Category: Examples

Simulation studies are crucial for understanding the behavior of statistical methods. Here’s an example of simulating the Central Limit Theorem. Step 1: Set Parameters Step 2: Simulate Sample Means Step 3: Plot the Distribution of Sample Means

Functional programming is a programming paradigm that treats computation as the evaluation of mathematical functions. In R, you can use functions like lapply, sapply, and map from the purrr package. Step 1: Install and Load purrr Step 2: Create a Sample List Step 3: Use lapply and sapply Step 4: Use map from purrr

R can connect to databases to perform data analysis on large datasets. Here’s how to connect to a MySQL database. Step 1: Install and Load Required Packages Step 2: Connect to the Database Step 3: Query Data Step 4: Disconnect from the Database

Geospatial data analysis is crucial for visualizing and analyzing spatial relationships. We’ll use the sf package for handling spatial data. Step 1: Install and Load sf Step 2: Load Geographic Data For this example, you can use built-in datasets or download shapefiles. Here, we’ll use a simple example with the nc dataset from the sf…

Mixed-effects models are useful when dealing with data that have both fixed and random effects. We’ll use the lme4 package for this. Step 1: Install and Load lme4 Step 2: Create a Sample Dataset Step 3: Fit a Mixed-Effects Model

Network analysis is essential for understanding relationships in data. We’ll use the igraph package. Step 1: Install and Load igraph Step 2: Create a Sample Graph Step 3: Analyze the Graph

Text analysis is vital for extracting insights from unstructured data. Here, we’ll analyze a simple text corpus. Step 1: Install and Load Required Packages Step 2: Create a Sample Text Corpus Step 3: Create a Term-Document Matrix Step 4: Generate a Word Cloud

Clustering is a powerful technique for grouping similar data points. We’ll use the k-means algorithm. Step 1: Create a Sample Dataset Step 2: Apply k-means Clustering

Clustering is a powerful technique for grouping similar data points. We’ll use the k-means algorithm. Step 1: Create a Sample Dataset Step 2: Apply k-means Clustering

We can create faceted plots and combine multiple visualizations. Faceted Plot Combining Plots You can also use the patchwork package to combine multiple plots: