varfun

Arguments

f

function to apply with prototype: y = f(x)

t

table or timeseries object in input

Name, Value

Name: 'GroupingVariables', Value: variable names or variable indices: variables values are used to create unique groups.

Name: 'InputVariables', Value: variable names or variable indices: the function will be apply only on this specified variables.

v

table or timeseries object (same object as t).

Description

The varfun function returns a table or timeseries (same object as t) where each variable (columns) contains the result of the function applied to each variable of t.

v = varfun(f, t) applies the function f to each variable of t.

To apply the function only on certain table variables, InputVariables must be specified: v = varfun(f, t, "InputVariables", ["Var1", ...,]). It is also possible to use the syntax v = varfun(f, t(:, ["Var1", ...,])).

v = varfun(f, t, "GroupingVariables", ["Var1", ...,]) allows to specify the variables used to group the table or timeseries. The function f will be applied to each group in each variable of t. The expected result of function f must be a scalar.

Examples

v = varfun(f, t)

function y=f(x)
    y = x + 1;
endfunction

rand("seed", 0)
x1 = floor(rand(5,1)*5)-1.5;
x2 = -floor(rand(5,1)*5)+0.5;
t = table(x1, x2, "VariableNames", ["x1", "x2"])

v = varfun(f, t)

// with sum 
v = varfun(sum, t)

// With timeseries
timestamp = hours([1 2 2 3 3])';
ts = timeseries(timestamp, x1, x2, "VariableNames", ["timestamp", "x1", "x2"])

v = varfun(f, ts)

// with sum 
v = varfun(sum, ts)

v = varfun(f, t, "InputVariables", ["Var1", ...])

function y=f(x)
    y = x + 1;
endfunction

rand("seed", 0)
x1 = floor(rand(5,1)*5)-1.5;
x2 = -floor(rand(5,1)*5)+0.5;
t = table(x1, x2, "VariableNames", ["x1", "x2"])

v = varfun(f, t, "InputVariables", "x2")

// with sum 
v = varfun(sum, t, "InputVariables", "x1")

// Possible with extraction: t(:, ["var1", ..., "varN"])
v = varfun(f, t(:, "x2"))

v = varfun(sum, t(:, "x1"))

// With timeseries
timestamp = hours([1 2 2 3 3])';
ts = timeseries(timestamp, x1, x2, "VariableNames", ["timestamp", "x1", "x2"])

v = varfun(f, ts, "InputVariables", "x2")

// with sum 
v = varfun(sum, ts, "InputVariables", "x1")

// Possible with extraction: ts(:, ["var1", ..., "varN"])
v = varfun(f, ts(:, "x2"))

v = varfun(sum, ts(:, "x1"))

v = varfun(f, t, "GroupingVariables", ["Var1", ...])

function y=f(x)
    y = x' * x;
endfunction

rand("seed", 0)
x = ["a"; "b"; "b"; "c"; "a"];
x1 = floor(rand(5,1)*5)-1.5;
x2 = -floor(rand(5,1)*5)+0.5;
t = table(x, x1, x2, "VariableNames", ["x", "x1", "x2"])

v = varfun(f, t, "GroupingVariables", "x")

// with sum 
v = varfun(sum, t, "GroupingVariables", "x", "InputVariables", "x1")

// With timeseries
timestamp = hours([1 2 2 3 3])';
ts = timeseries(timestamp, x1, x2, "VariableNames", ["timestamp", "x1", "x2"])

v = varfun(f, ts, "GroupingVariables", "timestamp")

// with sum 
v = varfun(sum, ts, "GroupingVariables", "timestamp", "InputVariables", "x1")

See also

• table — create a table from variables
• timeseries — create a timeseries - table with time as index
• rowfun — apply a function to each row of the table/timeseries
• groupcounts — returns the number of elements for each group
• groupsummary — create groups in table or timeseries and apply functions to variables within groups
• pivot — create a pivoted table providing a summary of data.