In this activity, we will explore real clinical data from breast cancer patients in **The Cancer Genome Atlas (TCGA)**. The goal is not only to learn R. The goal is to use R to ask scientific questions: - What information is collected from cancer patients? - How do we summarize a large clinical dataset? - What kinds of missing or uncertain values appear in real biomedical data? - How can clinical features help us understand breast cancer subtypes? Later in DREAM-High, we will connect this type of clinical information to **gene expression data**. That is where computational biology becomes especially powerful: we can ask how molecular patterns relate to patient and tumor characteristics.

`R` is a programming language and free software environment for statistical computing and graphics. It's not only a powerful statistical programming language but also the go-to data analysis tool for many computational genomics experts. We will explore how high-dimensional genomics datasets can be analyzed with core R packages and functions.

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.