Package 'arkhe'

Description

Convert Row Names to an Explicit Column

Usage

append_rownames(x, ...)

## S4 method for signature 'data.frame'
append_rownames(x, after = 0, remove = TRUE, var = "rownames")

Arguments

Value

A data.frame.

Author(s)

N. Frerebeau

See Also

Other data preparation tools: assign(), compact(), count(), detect(), discard(), get(), keep(), seek()

Examples

X <- data.frame(
  x = 1:5,
  y = 6:10,
  z = LETTERS[1:5]
)

## Assign column to row names
(Y <- assign_rownames(X, 3))

## Append row names to data.frame
(Z <- append_rownames(Y))

Description

Check Numeric Trend

Usage

assert_constant(x, ...)

assert_decreasing(x, ...)

assert_increasing(x, ...)

Arguments

Value

Throws an error, if any, and returns x invisibly otherwise.

Author(s)

N. Frerebeau

See Also

Other validation methods: assert_data, assert_length(), assert_lower(), assert_names(), assert_numeric, assert_package(), assert_square(), assert_type(), validate()

Description

• assert_missing() and assert_infinite() check if an object contains any missing (NA, NaN) or infinite (Inf) value.

• assert_unique() checks if an object contains duplicated elements.

Usage

assert_missing(x)

assert_infinite(x)

assert_unique(x)

Arguments

Value

Throws an error, if any, and returns x invisibly otherwise.

Author(s)

N. Frerebeau

See Also

Other validation methods: assert_constant(), assert_length(), assert_lower(), assert_names(), assert_numeric, assert_package(), assert_square(), assert_type(), validate()

Description

Check Object Length/Dimensions

Usage

assert_length(x, expected, empty = FALSE)

assert_lengths(x, expected)

assert_empty(x)

assert_filled(x)

assert_dimensions(x, expected)

Arguments

Value

Throws an error, if any, and returns x invisibly otherwise.

Author(s)

N. Frerebeau

See Also

Other validation methods: assert_constant(), assert_data, assert_lower(), assert_names(), assert_numeric, assert_package(), assert_square(), assert_type(), validate()

Description

Check Numeric Relations

Usage

assert_lower(x, y, ...)

assert_greater(x, y, ...)

Arguments

Value

Throws an error, if any, and returns x invisibly otherwise.

Author(s)

N. Frerebeau

See Also

Other validation methods: assert_constant(), assert_data, assert_length(), assert_names(), assert_numeric, assert_package(), assert_square(), assert_type(), validate()

Description

Check Object Names

Usage

assert_names(x, expected)

assert_dimnames(x, expected)

assert_rownames(x, expected)

assert_colnames(x, expected)

Arguments

Value

Throws an error, if any, and returns x invisibly otherwise.

Author(s)

N. Frerebeau

See Also

Other validation methods: assert_constant(), assert_data, assert_length(), assert_lower(), assert_numeric, assert_package(), assert_square(), assert_type(), validate()

Description

Check Numeric Values

Usage

assert_count(x, na.rm = FALSE, ...)

assert_whole(x, na.rm = FALSE, ...)

assert_positive(x, na.rm = FALSE, ...)

assert_negative(x, na.rm = FALSE, ...)

assert_odd(x, na.rm = FALSE, ...)

assert_even(x, na.rm = FALSE, ...)

Arguments

Value

Throws an error, if any, and returns x invisibly otherwise.

Author(s)

N. Frerebeau

See Also

Other validation methods: assert_constant(), assert_data, assert_length(), assert_lower(), assert_names(), assert_package(), assert_square(), assert_type(), validate()

Description

Check the Availability of a Package

Usage

assert_package(x, ask = TRUE)

needs(x, ask = TRUE)

Arguments

Details

assert_package() is designed for use inside other functions in your own package to check for the availability of a suggested package.

If the required packages are not available and R is running interactively, the user will be asked to install the packages.

needs() is an alias for assert_package().

Value

Invisibly returns NULL.

