R first appeared in the 1990s in New Zealand, as an implementation of the S statistical programming language. R was written by statisticians, with statistics and data in mind. It is a perfect choice for data analysis, statistical modeling, simulation
R allows you to manipulate (e.g., subset, recode, merge) data quickly. Some R packages have been designed specifically for these purposes, e.g.,
Anyone (including you) can contribute packages to the community to improve its functionality. The number of R packages contributed to the community is increasing at a rapid rate. Chances are, if there’s an analysis you need to do, you will find R packages to do it.
R has advanced graphics capabilities (to see examples go here and here). You can create beautiful graphics using R packages. In general, people like to digest and understand statistics visually, and R provides great tools for achieving exactly this.
Many tools have restrictions on how large your dataset can be. Processing large datasets, even when it does not technically exceed the maximum size of the tool you're using, can be a rather slow process (especially after you add tabs, formulas, and references). R supports larger dataset and supports big data.
R has features that make it much easier to reproduce the findings of your analysis, which is important for detecting errors.
R scripting language provides an easy way to automate processes. It can save you loads of time, especially when you plan to re-run the same analysis multiple times (e.g., a project being conducted on a recurring basis).
Want to learn to apply the porwer of R into your fieldwork/marketing research projects? Sign in for upcoming 3-day training designed for specific needs of marketing researchrs.
Nebu BV
Pakhuisplein 42V
1531 MZ Wormer
The Netherlands
T +31 251 311 413
E nebu@nebu.com
UK Sales: +44 33 080 87820
US Sales: +1 347 708 1633
Submit a commment