Organized processes to clean data
R contains some standard functions for data manipulation, which can be used for data cleaning, in its base package (gsub, transform, etc.), as well as in various third-party packages, such as stringr, reshape/reshape2, and plyr/dplyr. Examples and best practices of usage for these packages and their functions are described in the following paper: http://vita.had.co.nz/papers/tidy-data.pdf.

Additionally, R offers some packages specifically focused on data cleaning and transformation:


editrules (http://cran.r-project.org/web/packages/editrules/index.html)
deducorrect (http://cran.r-project.org/web/packages/deducorrect/index.html)
StatMatch (http://cran.r-project.org/web/packages/StatMatch/index.html)
MatchIt (http://cran.r-project.org/web/packages/MatchIt/index.html)
DataCombine (http://cran.r-project.org/web/packages/DataCombine)
data.table (http://cran.r-project.org/web/packages/data.table)


A comprehensive and coherent approach to data cleaning in R, including examples and use of editrules and deducorrect packages, as well as a description of workflow (framework) of data cleaning in R, is presented in the following paper, which I highly recommend: http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf.