This manual is for Tablicious, version 0.3.7.
Time is an illusion. Lunchtime doubly so.
This is the manual for the Tablicious package version 0.3.7 for GNU Octave.
Tablicious provides Matlab-compatible tabular data and date/time support for GNU Octave.
This includes a table class with support for filtering and join operations;
datetime, duration, and related classes;
Missing Data support; string and categorical data types;
and other miscellaneous things.
This document is a work in progress. You are invited to help improve it and submit patches.
Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.
Tablicious was written by Andrew Janke <floss@apjanke.net>. Support can be found on the Tablicious project GitHub page.
The easiest way to obtain Tablicious is by using Octave’s pkg package manager.
To install the development prerelease of Tablicious, run this in Octave:
pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.3.7/tablicious-0.3.7.tar.gz
(Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)
For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the inst directory in the repo to your Octave path.
Tablicious provides the table class for representing tabular data.
A table is an array object that represents a tabular data structure. It holds
multiple named “variables”, each of which is a column vector, or a 2-D matrix whose
rows are read as records.
A table is composed of multiple “variables”, each with a name, which all have
the same number of rows. (A table variable is like a “column” in SQL tables
or in R or Python/pandas dataframes. Whenever you read “variable” here, think
“column”.) Taken together, the i-th element or row of each variable compose
a single record or observation.
Tables are good ways of arranging data if you have data that would otherwise be stored
in a few separate variables which all need to be kept in the same shape and order,
especially if you might want to do element-wise comparisons involving two or more of
those variables. That’s basically all a table is: it holds a collection of
variables, and makes sure they are all kept aligned and ordered in the same way.
Tables are a lot like SQL tables or result sets, and are based on the same relational
algebra theory that SQL is. Many common, even powerful, SQL operations can be done
in Octave using table arrays. It’s like having your own in-memory SQL engine.
There are two main ways to construct a table array: build one up by combining
multiple variables together, or convert an existing tabular-organized array into a
table.
To build an array from multiple variables, use the table(…) constructor, passing
in all of your variables as separate inputs. It takes any number of inputs. Each input
becomes a table variable in the new table object. If you pass your constructor
inputs directly from variables, it automatically picks up their names and uses them
as the table variable names. Otherwise, if you’re using more complex expressions, you’ll
need to supply the 'VariableNames' option.
To convert a tabular-organized array of another type into a table, use the
conversion functions like array2table, struct2table and cell2table.
array2table and cell2table take each column of the input array and turn
it into a separate table variable in the resulting table. struct2table takes
the fields of a struct and puts them into table variables.
Here’s a table (ha!) of what SQL and relational algebar operations correspond to
what Octave table operations.
In this table, t is a variable holding a table array, and ix is
some indexing expression.
| SQL | Relational | Octave table |
|---|---|---|
| SELECT | PROJECT | subsetvars, t(:,ix) |
| WHERE | RESTRICT | subsetrows, t(ix,:) |
| INNER JOIN | JOIN | innerjoin |
| OUTER JOIN | OUTER JOIN | outerjoin |
FROM table1, table2, … | Cartesian product | cartesian |
| GROUP BY | SUMMARIZE | groupby |
| DISTINCT | (automatic) | unique(t) |
Note that there is one big difference between relational algebra and SQL & Octave
table: Relations in relational algebra are sets, not lists.
There are no duplicate rows in relational algebra, and there is no ordering.
So every operation there does an implicit DISTINCT/unique() on its
results, and there‘s no ORDER BY/sort(). This is not the case in SQL
or Octave table.
Note for users coming from Matlab: Matlab does not provide a general groupby
function. Instead, you have to variously use rowfun, grpstats,
groupsummary, and manual code to accomplish “group by” operations.
Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke
Tablicious provides the datetime class for representing points in time.
There’s also duration and calendarDuration for representing
periods or durations of time. Like vector quantities along the time line,
as opposed to datetime being a point along the time line.
datetime ClassA datetime is an array object that represents points in time in the familiar
Gregorian calendar.
This is an attempt to reproduce the functionality of Matlab’s datetime. It
also contains some Octave-specific extensions.
The underlying representation is that of a datenum (a double
containing the number of days since the Matlab epoch), but encapsulating it in an
object provides several benefits: friendly human-readable display, type safety,
automatic type conversion, and time zone support. In addition to the underlying
datenum array, a datetime inclues an optional TimeZone property
indicating what time zone the datetimes are in.
So, basically, a datetime is an object wrapper around a datenum array,
plus time zone support.
While the underlying data representation of datetime is compatible with
(in fact, identical to) that of datenums, you cannot directly combine them
via assignment, concatenation, or most arithmetic operations.
This is because of the signature of the datetime constructor. When combining
objects and primitive types like double, the primitive type is promoted to an
object by calling the other object’s one-argument constructor on it. However, the
one-argument numeric-input consstructor for datetime does not accept datenums:
it interprets its input as datevecs instead. This is due to a design decision on
Matlab’s part; for compatibility, Octave does not alter that interface.
To combine datetimes with datenums, you can convert the datenums to datetimes
by calling datetime.ofDatenum or datetime(x, 'ConvertFrom', 'datenum'), or you
can convert the datetimes to datenums by accessing its dnums field with
x.dnums.
Examples:
dt = datetime('2011-03-04')
dn = datenum('2017-01-01')
[dt dn]
⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
[dt datetime.ofDatenum(dn)]
⇒ 04-Mar-2011 01-Jan-2017
Also, if you have a zoned datetime, you can’t combine it with a datenum, because datenums
do not carry time zone information.
Tablicious has support for representing dates in time zones and for converting between time zones.
A datetime may be "zoned" or "zoneless". A zoneless datetime does not have a time zone
associated with it. This is represented by an empty TimeZone property on the datetime
object. A zoneless datetime represents the local time in some unknown time zone, and assumes a
continuous time scale (no DST shifts).
A zoned datetime is associated with a time zone. It is represented by having the time zone’s
IANA zone identifier (e.g. 'UTC' or 'America/New_York') in its TimeZone
property. A zoned datetime represents the local time in that time zone.
By default, the datetime constructor creates unzoned datetimes. To
make a zoned datetime, either pass the 'TimeZone' option to the constructor,
or set the TimeZone property after object creation. Setting the TimeZone
property on a zoneless datetime declares that it’s a local time in that time zone.
Setting the TimeZone property on a zoned datetime turns it back into a
zoneless datetime without changing the local time it represents.
You can tell a zoned from a zoneless time zone in the object display because the time zone
is included for zoned datetimes.
% Create an unzoned datetime
d = datetime('2011-03-04 06:00:00')
⇒ 04-Mar-2011 06:00:00
% Create a zoned datetime
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
⇒ 04-Mar-2011 06:00:00 America/New_York
% This is equivalent
d_ny = datetime('2011-03-04 06:00:00');
d_ny.TimeZone = 'America/New_York'
⇒ 04-Mar-2011 06:00:00 America/New_York
% Convert it to Chicago time
d_chi.TimeZone = 'America/Chicago'
⇒ 04-Mar-2011 05:00:00 America/Chicago
When you combine two zoned datetimes via concatenation, assignment, or
arithmetic, if their time zones differ, they are converted to the time zone of
the left-hand input.
d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
d_la - d_ny
⇒ 03:00:00
You cannot combine a zoned and an unzoned datetime. This results in an error
being raised.
Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.
Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.
You can use the timezones function to list the time zones known to Tablicious. These will be
all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA
time zone database redistributed with Tablicious (for Windows).
Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for
datetimes outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)
duration ClassA duration represents a period of time in fixed-length seconds (or minutes, hours,
or whatever you want to measure it in.)
A duration has a resolution of about a nanosecond for typical dates. The underlying
representation is a double representing the number of days elapsed, similar to a
datenum, except it’s interpreted as relative to some other reference point you provide,
instead of being relative to the Matlab/Octave epoch.
You can add or subtract a duration to a datetime to get another datetime.
You can also add or subtract durations to each other.
calendarDuration ClassA calendarDuration represents a period of time in variable-length calendar
components. For example, years and months can have varying numbers of days, and days
in time zones with Daylight Saving Time have varying numbers of hours. A
calendarDuration does arithmetic with "whole" calendar periods.
calendarDurations and durations cannot be directly combined, because
they are not semantically equivalent. (This may be relaxed in the future to allow
durations to be interpreted as numbers of days when combined with
calendarDurations.)
d = datetime('2011-03-04 00:00:00')
⇒ 04-Mar-2011
cdur = calendarDuration(1, 3, 0)
⇒ 1y 3mo
d2 = d + cdur
⇒ 04-Jun-2012
Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.
These validation functions are named following the pattern mustBeXxx, where Xxx
is some property of the input it is testing. Validation functions may check the type,
size, or other aspects of their inputs.
The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.
Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.
When a validation function’s condition is violated, it raises an error that includes a
description of the violation in the error message. This message will include a label for
the input that describes what is being tested. By default, this label is initialized
with inputname(), so when you are calling a validator on a function argument or
variable, you will generally not need to supply a label. But if you’re calling it on
an object property or an expression more complex than a simple variable reference, the
validator cannot automatically detect the input name for use in the label. In this case,
make use of the optional trailing argument(s) to the functions to manually supply a
label for the value being tested.
% Validation of a simple variable does not need a label mustBeScalar (x); % Validation of a field or property reference does need a label mustBeScalar (this.foo, 'this.foo');
Tablicious comes with several example data sets that you can use to explore how
its functions and objects work. These are accessed through the
octave.datasets and octave.dataset classes.
To see a list of the available data sets, run octave.datasets.list().
Then to load one of the example data sets, run
octave.datasets.load('examplename'). For example:
octave.datasets.list
t = octave.datasets.load('cupcake')
You can also load it by calling octave.dataset.<name>. This does
the same thing. For example:
t = octave.dataset.cupcake
When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).
Each example data set comes with help text that describes the data set and
provides examples of how to work with it. This help is found using the doc
command on octave.dataset.<name>, where <name> is the name of
the data set.
For example:
doc octave.dataset.cupcake
(The command help octave.dataset.<name> ought to work too, but it
currently doesn’t. This may be due to an issue with Octave’s help
command.)
Many of Tablicious’ example data sets are based on the example datasets
found in R’s datasets package. R can be found at
https://www.r-project.org/, and documentation for its datasets
is at https://rdrr.io/r/datasets/datasets-package.html.
Thanks to the R developers for producing the original data sets here.
Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.
Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.
Tablicious is based on Matlab’s table and date/time APIs and supports most of their major functionality. But not all of it is implemented yet. The missing parts are currently:
readtable() and writetable()
summary() categorical
.-indexing
timetable
'ConvertFrom' forms for datetime and duration
datetime
between
caldiff
dateshift
week
isdst, isweekend
calendarDuration.split
duration.Format support
fillmissing
UTCOffset and DSTOffset fields in the output of timezones()
It is the author’s hope that all these will be implemented some day.
These areas of missing functionality are all tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/1.
Tabular data array containing multiple columnar variables.
Convert an array to a table.
Convert a cell array to a table.
Convert struct to a table.
Outer fill value for variable within a table.
Filter by variable type for use in suscripting.
A string array of Unicode strings.
Test if strings start with a pattern.
Test if strings end with a pattern.
Test if strings contain a pattern.
Categorical variable array.
Group data into discrete bins or categories.
Represents points in time using the Gregorian calendar.
Represents a complete day using the Gregorian calendar.
True if input is a ‘datetime’ array, false otherwise.
“Not-a-Time”.
Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.
Create a ‘calendarDuration’ that is a given number of calendar months long.
Construct a ‘calendarDuration’ a given number of years long.
Duration in days.
Represents durations or periods of time as an amount of fixed-length time (i.e.
Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.
True if input is a ‘duration’ array, false otherwise.
Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.
Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.
Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.
List all the time zones defined on this system.
Create a ‘duration’ X years long, or get the years in a ‘duration’ X.
Fill missing values.
Find missing values.
Remove missing values.
Insert standard missing values.
Generic auto-converting missing value.
Test if elements are NaN or NaN-like
Determine element-wise equality, treating NaNs as equal
Requires that input is of a given type.
Requires that input is a cellstr.
Requires that input is a char row vector.
Requires that the inputs are the same size.
Requires that input is scalar.
Requires that input is a scalar logical.
Requires that input is a vector or empty.
Apply a function to column vectors in array.
Display strings for array.
Alias for prettyprint, for interactive use.
Expand scalar inputs to match size of non-scalar inputs.
Format an array size for display.
Split data into groups and apply function.
Apply function to vectors in array along arbitrary dimension.
Example dataset collection.
The ‘dataset’ class provides convenient access to the various datasets included with Tablicious.
Plot pairs of variables against each other.
True if input is a ‘categorical’ array, false otherwise.
A dummy function just for testing the doco tools.
A do-nothing class just for testing the doco tools.
Conditioning plot.
out = array2table (c) ¶out = array2table (…, 'VariableNames', VariableNames) ¶out = array2table (…, 'RowNames', RowNames) ¶Convert an array to a table.
Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.
You may not want to use this on cell arrays, though, because you will
end up with a table that has all its variables of type cell. If you use
cell2table instead, columns of the cell array which can be
condensed into primitive arrays will be. With array2table, they
won’t be.