Author(s)

N. Frerebeau

See Also

Other validation methods: assert_constant(), assert_data, assert_length(), assert_lower(), assert_names(), assert_numeric, assert_square(), assert_type(), validate()

Description

Check Matrix

Usage

assert_square(x)

assert_symmetric(x)

Arguments

Value

Throw an error, if any, and returns x invisibly otherwise.

Author(s)

N. Frerebeau

See Also

Other validation methods: assert_constant(), assert_data, assert_length(), assert_lower(), assert_names(), assert_numeric, assert_package(), assert_type(), validate()

Description

Check Data Types

Usage

assert_type(x, expected)

assert_scalar(x, expected)

assert_function(x)

Arguments

Value

Throws an error, if any, and returns x invisibly otherwise.

Author(s)

N. Frerebeau

See Also

Other validation methods: assert_constant(), assert_data, assert_length(), assert_lower(), assert_names(), assert_numeric, assert_package(), assert_square(), validate()

Description

Assign a Specific Row/Column to the Column/Row Names

Usage

assign_colnames(x, ...)

assign_rownames(x, ...)

## S4 method for signature 'data.frame'
assign_rownames(x, column, remove = TRUE)

## S4 method for signature 'data.frame'
assign_colnames(x, row, remove = TRUE)

Arguments

Value

A data.frame.

Author(s)

N. Frerebeau

See Also

Other data preparation tools: append(), compact(), count(), detect(), discard(), get(), keep(), seek()

Examples

X <- data.frame(
  x = 1:5,
  y = 6:10,
  z = LETTERS[1:5]
)

## Assign column to row names
(Y <- assign_rownames(X, 3))

## Append row names to data.frame
(Z <- append_rownames(Y))

Description

Samples randomly from the elements of object with replacement.

Usage

bootstrap(object, ...)

## S4 method for signature 'numeric'
bootstrap(object, do, n, ..., f = NULL)

Arguments

Value

If f is NULL (the default), bootstrap() returns a named numeric vector with the following elements:

original

The observed value of do applied to object.

mean

The bootstrap estimate of mean of do.

bias

The bootstrap estimate of bias of do.

error

he bootstrap estimate of standard error of do.

If f is a function, bootstrap() returns the result of f applied to the n values of do.

Author(s)

N. Frerebeau

See Also

Other resampling methods: jackknife()

Examples

x <- rnorm(20)

## Bootstrap
bootstrap(x, do = mean, n = 100)

## Estimate the 25th and 95th percentiles
quant <- function(x) { quantile(x, probs = c(0.25, 0.75)) }
bootstrap(x, n = 100, do = mean, f = quant)

## Get the n bootstrap values
bootstrap(x, n = 100, do = mean, f = function(x) { x })

## Jackknife
jackknife(x, do = mean) # Sample mean

## Get the leave-one-out values instead of summary
jackknife(x, do = mean, f = function(x) { x })

Description

Remove Leading/Trailing Whitespace

Usage

clean_whitespace(x, ...)

## S4 method for signature 'data.frame'
clean_whitespace(x, which = c("both", "left", "right"), squish = TRUE)

## S4 method for signature 'matrix'
clean_whitespace(x, which = c("both", "left", "right"), squish = TRUE)

Arguments

Author(s)

N. Frerebeau

See Also

trimws()

Other data cleaning tools: remove_Inf(), remove_NA(), remove_constant(), remove_empty(), remove_zero(), replace_Inf(), replace_NA(), replace_empty(), replace_zero()

Examples

x <- data.frame(
  A = c("  Both ", "  Left", "Right  "),
  B = 1:3
)

clean_whitespace(x, which = "both")
clean_whitespace(x, which = "left")
clean_whitespace(x, which = "right")

Description

Removes empty rows/columns in an array-like object.

Usage

compact(x, ...)

compact_cols(x, ...)

compact_rows(x, ...)

## S4 method for signature 'ANY'
compact(x, margin = 1, na.rm = FALSE, verbose = getOption("arkhe.verbose"))

## S4 method for signature 'ANY'
compact_cols(x, na.rm = FALSE, verbose = getOption("arkhe.verbose"))

## S4 method for signature 'ANY'
compact_rows(x, na.rm = FALSE, verbose = getOption("arkhe.verbose"))

Arguments

Details

A row/column is empty if it contains only zeros (if of type numeric) or zero length character strings (if of type character).

Author(s)

N. Frerebeau

See Also

Other data preparation tools: append(), assign(), count(), detect(), discard(), get(), keep(), seek()

Examples

## Create a data.frame
X <- data.frame(A = 0, B = 1:5, C = 6, D = "", F = letters[1:5])
X

## Remove empty columns
compact(X, margin = 2)

Description

Concatenates character vectors.

Usage

x %+% y

Arguments

Value

A character vector.

See Also

Other utilities: null

Description

Computes a Wald interval for a proportion at a desired level of significance.

Usage

confidence_binomial(object, ...)

## S4 method for signature 'numeric'
confidence_binomial(
  object,
  n,
  level = 0.95,
  method = "wald",
  corrected = FALSE
)

Arguments

Value

A length-two numeric vector giving the lower and upper confidence limits.

Author(s)

N. Frerebeau

See Also

Other summary statistics: confidence_mean(), confidence_multinomial(), interval_credible(), interval_hdr()

Examples

## Confidence interval for a mean
x <- seq(from = -4, to = 4, by = 0.01)
y <- dnorm(x)

confidence_mean(y, type = "student")
confidence_mean(y, type = "normal")

## Confidence interval for a propotion
confidence_binomial(118, n = 236)

x <- c(35, 74, 22, 69)
confidence_multinomial(x)

Description

Computes a confidence interval for a mean at a desired level of significance.

Usage

confidence_mean(object, ...)

## S4 method for signature 'numeric'
confidence_mean(object, level = 0.95, type = c("student", "normal"))

Arguments

Value

A length-two numeric vector giving the lower and upper confidence limits.

Author(s)

N. Frerebeau

See Also

Other summary statistics: confidence_binomial(), confidence_multinomial(), interval_credible(), interval_hdr()

Examples

## Confidence interval for a mean
x <- seq(from = -4, to = 4, by = 0.01)
y <- dnorm(x)

confidence_mean(y, type = "student")
confidence_mean(y, type = "normal")

## Confidence interval for a propotion
confidence_binomial(118, n = 236)

x <- c(35, 74, 22, 69)
confidence_multinomial(x)

Description

Computes a Wald interval for a proportion at a desired level of significance.

Usage

confidence_multinomial(object, ...)

## S4 method for signature 'numeric'
confidence_multinomial(
  object,
  level = 0.95,
  method = "wald",
  corrected = FALSE
)

Arguments

Value

A two column numeric matrix giving the lower and upper confidence limits.

Author(s)

N. Frerebeau

See Also

Other summary statistics: confidence_binomial(), confidence_mean(), interval_credible(), interval_hdr()

Examples

## Confidence interval for a mean
x <- seq(from = -4, to = 4, by = 0.01)
y <- dnorm(x)

confidence_mean(y, type = "student")
confidence_mean(y, type = "normal")

## Confidence interval for a propotion
confidence_binomial(118, n = 236)

x <- c(35, 74, 22, 69)
confidence_multinomial(x)

Description

Counts values by rows/columns using a predicate function.

Usage

count(x, ...)

## S4 method for signature 'data.frame'
count(x, f, margin = 1, negate = FALSE, na.rm = FALSE, ...)

## S4 method for signature 'matrix'
count(x, f, margin = 1, negate = FALSE, na.rm = FALSE, ...)

Arguments

Value

A numeric vector.

Author(s)

N. Frerebeau

See Also

Other data preparation tools: append(), assign(), compact(), detect(), discard(), get(), keep(), seek()

Examples

## Create a data matrix
X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)

## Add NA
k <- sample(1:25, 3, FALSE)
X[k] <- NA
X

## Count missing values in rows
count(X, f = is.na, margin = 1)
## Count non-missing values in columns
count(X, f = is.na, margin = 2, negate = TRUE)

Description

Describes an object.

Usage

describe(x, ...)

## S4 method for signature 'ANY'
describe(x)

Arguments

Value

describe() is called for its side-effects. Invisibly returns x.

