Learning R

Kevin Feasel

2015-12-23

R

Grant Fritchey is learning R:

Awesome. Fixed that algorithm problem, right?

Wrong.

That’s because algorithms are not the problem… the only problem. The real problem is data preparation. A lot of the examples you’ll read online are very straight forward with nice neat data sets. That’s because they were carefully groomed and prepared. Here I am looking at the wooly wild real data and I’m utterly lost in how to properly prepare this so that it’s appropriately set up as a continuous distribution(or a distribution at all). WOOF! The reason this is so hard is because I actually don’t understand the data fundamentals of the problem I’m trying to solve in exactly the way needed to solve the problem. More cogitation is necessary.

Just because you can write R code doesn’t mean you are a data scientist.  Grant has the right mindset, but this post is fair warning that R’s complexity isn’t so much in its being a DSL, but rather in the domain itself.

Related Posts

Reasons For Using Docker With R

Jeroen Ooms gives us a few reasons why we might want to containerize our R-based products: The flagship of the OpenCPU system is the OpenCPU server: a mature and powerful Linux stack for embedding R in systems and applications. Because OpenCPU is completely open source we can build and ship on DockerHub. A ready-to-go linux server […]

Read More

Linear Discriminant Analysis

Jake Hoare explains Linear Discriminant Analysis: Linear Discriminant Analysis takes a data set of cases (also known as observations) as input. For each case, you need to have a categorical variable to define the class and several predictor variables (which are numeric). We often visualize this input data as a matrix, such as shown below, with each case being a row and each variable a column. In this […]

Read More

Categories

December 2015
MTWTFSS
« Nov Jan »
 123456
78910111213
14151617181920
21222324252627
28293031