In this activity, I practiced: - working in RStudio, - editing an R Markdown notebook, - running R code chunks, - inspecting a dataset, - converting a data frame to a matrix, - using the assignment operator `<-`, - indexing rows and columns, - making heatmaps, - scaling data, - changing color palettes, - interpreting clusters and correlations, - and connecting a simple dataset to future cancer biology analyses.

Big idea: data can hide patterns Large biological datasets are often too big to understand by reading numbers in a table. In DREAM-High, we will eventually use heatmaps to look for patterns in breast cancer gene expression data from patients in The Cancer Genome Atlas. A heatmap can help us ask questions such as: - Which samples look similar to each other? - Which genes behave similarly across patients? - Can visual patterns help us discover tumor subtypes? **Main idea:** A heatmap turns numbers into colors so that hidden structure becomes easier to see.

Peer-graded Assignment: R Markdown Presentation & Plotly

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R Markdown and Leaflet Coursera Project

Practice questions from Chapter 6: Linear Model Selection and Regularization from ISLR textbook.