A consistent set of tools to perform in-database analytics on Teradata Aster Big Data Discovery Platform. toaster (a.k.a 'to Aster') embraces simple 2-step approach: compute in Aster - visualize and analyze in R. Its `compute` functions use combination of parallel SQL, SQL-MR and SQL-GR executing in Aster database - highly scalable parallel and distributed analytical platform. Then `create` functions visualize results with boxplots, scatterplots, histograms, heatmaps, word clouds, maps, networks, or slope graphs. Advanced options such as faceting, coloring, labeling, and others are supported with most visualizations.
toaster (to Aster) is a set of tools for computing and analyzing data with Teradata Aster Big Data database. It brings the power of Teradata Aster's distributed SQL, MapReduce (SQL-MR), and Graph Engine (SQL-GR) to R on desktop and complements analysis of results with a convenient set of plotting functions.
toaster acheives most tasks in 2 distinct steps:
Compute in Aster using Aster's rich, fully scalable set of analyical functions, transparently running in distributed and parallel environement.
Deliver and visualize results in R for further exploration and analysis.
toaster performs all big data, processing intensive computations in Aster, making results and visualizations available in R. Summary statistics, aggregates, histograms, heatmaps, and coefficients from linear regression models are among results available in R after processing in Aster. Most results have toaster visualization functions to aid further analysis.
You can install:
the latest released version from CRAN with
install.packages("toaster")
the latest development version from github with
devtools::install_github("toaster", "teradata-aster-field")
evaluation version of Aster analytic platform - Aster Express - to run on your PC here and get started with this Tutorial Series.
If you encounter a clear bug, please file a minimal reproducible example on github.
Attribution:
NEW FEATURES
MINOR FEATURES
BUG FIXES
NEW FEATURES
Both explicit and implicit support for kmeans functions in AAF 6.21. Package will recognize versions based on the function's output or using new argument version. since new output now includes more kmeans statistics computeKmeans will run faster with newer version of AAF (#56).
Kmeans clustering can now persist clustered data for both optimized performance and convinience using new argument persist=TRUE (#56).
Kmeans clustering now supports initial centers obtained with canopy clustering. Use new functinality computeCanopy to quickly seed initial centroids and run kmeans with canopy object (#61).
MINOR FEATURES
Compute functions computeAggregates, computeBarchart, computeSample now allow passing any parameter to sqlQuery via ... syntax to better control performance and data type conversion (#60).
computeClusterSample now includes id by default (#60).
createClusterPairsPlot added argument include and except to selectively control features to plot (#63).
BUG FIXES
createBoxplot with coordFlip=TRUE now labels axises correctly (#58).
Updated createClusterPairsPlot to work with the latest version of GGally (#62).
NEW FEATURES
Graph function computeGraphClusters
performs various types
of graph decomposition including connected components and
modularity (future release) (#33).
Graph function computeGraphClustersAsGraphs
uses community object
produced by computeGraphClusters
to create graphs corresponding
to its components (#33).
Function validateGraph
validates and tests for consistency graph
tables in Aster (#34).
Function computeSample
now supports sample stratum based on
table column or custom stratum condition (#28).
Function getTableCounts
is handy when reviewing database tables
first time: it reports the number of rows and columns in each
table (#50).
Function computeCorrelations
now supports group columns in Aster
(with argument by
) (#49).
MINOR FEATURES
Graph vertices table is now always read when constructing corresponding network object (#46).
Minimal graph must have single vertex and no edges (#42).
All plotting functions now support subtitle (#41).
Deprecated function theme_empty - use ggplot2::theme_void instead (#53).
BUG FIXES
getTableSummary
fails when table has one or more temporal column
types (#37)
getTablesummary
fails when numerical data contains 'NaN' or other
special values (#39, #51)
computeHeatmap
argument by
now supports multiple columns
to group results (#44).
Temporary table names allow underscore characters now (#40).
NEW FEATURES
New graph functionality expands toaster's reach to Aster graph engine and SQL/GR functions (#32).
