diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index aedb5967..fd416f0a 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.11.1","generation_timestamp":"2024-10-17T17:55:16","documenter_version":"1.7.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.11.1","generation_timestamp":"2024-10-17T20:06:12","documenter_version":"1.7.0"}} \ No newline at end of file diff --git a/dev/devguide/index.html b/dev/devguide/index.html index fefd708e..7be89410 100644 --- a/dev/devguide/index.html +++ b/dev/devguide/index.html @@ -53,4 +53,4 @@ # specify that it is invertible TransformsBase.isinvertible(::Type{Identity}) = true # implement Base.inv -Base.inv(::Identity) = Identity()

which implies that inv(Identity()) would return an identity transform.

+Base.inv(::Identity) = Identity()

which implies that inv(Identity()) would return an identity transform.

diff --git a/dev/index.html b/dev/index.html index 97c88ae5..1cfd3778 100644 --- a/dev/index.html +++ b/dev/index.html @@ -24,8 +24,8 @@ pipeline = (f1 → f2 → f3) ⊔ (f4 → f5) # feed data into the pipeline -table |> pipeline |> pairplotExample block output

Each branch is a sequence of transforms constructed with the → (\to<tab>) operator. The branches are placed in parallel with the ⊔ (\sqcup<tab>) operator.

TransformsBase.:→ — Function
transform₁ → transform₂ → ⋯ → transformₙ

Create a SequentialTransform transform with [transform₁, transform₂, …, transformₙ].

source
TransformsBase.SequentialTransform — Type
SequentialTransform(transforms)

A transform where transforms are applied in sequence.

source
TableTransforms.:⊔ — Function
transform₁ ⊔ transform₂ ⊔ ⋯ ⊔ transformₙ

Create a ParallelTableTransform transform with [transform₁, transform₂, …, transformₙ].

source
TableTransforms.ParallelTableTransform — Type
ParallelTableTransform(transforms)

A transform where transforms are applied in parallel. It isrevertible if any of the constituent transforms is revertible. In this case, the revert is performed with the first revertible transform in the list.

Examples

LowHigh(low=0.3, high=0.6) ⊔ EigenAnalysis(:VDV)
-ZScore() ⊔ EigenAnalysis(:V)

Notes

  • Metadata is transformed with the first revertible transform in the list of transforms.
source

Reverting transforms

To revert a pipeline or single transform, use the apply and revert functions instead. The function isrevertible can be used to check if a transform is revertible.

TransformsBase.apply — Function
newobject, cache = apply(transform, object)

Apply transform on the object. Return the newobject and a cache to revert the transform later.

source
TransformsBase.revert — Function
object = revert(transform, newobject, cache)

Revert the transform on the newobject using the cache from the corresponding apply call and return the original object. Only defined when the transform isrevertible.

source
TransformsBase.isrevertible — Function
isrevertible(transform)

Tells whether or not the transform is revertible, i.e. supports a revert function. Defaults to false for new transform types.

Transforms can be revertible and yet don't be invertible. Invertibility is a mathematical concept, whereas revertibility is a computational concept.

See also isinvertible.

source

To exemplify the use of these functions, let's create a table:

a = [-1.0, 4.0, 1.6, 3.4]
+table |> pipeline |> pairplot
Example block output

Each branch is a sequence of transforms constructed with the → (\to<tab>) operator. The branches are placed in parallel with the ⊔ (\sqcup<tab>) operator.

TransformsBase.:→ — Function
transform₁ → transform₂ → ⋯ → transformₙ

Create a SequentialTransform transform with [transform₁, transform₂, …, transformₙ].

source
TransformsBase.SequentialTransform — Type
SequentialTransform(transforms)

A transform where transforms are applied in sequence.

source
TableTransforms.:⊔ — Function
transform₁ ⊔ transform₂ ⊔ ⋯ ⊔ transformₙ

Create a ParallelTableTransform transform with [transform₁, transform₂, …, transformₙ].

source
TableTransforms.ParallelTableTransform — Type
ParallelTableTransform(transforms)

A transform where transforms are applied in parallel. It isrevertible if any of the constituent transforms is revertible. In this case, the revert is performed with the first revertible transform in the list.

Examples

LowHigh(low=0.3, high=0.6) ⊔ EigenAnalysis(:VDV)
+ZScore() ⊔ EigenAnalysis(:V)

Notes

  • Metadata is transformed with the first revertible transform in the list of transforms.
source

Reverting transforms

To revert a pipeline or single transform, use the apply and revert functions instead. The function isrevertible can be used to check if a transform is revertible.

TransformsBase.apply — Function
newobject, cache = apply(transform, object)

Apply transform on the object. Return the newobject and a cache to revert the transform later.

source
TransformsBase.revert — Function
object = revert(transform, newobject, cache)

Revert the transform on the newobject using the cache from the corresponding apply call and return the original object. Only defined when the transform isrevertible.

source
TransformsBase.isrevertible — Function
isrevertible(transform)

Tells whether or not the transform is revertible, i.e. supports a revert function. Defaults to false for new transform types.

Transforms can be revertible and yet don't be invertible. Invertibility is a mathematical concept, whereas revertibility is a computational concept.

See also isinvertible.

source

To exemplify the use of these functions, let's create a table:

a = [-1.0, 4.0, 1.6, 3.4]
 b = [1.6, 3.4, -1.0, 4.0]
 c = [3.4, 2.0, 3.6, -1.0]
 table = (; a, b, c)
(a = [-1.0, 4.0, 1.6, 3.4], b = [1.6, 3.4, -1.0, 4.0], c = [3.4, 2.0, 3.6, -1.0])

Now, let's choose a transform and check that it is revertible:

transform = Center()
@@ -45,4 +45,4 @@
 
 # we can reuse the same values of μ and σ with test data
 newtable = reapply(transform, testtable, cache)
(a = [3.664372586770125, 1.3010543105102819, 0.20659622020787544], b = [0.34718775148637704, -0.26274719825965603, 1.2490776324474353], c = [0.33062949892284726, 1.4746576094403234, 0.04883085864213076])

Note that this result is different from the result returned by the apply function:

newtable, cache = apply(transform, testtable)
-newtable
(a = [1.0979486034606596, -0.23932668246111835, -0.8586219209995408], b = [-0.12795001946747547, -0.929866825966845, 1.0578168454343209], c = [-0.38061724707911765, 1.1344208432568101, -0.7538035961776925])
+newtable
(a = [1.0979486034606596, -0.23932668246111835, -0.8586219209995408], b = [-0.12795001946747547, -0.929866825966845, 1.0578168454343209], c = [-0.38061724707911765, 1.1344208432568101, -0.7538035961776925])
diff --git a/dev/related/index.html b/dev/related/index.html index 91c70fc8..fc8d2f64 100644 --- a/dev/related/index.html +++ b/dev/related/index.html @@ -1,2 +1,2 @@ -Related · TableTransforms.jl
GitHub

Related

  • FeatureTransforms.jl has transforms, but they are not fully revertible. Some of their transforms such as MeanStdScaling are constructed for a specific table and cannot be inserted in the middle of a pipeline for example.
  • AutoMLPipeline.jl relies on the Python stack via PyCall.jl. They provide pipelines with Julia's pipe |> operator and follow a more "Pythonic" interface. They do not support general Tables.jl.
  • Impute.jl, Cleaner.jl, DataConvenience.jl all have a small set of transforms related to fixing column names as well as other basic transforms that we plan to absorb in the long term.
  • DataFramesMeta.jl is a package to manipulate DataFrames.jl tables. It is not intended for statistical transforms such as PCA, Quantile, etc, which rely on complex interactions between the rows and columns of a table. The usage of macros in the package promotes one-shot scripts as opposed to general pipelines that can be passed around to different places in the program.
  • Query.jl is a package to query IterableTables.jl. Similar to other alternatives above, the package is not intended for advanced statistical transforms.
  • MLJ.jl is a popular machine learning framework in Julia that adopts a different design for pipelines based on mutability of structs.
+Related · TableTransforms.jl
GitHub

Related

  • FeatureTransforms.jl has transforms, but they are not fully revertible. Some of their transforms such as MeanStdScaling are constructed for a specific table and cannot be inserted in the middle of a pipeline for example.
  • AutoMLPipeline.jl relies on the Python stack via PyCall.jl. They provide pipelines with Julia's pipe |> operator and follow a more "Pythonic" interface. They do not support general Tables.jl.
  • Impute.jl, Cleaner.jl, DataConvenience.jl all have a small set of transforms related to fixing column names as well as other basic transforms that we plan to absorb in the long term.
  • DataFramesMeta.jl is a package to manipulate DataFrames.jl tables. It is not intended for statistical transforms such as PCA, Quantile, etc, which rely on complex interactions between the rows and columns of a table. The usage of macros in the package promotes one-shot scripts as opposed to general pipelines that can be passed around to different places in the program.
  • Query.jl is a package to query IterableTables.jl. Similar to other alternatives above, the package is not intended for advanced statistical transforms.
  • MLJ.jl is a popular machine learning framework in Julia that adopts a different design for pipelines based on mutability of structs.
diff --git a/dev/transforms/index.html b/dev/transforms/index.html index a8eb49c1..30549186 100644 --- a/dev/transforms/index.html +++ b/dev/transforms/index.html @@ -5,7 +5,7 @@ Assert([2, 3, 5], cond=x -> sum(x) > 100) Assert([:b, :c, :e], cond=x -> eltype(x) <: Integer) Assert(("b", "c", "e"), cond=allunique, msg=nm -> "error in column $nm") -Assert(r"[bce]", cond=x -> sum(x) > 100)source

Select

TableTransforms.Select — Type
Select(col₁, col₂, ..., colₙ)
+Assert(r"[bce]", cond=x -> sum(x) > 100)
source

Select

TableTransforms.Select — Type
Select(col₁, col₂, ..., colₙ)
 Select([col₁, col₂, ..., colₙ])
 Select((col₁, col₂, ..., colₙ))

The transform that selects columns col₁, col₂, ..., colₙ.

Select(col₁ => newcol₁, col₂ => newcol₂, ..., colₙ => newcolₙ)

Selects the columns col₁, col₂, ..., colₙ and rename them to newcol₁, newcol₂, ..., newcolₙ.

Select(regex)

Selects the columns that match with regex.

Examples

Select(1, 3, 5)
 Select([:a, :c, :e])
@@ -13,31 +13,31 @@
 Select(1 => :x, 3 => :y)
 Select(:a => :x, :b => :y)
 Select("a" => "x", "b" => "y")
-Select(r"[ace]")
source

Reject

TableTransforms.Reject — Type
Reject(col₁, col₂, ..., colₙ)
+Select(r"[ace]")
source

Reject

TableTransforms.Reject — Type
Reject(col₁, col₂, ..., colₙ)
 Reject([col₁, col₂, ..., colₙ])
 Reject((col₁, col₂, ..., colₙ))

The transform that discards columns col₁, col₂, ..., colₙ.

Reject(regex)

Discards the columns that match with regex.

Examples

Reject(:b, :d, :f)
 Reject(["b", "d", "f"])
 Reject((2, 4, 6))
-Reject(r"[bdf]")
source

Satisfies

TableTransforms.Satisfies — Type
Satisfies(pred)

Selects the columns where pred(column) returns true.

Examples

Satisfies(allunique)
+Reject(r"[bdf]")
source

Satisfies

TableTransforms.Satisfies — Type
Satisfies(pred)

Selects the columns where pred(column) returns true.

Examples

Satisfies(allunique)
 Satisfies(x -> sum(x) > 100)
-Satisfies(x -> eltype(x) <: Integer)
source

Only

TableTransforms.Only — Function
Only(S)

Selects the columns that have scientific type S.

Examples

using DataScienceTraits
+Satisfies(x -> eltype(x) <: Integer)
source

Only

TableTransforms.Only — Function
Only(S)

Selects the columns that have scientific type S.

Examples

using DataScienceTraits
 
-Only(Continuous)
source

Except

TableTransforms.Except — Function
Except(S)

Selects the columns that don't have scientific type S.

Examples

using DataScienceTraits
+Only(Continuous)
source

Except

TableTransforms.Except — Function
Except(S)

Selects the columns that don't have scientific type S.

Examples

using DataScienceTraits
 
-Except(Categorical)
source

Rename

TableTransforms.Rename — Type
Rename(:col₁ => :newcol₁, :col₂ => :newcol₂, ..., :colₙ => :newcolₙ)
+Except(Categorical)
source

Rename

TableTransforms.Rename — Type
Rename(:col₁ => :newcol₁, :col₂ => :newcol₂, ..., :colₙ => :newcolₙ)
 Rename([:col₁ => :newcol₁, :col₂ => :newcol₂, ..., :colₙ => :newcolₙ])

Renames the columns col₁, col₂, ..., colₙ to newcol₁, newcol₂, ..., newcolₙ.

Rename(fun)

Renames the table columns using the modification function fun that takes a string as input and returns another string with the new name.

