1. Applying all the code on your selected dataset, complete all codes from Chapter 4 Bivariate Graphs. Make sure you submit to this link two things 1. Your report file showing screenshots of all commands from Rstudio GUI Make sure you show all Rstudio GUIs 2. Submit your R script code Due Friday midnight May 29th By submitting this paper, you agree: (1) that you are submitting your paper to be used and stored as part of the SafeAssign™ services in accordance with the ; (2) that your institution may use your paper in accordance with your institution’s policies; and (3) that your use of SafeAssign will be without recourse against Blackboard Inc. and its affiliates. Applying all the code on your selected dataset, complete all codes from Chapter 5 Multivariate Graphs. Make sure you submit to this link two things 1. Your report file showing screenshots of all commands from Rstudio GUI Make sure you show all Rstudio GUIs 2 Submit your R script code Due Friday midnight May 29th By submitting this paper, you agree: (1) that you are submitting your paper to be used and stored as part of the SafeAssign™ services in accordance with the

Chapter 4 of the course material focuses on bivariate graphs in R. Bivariate graphs are a type of visualization that allows us to explore the relationship between two variables. In this assignment, you are required to apply the code on your selected dataset and complete all the codes from Chapter 4.

To begin, you will need to open RStudio and load your dataset into the R environment. This can be done using the `read_csv()` function, specifying the file path to your dataset. Once the dataset is loaded, you can start applying the code from the chapter.

The chapter covers various types of bivariate graphs, including scatter plots, line graphs, bar graphs, and box plots. Each graph serves a different purpose in visualizing the relationship between two variables.

For scatter plots, you can use the `plot()` function and specify the variables you want to plot on the x and y-axis. This will create a scatter plot with the data points representing the relationship between the two variables.

Line graphs are useful for visualizing trends over time. You can use the `plot()` function again, but this time specifying the variable on the x-axis as the time variable (e.g., date) and the variable on the y-axis as the variable you want to track over time.

Bar graphs are commonly used to compare categories or groups. You can use the `barplot()` function, specifying the variables you want to compare on the x and y-axis. This will create a bar graph with bars of different heights representing the values of each category.

Box plots are helpful for visualizing the spread and distribution of a variable. The `boxplot()` function can be used to create a box plot, specifying the variable you want to examine on the y-axis.

Throughout the chapter, you will also learn how to add labels, titles, legends, and customize the appearance of the graphs. It is important to capture screenshots of each graph you create in RStudio GUI and include them in your report file.

In addition to the report file, you are also required to submit your R script code. The R script should contain all the codes you have executed in RStudio to generate the graphs.

The deadline for this assignment is Friday midnight, May 29th. By submitting the paper, you agree to the terms and conditions stated regarding the use and storage of your paper.

You should ensure that you thoroughly understand the concepts and code from Chapter 4 before attempting to complete this assignment. It is recommended to review the chapter and practice the code examples on a sample dataset before applying them to your selected dataset.