Read data from various types of text files and Stata data setsManage your data through transformations, recodes, and combining data sets from both the add-cases and add-variables approaches and restructuring data from wide to long formats and vice versaCreate publication quality graphsPerform the basic types of analyses to measure strength of association and group differences and be able to know where to turn to cover much more complex methods
Installing and Updating R.- Running R.- Help and Documentation.- Programming Language Basics.- Data Acquisition.- Selecting Variables.- Selecting Observations.- Selecting Variables and Observations.- Data Management.- Enhancing Your Output.- Generating Data.- Managing Your Files and Workspace.- Graphics Overview.- Traditional Graphics.- Graphics with ggplot2.- Statistics.- Conclusion.
Stata is the most flexible and extensible data analysis package available from a commercial vendor. R is a similarly flexible free and open source package for data analysis, with over 3,000 add-on packages available. This book shows you how to extend the power of Stata through the use of R. It introduces R using Stata terminology with which you are already familiar. It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches. When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses.
A glossary defines over 50 R terms using Stata jargon and again using more formal R terminology. The table of contents and index allow you to find equivalent R functions by looking up Stata commands and vice versa. The example programs and practice datasets for both R and Stata are available for download.
Robert A. Muenchen is the author of the book, R for SAS and SPSS Users, and is a consulting statistician with 29 years of experience. He has served on the advisory boards of SAS Institute, SPSS Inc., and the Statistical Graphics Corporation. He currently manages Research Computing Support at The University of Tennessee.
Joseph M. Hilbe is Solar System Ambassador with NASA/Jet Propulsion Laboratory, California Institute of Technology, an adjunct professor of statistics at Arizona State, and emeritus professor at the University of Hawaii. He is a Fellow of the American Statistical Association and elected member of the International Statistical Institute. Hilbe was the first editor of the Stata Technical Bulletin, (later named the Stata Journal) and is author of a number of textbooks, including Logistic Regression Models and Negative Binomial Regression.
From the reviews:
"The goal of the authors `is to help you translate what you know about Stata into a working knowledge of R as quickly and easily as possible´ ... . the book is apparently aimed at someone who wishes to switch from Stata to R. ... many chapters include Stata code and R code to do exactly the same thing (particularly in the data manipulation chapters) and these are helpful.” (Richard Goldstein, Technometrics, Vol. 53 (4), November, 2011)
When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses.A glossary defines over 50 R terms using Stata jargon and again using more formal R terminology.
Stata and R are two very flexible data analysis packages. This book details how to extend the power of Stata through the use of R. It steps through more than thirty packages written in both languages, comparing and contrasting their different approaches.
Introduction.- Installing and Updating R.- Running R.- Help and Documentation.- Programming Language Basics.- Data Acquisition.- Selecting Variables.- Selecting Observations.- Selecting Variables and Observations.- Data Management.- Enhancing Your Output.- Generating Data.- Managing Your Files and Workspace.- Graphics Overview.- Traditional Graphics.- Graphics with ggplot2.- Statistics.- Conclusion.
From the reviews:
"The goal of the authors 'is to help you translate what you know about Stata into a working knowledge of R as quickly and easily as possible' ... . the book is apparently aimed at someone who wishes to switch from Stata to R. ... many chapters include Stata code and R code to do exactly the same thing (particularly in the data manipulation chapters) and these are helpful." (Richard Goldstein, Technometrics, Vol. 53 (4), November, 2011)
Joseph M. Hilbe, is Chair of both the International Astrostatistics Network, and ISI astrostatistics committee. ISI is the International Statistical Institute, the world association of statisticians. Prof. Hilbe is also editor-in-chief of the Springer Series in Astrostatistics and chaired the World Statistics Congress invited and special topics sessions from which eight of the book's monographs originated. An elected Fellow of the American Statistical Association and Elected Member of the ISI, Professor Hilbe is author of several leading texts in statistical modeling.
Inhaltsverzeichnis
Installing and Updating R.- Running R.- Help and Documentation.- Programming Language Basics.- Data Acquisition.- Selecting Variables.- Selecting Observations.- Selecting Variables and Observations.- Data Management.- Enhancing Your Output.- Generating Data.- Managing Your Files and Workspace.- Graphics Overview.- Traditional Graphics.- Graphics with ggplot2.- Statistics.- Conclusion.
Klappentext
Stata is the most flexible and extensible data analysis package available from a commercial vendor. R is a similarly flexible free and open source package for data analysis, with over 3,000 add-on packages available. This book shows you how to extend the power of Stata through the use of R. It introduces R using Stata terminology with which you are already familiar. It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches. When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses.
A glossary defines over 50 R terms using Stata jargon and again using more formal R terminology. The table of contents and index allow you to find equivalent R functions by looking up Stata commands and vice versa. The example programs and practice datasets for both R and Stata are available for download.
Read data from various types of text files and Stata data sets
Manage your data through transformations, recodes, and combining data sets from both the add-cases and add-variables approaches and restructuring data from wide to long formats and vice versa
Create publication quality graphs
Perform the basic types of analyses to measure strength of association and group differences and be able to know where to turn to cover much more complex methods
Includes supplementary material: sn.pub/extras
