A brief overview of the R statistical computing and programming environment is given that explains why many time series researchers in both applied and the-oretical research may find R useful. The core features of R for basic time series analysis are outlined. Some intermediate level and advanced topics in time series analysis that are supported in R are discussed such as including state-space mod-els, structural change, generalized linear models, threshold models, neural nets, co-integration, GARCH, wavelets, and stochastic differential equations. Numer-ous examples of beautiful graphs constructed using R for time series are shown. R code for reproducing all the graphs and tables is given on my homepage.