Start with a one-paragraph roaRoadmap Introduction This paper explores three foundational R commands commonly used in data analysis: summary(), plot(), and lm(). Each of these functions plays a crucial role in understanding, visualizing, and modeling data. By examining what these commands do and how they contribute to the analytical process, this paper will provide a clearer understanding of their utility. The following paragraphs will describe each command, its syntax, and its purpose in a data science workflow, offering both context and interpretation of the outputs these functions generate. The summary() Command The summary() command in R provides a quick statistical overview of data, whether it's a dataset, vector, or model. For example, when you run summary(mtcars), R returns the minimum, 1st quartile, median, mean, 3rd quartile, and maximum for each variable in the mtcars dataset. This command is particularly useful because it summarizes key descriptive statistics with a single line of code, allowing analysts to rapidly assess the distribution and central tendency of data. It is often used in the early stages of data analysis to identify potential anomalies or confirm expectations. According to ChatGPT, this function helps users "quickly understand the structure and summary statistics of various data objects