See also: cell2table, table, struct2table
Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)
calendarDuration: charThe sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.
calendarDuration: charThe number of whole calendar years in this duration. Must be integer-valued.
calendarDuration: charThe number of whole calendar months in this duration. Must be integer-valued.
calendarDuration: charThe number of whole calendar days in this duration. Must be integer-valued.
calendarDuration: charThe number of whole hours in this duration. Must be integer-valued.
calendarDuration: charThe number of whole minutes in this duration. Must be integer-valued.
calendarDuration: charThe number of seconds in this duration. May contain fractional values.
calendarDuration: charThe format to display this calendarDuration in. Currently unsupported.
This is a single value that applies to the whole array.
obj = calendarDuration () ¶Constructs a new scalar calendarDuration of zero elapsed time.
obj = calendarDuration (Y, M, D) ¶obj = calendarDuration (Y, M, D, H, MI, S) ¶Constructs new calendarDuration arrays based on input values.
out = isnat (obj) ¶True if input elements are NaT.
Returns logical array the same size as obj.
out = plus (A, B) ¶Addition: add two calendarDurations.
All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.
If B is numeric, it is converted to a calendarDuration
using calendarDuration.ofDays.
Returns a calendarDuration.
out = times (obj, B) ¶Multiplication: Multiplies a calendarDuration by a numeric factor.
Returns a calendarDuration.
out = times (A, B) ¶Subtraction: Subtracts one calendarDuration from another.
Returns a calendarDuration.
out = dispstrs (obj) ¶Get display strings for each element of obj.
Returns a cellstr the same size as obj.
out = isnan (obj) ¶True if input elements are NaT. This is just an alias for isnat,
provided for compatibility and polymorphic programming purposes.
Returns logical array the same size as obj.
out = calmonths (x) ¶Create a calendarDuration that is a given number of calendar months
long.
Input x is a numeric array specifying the number of calendar months.
This is a shorthand alternative to calling the calendarDuration
constructor with calendarDuration(0, x, 0).
Returns a new calendarDuration object of the same size as x.
See calendarDuration.
out = calyears (x) ¶Construct a calendarDuration a given number of years long.
This is a shorthand for calling calendarDuration(x, 0, 0).
See calendarDuration.
Categorical variable array.
A categorical array represents an array of values of a categorical
variable. Each categorical array stores the element values along
with a list of the categories, and indicators of whether the categories
are ordinal (that is, they have a meaningful mathematical ordering), and
whether the set of categories is protected (preventing new categories
from being added to the array).
In addition to the categories defined in the array, a categorical array
may have elements of "undefined" value. This is not considered a
category; rather, it is the absence of any known value. It is
analagous to a NaN value.
This class is not fully implemented yet. Missing stuff: - gt, ge, lt, le - Ordinal support in general - countcats - summary
categorical: uint16The numeric codes of the array element values. These are indexes into the
cats category list.
This is a planar property.
categorical: logicalA logical mask indicating whether each element of the array is missing (that is, undefined).
This is a planar property.
categorical: cellstrThe names of the categories in this array. This is the list into which
the code values are indexes.
categorical: scalar_logicalA scalar logical indicating whether the categories in this array have an ordinal relationship.
out = categorical.undefined () ¶out = categorical.undefined (sz) ¶Create an array of undefined categoricals.
Creates a categorical array whose elements are all <undefined>.
sz is the size of the array to create. If omitted or empty, creates a scalar.
Returns a categorical.
obj = categorical () ¶Constructs a new scalar categorical whose value is undefined.
obj = categorical (vals) ¶obj = categorical (vals, valueset) ¶obj = categorical (vals, valueset, category_names) ¶obj = categorical (…, 'Ordinal', Ordinal) ¶obj = categorical (…, 'Protected', Protected) ¶Constructs a new categorical array from the given values.
vals is the array of values to convert to categoricals.
valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.
category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.
Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.
Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.
out = categories (obj) ¶Get a list of the categories in obj.
Gets a list of the categories in obj, identified by their category names.
Returns a cellstr column vector.
out = iscategory (obj, catnames) ¶Test whether input is a category on a categorical array.
catnames is a cellstr listing the category names to check against obj.
Returns a logical array the same size as catnames.
out = isordinal (obj) ¶Whether obj is ordinal.
Returns true if obj is ordinal (as determined by its
IsOrdinal property), and false otherwise.
out = string (obj) ¶Convert to string array.
Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.
Returns a string array the same size as obj.
out = cellstr (obj) ¶Convert to cellstr.
Converts obj to a cellstr array. The strings will be the
category names for corresponding values, or '' for undefined
values.
Returns a cellstr array the same size as obj.
out = dispstrs (obj) ¶Display strings.
Gets display strings for each element in obj. The display strings are
either the category string, or '<undefined>' for undefined values.
Returns a cellstr array the same size as obj.
(obj) ¶Display summary of array’s values.
Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.
out = addcats (obj, newcats) ¶Add categories to categorical array.
Adds the specified categories to obj, without changing any of its values.
newcats is a cellstr listing the category names to add to obj.
out = removecats (obj) ¶Removes all unused categories from obj. This is equivalent to
out = squeezecats (obj).
out = removecats (obj, oldcats) ¶Remove categories from categorical array.
Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.
newcats is a cellstr listing the category names to add to obj.
out = mergecats (obj, oldcats) ¶out = mergecats (obj, oldcats, newcat) ¶Merge multiple categories.
Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.
If newcat is not provided, all of odcats are merged into
oldcats{1}.
out = renamecats (obj, newcats) ¶out = renamecats (obj, oldcats, newcats) ¶Rename categories.
Renames some or all of the categories in obj, without changing any of its values.
out = reordercats (obj) ¶out = reordercats (obj, newcats) ¶Reorder categories.
Reorders the categories in obj to match newcats.
newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.
out = setcats (obj, newcats) ¶Set categories for categorical array.
Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.
out = isundefined (obj) ¶Test whether elements are undefined.
Checks whether each element in obj is undefined. "Undefined" is
a special value defined by categorical. It is equivalent to
a NaN or a missing value.
Returns a logical array the same size as obj.
out = ismissing (obj) ¶Test whether elements are missing.
For categorical arrays, undefined elements are considered to be missing.
Returns a logical array the same size as obj.
out = isnanny (obj) ¶Test whethere elements are NaN-ish.
Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.
Returns a logical array the same size as obj.
out = squeezecats (obj) ¶Remove unused categories.
Removes all categories which have no corresponding values in obj’s elements.
This is currently unimplemented.
out = cell2table (c) ¶out = cell2table (…, 'VariableNames', VariableNames) ¶out = cell2table (…, 'RowNames', RowNames) ¶Convert a cell array to a table.
Converts a 2-dimensional cell matrix into a table. Each column in the
input c becomes a variable in out. For columns that contain
all scalar values of cat-compatible types, they are “popped out”
of their cells and condensed into a homogeneous array of the contained
type.
See also: array2table, table, struct2table
out = colvecfun (fcn, x) ¶Apply a function to column vectors in array.
Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.
fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.
Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.
out = colvecfun (str, pattern) ¶out = colvecfun (…, 'IgnoreCase', IgnoreCase) ¶Test if strings contain a pattern.
Tests whether the given strings contain the given pattern(s).
str (char, cellstr, or string) is a list of strings to compare against pattern.
pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.
Returns a logical array of the same size as the string array represented by str.
Represents points in time using the Gregorian calendar.
The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.
A datetime array is an array of date/time values, with each element
holding a complete date/time. The overall array may also have a TimeZone and a
Format associated with it, which apply to all elements in the array.
This is an attempt to reproduce the functionality of Matlab’s datetime. It
also contains some Octave-specific extensions.
datetime: doubleThe underlying datenums that represent the points in time. These are always in UTC.
This is a planar property: the size of dnums is the same size as the
containing datetime array object.
datetime: charThe time zone this datetime array is in. Empty if this does not have a
time zone associated with it (“unzoned”). The name of an IANA time zone if
this does.
Setting the TimeZone of a datetime array changes the time zone it
is presented in for strings and broken-down times, but does not change the
underlying UTC times that its elements represent.
datetime: charThe format to display this datetime in. Currently unsupported.
obj = datetime () ¶Constructs a new scalar datetime containing the current local time, with
no time zone attached.
obj = datetime (datevec) ¶obj = datetime (datestrs) ¶obj = datetime (in, 'ConvertFrom', inType) ¶obj = datetime (Y, M, D, H, MI, S) ¶obj = datetime (Y, M, D, H, MI, MS) ¶obj = datetime (…, 'Format', Format, 'InputFormat', InputFormat, 'Locale', InputLocale, 'PivotYear', PivotYear, 'TimeZone', TimeZone) ¶Constructs a new datetime array based on input values.
obj = datetime.ofDatenum (dnums) ¶Converts a datenum array to a datetime array.
Returns an unzoned datetime array of the same size as the input.
obj = datetime.ofDatestruct (dstruct) ¶Converts a datestruct to a datetime array.
A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.
Returns an unzoned datetime array.
out = datetime.NaT () ¶out = datetime.NaT (sz) ¶“Not-a-Time”: Creates NaT-valued arrays.
Constructs a new datetime array of all NaT values of
the given size. If no input sz is given, the result is a scalar NaT.
NaT is the datetime equivalent of NaN. It represents a missing
or invalid value. NaT values never compare equal to, greater than, or less
than any value, including other NaTs. Doing arithmetic with a NaT and
any other value results in a NaT.
dnums = datetime.posix2datenum (pdates) ¶Converts POSIX (Unix) times to datenums
Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.
out = datetime.datenum2posix (dnums) ¶Converts Octave datenums to Unix dates.
The input datenums are assumed to be in UTC.
Returns a double, which may have fractional seconds.
[keysA, keysB] = proxyKeys (a, b) ¶Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.
This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.
Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.
[y, m, d] = ymd (obj) ¶Get the Year, Month, and Day components of obj.
For zoned datetimes, these will be local times in the associated time zone.
Returns double arrays the same size as obj.
[h, m, s] = hms (obj) ¶Get the Hour, Minute, and Second components of a obj.
For zoned datetimes, these will be local times in the associated time zone.
Returns double arrays the same size as obj.
[y, m, d, h, mi, s] = ymdhms (obj) ¶Get the Year, Month, Day, Hour, Minute, and Second components of a obj.
For zoned datetimes, these will be local times in the associated time zone.
Returns double arrays the same size as obj.
out = timeofday (obj) ¶Get the time of day (elapsed time since midnight).
For zoned datetimes, these will be local times in the associated time zone.
Returns a duration array the same size as obj.
out = week (obj) ¶Get the week of the year.
This method is unimplemented.
out = dispstrs (obj) ¶Get display strings for each element of obj.
Returns a cellstr the same size as obj.
out = datestr (obj) ¶out = datestr (obj, …) ¶Format obj as date strings. Supports all arguments that core Octave’s
datestr does.
Returns date strings as a 2-D char array.
out = datestrs (obj) ¶out = datestrs (obj, …) ¶Format obj as date strings, returning cellstr.
Supports all arguments that core Octave’s datestr does.
Returns a cellstr array the same size as obj.
out = datestruct (obj) ¶Converts this to a "datestruct" broken-down time structure.
A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.
The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.
Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.
out = posixtime (obj) ¶Converts this to POSIX time values (seconds since the Unix epoch)
Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.
Returns double array of same size as this.
out = datenum (obj) ¶Convert this to datenums that represent the same local time
Returns double array of same size as this.
out = gmtime (obj) ¶Convert to TM_STRUCT structure in UTC time.
Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.
Returns a struct array in TM_STRUCT style.
out = localtime (obj) ¶Convert to TM_STRUCT structure in UTC time.
Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.
If obj is unzoned, it is assumed to be in UTC time.
Returns a struct array in TM_STRUCT style.
Example:
dt = datetime; dt.TimeZone = datetime.SystemTimeZone; tm_struct = localtime (dt);
out = isnat (obj) ¶True if input elements are NaT.
Returns logical array the same size as obj.
out = isnan (obj) ¶True if input elements are NaT. This is an alias for isnat
to support type compatibility and polymorphic programming.
Returns logical array the same size as obj.
out = lt (A, B) ¶True if A is less than B. This defines the < operator
for datetimes.
Inputs are implicitly converted to datetime using the one-arg
constructor or conversion method.
Returns logical array the same size as obj.
out = le (A, B) ¶True if A is less than or equal toB. This defines the <= operator
for datetimes.
Inputs are implicitly converted to datetime using the one-arg
constructor or conversion method.
Returns logical array the same size as obj.
out = ne (A, B) ¶True if A is not equal to B. This defines the != operator
for datetimes.
Inputs are implicitly converted to datetime using the one-arg
constructor or conversion method.
Returns logical array the same size as obj.
out = eq (A, B) ¶True if A is equal to B. This defines the == operator
for datetimes.
Inputs are implicitly converted to datetime using the one-arg
constructor or conversion method.
Returns logical array the same size as obj.
out = ge (A, B) ¶True if A is greater than or equal to B. This defines the >= operator
for datetimes.
Inputs are implicitly converted to datetime using the one-arg
constructor or conversion method.
Returns logical array the same size as obj.
out = gt (A, B) ¶True if A is greater than B. This defines the > operator
for datetimes.
Inputs are implicitly converted to datetime using the one-arg
constructor or conversion method.
Returns logical array the same size as obj.
out = plus (A, B) ¶Addition (+ operator). Adds a duration, calendarDuration,
or numeric B to a datetime A.
A must be a datetime.
Numeric B inputs are implicitly converted to duration using
duration.ofDays.
