Data Manipulation and PCA (Principal Component Analysis )

Explore core concepts of data manipulation and an introduction to principal component analysis, setting the foundation for mastering PCA techniques.

Learn how to download and install R on Windows and macOS, set up RStudio, manage packages, and configure a working directory for data analysis.

Learn how to install a package and import a library, then start, run, and stop processes to access and use the library in your code.

Learn to import data from csv and txt formats and set a working directory to prepare and manage datasets for effective data manipulation.

Identify duplicate rows and keep one observation per unique entry, showing how removing duplicates affects the collection.

Identify missing values in a dataset, learn when to remove or impute them, visualize missingness, and apply mean or median imputation to numeric features for data prepared for PCA.

Learn to detect strongly correlated variables using correlation functions and visual plots, compare positive and negative correlations, and tailor visualizations with data subsets and consistent scales.

Learn how to select a subset of the data based on specified criteria, such as year greater than 2000 and age less than 30, and compute the proportions for analysis.

Apply operations across columns using the apply function to perform additions and subtractions on a dataset. Standardize data with scaling and manipulate data frames and vectors.

Select the number of principal components in PCA by examining the curve, noting a big drop at five, and diagnose each component with a correlation matrix.

Compute the correlation matrix from standardized data and derive its eigenvalues and eigenvectors, using code examples to interpret PCA results.

Explore data manipulation and principal component analysis, computing principal components from a standardized correlation matrix using eigenvectors, projecting data into reduced five-component space.