To take a spreadsheet beyond what it's designed for — data presentation, summarization and simple calculations — into the world of complex data analysis can be an alluring prospect. But it can also be dangerous: consider these examples of spreadsheet errors that led to monumental financial losses, mistaken government policies, and even the wrong drugs being given to cancer patients.
The answer is to move your analysis into a computing environment specifically designed for data analysis: R. Burns Statistics provides a step by step tutorial on transitioning from spreadsheets to R: if you care about the accuracy if your analysis, or even just being able to reproduce your results again in the future, you should check it out at the link below.
Burns Statistics: A first step towards R from spreadsheets
R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...