Returns datetime array the same size as A.
out = minus (A, B) ¶Subtraction (- operator). Subtracts a duration,
calendarDuration or numeric B from a datetime A,
or subtracts two datetimes from each other.
If both inputs are datetime, then the output is a duration.
Otherwise, the output is a datetime.
Numeric B inputs are implicitly converted to duration using
duration.ofDays.
Returns an array the same size as A.
out = diff (obj) ¶Differences between elements.
Computes the difference between each successive element in obj, as a
duration.
Returns a duration array the same size as obj.
out = isbetween (obj, lower, upper) ¶Tests whether the elements of obj are between lower and upper.
All inputs are implicitly converted to datetime arrays, and are subject
to scalar expansion.
Returns a logical array the same size as the scalar expansion of the inputs.
out = linspace (from, to, n) ¶Linearly-spaced values in date/time space.
Constructs a vector of datetimes that represent linearly spaced points
starting at from and going up to to, with n points in the
vector.
from and to are implicitly converted to datetimes.
n is how many points to use. If omitted, defaults to 100.
Returns an n-long datetime vector.
out = datetime.convertDatenumTimeZone (dnum, fromZoneId, toZoneId) ¶Convert a datenum from one time zone to another.
dnum is a datenum array to convert.
fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.
toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.
Returns a datenum array the same size as dnum.
out = days (x) ¶Duration in days.
If x is numeric, then out is a duration array in units
of fixed-length 24-hour days, with the same size as x.
If x is a duration, then returns a double array the same
size as x indicating the number of fixed-length days that each duration
is.
[Y, E] = discretize (X, n) ¶[Y, E] = discretize (X, edges) ¶[Y, E] = discretize (X, dur) ¶[Y, E] = discretize (…, 'categorical') ¶[Y, E] = discretize (…, 'IncludedEdge', IncludedEdge) ¶Group data into discrete bins or categories.
n is the number of bins to group the values into.
edges is an array of edge values defining the bins.
dur is a duration value indicating the length of time of each
bin.
If 'categorical' is specified, the resulting values are a categorical
array instead of a numeric array of bin indexes.
Returns: Y - the bin index or category of each value from X E - the list of bin edge values
out = dispstrs (x) ¶Display strings for array.
Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.
The default implementation of dispstrs can accept input of any
type, and has decent implementations for Octave’s standard built-in types,
but will have opaque displays for most user-defined objects.
This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.
Returns a cell array the same size as x.
Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.
This is an attempt to reproduce the functionality of Matlab’s duration. It
also contains some Octave-specific extensions.
duration: doubleThe underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.
This is a planar property: the size of days is the same size as the
containing duration array object.
duration: charThe format to display this duration in. Currently unsupported.
obj = duration.ofDays (dnums) ¶Converts a double array representing durations in whole and fractional days
to a duration array. This is the method that is used for implicit conversion
of numerics in many cases.
Returns a duration array of the same size as the input.
out = years (obj) ¶Equivalent number of years.
Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.
Returns double array the same size as obj.
out = hours (obj) ¶Equivalent number of hours.
Gets the number of fixed-length 60-minute hours that is equivalent to this duration.
Returns double array the same size as obj.
out = minutes (obj) ¶Equivalent number of minutes.
Gets the number of fixed-length 60-second minutes that is equivalent to this duration.
Returns double array the same size as obj.
out = seconds (obj) ¶Equivalent number of seconds.
Gets the number of seconds that is equivalent to this duration.
Returns double array the same size as obj.
out = milliseconds (obj) ¶Equivalent number of milliseconds.
Gets the number of milliseconds that is equivalent to this duration.
Returns double array the same size as obj.
out = duration (obj) ¶Get display strings for each element of obj.
Returns a cellstr the same size as obj.
out = char (obj) ¶Convert to char. The contents of the strings will be the same as
returned by dispstrs.
This is primarily a convenience method for use on scalar objs.
Returns a 2-D char array with one row per element in obj.
out = linspace (from, to, n) ¶Linearly-spaced values in time duration space.
Constructs a vector of durations that represent linearly spaced points
starting at from and going up to to, with n points in the
vector.
from and to are implicitly converted to durations.
n is how many points to use. If omitted, defaults to 100.
Returns an n-long datetime vector.
out = endsWith (str, pattern) ¶out = endsWith (…, 'IgnoreCase', IgnoreCase) ¶Test if strings end with a pattern.
Tests whether the given strings end with the given pattern(s).
str (char, cellstr, or string) is a list of strings to compare against pattern.
pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.
Returns a logical array of the same size as the string array represented by str.
out = eqn (A, B) ¶Determine element-wise equality, treating NaNs as equal
out = eqn (A, B)
eqn is just like eq (the function that implements the
== operator), except
that it considers NaN and NaN-like values to be equal. This is the element-wise
equivalent of isequaln.
eqn uses isnanny to test for NaN and NaN-like values,
which means that NaNs and NaTs are considered to be NaN-like, and
string arrays’ “missing” and categorical objects’ “undefined” values
are considered equal, because they are NaN-ish.
Developer’s note: the name “eqn” is a little unfortunate,
because “eqn” could also be an abbreviation for “equation”. But this
name follows the isequaln pattern of appending an “n” to the
corresponding non-NaN-equivocating function.
See also: eq, isequaln, isnanny
[out, tfFilled] = fillmissing (X, method) ¶[out, tfFilled] = fillmissing (X, 'constant', fill_val) ¶[out, tfFilled] = fillmissing (X, movmethod, window) ¶Fill missing values.
Fills missing values in X according to the method specified by method.
This method is only partially implemented.
method may be:
'constant'
'previous'
'next'
'nearest'
'linear'
'spline'
'pchip'
movmethod may be:
'movmean'
'movmedian'
Returns out, which is X but with missing values filled in, and tfFilled, a logical array the same size as X which indicates which elements were filled.
out = hours (x) ¶Create a duration x hours long, or get the hours in a duration
x.
If input is numeric, returns a duration array that is that many hours in
time.
If input is a duration, converts the duration to a number of hours.
Returns an array the same size as x.
out = iscategorical (x) ¶True if input is a categorical array, false otherwise.
Returns a scalar logical.
out = isdatetime (x) ¶True if input is a datetime array, false otherwise.
Returns a scalar logical.
out = isduration (x) ¶True if input is a duration array, false otherwise.
Returns a scalar logical.
Not documented
Not documented
out = ismissing (X) ¶out = ismissing (X, indicator) ¶Find missing values.
Determines which elements of X contain missing values. If an indicator input is not provided, standard missing values depending on the input type of X are used.
Standard missing values depend on the data type:
' ' for char
{''} for cellstrs
ismissing on them without providing an indicator.
ismissing on them
results in an error.
ismissing should polymorphically
detect isnan()/isnat()/isnanny() methods and use those, or whether we should
require classes to override ismissing() itself.
If indicator is supplied, it is an array containing multiple values, all of
which are considered to be missing values. Only indicator values that are
type-compatible with the input are considered; other indicator value types are
silently ignored. This is by design, so you can pass an indicator that holds
sentinel values for disparate types in to ismissing() used for any type, or
for compound types like table.
Indicators are currently not supported for struct or logical inputs. This is probably a bug.
Table defines its own ismissing() method which respects individual variables’
data types; see table.ismissing.
out = isnanny (X) ¶Test if elements are NaN or NaN-like
Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true
if isnan() or isnat() returns true, and is false for types that do not support
isnan() or isnat().
This function only exists because:
isnanny() smooths over those differences so you can call it polymorphically on
any input type.
Under normal operation, isnanny() should not throw an error for any type or
value of input.
Represents a complete day using the Gregorian calendar.
This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.
localdate: doubleThe underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.
These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.
localdate: charThe format to display this localdate in. Currently unsupported.
obj = localdate () ¶Constructs a new scalar localdate containing the current local date.
obj = localdate (datenums) ¶obj = localdate (datestrs) ¶obj = localdate (Y, M, D) ¶obj = localdate (…, 'Format', Format) ¶Constructs a new localdate array based on input values.
out = localdate.NaT () ¶out = localdate.NaT (sz) ¶“Not-a-Time”: Creates NaT-valued arrays.
Constructs a new datetime array of all NaT values of
the given size. If no input sz is given, the result is a scalar NaT.
NaT is the datetime equivalent of NaN. It represents a missing
or invalid value. NaT values never compare equal to, greater than, or less
than any value, including other NaTs. Doing arithmetic with a NaT and
any other value results in a NaT.
This static method is provided because the global NaT function creates
datetimes, not localdates
[y, m, d] = ymd (obj) ¶Get the Year, Month, and Day components of obj.
Returns double arrays the same size as obj.
out = dispstrs (obj) ¶Get display strings for each element of obj.
Returns a cellstr the same size as obj.
out = datestr (obj) ¶out = datestr (obj, …) ¶Format obj as date strings. Supports all arguments that core Octave’s
datestr does.
Returns date strings as a 2-D char array.
out = datestrs (obj) ¶out = datestrs (obj, …) ¶Format obj as date strings, returning cellstr.
Supports all arguments that core Octave’s datestr does.
Returns a cellstr array the same size as obj.
out = datestruct (obj) ¶Converts this to a “datestruct” broken-down time structure.
A “datestruct” is a format of struct that Tablicious came up with. It is a scalar
struct with fields Year, Month, and Day, each containing
a double array the same size as the date array it represents. This format
differs from the “datestruct” used by datetime in that it lacks
Hour, Minute, and Second components. This is done for efficiency.
The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.
Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.
out = posixtime (obj) ¶Converts this to POSIX time values for midnight of obj’s days.
Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.
Returns double array of same size as this.
out = datenum (obj) ¶Convert this to datenums that represent midnight on obj’s days.
Returns double array of same size as this.
out = isnat (obj) ¶True if input elements are NaT.
Returns logical array the same size as obj.
out = isnan (obj) ¶True if input elements are NaT. This is an alias for isnat
to support type compatibility and polymorphic programming.
Returns logical array the same size as obj.
out = milliseconds (x) ¶Create a duration x milliseconds long, or get the milliseconds in a duration
x.
If input is numeric, returns a duration array that is that many milliseconds in
time.
If input is a duration, converts the duration to a number of milliseconds.
Returns an array the same size as x.
out = hours (x) ¶Create a duration x hours long, or get the hours in a duration
x.
Generic auto-converting missing value.
missing is a generic missing value that auto-converts to other
types.
A missing array indicates a missing value, of no particular type. It auto-
converts to other types when it is combined with them via concatenation or
other array combination operations.
This class is currently EXPERIMENTAL. Use at your own risk.
Note: This class does not actually work for assignment. If you do this:
x = 1:5 x(3) = missing
It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.
obj = missing () ¶Constructs a scalar missing array.
The constructor takes no arguments, since there’s only one
missing value.
out = dispstrs (obj) ¶Display strings.
Gets display strings for each element in obj.
For missing, the display strings are always '<missing>'.
Returns a cellstr the same size as obj.
out = ismissing (obj) ¶Test whether elements are missing values.
ismissing is always true for missing arrays.
Returns a logical array the same size as obj.
out = isnan (obj) ¶Test whether elements are NaN.
isnan is always true for missing arrays.
Returns a logical array the same size as obj.
out = isnanny (obj) ¶Test whether elements are NaN-like.
isnanny is always true for missing arrays.
Returns a logical array the same size as obj.
x = mustBeA (x, type) ¶x = mustBeA (x, type, label) ¶Requires that input is of a given type.
Raises an error if the input x is not of type type, as determined by
isa (x, type).
label is an optional input that determines how the input will be described in
error messages. If not supplied, inputname (1) is used, and if that is
empty, it falls back to "input".
x =(x, label) ¶Requires that input is a cellstr.
Raises an error if the input x is not a cellstr (a cell array
of char arrays).
TODO: Decide whether to require the contained char arrays be rowvec/empty.
label is an optional input that determines how the input will be described in
error messages. If not supplied, inputname (1) is used, and if that is
empty, it falls back to "input".
x = mustBeCharvec (x, label) ¶Requires that input is a char row vector.
Raises an error if the input x is not a row vector of chars.
char row vectors are Octave’s normal representation of single strings.
(They are what are produced by '...' string literals.) As a special
case, 0-by-0 empty chars (what is produced by the string literal '')
are also considered charvecs.
This does not differentiate between single-quoted and double-quoted strings.
label is an optional input that determines how the input will be described in
error messages. If not supplied, inputname (1) is used, and if that is
empty, it falls back to "input".
Not documented
Not documented
Not documented
Not documented
Not documented
Not documented
[a, b] = mustBeSameSize (a, b, labelA, labelB) ¶Requires that the inputs are the same size.
Raises an error if the inputs a and b are not the same size,
as determined by isequal (size (a), size (b)).
labelA and labelB are optional inputs that determine how
the input will be described in error messages. If not supplied,
inputname (...) is used, and if that is empty, it falls back to
"input 1" and "input 2".
x = mustBeScalar (x, label) ¶Requires that input is scalar.
Raises an error if the input x is not scalar, as determined by
isscalar (x).
label is an optional input that determines how the input will be described in
error messages. If not supplied, inputname (1) is used, and if that is
empty, it falls back to "input".
x = mustBeScalarLogical (x, label) ¶Requires that input is a scalar logical.
