Install/Update R & RStudio
The most difficult time people have learning R is when functions don’t do the “obvious” thing. For example when sorting data, SAS, SPSS and Stata all use commands appropriately named “sort.” Turning to R, they look for such a command and, sure enough, there is one named exactly that. Instead it sorts individual variables, which is often a very dangerous thing to do. In R, the order function sorts data sets and it does so in a somewhat convoluted way.
Even when a function offers that argument, it only works if you specify a model formula too (e.g. paired tests don’t need formulas). R has many data structures that give it great flexibility, and each can use slightly different approaches to variable selection.
If this is so easy in other software but so confusing in R, what’s the point? R is the only software I know of that allows you to include variables from multiple datasets in a single analysis. So you need ways to change datasets in the middle of an analysis. However, part of that may simply be design choices that could have been better in hindsight.
Is Python better than R?
R and Python are both open-source programming languages with a large community. R is mainly used for statistical analysis while Python provides a more general approach to data science. R and Python are state of the art in terms of programming language oriented towards data science.
As previously mentioned, R has vectors, factors, matrices, arrays, data frames (datasets) and lists. Modeling functions create many variations on these structures and they also create whole new ones.
I can’t help but be biased on this question, but my books, R for SAS and SPSS Users and R for Stata Users start at the very beginning, and build slowly until you have all the fundamentals of base R down pat. For example, many books show how to recode a variable, but don’t extend that to copying the originals, giving them new names, then recoding them and (optionally) adding them back to the original data set. I try to provide all the steps that you need in everyday use. Despite their names, you don’t need to know any other packages to read them, they just start each section describing very briefly how the other languages work so you’re more aware of how R differs.
Any individual who wants to play around with the concepts of data analysis, data science or any other mathematical operations without involving much of the coding can use this tool. Since it is a paid version the online availability of this tool could be an issue.
So it’s true that in R, functions act like both procedures and functions from other packages. If you’re coming to R from other software, that’s a radically new approach.
For computationally intensive tasks, C, C++, and Fortran code can be linked and called at run time. Advanced users can write C, C++, Java, .NET or Python code to manipulate R objects directly. R is highly extensible through the use of user-submitted packages for specific functions or specific areas of study. Due to its S heritage, R has stronger object-oriented programming facilities than most statistical computing languages. Extending R is also eased by its lexical scoping rules. Matlab is a multi-paradigm numerical computing environment and proprietary programming language developed by the company called MathWorks.
- Many of R’s standard functions are written in R itself, which makes it easy for users to follow the algorithmic choices made.
- R and its libraries implement a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others.
Their macro and matrix languages, and output management systems, are separated enough as to be almost invisible to beginners and even intermediate users. Most packages let you repeat any analysis simply by adding a command like “by group” to it. It requires you to create a macro-like function that does the analysis steps you need. Other languages let you avoid learning that type of programming until you’re doing more complex tasks. However, the deeper integration of such macro-like facilities in R means that the functions you write are much more integrated into the complete system.
R vs. RStudio
So, anybody who wants to begin their learning in the field of data science on their own can use R language since it is open-source. However, the converse of that is provided by its competitors, such as SAS, SPSS and Stata.
R and its libraries implement a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others. R is easily extensible through functions and extensions, and the R community is noted for its active contributions in terms of packages. Many of R’s standard functions are written in R itself, which makes it easy for users to follow the algorithmic choices made.
In most data science software, a variable is a variable, and all procedures accept them. In R however, a variable could be a vector, a factor, a member of a data frame or even a component of a complex structure in R called a list. For each function you have to learn what it will accept for processing. For example, most simple statistical functions for the mean, median, etc. will accept variables stored as vectors. They’ll also accept variables in datasets or lists, but only if you select them in such a way that they become vectors on the fly.
However, the dplyr package has an arrange function that sorts data sets and it is quite easy to use. Many other packages, including SAS, SPSS, and Stata have procedures or commands that do typical data analyses which go “down” through all the observations. They also have functions that usually do a single calculation across rows, such as taking the mean of some scores for each observation in the data set. Functions may have a preference to go down rows or across columns but for many functions you can use the “apply” family of functions to force them to go in either direction.
For example, it could have been designed with a data argument in all functions, with a system-wide option to look for a default data set, as SAS has. As we have listed down most of the differences and compared both MATLAB vs R languages with each other. Also considering the educational value of each programming language in terms of teaching, we can say that R can provide a competitive advantage while looking for a job in the analysis. Since R is an open-source an individual can contribute to it and provide a lot of online code which might help others to learn the language. As Matlab is concerned this is also one of the languages that are widely being used.
Users are free to create their own data structures, and some of these have become quite popular. Along with all these structures comes a set of conversion functions that switch an object’s structure from one type to another, when possible. Given that so many other analytics packages get by with just one structure, the dataset, why go to all this trouble? If you added the various data structures that exist in other packages’ matrix languages, you would see a similar amount of complexity.
The last few of examples above come from the dplyr package, which makes variable selection much easier, but of course that also means having to learn more. R generally lacks a built-in ability to make these selections easily, but the dplyr package’s select function is both easy and powerful. It is important to note the differences between R and RStudio.
The first task in any data analysis is selecting a data set to work with. Most other software have ways to specify the data set to use that is easy, safe and consistent. R offers several ways to select a data set, but none that meets all three criteria. Referring to variables as mydata$myvar works in many situations, but it’s not easy as you end up typing “mydata” over and over.
R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. R and its libraries implement wide varieties of statistical and graphical techniques which include machine learning algorithms like classification, clustering, time-series analysis, data modeling and many more.