Latin;Latin-ASCII").

Usage

make_clean_names(
  string,
  case = "snake",
  replace = c(`'` = "", `"` = "", `%` = "_percent_", `#` = "_number_"),
  ascii = TRUE,
  use_make_names = TRUE,
  allow_dupes = FALSE,
  sep_in = "\\.",
  transliterations = "Latin-ASCII",
  parsing_option = 1,
  numerals = "asis",
  ...
)

Arguments

string	A character vector of names to clean.
case	The desired target case (default is "snake") will be passed to snakecase::to_any_case() with the exception of "old_janitor", which exists only to support legacy code (it preserves the behavior of clean_names() prior to addition of the "case" argument (janitor versions <= 0.3.1). "old_janitor" is not intended for new code. See snakecase::to_any_case() for a wide variety of supported cases, including "sentence" and "title" case.
replace	A named character vector where the name is replaced by the value.
ascii	Convert the names to ASCII (TRUE, default) or not (FALSE).
use_make_names	Should make.names() be applied to ensure that the output is usable as a name without quoting? (Avoiding make.names() ensures that the output is locale-independent but quoting may be required.)
allow_dupes	Allow duplicates in the returned names (TRUE) or not (FALSE, the default).
sep_in	(short for separator input) if character, is interpreted as a regular expression (wrapped internally into stringr::regex()). The default value is a regular expression that matches any sequence of non-alphanumeric values. All matches will be replaced by underscores (additionally to "_" and " ", for which this is always true, even if NULL is supplied). These underscores are used internally to split the strings into substrings and specify the word boundaries.
transliterations	A character vector (if not NULL). The entries of this argument need to be elements of stringi::stri_trans_list() (like "Latin-ASCII", which is often useful) or names of lookup tables (currently only "german" is supported). In the order of the entries the letters of the input string will be transliterated via stringi::stri_trans_general() or replaced via the matches of the lookup table. When named character elements are supplied as part of 'transliterations', anything that matches the names is replaced by the corresponding value. You should use this feature with care in case of case = "parsed", case = "internal_parsing" and case = "none", since for upper case letters, which have transliterations/replacements of length 2, the second letter will be transliterated to lowercase, for example Oe, Ae, Ss, which might not always be what is intended. In this case you can make usage of the option to supply named elements and specify the transliterations yourself.
parsing_option	An integer that will determine the parsing_option. 1: "RRRStudio" -> "RRR_Studio" 2: "RRRStudio" -> "RRRS_tudio" 3: "RRRStudio" -> "RRRSStudio". This will become for example "Rrrstudio" when we convert to lower camel case. -1, -2, -3: These parsing_options's will suppress the conversion after non-alphanumeric values. 0: no parsing
numerals	A character specifying the alignment of numerals ("middle", left, right, asis or tight). I.e. numerals = "left" ensures that no output separator is in front of a digit.
...	Arguments passed on to snakecase::to_any_case abbreviations character. (Case insensitive) matched abbreviations are surrounded by underscores. In this way, they can get recognized by the parser. This is useful when e.g. parsing_option 1 is needed for the use case, but some abbreviations but some substrings would require parsing_option 2. Furthermore, this argument also specifies the formatting of abbreviations in the output for the cases title, mixed, lower and upper camel. E.g. for upper camel the first letter is always in upper case, but when the abbreviation is supplied in upper case, this will also be visible in the output. Use this feature with care: One letter abbreviations and abbreviations next to each other are hard to read and also not easy to parse for further processing. sep_out (short for separator output) String that will be used as separator. The defaults are "_" and "", regarding the specified case. When length(sep_out) > 1, the last element of sep_out gets recycled and separators are incorporated per string according to their order. unique_sep A string. If not NULL, then duplicated names will get a suffix integer in the order of their appearance. The suffix is separated by the supplied string to this argument. empty_fill A string. If it is supplied, then each entry that matches "" will be replaced by the supplied string to this argument. prefix prefix (string). postfix postfix (string).

Value

Returns the "cleaned" character vector.

See Also

snakecase::to_any_case()

Examples

# cleaning the names of a vector:
x <- structure(1:3, names = c("name with space", "TwoWords", "total $ (2009)"))
x
names(x) <- make_clean_names(names(x))
x # now has cleaned names

# if you prefer camelCase variable names:
make_clean_names(names(x), "small_camel")

# similar to janitor::clean_names(poorly_named_df):
# not run:
# make_clean_names(names(poorly_named_df))

---

Constant to help map from mu to u

Description

This is a character vector with names of all known Unicode code points that look like the Greek mu or the micro symbol and values of "u". This is intended to simplify mapping from mu or micro in Unicode to the character "u" with clean_names() and make_clean_names().

Usage

mu_to_u

Format

An object of class character of length 10.

Details

See the help in clean_names() for how to use this.

See Also

Other Set names: clean_names(), find_header(), row_to_names()

---

Like paste(), but missing values are omitted

Description

Like paste(), but missing values are omitted

Usage

paste_skip_na(..., sep = " ", collapse = NULL)

Arguments

..., sep, collapse

See base::paste()

Details

If all values are missing, the value from the first argument is preserved.

Value

A character vector of pasted values.

Examples

paste_skip_na(NA) # NA_character_
paste_skip_na("A", NA) # "A"
paste_skip_na("A", NA, c(NA, "B"), sep = ",") # c("A", "A,B")

---

Remove constant columns from a data.frame or matrix.

Description

Remove constant columns from a data.frame or matrix.

Usage

remove_constant(dat, na.rm = FALSE, quiet = TRUE)

Arguments

dat	the input data.frame or matrix.
na.rm	should NA values be removed when considering whether a column is constant? The default value of FALSE will result in a column not being removed if it's a mix of a single value and NA.
quiet	Should messages be suppressed (TRUE) or printed (FALSE) indicating the summary of empty columns or rows removed?

See Also

remove_empty() for removing empty columns or rows.

Other remove functions: remove_empty()

Examples

remove_constant(data.frame(A = 1, B = 1:3))

# To find the columns that are constant
data.frame(A = 1, B = 1:3) %>%
  dplyr::select(!dplyr::all_of(names(remove_constant(.)))) %>%
  unique()

---

Remove empty rows and/or columns from a data.frame or matrix.

Description

Removes all rows and/or columns from a data.frame or matrix that are composed entirely of NA values.

Usage

remove_empty(dat, which = c("rows", "cols"), cutoff = 1, quiet = TRUE)

Arguments

dat	the input data.frame or matrix.
which	one of "rows", "cols", or c("rows", "cols"). Where no value of which is provided, defaults to removing both empty rows and empty columns, declaring the behavior with a printed message.
cutoff	What fraction (>0 to <=1) of rows or columns must be empty to be removed?
quiet	Should messages be suppressed (TRUE) or printed (FALSE) indicating the summary of empty columns or rows removed?

Value

Returns the object without its missing rows or columns.

See Also

remove_constant() for removing constant columns.

Other remove functions: remove_constant()

Examples

# not run:
# dat %>% remove_empty("rows")
# addressing a common untidy-data scenario where we have a mixture of
# blank values in some (character) columns and NAs in others:
library(dplyr)
dd <- tibble(
  x = c(LETTERS[1:5], NA, rep("", 2)),
  y = c(1:5, rep(NA, 3))
)
# remove_empty() drops row 5 (all NA) but not 6 and 7 (blanks + NAs)
dd %>% remove_empty("rows")
# solution: preprocess to convert whitespace/empty strings to NA,
# _then_ remove empty (all-NA) rows
dd %>%
  mutate(across(where(is.character), ~ na_if(trimws(.), ""))) %>%
  remove_empty("rows")

---

Round a numeric vector; halves will be rounded up, ala Microsoft Excel.

Description

In base R round(), halves are rounded to even, e.g., 12.5 and 11.5 are both rounded to 12. This function rounds 12.5 to 13 (assuming digits = 0). Negative halves are rounded away from zero, e.g., -0.5 is rounded to -1.

This may skew subsequent statistical analysis of the data, but may be desirable in certain contexts. This function is implemented exactly from https://stackoverflow.com/a/12688836; see that question and comments for discussion of this issue.

Usage

round_half_up(x, digits = 0)

Arguments

x	a numeric vector to round.
digits	how many digits should be displayed after the decimal point?

Value

A vector with the same length as x

Examples

round_half_up(12.5)
round_half_up(1.125, 2)
round_half_up(1.125, 1)
round_half_up(-0.5, 0) # negatives get rounded away from zero

---

Round to the nearest fraction of a specified denominator.

Description

Round a decimal to the precise decimal value of a specified fractional denominator. Common use cases include addressing floating point imprecision and enforcing that data values fall into a certain set.

E.g., if a decimal represents hours and values should be logged to the nearest minute, round_to_fraction(x, 60) would enforce that distribution and 0.57 would be rounded to 0.566667, the equivalent of 34/60. 0.56 would also be rounded to 34/60.

Set denominator = 1 to round to whole numbers.

The digits argument allows for rounding of the subsequent result.

Usage

round_to_fraction(x, denominator, digits = Inf)

Arguments

x	A numeric vector
denominator	The denominator of the fraction for rounding (a scalar or vector positive integer).
digits	Integer indicating the number of decimal places to be used after rounding to the fraction. This is passed to base::round()). Negative values are allowed (see Details). (Inf indicates no subsequent rounding)

Details

If digits is Inf, x is rounded to the fraction and then kept at full precision. If digits is "auto", the number of digits is automatically selected as ceiling(log10(denominator)) + 1.

Value

the input x rounded to a decimal value that has an integer numerator relative to denominator (possibly subsequently rounded to a number of decimal digits).

Examples

round_to_fraction(1.6, denominator = 2)
round_to_fraction(pi, denominator = 7) # 22/7
round_to_fraction(c(8.1, 9.2), denominator = c(7, 8))
round_to_fraction(c(8.1, 9.2), denominator = c(7, 8), digits = 3)
round_to_fraction(c(8.1, 9.2, 10.3), denominator = c(7, 8, 1001), digits = "auto")

---

Elevate a row to be the column names of a data.frame.

Description

Elevate a row to be the column names of a data.frame.

Usage

row_to_names(
  dat,
  row_number,
  ...,
  remove_row = TRUE,
  remove_rows_above = TRUE,
  sep = "_"
)

Arguments

dat	The input data.frame
row_number	The row(s) of dat containing the variable names or the string "find_header" to use find_header(dat=dat, ...) to find the row_number. Allows for multiple rows input as a numeric vector. NA's are ignored, and if a column contains only NA value it will be named "NA".
...	Sent to find_header(), if row_number = "find_header". Otherwise, ignored.
remove_row	Should the row row_number be removed from the resulting data.frame?
remove_rows_above	If row_number != 1, should the rows above row_number - that is, between 1:(row_number-1) - be removed from the resulting data.frame?
sep	A character string to separate the values in the case of multiple rows input to row_number.

Value

A data.frame with new names (and some rows removed, if specified)

See Also

Other Set names: clean_names(), find_header(), mu_to_u

Examples

x <- data.frame(
  X_1 = c(NA, "Title", 1:3),
  X_2 = c(NA, "Title2", 4:6)
)
x %>%
  row_to_names(row_number = 2)

x %>%
  row_to_names(row_number = "find_header")

---

Convert a SAS date, time or date/time to an R object

Description

Convert a SAS date, time or date/time to an R object

Usage

sas_numeric_to_date(date_num, datetime_num, time_num, tz = "UTC")

Arguments

date_num	numeric vector of serial numbers to convert.
datetime_num	numeric vector of date/time numbers (seconds since midnight 1960-01-01) to convert
time_num	numeric vector of time numbers (seconds since midnight on the current day) to convert
tz	Time zone, used when include_time = TRUE (see details for more information on timezones).

Value

If a date and time or datetime are provided, a POSIXct object. If a date is provided, a Date object. If a time is provided, an hms::hms object

References

SAS Date, Time, and Datetime Values reference (retrieved on 2022-03-08): https://v8doc.sas.com/sashtml/lrcon/zenid-63.htm

See Also

Other date-time cleaning: convert_to_date(), excel_numeric_to_date(), excel_time_to_numeric()

Examples

sas_numeric_to_date(date_num = 15639) # 2002-10-26
sas_numeric_to_date(datetime_num = 1217083532, tz = "UTC") # 1998-07-26T14:45:32Z
sas_numeric_to_date(date_num = 15639, time_num = 3600, tz = "UTC") # 2002-10-26T01:00:00Z
sas_numeric_to_date(time_num = 3600) # 01:00:00

---

Round a numeric vector to the specified number of significant digits; halves will be rounded up.

Description

In base R signif(), halves are rounded to even, e.g., signif(11.5, 2) and signif(12.5, 2) are both rounded to 12. This function rounds 12.5 to 13 (assuming digits = 2). Negative halves are rounded away from zero, e.g., signif(-2.5, 1) is rounded to -3.

This may skew subsequent statistical analysis of the data, but may be desirable in certain contexts. This function is implemented from https://stackoverflow.com/a/1581007/; see that question and comments for discussion of this issue.

Usage

signif_half_up(x, digits = 6)

Arguments

x	a numeric vector to round.
digits	integer indicating the number of significant digits to be used.

Examples

signif_half_up(12.5, 2)
signif_half_up(1.125, 3)
signif_half_up(-2.5, 1) # negatives get rounded away from zero

---

Ensure that a vector has only a single value throughout.

