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Mastering Data analysis with R

You're reading from   Mastering Data analysis with R Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781783982028
Length 396 pages
Edition 1st Edition
Languages
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Author (1):
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Gergely Daróczi Gergely Daróczi
Author Profile Icon Gergely Daróczi
Gergely Daróczi
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Table of Contents (17) Chapters Close

Preface 1. Hello, Data! 2. Getting Data from the Web FREE CHAPTER 3. Filtering and Summarizing Data 4. Restructuring Data 5. Building Models (authored by Renata Nemeth and Gergely Toth) 6. Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth) 7. Unstructured Data 8. Polishing Data 9. From Big to Small Data 10. Classification and Clustering 11. Social Network Analysis of the R Ecosystem 12. Analyzing Time-series 13. Data Around Us 14. Analyzing the R Community A. References Index

Importing data from other statistical systems

In a recent academic project, where my task was to implement some financial models in R, I got the demo dataset to be analyzed as Stata dta files. Working as a contractor at the university, without access to any Stata installations, it might have been problematic to read the binary file format of another statistical software, but as the dta file format is documented and the specification is publicly available at http://www.stata.com/help.cgi?dta, some members of the Core R Team have already implemented an R parser in the form of the read.dta function in the foreign package.

To this end, loading (and often writing) Stata—or for example SPSS, SAS, Weka, Minitab, Octave, or dBase files—just cannot be easier in R. Please see the complete list of supported file formats and examples in the package documentation or in the R Data Import/Export manual: http://cran.r-project.org/doc/manuals/r-release/R-data.html#Importing-from-other-statistical-systems.

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