The process of ML is comparable to data processing. Both systems search through information to appear for patterns. However, rather than extracting information for human comprehension as just in case of knowledge mining, ML uses that information to discover trends in data and alter program actions.
If you haven’t already, I encourage you to read the first article in my series to gain a basic foundation of R and R Studio. You will also find the links for downloading the programs there as well.
To begin, I want to mention a bit about the “packages” found in R. The incredible thing about R is that is a dynamically evolving language that gains functionality on a daily basis. Packages allow for anyone to compile functions and data sets together in one convenient bundle to extend to the base system functionality of R. There are two main repositories that host packages, CRAN and bioconductor. At the time of writing this article there are more than 9,000 individual packages available for use with R.
A majority of the packages will be installed from CRAN, so I will highlight the steps to install and load packages hosted there. The first thing you will need to do is to install the package, as an example we will install the ggplot2 (one of R’s most popular graphing packages). Once you have the package installed, you must then load it. The code is as follows:
Having just graduated from college and entered the workforce, I don’t find myself always using the tools and programs I grew accustomed to over the past four years. However, there is one program I continually find myself reverting back to…R.
So what is R? R is a programming language and environment with an ever-growing bucket of tools for statistical processing and graphic creation. According to the R-project website, R includes
an effective data handling and storage facility,
a suite of operators for calculations on arrays, in particular matrices,
a large, coherent, integrated collection of intermediate tools for data analysis,
graphical facilities for data analysis and display either on-screen or on hardcopy, and
a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
Sure, graduating with a minor in Statistics and my love for data manipulation may make me partially biased with using R but I feel like its uses are incredibly wide-reaching. The great thing about R is it allows the user to perform simple arithmetic calculations with matrices or dive in deeper and create complex graphs or even create dynamic reports to incorporate LaTeX.
Another incredible feature is you have an overwhelming amount of “packages” at your disposal to increase …