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![R Objects
• Almost all things in R – functions, datasets, results,
etc. – are OBJECTS.
– (graphics are written out and are not stored as objects)
• Script can be thought of as a way to make objects.
Your goal is usually to write a script that, by its end,
has created the objects (e.g., statistical results) and
graphics you need.
• Objects are classified by two criteria:
– MODE: how objects are stored in R - character, numeric,
logical, list, & function
– CLASS: how objects are treated by functions (important to
know!) - [vector], matrix, array, factor, data.frame, & 1000s of
special classes created by specific functions](https://image.slidesharecdn.com/averybriefintroductiontor-240907174606-3a76ff28/75/a_very_brief_introduction_to_r-pdfhshkdjdn-21-2048.jpg)
![R Classes
The CLASS of Z is either set by default
depending, on how it was created, or is
explicitly set by user. You can check the
objects’class and change it. It determines
how functions deal with Z. If of class “lm”,
R searches for a function fun.lm
x1 x2 x3 x4 x5 x6
1
2
3
4
5
6
7
8
R classes (to check, use class() function):
[for vectors, mode & class are same] - logical, numeric, character
[modes & class are same for these 2 as well] - function, list (when generic)
factor
matrix
array
data.frame
NOTE: If an object has two classes - c("first", "second") - R searches for a function
called fun.first and, if it finds it, applies it to the object. If no such function is found, a function called
fun.second is tried. If no class name produces a suitable function, the function fun.default is used.](https://image.slidesharecdn.com/averybriefintroductiontor-240907174606-3a76ff28/75/a_very_brief_introduction_to_r-pdfhshkdjdn-24-2048.jpg)
![R Objects
• Almost all things in R – functions, datasets, results,
etc. – are OBJECTS.
– (graphics are written out and are not stored as objects)
• Script can be thought of as a way to make objects.
Your goal is usually to write a script that, by its end,
has created the objects (e.g., statistical results) and
graphics you need.
• Objects are classified by two criteria:
– MODE: how objects are stored in R - character, numeric,
logical, list, & function
– CLASS: how objects are treated by functions (important to
know!) - [vector], matrix, array, factor, data.frame, & 1000s of
special classes created by specific functions](https://crownmelresort.com/image.slidesharecdn.com/averybriefintroductiontor-240907174606-3a76ff28/75/a_very_brief_introduction_to_r-pdfhshkdjdn-21-2048.jpg)
![R Classes
The CLASS of Z is either set by default
depending, on how it was created, or is
explicitly set by user. You can check the
objects’class and change it. It determines
how functions deal with Z. If of class “lm”,
R searches for a function fun.lm
x1 x2 x3 x4 x5 x6
1
2
3
4
5
6
7
8
R classes (to check, use class() function):
[for vectors, mode & class are same] - logical, numeric, character
[modes & class are same for these 2 as well] - function, list (when generic)
factor
matrix
array
data.frame
NOTE: If an object has two classes - c("first", "second") - R searches for a function
called fun.first and, if it finds it, applies it to the object. If no such function is found, a function called
fun.second is tried. If no class name produces a suitable function, the function fun.default is used.](https://crownmelresort.com/image.slidesharecdn.com/averybriefintroductiontor-240907174606-3a76ff28/75/a_very_brief_introduction_to_r-pdfhshkdjdn-24-2048.jpg)
a_very_brief_introduction_to_r.pdfhshkdjdn
- 1. A very briefintroduction to R - Matthew Keller Some material cribbed from: UCLA Academic Technology Services Technical Report Series (by Patrick Burns) and presentations (found online) by Bioconductor, Wolfgang Huber and Hung Chen, & various Harry Potter websites
- 2. R, And theRise of the Best Software Money Can’t Buy R programming language is a lot like magic... except instead of spells you have functions.
- 3. = muggle SPSS and SASusers are like muggles. They are limited in their ability to change their environment. They have to rely on algorithms that have been developed for them. The way they approach a problem is constrained by how SAS/SPSS employed programmers thought to approach them. And they have to pay money to use these constraining algorithms.
- 4. = wizard R users arelike wizards. They can rely on functions (spells) that have been developed for them by statistical researchers, but they can also create their own. They don’t have to pay for the use of them, and once experienced enough (like Dumbledore), they are almost unlimited in their ability to change their environment.
- 5. History of R •S: language for data analysis developed at Bell Labs circa 1976 • Licensed by AT&T/Lucent to Insightful Corp. Product name: S-plus. • R: initially written & released as an open source software by Ross Ihaka and Robert Gentleman at U Auckland during 90s (R plays on name “S”) • Since 1997: international R-core team ~15 people & 1000s of code writers and statisticians happy to share their libraries! AWESOME!
- 6. “Open source”... thatjust means I don’t have to pay for it, right? 5 •No. Much more: –Provides full access to algorithms and their implementation. Most of R is written in… R, making it easy to see what functions are actually doing. –Gives the community ability to fix bugs/extend software –Provides a forum allowing researchers to explore and expand the methods used to analyze data –Ensures that scientists around the world - and not just ones in rich countries - are the co-owners to the software tools needed to carry out research –Promotes reproducible research by providing open and accessible tools –Product of 1000s of leading experts in the fields they know best. It is CUTTING EDGE.
