R-Shiny

Shiny is a web application framework for R language from the developers of RStudio. Shiny can easily turn your analyses into interactive web applications. There is no need to know HTML, CSS, or JavaScript.

Some Shiny app examples

More examples and detailed tutorials how to create them can be found on Shiny website.

Shiny Tutorials

Shiny website provides plenty of tutorials to encourage users to develop thier own shiny application and simply share thier results on the internet.

Video tutorials - The How to Start Shiny video series will take you from R programmer to Shiny developer. The entire tutorial is two hours and 25 minutes long. You can watch the complete tutorial, or jump to a specific chapter.

Written tutorials - This seven lesson tutorial from 2014 will teach you the basics of Shiny in a self-paced format. Each lesson takes about 20 minutes and teaches one new Shiny skill. By the end of the lessons, you will know how to build and deploy a Shiny app.

Graphs in R

R comes with several packages for data visialization. R enables you to chose from a variety of graphs and customize them. Moreover, all graphs can be easily integrated in any shiny application.

Examples of popular graph visualization packages

plotly

dygraphs

ggplot2

More examples and detailed tutorials how to create graphs can be found on Simple Graphs with R, Advanced Graphs, R Graph Gallery or Cookbook for ggplot2.

Time Series Data Mining in R

This section is belonging to tutorials, which are written within a blog of our member - Peter Laurinec.
Tutorials deal with forecasting methods, which are suitable for seasonal time series. In more detail, with forecasting methods which can handle multiple seasonalities in time series of electricity consumption. Website, petolau.github.io, source files can be simply checked on GitHub too - github.com/PetoLau/petolau.github.io.

Ensemble learning for time series forecasting in R

Written on 2017-10-19

Ensemble learning methods are widely used nowadays for its predictive performance improvement. Ensemble learning combines multiple predictions (forecasts) from one or multiple methods to overcome accuracy of simple prediction and to avoid possible overfit. In the domain of time series forecasting, we have somehow obstructed situation because of dynamic changes in coming data... READ MORE

Using regression trees for forecasting double-seasonal time series with trend in R

Written on 2017-08-22

After blogging break caused by writing research papers, I managed to secure time to write something new about time series forecasting. This time I want to share with you my experiences with seasonal-trend time series forecasting using simple regression trees. Classification and regression tree (or decision tree) is broadly used machine learning method for modeling... READ MORE

Doing magic and analyzing seasonal time series with GAM (Generalized Additive Model) in R

Written on 2017-01-24

In this post (tutorial), I will fully introduce a “magical” Generalized Additive Model (GAM) to model time series of electricity consumption. You may wonder, why words “magic” or “magical” are mentioned? You will get your answer later in this post... READ MORE

Forecast double seasonal time series with multiple linear regression in R

Written on 2016-12-03

I will continue in describing forecast methods, which are suitable to seasonal (or multi-seasonal) time series. In the previous post smart meter data of electricity consumption were introduced and a forecast method using similar day approach was proposed. ARIMA and exponential smoothing (common methods of time series analysis) were used as forecast methods... READ MORE

Enernoc smart meter data - forecast electricity consumption with similar day approach in R

Written on 2016-11-12

Deployment of smart grids gives space to an occurrence of new methods of machine learning and data analysis. Smart grids can contain of millions of smart meters, which produce a large amount of data of electricity consumption (long time series). In addition to time series of electricity consumption, we can have extra information about the consumer like ZIP code, type of consumer (consumer vs. prosumer) and so on... READ MORE