Data Science Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface Data Science Introducing R R Constructs R Tasks R Strings R Read, Write, and Create Data Template Data Exploration Data Wrangling Data Visualisation Statistics ML Template ML Scenarios ML Activities ML Applications ML Algorithms Cluster Analysis Decision Trees Computer Vision Graph Data Privacy Literate Data Science Coding with Style Resources Bibliography Index

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Chapter: R Read, Write, and Create

20200104 Data is available in an enormous variety of formats and stored in a diverse number of locations, including our own data stores, local disks, cloud stores, database systems, and from the Internet sites like http://data.gov, http://data.gov.uk/, and http://data.gov.au/. A major task we face as Data Scientists is ingesting that data into R in order for us to perform our analyses. R provides extensive capabilities for reading/importing/ingesting data and for writing/exporting data. Here we explore the options, including a simple and widely used format known as comma separated values (CSV) files. A variation of this is the tab separated values, or indeed other special characters used to separate the columns of the data. But data comes in an amazing variety of formats, including many proprietary formats that need special effort to decipher. R supports almost every known format through many different packages. This chapter introduces the numerous options available. Also included is a guide to creating your own random dataset for testing ideas and building systems where the actual data may not be so readily available due to privacy, for example.