Examples

Rename(1 => :x, 3 => :y)
 Rename(:a => :x, :c => :y)
 Rename("a" => "x", "c" => "y")
 Rename([1 => "x", 3 => "y"])
 Rename([:a => "x", :c => "y"])
 Rename(["a", "c"] .=> [:x, :y])
-Rename(nm -> nm * "_suffix")
source

StdNames

TableTransforms.StdNames — Type
StdNames(spec = :uppersnake)

Standardizes column names according to given spec. Default to :uppersnake case specification.

Specs

  • :uppersnake - Upper Snake Case, e.g. COLUMN_NAME
  • :uppercamel - Upper Camel Case, e.g. ColumnName
  • :upperflat - Upper Flat Case, e.g. COLUMNNAME
  • :snake - Snake Case, e.g. column_name
  • :camel - Camel Case, e.g. columnName
  • :flat - Flat Case, e.g. columnname
source

StdFeats

TableTransforms.StdFeats — Type
StdFeats()

Standardizes the columns of the table based on scientific types:

  • Continuous: ZScore
  • Categorical: Identity
  • Unknown: Identity
source

Sort

TableTransforms.Sort — Type
Sort(col₁, col₂, ..., colₙ; kwargs...)
+Rename(nm -> nm * "_suffix")
source

StdNames

TableTransforms.StdNames — Type
StdNames(spec = :uppersnake)

Standardizes column names according to given spec. Default to :uppersnake case specification.

Specs

  • :uppersnake - Upper Snake Case, e.g. COLUMN_NAME
  • :uppercamel - Upper Camel Case, e.g. ColumnName
  • :upperflat - Upper Flat Case, e.g. COLUMNNAME
  • :snake - Snake Case, e.g. column_name
  • :camel - Camel Case, e.g. columnName
  • :flat - Flat Case, e.g. columnname
source

StdFeats

TableTransforms.StdFeats — Type
StdFeats()

Standardizes the columns of the table based on scientific types:

  • Continuous: ZScore
  • Categorical: Identity
  • Unknown: Identity
source

Sort

TableTransforms.Sort — Type
Sort(col₁, col₂, ..., colₙ; kwargs...)
 Sort([col₁, col₂, ..., colₙ]; kwargs...)
 Sort((col₁, col₂, ..., colₙ); kwargs...)

Sort the rows of selected columns col₁, col₂, ..., colₙ by forwarding the kwargs to the sortperm function.

Sort(regex; kwargs...)

Sort the rows of columns that match with regex.

Examples

Sort(:a)
 Sort(:a, :c, rev=true)
 Sort([1, 3, 5], by=row -> abs.(row))
 Sort(("a", "c", "e"))
-Sort(r"[ace]")
source

Sample

TableTransforms.Sample — Type
Sample(size, [weights]; replace=true, ordered=false, rng=default_rng())

Sample size rows of table using weights with or without replacement depending on the option replace. The option ordered can be used to return samples in the same order of the original table.

Examples

Sample(1000)
+Sort(r"[ace]")
source

Sample

TableTransforms.Sample — Type
Sample(size, [weights]; replace=true, ordered=false, rng=default_rng())

Sample size rows of table using weights with or without replacement depending on the option replace. The option ordered can be used to return samples in the same order of the original table.

Examples

Sample(1000)
 Sample(1000, replace=false)
 Sample(1000, replace=false, ordered=true)
 
@@ -47,25 +47,25 @@
 Sample(1000, rng=rng)
 
 # with weights
-Sample(10, rand(100))
source

Filter

TableTransforms.Filter — Type
Filter(pred)

Filters the table returning only the rows where the predicate pred is true.

Examples

Filter(row -> sum(row) > 10)
+Sample(10, rand(100))
source

Filter

TableTransforms.Filter — Type
Filter(pred)

Filters the table returning only the rows where the predicate pred is true.

Examples

Filter(row -> sum(row) > 10)
 Filter(row -> row.a == true && row.b < 30)
 Filter(row -> row."a" == true && row."b" < 30)
 Filter(row -> row[1] == true && row[2] < 30)
 Filter(row -> row[:a] == true && row[:b] < 30)
-Filter(row -> row["a"] == true && row["b"] < 30)

Notes

  • The schema of the table is preserved by the transform.
source

DropMissing

TableTransforms.DropMissing — Type
DropMissing()
+Filter(row -> row["a"] == true && row["b"] < 30)

Notes

  • The schema of the table is preserved by the transform.
source

DropMissing

TableTransforms.DropMissing — Type
DropMissing()
 DropMissing(:)

Drop all rows with missing values in table.

DropMissing(col₁, col₂, ..., colₙ)
 DropMissing([col₁, col₂, ..., colₙ])
 DropMissing((col₁, col₂, ..., colₙ))

Drop all rows with missing values in selected columns col₁, col₂, ..., colₙ.

DropMissing(regex)

Drop all rows with missing values in columns that match with regex.

Examples

DropMissing()
 DropMissing("b", "c", "e")
 DropMissing([2, 3, 5])
 DropMissing((:b, :c, :e))
-DropMissing(r"[bce]")

Notes

  • The transform can alter the element type of columns from Union{Missing,T} to T.
  • If the transformed column has only missing values, it will be converted to an empty column of type Any.
source

DropNaN

TableTransforms.DropNaN — Type
DropNaN()
+DropMissing(r"[bce]")

Notes

  • The transform can alter the element type of columns from Union{Missing,T} to T.
  • If the transformed column has only missing values, it will be converted to an empty column of type Any.
source

DropNaN

TableTransforms.DropNaN — Type
DropNaN()
 DropNaN(:)

Drop all rows with NaN values in table.

DropNaN(col₁, col₂, ..., colₙ)
 DropNaN([col₁, col₂, ..., colₙ])
 DropNaN((col₁, col₂, ..., colₙ))

Drop all rows with NaN values in selected columns col₁, col₂, ..., colₙ.

DropNaN(regex)

Drop all rows with NaN values in columns that match with regex.

Examples

DropNaN(2, 3, 4)
 DropNaN([:b, :c, :d])
 DropNaN(("b", "c", "d"))
-DropNaN(r"[bcd]")
source

DropExtrema

TableTransforms.DropExtrema — Type
DropExtrema(; low=0.25, high=0.75)

Drops rows where any of the values in all columns are outside the interval ([quantile(col, low), quantile(col, high)]).

DropExtrema(col₁, col₂, ..., colₙ; low=0.25, high=0.75)
+DropNaN(r"[bcd]")
source

DropExtrema

TableTransforms.DropExtrema — Type
DropExtrema(; low=0.25, high=0.75)

Drops rows where any of the values in all columns are outside the interval ([quantile(col, low), quantile(col, high)]).

