Add results of prop.test to data frame

Description

Adds prop.test results as columns to data frame based on data in columns For use with a tibble in a pipe: Using one-group prop.test, adds confidence intervals (with given conf.level) for the proportion of x positive results in n trials, and the p value that the proportion is equal to p (default: 0.5) (to add the estimated proportion itself, use count_by)

Usage

add_prop_test(
  .df,
  x,
  n,
  p = NULL,
  CI_lower_name = "CI_lower",
  CI_upper_name = "CI_upper",
  p_name = "p",
  alternative = c("two.sided", "less", "greater"),
  conf.level = 0.95,
  correct = TRUE
)

Arguments

Value

Data frame with columns added

See Also

count_by()

Examples

library(magrittr)
if (requireNamespace("survival", quietly = TRUE))
{
   survival::aml %>%
   count_by(x) %>%
   add_prop_test(n, sum(n), rel)
}

All() giving NA only if all values are NA

Description

All() giving NA only if all values are NA

Usage

all_or_all_na(...)

Arguments

Value

NA if and only if all ... are NA, else all(...), ignoring NA values

Any() giving NA only if all values are NA

Description

Any() giving NA only if all values are NA

Usage

any_or_all_na(...)

Arguments

Value

NA if and only if all ... are NA, else any(...), ignoring NA values

Appending in a pipe, never unlisting

Description

Append to a given list, while considering as a single object and not unlisting as base::append does. Argument order is reversed compared to base::append to allow a different pattern of use in a pipe.

Usage

append_object(x, .l, name = NULL)

Arguments

Value

The list .l with x appended

Examples

library(magrittr)
results <- list(first=c(3,4), second=list(5,6))
list(7,8) %>%
  append_object(results, "third result") ->
results
# results has length 1, containing one list named "first"

Vectorised conversion to logical, treating NA as False

Description

Vectorised conversion to logical, treating NA as False

Usage

are_true(x)

Arguments

Value

A logical vector of same size as x which is true where x is true (rlang::as_logical) and not NA

Format numeric value for output

Description

Vectorised conversion

Usage

as_formatted_number(x, decimal_places = 1, remove_trailing_zeroes = T)

Arguments

Value

Character vector

Examples

as_formatted_number(0.74167, 2) # gives "0.74"

Formatting p values

Description

Vectorised conversion

Usage

as_formatted_p_value(
  x,
  decimal_places = 3,
  prefix = "p",
  less_than_cutoff = 0.001,
  remove_trailing_zeroes = T,
  alpha = 0.05,
  ns_replacement = NULL
)

Arguments

Value

Character vector

Examples

as_formatted_p_value(0.02) # "p=0.02"
as_formatted_p_value(0.00056) # "p<0.001"

Format as percentage for output

Description

Vectorised conversion

Usage

as_percentage_label(x, decimal_places = 1, include_plus_sign = F)

Arguments

Value

Character vector

Examples

as_percentage_label(0.746) # gives "74.6%"

Categorical test in a pipe

Description

Performs classical categorical tests on two columns of a data frame. Per default, will perform chisq.test or fisher.test on the contingency table created by var1 and var2.

Usage

categorical_test_by(
  .tbl,
  var1,
  var2,
  na.rm = T,
  test_function_generator = NULL,
  ...
)

Arguments

Details

Returns a one-line data frame as result and thus plays nicely with for example map_dfr.

Value

A one-row data frame with the columns:

• "var1,var2": The tested variables

• "test": Label of the test function (default: fisher or chisq)

• "p-value": P value

• "result": List column with full result object (default: htest)

• "contingency_table": List column with contingency table data frame as return by contingency_table_by

Examples

library(magrittr)
if (requireNamespace("datasets", quietly = TRUE))
{
   mtcars %>% categorical_test_by(cyl >= 6, gear)
}

Convert contingency table to classical R matrix

Description

Converts the result of contingency_table_by to a classical matrix

Usage

contingency_table_as_matrix(table_frame)

Arguments

Value

A matrix

Create data frame formed like a contingency-table

Description

Counts by the specified two variables and the pivots the count data frame wider to a two-dimensional contingency table. Please note that the resulting data frame is suitable for convenient output or use with functions that work on matrix-like data, but does not fulfill the tidy data criteria.