- 7. What is it? •Ris an interpreted computer language. –Most user-visible functions are written in R itself, calling upon a smaller set of internal primitives. –It is possible to interface procedures written in C, C+, or FORTRAN languages for efficiency, and to write additional primitives. –System commands can be called from within R •R is used for data manipulation, statistics, and graphics. It is made up of: –operators (+ - <- * %*% …) for calculations on arrays & matrices –large, coherent, integrated collection of functions –facilities for making unlimited types of publication quality graphics –user written functions & sets of functions (packages); 16000+ contributed packages so far & growing
- 8. R Advantages Disadvantages oFast andfree. oState of the art: Statistical researchers provide their methods as R packages. SPSS and SAS are years behind R! o2nd only to MATLAB for graphics. oMx, WinBugs, and other programs use R. oActive user community oExcellent for simulation, programming, computer intensive analyses, etc. oForces you to think about your analysis. oInterfaces with database storage software (SQL)
- 9. R Advantages Disadvantages oNot userfriendly @ start - steep learning curve, minimal GUI. oNo commercial support; figuring out correct methods or how to use a function on your own can be frustrating. oWorking with large datasets is limited by RAM and some operations don’t work on vectors > 2^31 length oNot natively multi-threaded (easy work- arounds though) oIn the beginning, data prep & cleaning can be messier & more mistake prone in R vs. SPSS or SAS oSome users complain about hostility on the R listserve oFast and free. oState of the art: Statistical researchers provide their methods as R packages. SPSS and SAS are years behind R! o2nd only to MATLAB for graphics. oMx, WinBugs, and other programs use R. oActive user community oExcellent for simulation, programming, computer intensive analyses, etc. oForces you to think about your analysis. oInterfaces with database storage software (SQL) oLarge vectors in 64 bit: 2^52 length
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- 12. There are over16K add-on packages (http://cran.r-project.org/src/contrib/PACKAGES.html http://www.bioconductor.org https://github.com/trending?l=r ) • This is an enormous advantage - new techniques available without delay, and they can be performed using the R language you already know. • Allows you to build a customized statistical program suited to your own needs. • Downside = as the number of packages grows, it is becoming difficult to choose the best package for your needs, & QC is an issue.
- 13. Growth of Rpackages through 2012
- 14. Will anything replaceR in the future? • Probably, but it’s hard to know when, and I’d be my bottom dollar that it will be an object oriented, open- sourced language like R. (Thus translating your R knowledge will not be tough). • One possible guess at this next language: JULIA (http://julialang.org ), which is faster than R, able to work with very large datasets, and has sensible syntax (something R sometimes lacks). It already has 473 packages.
- 15. Typical Rstudio session •Console – output & temporary input - usually unsaved • Script – tells R what to do. Save this Environment • Misc. windows, including help, files, etc.
- 16. Typical R session •R sessions are interactive Write small bits of code here and run it
- 17. Typical R session •R sessions are interactive Output appears here. Did you get what you wanted? Write small bits of code here and run it
- 18. Typical R session •R sessions are interactive Adjust your syntax here depending on this answer. Output appears here. Did you get what you wanted?
- 19. Typical R session •R sessions are interactive
- 20. Typical R session •R sessions are interactive At end, all you need to do is save your script file(s) - which can easily be rerun later.
- 21. R Objects • Almostall things in R – functions, datasets, results, etc. – are OBJECTS. – (graphics are written out and are not stored as objects) • Script can be thought of as a way to make objects. Your goal is usually to write a script that, by its end, has created the objects (e.g., statistical results) and graphics you need. • Objects are classified by two criteria: – MODE: how objects are stored in R - character, numeric, logical, list, & function – CLASS: how objects are treated by functions (important to know!) - [vector], matrix, array, factor, data.frame, & 1000s of special classes created by specific functions
- 22. R Objects x1 x2x3 x4 x5 x6 1 2 3 4 5 6 7 8 Z <-
- 23. R Objects The MODEof Z is determined automatically by the types of things stored in Z – numbers, characters, etc. Vectors & matrices must have their values all of the same mode. Lists can be a mix of modes. x1 x2 x3 x4 x5 x6 1 2 3 4 5 6 7 8 R modes (to check, use mode() function): numeric – numbers character list – a concatenation of elements of different modes logical – TRUE/FALSE function
- 24. R Classes The CLASSof Z is either set by default depending, on how it was created, or is explicitly set by user. You can check the objects’class and change it. It determines how functions deal with Z. If of class “lm”, R searches for a function fun.lm x1 x2 x3 x4 x5 x6 1 2 3 4 5 6 7 8 R classes (to check, use class() function): [for vectors, mode & class are same] - logical, numeric, character [modes & class are same for these 2 as well] - function, list (when generic) factor matrix array data.frame NOTE: If an object has two classes - c("first", "second") - R searches for a function called fun.first and, if it finds it, applies it to the object. If no such function is found, a function called fun.second is tried. If no class name produces a suitable function, the function fun.default is used.
- 25. Learning R • Readthrough the CRAN website & intro manual • Know your objects’ modes & classes: mode(x); class(x) • Because R is interactive, errors are your friends! • ?lm gives you help on lm function. Reading help files can be very… helpful • MOST IMPORTANT - the more time you spend using R, the more comfortable you become with it. After doing your first real project in R, you won’t look back. I promise.
- 26. Recommended Book • AnR and S-PLUS Companion to Applied Regression: An excellent overview of R, not just regression in R. Highly recommended. Many of the HWs we will do were inspired by Fox’s book. If you are the type of person who likes to have a book, buy this one. $56 at Amazon.