How does R compare to SQL for regression?

1 What is R?

R is a programming language and environment for statistical computing and graphics. It offers a wide range of functions and packages for data manipulation, visualization, modeling, and testing. R is especially suited for exploratory data analysis, as it allows you to interact with your data and try different methods quickly and easily.

3 How to do regression in R?

To do regression in R, you need to use a function that fits a model to your data and returns an object with the results. For example, you can use the lm function to perform linear regression, or the glm function to perform generalized linear regression. You can then use other functions to inspect, summarize, or plot the model object. For example, you can use the summary function to see the coefficients, standard errors, and p-values of the model, or the plot function to see the diagnostic plots.

5 Advantages of R for regression

One of the main advantages of R for regression is that it offers a lot of flexibility and functionality for data analysis. You can choose from a variety of regression models, such as linear, logistic, polynomial, or multivariate, and customize them with different options and parameters. You can also use R to perform data preprocessing, feature engineering, validation, and visualization, as well as apply advanced techniques such as regularization, cross-validation, or bootstrapping. R also has a large and active community of users and developers who contribute to its documentation, support, and innovation.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?