Usage

contingency_table_by(.tbl, var1, var2, na.rm = F, add_margins = F)

Arguments

Value

A data frame

Examples

library(magrittr)
if (requireNamespace("datasets", quietly = TRUE))
{
   mtcars %>% contingency_table_by(cyl, gear)
}

Count by multiple variables

Description

Count by multiple variables

Usage

count_at(
  .tbl,
  .vars,
  .grouping = vars(),
  label_style = "long",
  long_label_column_names = c("variable", "category"),
  column_names = c("n", "rel", "percent"),
  na_label = "missing",
  percentage_label_decimal_places = 1,
  add_grouping = T,
  na.rm = F
)

Arguments

Value

A data frame concatenated from individual count_by results, with labels as per label_style.

Examples

library(magrittr)
library(datasets)
library(dplyr)
mtcars %>% count_at(vars(gear, cyl))

Count according to grouping

Description

Similar to dplyr::count(), but also adds the relative proportion and a percent-formatted string of the relative proportion, and allows to specify the column names.

Usage

count_by(
  .tbl,
  ...,
  column_names = c("n", "rel", "percent"),
  percentage_label_decimal_places = 1,
  add_grouping = T,
  na.rm = F
)

Arguments

Value

The counted data frame

Examples

library(magrittr)
if (requireNamespace("survival", quietly = TRUE))
{
   survival::aml %>%
   count_by(x)
}

The DIN A paper formats

Description

The DIN A paper formats

Usage

dinAFormat()

dinA_format()

dinA(n)

dinAWidth(n)

dinA_width(n)

dinAHeight(n)

dinA_height(n)

Arguments

Value

A named list (0-10) of named vectors (long, short) of unit objects with the size in inches of the DIN A paper formats

named unit vector (long, short) with the size in inches of the requested DIN A paper format

the long side / width in landscape as a unit object in inches

the short side / height in landscape as a unit object in inches

See Also

unit

Compare vectors, treating NA like a value

Description

Compare vectors, treating NA like a value

Usage

equal_including_na(v1, v2)

Arguments

Value

Returns a logical vector of the same size as v1 and v2, TRUE wherever elements are the same. NA is treated like a value level, i.e., NA == NA is true, NA == 1 is false.

Execute code after tidy evaluation

Description

This function takes R code as arguments and executes this code in the calling environment. All quoted variables (using rlang's quasiquotation, !! or !!!) will be unquoted prior to evaluation. This results in executed in code in which the variable is replaced verbatim by its value, as if you had typed the variable's value. This is particularly useful for functions using base R's substitute() approach, such as functions taking formulas, and you have built the formula dynamically. It is unnecessary for all functions based on tidy_eval (dplyr).

Usage

eval_unquoted(...)

Arguments

Value

The value of the last evaluated expression.

Examples

library(rlang)
# Note that evaluation takes place in the calling environment!
l <- quo(l <- 1) # l is a quosure in our env
eval_unquoted(!!l)
l == 1 # TRUE: l is now a vector

Extract symbols from an expression of symbols and operators

Description

Extract symbols from an expression of symbols and operators

Usage

expression_list(expr, seps = "+")

quosure_list(expr, seps = "+", env = caller_env())

symbol_string_list(expr, seps = "+")

Arguments

Value

A list of all symbols in the expression, as symbol, quosure or text.

Examples

expression_list(a+b+c+d)

Row-wise first value which is not NA

Description

This is useful in conjunction with dplyr's mutate to condense multiple columns to one, where in each sample typically only one of n columns has a value, while the others are NA. Returns one vector of the same length as each input vector containing the result. Note that factors will be converted to character vectors (with a warning).

Usage

first_non_nas(...)

Arguments

Value

Returns a vector of type and size as any of the given vectors (vectors regarded a column, number of rows is size of each vectors) For each "row", returns the first value that is not NA, or NA iff all values in the row are NA.

Examples

library(tibble)
library(magrittr)
library(dplyr)
# Creates a column containing (4, 2, 2)
tibble(a=c(NA, NA, 2), b=c(4, NA, 5), c=c(1, 2, 3)) %>%
  mutate(essence=first_non_nas(a, b, c))

Row-wise first value that is not NA

Description

Row-wise first value that is not NA

Usage

first_non_nas_at(.tbl, ...)

Arguments

Value

A vector of length nrow(.tbl) containing the first found non-na value

First argument that does not equal a given value

Description

First argument that does not equal a given value

Usage

first_not(not, ...)

Arguments

Value

The first value that does not equal "not", or NA iff all equal "not"

Examples

# 5
first_not(1, 1,1,1,5)

First argument that is not NA

Description

First argument that is not NA

Usage

first_not_na(...)

Arguments

Value

The first argument that is not NA, or NA iff all are NA

Row-wise first index of column that is not NA

Description

Row-wise first index of column that is not NA

Usage

first_which_non_na_at(.tbl, ...)

Arguments

Value

A numeric vector of length nrow(.tbl) containing the index of the first found non-na value in the given columns. Possible values are NA (all values in that row are NA), and 1 ... number of columns in selection

First which() is not na

Description

First which() is not na

Usage

first_which_not_na(...)

Arguments

Value

The index of the first value which is not NA, or NA iff all elements are NA.

Examples

# 4
first_which_not_na(NA, NA, NA, 56)

Format numeric columns for display

Description

Combines mutate_at() and as_formatted_number()

Usage

format_numbers_at(.tbl, .vars, decimal_places = 1, remove_trailing_zeroes = T)

Arguments

Value

Value of mutate_at

See Also

format_p_values_at

Examples

library(tibble)
library(magrittr)
library(dplyr)
tibble(a=c(0.1, 0.238546)) %>%
    format_numbers_at(vars(a))

Format numeric columns for display

Description

Combines mutate_at() and as_formatted_p_value()

Usage

format_p_values_at(
  .tbl,
  .vars,
  decimal_places = 3,
  prefix = "p",
  less_than_cutoff = 0.001,
  remove_trailing_zeroes = T,
  alpha = 0.05,
  ns_replacement = NULL
)

Arguments

Value

Value of mutate_at

See Also

format_numbers_at

Examples

library(tibble)
library(magrittr)
library(dplyr)
tibble(p=c(0.05, 0.0001)) %>%
    format_numbers_at(vars(p))

Ordering function: identity order

Description

This can be used in a place where a function with a signature like order is required. It simply retains the original order.

Usage

identity_order(x, ...)

Arguments

Value

An integer vector

A notion of valid and invalid

Description

An object is valid if it is not null, not missing (NA), and is not an empty vector. Note that this is per se not vectorised, because a non-empty list or vector is valid as such.

Usage

invalid(x)

valid(x)

Arguments

Value

logical

Functions

• valid: x is not invalid

Examples

invalid(NULL) # TRUE
invalid(NA) # TRUE
invalid(list()) # TRUE
invalid("a") # FALSE
invalid(c(1,2,3)) # FALSE

Inverting name and value

Description

Inverting name and value

Usage

invert_value_and_names(v)

Arguments

Value

A vector where names(v) are the values and the values of v are the names

"Variable generating" functions

Description

A pair of functions that allows a "variable generating" function and read this function's local vars into the environment of the caller.

Usage

local_variables(env = parent.frame())

localVariables(env = parent.frame())

source_variables(localVars)

sourceVariables(localVars)

Arguments

Value

Named vector of created local variables

The updated environment

Examples

myVariableGeneratingFunction <- function()
{
  x <- 1
  y <- 2
  local_variables()
}
myMainFunction <- function()
{
  source_variables(myVariableGeneratingFunction())
  print(c(x, y))
}

Lookup in a dictionary

Description

Looks up all values as keys of the dictionary and returns the values.

Usage

lookup(dict, ..., default = NA, dict_key_is_regex = F, key_is_regex = F)

lookup_int(dict, ..., default = NA, dict_key_is_regex = F, key_is_regex = F)

lookup_chr(dict, ..., default = NA, dict_key_is_regex = F, key_is_regex = F)

lookup_lgl(dict, ..., default = NA, dict_key_is_regex = F, key_is_regex = F)

lookup_dbl(dict, ..., default = NA, dict_key_is_regex = F, key_is_regex = F)

lookup_num(dict, ..., default = NA, dict_key_is_regex = F, key_is_regex = F)

Arguments

Value

A list of the same size as ..., containing the lookup results. For the type-specific functions, returns a vector typed as requested, requiring all lookup results to have matching type.

Examples

a <- list("x", "y", "z")
dict <- c(x="xc", y="yv")
# returns c("xc", "yv", na_chr)
lookup_chr(dict, a)#'
# returns c("xc", "yv", "z")
lookup_chr(dict, "x", "y", "z", default=identity)

Creating a lookup function from dictionary

Description

Creating a lookup function from dictionary

Usage

lookup_function_from_dict(dict, default = identity, dict_key_is_regex = F)

Arguments

Value

A function which can be called with keys and performs the described lookup, returning the value (string)

Generic lumping

Description

Takes levels (labels, factor levels) and corresponding counts and "lumps" according to specified criteria (either n or prop), i.e. preserves some rows and summarises the rest in a single "Other" row

Usage

lump(
  levels,
  count,
  n,
  prop,
  other_level = "Other",
  ties.method = c("min", "average", "first", "last", "random", "max")
)

Arguments

Value

A dictionary (named vector) of levels -> new levels

Lump rows of a tibble

Description

A verb for a dplyr pipeline: In the given data frame, take the .level column as a set of levels and the .count column as corresponding counts. Return a data frame where the rows are lumped according to levels/counts using the parameters n, prop, other_level, ties.method like for lump(). The resulting row for other_level has level=other level, count=sum(count of all lumped rows). For the remaining columns, either a default concatenation is used, or you can provide custom summarising statements via the summarising_statements parameter. Provide a list named by the column you want to summarize, giving statements wrapped in quo(), using syntax as you would for a call to summarise().

Usage

lump_rows(
  .df,
  .level,
  .count,
  summarising_statements = quos(),
  n,
  prop,
  remaining_levels,
  other_level = "Other",
  ties.method = c("min", "average", "first", "last", "random", "max")
)

Arguments

Value

The lumped data frame

See Also

lump

Named color palette

Description

Returns the palette named by names. This is useful to pick only a few specific colors from a larger palette.

Usage

named_palette(palette, names, color_order = NULL)

Arguments

Value

A named palette. If the palette is longer than names, will only use the first n entries. If names is longer than palette, will recycle colors.

Reorder a factor

Description

Makes f a factor ordered according to ... (which is passed to order)

Usage

order_factor_by(.f, ...)

Arguments

Details

This is a thin wrapper around forcats::fct_reorder(), which is unintuitive in conjunction with order().

Value

Reordered factor

See Also

rename_reorder_factor, rename_factor, forcats::fct_reorder

Orderer function for complex sorting

Description

If you want to order by multiple features and have sorted vectors for each feature which describe the intended order

Usage

orderer_function_from_sorted_vectors(...)

Arguments

Value

A function which takes (at least) k vectors This function will return an order for these vectors determined by the sorted vectors

Pluck with simplified return value

Description

Like purrr::pluck(), but will return simplify()'ed as a vector

Usage

pluck_vector(.x, ..., .default = NULL)

Arguments

Value

Result of purrr::pluck(), transformed y purrr::simplify()

Directory creation

Description

Creates directory if it does not yet exist

Usage

prepare_directory(folder)

Arguments

Value

Folder path

Directory creation and file path concatenation

Description

Given a folder, file base name and suffix, ensures the directory exists, and returns the ready file path.

Usage

prepare_path(folder, fileBaseName, fileSuffix)

Arguments

Value

Complete file path

Prepending in a pipe, never unlisting

Description

Prepend to a given list, while considering as a single object and not unlisting. Argument order is reversed compared to base::append or purrr::prepend to allow a different pattern of use in a pipe.

Usage

prepend_object(x, .l, name = NULL, before = 1)

Arguments

Value

The list .l with x prepended

Examples

#' library(tibble)
library(magrittr)
library(dplyr)
results <- list(second=list(1,2), third=list(3))
list(-1, 1) %>%
  prepend_object(results, "first") ->
results
# results has length 3, containing three lists

Print deparsed language

Description

Prints deparsed R language tree of given expression

Usage

print_deparsed(language)

Arguments

Value

Invisible null

Rename a factor.

Description

Renames the levels of a factor.

Usage

rename_factor(.f, ..., reorder = F)

Arguments

Value

A renamed and reordered factor

See Also

rename_reorder_factor, order_factor_by, forcats::fct_recode, forcats::fct_relevel

Rename and reorder a factor.

Description

The factor will be recoded according to value_label_dict and, if requested, also reordered by the order of this vector. Secondly, the vector will be reordered according to reorder_vector, if given.

Usage

rename_reorder_factor(
  .f,
  value_label_dict,
  reorder_vector,
  reorder_by_value_label_dict = T
)

Arguments

Value

A renamed and reordered factor

See Also

rename_factor, order_factor_by, forcats::fct_recode, forcats::fct_relevel

Replace sequential duplicates

Description

Replace sequential duplicates

Usage

replace_sequential_duplicates(strings, replace_with = "", ordering = NULL)

Arguments

Value

A character vector with strings identical to the previous string replaced with replace_with

Examples

# returns c("a", "", "b", "", "", "a")
replace_sequential_duplicates(c("a", "a", "b", "b", "b", "a"))

Save plot as PDF

Description

Save plot as PDF

Usage

save_pdf(plot, folder, fileBaseName, width, height, ...)

Arguments

Save plot as PNG

Description

Save plot as PNG

Usage

save_png(
  plot,
  folder,
  fileBaseName,
  width,
  height,
  dpi = 300,
  background = c("white", "transparent"),
  ...
)

Arguments

Value

invisible NULL

Detect sequential duplicates

Description

Detect sequential duplicates

Usage

sequential_duplicates(strings, ordering = NULL)

Arguments

Value

A logical vector which indicates if a string is identical to the previous string.

Examples

# return c(F, T, F, T, T, F)
sequential_duplicates(c("a", "a", "b", "b", "b", "a"))

Combine str_match and str_locate

Description

For every pattern, return the index of the first match of pattern in strings

Usage

str_locate_match(patterns, strings)

Arguments

Value

Integer vector of length(patterns) where entry i gives the index in strings where pattern i first matched

Make quosure from symbol

Description

Make quosure from symbol

Usage

symbol_as_quosure(x, env = caller_env())

Arguments

Value

Quosure containing the symbol

Syntactically safe names

Description

Makes the names syntactically safe by wrapping them in “ if necessary

Usage

syntactically_safe(expr_strings)

Arguments

Value

Strings converted to syntactically safe form

Test for logical true or NA

Description

Test for logical true or NA

Usage

true_or_na(x)

Arguments

Value

True if and only if x is TRUE or x is NA, False otherwise.

A python / javascript-like "truthy" notion

Description

Values are truthy that are not null, NA, empty, 0, or FALSE.

Usage

truthy(x)

falsy(x)

Arguments

Details

Note that this is per se not vectorised, because a non-empty list or vector is "truthy" as such.

Value

logical

Functions

• falsy: x is not truthy

Infix operator for python-style tuple assignment

Description

Infix operator for python-style tuple assignment

Usage

l %=% r

g(...)

Arguments

Value

Last assigned value

Examples

g(a,b) %=% c(1,2) # equivalent to a <- 1; b <- 2

Get indices of non-NA values

Description

Get indices of non-NA values

Usage

which_non_na(...)

Arguments

Value

A list of n numerical vectors. Each numerical vector has a size between 0 and k and contains the indices of the vectors whose elements are not na in the corresponding row.

Examples

library(tibble)
library(magrittr)
library(dplyr)
# Creates a list column containing (2,3);(3);(1,2,3)
tibble(a=c(NA, NA, 2), b=c(4, NA, 5), c=c(1, 2, 3)) %>%
  mutate(non_na_idc=which_non_na(a, b, c))

Slice by name

Description

Slices of a vector with elements of given name, or containing given patterns. Analogous accessor functions for purrr::pluck

Usage

with_name(v, name)

with_name_containing(v, pattern)

named(name)

name_contains(pattern)

Arguments

Value

A slice from v containing all elements in v with the given name, or the name of which contains pattern

Slice by value

Description

Slices of a vector with elements containing given patterns. Analogous accessor function for purrr::pluck

Usage

with_value_containing(v, pattern)

value_contains(pattern)

Arguments

Value

A slice from v containing all elements in v with the given name, or the name of which contains pattern