RStudio Lesson 4 | BehindTheBall

This lecture introduces students to basic graphing in R using ggplot, focusing specifically on how to create histograms. The instructor begins by explaining the value of visualization in R compared to other coding languages and highlights ggplot as a powerful and user-friendly tool for generating plots. Using a dataset of NBA three-point statistics from the previous video, students learn how to build a histogram by specifying the x-axis variable and controlling how the data is grouped using either the bins or binwidth arguments. The lesson explains what histograms represent, how to group similar data points for clearer visuals, and the trade-offs of bin width versus bin count. It ends with a brief preview of z-scoring and how histograms are used in statistical analysis, setting up the next video on scatter plots and correlation.

Histograms: Graphs that show frequency distributions.
ggplot syntax: The structure of a basic ggplot() call, including the use of + to add layers.
aes(): Used for aesthetic mappings like setting the x-axis.
bins vs binwidth: Two ways to group data in histograms—either by specifying how many groups or how wide each group should be.
Outliers: Players like Steph Curry and Damian Lillard serve as examples of data points far from the norm.
Z-scoring (briefly introduced): Using histograms to visualize normal distributions and statistical comparisons.

Lesson 4: Graphing

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