SMHS Usage - Revision history

Dinov: /* Why Use R */

2017-07-21T15:32:53Z

‎Why Use R

Dinov: /* Why Use R */

2017-07-21T15:16:02Z

‎Why Use R

Dinov: /* Why Use R */

2017-07-21T15:14:08Z

‎Why Use R

Dinov at 15:12, 21 July 2017

2017-07-21T15:12:14Z

Dinov: /* R Statistical Computing Software */ added links to SWC

2017-01-26T19:58:36Z

‎R Statistical Computing Software: added links to SWC

Dinov at 18:40, 24 June 2016

2016-06-24T18:40:42Z

Dinov: /* Software Help */

2016-04-12T12:30:47Z

‎Software Help

Dinov: /* Software Help */

2016-04-12T12:30:31Z

‎Software Help

Glenbrau: /* Scientific Methods for Health Sciences - Learning and Instructional Usage */

2015-03-18T19:18:59Z

‎Scientific Methods for Health Sciences - Learning and Instructional Usage

Dinov: /* Software help */

2014-11-20T15:03:03Z

‎Software help

@@ Line 31: / Line 31: @@
 ===Why Use R===
-There are marked differences between different types of computational environments for data wrangling, preprocessing, analytics, visualization and interpretation.  The table below provides some rough comparisons between some of the most popular data computational platforms (''higher scores'' represent better performance within the specific category, but the scales are not normalizes between categories).
+There are marked differences between different types of computational environments for data wrangling, preprocessing, analytics, visualization and interpretation.  The table below provides some rough comparisons between some of the most popular data computational platforms (''higher scores'' represent better performance within the specific category, but the scales are not normalized between categories).
 More information about [http://www.socr.umich.edu/people/dinov/2017/Spring/DSPA_HS650/notes/01_Foundation.html#1_why_use_r the rationale for using R is provided on the DSPA resources].

@@ Line 31: / Line 31: @@
 ===Why Use R===
-There are marked differences between different types of computational environments for data wrangling, preprocessing, analytics, visualization and interpretation.  The table below provides some rough comparisons between some of the most popular data computational platforms.
+There are marked differences between different types of computational environments for data wrangling, preprocessing, analytics, visualization and interpretation.  The table below provides some rough comparisons between some of the most popular data computational platforms (''higher scores'' represent better performance within the specific category, but the scales are not normalizes between categories).
 More information about [http://www.socr.umich.edu/people/dinov/2017/Spring/DSPA_HS650/notes/01_Foundation.html#1_why_use_r the rationale for using R is provided on the DSPA resources].

@@ Line 32: / Line 32: @@
 There are marked differences between different types of computational environments for data wrangling, preprocessing, analytics, visualization and interpretation.  The table below provides some rough comparisons between some of the most popular data computational platforms.
 <center>
@@ Line 54: / Line 56: @@
 | Fortran || Yes || 1315 || 0.76 || 95 || 25 || 15 || 95
 |}
 * [https://modelingguru.nasa.gov/docs/DOC-2625 NASA Comparison of Python, Julia, R, Matlab and IDL]
 * [http://sebastianraschka.com/Articles/2014_matrix_cheatsheet.html Sebastian Raschka, Numeric matrix manipulation - The cheat sheet for MATLAB, Python Nympy, R and Julia, June 2014.]
 <hr>

@@ Line 22: / Line 22: @@
 ===[http://www.r-project.org/ R Statistical Computing Software]===
 * [[SMHS_Usage_Rfundamentals|Some R fundamentals]]
-* The SOftware Carpentry FOundation provides useful [http://swcarpentry.github.io/r-novice-inflammation Programming with R] and [http://swcarpentry.github.io/r-novice-gapminder R for Reproducible Scientific Analysis] materials.
+* The Software Carpentry Foundation provides useful [http://swcarpentry.github.io/r-novice-inflammation Programming with R] and [http://swcarpentry.github.io/r-novice-gapminder R for Reproducible Scientific Analysis] materials.
 * [http://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf A very gentle stats intro using R Book (Verzani)]
 * [http://www.statmethods.net/index.html Quick-R examples]
@@ Line 29: / Line 29: @@
 * [http://www.ats.ucla.edu/stat/r/ UCLA ITS/IDRE R Resources]
 <hr>

