Can I use R without having to learn the details of the R language?
Yes, 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. For example, a graphical user interface (or "GUI") allows the analyst to carry out data analysis tasks by selecting items from menus and lists, rather than entering commands.
One such GUI is the R Commander, written by John Fox. The R Commander is accessed by installing and loading the Rcmdr package within R. The R Commander provides an easy-to-use, menu-based system for loading data into R, manipulating data values, performing statistical analyses, creating graphical displays, and carrying out diagnostic tests on statistical models. Documentation for the R Commander is available on John Fox's Web site and in the following paper:
Fox, John. 2005. "The R Commander: A Basic-Statistics Graphical User Interface to R." Journal of Statistical Software 14(9).
There are several other GUI systems, in addition to the R Commander, for interacting with R. A useful discussion of R and GUIs, along with a list of current GUI projects for R is available.
The advantage provided by the R Commander or another GUI is that the user does not need to learn a language in order to carry out his or her analysis. Instead, each step is taken by making one or more selections from a menu of available options. The disadvantage of interacting with the R environment through a GUI is that the course of the analysis is limited to those actions that have been programmed into the GUI. Thus, one could argue that using a GUI removes much of the flexibility that is inherent in the R environment.
In order to overcome the preceding limitation, the R Commander and most other GUIs allow the user to employ both methods of interacting with the environment within a single R session. For example, one could invoke the R Commander, and use its GUI to read the contents of an external file and create an R data frame. For many types of analyses, other features of the R Commander could be used to estimate model parameters, construct graphical displays, and so on. But, if the user wanted to carry out a task that is not available in the R Commander (e.g., a multidimensional scaling analysis), then the data frame created in the GUI could still be treated like any other currently defined R object (say as an argument to a function or the target of an assignment) on the R command line. In this manner, a user could exploit the advantages of both the GUI and the command-line interface.