Analyze Data with R | Codecademy (2024)

FAQs

Can you use R to analyze data? ›

R is a free, open source statistical programming language. It is useful for data cleaning, analysis, and visualization.

R analytics (or R programming language) is a free, open-source software used for all kinds of data science, statistics, and visualization projects. R programming language is powerful, versatile, AND able to be integrated into BI platforms like Sisense, to help you get the most out of business-critical data.

While Python and R were created with different purposes –Python as a general-purpose programming language and R for statistical analysis–nowadays, both are suitable for any data science task.

It is evident that the source code of R can be used repeatedly and with different data sets in ways that Excel formulas cannot. R clearly shows the code (instructions), data and columns used for an analysis in ways that Excel does not.

They're both very powerful languages, so the answer has a lot to do with what you intend to do. If you're primarily looking to create and visualize statistical models, R will be the better choice. If your project goes beyond statistics, Python will offer you far more possibilities.

R is considered one of the more difficult programming languages to learn due to how different its syntax is from other languages like Python and its extensive set of commands. It takes most learners without prior coding experience roughly four to six weeks to learn R. Of course, this depends on several factors.

Introduction to R

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible.

The truth is, R is far from dead. While it's true that Python has gained significant traction in recent years, R remains a powerful language that offers unique benefits for data scientists. One of the critical advantages of R is its focus on statistics and data visualization.

Should I start with R or Python? ›

Both Python and R are considered fairly easy languages to learn. Python was originally designed for software development. If you have previous experience with Java or C++, you may be able to pick up Python more naturally than R. If you have a background in statistics, on the other hand, R could be a bit easier.

R is slower than other programming languages like Python or MATLAB. It takes up a lot of memory. Memory management isn't one of R's strong points. R's data must be stored in physical memory.

The contents of a CSV file can be read as a data frame in R using the read. csv(…) function. The CSV file to be read should be either present in the current working directory or the directory should be set accordingly using the setwd(…)

The readxl package makes it easy to get data out of Excel and into R.

To sort a data frame in R, use the order( ) function. By default, sorting is ASCENDING. Prepend the sorting variable by a minus sign to indicate DESCENDING order.

R offers a broad collection of visualization libraries along with extensive online guidance on their usage. R also offers data visualization in the form of 3D models and multipanel charts. Through R, we can easily customize our data visualization by changing axes, fonts, legends, annotations, and labels.

R is a popular choice for public data web scraping, and deservedly so. It's open-source, has powerful libraries, and is relatively easy to use. Since R has built-in data analysis functionalities, it's commonly used for statistical analysis.

RStudio is a must-know tool for everyone who works with the R programming language. It's used in data analysis to import, access, transform, explore, plot, and model data, and for machine learning to make predictions on data.