Author(s)

N. Frerebeau

See Also

Other data summaries: sparsity()

Examples

## Create a data matrix
X <- matrix(sample(0:9, 15, TRUE), nrow = 3, ncol = 5)

## Add NA
k <- sample(1:15, 3, FALSE)
X[k] <- NA

## Sparsity
sparsity(X)

## Quick description
describe(X)

Description

Finds rows/columns in an array-like object using a predicate function.

Usage

detect(x, ...)

## S4 method for signature 'ANY'
detect(x, f, margin = 1, negate = FALSE, all = FALSE, na.rm = FALSE, ...)

Arguments

Value

A logical vector.

Author(s)

N. Frerebeau

See Also

Other data preparation tools: append(), assign(), compact(), count(), discard(), get(), keep(), seek()

Examples

## Create a data matrix
X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)

## Add NA
k <- sample(1:25, 3, FALSE)
X[k] <- NA
X

## Find row with NA
detect(X, f = is.na, margin = 1)
## Find column without any NA
detect(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)

Description

Removes rows/columns in an array-like object using a predicate function.

Usage

discard(x, ...)

discard_cols(x, ...)

discard_rows(x, ...)

## S4 method for signature 'ANY'
discard(
  x,
  f,
  margin = 1,
  negate = FALSE,
  all = FALSE,
  na.rm = FALSE,
  verbose = getOption("arkhe.verbose"),
  ...
)

## S4 method for signature 'ANY'
discard_rows(
  x,
  f,
  negate = FALSE,
  all = FALSE,
  na.rm = FALSE,
  verbose = getOption("arkhe.verbose"),
  ...
)

## S4 method for signature 'ANY'
discard_cols(
  x,
  f,
  negate = FALSE,
  all = FALSE,
  na.rm = FALSE,
  verbose = getOption("arkhe.verbose"),
  ...
)

Arguments

Author(s)

N. Frerebeau

See Also

Other data preparation tools: append(), assign(), compact(), count(), detect(), get(), keep(), seek()

Examples

## Create a data matrix
X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)

## Add NA
k <- sample(1:25, 3, FALSE)
X[k] <- NA
X

## Remove row with any NA
discard(X, f = is.na, margin = 1, all = FALSE)
## Remove column with any NA
discard(X, f = is.na, margin = 2, all = FALSE)

Description

Returns rows/columns selected by name in an array-like object.

Usage

get_columns(x, ...)

get_rows(x, ...)

## S4 method for signature 'data.frame'
get_columns(x, select = NULL, ...)

## S4 method for signature 'data.frame'
get_rows(x, select = NULL, ...)

Arguments

Value

An object of the same sort as x.

Author(s)

N. Frerebeau

See Also

Other data preparation tools: append(), assign(), compact(), count(), detect(), discard(), keep(), seek()

Examples

## Seek columns
seek_columns(iris, select = startsWith, prefix = "Sepal")

## Get columns
x <- get_columns(iris, select = startsWith, prefix = "Sepal")
head(x)

Description

Computes the shortest credible interval within which an unobserved parameter value falls with a particular probability.

Usage

interval_credible(x, ...)

## S4 method for signature 'numeric'
interval_credible(x, level = 0.95)

Arguments

Value

A three-columns numeric matrix giving the lower and upper boundaries of the credible interval and associated probability.

Author(s)

N. Frerebeau

See Also

Other summary statistics: confidence_binomial(), confidence_mean(), confidence_multinomial(), interval_hdr()

Examples

## HDR of the Old Faithful eruption times
interval_hdr(faithful$eruptions)

Description

Highest Density Regions

Usage

interval_hdr(x, y, ...)

## S4 method for signature 'numeric,numeric'
interval_hdr(x, y, level = 0.954)

## S4 method for signature 'numeric,missing'
interval_hdr(x, level = 0.954, ...)

Arguments

Value

A three-columns numeric matrix giving the lower and upper boundaries of the HPD interval and associated probabilities.

Author(s)

N. Frerebeau

References

Hyndman, R. J. (1996). Computing and graphing highest density regions. American Statistician, 50: 120-126. doi:10.2307/2684423.

