An Introduction to R

The simplest way to construct a data frame from scratch is to use theread.table() function to read an entire data frame from anexternal file. A data frame may for many purposes be regarded as a matrix with columnspossibly of differing modes and attributes. It may be displayed inmatrix form, and its rows and columns extracted using matrix indexingconventions. The entities R operates on are technically known as objects.Examples are vectors of numeric (real) or complex values, vectors oflogical values and vectors of character strings. These are known as“atomic” structures since their components are all of the same type,or mode, namely numeric10, complex,logical, character and raw. Character quantities and character vectors are used frequently in R,for example as plot labels.

They use C-style escape sequences, using\ as the escape character, so \ is entered and printed as\\, and inside double quotes ” is entered as \”.Other useful escape sequences are \n, newline, \t, tab and\b, backspace—see ? If the argument to var() is ann-by-p matrix the value is a p-by-p samplecovariance matrix got by regarding the rows as independentp-variate sample vectors. (alternatively, ls()) can be used to display the names of (mostof) the objects which are currently stored within R. The collectionof objects currently stored is called the workspace.

Gives the results of a least squares fit where y is the vector ofobservations and X is the design matrix. See the help facilityfor more details, and also for the follow-up function ls.diag()for, among other things, regression diagnostics. Note that a grand meanterm is automatically included and need not be included explicitly as acolumn of X. Further note that you almost always will preferusing lm(.) (see Linear models) to lsfit() forregression modelling. The result is a structure of the same length as the levelsattribute of the factor containing the results.

• Lexical scope can also be used to give functions mutable state.In the following example we show how R can be used to mimic a bankaccount.
• Other functions such as matrix() and array() are availablefor simpler and more natural looking assignments, as we shall see inThe array() function.
• Where appropriate,axes, labels and titles are automatically generated (unless you requestotherwise.) High-level plotting commands always start a new plot,erasing the current plot if necessary.
• Users of R on Windows or macOS should read the OS-specific sectionfirst, but command-line use is also supported.
• All objects in R have a class, reported by the functionclass.
• It should be noted that whereas cbind() and rbind() areconcatenation functions that respect dim attributes, the basicc() function does not, but rather clears numeric objects of alldim and dimnames attributes.

Notice that in the case of a character vector, “sorted” means sortedin alphabetical order. And vectors can be extended (by missing values) in the same way. The function is.na(x) gives a logical vector of the same size asx with value TRUE if and only if the corresponding elementin x is NA. Sets temp as a vector of the same length as x with valuesFALSE corresponding to elements of x where the conditionis not met and TRUE where it is. Command lines entered at the console are limited4 to about 4095 bytes (not characters). Most users will find r&d tax credit it necessary to interact directly with theoperating system on their computer from time to time.

• Fit the sub-model omitting ‘runs’, and compare using a formal analysisof variance.
• We achieve this by creatingthe three functions within account and then returning a listcontaining them.
• R allows simple facilities forcreating and handling arrays, and in particular the special case ofmatrices.
• The community support with the R language is very large and it works on all OS.
• The integrand is evaluated at the end points ofthe range and in the middle.
• It compiles and runs on a wide variety of UNIX platforms,Windows and MacOS.

4 The ‘…’ argument ¶

When the subclass sizes are all the same theindexing may be done implicitly and much more efficiently, as we see inthe next section. It is recommended that you should use separate working directories foranalyses conducted with R. It is quite common for objects with namesx and y to be created during an analysis. Names like thisare often meaningful in the context of a single analysis, but it can bequite hard to decide what they might be when the several analyses havebeen conducted in the same directory. R is very much a vehicle for newly developing methods of interactivedata analysis. It has developed rapidly, and has been extended by alarge collection of packages.

9 Classes, generic functions and object orientation ¶

To read an entire data frame directly, the external file will normallyhave a special form. The $ notation, such as accountants$home, for listcomponents is not always very convenient. A useful facility would besomehow to make the components of a list or data frame temporarilyvisible as variables under their component name, without the need toquote the list name explicitly each time. Recall that with vector objects as arguments the concatenation functionsimilarly joined together all arguments into a single vector structure.In this case all other attributes, such as dim attributes, arediscarded. Similarly a pairof factors defines a two way cross classification, and so on.The function table() allows frequency tables to be calculatedfrom equal length factors.

Documentation

Note, however, that such functions, or indeed variables,are not inherited by called functions in higher evaluation frames asthey would be if they were on the search path. The result of the function is a list giving not only the efficiencyfactors as the first component, but also the block and variety canonicalcontrasts, since sometimes these give additional useful qualitativeinformation. It is important to note that defaults may be arbitrary expressions, eveninvolving other arguments to the same function; they are not restrictedto be constants as in our simple example here. The classical R function lsfit() does this job quite well, andmore21.

Introduction to R

The precise rule affecting element by element mixed calculations withvectors and arrays is somewhat quirky and hard to find in thereferences. From experience we have found the following to be a reliableguide. Makes D a similar array with its data vector being the result ofthe given element-by-element operations. However the precise ruleconcerning mixed array and vector calculations has to be considered alittle more carefully.

6.2 The glm() function ¶

Compare a submodel with an outer model and produce an analysis ofvariance table. Is an operator, either + or -, implying the inclusion orexclusion of a term in the model, (the first is optional). Similarly a function .Last(), if defined, is (normally) executedat the very end of the session. Thus, the sequence in which files are executed is, Rprofile.site,the user profile, .RData and then .First(). A definitionin later files will mask definitions in earlier files. This is particularly useful for large integer arrays, where patterns arethe real interest rather than the values.

Data Visualization

We have still not finished, as the contrast scheme to be used can be setfor each term in the model using the functions contrasts andC. Is a vector or matrix, (or expression evaluating to a vector or matrix)defining the response variable(s). An experiment with two treatment factors, A and B, anderror strata determined by factor C. For example a split plotexperiment, with whole plots (and hence also subplots), determined byfactor C. Single classification analysis of variance model of y, withclasses determined by A. Multiple regression y with model matrix consisting of the matrixX as well as polynomial terms in x to degree 2.

(Thus the implicit parameterization isto contrast the response at each level with that at the first.) Forordered factors the k – 1 columns are the orthogonalpolynomials on 1, …, k, omitting the constant term. In all cases each term defines a collection of columns either to beadded to or removed from the model matrix. A 1 stands for anintercept column and is by default included in the model matrix unlessexplicitly removed. The requirements for fitting statistical models are sufficiently welldefined to make it possible to construct general tools that apply in abroad spectrum of problems. Functions may be recursive, and may themselves define functions withinthemselves.