Raises an error if the input x is not scalar, as determined by
isscalar (x) && islogical (x).
label is an optional input that determines how the input will be described in
error messages. If not supplied, inputname (1) is used, and if that is
empty, it falls back to "input".
x = mustBeVector (x, label) ¶Requires that input is a vector or empty.
Raises an error if the input x is not a row vector and is not 0-by-0 empty.
label is an optional input that determines how the input will be described in
error messages. If not supplied, inputname (1) is used, and if that is
empty, it falls back to "input".
out = NaT () ¶out = NaT (sz) ¶“Not-a-Time”. Creates NaT-valued arrays.
Constructs a new datetime array of all NaT values of
the given size. If no input sz is given, the result is a scalar NaT.
NaT is the datetime equivalent of NaN. It represents a missing
or invalid value. NaT values never compare equal to, greater than, or less
than any value, including other NaTs. Doing arithmetic with a NaT and
any other value results in a NaT.
NaT currently cannot create NaT arrays of type localdate. To do that,
use localdate.NaT instead.
out = dummy_function (x) ¶A dummy function just for testing the doco tools.
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A do-nothing class just for testing the doco tools.
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DummyClass: doubleAn x. Has no semantics.
DummyClass: doubleA y. Has no semantics.
obj = octave.chrono.DummyClass () ¶Constructs a new scalar DummyClass with default values.
obj = octave.chrono.DummyClass (x, y) ¶Constructs a new DummyClass with the specified values.
out = foo (obj) ¶Computes a foo value.
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out = bar (obj) ¶Computes a bar value.
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The dataset class provides convenient access to the various
datasets included with Tablicious.
This class just contains a bunch of static methods, each of which loads the dataset of that name. It’s provided so you can use tab completion on the dataset list.
out = airmiles () ¶Passenger Miles on Commercial US Airlines, 1937-1960
The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.
F.A.A. Statistical Handbook of Aviation.
t = octave.dataset.airmiles;
plot (t.year, t.miles);
title ("airmiles data");
xlabel ("Passenger-miles flown by U.S. commercial airlines")
ylabel ("airmiles");
out = AirPassengers () ¶Monthly Airline Passenger Numbers 1949-1960
The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.
Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.
## TODO: This example needs to be ported from R.
out = airquality () ¶New York Air Quality Measurements from 1973
Daily air quality measurements in New York, May to September 1973.
OzoneOzone concentration (ppb)
SolarRSolar R (lang)
WindWind (mph)
TempTemperature (degrees F)
MonthMonth (1-12)
DayDay of month (1-31)
New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).
Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.
t = octave.dataset.airquality
# Plot a scatter-plot plus a fitted line, for each combination of measurements
vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
n_vars = numel (vars);
figure;
for i = 1:n_vars
for j = 1:n_vars
if i == j
continue
endif
ix_subplot = (n_vars*(j - 1) + i);
hax = subplot (n_vars, n_vars, ix_subplot);
var_x = vars{i};
var_y = vars{j};
x = t.(var_x);
y = t.(var_y);
scatter (hax, x, y, 10);
# Fit a cubic line to these points
# TODO: Find out exactly what kind of fitted line R's example is using, and
# port that.
hold on
p = polyfit (x, y, 3);
x_hat = unique(x);
p_y = polyval (p, x_hat);
plot (hax, x_hat, p_y, "r");
endfor
endfor
out = anscombe () ¶Anscombe’s Quartet of “Identical” Simple Linear Regressions
Four sets of x/y pairs which have the same statistical properties, but are very different.
The data comes in an array of 4 structs, each with fields as follows:
xThe X values for this pair.
yThe Y values for this pair.
Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.
Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.
data = octave.dataset.anscombe
# Pick good limits for the plots
all_x = [data.x];
all_y = [data.y];
x_limits = [min(0, min(all_x)) max(all_x)*1.2];
y_limits = [min(0, min(all_y)) max(all_y)*1.2];
# Do regression on each pair and plot the input and results
figure;
haxs = NaN (1, 4);
for i_pair = 1:4
x = data(i_pair).x;
y = data(i_pair).y;
# TODO: Port the anova and other characterizations from the R code
# TODO: Do a linear regression and plot its line
hax = subplot (2, 2, i_pair);
haxs(i_pair) = hax;
xlabel (sprintf ("x%d", i_pair));
ylabel (sprintf ("y%d", i_pair));
scatter (x, y, "r");
endfor
# Fiddle with the plot axes parameters
linkaxes (haxs);
xlim(haxs(1), x_limits);
ylim(haxs(1), y_limits);
out = attenu () ¶Joyner-Boore Earthquake Attenuation Data
Event data for 23 earthquakes in California, showing peak accelerations.
eventEvent number
magMoment magnitude
stationStation identifier
distStation-hypocenter distance (km)
accelPeak acceleration (g)
Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.
Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.
# TODO: Port the example code from R # It does coplot() and pairs(), which are higher-level plotting tools # than core Octave provides. This could turn into a long example if we # just use base Octave here.
out = attitude () ¶The Chatterjee-Price Attitude Data
Aggregated data from a survey of clerical employees at a large financial organization.
ratingOverall rating.
complaintsHandling of employee complaints.
privilegesDoes not allow special privileges.
learningOpportunity to learn.
raisesRaises based on performance.
criticalToo critical.
advanceAdvancement.
Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)
t = octave.dataset.attitude octave.examples.plot_pairs (t); # TODO: Display table summary # TODO: Whatever those statistical linear-model plots are that R is doing
out = austres () ¶Australian Population
Numbers of Australian residents measured quarterly from March 1971 to March 1994.
dateThe month of the observation.
residentsThe number of residents.
Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.
t = octave.dataset.austres
plot (datenum (t.date), t.residents);
datetick x
xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");
out = beavers () ¶Body Temperature Series of Two Beavers
Body temperature readings for two beavers.
dayDay of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).
timeTime of observation, in the form 0330 for 3:30am
tempMeasured body temperature in degrees Celsius.
activIndicator of activity outside the retreat.
P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.
# TODO: This example needs to be ported from R.
out = BJsales () ¶Sales Data with Leading Indicator
Sales Data with Leading Indicator
recordIndex of the record.
leadLeading indicator.
salesSales volume.
The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.
Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.
Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.
# TODO: Come up with example code here
out = BOD () ¶Biochemical Oxygen Demand
Contains biochemical oxygen demand versus time in an evaluation of water quality.
TimeTime of the measurement (in days).
demandBiochemical oxygen demand (mg/l).
Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.
Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.
# TODO: Port this example from R
out = cars () ¶Speed and Stopping Distances of Cars
Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.
speedSpeed (mph).
distStopping distance (ft).
Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.cars;
# TODO: Add Lowess smoothed lines to the plots
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars data");
figure;
loglog (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars data (logarithmic scales)");
# TODO: Do the linear model plot
# Polynomial regression
figure;
plot (t.speed, t.dist, "o");
xlabel ("Speed (mph)"); ylabel("Stopping distance (ft)");
title ("cars polynomial regressions");
hold on
xlim ([0 25]);
x2 = linspace (0, 25, 200);
for degree = 1:4
[P, S, mu] = polyfit (t.speed, t.dist, degree);
y2 = polyval(P, x2, [], mu);
plot (x2, y2);
endfor
out = ChickWeight () ¶Weight versus age of chicks on different diets
weighta numeric vector giving the body weight of the chick (gm).
Timea numeric vector giving the number of days since birth when the measurement was made.
Chickan ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.
Dieta factor with levels 1, ..., 4 indicating which experimental diet the chick received.
Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)
Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)
Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.
t = octave.dataset.ChickWeight octave.examples.coplot (t, "Time", "weight", "Chick");
out = chickwts () ¶Chicken Weights by Feed Type
An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.
Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.
weightChick weight at six weeks (gm).
feedFeed type.
Anonymous (1948) Biometrika, 35, 214.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
# This example requires the statistics package from Octave Forge
t = octave.dataset.chickwts
# Boxplot by group
figure
g = groupby (t, "feed", {
"weight", @(x) {x}, "weight"
});
boxplot (g.weight, 1);
xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
xticklabels ([{""} cellstr(g.feed')]);
# Linear model
# TODO: This linear model thing and anova
out = co2 () ¶Mauna Loa Atmospheric CO2 Concentration
Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.
dateDate of the month of the observation, as datetime.
co2CO2 concentration (ppm).
The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.
Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.
ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.
Cleveland, W. S. (1993). Visualizing Data. New Jersey: Summit Press.
t = octave.dataset.co2;
plot (datenum (t.date), t.co2);
datetick ("x");
xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
title ("co2 data set");
out = crimtab () ¶Student’s 3000 Criminals Data
Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.
This dataset contains three separate variables. The finger_length and
body_height variables correspond to the rows and columns of the
count matrix.
finger_lengthMidpoints of intervals of finger lengths (cm).
body_heightBody heights (cm).
countNumber of prisoners in this bin.
Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:
“Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”
The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").
MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”
http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.
Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.
MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.
Student (1908). The probable error of a mean. Biometrika, 6, 1–25.
# TODO: Port this from R
out = cupcake () ¶Google Search popularity for "cupcake", 2004-2019
Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.
MonthMonth when searches took place
CupcakeAn indicator of search volume, in unknown units
Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.
t = octave.dataset.cupcake
plot(datenum(t.Month), t.Cupcake)
title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric")
out = discoveries () ¶Yearly Numbers of Important Discoveries
The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.
yearYear.
discoveriesNumber of “great” discoveries that year.
The World Almanac and Book of Facts, 1975 Edition, pages 315–318.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.discoveries;
plot (t.year, t.discoveries);
xlabel ("Time"); ylabel ("Number of important discoveries");
title ("discoveries data set");
out = DNase () ¶Elisa assay of DNase
Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.
RunOrdered categorical indicating the assay run.
concKnown concentration of the protein (ng/ml).
densityMeasured optical density in the assay (dimensionless).
Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)
Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.
t = octave.dataset.DNase;
# TODO: Port this from R
octave.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
octave.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
"PlotArgs", {"o"});
out = esoph () ¶Smoking, Alcohol and Esophageal Cancer
Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.
itemAge group (years).
alcgpAlcohol consumption (gm/day).
tobgpTobacco consumption (gm/day).
ncasesNumber of cases.
ncontrolsNumber of controls
Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.
# TODO: Port this from R # TODO: Port the anova output # TODO: Port the fancy plot # This involves a "mosaic plot", which is not supported by Octave, so this will # take some work.
out = euro () ¶Conversion Rates of Euro Currencies
Conversion rates between the various Euro currencies.
This data comes in two separate variables.
euroAn 11-long vector of the value of 1 Euro in all participating currencies.
euro_crossAn 11-by-11 matrix of conversion rates between various Euro currencies.
euro_dateThe date upon which these Euro conversion rates were fixed.
The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.
Unknown.
This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.
# TODO: Port this from R # TODO: Example conversion # TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa
out = eurodist () ¶Distances Between European Cities and Between US Cities
eurodist gives road distances (in km) between 21 cities in Europe. The
data are taken from a table in The Cambridge Encyclopaedia.
UScitiesD gives “straight line” distances between 10 cities in the US.
eurodist?????
TODO: Finish this.
Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.
The US cities distances were provided by Pierre Legendre.
out = EuStockMarkets () ¶Daily Closing Prices of Major European Stock Indices
Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.
A multivariate time series with 1860 observations on 4 variables.
The starting date is the 130th day of 1991, with a frequency of 260 observations per year.
The data were kindly provided by Erste Bank AG, Vienna, Austria.
t = octave.dataset.EuStockMarkets;
# The fact that we're doing this munging means that table might have
# been the wrong structure for this data in the first place
t2 = removevars (t, "day");
index_names = t2.Properties.VariableNames;
day = 1:height (t2);
price = table2array (t2);
price0 = price(1,:);
rel_price = price ./ repmat (price0, [size(price,1) 1]);
figure;
plot (day, rel_price);
legend (index_names);
xlabel ("Business day");
ylabel ("Relative price");
out = faithful () ¶Old Faithful Geyser Data
Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.
eruptionsEruption time (mins).
waitingWaiting time to next eruption (mins).
W. Härdle.
Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.
Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.
t = octave.dataset.faithful;
% Munge the data, rounding eruption time to the second
e60 = 60 * t.eruptions;
ne60 = round (e60);
# TODO: Port zapsmall to Octave
eruptions = ne60 / 60;
# TODO: Display mean relative difference and bins summary
% Histogram of rounded eruption times
figure
hist (ne60, max (ne60))
xlabel ("Eruption time (sec)")
ylabel ("n")
title ("faithful data: Eruptions of Old Faithful")
% Scatter plot of eruption time vs waiting time
figure
scatter (t.eruptions, t.waiting)
xlabel ("Eruption time (min)")
ylabel ("Waiting time to next eruption (min)")
title ("faithful data: Eruptions of Old Faithful")
# TODO: Port Lowess smoothing to Octave
out = Formaldehyde () ¶Determination of Formaldehyde
These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.
recordObservation record number.
carbCarbohydrate (ml).
optdenOptical Density
Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.Formaldehyde;
figure
scatter (t.carb, t.optden)
% TODO: Add a linear model line
xlabel ("Carbohydrate (ml)")
ylabel ("Optical Density")
title ("Formaldehyde data")
% TODO: Add linear model summary output
% TOD: Add linear model summary plot
out = freeny () ¶Freeny’s Revenue Data
Freeny’s data on quarterly revenue and explanatory variables.
Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).
dateStart date of the quarter for the observation.
yObserved quarterly revenue. TODO: Determine units (probably millions of USD?)
lag_quarterly_revenueQuarterly revenue (y), lagged 1 quarter.
price_indexA price index
income_level??? TODO: Fill this in
market_potential??? TODO: Fill this in
Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.
t = octave.dataset.freeny; summary(t) octave.examples.plot_pairs (removevars (t, "date")) # TODO: Create linear model and print summary # TODO: Linear model plot
out = HairEyeColor () ¶Hair and Eye Color of Statistics Students
Distribution of hair and eye color and sex in 592 statistics students.
This data set comes in multiple variables
nA 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.
hairHair colors for the indexes along dimension 1.
eyeEye colors for the indexes along dimension 2.
sexSexes for the indexes along dimension 3.
The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.
This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.
http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas
Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).
Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.
Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html
Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html
Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.
octave.dataset.HairEyeColor # TODO: Aggregate over sex and display a table of counts # TODO: Port mosaic plot to Octave
out = Harman23cor () ¶Harman Example 2.3
A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.
covAn 8-by-8 correlation matrix.
namesNames of the variables corresponding to the indexes of the correlation matrix’s dimensions.
Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.
octave.dataset.Harman23cor; # TODO: Port factanal to Octave
out = Harman74cor () ¶Harman Example 7.4
A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.
covA 2-dimensional correlation matrix.
varsNames of the variables corresponding to the indexes along the dimensions of
cov.
Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.
octave.dataset.Harman74cor; # TODO: Port factanal to Octave
out = Indometh () ¶Pharmacokinetics of Indomethacin
Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).
SubjectSubject identifier.
timeTime since drug administration at which samples were drawn (hours).
concPlasma concentration of indomethacin (mcg/ml).
Each of the six subjects were given an intravenous injection of indometacin.
Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.
Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)
Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.
out = infert () ¶Infertility after Spontaneous and Induced Abortion
This is a matched case-control study dating from before the availability of conditional logistic regression.
educationIndex of the record.
ageAge in years of case.
parityCount.
inducedNumber of prior induced abortions, grouped into “0”, “1”, or “2 or more”.
case_status0 = control, 1 = case.
spontaneousNumber of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.
stratumMatched set number.
pooled_stratumStratum number.
One case with two prior spontaneous abortions and two prior induced abortions is omitted.
Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.
t = octave.dataset.infert; # TODO: Port glm() (generalized linear model) stuff to Octave
out = InsectSprays () ¶Effectiveness of Insect Sprays
The counts of insects in agricultural experimental units treated with different insecticides.
sprayThe type of spray.
countInsect count.
Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.
McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.InsectSprays; # TODO: boxplot # TODO: AOV plots
out = iris () ¶The Fisher Iris dataset: measurements of various flowers
This is the classic Fisher Iris dataset.
SpeciesThe species of flower being measured.
SepalLengthLength of sepals, in centimeters.
SepalWidthWidth of sepals, in centimeters.
PetalLengthLength of petals, in centimeters.
PetalWidthWidth of petals, in centimeters.
http://archive.ics.uci.edu/ml/datasets/Iris
https://en.wikipedia.org/wiki/Iris_flower_data_set
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).
Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.
# TODO: Port this example from R
out = islands () ¶Areas of the World’s Major Landmasses
The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.
nameThe name of the island.
areaThe area, in thousands of square miles.
The World Almanac and Book of Facts, 1975, page 406.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.islands; # TODO: Port dot chart to Octave
out = JohnsonJohnson () ¶Quarterly Earnings per Johnson & Johnson Share
Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.
dateStart date of the quarter.
earningsEarnings per share (USD).
Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.
t = octave.dataset.JohnsonJohnson # TODO: Yikes, look at all those plots. Port them to Octave.
out = LakeHuron () ¶Level of Lake Huron 1875-1972
Annual measurements of the level, in feet, of Lake Huron 1875–1972.
yearYear of the measurement
levelLake level (ft).
Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.
Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.
t = octave.dataset.LakeHuron;
plot (t.year, t.level)
xlabel ("Year")
ylabel ("Lake level (ft)")
title ("Level of Lake Huron")
out = lh () ¶Luteinizing Hormone in Blood Samples
A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.
sampleThe number of the observation.
lhLevel of luteinizing hormone.
P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.
t = octave.dataset.lh;
plot (t.sample, t.lh);
xlabel ("Sample Number");
ylabel ("lh level");
out = LifeCycleSavings () ¶Intercountry Life-Cycle Savings Data
Data on the savings ratio 1960–1970.
countryName of the country.
srAggregate personal savings.
pop15Percentage of population under 15.
pop75Percentage of population over 75.
dpiReal per-capita disposable income.
ddpiPercent growth rate of dpi.
Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.
The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).
Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.
Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.
t = octave.dataset.LifeCycleSavings; # TODO: linear model # TODO: pairs plot with Lowess smoothed line
out = Loblolly () ¶Growth of Loblolly pine trees
Records of the growth of Loblolly pine trees.
heightTree height (ft).
ageTree age (years).
SeedSeed source for the tree. Ordering is according to increasing maximum height.
Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.
Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.
t = octave.dataset.Loblolly;
t2 = t(t.Seed == "329",:);
scatter (t2.age, t2.height)
xlabel ("Tree age (yr)");
ylabel ("Tree height (ft)");
title ("Loblolly data and fitted curve (Seed 329 only)")
# TODO: Compute and plot fitted curve
out = longley () ¶Longley’s Economic Regression Data
A macroeconomic data set which provides a well-known example for a highly collinear regression.
YearThe year.
GNP_deflatorGNP implicit price deflator (1954=100).
GNPGross National Product.
UnemployedNumber of unemployed.
Armed_ForcesNumber of people in the armed forces.
Population“Noninstitutionalized” population ≥ 14 years of age.
EmployedNumber of people employed.
J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.
t = octave.dataset.longley; # TODO: Linear model # TODO: opar plot
out = lynx () ¶Annual Canadian Lynx trappings 1821-1934
Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).
yearYear of the record.
lynxNumber of lynx trapped.
Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.
Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.
t = octave.dataset.lynx;
plot (t.year, t.lynx);
xlabel ("Year");
ylabel ("Lynx Trapped");
out = morley () ¶Michelson Speed of Light Data
A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).
ExptThe experiment number, from 1 to 5.
RunThe run number within each experiment.
SpeedSpeed-of-light measurement.
The data is here viewed as a randomized block experiment with experiment
and run as the factors. run may also be considered a quantitative
variate to account for linear (or polynomial) changes in the measurement over
the course of a single experiment.
A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.
S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)
A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).
t = octave.dataset.morley; # TODO: Port to Octave
out = mtcars () ¶Motor Trend 1974 Car Road Tests
The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).
mpgFuel efficiency in miles/gallon
cylNumber of cylinders
dispDisplacement (cu. in.)
hpGross horsepower
dratRear axle ratio
wtWeight (1,000 lbs)
qsec1/4 mile time
vsEngine type (0 = V-shaped, 1 = straight)
amTransmission type (0 = automatic, 1 = manual)
gearNumber of forward gears
carbNumber of carburetors
Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”
Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.
# TODO: Port this example from R
out = nhtemp () ¶Average Yearly Temperatures in New Haven
The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.
yearYear of the observation.
tempMean annual temperature (degrees F).
Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.nhtemp;
plot (t.year, t.temp);
title ("nhtemp data");
xlabel ("Mean annual temperature in New Haven, CT (deg. F)");
out = Nile () ¶Flow of the River Nile
Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).
yearYear of the record.
flowAnnual flow (cubic meters).
Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html
Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.
Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.
t = octave.dataset.Nile; figure plot (t.year, t.flow); # TODO: Port the rest of the example to Octave
out = nottem () ¶Average Monthly Temperatures at Nottingham, 1920-1939
A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.
recordIndex of the record.
leadLeading indicator.
salesSales volume.
Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.
# TODO: Come up with example code here
out = npk () ¶Classical N, P, K Factorial Experiment
A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.
blockWhich block (1 to 6).
NIndicator (0/1) for the application of nitrogen.
PIndicator (0/1) for the application of phosphate.
KIndicator (0/1) for the application of potassium.
yieldYield of peas, in pounds/plot. Plots were 1/70 acre.
Imperial College, London, M.Sc. exercise sheet.
Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.
t = octave.dataset.npk; # TODO: Port aov() and LM to Octave
out = occupationalStatus () ¶Occupational Status of Fathers and their Sons
Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.
An 8-by-8 matrix of counts, with classifying fators origin (father’s
occupational status, levels 1:8) and destination (son’s
occupational status, levels 1:8).
Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.
# TODO: Come up with example code here
out = Orange () ¶Growth of Orange Trees
Records of the growth of orange trees.
TreeA categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.
ageAge of the tree (days since 1968-12-31).
circumferenceTrunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.
The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.
Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).
Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.
t = octave.dataset.Orange; # TODO: Port coplot to Octave # TODO: Linear model
out = OrchardSprays () ¶Potency of Orchard Sprays
An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.
rowposRow of the design.
colposColumn of the design
treatmentTreatment level.
decreaseResponse.
Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.
The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.
An 8 x 8 Latin square design was used and the treatments were coded as follows:
A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur
Finney, D. J. (1947). Probit Analysis. Cambridge.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.OrchardSprays; octave.examples.plot_pairs (t);
out = PlantGrowth () ¶Results from an Experiment on Plant Growth
Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.
groupTreatment condition group.
weightWeight of plants.
Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.
t = octave.dataset.PlantGrowth; # TODO: Port anova to Octave
out = precip () ¶Annual Precipitation in US Cities
The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.
cityCity observed.
precipAnnual precipitation (in).
Statistical Abstracts of the United States, 1975.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.precip; # TODO: Port dot plot to Octave
out = presidents () ¶Quarterly Approval Ratings of US Presidents
The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.
dateApproximate date of the observation.
approvalApproval rating (%).
The data are actually a fudged version of the approval ratings. See McNeil’s book for details.
The Gallup Organisation.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.presidents;
figure
plot (datenum (t.date), t.approval)
datetick ("x")
xlabel ("Date")
ylabel ("Approval rating (%)")
title ("presidents data")
out = pressure () ¶Vapor Pressure of Mercury as a Function of Temperature
Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).
temperatureTemperature (deg C).
pressurePressure (mm Hg).
Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.pressure;
figure
plot (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")
figure
semilogy (t.temperature, t.pressure)
xlabel ("Temperature (deg C)")
ylabel ("Pressure (mm of Hg)")
title ("pressure data: Vapor Pressure of Mercury")
out = Puromycin () ¶Reaction Velocity of an Enzymatic Reaction
Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.
stateWhether the cell was treated.
concSubstrate concentrations (ppm).
rateInstantaneous reaction rates (counts/min/min).
Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.
The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.
Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.
Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.
t = octave.dataset.Puromycin; # TODO: Port example to Octave
out = quakes () ¶Locations of Earthquakes off Fiji
The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.
latLatitude of event.
longLongitude of event.
depthDepth (km).
magRichter magnitude.
stationsNumber of stations reporting.
There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.
This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.
G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.
P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.
# TODO: Come up with example code here
out = randu () ¶Random Numbers from Congruential Generator RANDU
400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.
recordIndex of the record.
xX value of the triple.
yY value of the triple.
zZ value of the triple.
In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.
These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.
Under VMS versions 2.0 and higher, this problem has been fixed.
David Donoho
t = octave.dataset.randu;
out = rivers () ¶Lengths of Major North American Rivers
This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.
riversA vector containing 141 observations.
World Almanac and Book of Facts, 1975, page 406.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
octave.dataset.rivers; longest_river = max (rivers) shortest_river = min (rivers)
out = rock () ¶Measurements on Petroleum Rock Samples
Measurements on 48 rock samples from a petroleum reservoir.
areaArea of pores space, in pixels out of 256 by 256.
periPerimeter in pixels.
shapePerimeter/sqrt(area).
permPermeability in milli-Darcies.
Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.
Data from BP Research, image analysis by Ronit Katz, U. Oxford.
t = octave.dataset.rock;
figure
scatter (t.area, t.perm)
xlabel ("Area of pores space (pixels out of 256x256)")
ylabel ("Permeability (milli-Darcies)")
out = sleep () ¶Student’s Sleep Data
Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.
idPatient ID.
groupDrug given.
extraIncrease in hours of sleep.
The group variable name may be misleading about the data: They
represent measurements on 10 persons, not in groups.
Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.
Student (1908). The probable error of the mean. Biometrika, 6, 20.
Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.
t = octave.dataset.sleep; # TODO: Port to Octave
out = stackloss () ¶Brownlee’s Stack Loss Plant Data
Operational data of a plant for the oxidation of ammonia to nitric acid.
AirFlowFlow of cooling air.
WaterTempCooling Water Inlet temperature.
AcidConcConcentration of acid (per 1000, minus 500).
StackLossStack loss
“Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)
AirFlow represents the rate of operation of the plant. WaterTemp is the
temperature of cooling water circulated through coils in the absorption tower.
AcidConc is the concentration of the acid circulating, minus 50, times 10:
that is, 89 corresponds to 58.9 per cent acid. StackLoss (the dependent variable)
is 10 times the percentage of the ingoing ammonia to the plant that escapes from
the absorption column unabsorbed; that is, an (inverse) measure of the over-all
efficiency of the plant.
Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.
Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.
t = octave.dataset.stackloss; # TODO: Create linear model and print summary
out = state () ¶US State Facts and Figures
Data related to the 50 states of the United States of America.
abbState abbreviation.
nameState name.
areaArea (sq mi).
latApproximate center (latitude).
lonApproximate center (longitude).
divisionState division.
revionState region.
PopulationPopulation estimate as of July 1, 1975.
IncomePer capita income (1974).
IlliteracyIlliteracy as of 1970 (percent of population).
LifeExpLfe expectancy in years (1969-71).
MurderMurder and non-negligent manslaughter rate per 100,000 population (1976).
HSGradPercent high-school graduates (1970).
FrostMean number of days with minimum temperature below freezing (1931-1960) in capital or large city.
U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.
U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.
t = octave.dataset.state;
out = sunspot_month () ¶Monthly Sunspot Data, from 1749 to “Present”
Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.
monthMonth of the observation.
sunspotsNumber of sunspots.
WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.
t = octave.dataset.sunspot_month;
out = sunspot_year () ¶Yearly Sunspot Data, 1700-1988
Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).
yearYear of the observation.
sunspotsNumber of sunspots.
H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.
t = octave.dataset.sunspot_year;
figure
plot (t.year, t.sunspots)
xlabel ("Year")
ylabel ("Sunspots")
out = sunspots () ¶Monthly Sunspot Numbers, 1749-1983
Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.
monthMonth of the observation.
sunspotsNumber of observed sunspots.
Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.
t = octave.dataset.sunspots;
figure
plot (datenum (t.month), t.sunspots)
datetick ("x")
xlabel ("Date")
ylabel ("Monthly sunspot numbers")
title ("sunspots data")
out = swiss () ¶Swiss Fertility and Socioeconomic Indicators (1888) Data
Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.
FertilityIg, ‘common standardized fertility measure’.
Agriculture% of males involved in agriculture as occupation.
Examination% draftees receiving highest mark on army examination.
Education% education beyond primary school for draftees.
Catholic% ‘Catholic’ (as opposed to ‘Protestant’).
InfantMortalityLive births who live less than 1 year.
All variables but ‘Fertility’ give proportions of the population.
(paraphrasing Mosteller and Tukey):
Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.
The data collected are for 47 French-speaking “provinces” at about 1888.
Here, all variables are scaled to [0, 100], where in the original, all but
Catholic were scaled to [0, 1].
Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.
They state that variables Examination and Education are averages
for 1887, 1888 and 1889.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.
t = octave.dataset.swiss; # TODO: Port linear model to Octave
out = Theoph () ¶Pharmacokinetics of Theophylline
An experiment on the pharmacokinetics of theophylline.
SubjectCategorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.
WtWeight of the subject (kg).
DoseDose of theophylline administerred orally to the subject (mg/kg).
TimeTime since drug administration when the sample was drawn (hr).
concTheophylline concentration in the sample (mg/L).
Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.
These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.
The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.
Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.
Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)
Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)
t = octave.dataset.Theoph; # TODO: Coplot # TODO: Yet another linear model to port to Octave
out = Titanic () ¶Survival of passengers on the Titanic
This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.
n is a 4-dimensional array resulting from cross-tabulating 2201 observations
on 4 variables. The dimensions of the array correspond to the following variables:
Class1st, 2nd, 3rd, Cre.
SexMale, Female.
AgeChild, Adult.
SurvivedNo, Yes.
The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.
These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.
Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).
Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.
The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:
British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.
octave.dataset.Titanic; # TODO: Port mosaic plot to Octave # TODO: Check for higher survival rates in children and females
out = ToothGrowth () ¶The Effect of Vitamin C on Tooth Growth in Guinea Pigs
The response is the length of odontoblasts (cells responsible for tooth growth)
in 60 guinea pigs. Each animal received one of three dose levels of vitamin C
(0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or
ascorbic acid (a form of vitamin C and coded as VC).
suppSupplement type.
doseDose (mg/day).
lenTooth length.
C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.
t = octave.dataset.ToothGrowth; octave.examples.coplot (t, "dose", "len", "supp"); # TODO: Port Lowess smoothing to Octave
out = treering () ¶Yearly Treering Data, -6000-1979
Contains normalized tree-ring widths in dimensionless units.
A univariate time series with 7981 observations.
Each tree ring corresponds to one year.
The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.
Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.
For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.
t = octave.dataset.treering;
out = trees () ¶Diameter, Height and Volume for Black Cherry Trees
This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.
GirthTree diameter (rather than girth, actually) in inches.
HeightHeight in ft.
VolumeVolume of timber in cubic feet.
Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.
Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.
t = octave.dataset.trees;
figure
octave.examples.plot_pairs (t);
figure
loglog (t.Girth, t.Volume)
xlabel ("Girth")
ylabel ("Volume")
# TODO: Transform to log space for the coplot
# TODO: Linear model
out = UCBAdmissions () ¶Student Admissions at UC Berkeley
Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.
A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:
AdmitAdmitted, Rejected.
GenderMale, Female.
DeptA, B, C, D, E, F.
This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).
The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.
Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.
octave.dataset.UCBAdmissions; # TODO: Port mosaic plot to Octave
out = UKDriverDeaths () ¶Road Casualties in Great Britain 1969-84
UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed
or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts
was introduced on 31 Jan 1983.
Seatbelts is more information on the same problem.
UKDriverDeaths is a table with the following variables:
monthMonth of the observation.
deathsNumber of deaths.
Seatbelts is a table with the following variables:
monthMonth of the observation.
DriversKilledCar drivers killed.
driversSame as UKDriverDeaths deaths count.
frontFront-seat passengers killed or seriously injured.
rearRear-seat passengers killed or seriously injured.
kmsDistance driven.
PetrolPricePetrol price.
VanKilledNumber of van (“light goods vehicle”) drivers killed.
law0/1: was the seatbelt law in effect that month?
Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.
Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/
Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.
octave.dataset.UKDriverDeaths; d = UKDriverDeaths; s = Seatbelts; # TODO: Port the model and plots to Octave
out = UKgas () ¶UK Quarterly Gas Consumption
Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.
dateQuarter of the observation
gasGas consumption (MM therms).
Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.
t = octave.dataset.UKgas;
plot (datenum (t.date), t.gas);
datetick ("x")
xlabel ("Month")
ylabel ("Gas consumption (MM therms)")
out = UKLungDeaths () ¶Monthly Deaths from Lung Diseases in the UK
Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.
dateMonth of the observation.
ldeathsTotal lung deaths.
fdeathsLung deaths among females.
mdeathsLung deaths among males.
P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3
t = octave.dataset.UKLungDeaths;
figure
plot (datenum (t.date), t.ldeaths);
title ("Total UK Lung Deaths")
xlabel ("Month")
ylabel ("Deaths")
figure
plot (datenum (t.date), [t.fdeaths t.mdeaths]);
title ("UK Lung Deaths buy sex")
legend ({"Female", "Male"})
xlabel ("Month")
ylabel ("Deaths")
out = USAccDeaths () ¶Accidental Deaths in the US 1973-1978
A time series giving the monthly totals of accidental deaths in the USA.
monthMonth of the observation.
deathsAccidental deaths.
Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.
t = octave.dataset.USAccDeaths;
out = USArrests () ¶Violent Crime Rates by US State
This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.
StateState name.
MurderMurder arrests (per 100,000).
AssaultAssault arrests (per 100,000).
UrbanPopPercent urban population.
RapeRape arrests (per 100,000).
USArrests contains the data as in McNeil’s monograph. For the
UrbanPop percentages, a review of the table (No. 21) in the
Statistical Abstracts 1975 reveals a transcription error for Maryland
(and that McNeil used the same “round to even” rule), as found by
Daniel S Coven (Arizona).
See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.
World Almanac and Book of Facts 1975. (Crime rates).
Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.USArrests; summary (t); octave.examples.plot_pairs (t(:,2:end)); # TODO: Difference between USArrests and its correction # TODO: +/- 0.5 to restore the original <n>.5 percentages
out = USJudgeRatings () ¶Lawyers’ Ratings of State Judges in the US Superior Court
Lawyers’ ratings of state judges in the US Superior Court.
CONTNumber of contacts of lawyer with judge.
INTGJudicial integrity.
DMNRDemeanor.
DILGDiligence.
CFMGCase flow managing.
DECIPrompt decisions.
PREPPreparation for trial.
FAMIFamiliarity with law.
ORALSound oral rulings.
WRITSound written rulings.
PHYSPhysical ability.
RTENWorthy of retention.
New Haven Register, 14 January, 1977 (from John Hartigan).
t = octave.dataset.USJudgeRatings;
figure
octave.examples.plot_pairs (t(:,2:end));
title ("USJudgeRatings data")
out = USPersonalExpenditure () ¶Personal Expenditure Data
This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.
A 2-dimensional matrix x with Category along dimension 1 and Year along dimension 2.
The World Almanac and Book of Facts, 1962, page 756.
Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
octave.dataset.USPersonalExpenditure; # TODO: Port medpolish() from R, whatever that is.
out = uspop () ¶Populations Recorded by the US Census
This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.
yearYear of the census.
populationPopulation, in millions.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.uspop;
figure
semilogy (t.year, t.population)
xlabel ("Year")
ylabel ("U.S. Population (millions)")
out = VADeaths () ¶Death Rates in Virginia (1940)
Death rates per 1000 in Virginia in 1940.
A 2-dimensional matrix deaths, with age group along dimension 1 and
demographic group along dimension 2.
The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.
This provides a rather nice 3-way analysis of variance example.
Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
octave.dataset.VADeaths; # TODO: Port to Octave
out = volcano () ¶Topographic Information on Auckland’s Maunga Whau Volcano
Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.
A matrix volcano with 87 rows and 61 columns, rows corresponding
to grid lines running east to west and columns to grid lines running south
to north.
Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.
Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.
Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.
octave.dataset.volcano; # TODO: Figure out how to do a topo map in Octave. Just a gridded color plot # should be fine. And then maybe do a 3-d mesh plot.
out = warpbreaks () ¶The Number of Breaks in Yarn during Weaving
This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.
woolType of wool (A or B).
tensionThe level of tension (L, M, H).
breaksNumber of breaks.
There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).
Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.
Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.warpbreaks; summary (t) # TODO: Port the plotting code and OPAR to Octave
out = women () ¶Average Heights and Weights for American Women
This data set gives the average heights and weights for American women aged 30–39.
heightHeight (in).
weightWeight (lbs).
The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.
The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.
The World Almanac and Book of Facts, 1975.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
t = octave.dataset.women;
figure
scatter (t.height, t.weight)
xlabel ("Height (in)")
ylabel ("Weight (lb")
title ("women data: American women aged 30-39")
out = WorldPhones () ¶The World’s Telephones
The number of telephones in various regions of the world (in thousands).
A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.
The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.
The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.
AT&T (1961) The World’s Telephones.
McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.
octave.dataset.WorldPhones; # TODO: Port matplot() to Octave
out = WWWusage () ¶WWWusage
A time series of the numbers of users connected to the Internet through a server every minute.
A time series of length 100.
Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/
Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.
# TODO: Come up with example code here
out = zCO2 () ¶Carbon Dioxide Uptake in Grass Plants
The CO2 data set has 84 rows and 5 columns of data from an experiment
on the cold tolerance of the grass species Echinochloa crus-galli.
The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.
Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.
Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.
t = octave.dataset.zCO2; # TODO: Coplot # TODO: Port the linear model to Octave
Example dataset collection.
datasets is a collection of example datasets to go with the
Tablicious package.
The datasets class provides methods for listing and loading
the example datasets.
() ¶out = list () ¶List all datasets.
Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.
Returns a table with variables Name, Description, and possibly more.
load (datasetName) ¶out = load (datasetName) ¶Load a specified dataset.
datasetName is the name of the dataset to load, as found in the
Name column of the dataset list.
description (datasetName) ¶out = description (datasetName) ¶Get or display the description for a dataset.
Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.
[fig, hax] = coplot (tbl, xvar, yvar, gvar) ¶[fig, hax] = coplot (fig, tbl, xvar, yvar, gvar) ¶[fig, hax] = coplot (…, OptionName, OptionValue, …) ¶Conditioning plot.
coplot produces conditioning plots. This is a kind of plot that breaks up the
data into groups based on one or two grouping variables, and plots each group of data
in a separate subplot.
tbl is a table containing the data to plot.
xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.
yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.
gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.
fig is the figure handle to plot into. If fig is not provided, a new figure is created.
Name/Value options:
PlotFcnThe plotting function to use, supplied as a function handle. Defaults to @plot.
It must be a function that provides the signature fcn(hax, X, Y, …).
PlotArgsA cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.
Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots
out = plot_pairs (data) ¶out = plot_pairs (data, plot_type) ¶out = plot_pairs (fig, …) ¶Plot pairs of variables against each other.
data is the data holding the variables to plot. It may be either a
table or a struct. Each variable or field in the table
or struct is considered to be one variable. Each must hold a vector, and
all the vectors of all the variables must be the same size.
plot_type is a charvec indicating what plot type to do in each subplot.
("scatter" is the default.) Valid plot_type values are:
"scatter"A plain scatter plot.
"smooth"A scatter plot + fitted line, like R’s panel.smooth does.
fig is an optional figure handle to plot into. If omitted, a new figure is created.
Returns the created figure, if the output is captured.
(X) ¶(A, B, C, …) ¶('A', 'B', 'C', …) ¶A B C …Alias for prettyprint, for interactive use.
This is an alias for prettyprint(), with additional name-conversion magic.
If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.