See Also

Other summary statistics: confidence_binomial(), confidence_mean(), confidence_multinomial(), interval_credible()

Examples

## HDR of the Old Faithful eruption times
interval_hdr(faithful$eruptions)

Description

Scalar Type Predicates

Usage

is_scalar_list(x)

is_scalar_atomic(x)

is_scalar_vector(x)

is_scalar_numeric(x)

is_scalar_integer(x)

is_scalar_double(x)

is_scalar_character(x)

is_scalar_logical(x)

Arguments

Value

A logical scalar.

See Also

Other predicates: predicate-matrix, predicate-numeric, predicate-trend, predicate-type, predicate-utils

Description

Jackknife Estimation

Usage

jackknife(object, ...)

## S4 method for signature 'numeric'
jackknife(object, do, ..., f = NULL)

Arguments

Value

If f is NULL (the default), jackknife() returns a named numeric vector with the following elements:

original

The observed value of do applied to object.

mean

The jackknife estimate of mean of do.

bias

The jackknife estimate of bias of do.

error

he jackknife estimate of standard error of do.

If f is a function, jackknife() returns the result of f applied to the leave-one-out values of do.

Author(s)

N. Frerebeau

See Also

Other resampling methods: bootstrap()

Examples

x <- rnorm(20)

## Bootstrap
bootstrap(x, do = mean, n = 100)

## Estimate the 25th and 95th percentiles
quant <- function(x) { quantile(x, probs = c(0.25, 0.75)) }
bootstrap(x, n = 100, do = mean, f = quant)

## Get the n bootstrap values
bootstrap(x, n = 100, do = mean, f = function(x) { x })

## Jackknife
jackknife(x, do = mean) # Sample mean

## Get the leave-one-out values instead of summary
jackknife(x, do = mean, f = function(x) { x })

Description

Keeps rows/columns in an array-like object using a predicate function.

Usage

keep(x, ...)

keep_cols(x, ...)

keep_rows(x, ...)

## S4 method for signature 'ANY'
keep(
  x,
  f,
  margin = 1,
  negate = FALSE,
  all = FALSE,
  na.rm = FALSE,
  verbose = getOption("arkhe.verbose"),
  ...
)

## S4 method for signature 'ANY'
keep_rows(
  x,
  f,
  negate = FALSE,
  all = FALSE,
  na.rm = FALSE,
  verbose = getOption("arkhe.verbose"),
  ...
)

## S4 method for signature 'ANY'
keep_cols(
  x,
  f,
  negate = FALSE,
  all = FALSE,
  na.rm = FALSE,
  verbose = getOption("arkhe.verbose"),
  ...
)

Arguments

Author(s)

N. Frerebeau

See Also

Other data preparation tools: append(), assign(), compact(), count(), detect(), discard(), get(), seek()

Examples

## Create a data matrix
X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)

## Add NA
k <- sample(1:25, 3, FALSE)
X[k] <- NA
X

## Keep row without any NA
keep(X, f = is.na, margin = 1, negate = TRUE, all = TRUE)
## Keep row without any NA
keep(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)

Description

Computes the greatest common divisor (GCD) of two integer using the Euclidean algorithm.

Usage

math_gcd(x, y)

## S4 method for signature 'numeric,numeric'
math_gcd(x, y)

Arguments

Value

A numeric vector.

Author(s)

N. Frerebeau

See Also

Other mathematic functions: math_lcm()

Description

Computes the lowest common multiple of the denominators of a set of fractions.

Usage

math_lcm(x, y)

## S4 method for signature 'numeric,numeric'
math_lcm(x, y)

Arguments

Value

A numeric vector.

Author(s)

N. Frerebeau

See Also

Other mathematic functions: math_gcd()

Description

Replaces NULL with a default value.

Usage

x %||% y

Arguments

Value

If x is NULL, returns y; otherwise returns x.

See Also

Other utilities: concat

Description

Color Mapping

Usage

palette_color_continuous(x, palette = NULL, domain = NULL, na = "#DDDDDD", ...)

palette_color_discrete(
  x,
  palette = NULL,
  domain = NULL,
  ordered = FALSE,
  na = "#DDDDDD",
  ...
)

Arguments

Value

A character vector of colors.