Simply define a metadata 'toagraph' object that describes a graph data in Aster (one object per graph) to compute various graph metrics and distributions:
computeGraphHistogram
returns degree, clustering,
shortest path, and various centrality measure distributions;computeGraphMetric
returns top graph vertices for
given graph metric including degree, local clustering, and
various centrality measures.New functions computeGraph
, 'showGraph, and
computeEgoGraph`
query Aster graph data to filter, inspect and visualize whole
or sub-graphs, including ego graphs (neighborhood graphs).
MINOR FEATURES
getTableSummary
can skip computing percentiles by setting
percentiles value to logical \code{FALSE} (#31).
getNullCounts()
new argument support percent and data frame
with no factors (#29).
isTable
now supports schema in table name, queries (by returning
NA), and expanded result format (#30).
MINOR FEATURES
createMap
support for:
metrics
. This deprecates
parameter metricName
(#25);shapeAlpha
for the shapes on
the map (#24);shape
and shapeStroke
to manage
appearance of artifacts on the map.all plotting functions gain new guide parameter(s) that control appearance of fill, size and other legend(s) using ggplot2 guide object name or object itself.
BUG FIXES
Kmeans cluster and total within sum of squares calculations now
work when scale=FALSE
. (fixes #23)
Kmeans SQL is correct now when id
is exactly one of the table columns
and default aliasId
. (fixes #22)
showData
now uses same default theme_tufte as the rest of plotting
functions (missed it 0.4.1).
fixed histograms in showData
after upgrading to ggplot2 2.0.0
(missed it 0.4.1).
NEW FEATURES
K-means clustering function performs in-database data prep, scaling, clustering, computing standard k-means measures and aggregated metrics on produced clusters. Other functions include the silhouette method of evaluating cluster consistency and validity and variety of visualizations options. Result of k-means function is compatible with stats 'kmeans' object.
Utility getNullCounts function returns NULL counts per column in the table.
MINOR FEATURES
NEW FEATURES
New text analysis functions computeTf
and computeTfIdf
process corpora in Aster and produce results compatible with package tm,
in particular term document matrix.
Both computeTf
and computeTfIdf
rank terms to return top ranked
ones. Ranking and number of terms to return are provided by
parameters top
and rankFunction
. Unlimited (all terms) are
returned by default with top = NULL
.
S3 classes nGram
and token
provide pluggable parsers to extract text
tokens to use in the functions 'computeTf' and 'computeTfIdf'.
Text functions support stop words in both Aster (installed stopwords file) and R (post-processing of results).
Linear regression now is compatible with R standard lm functions returning
object of both classes c('toalm', 'lm'). This means methods summary
,
coefficients
, etc. work with the object returned by computeLm
.
This change is not backward compatible: to obtain result returned in 0.2.5
list contains element old.result
.
To compute results similar to lm
computeLm
uses sample (default 1000
rows) to calculate stats like residuals, R-square, etc. in Aster. As before,
linear regression coefficients are calculated on full data set with
SQL/MR linreg function.
getTableSummary
is enabled for parallel execution. Simply create and
register parallel cluster of your choice with doParallel package and set
parameter parallel=TRUE. Performance gains may be up to 50% or better
depending on size of the table, number of parallel processes, and number
of columns. Run demo("baseball-parallel")
for examples.
computePercentiles
is enabled for parallel execution. Simply create and
register parallel cluster of your choice with doParallel package and set
parameter parallel=TRUE. Performance gains may be up to 50% or better
depending on size of the table, number of parallel processes, and number
of columns. Run demo("baseball-parallel")
for examples.
Added support of temporal Aster data types in getTableSummary
and
computePercentiles
. Temporal types are date, time, timestamp, and interval.
in computePercentiles
set parameter temporal=TRUE to calculate
temporal columns and run it separately from numerical ones.
MINOR FEATURES
Added factory functions getDiscretePaletteFactory
and getGradientPaletteFactory
to dynamically generate palettes with n number of colors.
