Analysis with Programming

As promised from my recent article , here's my tutorial on how to layout and design an infographic in R. This article will serve as a template for more infographic design that I plan to share on future posts. Hence, we will go through the following sections: Layout - mainly handles by grid package. Design - style of the elements in the layout. Texts - use extrafont package for custom fonts; Shapes (lines and point characters) - use grid , although this package has been removed from CRAN (as of February 26, 2015), the compressed file of the source code of the package is still available. But if I am not mistaken, by default this package is included in R. You might check it first before installing. Plots - several choices for plotting data in R: base plot, lattice , or ggplot2 package. The Infographic We aim to obtain the following layout and design in the final output of our code:

The Philippine government has launched Open Data Philippines (data.gov.ph) last year, January 16, 2014. Accordingly, the data.gov.ph aims to make national government data searchable, accessible, and useful, with the help of the different agencies of government, and with the participation of the public. This website consolidates the data sets of different government agencies, allowing users to find specific information from a rich and continuously growing collection of public data sets. Data.gov.ph provides information on how to access these datasets and tools, such infographics and other applications, to make the information easy to understand. Users may not only view the datasets, but also share and download them as spreadsheets and other formats, for their own use. The primary goal of data.gov.ph is to foster a citizenry empowered to make informed decisions, and to promote efficiency and transparency in government. For more, check out the video:

Analysis with Programming has recently been syndicated to Planet Python . And as a first post being a contributing blog on the said site, I would like to share how to get started with data analysis on Python. Specifically, I would like to do the following: Importing the data Importing CSV file both locally and from the web; Data transformation; Descriptive statistics of the data; Hypothesis testing One-sample t test; Visualization; and Creating custom function. Importing the data This is the crucial step, we need to import the data in order to proceed with the succeeding analysis. And often times data are in CSV format, if not, at least can be converted to CSV format. In Python we can do this using the following codes:

To probability lovers, I just want to share (and discuss) few simple problems I solved in Chapter 4 of Casella, G. and Berger, R.L. (2002). Statistical Inference . A random point $(X,Y)$ is distributed uniformly on the square with vertices $(1, 1),(1,-1),(-1,1),$ and $(-1,-1)$. That is, the joint pdf is $f(x,y)=\frac{1}{4}$ on the square. Determine the probabilities of the following events. $X^2 + Y^2 < 1$ $2X-Y>0$ $|X+Y| Solutions: $X^2 + Y^2 < 1$ We need to consider the boundary of this inequality first in the unit square, so below is the plot of $X^2 + Y^2 = 1$,

So I started using SAS® University Edition which is a FREE version of SAS® software. Again it's FREE, and that's the main reason why I want to relearn the language. The software was announced on March 24, 2014 and the download went available on May of that year . And for that, I salute Dr. Jim Goodnight . At least we can learn SAS® without paying for the expensive price tag, especially for single user like me. The software requires a virtual machine, where it runs on top of that; and a 64-bit processor. To install, just follow the instruction in this video . Although the installation in the video is done in Windows, it also works on Mac. Below is the screenshot of my SAS® Studio running on Safari.

In imaging, we deal with multivariate data, like in array form with several spectral bands. And trying to come up with interpretation across correlations of its dimensions is very challenging, if not impossible. For example let's recall the number of spectral bands of AVIRIS data we used in the previous post . There are 152 bands, so in total there are 152$\cdot$152 = 23104 correlations of pairs of random variables. How will you be able to interpret that huge number of correlations? To engage on this, it might be better if we group these variables into two and study the relationship between these sets of variables. Such statistical procedure can be done using the canonical correlation analysis (CCA). An example of this on health sciences (from Reference 2) is variables related to exercise and health. On one hand you have variables associated with exercise, observations such as the climbing rate on a stair stepper, how fast you can run, the amount of weight lifted on bench press,...

Ever wonder what's the mathematics behind face recognition on most gadgets like digital camera and smartphones? Well for most part it has something to do with statistics. One statistical tool that is capable of doing such feature is the Principal Component Analysis (PCA). In this post, however, we will not do (sorry to disappoint you) face recognition as we reserve this for future post while I'm still doing research on it. Instead, we go through its basic concept and use it for data reduction on spectral bands of the image using R. Let's view it mathematically Consider a line $L$ in a parametric form described as a set of all vectors $k\cdot\mathbf{u}+\mathbf{v}$ parameterized by $k\in \mathbb{R}$, where $\mathbf{v}$ is a vector orthogonal to a normalized vector $\mathbf{u}$. Below is the graphical equivalent of the statement: