cd allows you to set up and move through a hierarchically-organized set of Rworkspaces, each corresponding to a directory. While working at any level of the hierarchy, all higher levels are attached on the search path, so you can see objects in the "parents". You can easily switch between workspaces in the same session, you can move objects around in the hierarchy, and you can do several hierarchy-wide things such as searching, even on parts of the hierarchy that aren't currently attached.cd()
cd(to)
cd(to, execute.First = TRUE, execute.Last = TRUE).First.task code be executed on attachment? Yes, unless there's a bug in it..Last.task code be executed on detachment? Yes, unless there's a bug in it.options() can be set, as follows. Remember to put these into your .First function, too.
write.mvb.tasks=TRUE causes a sourceable text representation of the tasks object to be maintained in each directory, in the file tasks.r. This helps in case you accidentally wipe out the .Rdata file and lose track of where the child tasks live. To create these text representations for the first time throughout the hierarchy, call cd.write.mvb.tasks(0). You need to put the the options call in your .First.
options(abbreviate.cdprompt(n)) controls the length of the prompt string. Only the first n characters of all ancestral task names will be shown. For example, n=1 would replace the prompt long.task.name/data/funcs> with l/d/funcs>.cd helps with all such problems, by letting you organize all your projects into a single tree structure, regardless of where they are stored on disk. Each workspace is referred to (for historical reasons) as a "task".
Note that there is a basic choice when working with R: do you keep everything you write in a text file which you source every time you start; or do you store all the objects in a workspace as a binary image in a .Rdata file, and rely on save and load? [Hybrids are possible, too.] Some people prefer the text-based approach, but others prefer the binary image approach (my own reasons being mainly that binary images let me organize my work across tasks more easily, and that repeated text-sourcing is much too slow when lengthy analyses or data extractions are involved). The cd system is really geared to the binary image model and, before cd moves to a new task, either up or down the hierarchy, the current workspace is automatically saved to a binary image. Nevertheless, I don't think cd is incompatible with other ways of working, as long as the .Rdata file (actually the tasks object) is not destroyed from session to session. At any rate, some people who work by sourceing large code files still seem to find cd useful. With the .Rdata-only approach, it is highly advisable to have some way of keeping separate text backups, at least of function code. The fixr editing system is geared up to this, and I presume other systems such as ESS are too.
To use the cd system, you will need to start Rin the same workspace every time. This will become your ROOT or home task, from which all other tasks stem. There need not be much in this workspace except for an object called tasks (see below), though you can use it for shared functions that you don't want to organize into a package or quasi-package. From the ROOT task, your first action in a new Rsession will normally be to use cd to switch to a real task. The cd command is used both to switch between existing tasks, and to create new ones.
To set yourself up for working with cd, it's probably a good idea to make the ROOT task a completely new blank workspace. [In MS-Windows, I'd suggest putting it near the top of the disk directory structure, too.] Start Rin this workspace, type library( mvbutils), and then start linking your existing projects into the task hierarchy. To link in a project, just type cd() and a menu will appear. The first time, there will be only one option: "CREATE NEW TASK". Select it (or type 0 to quit if you are feeling nervous), and you will be prompted for a "task name", by which Rwill always subsequently refer to the task. Keep the name short; it doesn't have to be related to the location of the disk directory where the .Rdata lives. Avoid spaces and weird characters-- use periods as separators. Task names are case-sensitive. Next, you'll be asked which disk directory this task refers to. By default, cd expects that you are creating a new task, and therefore suggests putting the directory immediately below the current task directory. However, if you are linking in an existing project, you'll need to supply the directory name. Next, you'll be returned to the Rcommand prompt-- but the prompt will have changed, so that the ">" is preceded by the task name. If you type search(), you'll see your ROOT task in position 2, below .GlobalEnv as usual. Despite the name, though, the new .GlobalEnv contains the project you've just linked, and if you type ls(), you should see some familiar objects. Now type cd(0) to move back to the ROOT task (note the changed prompt), type search() and ls() again to orient yourself, and proceed as before to link the rest of your pre-existing tasks into the hierarchy. When you type cd(), the menu will have more choices. If you select an existing task rather than creating a new one, you will switch straightaway to that workspace; watch the prompt.
Once you have a hierarchy set up, you can switch the current workspace within the hierarchy by calling e.g. cd(existing.task) (note the lack of quotes), or by calling cd() and picking off the menu. You can move through several levels of the hierarchy at once, using a path specifier such as cd(mytask/data/funcs) or cd(../child.of.sibling). Path specifiers are just like Unix or DOS disk paths with "/" as the separator, so that "." means "current task" and ".." means "parent". However, the character 0 must be used to denote the ROOT task, so that you have to type cd(0/different.task) rather than cd(/different.task). You can display the entire hiearchy by calling cdtree(0), or graphically via plot( cdtree( 0)).
When you first set up your task hierarchy, you'll also want to create or modify the .First function in your ROOT task. At a minimum, this should call library( mvbutils), but you may also want to set some options controlling the behaviour of cd (see the OPTIONS section). If you use other features of mvbutils such as the function-editing interface in fixr, there will be further options to be set in .First.
You can create a fully hierarchical structure, with subtasks within subtasks within tasks, etc. Even if your projects don't naturally look like this, you may find the facility useful. When I create a new task, I tend to start with just one level of hierarchy, containing data, function code, and results. When this gets unspeakably messy, I often create one (or more) subtasks, usually putting the basic data at the top level, and functions and results at the lower level. Apart from tidiness, this provides some degree of protection against overwriting the original data. And when even this gets too messy-- in one task, I have more than 150 functions, and it is very easy to generate 100s of analysis results-- I create another level, keeping "established" functions at the second tier and using the third tier for temporary workspace and results. There are no hard-and-fast rules here, of course, and different people use Rin very different ways.
A task can have .First.task and/or .Last.task functions, which get called immediately after cding into the task from its parent, or immediately before cding back to its parent, respectively (see ARGUMENTS). These can be useful for dynamic loading, attaching & detaching datasets, etc., and facilitate the use of tasks as informal libraries/packages (see also flatdoc and mlibrary).
Formal and semi-formal packages can also be tasks. For example, my debug and mlibrary when I start R(in my .First), but are also linked into my task hierarchy. This lets me cd(debug) when I intend to create or remove a number of functions in debug. It's also useful to be able to have subtasks below such packages, e.g. so that probably-obsolete functions can be moved there pending a final decision. When you cd to something that's already attached as a package, search() will show e.g. "PLACEHOLDER:debug" in the position formerly occupied by the package; when you cd back up, the package will return to the placeholder slot. mlibrary is not fussy about package requirements; you can use mlibrary to attach any task as a package, without going through the complex package-building procedures required by library. You can use flatdoc to add informal documentation to the help system. If & when you decide to turn an informal package into a proper CRAN-able Rpackage, you might find formalize.package useful.move, task.home, cdtree, cdfind, cditerate, cd.change.all.paths, cd.write.mvb.tasks, cdprompt, fixr