[out, tf] = rmmissing (X) ¶[out, tf] = rmmissing (X, dim) ¶[out, tf] = rmmissing (…, 'MinNumMissing', MinNumMissing) ¶Remove missing values.
If x is a vector, removes elements with missing values. If x is a matrix, removes rows or columns with missing data elements.
dim is the dimension to operate along. Specifying a dimension forces rmmissing
to operate in matrix instead of vector mode.
MinNumMissing indicates how many missing element values there must be in a row or column for it to be considered missing and this removed. This option is only used in matrix mode; it is silently ignored in vector mode.
Returns: out - the input, with missing elements or rows or columns removed tf - a logical index vector indicating which elements, rows, or columns were removed
[out1, out2, …, outN] = scalarexpand (x1, x2, …, xN) ¶Expand scalar inputs to match size of non-scalar inputs.
Expands each scalar input argument to match the size of the non-scalar
input arguments, and returns the expanded values in the corresponding
output arguments. repmat is used to do the expansion.
Works on any input types that support size, isscalar, and
repmat.
It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.
Returns as many output arguments as there were input arguments.
Examples:
x1 = rand(3); x2 = 42; x3 = magic(3); [x1, x2, x3] = scalarexpand (x1, x2, x3)
out = seconds (x) ¶Create a duration x seconds long, or get the seconds in a duration
x.
If input is numeric, returns a duration array that is that many seconds in
time.
If input is a duration, converts the duration to a number of seconds.
Returns an array the same size as x.
out = size2str (sz) ¶Format an array size for display.
Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.
sz is an array of dimension sizes, in the format returned by
the size function.
Returns a charvec.
Examples:
str = size2str (size (magic (4)))
⇒ str = 4-by-4
out = splitapply (func, X, G) ¶out = splitapply (func, X1, …, XN, G) ¶[Y1, …, YM] = splitapply (…) ¶Split data into groups and apply function.
func is a function handle to call on each group of inputs in turn.
X, X1, …, XN are the input variables that are split into
groups for the function calls. If X is a table, then its contained
variables are “popped out” and considered to be the X1 … XN
input variables.
G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.
Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.
Returns the concatenated outputs of applying func to each group.
See also: table.groupby, table.splitapply
out = standardizeMissing (X, indicator) ¶Insert standard missing values.
Standardizes missing values in X by replacing the values listed in indicator with the standard missing values for the type of X.
Standard missing values depend on the data type:
* NaN for double, single, duration, and calendarDuration
* NaT for datetime
* ' ' for char
* {''} for cellstrs
* Integer numeric types have no standard missing value; they are never
considered missing.
* Structs are never considered missing.
* Logicals are never considered missing.
See also: table.standardizeMissing
out = startsWith (str, pattern) ¶out = startsWith (…, 'IgnoreCase', IgnoreCase) ¶Test if strings start with a pattern.
Tests whether the given strings start with the given pattern(s).
str (char, cellstr, or string) is a list of strings to compare against pattern.
pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.
Returns a logical array of the same size as the string array represented by str.
A string array of Unicode strings.
A string array is an array of strings, where each array element is a single string.
The string class represents strings, where: - Each element of a string array is a single string - A single string is a 1-dimensional row vector of Unicode characters - Those characters are encoded in UTF-8 - This last bit depends on the fact that Octave chars are UTF-8 now
This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.
String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.
This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)
Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.
This class really serves three roles. - It is a type-safe object wrapper around Octave’s base primitive character types. - It adds ismissing() semantics. - And it introduces Unicode support. Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.
“Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.
TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.
TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.
out = empty (sz) ¶Get an empty string array of a specified size.
The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].
The size may also be supplied as multiple arguments containing scalar numerics.
Returns an empty string array of the requested size.
obj = string () ¶obj = string (in) ¶Construct a new string array.
The zero-argument constructor creates a new scalar string array whose value is the empty string.
The other constructors construct a new string array by converting various types of inputs. - chars and cellstrs are converted via cellstr() - numerics are converted via num2str() - datetimes are converted via datestr()
out = isstring (obj) ¶Test if input is a string array.
isstring is always true for string inputs.
Returns a scalar logical.
out = dispstrs (obj) ¶Display strings for array elements.
Gets display strings for all the elements in obj. These display strings
will either be the string contents of the element, enclosed in "...",
and with CR/LF characters replaced with '\r' and '\n' escape sequences,
or "<missing>" for missing values.
Returns a cellstr of the same size as obj.
out = ismissing (obj) ¶Test whether array elements are missing.
For string arrays, only the special “missing” value is
considered missing. Empty strings are not considered missing,
the way they are with cellstrs.
Returns a logical array the same size as obj.
out = isnanny (obj) ¶Test whether array elements are NaN-like.
Missing values are considered nannish; any other string value is not.
Returns a logical array of the same size as obj.
out = cellstr (obj) ¶Convert to cellstr.
Converts obj to a cellstr. Missing values are converted to ''.
Returns a cellstr array of the same size as obj.
out = cell (obj) ¶Convert to cell array.
Converts this to a cell, which will be a cellstr. Missing values are
converted to ''.
This method returns the same values as cellstr(obj); it is just provided
for interface compatibility purposes.
Returns a cell array of the same size as obj.
out = char (obj) ¶Convert to char array.
Converts obj to a 2-D char array. It will have as many rows as obj has elements.
It is an error to convert missing-valued string arrays to
char. (NOTE: This may change in the future; it may be more appropriate)
to convert them to space-padded empty strings.)
Returns 2-D char array.
out = encode (obj, charsetName) ¶Encode string in a given character encoding.
obj must be scalar.
charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)
Returns the encoded string as a uint8 vector.
See also: string.decode.
out = strlength_bytes (obj) ¶String length in bytes.
Gets the length of each string in obj, counted in Unicode UTF-8
code units (bytes). This is the same as numel(str) for the corresponding
Octave char vector for each string, but may not be what you
actually want to use. You may want strlength instead.
Returns double array of the same size as obj. Returns NaNs for missing strings.
See also: string.strlength
out = strlength (obj) ¶String length in characters (actually, UTF-16 code units).
Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.
The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.
This is the string length method you probably want to use,
not strlength_bytes.
Returns double array of the same size as obj. Returns NaNs for missing strings.
See also: string.strlength_bytes
out = reverse_bytes (obj) ¶Reverse string, byte-wise.
Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.
This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.
You probably do not want to use this method. You probably want to use
string.reverse instead.
Returns a string array the same size as obj.
See also: string.reverse
out = reverse (obj) ¶Reverse string, character-wise.
Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.
Returns a string array the same size as obj.
out = strcat (varargin) ¶String concatenation.
Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.
The semantics of concatenating missing strings with non-missing strings has not been determined yet.
Returns a string array the same size as the scalar expansion of its inputs.
out = lower (obj) ¶Convert to lower case.
Converts all the characters in all the strings in obj to lower case.
This currently delegates to Octave’s own lower() function to
do the conversion, so whatever character class handling it has, this
has.
Returns a string array of the same size as obj.
out = upper (obj) ¶Convert to upper case.
Converts all the characters in all the strings in obj to upper case.
This currently delegates to Octave’s own upper() function to
do the conversion, so whatever character class handling it has, this
has.
Returns a string array of the same size as obj.
out = erase (obj, match) ¶Erase matching substring.
Erases the substrings in obj which match the match input.
Returns a string array of the same size as obj.
out = strrep (obj, match, replacement) ¶out = strrep (…, varargin) ¶Replace occurrences of pattern with other string.
Replaces matching substrings in obj with a given replacement string.
varargin is passed along to the core Octave strrep function. This
supports whatever options it does.
TODO: Maybe document what those options are.
Returns a string array of the same size as obj.
out = strfind (obj, pattern) ¶out = strfind (…, varargin) ¶Find pattern in string.
Finds the locations where pattern occurs in the strings of obj.
TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.
Returns either an index vector, or a cell array of index vectors.
out = regexprep (obj, pat, repstr) ¶out = regexprep (…, varargin) ¶Replace based on regular expression matching.
Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.
Returns a string array of the same size as obj.
out = strcmp (A, B) ¶String comparison.
Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.
This does the same comparison as A == B, but is not polymorphic.
Generally, there is no reason to use strcmp instead of ==
or eq on string arrays, unless you want to be compatible with
cellstr inputs as well.
Returns logical array the size of the scalar expansion of A and B.
[out, outA, outB] = cmp (A, B) ¶Value ordering comparison, returning -1/0/+1.
Compares each element of A and B, returning for
each element i whether A(i) was less than (-1),
equal to (0), or greater than (1) the corresponding B(i).
TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.
Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.
Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.
out = string.missing (sz) ¶Missing string value.
Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.
Returns a string array of size sz.
out = string.decode (bytes, charsetName) ¶Decode encoded text from bytes.
Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.
Returns a scalar string.
See also: string.encode
out = struct2table (s) ¶out = struct2table (…, 'AsArray', AsArray) ¶Convert struct to a table.
Converts the input struct s to a table.
s may be a scalar struct or a nonscalar struct array.
The AsArray option is not implemented yet.
Returns a table.
Tabular data array containing multiple columnar variables.
A table is a tabular data structure that collects multiple parallel
named variables.
Each variable is treated like a column. (Possibly a multi-columned column, if
that makes sense.)
The types of variables may be heterogeneous.
A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.
A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.
A note on accessing properties of a table array: Because .-indexing is
used to access the variables inside the array, it can’t also be directly used
to access properties as well. Instead, do t.Properties.<property> for
a table t. That will give you a property instead of a variable.
(And due to this mechanism, it will cause problems if you have a table
with a variable named Properties. Try to avoid that.)
table: cellstrThe names of the variables in the table, as a cellstr row vector.
table: cellA cell vector containing the values for each of the variables.
VariableValues(i) corresponds to VariableNames(i).
table: cellstrAn optional list of row names that identify each row in the table. This is a cellstr column vector, if present.
obj = table () ¶Constructs a new empty (0 rows by 0 variables) table.
obj = table (var1, var2, …, varN) ¶Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.
obj = table ('Size', sz, 'VariableTypes', varTypes) ¶Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.
obj = table (…, 'VariableNames', varNames) ¶obj = table (…, 'RowNames', rowNames) ¶Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.
summary (obj) ¶Summary of table’s data.
Displays a summary of data in the input table. This will contain some statistical information on each of its variables.
(obj) ¶Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.
Variables which contain objects are displayed using the strings
returned by their dispstrs method, if they define one.
c = table2cell (obj) ¶Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.
Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.
s = table2struct (obj) ¶s = table2struct (…, 'ToScalar', trueOrFalse) ¶Converts obj to a scalar structure or structure array.
Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;
Returns a scalar struct or struct array, depending on the value of the
ToScalar option.
out = varnames (obj) ¶out = varnames (obj, varNames) ¶Get or set variable names for a table.
Returns cellstr in the getter form. Returns an updated datetime in the setter form.
sz = size (obj) ¶[nr, nv] = size (obj) ¶[nr, nv, …] = size (obj) ¶Gets the size of a table.
For tables, the size is [number-of-rows x number-of-variables].
This is the same as [height(obj), width(obj)].
out = length (obj) ¶Length along longest dimension
Use of length is not recommended. Use numel
or size instead.
out = ndims (obj) ¶Number of dimensions
For tables, ndims(obj) is always 2.
obj = squeeze (obj) ¶Remove singleton dimensions.
For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions.
out = size_equal (varargin) ¶True if the dimensions of all arguments agree.
out = sizeof (obj) ¶Approximate size of array in bytes. For tables, this returns the sume
of sizeof for all of its variables’ arrays, plus the size of the
VariableNames and any other metadata stored in obj.
This is currently unimplemented.
out = width (obj) ¶Number of variables in table.
Note that this is not the sum of the number of columns in each variable. It is just the number of variables.
out = columns (obj) ¶Number of variables in table.
Note that this is not the sum of the number of columns in each variable. It is just the number of variables.
out = numel (obj) ¶Total number of elements in table (actually 1).
For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.
out = isempty (obj) ¶Test whether array is empty.
For tables, isempty is true if the number of rows is 0 or the number
of variables is 0.
out = ismatrix (obj) ¶Test whether array is a matrix.
For tables, ismatrix is always true, by definition.
out = iscol (obj) ¶Test whether array is a column vector.
For tables, iscol is true if the input has a single variable.
The number of columns within that variable does not matter.
out = hasrownames (obj) ¶True if this table has row names defined.
out = vertcat (varargin) ¶Vertical concatenation.
Combines tables by vertically concatenating them.
Inputs that are not tables are automatically converted to tables by calling table() on them.
The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible.
out = horzcat (varargin) ¶Horizontal concatenation.
Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.
Output has the same RowNames as varargin{1}. The variable names and values
are the result of the concatenation of the variable names and values lists
from the inputs.
out = repmat (obj, sz) ¶Replicate matrix.
Repmats a table by repmatting each of its variables vertically.
For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1.
Returns a new table with the same variable names and types as tbl, but with a possibly different row count.
out = repelem (obj, R) ¶out = repelem (obj, R_1, R_2) ¶Replicate elements of matrix.
Replicates elements of this table matrix by applying repelem to each of its variables.
Only two dimensions are supported for repelem on tables.
out = setVariableNames (obj, names) ¶out = setVariableNames (obj, ix, names) ¶Set variable names.
Sets the VariableNames for this table to a new list of names.
names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.
ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.
This method exists because the obj.Properties.VariableNames = …
assignment form does not work, possibly due to an Octave bug.
out = setDimensionNames (obj, names) ¶out = setDimensionNames (obj, ix, names) ¶Set dimension names.
