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To be concrete, many of the plots in R are simply impossible to produce with Excel, SPSS, or SAS, and would take a tremendous amount of work to produce with Python, Java and lower level programming languages. R has many plotting mechanisms, allowing the user a tremendous amount of flexibility, while abstracting away a lot of the tedious details. Whether you are doing EDA, or preparing your results for publication, you need plots.

22.2 Causal Inference from Observational Data.

22.1 Causal Inference From Designed Experiments.

17.7 Numerical Libraries for Linear Algebra.

16.4.2 Parallel Data Munging with data.table.

15.10 Computing from a Distributed File System.

15.3 Computing From Efficient File Structrures.

14.2 Sparse Matrices and Sparse Models in R.

14.1.3 Compressed Column Oriented Representation.

14.1.2 Compressed Row Oriented Representation.

12.4 Other R Interfaces to JavaScript Plotting.

12.2.1 Extensions of the ggplot2 System.

12.1.1 Using Existing Plotting Functions.

11.2.1 Latent Variable Generative Approaches.

11.1.3 Latent Variable Generative Approaches.

11.1.2 Dimensionality Reduction Preliminaries.

10.2.6 Linear Discriminant Analysis (LDA).

10.2.4 Classification and Regression Trees (CART).

10.2.1 Linear Models with Least Squares Loss.

9.1.2 Various Types of Signal to Detect.

8.4.3 Testing Hypotheses on Correlations.

8.1.2 Generalized Linear Mixed Models (GLMM).

6.3.5 Testing a Hypothesis on a Single Contrast (*).

6.3.2 Constructing a Confidence Interval on a Single Coefficient.

6.3.1 Testing a Hypothesis on a Single Coefficient.