"R"의 두 판 사이의 차이

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===위키데이터===
 
===위키데이터===
 
* ID :  [https://www.wikidata.org/wiki/Q206904 Q206904]
 
* ID :  [https://www.wikidata.org/wiki/Q206904 Q206904]
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===Spacy 패턴 목록===
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* [{'LEMMA': 'r'}]
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* [{'LOWER': 'gnu'}, {'LEMMA': 'r'}]
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* [{'LOWER': 'gnu'}, {'LEMMA': 'S'}]
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* [{'LOWER': 'the'}, {'LOWER': 'r'}, {'LOWER': 'project'}, {'LOWER': 'for'}, {'LOWER': 'statistical'}, {'LEMMA': 'computing'}]
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* [{'LOWER': 'r'}, {'LEMMA': 'language'}]

2021년 2월 17일 (수) 01:14 기준 최신판

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말뭉치

  1. The R language is an open source environment for statistical computing and graphics, and runs on a wide variety of computing platforms.[1]
  2. The R language has enjoyed significant growth, and now supports over 2 million users.[1]
  3. A broad range of industries have adopted the R language, including biotech, finance, research and high technology industries.[1]
  4. The R language is often integrated into third party analysis, visualization and reporting applications.[1]
  5. R is not the only language that you can use for statistical computing and graphics.[2]
  6. The choice between R vs Python also depends on what you are trying to accomplish with your code.[2]
  7. If you are trying to analyze a dataset and present the findings in a research paper, then R is probably a better choice.[2]
  8. But R and Python are gaining momentum in the enterprise space and companies are also trying to move towards open-source technologies.[2]
  9. One of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.[3]
  10. Data Analysts Captivated by R's Power R is used by majority of academic statisticians.[4]
  11. R has the best help resources both online (just google any issue/question) and using help(...), e.g. help(lm).[4]
  12. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data.[5]
  13. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website.[5]
  14. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data.[5]
  15. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis.[5]
  16. Who uses R?[6]
  17. Google: There are more than 500 R users at Google, according to David Smith at Revolution Analytics, doing tasks such as making online advertising more effective.[6]
  18. R-Studio can recognize all RAID parameters for RAID 5 and 6.[7]
  19. NUIT Research Computing sponsors a university-wide R User Group.[8]
  20. The group welcomes members of the Northwestern community using, or interested in using, the R statistical computing environment.[8]
  21. Go to the Comprehensive R Archive Network (CRAN), which is mirrored on dozens of servers around the world, and choose the location closest to you.[9]
  22. Click the “base” link and then click the “Download R…” link on the following page.[9]
  23. Rstudio should be able to detect your latest installed R version.[9]
  24. Research Software Support can only provide limited support for programs such as R whose binaries and source originate offsite.[9]
  25. The statistical software R has come into prominence due to its flexibility as an efficient language that builds a bridge between software development and data analysis.[10]
  26. using some combination of RMarkdown, R and/or Shiny.[10]
  27. The statistical computing language R has become commonplace for many applications in industry, government and academia.[10]
  28. https://cran.r-project.org/ While R certainly can be used “as is” for many purposes, we strongly recommend using an IDE called RStudio.[10]
  29. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.[11]
  30. Its interface is organized so that the user can clearly view graphs, data tables, R code, and output all at the same time.[12]
  31. What file types are typically associated with R?[12]
  32. For a fuller description of R, see What is R?[13]
  33. With R, it is easier to document, reuse, and reproduce all the steps of your statistical analysis, compared to other statistical packages.[13]
  34. Packages are collections of R functions, data and code written by a very activity community of R users.[13]
  35. R's Help pages include extensive documentation.[13]
  36. However, most code written in S will run successfully in the R environment.[14]
  37. R performs a wide variety of basic to advanced statistical and graphical techniques at little to no cost to the user.[14]
  38. Yes (at least for the basics), there are a number of "front ends" that have been constructed in order to make it easier for users to interact with the R statistical computing environment.[14]
  39. One such GUI is the R Commander, written by John Fox.[14]
  40. According to Comprehensive R Archive Network (CRAN), R is “GNU S,” which is similar to the S system (see SPlus).[15]
  41. You can choose to either install R locally in your personal computer or invoke it from a Research Computing Server.[15]
  42. R is used for Statistical Computing.[16]
  43. It can be downloaded from https://www.r-project.org/. R runs on a wide variety of platforms (e.g. Linux, Windows and MacOS).[16]
  44. The contents of The R Software are presented so as to be both comprehensive and easy for the reader to use.[17]
  45. Besides its application as a self-learning text, this book can support lectures on R at any level from beginner to advanced.[17]
  46. This book can serve as a textbook on R for beginners as well as more advanced users, working on Windows, MacOs or Linux OSes.[17]
  47. The last chapter in this part deals with oriented object programming as well as interfacing R with C/C++ or Fortran, and contains a section on debugging techniques.[17]
  48. R is a programming language and free software developed by Ross Ihaka and Robert Gentleman in 1993.[18]
  49. R possesses an extensive catalog of statistical and graphical methods.[18]
  50. If we break down the use of R by industry, we see that academics come first.[18]
  51. The primary uses of R is and will always be, statistic, visualization, and machine learning.[18]
  52. R is very much a vehicle for newly developing methods of interactive data analysis.[19]
  53. R provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others.[19]
  54. R is easily extensible through functions and extensions, and the R community is noted for its active contributions in terms of packages.[19]
  55. Scripting in R itself is possible via littler.[19]
  56. One button deployment of Shiny applications, R Markdown reports, Jupyter Notebooks, and more.[20]
  57. In 1995 Martin Maechler convinced Ihaka and Gentleman to make R free and open-source software under the GNU General Public License.[21]
  58. Another strength of R is static graphics, which can produce publication-quality graphs, including mathematical symbols.[21]
  59. R is an interpreted language; users typically access it through a command-line interpreter.[21]
  60. Like other similar languages such as APL and MATLAB, R supports matrix arithmetic.[21]
  61. R is free software - see the R site above for the terms of use.[22]
  62. In R you can enter each line of code at the prompt in a step-by-step approach.[22]
  63. This text is not read by the R application.[22]
  64. This program can either be copied and pasted into the R command line, line by line or as an entire program.[22]

소스

  1. ↑ 1.0 1.1 1.2 1.3 R Consortium
  2. ↑ 2.0 2.1 2.2 2.3 Learn R Programming
  3. ↑ R software packages
  4. ↑ 4.0 4.1 Intro to R
  5. ↑ 5.0 5.1 5.2 5.3 Statistical and Mathematical Software
  6. ↑ 6.0 6.1 R tutorial: A beginner's guide to R programming
  7. ↑ Disk Recovery Software and Hard Drive Recovery tool for Windows, Mac, and Linux
  8. ↑ 8.0 8.1 Research Guides at Northwestern University
  9. ↑ 9.0 9.1 9.2 9.3 University of Virginia Library Research Data Services + Sciences
  10. ↑ 10.0 10.1 10.2 10.3 An Introduction to Statistical Programming Methods with R
  11. ↑ Software Licensing
  12. ↑ 12.0 12.1 Statistical & Qualitative Data Analysis Software
  13. ↑ 13.0 13.1 13.2 13.3 R: Downloading, learning about, and support for R software
  14. ↑ 14.0 14.1 14.2 14.3 What is R? How do I use it?
  15. ↑ 15.0 15.1 Software Distribution -
  16. ↑ 16.0 16.1 R Software
  17. ↑ 17.0 17.1 17.2 17.3 The R Software
  18. ↑ 18.0 18.1 18.2 18.3 What is R Programming Language? Introduction & Basics of R
  19. ↑ 19.0 19.1 19.2 19.3 R Software Environment in 2020
  20. ↑ RStudio | Open source & professional software for data science teams
  21. ↑ 21.0 21.1 21.2 21.3 R (programming language)
  22. ↑ 22.0 22.1 22.2 22.3 STAT ONLINE

메타데이터

위키데이터

Spacy 패턴 목록

  • [{'LEMMA': 'r'}]
  • [{'LOWER': 'gnu'}, {'LEMMA': 'r'}]
  • [{'LOWER': 'gnu'}, {'LEMMA': 'S'}]
  • [{'LOWER': 'the'}, {'LOWER': 'r'}, {'LOWER': 'project'}, {'LOWER': 'for'}, {'LOWER': 'statistical'}, {'LEMMA': 'computing'}]
  • [{'LOWER': 'r'}, {'LEMMA': 'language'}]
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