Sets the DimensionNames for this table to a new list of names.
names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.
ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.
This method exists because the obj.Properties.DimensionNames = …
assignment form does not work, possibly due to an Octave bug.
out = setRowNames (obj, names) ¶Set row names.
Sets the row names on obj to names.
names is a cellstr column vector, with the same number of rows as obj has.
out = removevars (obj, vars) ¶Remove variables from table.
Deletes the variables specified by vars from obj.
vars may be a char, cellstr, numeric index vector, or logical index vector.
out = movevars (obj, vars, relLocation, location) ¶Move around variables in a table.
vars is a list of variables to move, specified by name or index.
relLocation is 'Before' or 'After'.
location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.
Returns a table with the same variables as obj, but in a different order.
[out, name] = getvar (obj, varRef) ¶Get value and name for single table variable.
varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.
Returns: out – the value of the referenced table variable name – the name of the referenced table variable
[out1, …] = getvars (obj, varRef) ¶Get values for one ore more table variables.
varRef is a variable reference in the form of variable names or indexes.
Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.
out = setvar (obj, varRef, value) ¶Set value for a variable in table.
This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.
This method exists primarily because I cannot get obj.foo = value to work,
apparently due to an issue with Octave’s subsasgn support.
varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.
value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.
out = addvars (obj, var1, …, varN) ¶out = addvars (…, 'Before', Before) ¶out = addvars (…, 'After', After) ¶out = addvars (…, 'NewVariableNames', NewVariableNames) ¶Add variables to table
Adds the specified variables to a table.
out = convertvars (obj, vars, dataType) ¶Convert variables to specified data type.
Converts the variables in obj specified by vars to the specified data type.
vars is a cellstr or numeric vector specifying which variables to convert.
dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.
Returns a table with the same variable names as obj, but with converted types.
out = mergevars (obj, vars) ¶out = mergevars (…, 'NewVariableName', NewVariableName) ¶out = mergevars (…, 'MergeAsTable', MergeAsTable) ¶Merge table variables into a single variable.
out = splitvars (obj) ¶out = splitvars (obj, vars) ¶out = splitvars (…, 'NewVariableNames', NewVariableNames) ¶Split multicolumn table variables.
Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.
out = stack (obj, vars) ¶out = stack (…, 'NewDataVariableName', NewDataVariableName) ¶out = stack (…, 'IndexVariableName', IndexVariableName) ¶Stack multiple table variables into a single variable.
out = head (obj) ¶out = head (obj, k) ¶Get first K rows of table.
Returns the first k rows of obj, as a table.
k defaults to 8.
If there are less than k rows in obj, returns all rows.
out = tail (obj) ¶out = tail (obj, k) ¶Get last K rows of table.
Returns the last k rows of obj, as a table.
k defaults to 8.
If there are less than k rows in obj, returns all rows.
[C, ib] = join (A, B) ¶[C, ib] = join (A, B, …) ¶Combine two tables by rows using key variables, in a restricted form.
This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.
You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.
See also: table.innerjoin, table.outerjoin
[out, ixa, ixb] = innerjoin (A, B) ¶[…] = innerjoin (A, B, …) ¶Combine two tables by rows using key variables.
Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.
TODO: Document options.
Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out
[out, ixa, ixb] = outerjoin (A, B) ¶[…] = outerjoin (A, B, …) ¶Combine two tables by rows using key variables, retaining unmatched rows.
Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.
TODO: Document options.
Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out
out = outerfillvals (obj) ¶Get fill values for outer join.
Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.
[outA, ixA, outB, ixB] = semijoin (A, B) ¶Natural semijoin.
Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.
This method also computes the semijoin of B and A, for convenience.
Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB
[outA, ixA, outB, ixB] = antijoin (A, B) ¶Natural antijoin (AKA “semidifference”).
Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.
Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB
[out, ixs] = cartesian (A, B) ¶Cartesian product of two tables.
Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.
Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.
The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.
This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.
The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.
[out] = groupby (obj, groupvars, aggcalcs) ¶Find groups in table data and apply functions to variables within groups.
This works like an SQL "SELECT ... GROUP BY ..." statement.
groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.
aggcalcs is a specification of the aggregate calculations to perform
on them, in the form {out_var, fcn, in_vars; ...}, where:
out_var (char) is the name of the output variable
fcn (function handle) is the function to apply to produce it
in_vars (cellstr) is a list of the input variables to pass to fcn
Returns a table.
[out] = grpstats (obj, groupvar) ¶[out] = grpstats (…, 'DataVars', DataVars) ¶Statistics by group.
See also: table.groupby.
out = splitapply (func, obj, G) ¶[Y1, …, YM] = splitapply (func, obj, G) ¶Split table data into groups and apply function.
Performs a splitapply, using the variables in obj as the input X variables
to the splitapply function call.
See also: splitapply, table.groupby
out = rows2vars (obj) ¶out = rows2vars (obj, 'VariableNamesSource', VariableNamesSource) ¶out = rows2vars (…, 'DataVariables', DataVariables) ¶Reorient table, swapping rows and variables dimensions.
This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.
The variable names become a new variable named “OriginalVariableNames”.
The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.
If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.
[outA, outB] = congruentize (A, B) ¶Make tables congruent.
Makes tables congruent by ensuring they have the same variables of the same types in the same order. Congruent tables may be safely unioned, intersected, vertcatted, or have other set operations done to them.
Variable names present in one input but not in the other produces an error. Variables with the same name but different types in the inputs produces an error. Inputs must either both have row names or both not have row names; it is an error if one has row names and the other doesn’t. Variables in different orders are reordered to be in the same order as A.
[C, ia, ib] = union (A, B) ¶Set union.
Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.
Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.
[C, ia, ib] = intersect (A, B) ¶Set intersection.
Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.
Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.
[C, ia, ib] = setxor (A, B) ¶Set exclusive OR.
Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.
Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.
[C, ia] = setdiff (A, B) ¶Set difference.
Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.
Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.
[tf, loc] = ismember (A, B) ¶Set membership.
Finds rows in A that are members of B.
Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.
out = ismissing (obj) ¶out = ismissing (obj, indicator) ¶Find missing values.
Finds missing values in obj’s variables.
If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.
All variables in this must be vectors. (This is due to the requirement
that size(out) == size(obj).)
Returns a logical array the same size as obj.
[out, tf] = rmmissing (obj) ¶[out, tf] = rmmissing (obj, indicator) ¶[out, tf] = rmmissing (…, 'DataVariables', vars) ¶[out, tf] = rmmissing (…, 'MinNumMissing', minNumMissing) ¶Remove rows with missing values.
Removes the rows from obj that have missing values.
If the ’DataVariables’ option is given, only the data in the specified variables is considered.
Returns: out - A table the same as obj, but with rows with missing values removed. tf - A logical index vector indicating which rows were removed.
out = standardizeMissing (obj, indicator) ¶out = standardizeMissing (…, 'DataVariables', vars) ¶Insert standard missing values.
Standardizes missing values in variable data.
If the DataVariables option is supplied, only the indicated variables are standardized.
indicator is passed along to standardizeMissing when it is called on each
of the data variables in turn. The same indicator is used for all
variables. You can mix and match indicator types by just passing in
mixed indicator types in a cell array; indicators that don’t match the
type of the column they are operating on are just ignored.
Returns a table with same variable names and types as obj, but with variable values standardized.
out = varfun (fcn, obj) ¶out = varfun (…, 'OutputFormat', outputFormat) ¶out = varfun (…, 'InputVariables', vars) ¶out = varfun (…, 'ErrorHandler', errorFcn) ¶Apply function to table variables.
Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.
out = varfun (func, obj) ¶out = varfun (…, 'OptionName', OptionValue, …) ¶Apply function to rows in table and collect outputs.
This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.
func is a function handle. It should take as many inputs as there
are variables in obj. Or, it can take a single input, and you must
specify 'SeparateInputs', false to have the input variables
concatenated before being passed to func. It may return multiple
argouts, but to capture those past the first one, you must explicitly
specify the 'NumOutputs' or 'OutputVariableNames' options.
Supported name/value options:
'OutputVariableNames'Names of table variables to store combined function output arguments in.
'NumOutputs'Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.
'SeparateInputs'If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.
'ErrorHandler'A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.
'ExtractCellContents'Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.
'InputVariables'If specified, only these variables from obj are used as the function inputs, instead of using all variables.
'GroupingVariables'Not yet implemented.
'OutputFormat'The format of the output. May be 'table' (the default),
'uniform', or 'cell'. If it is 'uniform' or 'cell',
the output variables are returned in multiple output arguments from
'rowfun'.
Returns a table whose variables are the collected output arguments
of func if OutputFormat is 'table'. Otherwise, returns
multiple output arguments of whatever type func returned (if
OutputFormat is 'uniform') or cells (if OutputFormat
is 'cell').
[G, TID] = findgroups (obj) ¶Find groups within a table’s row values.
Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.
Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.
out = evalWithVars (obj, expr) ¶Evaluate an expression against table’s variables.
Evaluates the M-code expression expr in a workspace where all of obj’s variables have been assigned to workspace variables.
expr is a charvec containing an Octave expression.
As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.
Returns the result of the evaluation.
Examples:
[s,p,sp] = table_examples.SpDb tmp = join (sp, p); shipment_weight = evalWithVars (tmp, "Qty .* Weight")
out = restrict (obj, expr) ¶out = restrict (obj, ix) ¶Subset rows using variable expression or index.
Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.
If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)
If the argument is a char, then it is evaulated as an M-code expression,
with all of this’ variables available as workspace variables, as with
evalWithVars. The output of expr must be a numeric or logical index
vector (This form is a shorthand for
out = subsetrows (this, evalWithVars (this, expr)).)
TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.
Examples:
[s,p,sp] = table_examples.SpDb; prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))
out = renamevars (obj, renameMap) ¶Rename variables in a table.
Renames selected variables in the table obj based on the mapping provided in renameMap.
renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.
Variables which are not included in renameMap are not modified.
It is an error if any variables named in the first column of renameMap are not present in obj.
Renames
[ixVar, varNames] = resolveVarRef (obj, varRef) ¶[ixVar, varNames] = resolveVarRef (obj, varRef, strictness) ¶Resolve a variable reference against this table.
A varRef is a numeric or char/cellstr indicator of which variables within obj are being referenced.
strictness controls what to do when the given variable references could not be resolved. It may be ’strict’ (the default) or ’lenient’.
Returns: ixVar - the indexes of the variables in obj varNames - a cellstr of the names of the variables in obj
Raises an error if any of the specified variables could not be resolved, unless strictness is ’lenient’, in which case it will return 0 for the index and ” for the name for each variable which could not be resolved.
out = subsetrows (obj, ixRows) ¶Subset table by rows.
Subsets this table by rows.
ixRows may be a numeric or logical index into the rows of obj.
out = subsetvars (obj, ixVars) ¶Subset table by variables.
Subsets table obj by subsetting it along its variables.
ixVars may be: - a numeric index vector - a logical index vector - ":" - a cellstr vector of variable names
The resulting table will have its variables reordered to match ixVars.
out = tableOuterFillValue (x) ¶Outer fill value for variable within a table.
Determines the fill value to usse for a given variable value x when that value is used as a variable in a table that is involved in an outer join.
The default implementation for tableOuterFillValue has support for
all Octave primitive types, plus cellstrs, datetime & friends, strings,
and table-valued variables.
This function may become private to table before version 1.0. It is currently global to make debugging more convenient. It (or an equivalent) will remain global if we want to allow user-defined classes to customize their fill value. It also has default logic that will determine the fill value for an arbitrary type by detecting the value used to fill elements during array expansion operations. This will be appropriate for most data types.
Returns a 1-by-ncols value of the same type as x, which may be any type, where ncols is the number of columns in the input.
out = timezones () ¶out = timezones (area) ¶List all the time zones defined on this system.
This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from /usr/share/zoneinfo. On Windows, it will be the database redistributed with the Tablicious package.
If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:
NameThe IANA zone name, as cellstr.
AreaThe geographical area the zone is in, as cellstr.
Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.
out = vartype (type) ¶Filter by variable type for use in suscripting.
Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.
type is the name of a type as charvec. This may be anything that
the isa function accepts, or 'cellstr' to select cellstrs,
as determined by iscellstr.
Returns an object of an opaque type. Don’t worry about what type it is;
just pass it into the second argument of a subscript into a table
object.
out = vecfun (fcn, x, dim) ¶Apply function to vectors in array along arbitrary dimension.
This function is not implemented yet.
Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.
fcn is a function handle to apply.
x is an array of arbitrary type which is to be sliced and passed in to fcn.
dim is the dimension along which the vector slices lay.
Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.
out = years (x) ¶Create a duration x years long, or get the years in a duration
x.
If input is numeric, returns a duration array in units of fixed-length
years of 365.2425 days each.
If input is a duration, converts the duration to a number of fixed-length
years as double.
Note: years creates fixed-length years, which may not be what you want.
To create a duration of calendar years (which account for actual leap days),
use calyears.
See calyears.
Tablicious for Octave is covered by the GNU GPLv3.
All the code in the package is GNU GPLv3.
The Fisher Iris dataset is Public Domain.
This manual is for Tablicious, version 0.3.7.
Copyright © 2019 Andrew Janke
Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies.
Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one.
Permission is granted to copy and distribute translations of this manual into another language, under the same conditions as for modified versions.