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R basics
Published in Rafael A. Irizarry, Introduction to Data Science, 2019
In this book, we will be using the R software environment for all our analysis. You will learn R and data analysis techniques simultaneously. To follow along you will therefore need access to R. We also recommend the use of an integrated development environment (IDE), such as RStudio, to save your work. Note that it is common for a course or workshop to offer access to an R environment and an IDE through your web browser, as done by RStudio cloud1. If you have access to such a resource, you don’t need to install R and RStudio. However, if you intend on becoming an advanced data analyst, we highly recommend installing these tools on your computer2. Both R and RStudio are free and available online.
Miscellaneous
Published in David E. Hiebeler, R and MATLAB®, 2018
One very popular third-party interface for is called RStudio. It is available in a free, open-source edition as well as a commercial version from http://www.rstudio.com. RStudio provides a richer integrated development environment, with debugging tools, an editor that performs syntax-aware text coloring, a workspace inspector showing your defined variables, and so on. It is widely used among the students in my classes because of its superior editor. It also provides facilities to run on a server and access it via the Web.
Introduction to R
Published in Jan Žižka, František Dařena, Arnošt Svoboda, Text Mining with Machine Learning, 2019
Jan Žižka, František Dařena, Arnošt Svoboda
RStudio is an IDE for R available in open source and commercial editions. It enables syntax highlighting, code completion, and smart indentation when writing the code that can be executed directly from the source editor. It facilitates the work of programmers by providing facilities like jumping to function definitions, integrated documentation and help, managing multiple working directories, multiple-file editing, interactive debugger for diagnosing and fixing errors, and package development tools.
The impact of extreme weather on peak electricity demand from homes heated by air source heat pumps
Published in Energy Sources, Part B: Economics, Planning, and Policy, 2021
Michael Chesser, Padraic O'Reilly, Padraig Lyons, Paula Carroll
The data were cleaned by removing meters with large amounts of missing data, and patching or repairing small amounts of missing data by interpolation. This left eight homes with complete electricity data sets for analysis. The average electricity per time step over the sample of customers is then calculated to create the average coincident demand per time step, TSt in Eq.1. Taking the maximum over the time series yields the ADMD in Eq.2. We use the software package RStudio for data analysis and modeling. The R package “fittdistrplus” provides functions for fitting univariate distributions to different types of data and allows different estimation methods (Delignette-Muller and Dutang 2015). We fit and evaluate statistical distributions for the data in TSt.
An economic approach to road condition assessment using road user feedback: A new model and its application
Published in International Journal of Pavement Engineering, 2022
The analysis was performed in RStudio. It is an open-sourced integrated development environment (IDE) for R. R is a language as well as an environment for statistical computing and graphics. This software is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. Rstudio includes a console, a syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging, and workspace management.
Smart support system of material procurement for waste reduction based on big data and predictive analytics
Published in International Journal of Logistics Research and Applications, 2021
Tsai-Chi Kuo, Chien-Yun Peng, Chien-Jou Kuo
For example, for D1, the original dataset volume was 2858. After the data science techniques were implemented and the overlapping data in the dataset were eliminated, the effective dataset volume was reduced to 2500. The software used in this study was RStudio software. Table 2 presents the dataset information.