See Also

Other palettes: palette_shape(), palette_size()

Description

Symbol Mapping

Usage

palette_shape(x, palette = NULL, domain = NULL, ordered = FALSE, ...)

Arguments

Value

A vector of symbols.

See Also

Other palettes: palette_color, palette_size()

Description

Symbol Size Mapping

Usage

palette_size(x, palette = NULL, domain = NULL, ...)

Arguments

Value

A numeric vector giving the amount by which plotting text and symbols should be magnified relative to the default (see par("cex")).

See Also

Other palettes: palette_color, palette_shape()

Description

• is_square() checks if a matrix is square.

• is_symmetric() checks if a matrix is symmetric.

Usage

is_square(x)

is_symmetric(x)

Arguments

Value

A logical scalar.

See Also

Other predicates: is_scalar, predicate-numeric, predicate-trend, predicate-type, predicate-utils

Description

Check numeric objects:

• is_zero() checks if an object contains only zeros.

• is_odd() and is_even() check if a number is odd or even, respectively.

• is_positive() and is_negative check if an object contains only (strictly) positive or negative numbers.

• is_whole() checks if an object only contains whole numbers.

Usage

is_zero(x, tolerance = sqrt(.Machine$double.eps), ...)

is_odd(x, ...)

is_even(x, ...)

is_positive(x, strict = FALSE, ...)

is_negative(x, strict = FALSE, ...)

is_whole(x, tolerance = sqrt(.Machine$double.eps), ...)

Arguments

Value

A logical vector.

See Also

Other predicates: is_scalar, predicate-matrix, predicate-trend, predicate-type, predicate-utils

Description

Check numeric objects:

• is_constant() checks for equality among all elements of a vector.

• is_increasing() and is_decreasing() check if a sequence of numbers is monotonically increasing or decreasing, respectively.

Usage

is_constant(x, tolerance = sqrt(.Machine$double.eps), na.rm = FALSE)

is_increasing(x, na.rm = FALSE)

is_decreasing(x, na.rm = FALSE)

is_greater(x, y, strict = FALSE, na.rm = FALSE)

is_lower(x, y, strict = FALSE, na.rm = FALSE)

Arguments

Value

A logical scalar.

See Also

Other predicates: is_scalar, predicate-matrix, predicate-numeric, predicate-type, predicate-utils

Description

Type Predicates

Usage

is_list(x)

is_atomic(x)

is_vector(x)

is_numeric(x)

is_integer(x)

is_double(x)

is_character(x)

is_logical(x)

is_error(x)

is_warning(x)

is_message(x)

Arguments

Value

A logical scalar.

See Also

Other predicates: is_scalar, predicate-matrix, predicate-numeric, predicate-trend, predicate-utils

Description

• is_empty() checks is an object is empty (any zero-length dimensions).

• has_length() checks how long is an object.

• has_names() checks if an object is named.

• has_duplicates() checks if an object has duplicated elements.

• has_missing() and has_infinite() check if an object contains missing or infinite values.

Usage

has_length(x, n = NULL)

is_empty(x)

has_names(x, names = NULL)

has_missing(x)

has_infinite(x)

is_unique(x, tolerance = sqrt(.Machine$double.eps), na.rm = FALSE)

has_duplicates(x)

Arguments

Value

A logical scalar.

See Also

Other predicates: is_scalar, predicate-matrix, predicate-numeric, predicate-trend, predicate-type

Description

Remove Constant Columns

Usage

remove_constant(x, ...)

## S4 method for signature 'ANY'
remove_constant(x, na.rm = FALSE, verbose = getOption("arkhe.verbose"))

Arguments

Author(s)

N. Frerebeau

See Also

Other data cleaning tools: clean_whitespace(), remove_Inf(), remove_NA(), remove_empty(), remove_zero(), replace_Inf(), replace_NA(), replace_empty(), replace_zero()

Examples

## Create a data.frame
X <- data.frame(A = 1, B = 1:3)
X

remove_constant(X)

## Add NA
X[1, 1] <- NA
remove_constant(X)
remove_constant(X, na.rm = TRUE)

Description

Removes rows/columns that contain empty strings.

