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 distribution
show(object)
# S3 method for distribution
print(
dist,
digits = if (inherits(dist, "probability")) 3 else 1,
syntaxHighlight = humdrumRoption("syntaxHighlight"),
zeros = "."
)
# S4 method for distribution
sort(x, decreasing = TRUE)
# S3 method for default
count(..., sort = FALSE, na.rm = FALSE, .drop = FALSE, binArgs = list())
# S3 method for humdrumR
count(x, ..., sort = FALSE, na.rm = FALSE, .drop = FALSE, binArgs = list())
# S3 method for table
count(..., sort = FALSE, na.rm = FALSE, .drop = FALSE)
# S3 method for count
pdist(
x,
...,
condition = NULL,
na.rm = FALSE,
sort = FALSE,
.drop = FALSE,
binArgs = list()
)
# S3 method for probability
pdist(
x,
...,
condition = NULL,
na.rm = FALSE,
sort = FALSE,
.drop = FALSE,
binArgs = list()
)
# S3 method for default
pdist(
...,
condition = NULL,
na.rm = FALSE,
sort = FALSE,
.drop = FALSE,
binArgs = list()
)
# S3 method for data.frame
pdist(
x,
...,
condition = NULL,
na.rm = FALSE,
sort = FALSE,
.drop = FALSE,
binArgs = list()
)
# S3 method for humdrumR
pdist(
x,
...,
condition = NULL,
na.rm = FALSE,
sort = FALSE,
.drop = FALSE,
binArgs = list()
)
# S3 method for 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 humdrumR
table(
x,
...,
exclude = if (useNA == "no") c(NA, NaN),
useNA = "no",
dnn = NULL,
deparse.level = 1
)
# S4 method for 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
#> humdrumR count distribution
#> generic n
#> a 1
#> b 3
#> c 2
#> e 1
#> g 1
#> generic n
#> humdrumR count distribution
complexTable
#> humdrumR count distribution
#> complex n
#> a 1
#> c 1
#> c# 1
#> e 1
#> f 1
#> g 1
#> g# 2
#> complex n
#> humdrumR count distribution
genericTable + complexTable
#> Error: Failed to parse glue component
#> Caused by error in `parse()`:
#> ! <text>:1:11: unexpected symbol
#> 1: NULL base
#> ^
cbind(genericTable, complexTable)
#> Error: Failed to parse glue component
#> Caused by error in `parse()`:
#> ! <text>:1:11: unexpected symbol
#> 1: NULL base
#> ^