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: