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This site is for people who develop content or code for rFactor 2. It contains information on the various aspects of the simulation and how to extend it. It is a living document that we will keep updating and extending. We've structured the information based on the different types of extensions you can work on. For each we try to list prerequisites, both in terms of tools and knowledge, and guides that help you with the actual creation process. If you're an end user of rFactor 2, looking for more information about the simulation, please go to the Users Guide.
rFactor 2 provides developers with a platform to make all kinds of extensions. You can add your own cars that include not just new 3D models, but also completely customisable physics and sounds. The simulation can handle anything from the tiniest radio controlled cars up to big trucks and everything in between. Tracks can be added with different layouts for different types of racing, including for example karting. These tracks can support dynamic weather with special rain effects as well as day to night transitions and lighting. There are also various ways to make them come alive, with animated elements and local ambient sounds. Software developers can also extend the simulation through a plugin API that provides telemetry information, scoring as well as ways to interact with the simulation. Such plugins can be used to analyse data, control motion platforms, hook up external controllers and other peripherals and many other things.
A few weeks ago we released a rather large update to our game which adjusted the lighting calculations in various places on all content. We have now released a small update which focuses on improving the compatibility of those updates with old content, although some content will just need to be updated due to settings used.
Installing Insulation is a great way of lowering your heating an cooling costs while improving the comfort of your home. However shopping for insulation can quickly become overwhelming when you are confronted with so many complicated technical terms that you may not be familiar with. one of the main technical terms associated with insulation is R-Value.
Structures such as walls, roofs, and floors tend to be made up of multiple different layers of materials. It is important to remember to factor in each materials individual R-Value when choosing what insulation to purchase.
At the end of the day its a no brainer. The higher the R-value, the better the thermal performance of the insulation. To increase your R-Value simply apply an extra layer of insulation over your initial layer.
As mentioned above, you can always increase your R-Value simply apply an extra layer of insulation over your initial layer. However there is another way of improving thermal performance on exterior walls. You can add Insulating Sheating along with your insulation to increase your overall R-Value.
Here at Insulation4US we are actively working to help guide all customers on their Insulation needs via the use of our Guides, Dedicated Live Chat or even by phone at 786 224 0029.
If there is anything we can do to help assist you further with your Insulation needs please do not hesitate to get in contact with us.
Permission is granted to copy and distribute modified versions of thismanual under the conditions for verbatim copying, provided that theentire resulting derived work is distributed under the terms of apermission notice identical to this one.
Permission is granted to copy and distribute translations of this manualinto another language, under the above conditions for modified versions,except that this permission notice may be stated in a translationapproved by the R Core Team.
We would like to extend warm thanks to Bill Venables (and DavidSmith) for granting permission to distribute this modified version ofthe notes in this way, and for being a supporter of R from way back.
Most R novices will start with the introductory session in AppendixA. This should give some familiarity with the style of R sessionsand more importantly some instant feedback on what actually happens.
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. However, most programs written inR are essentially ephemeral, written for a single piece of dataanalysis.
The evolution of the S language is characterized by four books byJohn Chambers and coauthors. For R, the basic reference is TheNew S Language: A Programming Environment for Data Analysis andGraphics by Richard A. Becker, John M. Chambers andAllan R. Wilks. The new features of the 1991 release of Sare covered in Statistical Models in S edited by John M.Chambers and Trevor J. Hastie. The formal methods and classes of the methods package are based on those described in Programmingwith Data by John M. Chambers. See References, for precisereferences.
There are now a number of books which describe how to use R for dataanalysis and statistics, and documentation for S/S-PLUS cantypically be used with R, keeping the differences between the Simplementations in mind. See What documentation exists for R? in R FAQ.
There is an important difference in philosophy between S (and henceR) and the other main statistical systems. In S a statisticalanalysis is normally done as a series of steps, with intermediateresults being stored in objects. Thus whereas SAS and SPSS will givecopious output from a regression or discriminant analysis, R willgive minimal output and store the results in a fit object for subsequentinterrogation by further R functions.
The most convenient way to use R is at a graphics workstation runninga windowing system. This guide is aimed at users who have thisfacility. In particular we will occasionally refer to the use of Ron an X window system although the vast bulk of what is said appliesgenerally to any implementation of the R environment.
Most users will find it necessary to interact directly with theoperating system on their computer from time to time. In this guide, wemainly discuss interaction with the operating system on UNIX machines.If you are running R under Windows or macOS you will need to makesome small adjustments.
Setting up a workstation to take full advantage of the customizablefeatures of R is a straightforward if somewhat tedious procedure, andwill not be considered further here. Users in difficulty should seeklocal expert help.
At this point you will be asked whether you want to save the data fromyour R session. On some systems this will bring up a dialog box, andon others you will receive a text prompt to which you can respondyes, no or cancel (a single letter abbreviation willdo) to save the data before quitting, quit without saving, or return tothe R session. Data which is saved will be available in future Rsessions.
To use R under Windows the procedure tofollow is basically the same. Create a folder as the working directory,and set that in the Start In field in your R shortcut.Then launch R by double clicking on the icon.
Elementary commands consist of either expressions orassignments. If an expression is given as a command, it isevaluated, printed (unless specifically made invisible), and the valueis lost. An assignment also evaluates an expression and passes thevalue to a variable but the result is not automatically printed.
on second and subsequent lines and continue to read input until thecommand is syntactically complete. This prompt may be changed by theuser. We will generally omit the continuation promptand indicate continuation by simple indenting.
Under many versions of UNIX and on Windows, R provides a mechanismfor recalling and re-executing previous commands. The vertical arrowkeys on the keyboard can be used to scroll forward and backward througha command history. Once a command is located in this way, thecursor can be moved within the command using the horizontal arrow keys,and characters can be removed with the DEL key or added with theother keys. More details are provided later: see The command-line editor.
All objects created during an R session can be stored permanently ina file for use in future R sessions. At the end of each R sessionyou are given the opportunity to save all the currently availableobjects. If you indicate that you want to do this, the objects arewritten to a file called .RData6 in thecurrent directory, and the command lines used in the session are savedto a file called .Rhistory.
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 operates on named data structures. The simplest suchstructure is the numeric vector, which is a single entityconsisting of an ordered collection of numbers. To set up a vectornamed x, say, consisting of five numbers, namely 10.4, 5.6, 3.1,6.4 and 21.7, use the R command
Vectors can be used in arithmetic expressions, in which case theoperations are performed element by element. Vectors occurring in thesame expression need not all be of the same length. If they are not,the value of the expression is a vector with the same length as thelongest vector which occurs in the expression. Shorter vectors in theexpression are recycled as often as need be (perhapsfractionally) until they match the length of the longest vector. Inparticular a constant is simply repeated. So with the above assignmentsthe command
The elementary arithmetic operators are the usual +, -,*, / and ^ for raising to a power.In addition all of the common arithmetic functions are available.log, exp, sin, cos, tan, sqrt,and so on, all have their usual meaning.max and min select the largest and smallest elements of avector respectively.range is a function whose value is a vector of length two, namelyc(min(x), max(x)).length(x) is the number of elements in x,sum(x) gives the total of the elements in x,and prod(x) their product.
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