Usage

remove_empty(x, ...)

## S4 method for signature 'ANY'
remove_empty(x, margin = 1, all = FALSE, verbose = getOption("arkhe.verbose"))

Arguments

Author(s)

N. Frerebeau

See Also

Other data cleaning tools: clean_whitespace(), remove_Inf(), remove_NA(), remove_constant(), remove_zero(), replace_Inf(), replace_NA(), replace_empty(), replace_zero()

Examples

## Create a data matrix
X <- matrix(sample(LETTERS, 25, TRUE), nrow = 5, ncol = 5)

## Add empty string
k <- sample(1:25, 3, FALSE)
X[k] <- ""
X

## Remove rows with empty strings
remove_empty(X, margin = 1)

## Replace empty strings
replace_empty(X, value = "XXX")

Description

Removes rows/columns that contain infinite values.

Usage

remove_Inf(x, ...)

## S4 method for signature 'ANY'
remove_Inf(x, margin = 1, all = FALSE, verbose = getOption("arkhe.verbose"))

Arguments

Author(s)

N. Frerebeau

See Also

Other data cleaning tools: clean_whitespace(), remove_NA(), remove_constant(), remove_empty(), remove_zero(), replace_Inf(), replace_NA(), replace_empty(), replace_zero()

Examples

## Create a data matrix
X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)

## Add Inf
k <- sample(1:25, 3, FALSE)
X[k] <- Inf
X

## Remove rows with Inf
remove_Inf(X, margin = 1)

## Replace Inf with zeros
replace_Inf(X, value = 0)

Description

Removes rows/columns that contain missing values.

Usage

remove_NA(x, ...)

## S4 method for signature 'ANY'
remove_NA(x, margin = 1, all = FALSE, verbose = getOption("arkhe.verbose"))

Arguments

Author(s)

N. Frerebeau

See Also

Other data cleaning tools: clean_whitespace(), remove_Inf(), remove_constant(), remove_empty(), remove_zero(), replace_Inf(), replace_NA(), replace_empty(), replace_zero()

Examples

## Create a data matrix
X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)

## Add NA
k <- sample(1:25, 3, FALSE)
X[k] <- NA
X

## Remove rows with NA
remove_NA(X, margin = 1)

## Replace NA with zeros
replace_NA(X, value = 0)

Description

Removes rows/columns that contain zeros.

Usage

remove_zero(x, ...)

## S4 method for signature 'ANY'
remove_zero(x, margin = 1, all = FALSE, verbose = getOption("arkhe.verbose"))

Arguments

Author(s)

N. Frerebeau

See Also

Other data cleaning tools: clean_whitespace(), remove_Inf(), remove_NA(), remove_constant(), remove_empty(), replace_Inf(), replace_NA(), replace_empty(), replace_zero()

Examples

## Create a data matrix
X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)

## Add zero
k <- sample(1:25, 3, FALSE)
X[k] <- 0
X

## Remove rows with zero
remove_zero(X, margin = 1)

## Replace zero
replace_zero(X, value = 1)

Description

Replaces empty strings.

Usage

replace_empty(x, ...)

## S4 method for signature 'matrix'
replace_empty(x, value)

## S4 method for signature 'data.frame'
replace_empty(x, value)

Arguments

Author(s)

N. Frerebeau

See Also

Other data cleaning tools: clean_whitespace(), remove_Inf(), remove_NA(), remove_constant(), remove_empty(), remove_zero(), replace_Inf(), replace_NA(), replace_zero()

Examples

## Create a data matrix
X <- matrix(sample(LETTERS, 25, TRUE), nrow = 5, ncol = 5)

## Add empty string
k <- sample(1:25, 3, FALSE)
X[k] <- ""
X

## Remove rows with empty strings
remove_empty(X, margin = 1)

## Replace empty strings
replace_empty(X, value = "XXX")

Description

Replaces infinite values values.

Usage

replace_Inf(x, ...)

## S4 method for signature 'matrix'
replace_Inf(x, value = 0)

## S4 method for signature 'data.frame'
replace_Inf(x, value = 0)

Arguments

Author(s)

N. Frerebeau

See Also

Other data cleaning tools: clean_whitespace(), remove_Inf(), remove_NA(), remove_constant(), remove_empty(), remove_zero(), replace_NA(), replace_empty(), replace_zero()

Examples

## Create a data matrix
X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)

## Add Inf
k <- sample(1:25, 3, FALSE)
X[k] <- Inf
X

## Remove rows with Inf
remove_Inf(X, margin = 1)

## Replace Inf with zeros
replace_Inf(X, value = 0)

Description

Replaces missing values values.

Usage

replace_NA(x, ...)

## S4 method for signature 'matrix'
replace_NA(x, value = 0)

## S4 method for signature 'data.frame'
replace_NA(x, value = 0)

Arguments

Author(s)

N. Frerebeau

See Also

Other data cleaning tools: clean_whitespace(), remove_Inf(), remove_NA(), remove_constant(), remove_empty(), remove_zero(), replace_Inf(), replace_empty(), replace_zero()

Examples

## Create a data matrix
X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)

## Add NA
k <- sample(1:25, 3, FALSE)
X[k] <- NA
X

## Remove rows with NA
remove_NA(X, margin = 1)

## Replace NA with zeros
replace_NA(X, value = 0)

Description

Replaces zeros.

Usage

replace_zero(x, ...)

## S4 method for signature 'matrix'
replace_zero(x, value)

## S4 method for signature 'data.frame'
replace_zero(x, value)

Arguments

Author(s)

N. Frerebeau

See Also

Other data cleaning tools: clean_whitespace(), remove_Inf(), remove_NA(), remove_constant(), remove_empty(), remove_zero(), replace_Inf(), replace_NA(), replace_empty()

Examples

## Create a data matrix
X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)

## Add zero
k <- sample(1:25, 3, FALSE)
X[k] <- 0
X

## Remove rows with zero
remove_zero(X, margin = 1)

## Replace zero
replace_zero(X, value = 1)

Description

Rescales continuous vector to have specified minimum and maximum.

Usage

scale_range(x, to = c(0, 1), from = range(x, finite = TRUE))

Arguments

Value

A numeric vector.

Note

For internal use only.

Description

Searches rows/columns by name in an array-like object.

Usage

seek_columns(x, ...)

seek_rows(x, ...)

## S4 method for signature 'data.frame'
seek_rows(x, select = NULL, ...)

## S4 method for signature 'data.frame'
seek_columns(x, select = NULL, ...)

Arguments

Value

An integer vector or NULL.

Author(s)

N. Frerebeau

See Also

Other data preparation tools: append(), assign(), compact(), count(), detect(), discard(), get(), keep()

Examples

## Seek columns
seek_columns(iris, select = startsWith, prefix = "Sepal")

## Get columns
x <- get_columns(iris, select = startsWith, prefix = "Sepal")
head(x)

Description

Computes data sparsity (proportion of zeros).

Usage

sparsity(x, ...)

## S4 method for signature 'matrix'
sparsity(x, count = FALSE)

## S4 method for signature 'data.frame'
sparsity(x, count = FALSE)

Arguments

Details

If x is a data.frame, sparsity is computed on numeric variables only.

Value

A length-one numeric vector.

Author(s)

N. Frerebeau

See Also

Other data summaries: describe()

Examples

## Create a data matrix
X <- matrix(sample(0:9, 15, TRUE), nrow = 3, ncol = 5)

## Add NA
k <- sample(1:15, 3, FALSE)
X[k] <- NA

## Sparsity
sparsity(X)

## Quick description
describe(X)

Description

Validate a Condition

Usage

validate(expr)

Arguments

Value

Returns NULL on success, otherwise returns the error as a string.

Author(s)

N. Frerebeau

See Also

Other validation methods: assert_constant(), assert_data, assert_length(), assert_lower(), assert_names(), assert_numeric, assert_package(), assert_square(), assert_type()