distributions: Distributions

In Computational-Cognitive-Musicology-Lab/humdrumR: humdrumR

Distributions

Description

HUmdrumR has ways to...

The count() function is exactly like R's fundamental table() function, except that 1) will give special treatment to humdrumR token() data 2) has more intuitive/simple argument names 3) makes it easier to combine/manipulate disparate output tables.

Usage

## S4 method for signature 'distribution'
show(object)

## S3 method for class 'distribution'
print(
  dist,
  digits = if (inherits(dist, "probability")) 3 else 1,
  syntaxHighlight = humdrumRoption("syntaxHighlight"),
  zeros = "."
)

## S4 method for signature 'distribution'
sort(x, decreasing = TRUE)

## Default S3 method:
count(..., sort = FALSE, na.rm = FALSE, .drop = FALSE, binArgs = list())

## S3 method for class 'humdrumR'
count(x, ..., sort = FALSE, na.rm = FALSE, .drop = FALSE, binArgs = list())

## S3 method for class 'table'
count(..., sort = FALSE, na.rm = FALSE, .drop = FALSE)

## S3 method for class 'count'
pdist(
  x,
  ...,
  condition = NULL,
  na.rm = FALSE,
  sort = FALSE,
  .drop = FALSE,
  binArgs = list()
)

## S3 method for class 'probability'
pdist(
  x,
  ...,
  condition = NULL,
  na.rm = FALSE,
  sort = FALSE,
  .drop = FALSE,
  binArgs = list()
)

## Default S3 method:
pdist(
  ...,
  condition = NULL,
  na.rm = FALSE,
  sort = FALSE,
  .drop = FALSE,
  binArgs = list()
)

## S3 method for class 'data.frame'
pdist(
  x,
  ...,
  condition = NULL,
  na.rm = FALSE,
  sort = FALSE,
  .drop = FALSE,
  binArgs = list()
)

## S3 method for class 'humdrumR'
pdist(
  x,
  ...,
  condition = NULL,
  na.rm = FALSE,
  sort = FALSE,
  .drop = FALSE,
  binArgs = list()
)

## S3 method for class 'table'
pdist(x, ..., condition = NULL, na.rm = FALSE, sort = FALSE, binArgs = list())

table(
  x,
  ...,
  exclude = if (useNA == "no") c(NA, NaN),
  useNA = "no",
  dnn = NULL,
  deparse.level = 1
)

## S4 method for signature 'humdrumR'
table(
  x,
  ...,
  exclude = if (useNA == "no") c(NA, NaN),
  useNA = "no",
  dnn = NULL,
  deparse.level = 1
)

## S4 method for signature 'distribution'
table(x)

Details

The count() function is essentially a wrapper around base::table() function. However, any token() class arguments are treated like factors(), calling generating their own levels. This assures that, for example, pitch data is tabulated in order of pitch height, and "missing" pitches are counted as zero.

count() will, by default, count NA values if they are present—if you don't want to count NAs, specify na.rm = TRUE. You can also tell count() to exclude (not count) any other arbitrary values you provide as a vector to the exclude argument.

count() will always give names to the dimensions of the table it creates. You can specify these names directly as argument names, like count(Kern = kern(Token)); if you don't specify a name, count() will make up a name(s) based on expression(s) it is tallying. (Note that count() does not copy base::table()'s obtusely-named dnn or deparse.level arguments.)

Manipulating humdrum tables

The output of count() is a special form of R table, a humdrumR.table. Given two or more humdrumR.tables, if you apply basic R operators (e.g., arithmetic, comparisons) or row/column binding (cbind/rbind) humdrumR will align the tables by their dimension-names before doing the operation. This means, that if you have two tables of pitch data, but one table includes specific pitch and other doesn't, you can still add them together or bind them into a matrix. See the examples!

Examples

generic <- c('c', 'c', 'e', 'g', 'a', 'b', 'b', 'b')
complex <- c('c', 'c#', 'e', 'f', 'g','g#', 'g#', 'a')

genericTable   <- count(generic)
complexTable <- count(complex)

genericTable
complexTable

genericTable + complexTable

cbind(genericTable, complexTable)

Computational-Cognitive-Musicology-Lab/humdrumR documentation built on Oct. 22, 2024, 9:28 a.m.