@@ Line 22: / Line 22: @@
 ===[http://www.r-project.org/ R Statistical Computing Software]===
 * [[SMHS_Usage_Rfundamentals|Some R fundamentals]]
 * [http://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf A very gentle stats intro using R Book (Verzani)]
 * [http://www.statmethods.net/index.html Quick-R examples]

@@ Line 12: / Line 12: @@
 The novelty of the Scientific Methods for Health Sciences EBook is derived from the fact that in the context of health sciences training, it integrates mathematical foundations, statistical concepts, interactive experiments, statistical computing resources, simulations, and tools for data analysis and visualization.
-===Software Help===
+===SOCR Help===
 The SMHS Ebook uses [[SOCR|SOCR Tools]] and [http://www.r-project.org/ R] for all examples, computational demonstrations, data analytics and visualization. The following links provide useful tutorials and help with both software environments:
 * [[SOCR]]
-** [[SOCR_Help_Pages| SOCR Help]]
+* [[SOCR_Help_Pages| SOCR Help]]
-** [[SOCR_About_Pages| SOCR About]]
+* [[SOCR_About_Pages| SOCR About]]
-** [[SOCR_Activities| SOCR Activities]]
+* [[SOCR_Activities| SOCR Activities]]
-** [[SOCR_Videos| SOCR Videos]]
+* [[SOCR_Videos| SOCR Videos]]

@@ Line 26: / Line 26: @@
 ** [http://cran.r-project.org/doc/manuals/r-release/R-intro.html R project Intro]
 ** [http://www.ats.ucla.edu/stat/r/ UCLA ITS/IDRE R Resources]
-** [[SMHS_Usage|Some R fundamentals]]
+** [[SMHS_Usage_Rfundamentals|Some R fundamentals]]

@@ Line 2: / Line 2: @@
 This [[SMHS|Scientific Methods for Health Sciences EBook]] provides three types of instructional and learning materials:
-* Datasets and natural driving motivational problems.
+* Datasets and natural driving motivational problems
-* Mathematical techniques and modeling methodologies.
+* Mathematical techniques and modeling methodologies
-* Applications with interactive Graphical interfaces for statistical computing, data exploration and IT-blended instruction.
+* Applications with interactive graphical interfaces for statistical computing, data exploration and IT-blended instruction
-This EBook is not intended to be an ''one-book-fits-all-curricula'' textbook. Most instructors that use the Scientific Methods for Health Sciences Ebook may customize some of the content, develop additional applications, discuss course-specific data and expand the materials as appropriate to their curricula.
+Furthermore, all learning materials and instructional resources of this EBook are freely accessible via the Internet. This publication also includes multi-language support; at the bottom of any chapter, section or page, the reader may obtain a machine translation of all page content into a different language.
-The novelty of the Scientific Methods for Health Sciences EBook is derived from the fact that in the context of healths science training, it integrates mathematical foundations, statistical concepts, interactive experiments, statistical computing resources, simulations, and tools for data analysis and visualization.
+This EBook is not intended to be an ''one-book-fits-all-curricula'' textbook. Instructors that use the Scientific Methods for Health Sciences Ebook may customize some of the content, develop additional applications, discuss course-specific data, or even expand the materials as appropriate to their curricula.
-===Software help===
+The novelty of the Scientific Methods for Health Sciences EBook is derived from the fact that in the context of health sciences training, it integrates mathematical foundations, statistical concepts, interactive experiments, statistical computing resources, simulations, and tools for data analysis and visualization.
 The SMHS Ebook uses [[SOCR|SOCR Tools]] and [http://www.r-project.org/ R] for all examples, computational demonstrations, data analytics and visualization. The following links provide useful tutorials and help with both software environments:
 * [[SOCR]]

@@ Line 22: / Line 22: @@
 * [http://www.r-project.org/ R]
 ** [http://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf A very gentle stats intro using R Book (Verzani)]
 ** [http://www.r-tutor.com/r-introduction R-tutor Introduction]
 ** [http://cran.r-project.org/doc/manuals/r-release/R-intro.html R project Intro]