DropExtrema(col₁, col₂, ..., colₙ; low=0.25, high=0.75)
 DropExtrema([col₁, col₂, ..., colₙ]; low=0.25, high=0.75)
 DropExtrema((col₁, col₂, ..., colₙ); low=0.25, high=0.75)

Drops rows where any of the values in columns col₁, col₂, ..., colₙ are outside the interval.

DropExtrema(regex; low=0.25, high=0.75)

Drops rows where any of the values in columns that match with regex are outside the interval.

Examples

DropExtrema(low=0.3, high=0.7)
 DropExtrema(1, low=0.3, high=0.7)
@@ -74,59 +74,59 @@
 DropExtrema(1, 3, 5, low=0, high=1)
 DropExtrema([:a, :c, :e], low=0.3, high=0.7)
 DropExtrema(("a", "c", "e"), low=0.25, high=0.75)
-DropExtrema(r"[ace]", low=0.3, high=0.7)
source

DropUnits

TableTransforms.DropUnits — Type
DropUnits()
+DropExtrema(r"[ace]", low=0.3, high=0.7)
source

DropUnits

TableTransforms.DropUnits — Type
DropUnits()
 DropUnits(:)

Drop units from all columns in the table.

DropUnits(col₁, col₂, ..., colₙ)
 DropUnits([col₁, col₂, ..., colₙ])
 DropUnits((col₁, col₂, ..., colₙ))

Drop units from selected columns col₁, col₂, ..., colₙ.

DropUnits(regex)

Drop units from columns that match with regex.

Examples

DropUnits()
 DropUnits([2, 3, 5])
 DropUnits([:b, :c, :e])
 DropUnits(("b", "c", "e"))
-DropUnits(r"[bce]")
source

DropConstant

TableTransforms.DropConstant — Type
DropConstant()

Drops the constant columns using the allequal function.

source

AbsoluteUnits

TableTransforms.AbsoluteUnits — Type
AbsoluteUnits()
+DropUnits(r"[bce]")
source

DropConstant

TableTransforms.DropConstant — Type
DropConstant()

Drops the constant columns using the allequal function.

source

AbsoluteUnits

TableTransforms.AbsoluteUnits — Type
AbsoluteUnits()
 AbsoluteUnits(:)

Converts the units of all columns in the table to absolute units.

AbsoluteUnits(col₁, col₂, ..., colₙ)
 AbsoluteUnits([col₁, col₂, ..., colₙ])
 AbsoluteUnits((col₁, col₂, ..., colₙ))

Converts the units of selected columns col₁, col₂, ..., colₙ to absolute units.

AbsoluteUnits(regex)

Converts the units of columns that match with regex to absolute units.

Examples

AbsoluteUnits()
 AbsoluteUnits([2, 3, 5])
 AbsoluteUnits([:b, :c, :e])
 AbsoluteUnits(("b", "c", "e"))
-AbsoluteUnits(r"[bce]")
source

Unitify

TableTransforms.Unitify — Type
Unitify()

Add units to columns of the table using bracket syntax. A column named col [unit] will be renamed to a unitful column col with a valid unit from Unitful.jl.

In the case that the unit is not recognized by Unitful.jl, no units are added. Empty brackets are also allowed to represent columns without units, e.g. col [].

source

Unit

TableTransforms.Unit — Type
Unit(unit)

Converts the units of all columns in the table to unit.

Unit(cols₁ => unit₁, cols₂ => unit₂, ..., colsₙ => unitₙ)

Converts the units of selected columns cols₁, cols₂, ..., colsₙ to unit₁, unit₂, ... unitₙ.

The column selection can be a single column identifier (index or name), a collection of identifiers or a regular expression (regex).

Examples

Unit(u"m")
+AbsoluteUnits(r"[bce]")
source

Unitify

TableTransforms.Unitify — Type
Unitify()

Add units to columns of the table using bracket syntax. A column named col [unit] will be renamed to a unitful column col with a valid unit from Unitful.jl.

In the case that the unit is not recognized by Unitful.jl, no units are added. Empty brackets are also allowed to represent columns without units, e.g. col [].

source

Unit

TableTransforms.Unit — Type
Unit(unit)

Converts the units of all columns in the table to unit.

Unit(cols₁ => unit₁, cols₂ => unit₂, ..., colsₙ => unitₙ)

Converts the units of selected columns cols₁, cols₂, ..., colsₙ to unit₁, unit₂, ... unitₙ.

The column selection can be a single column identifier (index or name), a collection of identifiers or a regular expression (regex).

Examples

Unit(u"m")
 Unit(1 => u"km", :b => u"K", "c" => u"s")
 Unit([2, 3] => u"cm")
 Unit([:a, :c] => u"cm")
 Unit(["a", "c"] => u"cm")
-Unit(r"[abc]" => u"km")
source

Map

TableTransforms.Map — Type
Map(cols₁ => fun₁ => target₁, cols₂ => fun₂, ..., colsₙ => funₙ => targetₙ)

Applies the funᵢ function to the columns selected by colsᵢ using the map function and saves the result in a new column named targetᵢ.

The column selection can be a single column identifier (index or name), a collection of identifiers or a regular expression (regex).

Passing a target column name is optional and when omitted a new name is generated by joining the function name with the selected column names. If the target column already exists in the table, the original column will be replaced.

Examples

Map(1 => sin)
+Unit(r"[abc]" => u"km")
source

Map

TableTransforms.Map — Type
Map(cols₁ => fun₁ => target₁, cols₂ => fun₂, ..., colsₙ => funₙ => targetₙ)

Applies the funᵢ function to the columns selected by colsᵢ using the map function and saves the result in a new column named targetᵢ.

The column selection can be a single column identifier (index or name), a collection of identifiers or a regular expression (regex).

Passing a target column name is optional and when omitted a new name is generated by joining the function name with the selected column names. If the target column already exists in the table, the original column will be replaced.

Examples

Map(1 => sin)
 Map(:a => sin, "b" => cos => :cos_b)
 Map([2, 3] => ((b, c) -> 2b + c))
 Map([:a, :c] => ((a, c) -> 2a * 3c) => :col1)
 Map(["c", "a"] => ((c, a) -> 3c / a) => :col1, "c" => tan)
-Map(r"[abc]" => ((a, b, c) -> a^2 - 2b + c) => "col1")

Notes

  • Anonymous functions must be passed with parentheses as in the examples above;
  • Some function names are treated in a special way, they are:
    • Anonymous functions: #1 -> f1;
    • Composed functions: outer ∘ inner -> outer_inner;
    • Base.Fix1 functions: Base.Fix1(f, x) -> fix1_f;
    • Base.Fix2 functions: Base.Fix2(f, x) -> fix2_f;
source

Replace

TableTransforms.Replace — Type
Replace(cols₁ => pred₁ => new₁, pred₂ => new₂, ..., colsₙ => predₙ => newₙ)

Replaces all values where predᵢ predicate returns true with newᵢ value in the the columns selected by colsᵢ.

