Manipulating Data

Abstract

R has different types of data storage such as lists, arrays, and data frames. This can be confusing for some analysts with a pure background in handling rectangular datasets like data (with rows for records and variables for columns). The first and often the toughest or most time-consuming task in an analytical environment for a new project is getting the data loaded into the analytical software. This chapter discusses the techniques for reading in data from various formats. The two main methods of inputting data are through the command line and a GUI, and different packages for bigger datasets (¿1 GB) are discussed. In addition, obtaining data from various types of databases is specifically mentioned. Analyzing data can have many challenges associated with it. In the case of business analytics data, these challenges or constraints can have a marked effect on the quality and timeliness of the analysis as well as the expected versus actual payoff from the analytical results.