Review of Basic Data Analytic Methods Using R

Key Concepts

Basic features of R

Data exploration and analysis with R

Statistical methods for evaluation

The previous chapter presented the six phases of the Data Analytics Lifecycle.

  • Phase 1: Discovery
  • Phase 2: Data Preparation
  • Phase 3: Model Planning
  • Phase 4: Model Building
  • Phase 5: Communicate Results
  • Phase 6: Operationalize

The first three phases involve various aspects of data exploration. In general, the success of a data analysis project requires a deep understanding of the data. It also requires a toolbox for mining and presenting the data. These activities include the study of the data in terms of basic statistical measures and creation of graphs and plots to visualize and identify relationships and patterns. Several free or commercial tools are available for exploring, conditioning, modeling, and presenting data. Because of its popularity and versatility, the open-source programming language R is used to illustrate many of the presented analytical tasks and models in this book.

This chapter introduces the basic functionality of the R programming language and environment. The first section gives an overview of how to use R to acquire, parse, and filter the data as well as how to obtain some basic descriptive statistics on a dataset. The second section examines using R to perform exploratory data analysis tasks using visualization. The final section focuses on statistical inference, such as hypothesis testing and analysis ...

Get Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.