Difference between revisions of "SMHS LinearModeling StatsSoftware"
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This section briefly describes the pros and cons of different statistical software platforms. | This section briefly describes the pros and cons of different statistical software platforms. | ||
| − | [[ | + | <center> |
| + | {| class="wikitable" style="text-align:left; width:99%" border="1" | ||
| + | |- | ||
| + | !Statistical Software||Advantages||Disadvantages | ||
| + | |- | ||
| + | | [http://r-project.org 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 | ||
| + | |- | ||
| + | | [http://www.sas.com SAS] || | ||
| + | * Large datasets | ||
| + | * Commonly used in business & Government | ||
| + | || | ||
| + | * Expensive | ||
| + | * Somewhat dated programming language | ||
| + | * Expensive/proprietary | ||
| + | |- | ||
| + | | [http://www.stata.com Stata] || | ||
| + | * Easy statistical analyses | ||
| + | || | ||
| + | * Mostly classical stats | ||
| + | |- | ||
| + | | [http://www.ibm.com/analytics/us/en/technology/spss SPSS] || | ||
| + | * Appropriate for beginners | ||
| + | * Simple interfaces | ||
| + | || | ||
| + | * weak in more cutting edge statistical procedures lacking in robust methods and survey methods | ||
| + | |- | ||
| + | | colspan=3| More comparisons are available online: [http://www.ats.ucla.edu/stat/mult_pkg/compare_packages.htm UCLA/ATS] and [https://en.wikipedia.org/wiki/Comparison_of_statistical_packages Wikipedia]. | ||
| + | |} | ||
| + | </center> | ||
<center> | <center> | ||
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|2011||14600||32100||32000 | |2011||14600||32100||32000 | ||
|} | |} | ||
| + | </CENTER> | ||
require(ggplot2) | require(ggplot2) | ||
require(reshape) | require(reshape) | ||
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ggplot(data=df, aes(x=Year, y=value, colour=variable, group = variable)) + geom_line() + geom_line(size=4) + labs(x='Year', y='Citations') | ggplot(data=df, aes(x=Year, y=value, colour=variable, group = variable)) + geom_line() + geom_line(size=4) + labs(x='Year', y='Citations') | ||
| − | + | <CENTER> | |
[[Image:SMHS_LinearModeling_Fig002.png|500px]] | [[Image:SMHS_LinearModeling_Fig002.png|500px]] | ||
| − | |||
</center> | </center> | ||
| + | ===Next see=== | ||
| + | [[SMHS_LinearModeling_QC|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. | ||
Latest revision as of 12:19, 21 May 2016
SMHS Linear Modeling - Statistical Software
This section briefly describes the pros and cons of different statistical software platforms.
| Statistical Software | Advantages | Disadvantages |
|---|---|---|
| R |
|
|
| SAS |
|
|
| Stata |
|
|
| SPSS |
|
|
| 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.
- SOCR Home page: http://www.socr.umich.edu
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