Passing a column selection is optional and when omitted all columns in the table will be selected. The column selection can be a single column identifier (index or name), a collection of identifiers, or a regular expression (regex).

The predicate can be a function that accepts a single argument and returns a boolean, or a value. If the predicate is a value, it will be transformed into the following function: x -> x === value.

Examples

Replace(1 => -1, 5 => -5)
+Map(r"[abc]" => ((a, b, c) -> a^2 - 2b + c) => "col1")

Notes

  • Anonymous functions must be passed with parentheses as in the examples above;
  • Some function names are treated in a special way, they are:
    • Anonymous functions: #1 -> f1;
    • Composed functions: outer ∘ inner -> outer_inner;
    • Base.Fix1 functions: Base.Fix1(f, x) -> fix1_f;
    • Base.Fix2 functions: Base.Fix2(f, x) -> fix2_f;
source

Replace

TableTransforms.Replace — Type
Replace(cols₁ => pred₁ => new₁, pred₂ => new₂, ..., colsₙ => predₙ => newₙ)

Replaces all values where predᵢ predicate returns true with newᵢ value in the the columns selected by colsᵢ.

Passing a column selection is optional and when omitted all columns in the table will be selected. The column selection can be a single column identifier (index or name), a collection of identifiers, or a regular expression (regex).

The predicate can be a function that accepts a single argument and returns a boolean, or a value. If the predicate is a value, it will be transformed into the following function: x -> x === value.

Examples

Replace(1 => -1, 5 => -5)
 Replace(2 => 0.0 => 1.5, 5.0 => 5.5)
 Replace(:b => 0.0 => 1.5, 5.0 => 5.5)
 Replace("b" => 0.0 => 1.5, 5.0 => 5.5)
 Replace([1, 3] => >(5) => 5)
 Replace([:a, :c] => isequal(2) => -2)
 Replace(["a", "c"] => (x -> 4 < x < 6) => 0)
-Replace(r"[abc]" => (x -> isodd(x) && x > 10) => 2)

Notes

  • Anonymous functions must be passed with parentheses as in the examples above.
  • Replacements are applied in the sequence in which they are defined, therefore, if there is more than one replacement for the same column, the first valid one will be applied.
source

Coalesce

TableTransforms.Coalesce — Type
Coalesce(; value)

Replaces all missing values from the table with value.

Coalesce(col₁, col₂, ..., colₙ; value)
+Replace(r"[abc]" => (x -> isodd(x) && x > 10) => 2)

Notes

  • Anonymous functions must be passed with parentheses as in the examples above.
  • Replacements are applied in the sequence in which they are defined, therefore, if there is more than one replacement for the same column, the first valid one will be applied.
source

Coalesce

TableTransforms.Coalesce — Type
Coalesce(; value)

Replaces all missing values from the table with value.

Coalesce(col₁, col₂, ..., colₙ; value)
 Coalesce([col₁, col₂, ..., colₙ]; value)
 Coalesce((col₁, col₂, ..., colₙ); value)

Replaces all missing values from the columns col₁, col₂, ..., colₙ with value.

Coalesce(regex; value)

Replaces all missing values from the columns that match with regex with value.

Examples

Coalesce(value=0)
 Coalesce(1, 3, 5, value=1)
 Coalesce([:a, :c, :e], value=2)
 Coalesce(("a", "c", "e"), value=3)
-Coalesce(r"[ace]", value=4)

Notes

  • The transform can alter the element type of columns from Union{Missing,T} to T.
source

Coerce

TableTransforms.Coerce — Type
Coerce(col₁ => S₁, col₂ => S₂, ..., colₙ => Sₙ)

Return a copy of the table, ensuring that the scientific types of the columns match the new specification.

Coerce(S)

Coerce all columns of the table with scientific type S.

This transform uses the DataScienceTraits.coerce function. Please see their docstring for more details.

Examples

import DataScienceTraits as DST
+Coalesce(r"[ace]", value=4)

Notes

  • The transform can alter the element type of columns from Union{Missing,T} to T.
source

Coerce

TableTransforms.Coerce — Type
Coerce(col₁ => S₁, col₂ => S₂, ..., colₙ => Sₙ)

Return a copy of the table, ensuring that the scientific types of the columns match the new specification.

Coerce(S)

Coerce all columns of the table with scientific type S.

This transform uses the DataScienceTraits.coerce function. Please see their docstring for more details.

Examples

import DataScienceTraits as DST
 Coerce(1 => DST.Continuous, 2 => DST.Continuous)
 Coerce(:a => DST.Continuous, :b => DST.Continuous)
-Coerce("a" => DST.Continuous, "b" => DST.Continuous)
source

Levels

TableTransforms.Levels — Type
Levels(col₁ => levels₁, col₂ => levels₂, ..., colₙ => levelsₙ; ordered=nothing)

Convert columns col₁, col₂, ..., colₙ to categorical arrays with given levels levels₁, levels₂, ..., levelsₙ. Optionally, specify which columns are ordered.

Examples

Levels(1 => 1:3, 2 => ["a", "b"], ordered=r"a")
+Coerce("a" => DST.Continuous, "b" => DST.Continuous)
source

Levels

TableTransforms.Levels — Type
Levels(col₁ => levels₁, col₂ => levels₂, ..., colₙ => levelsₙ; ordered=nothing)

Convert columns col₁, col₂, ..., colₙ to categorical arrays with given levels levels₁, levels₂, ..., levelsₙ. Optionally, specify which columns are ordered.

Examples

Levels(1 => 1:3, 2 => ["a", "b"], ordered=r"a")
 Levels(:a => 1:3, :b => ["a", "b"], ordered=[:a])
-Levels("a" => 1:3, "b" => ["a", "b"], ordered=["b"])
source

Indicator

TableTransforms.Indicator — Type
Indicator(col; k=10, scale=:quantile, categ=false)

Transforms continuous variable into k indicator variables defined by half-intervals of col values in a given scale. Optionally, specify the categ option to return binary categorical values as opposed to raw 1s and 0s.

Given a sequence of increasing threshold values t1 < t2 < ... < tk, the indicator transform converts a continuous variable Z into a sequence of k variables Z_1 = Z <= t1, Z_2 = Z <= t2, ..., Z_k = Z <= tk.

Scales:

  • :quantile - threshold values are calculated using the quantile(Z, p) function with a linear range of probabilities.
  • :linear - threshold values are calculated using a linear range.

Examples

Indicator(1, k=3)
+Levels("a" => 1:3, "b" => ["a", "b"], ordered=["b"])
source

Indicator

TableTransforms.Indicator — Type
Indicator(col; k=10, scale=:quantile, categ=false)

Transforms continuous variable into k indicator variables defined by half-intervals of col values in a given scale. Optionally, specify the categ option to return binary categorical values as opposed to raw 1s and 0s.

Given a sequence of increasing threshold values t1 < t2 < ... < tk, the indicator transform converts a continuous variable Z into a sequence of k variables Z_1 = Z <= t1, Z_2 = Z <= t2, ..., Z_k = Z <= tk.

Scales:

  • :quantile - threshold values are calculated using the quantile(Z, p) function with a linear range of probabilities.
  • :linear - threshold values are calculated using a linear range.

Examples

Indicator(1, k=3)
 Indicator(:a, k=6, scale=:linear)
-Indicator("a", k=9, scale=:linear, categ=true)
source

OneHot

TableTransforms.OneHot — Type
OneHot(col; categ=false)

Transforms categorical column col into one-hot columns of levels returned by the levels function of CategoricalArrays.jl. The categ option can be used to convert resulting columns to categorical arrays as opposed to boolean vectors.

Examples

OneHot(1)
+Indicator("a", k=9, scale=:linear, categ=true)
source

OneHot

TableTransforms.OneHot — Type
OneHot(col; categ=false)

Transforms categorical column col into one-hot columns of levels returned by the levels function of CategoricalArrays.jl. The categ option can be used to convert resulting columns to categorical arrays as opposed to boolean vectors.

Examples

OneHot(1)
 OneHot(:a)
 OneHot("a")
-OneHot("a", categ=true)
source

Identity

TransformsBase.Identity — Type
Identity()

The identity transform that maps any object to itself.

source

Center

TableTransforms.Center — Type
Center()

Applies the center transform to all columns of the table. The center transform of the column x, with mean μ, is defined by x .- μ.

Center(col₁, col₂, ..., colₙ)
+OneHot("a", categ=true)
source

Identity

TransformsBase.Identity — Type
Identity()

The identity transform that maps any object to itself.

source

Center

TableTransforms.Center — Type
Center()

Applies the center transform to all columns of the table. The center transform of the column x, with mean μ, is defined by x .- μ.

Center(col₁, col₂, ..., colₙ)
 Center([col₁, col₂, ..., colₙ])
 Center((col₁, col₂, ..., colₙ))

Applies the Center transform on columns col₁, col₂, ..., colₙ.

Center(regex)

Applies the Center transform on columns that match with regex.

Examples

Center(1, 3, 5)
 Center([:a, :c, :e])
 Center(("a", "c", "e"))
-Center(r"[ace]")
source

LowHigh

TableTransforms.LowHigh — Type
LowHigh(; low=0.25, high=0.75)

Applies the LowHigh transform to all columns of the table. The LowHigh transform of the column x is defined by (x .- xl) ./ (xh - xl), where xl = quantile(x, low) and xh = quantile(x, high).

LowHigh(col₁, col₂, ..., colₙ; low=0.25, high=0.75)
+Center(r"[ace]")
source

LowHigh

TableTransforms.LowHigh — Type
LowHigh(; low=0.25, high=0.75)

Applies the LowHigh transform to all columns of the table. The LowHigh transform of the column x is defined by (x .- xl) ./ (xh - xl), where xl = quantile(x, low) and xh = quantile(x, high).

LowHigh(col₁, col₂, ..., colₙ; low=0.25, high=0.75)
 LowHigh([col₁, col₂, ..., colₙ]; low=0.25, high=0.75)
 LowHigh((col₁, col₂, ..., colₙ); low=0.25, high=0.75)

Applies the LowHigh transform on columns col₁, col₂, ..., colₙ.

LowHigh(regex; low=0.25, high=0.75)

Applies the LowHigh transform on columns that match with regex.

Examples

LowHigh()
 LowHigh(low=0, high=1)
@@ -134,22 +134,22 @@
 LowHigh(1, 3, 5, low=0, high=1)
 LowHigh([:a, :c, :e], low=0.3, high=0.7)
 LowHigh(("a", "c", "e"), low=0.25, high=0.75)
-LowHigh(r"[ace]", low=0.3, high=0.7)
source

MinMax

TableTransforms.MinMax — Function
MinMax()

Applies the MinMax transform to all columns of the table. The MinMax transform is equivalent to LowHigh(low=0, high=1).

MinMax(col₁, col₂, ..., colₙ)
+LowHigh(r"[ace]", low=0.3, high=0.7)
source

MinMax

TableTransforms.MinMax — Function
MinMax()

Applies the MinMax transform to all columns of the table. The MinMax transform is equivalent to LowHigh(low=0, high=1).

MinMax(col₁, col₂, ..., colₙ)
 MinMax([col₁, col₂, ..., colₙ])
 MinMax((col₁, col₂, ..., colₙ))

Applies the MinMax transform on columns col₁, col₂, ..., colₙ.

MinMax(regex)

Applies the MinMax transform on columns that match with regex.

Examples

MinMax(1, 3, 5)
 MinMax([:a, :c, :e])
 MinMax(("a", "c", "e"))
-MinMax(r"[ace]")

See also LowHigh.

source

Interquartile

TableTransforms.Interquartile — Function
Interquartile()

Applies the Interquartile transform to all columns of the table. The Interquartile transform is equivalent to LowHigh(low=0.25, high=0.75).

Interquartile(col₁, col₂, ..., colₙ)
+MinMax(r"[ace]")

See also LowHigh.

source

Interquartile

TableTransforms.Interquartile — Function
Interquartile()

Applies the Interquartile transform to all columns of the table. The Interquartile transform is equivalent to LowHigh(low=0.25, high=0.75).

Interquartile(col₁, col₂, ..., colₙ)
 Interquartile([col₁, col₂, ..., colₙ])
 Interquartile((col₁, col₂, ..., colₙ))

Applies the Interquartile transform on columns col₁, col₂, ..., colₙ.

Interquartile(regex)

Applies the Interquartile transform on columns that match with regex.

Examples

Interquartile(1, 3, 5)
 Interquartile([:a, :c, :e])
 Interquartile(("a", "c", "e"))
-Interquartile(r"[ace]")

See also LowHigh.

source

ZScore

TableTransforms.ZScore — Type
ZScore()

Applies the z-score transform (a.k.a. normal score) to all columns of the table. The z-score transform of the column x, with mean μ and standard deviation σ, is defined by (x .- μ) ./ σ.

