![]() And these packages are freely available too. Because R is open and so widely used, it has become a standard tool in statistics, so many people write their own packages that extend the system. Packages are specific units of code that add more functionality to R. When you download and install R, you get all the basic “packages,” and those are very powerful on their own. Related to the previous point, R is highly extensible. If you are interested in science, it is highly likely that you will need to learn the basics of computer modeling, or you may want to develop useful apps, or automatize tasks in your business, or conduct surveys online, or communicate information through data visualization. As you get better at using R for data analysis, you are also learning to program. Because R is, basically, a programming language, it can be used for a variety of things, not just statistics. We include links to that information down below. In addition, there are many great websites, videos, and tutorials to get you started (and to acquire advanced knowledge) with R, and to explain you how to do statistics with R. ![]() So, R is free, and you can keep updating it without any cost and have it always available in your computer, regardless of the university or company in which you are. R is an open-source free programming software. In that case, you will be better off by using a programming language like R because that gives you the ultimate control, flexibility, and much power in analyzing your data in ways that specifically address the questions that matter to you. But you may have data that do not fit into the rows and columns that standard statistical applications expect or you may have questions that go beyond what the drop-down menus allow you to do. A non-commercial option is jamovi, an open-source application, with point-and-click interfaces for common tasks and data exploration and modeling. Some common and traditional statistical applications are SPSS and SAS, but they require a (very expensive) commercial license. But if you want to move beyond summaries and basic graphs, you will need more specialized statistical software. To start taking advantage of computer software for your data analysis, spreadsheets (like Excel) are good because they allow you to organize the data the way you want, you can sort and filter data, you can count and summarize values, calculate basic descriptive statistics, and make graphs. Using any kind of statistical software will allow you to avoid mistakes and be faster in the computation of your statistical analyses. Descriptive Statistics for Psychological Research Statistical Software for Data Analysis Introduction to Statistics for Psychological Science In addition to the information here, next units will include examples of how to conduct in R the different analyses explained in those units. In this unit you will learn the basics of how R works and how to get comfortable interacting with this software. Statistics with R: Introduction and Descriptive Statistics
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