- I didn’t learn the SAS Macro Language enough. SAS Macros are cool, and fast. Ditto for arrays. or ODS.
- Not keeping up with the changes in Version 9+. Especially the hash method.(Why name a technique after a recreational drug, most unfair)
- Not studying more statistics theory.
- Flunking SAS Certification Twice.
- Not making enough money because customers need a solution not a p value.
- There is no Proc common sense. There is no Proc Clean the Data.
- No Macros to automate the model. Here is dirty data. There is clean model. Wait till version 16.
- Not getting selected by SAS R & D.Not applying to SAS R & D.
- Google has better voice recognition for typing notes. No Voice Recognition in SAS langvuage to type syntax.
- Enhanced Editor and EG are both idiotic junk pushed by Marketing!
Inspired by true events at
The 3.0 Era for R starts today! Changes include better Big Data support.
Read the NEWS here
install.packages() has a new argument
quiet to reduce the amount of output shown.
- New functions
citeNatbib() have been added, to allow generation of in-text citations from
"bibentry" objects. A
cite() function may be added to
merge() works in more cases where the data frames include matrices. (Wish of PR#14974.)
sample.int() has some support for n >= 2^31: see its help for the limitations.A different algorithm is used for
(n, size, replace = FALSE, prob = NULL) for
n > 1e7 and
size <= n/2. This is much faster and uses less memory, but does give different results.
dir()) gains a new optional argument
no.. which allows to exclude
".." from listings.
- Profiling via
Rprof() now optionally records information at the statement level, not just the function level.
available.packages() gains a
"license/restricts_use" filter which retains only packages for which installation can proceed solely based on packages which are guaranteed not to restrict use.
- File ‘share/licenses/licenses.db’ has some clarifications, especially as to which variants of ‘BSD’ and ‘MIT’ is intended and how to apply them to packages. The problematic licence ‘Artistic-1.0’ has been removed.
breaks argument in
hist.default() can now be a function that returns the breakpoints to be used (previously it could only return the suggested number of breakpoints).
This section applies only to 64-bit platforms.
- There is support for vectors longer than 2^31 – 1 elements. This applies to raw, logical, integer, double, complex and character vectors, as well as lists. (Elements of character vectors remain limited to 2^31 – 1 bytes.)
- Most operations which can sensibly be done with long vectors work: others may return the error ‘long vectors not supported yet’. Most of these are because they explicitly work with integer indices (e.g.
match()) or because other limits (e.g. of character strings or matrix dimensions) would be exceeded or the operations would be extremely slow.
length() returns a double for long vectors, and lengths can be set to 2^31 or more by the replacement function with a double value.
- Most aspects of indexing are available. Generally double-valued indices can be used to access elements beyond 2^31 – 1.
- There is some support for matrices and arrays with each dimension less than 2^31 but total number of elements more than that. Only some aspects of matrix algebra work for such matrices, often taking a very long time. In other cases the underlying Fortran code has an unstated restriction (as was found for complex
dist() can produce dissimilarity objects for more than 65536 rows (but for example
hclust() cannot process such objects).
serialize() to a raw vector is unlimited in size (except by resources).
- The C-level function
R_alloc can now allocate 2^35 or more bytes.
grep() will return double vectors of indices for long vector inputs.
- Many calls to
.C() have been replaced by
.Call() to allow long vectors to be supported (now or in the future). Regrettably several packages had copied the non-API
.C() calls and so failed.
.Fortran() do not accept long vector inputs. This is a precaution as it is very unlikely that existing code will have been written to handle long vectors (and the R wrappers often assume that
length(x) is an integer).
- Most of the methods for
sort() work for long vectors.
order() support long vectors (slowly except for radix sorting).
sample() can do uniform sampling from a long vector.
- More use has been made of R objects representing registered entry points, which is more efficient as the address is provided by the loader once only when the package is loaded.
This has been done for packages
tcltk: it was already in place for the other standard packages.
Since these entry points are always accessed by the R entry points they do not need to be in the load table which can be substantially smaller and hence searched faster. This does mean that
.Call calls copied from earlier versions of R may no longer work – but they were never part of the API.
.Call() calls in package base have been migrated to
solve() makes fewer copies, especially when
b is a vector rather than a matrix.
eigen() makes fewer copies if the input has dimnames.
- Most of the linear algebra functions make fewer copies when the input(s) are not double (e.g. integer or logical).
- A foreign function call (
.C() etc) in a package without a
PACKAGE argument will only look in the first DLL specified in the ‘NAMESPACE’ file of the package rather than searching all loaded DLLs. A few packages needed
PACKAGE arguments added.
@<- operator is now implemented as a primitive, which should reduce some copying of objects when used. Note that the operator object must now be in package base: do not try to import it explicitly from package methods.
SIGNIFICANT USER-VISIBLE CHANGES
So I finally got my test plan accepted for a 1 month trial to the Oracle Public Cloud at https://cloud.oracle.com/ .
I am testing this for my next book R for Cloud Computing ( I have already covered Windows Azure, Amazon AWS, and in the middle of testing Google Compute).
Some initial thoughts- this Java cloud seemed more suitable for web apps, than for data science ( but I have to spend much more time on this).
I really liked the help and documentation and tutorials, Oracle has invested a lot in it to make it friendly to enterprise users.
Hopefully the Oracle R Enterprise ORE guys can talk to the Oracle Cloud department and get some common use case projects going.
In the meantime, I did a roundup on all R -Java projects.