Description

Missing values are replaced with the single value, and if all values are missing, the first value in missing is used throughout.

Usage

single_value(x, missing = NA, warn_if_all_missing = FALSE, info = NULL)

Arguments

x	The vector which should have a single value
missing	The vector of values to consider missing in x
warn_if_all_missing	Generate a warning if all values are missing?
info	If more than one value is found, append this to the warning or error to assist with determining the location of the issue.

Value

x as the scalar single value found throughout (or an error if more than one value is found).

Examples

# A simple use case with vectors of input

single_value(c(NA, 1))
# Multiple, different values of missing can be given
single_value(c(NA, "a"), missing = c(NA, "a"))

# A typical use case with a grouped data.frame used for input and the output
# (`B` is guaranteed to have a single value and only one row, in this case)
data.frame(
  A = rep(1:3, each = 2),
  B = c(rep(4:6, each = 2))
) %>%
  dplyr::group_by(A) %>%
  dplyr::summarize(
    B = single_value(B)
  )

try(
  # info is useful to give when multiple values may be found to see what
  # grouping variable or what calculation is causing the error
  data.frame(
    A = rep(1:3, each = 2),
    B = c(rep(1:2, each = 2), 1:2)
  ) %>%
    dplyr::group_by(A) %>%
    dplyr::mutate(
      C = single_value(B, info = paste("Calculating C for group A=", A))
    )
)

---

Generate a frequency table (1-, 2-, or 3-way).

Description

A fully-featured alternative to table(). Results are data.frames and can be formatted and enhanced with janitor's family of adorn_ functions.

Specify a data.frame and the one, two, or three unquoted column names you want to tabulate. Three variables generates a list of 2-way tabyls, split by the third variable.

Alternatively, you can tabulate a single variable that isn't in a data.frame by calling tabyl() on a vector, e.g., tabyl(mtcars$gear).

Usage

tabyl(dat, ...)

## Default S3 method:
tabyl(dat, show_na = TRUE, show_missing_levels = TRUE, ...)

## S3 method for class 'data.frame'
tabyl(dat, var1, var2, var3, show_na = TRUE, show_missing_levels = TRUE, ...)

Arguments

dat	A data.frame containing the variables you wish to count. Or, a vector you want to tabulate.
...	Additional arguments passed to methods.
show_na	Should counts of NA values be displayed? In a one-way tabyl, the presence of NA values triggers an additional column showing valid percentages (calculated excluding NA values).
show_missing_levels	Should counts of missing levels of factors be displayed? These will be rows and/or columns of zeroes. Useful for keeping consistent output dimensions even when certain factor levels may not be present in the data.
var1	The column name of the first variable.
var2	(optional) the column name of the second variable (the rows in a 2-way tabulation).
var3	(optional) the column name of the third variable (the list in a 3-way tabulation).

Value

A data.frame with frequencies and percentages of the tabulated variable(s). A 3-way tabulation returns a list of data frames.

Examples

tabyl(mtcars, cyl)
tabyl(mtcars, cyl, gear)
tabyl(mtcars, cyl, gear, am)

# or using the %>% pipe
mtcars %>%
  tabyl(cyl, gear)

# illustrating show_na functionality:
my_cars <- rbind(mtcars, rep(NA, 11))
my_cars %>% tabyl(cyl)
my_cars %>% tabyl(cyl, show_na = FALSE)

# Calling on a single vector not in a data.frame:
val <- c("hi", "med", "med", "lo")
tabyl(val)

---

Generate a frequency table of a factor grouped into top-n, bottom-n, and all other levels.

Description

Get a frequency table of a factor variable, grouped into categories by level.

Usage

top_levels(input_vec, n = 2, show_na = FALSE)

Arguments

input_vec	The factor variable to tabulate.
n	Number of levels to include in top and bottom groups
show_na	Should cases where the variable is NA be shown?

Value

A data.frame (actually a tbl_df) with the frequencies of the grouped, tabulated variable. Includes counts and percentages, and valid percentages (calculated omitting NA values, if present in the vector and show_na = TRUE.)

Examples

top_levels(as.factor(mtcars$hp), 2)

---

Remove tabyl attributes from a data.frame.

Description

Strips away all tabyl-related attributes from a data.frame.

Usage

untabyl(dat)

Arguments

dat	a data.frame of class tabyl.

Value

the same data.frame, but without the tabyl class and attributes.

Examples

mtcars %>%
  tabyl(am) %>%
  untabyl() %>%
  attributes() # tabyl-specific attributes are gone

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