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R Code and Data Coding

Saying that R code is idea for statistical data analysis and visualization isn’t an exaggeration. It’s really the truth because of its entire special arsenal of quality features. It’s a language that mirrors mathematics about as well as C, which makes it very popular with those who have a background in statistics before they came over to development. Development studios designed around the language are quite powerful as well, and these can be learned in really no time flat.

statistical data analysis schema

Tips for Using the Code

Those who want to learn how to program in the R coding language should take a moment to familiarize themselves with each of the features that the system is divided into.

r codeSince it’s really designed around the idea of data analysis rather than general development, R has the distinction of having functions divided as such:

  1. Expressions and variables, which make up a big part of the language. This is where users enter material into the programming environment for analysis, and it sort of mirrors the same function from the SPSS platform and other similar environments. Those with spreadsheet experience can often apply it here.
  2. Vector grouping is an extremely important feature, because it allows users to group values together. Arithmetic operations are much easier when broken down into vectors, and graphs can always be drawn around these programmable constructs.
  3. Two-dimensional data sets are great for those who want to draw a traditional graph, and these are always in everyone’s playbook. Basic statistics include the mean, median and standard deviation of a dataset.
  4. R is a great language for those who are plotting categorized data, and it’s always easy to set up a dataset around categories in the language. Rather than use an interface, it’s fairly easy to simply code it all directly. Values can be organized into data frames. Frames can be loaded from files and ultimately merged together.
  5. Correlation testing is the meat and potatoes of the software. Testing can be done between individual datasets as well as with linear models. In both cases, the testing system is considered superior to those provided by other environments so users should take the extra time to familiarize themselves with it.

 Additional Library Extensions

Individuals who are writing plenty of R code will eventually find some kind of repetitive task they need to duplicate, or perhaps they might stumble across something that they need done that the language can’t do natively. In either case, they can expand it via a library.

Rather than using plug-ins the way that other environments would, R allows users to include libraries the same way they might if they were programming in Java or C#. This makes it a good deal more powerful than other statistical languages, and in fact it’s possible to program entire applications in R if one so elected to do so.

To learn more about coding data in SPSS, go on reading our articles!

Having r code issues? Get in touch us with us then!