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Introduction to R

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An Introduction to Data Analysis in R

Part of the book series: Use R! ((USE R))

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Abstract

From Business Intelligence to advanced statistics applications, professionals are expected to access and manipulate large datasets, and R is the perfect tool for it. In this introductory chapter, we explain the principles of programming and the position of R in data science today. Then, a beginners level course on R starts introducing the main data types of this superior programming language. Examples and exercises are included to provide a hands-on training, guaranteeing the users control and understanding of R capabilities. Then, two main generic programming tools are introduced: control structures and functions. This will allow us to manipulate our datasets and generate all sorts of values and conclusions. In addition, this chapter includes specific R operators that highly simplify the use of R and enhance its capabilities.

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Notes

  1. 1.

    This algorithm is usually called long division method in US schools and many other places.

  2. 2.

    Assembly languages are often abbreviated asm.

  3. 3.

    Here, with machine we refer both to hardware, the architecture of the computer, and software, the operating system.

  4. 4.

    FORTRAN is the acronym of FORmula TRANslation.

  5. 5.

    Latest FORTRAN version was released on November 28, 2018, known as FORTRAN 2018, see https://wg5-fortran.org/f2018.html.

  6. 6.

    Message, by Peter Dalgaard, of the first beta version released https://stat.ethz.ch/pipermail/r-announce/2000/000127.html.

  7. 7.

    GNU is a recursive acronym for “GNU’s Not Unix.”

  8. 8.

    https://www.gnu.org.

  9. 9.

    Upon completion of this book, last stable versions are R 3.6.2, called Dark and Stormy Night, released on December 12, 2019.

  10. 10.

    https://www.r-project.org.

  11. 11.

    Visit https://stat.ethz.ch/pipermail/r-announce/1997/000001.html for the announcement by Kurt Hornik of the opening of CRAN site.

  12. 12.

    https://cran.r-project.org/.

  13. 13.

    In order to know the exact amount of available packages at a certain moment one can type nrow( available.packages( ) ) on the R console. .

  14. 14.

    https://stat.ethz.ch/mailman/listinfo/r-help.

  15. 15.

    https://stackoverflow.com/questions/tagged/r.

  16. 16.

    https://www.r-project.org/conferences.html.

  17. 17.

    https://journal.r-project.org.

  18. 18.

    Computational costs are defined as the amount of time and memory needed to run an algorithm.

  19. 19.

    Upon this book completion, the CRAN package repository features 15,368 available packages comprehending many possible extensions of the R core library.

  20. 20.

    https://www.tiobe.com/tiobe-index/.

  21. 21.

    https://julialang.org.

  22. 22.

    https://julialang.org/blog/2012/02/why-we-created-julia Ⓒ 2020 JuliaLang.org contributors.

  23. 23.

    https://github.com/ThinkR-open/companies-using-r.

  24. 24.

    https://www.rstudio.com.

  25. 25.

    Announcement of RStudio release on February 28, 2011, https://blog.rstudio.com/2011/02/28/rstudio-new-open-source-ide-for-r/. Upon completion of this book, last stable version released is RStudio 1.2.5033, on December 3, 2019.

  26. 26.

    For example, Oracle, OLBC, Spark, and many others.

  27. 27.

    A very useful shortcut is to use Control+ Enter in PC, or Command+ Enter in Mac, to run each code line.

  28. 28.

    Have a look at the menu Code in RStudio for different run options.

  29. 29.

    The package ggplot2 will be called in Sect. 4.2 to create ellaborated plots in R.

  30. 30.

    When calling library( ) quotation marks are not needed.

  31. 31.

    https://rmarkdown.rstudio.com.

  32. 32.

    We can access this list at https://cran.r-project.org/web/views/.

  33. 33.

    https://www.r-project.org/mail.html.

  34. 34.

    https://stackoverflow.com/.

  35. 35.

    Learn how to make your questions reading https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example and https://www.r-project.org/posting-guide.html.

  36. 36.

    The acronym RAM stands for Random-access memory.

  37. 37.

    The same result is achieved by typing assign( "x",4) .

  38. 38.

    A Boolean expression is a data type whose possible values are either TRUE or FALSE . It is named after the mathematician George Bool.

  39. 39.

    For simplicity, logical values can also be written as T or F. We will use the full word or the initial letter indistinctively throughout the book.

  40. 40.

    In Sect. 5.1 we will see how to remove these NAs when performing calculations over vectors containing them, with the argument na.rm .

  41. 41.

    The conversion between Celsius degrees ∘C and Fahrenheit degrees ∘F is ∘F = 1.8 ×∘C + 32. To go from Celsius to Kelvin we just shift the zero in the scale to 273.15.

  42. 42.

    See Sect. 5.1.1 for an explanation of the arithmetic mean and other statistical measures.

  43. 43.

    summary( ) is one of the most robust and powerful commands in R. Almost all kind of structures can be passed as an argument of this command and it will usually provide plenty of information.

  44. 44.

    Everything can be ordered, alphabetically for example, but nominal scales have no meaningful order related to anything intrinsic to the nature of the variable.

  45. 45.

    Thanks to the combination command c( ) , if data are of different types, all of them are stored in the most general type admitting all kinds appearing in the structure.

  46. 46.

    Unlike matrices, if the column lengths to be included in the data frame are not the same, the function returns an error and a data frame filling the gaps is not created.

  47. 47.

    In Spain and other countries, two family names are used, preserving both the last name of the father and the mother.

  48. 48.

    When applying as.data.frame, unless otherwise specified, the default names of the variables are V 1, V 2, etc., meaning variable 1, variable 2, etc.

  49. 49.

    Some R packages are specially designed for dealing with datasets, such as tibble and data.table, we will explore the later one in Chap. 3.

  50. 50.

    Technically speaking, when using 1 : 10, R is internally doing a loop, so the previous code could be simplified to print( 1 : 10) but it is valid as a first and easy example.

  51. 51.

    Note that, in the example, the logical evaluation of the expression 3!=3 is FALSE, whereas being or not a logical expression is TRUE. Try the command is.logical( "Hello") to see the difference.

  52. 52.

    Observe that f(1) is undefined, because we are dividing by zero. Despite this, R outputs Inf recovering the limits of f when x approaches 0.

  53. 53.

    A richer function is already implemented in the R base library under the name mat.or.vec( ) .

  54. 54.

    The computational advantages and disadvantages of using or not return( ) are beyond the scope of this book.

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Author information

Authors and Affiliations

  1. Department of Mathematics Applied to ICT, Technical University of Madrid, Madrid, Spain

    Alfonso Zamora Saiz

  2. Department of Applied Mathematics and Statistics, Universidad San Pablo CEU, Madrid, Spain

    Carlos Quesada González & Diego Mondéjar Ruiz

  3. eDreams ODIGEO, Barcelona, Spain

    Lluís Hurtado Gil

Authors
  1. Alfonso Zamora Saiz
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  2. Carlos Quesada González
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  3. Lluís Hurtado Gil
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  4. Diego Mondéjar Ruiz
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Zamora Saiz, A., Quesada González, C., Hurtado Gil, L., Mondéjar Ruiz, D. (2020). Introduction to R . In: An Introduction to Data Analysis in R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-030-48997-7_2

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