Thanks for trying out the SciDB package for R. I hope you enjoy using it.

Install the package from CRAN with

install.packages("scidb")

The current development version of the package can be installed directly from sources on GitHub using the devtools package as follows (requires an R development environment and the R tools package):

library('devtools')
install_github("SciDBR","paradigm4",quick=TRUE)

Note! The SciDBR package depends on the RCurl R package, which in turn requires support for the curl library in your operating system. This might mean that you need to install a libcurl development library RPM or deb package on your OS. On RHEL and CentOS, this package is usually called libcurl-devel and on Ubuntu it's called libcurl4-gnutls-dev.

The SciDB R package requires installation of a simple open-source HTTP network service called on the computer that SciDB is installed on. This service only needs to be installed on the SciDB machine, not on client computers that connect to SciDB from R. See http://github.com/paradigm4/shim for source code and installation instructions.

Developers please note that R CMD check-style unit tests are skipped unless a system environment variable named SCIDB_TEST_HOST is set to the host name or I.P. address of SciDB. See the tests directory for test code.

Wiki

Check out (and feel free to contribute to) examples in the wiki pages for this project here:

https://github.com/Paradigm4/SciDBR/wiki/_pages

This project also has a pretty web page on Github here:

https://Paradigm4.github.io/SciDBR

New features

More functions

Better handling of missing values

The array-like and dataframe-like classes now handle missing values in a uniform way. All SciDB missing codes are mapped to R NA values and R NA values are mapped to SciDB missing code zero.

Windowed and moving-window aggregates

Multidimensional windowed and moving-window aggregates are now supported with simple syntax in the aggregate function. Windows can be defined along coordinate axes or number of (sparse) data values.

Labeled coordinates

SciDB arrays now support labeled coordinate indices using the standard R rownames, colnames, or dimnames settings. Assigned labels are provided by 1-d SciDB arrays that map the integer coordinate to a string label. Here is a simple example:

# Upload a test matrix to SciDB:
X <- as.scidb( matrix(rnorm(20),nrow=5) )

# Assign rownames to the SciDB matrix X.  SciDB matrix objects like X default
# to zero-based indexing.  It's important that the label array have the same
# starting index:
rownames(X) <- as.scidb( data.frame(letters[1:5]), start=0)

# We can now use strings to select subarrays and otherwise index X:
X[c("b","a","d"), ]

More indexing goodness

Indexing SciDB array objects by other SciDB array objects to achieve the effect of filtering by boolean expressions and similar operations is now supported. Here is a simple example:

# Create a five-element SciDB vector:
y <- as.scidb(runif(5))

# Pick out rows of the SciDB matrix X fromt the last example that correspond
# to entries of y that are positive:
X[y > 0, ]

SciDB array promises

Most functions return objects that represent array promises--unevaluated SciDB query expressions with a result schema. Use the new scidbeval function or the optional eval function argument when available to force evaluation to a materialized SciDB backing array. Otherwise use the objects normally, deferring evaluation until required.

R Sparse matrix support

The package now supports double-precision valued R sparse matrices defined via the Matrix package. Sparse SciDB matrices that are materialized to R are returned as sparse R matrices and vice-versa.

Heatmaps

The package overloads the standard R image function to plot heatmaps of SciDB array objects (only applies to objects of class scidb).

library("devtools")
install_github("SciDBR","Paradigm4")
library("scidb")
scidbconnect()      # Fill in your SciDB hostname as required

# Create a SciDB array with some random entries
iquery("store(build(<v:double>[i=0:999,100,0,j=0:999,250,0],random()%100),A)")

# The SciDBR `image` function overloads the usual R image function to produce
# heatmaps using SciDB's `regrid` aggregation operator. The 'grid' argument
# specifies the output array size, and the 'op' argument specifies the
# aggregation operator to apply.

X = image(A, grid=c(100,100), op="avg(v)", useRaster=TRUE)
Example output

Example output

# Image accepts all the standard arguments to the R `image` function in
# addition to the SciDB-specific `grid` and `op` arguments. The output axes are
# labeled in the original array units. The scidb::image function returns the
# interpolated heatmap array:

dim(X)
[1] 100 100

The package has a completely new implementation of aggregation, merge, and related database functions. The new functions apply to SciDB array and data frame-like objects. A still growing list of the functions includes:

See for example help("subset", package="scidb") for help on the subset function, or any of the other functions.

Perhaps the coolest new feature associated with the functions listed above is that they can be composed in a way that defers computation in SciDB to avoid unnecessary creation of intermediate arrays. The new functions all accept an argument named eval which, when set to FALSE, returns a new SciDB array promise object in place of evaluating the query and returning an array or data frame object.

The eval argument is automatically set to FALSE when any of the above functions are directly composed in R, unless manually overriden by explicitly setting eval=TRUE. Consider the following example:

x = as.scidb(iris)
head(x)
  Sepal_Length Sepal_Width Petal_Length Petal_Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

a = aggregate(
      project(
        bind(x,name="PxP", FUN="Petal_Length*Petal_Width"),
        attributes=c("PxP","Species")),
      by="Species", FUN="avg(PxP)")

a[]
  Species_index PxP_avg    Species
0             0  0.3656     setosa
1             1  5.7204 versicolor
2             2 11.2962  virginica

The composed aggregate(project(bind(... functions were carried out in the above example within a single SciDB transaction, storing only the result of the composed query.