SMHS LinearModeling StatsSoftware

SMHS Linear Modeling - Statistical Software

This section briefly describes the pros and cons of different statistical software platforms.

Statistical Software	Advantages	Disadvantages
R	R is actively maintained (100,000 developers, 15K packages) Excellent connectivity to various types of data and other systems Versatile for solving problems in many domains It’s free, open-source code Anybody can access/review/extend the source code R is very stable and reliable If you change or redistribute the R source code, you have to make those changes available for anybody else to use R runs anywhere (platform agnostic) Extensibility: R supports extensions, e.g., for data manipulation, statistical modeling, and graphics Active and engaged community supports R Unparalleled question-and-answer (Q&A) websites R connects with other languages(Java/C/JavaScript/Python/Fortran) & database systems, and other programs, SAS, SPSS, etc. Other packages have add-ons to connect with R. SPSS has incorporated a link to R, and SAS has protocols to move data and graphics between the two packages	Mostly scripting language Steeper learning curve
SAS	Large datasets Commonly used in business & Government	Expensive Somewhat dated programming language Expensive/proprietary
Stata	Easy statistical analyses	Mostly classical stats
SPSS	Appropriate for beginners Simple interfaces	weak in more cutting edge statistical procedures lacking in robust methods and survey methods
More comparisons are available online: UCLA/ATS and Wikipedia.

GoogleScholar Research Article Pubs

Year	R	SAS	SPSS
1995	8	8620	6450
1996	2	8670	7600
1997	6	10100	9930
1998	13	10900	14300
1999	26	12500	24300
2000	51	16800	42300
2001	133	22700	68400
2002	286	28100	88400
2003	627	40300	78600
2004	1180	51400	137000
2005	2180	58500	147000
2006	3430	64400	142000
2007	5060	62700	131000
2008	6960	59800	116000
2009	9220	52800	61400
2010	11300	43000	44500
2011	14600	32100	32000

require(ggplot2)
require(reshape)
Data_R_SAS_SPSS_Pubs <-read.csv('https://umich.instructure.com/files/522067/download?download_frd=1', header=T)
df <- data.frame(Data_R_SAS_SPSS_Pubs) 
# convert to long format
df <- melt(df ,  id.vars = 'Year', variable.name = 'Time') 
ggplot(data=df, aes(x=Year, y=value, colour=variable, group = variable)) +  geom_line() + geom_line(size=4) + labs(x='Year', y='Citations')

Next see

Quality Control section for a discussion of data Quality Control (QC) and Quality Assurance (QA) which represent important components of data-driven modeling, analytics and visualization.

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• SOCR Home page: http://www.socr.umich.edu

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