R why use

After that, we will have a look at how to use the operator when working with dataframes. Specifically, we will have a look at how we can get a logical value for more specific elements, whether they are also present in a longer vector. In the code above we get an output as long as the longer vector i.

Furthermore, we used the seq function, to create the first one sequence of numbers in R and then another. In a real-world example, our vectors might not be containing sequences but just random numbers. If we, on the other hand, want to test which elements of a longer vector are in a short vector we do as follows:.

As you can see, both above methods will result in a boolean. Additionally, if we use the which function, we can the indexes of where the overlapping elements:. In the next example, we will see that we can apply the same methods for letters, or factors, in R. That is, we will test if two vectors, containing letters, are overlapping.

Note, this can also be done for words e. First, we will compare letters in a shorter vector and in a longer vector. Again we can test which letters in a long vector are in a short vector:.

Naturally, as with the examples where we used sequences of numbers in R, the result when working with letters, words, or factors is a boolean vector. Furthermore, as in the first example, we can use the which function to get indexes:. Speed - because it is a general computing language, Python is optimized to be fast assuming you write your code optimally.

As your data becomes larger or more complex, you might find Python to be faster than R for your analytical needs. Workflow - since Python is a general-purpose language, you can build entire applications using it. R, not so much. That said, there are also things it does not do as well as R: Visualizations - visual graphics libraries in Python are increasing in number and quality see matplotlib , pygal , and seaborn , but are still behind R in terms of comprehensiveness and ease of use.

Of course, once you wish to create interactive and advanced information visualizations, you can also used more specialized software such as Tableau or D3. Add-on libraries - previously Python was criticized for its lack of libraries to perform statistical analysis and data manipulation, especially relative to the plethora of libraries for R.

In recent years Python has begun to catch up with libraries for scientific computing numpy , data analysis pandas , and machine learning scikit-learn. However I personally have found immense difficulty installing and managing packages in Python, even with the use of a package manager such as conda.

This is not a helpful characterisation — Hadley Wickham hadleywickham April 20, This course could be taught exclusively in Python as it was in previous incarnations or a combination of R and Python as it was in fall Copy Download.

The R Commander provides an easy-to-use, menu-based system for loading data into R, manipulating data values, performing statistical analyses, creating graphical displays, and carrying out diagnostic tests on statistical models. Documentation for the R Commander is available on John Fox's website and in the following paper:. The advantage provided by the R Commander or another GUI is that the user does not need to learn a language in order to carry out his or her analysis.

Instead, each step is taken by making one or more selections from a menu of available options. The disadvantage of interacting with the R environment through a GUI is that the course of the analysis is limited to those actions that have been programmed into the GUI.

Thus, one could argue that using a GUI removes much of the flexibility that is inherent in the R environment. In order to overcome the preceding limitation, the R Commander and most other GUIs allow the user to employ both methods of interacting with the environment within a single R session.

For example, one could invoke the R Commander , and use its GUI to read the contents of an external file and create an R data frame. For many types of analyses, other features of the R Commander could be used to estimate model parameters, construct graphical displays, and so on. But, if the user wanted to carry out a task that is not available in the R Commander e. R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions.

Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. Advanced users can write C code to manipulate R objects directly. Many users think of R as a statistics system. We prefer to think of it as an environment within which statistical techniques are implemented.