R-help, and the various R Mailing Lists or SIGs, welcome any questions (provided they conform to the Posting Guide). Answers generally come within one or two days.
Quick-R gives a gentle overview of most of the basic R syntax for people coming from SAS, SPSS, or Stata. Stack Overflow also provides strong support for R questions. Additionally, rseek.org provides a custom Google search that facilitates queries related to R code, packages, articles, etc., while crantastic.org features useful reviews of current packages.
If you're looking for visualization ideas, visit the R Graph gallery and the Learn R blog, both of which feature a wide variety of plots and the accompanying code. The R Graphical Manual also provides a visualization of all CRAN R package example plots, and is searchable by topic. The Cookbook for R page provides multiple examples and recipes for plotting data (mostly using ggplot2) plus some additional information on using R.
To add the excellent resources list above, I (@michelle) have found the R Tutorial web site to be helpful. Also I have R bloggers as a feed. That has lots of useful posts from various bloggers and is an excellent way to keep up with new packages and new ways of using existing packages. If you're coming from SAS or SPSS, check out R for SAS and SPSS Users; there is a book with more information in it. An equivalent book for Stata users coming to R is R for Stata Users. Nice introductory tutorial can be found on R Tutorials page - it covers introduction to basics of R, using statistical tools such as t-tests, ANOVA, regression and other topics.
There are also some resources listed on our site here: Resources for learning R, and on our R tag wiki.
For learning on more advanced topics in R programming the best resource that is available online is Advanced R site by Hadley Wickham. It is an online version of book under the same title. Another resource covering programming issues is The R Inferno by Patrick Burns available as pdf file. Those two cover topics that are negligible to most people that use R for statistics but can be crucial if you do actual programming in R and can be helpful in understanding how R works 'under the hood'. If trying to understand better how some R function works, you can always check their source code as R is open-source.