Added utility function isTable
that checks if tables exist in Aster database.
Parameter formula
replaced defunct expr
in the function computeLm
for consistency with other model-fitting functions.
computePercentiles
now operates on multiple columns at once.
Improved database error handling to be more robust and informative. Error messages now include both ODBC and Aster error message and information (when applicable).
Added deprecated warning facility toa_dep
similar to ggplot2 gg_dep
function.
BUG FIXES
Legend position in showData
histogram format is completely removed if
legendPosition="none".
computePercentiles
now returns no rows for the column that contains all NULLs.
Before it threw error without completing.
fixed legend position in plotting functions.
Added error when histogram start value is greater than end value in (Issue #33)
DOCUMENTAION
Completely reworked demo scripts. Now they contain fully functional examples
running on baseball and openDallas data sets. The data sets are available
from github: https://bitbucket.org/grigory/toaster/downloads
Baseball Lahman data set now includes 2013 season.
NEW FEATURES
computeSample
: randomly sample data from the table specifying
fraction or size of desired data set.
createMap
: new visualization function for combining maps with data
artifacts from Aster database. Can be used to produce maps of
arbitrary scale (with exception of whole world) and type with shapes
of size and labels corresponding to data computed in Aster. It uses
ggmap and ggplot2 packages and Google API for geocoding data as
necessary. It implements smart logic to choose map tiles to place
geocoded data appropriately, and it also automatically geocodes
data if necessary (Google API restrictions apply).
Due to geocoding and map API restrictions createMap
supports
function caching suggesting function memoise
of memoise package.
(other functions are fine too). Properly following suggested practices
should significantly optimize both peformance and API usage when geocoding
or retrieving maps.
compute
: for executing arbitrary aggregations on Aster tables.
computeBarchart
: for computing data for barchart visualizations. This
is different from computeHistogram
as barchart is defined on factors
(categorical data) witch doesn't support defining bins like in histograms.
computePercentiles
: for computing multiple percentiles across one or
many subsets of a table in one go. Results are suitable for function
createBoxplot
(see next).
createBoxplot
: visualizes boxplots for single column across one or
multiple subsets.
computeLm
: compute linear model coefficients similar to lm function but
all performed inside Aster.
ENHANCEMENTS
added parameter test
to compute- functions (functions that access and
manipulate data in Aster) to produce SQL without executing it. Thus, when
test=TRUE
function returns string containing SQL that would have run
in Aster.
package depedencies moved from Depends to Imports section of DESCRIPTION file
except for RODBC package. Keeping RODBC in Depends because toaster requires
access to RODBC connection object and to its function odbcConnect
. Other
packages are not exposed by toaster functions so accessing them would have
been needed only for advanced usage (if any).
if you use any function from the packages other than RODBC then those packages
should be loaded with library
or require
or use their namespace.
facet parameter now supports both one-value and 2-value vector (if parameter is longer than the rest of values are ignored). Single value defines column name for wrapping facets in 1 or more column lattice. Two values define pair of columns to place facets in 2-dimensional grid for each combination of values found.
createHistogram
supports trend lines with parameter trendLine=TRUE.
computeHeatmap
converts dimension and facet columns to factors by default.
If undesired set parameter dimAsFactor = FALSE to disable (not recommended
with heat maps).
computeHeatmap
now supports withMelt to melt result using function melt
from package reshape2. This option simplifies visualizating with facets.
createBubblechart
now supports scaling shapes by size (default) or by area.
Correspondingly, use shapeSizeRange when scaling by size; and shapeMaxSize
when scaling by area.
createBubblechart
added parameters to control label positioning and
formatting. All parameters that position and format label text start
with prefix "label" now. Old parameters textSize, textColour, and
textVJust renamed to labelSize, labelColour, labelVJust.
createPopPyramid
support for facets.
added utility method to list Aster data types: getNumericTypes
,
getCharacterTypes
, getTemporalTypes
.
computeAggregates
is not an alias anymore and it replaced function
compute
which is no more.