ZScore(col₁, col₂, ..., colₙ)
+Interquartile(r"[ace]")

See also LowHigh.

source

ZScore

TableTransforms.ZScore — Type
ZScore()

Applies the z-score transform (a.k.a. normal score) to all columns of the table. The z-score transform of the column x, with mean μ and standard deviation σ, is defined by (x .- μ) ./ σ.

ZScore(col₁, col₂, ..., colₙ)
 ZScore([col₁, col₂, ..., colₙ])
 ZScore((col₁, col₂, ..., colₙ))

Applies the ZScore transform on columns col₁, col₂, ..., colₙ.

ZScore(regex)

Applies the ZScore transform on columns that match with regex.

Examples

ZScore(1, 3, 5)
 ZScore([:a, :c, :e])
 ZScore(("a", "c", "e"))
-ZScore(r"[ace]")
source

Quantile

TableTransforms.Quantile — Type
Quantile(; dist=Normal())

The quantile transform to a given distribution.

Quantile(col₁, col₂, ..., colₙ; dist=Normal())
+ZScore(r"[ace]")
source

Quantile

TableTransforms.Quantile — Type
Quantile(; dist=Normal())

The quantile transform to a given distribution.

Quantile(col₁, col₂, ..., colₙ; dist=Normal())
 Quantile([col₁, col₂, ..., colₙ]; dist=Normal())
 Quantile((col₁, col₂, ..., colₙ); dist=Normal())

Applies the Quantile transform on columns col₁, col₂, ..., colₙ.

Quantile(regex; dist=Normal())

Applies the Quantile transform on columns that match with regex.

Examples

using Distributions
 
@@ -158,23 +158,23 @@
 Quantile(1, 3, 5, dist=Beta())
 Quantile([:a, :c, :e], dist=Gamma())
 Quantile(("a", "c", "e"), dist=Beta())
-Quantile(r"[ace]", dist=Normal())
source

Functional

TableTransforms.Functional — Type
Functional(fun)

The transform that applies a fun elementwise.

Functional(col₁ => fun₁, col₂ => fun₂, ..., colₙ => funₙ)

Apply the corresponding funᵢ function to each colᵢ column.

Examples

Functional(exp)
+Quantile(r"[ace]", dist=Normal())
source

Functional

TableTransforms.Functional — Type
Functional(fun)

The transform that applies a fun elementwise.

Functional(col₁ => fun₁, col₂ => fun₂, ..., colₙ => funₙ)

Apply the corresponding funᵢ function to each colᵢ column.

Examples

Functional(exp)
 Functional(log)
 Functional(1 => exp, 2 => log)
 Functional(:a => exp, :b => log)
-Functional("a" => exp, "b" => log)
source

EigenAnalysis

TableTransforms.EigenAnalysis — Type
EigenAnalysis(proj; [maxdim], [pratio])

The eigenanalysis of the covariance with a given projection proj. Optionally specify the maximum number of dimensions in the output maxdim and the percentage of variance to retain pratio.

Projections

  • :V - Uncorrelated variables (PCA transform)
  • :VD - Uncorrelated variables and variance one (DRS transform)
  • :VDV - Uncorrelated variables and variance one (SDS transformation)

The :V projection used in the PCA transform projects the data on the eigenvectors V of the covariance matrix.

The :VD projection used in the DRS transform. Similar to the :V projection, but the eigenvectors are multiplied by the squared inverse of the eigenvalues D.

The :VDV projection used in the SDS transform. Similar to the :VD transform, but the data is projected back to the basis of the original variables using the Vᵀ matrix.

See https://geostatisticslessons.com/lessons/sphereingmaf for more details about these three variants of eigenanalysis.

Examples

EigenAnalysis(:V)
+Functional("a" => exp, "b" => log)
source

EigenAnalysis

TableTransforms.EigenAnalysis — Type
EigenAnalysis(proj; [maxdim], [pratio])

The eigenanalysis of the covariance with a given projection proj. Optionally specify the maximum number of dimensions in the output maxdim and the percentage of variance to retain pratio.

Projections

  • :V - Uncorrelated variables (PCA transform)
  • :VD - Uncorrelated variables and variance one (DRS transform)
  • :VDV - Uncorrelated variables and variance one (SDS transformation)

The :V projection used in the PCA transform projects the data on the eigenvectors V of the covariance matrix.

The :VD projection used in the DRS transform. Similar to the :V projection, but the eigenvectors are multiplied by the squared inverse of the eigenvalues D.

The :VDV projection used in the SDS transform. Similar to the :VD transform, but the data is projected back to the basis of the original variables using the Vᵀ matrix.

See https://geostatisticslessons.com/lessons/sphereingmaf for more details about these three variants of eigenanalysis.

Examples

EigenAnalysis(:V)
 EigenAnalysis(:VD)
 EigenAnalysis(:VDV)
 EigenAnalysis(:V, maxdim=3)
 EigenAnalysis(:VD, pratio=0.99)
-EigenAnalysis(:VDV, maxdim=3, pratio=0.99)
source

PCA

TableTransforms.PCA — Function
PCA([options])

Principal component analysis.

See EigenAnalysis for detailed description of the available options.

Examples

PCA(maxdim=2)
+EigenAnalysis(:VDV, maxdim=3, pratio=0.99)
source

PCA

TableTransforms.PCA — Function
PCA([options])

Principal component analysis.

See EigenAnalysis for detailed description of the available options.

Examples

PCA(maxdim=2)
 PCA(pratio=0.86)
-PCA(maxdim=2, pratio=0.86)

Notes

  • PCA() is shortcut for ZScore() → EigenAnalysis(:V).
source

DRS

TableTransforms.DRS — Function
DRS([options])

Dimension reduction sphering.

See EigenAnalysis for detailed description of the available options.

Examples

DRS(maxdim=3)
+PCA(maxdim=2, pratio=0.86)

Notes

  • PCA() is shortcut for ZScore() → EigenAnalysis(:V).
source

DRS

TableTransforms.DRS — Function
DRS([options])

Dimension reduction sphering.

See EigenAnalysis for detailed description of the available options.

Examples

DRS(maxdim=3)
 DRS(pratio=0.87)
-DRS(maxdim=3, pratio=0.87)

Notes

  • DRS() is shortcut for ZScore() → EigenAnalysis(:VD).
source

SDS

TableTransforms.SDS — Function
SDS([options])

Standardized data sphering.

See EigenAnalysis for detailed description of the available options.

Examples

SDS()
+DRS(maxdim=3, pratio=0.87)

Notes

  • DRS() is shortcut for ZScore() → EigenAnalysis(:VD).
source

SDS

TableTransforms.SDS — Function
SDS([options])

Standardized data sphering.

See EigenAnalysis for detailed description of the available options.

Examples

SDS()
 SDS(maxdim=4)
 SDS(pratio=0.88)
-SDS(maxdim=4, pratio=0.88)

Notes

  • SDS() is shortcut for ZScore() → EigenAnalysis(:VDV).
source

ProjectionPursuit

TableTransforms.ProjectionPursuit — Type
ProjectionPursuit(; tol=1e-6, maxiter=100, deg=5, perc=0.9, n=100, rng=Random.default_rng())

The projection pursuit multivariate transform converts any multivariate distribution into the standard multivariate Gaussian distribution.

This iterative algorithm repeatedly finds a direction of projection α that maximizes a score of non-Gaussianity known as the projection index I(α). The samples projected along α are then transformed with the Quantile transform to remove the non-Gaussian structure. The other coordinates in the rotated orthonormal basis Q = [α ...] are left untouched.

The non-singularity of Q is controlled by assuring that norm(det(Q)) ≥ tol. The iterative process terminates whenever the transformed samples are "more Gaussian" than perc% of n randomly generated samples from the standard multivariate Gaussian distribution, or when the number of iterations reaches a maximum maxiter.

Examples

ProjectionPursuit()
+SDS(maxdim=4, pratio=0.88)

Notes

  • SDS() is shortcut for ZScore() → EigenAnalysis(:VDV).
source

ProjectionPursuit

TableTransforms.ProjectionPursuit — Type
ProjectionPursuit(; tol=1e-6, maxiter=100, deg=5, perc=0.9, n=100, rng=Random.default_rng())

The projection pursuit multivariate transform converts any multivariate distribution into the standard multivariate Gaussian distribution.

This iterative algorithm repeatedly finds a direction of projection α that maximizes a score of non-Gaussianity known as the projection index I(α). The samples projected along α are then transformed with the Quantile transform to remove the non-Gaussian structure. The other coordinates in the rotated orthonormal basis Q = [α ...] are left untouched.

The non-singularity of Q is controlled by assuring that norm(det(Q)) ≥ tol. The iterative process terminates whenever the transformed samples are "more Gaussian" than perc% of n randomly generated samples from the standard multivariate Gaussian distribution, or when the number of iterations reaches a maximum maxiter.

Examples

ProjectionPursuit()
 ProjectionPursuit(deg=10)
 ProjectionPursuit(perc=0.85, n=50)
 ProjectionPursuit(tol=1e-4, maxiter=250, deg=5, perc=0.95, n=100)
@@ -182,10 +182,10 @@
 # with rng
 using Random
 rng = MersenneTwister(2)
-ProjectionPursuit(perc=0.85, n=50, rng=rng)

See https://doi.org/10.2307/2289161 for further details.

source

Closure

TableTransforms.Closure — Type
Closure()

The transform that applies the closure operation (i.e. x ./ sum(x)), to all rows of the input table. The rows of the output table sum to one.

See also Remainder.

source

Remainder

TableTransforms.Remainder — Type
Remainder([total])

The transform that takes a table with columns x₁, x₂, …, xₙ and returns a new table with an additional column containing the remainder value xₙ₊₁ = total .- (x₁ + x₂ + ⋯ + xₙ) If the total value is not specified, then default to the maximum sum across rows.

See also Closure.

source

Compose

TableTransforms.Compose — Type
Compose(; as=:CODA)

Converts all columns of the table into parts of a composition in a new column named as, using the CoDa.compose function.

Compose(col₁, col₂, ..., colₙ; as=:CODA)
+ProjectionPursuit(perc=0.85, n=50, rng=rng)

See https://doi.org/10.2307/2289161 for further details.

source

Closure

TableTransforms.Closure — Type
Closure()

The transform that applies the closure operation (i.e. x ./ sum(x)), to all rows of the input table. The rows of the output table sum to one.

See also Remainder.

source

Remainder

TableTransforms.Remainder — Type
Remainder([total])

The transform that takes a table with columns x₁, x₂, …, xₙ and returns a new table with an additional column containing the remainder value xₙ₊₁ = total .- (x₁ + x₂ + ⋯ + xₙ) If the total value is not specified, then default to the maximum sum across rows.

See also Closure.

source

Compose

TableTransforms.Compose — Type
Compose(; as=:CODA)

Converts all columns of the table into parts of a composition in a new column named as, using the CoDa.compose function.

Compose(col₁, col₂, ..., colₙ; as=:CODA)
 Compose([col₁, col₂, ..., colₙ]; as=:CODA)
 Compose((col₁, col₂, ..., colₙ); as=:CODA)

Converts the selected columns col₁, col₂, ..., colₙ into parts of a composition.

Compose(regex; as=:CODA)

Converts the columns that match with regex into parts of a composition.

Examples

Compose(as=:comp)
 Compose([2, 3, 5])
 Compose([:b, :c, :e])
 Compose(("b", "c", "e"))
-Compose(r"[bce]", as="COMP")
source

ALR

TableTransforms.ALR — Type
ALR([refvar])

Additive log-ratio transform.

Optionally, specify the reference variable refvar for the ratios. Default to the last column of the input table.

source

CLR

TableTransforms.CLR — Type
CLR()

Centered log-ratio transform.

source

ILR

TableTransforms.ILR — Type
ILR([refvar])

Isometric log-ratio transform.

Optionally, specify the reference variable refvar for the ratios. Default to the last column of the input table.

source

RowTable

TableTransforms.RowTable — Type
RowTable()

The transform that applies the function Tables.rowtable to to the input table.

source

ColTable

TableTransforms.ColTable — Type
ColTable()

The transform that applies the function Tables.columntable to to the input table.

source
+Compose(r"[bce]", as="COMP")source

ALR

TableTransforms.ALR — Type
ALR([refvar])

Additive log-ratio transform.

Optionally, specify the reference variable refvar for the ratios. Default to the last column of the input table.

source

CLR

TableTransforms.CLR — Type
CLR()

Centered log-ratio transform.

source

ILR

TableTransforms.ILR — Type
ILR([refvar])

Isometric log-ratio transform.

Optionally, specify the reference variable refvar for the ratios. Default to the last column of the input table.

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RowTable

TableTransforms.RowTable — Type
RowTable()

The transform that applies the function Tables.rowtable to to the input table.

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ColTable

TableTransforms.ColTable — Type
ColTable()

The transform that applies the function Tables.columntable to to the input table.

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