Wait till the game is over (for the 7%)

Wait till the game is over!

This phrase haunted me during my childhood. 
Image:Wilsonnflfootball.jpg, modified to have ...

Now, I am an adult. My daughter has a neurological disease (a topic for another day).
There are times when she needs our utmost attention. 

We know better than to have her around others during a football game, less she disturb them or stand in front of the television, or do something else that may take their precious attention away from the large screen TV (that has a pause button). 

I sincerely believe something is wrong with me, because while so many other people I know can, and do speak incessantly about 1 hour and 20 minutes of entertainment, I find it not the least bit interesting.

As it turns out, I am one of the 7% of people who have little to no interest in watching sports.

Of course, I am a Nerd. I spend my time reading books, and writing about things. (Hopefully, by now the reader will have ascertained that this particular post is a little off-topic of my regular posts.)
Football is a relatively simple entertainment past time, but it is not life and death. 

 There are no redeeming qualities to watching football. Few people become altruistic watching football.

There are people that make money off the game, and chances are some good use-cases for using R or some other statistical tools may find the statistics useful. I am sure some teams even have predictive models for what to do under any given situation. 

Which means the entire game is practically scripted out by computer models before the first whistle is even blown.
The quote of Dumbledore about the Mirror of Erised can be applied to watching a football game: 

Dumbledore as portrayed by the late Richard Ha..."However, this mirror will give us neither knowledge or truth. Men have wasted away before it, entranced by what they have seen, or been driven mad, not knowing if what it shows is real or even possible." 

You learn nothing from watching it. 

If your goal is to simply waste your time, watching the game will certainly do that for you. 

If you have a loved one participating in the play, then by all means support them, and let them know you are there for them. 

If you don't actually know anyone personally on the field, why are you watching? 

I do not hate Football. I do however, consider it a huge waste of time.

I do my own version of wasting time. Reading books, and watching movies.  This is my entertainment. 

Enjoy your entertainment, but remember, there are times when things need to be done.  There are times when things are more important than Football.

Life happens, and when it does it is time for the game to wait. 


R on RHEL 7 in AWS

Tux, the Linux penguin

R on RHEL 7 in AWS

If I have start to run some software, and it ends up being less than straightforward, I always try to make a checklist of the procedure for how to build it. 

Recently I was testing the performance of some R code. 

I launched an AWS server Red Hat Linux 7 (RHEL 7) and found R was not installed. 

Ok, so this is a simple yum install, right? 

Not so fast. 

Here is everything I needed to do, in order to get R and rstudio running on my server: 

sudo su -c 'rpm -Uvh'
sudo yum update
sudo yum locallinstall lapack-devel-3.4.2-5.el7.x86_64.rpm
sudo yum locallinstall blas-devel-3.2.2-5.el7.x86_64.rpm
sudo yum locallinstall texlive-epsf-svn21461.2.7.4-38.el7.noarch.rpm
sudo yum locallinstall texinfo-tex-5.1-4.el7.x86_64.rpm
sudo yum install R

yum localinstall --nogpgcheck rstudio-server-rhel-0.99.491-x86_64.rpm
service rstudio-server
service rstudio-server status

All of this came about from google searches on stackoverflow, and other places where I found hiccup after hiccup just getting everything installed. 

These commands will save you some time. 


My introduction to R


English: Logo for R
Some time ago, I began down a path of learning statistics. This was not a topic I had studied in detail before. I had learned a bit of statistics over time while doing other things but never formally. 

I recognized I needed to learn more formally. 

During a number of the lectures I followed multiple professors referenced the R language. 

In my experience I had heard about SAS, and even worked with people that needed data from the various data repositories I managed imported into SAS. 

So I called one of my friends who is a big SAS user, and asked him about R. 

His response, R is basically a cleanroom version of SAS, but you have to write a lot of code to do the same things SAS does. 

Now this is something I could get into. 

So formally from the wiki: 
"R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R's popularity has increased substantially in recent years." -- R programming language

I have been a DBA, and a data architect for quite some time. Generally, the types of systems I had built up to that point in time were analytical frameworks. The need for these is apparent with the majority of the Business Intelligence tools that are out there.

As a general rule, the performance of a BI tools is almost entirely dependent on the data model (dimensional) that the BI tool reads from.

There are a few exceptions to this rule, but it has been a guiding rule for the majority of my career.

Now, with R there is not as much of a need for the structuring of the data to support the analysis. R is a programming language, as such you can do the Data Munging necessary within your code.

And since R is a vector based language you can do set operations which are incredibly faster than doing for loops, cursors and the like.

R has many various packages for doing various types of analysis. Machine learning, Sentiment Analysis, Data Mining, various types of regressions.

All of which, only a few years ago I would have needed a SAS license to be able to attempt.

Since R is open-source, I am able to download it and run with it with no "request" and "approval" process. I don't have to justify an expenditure to get a tool that helps me do my job.

If you are in a data architect, DBA, or other DataOps  I encourage you to check-out R. You will find a new powerful tool in your toolbox.

I will be writing a bit more about R over time as well.


I wrote for 30 days

I did it!

I wrote, and published something every day for 30 days. 

As I said in my first thoughts about this Challenge I was going to clean out all my drafts, then write some little thing, and push the publish button. 

Stephen King, American author best known for h...I have been doing so. 

My drafts on Data Warehousing, Comments about Search Engine Optimization, Being a DBA all of the original hastily scribbled notes have been polished, updated an published. 

Now what? 

I accomplished what I set out to accomplish. 

I hope that some of the notes I have shared on this forum have been satisfying, even in some small capacity. 

I do not know if I will keep it up and continue writing every day. I do have some ideas of things to continue writing about mostly R, or maybe a few commentaries that run through my mind. 

I will be putting out a Poll shortly to ask everyone if I should keep writing, publishing, and updating the world about my progress. 

What do you think I should do?