I put together a short intro presentation for some people explaining a little bit about R from an introductory point of view. Slides put together with R/markdown and ioslides. Presentation here.

I put together a short intro presentation for some people explaining a little bit about R from an introductory point of view. Slides put together with R/markdown and ioslides. Presentation here.

Here are some examples of using ggplot2 and kdb+ together to produce some simple graphs of data stored in kdb+. I am using the qserver extension for R (http://code.kx.com/wsvn/code/cookbook_code/r/) to connect to a running kdb+ instance from within R. First, … Continue reading →

In the first two parts of this series, I looked at the basics of the interface I created between rmathlib and kdb+. In this post, I’ll go through some of the convenience functions I wrote to emulate some basic R … Continue reading →

Following on from the last post on integrating some rmathlib functionality with kdb+, here is a sample walkthrough of how some of the functionality can be used, including some of the R-style wrappers I wrote to emulate some of the … Continue reading →

The R engine is usable in a variety of ways – one of the lesser-known features is that it provides a standalone math library that can be linked to from an external application. This library provides some nice functionality such … Continue reading →

Here is the beginnings of a simple routine to convert R data frames to Q format (in this case a dictionary). It uses the S3 dispatch mechanism to handle the conversion of different data types. Extremely basic (I havent even … Continue reading →

Frequently we will want to estimate the empirical probability density function of real-world data and compare it to the theoretical density from one or more probability distributions. The following example shows the empirical and theoretical normal density for EUR/USD high-frequency … Continue reading →

Binomial Tree Simulation The binomial model is a discrete grid generation method from \(t=0\) to \(T\). At each point in time (\(t+\Delta t\)) we can move up with probability \(p\) and down with probability \((1-p)\). As the probability of an … Continue reading →

Random number generation is a core topic in numerical computer science. There are many efficient algorithms for generating random (strictly speaking, pseudo-random) variates from different probability distributions. The figure below shows a sampling of 1000 two-dimensional random variates from the … Continue reading →

The Euler Method is a very simple method used for numerical solution of initial-value problems. Although there are much better methods in practise, it is a nice intuitive mechanism. The objective is to find a solution to the equation $$ … Continue reading →

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