This example will show how data sets can seamlessly be written into a SQLite database for subsequent analysis using familiar SQL statements (the data in question pertains to automobile performance and fuel efficiency). The dataset is included with R during its initial installation. Since the included data is readily available within R, it is not necessary to import the data from a spreadsheet or other external source. Such datasets are used in examples in R documentation, so they are available within R during its initial installation, or are added along with code when new packages are installed.
icrosoft’s acquisition of Revolution R has been followed quickly by several exciting announcements. SQLServer 2016 will include R placing it at the finger-tips of developers and DBAs. A Microsoft Online Course through EDX introducing R is also now available. Microsoft is already known for its proprietary .NET programming languages including Visual Basic and C# as well as its use of SQL and T-SQL in the SQLServer database. With all of these options already available, why would Microsoft be expending resources on promoting the use of R? There are a number of compelling answers to this question, including the community that has grown up around R and the availability of over 7000 special purpose packages. In R Basics, we mentioned these strengths and demonstrated how succinct and expressive R can be at data intensive tasks. Another appeal of R is its graphical capabilities. There are a number of packages available for creating charts, plots and visualizations in R. In this article, we will look at the three established foundational graphics systems available in R.
The Kalman filter has numerous applications in technology – including IoT. Specifically, Kalman filters are used in Sensor fusion. Sensor fusion helps to determine the State of an IoT based computing system which infers from different sensors.
Natural language processing, or NLP, is the machine handling of written and spoken human communications. Methods draw on linguistics and statistics, coupled with machine learning, to model language in the service of automation.