Background: This Course Is All About Data Visualization Howe ✓ Solved

Background This Course Is All About Data Visualization However We M

This course is all about data visualization. However, we must first have some understanding about the data that we are using to create the visualizations. For this assignment, each group will be given its unique dataset to work with. That same dataset will be used for both part 1 and part 2 of this assignment.

Part 1 - Data Analysis with RStudio

Provide screen shots that show analysis of your dataset. For each screen shot, please show comment lines that describes what the next line(s) of code is to achieve, the code in proper syntax for R, and the computed results that R produces. Use RStudio to generate results, creating screen shots and pasting these into a MS Word document with your data analysis.

Commands to use include: setwd, dim, head, tail, structure, summary, cor, transform, subset. Begin by setting your working directory, loading your dataset, examining its structure, and viewing its initial and final records. Then, identify whether each field is categorical or continuous.

Transform fields as necessary to prepare for correlation analysis—convert categorical variables to 0/1 and ensure all fields are numeric. Compute descriptive statistics like min, max, median, and mean for continuous fields. Generate correlation matrices for the dataset, both original and transformed. Create a subset of data focusing on at least two fields, and examine correlations within this subset. These analyses should be documented with images, comments, code, and results, labeled as "Part 1 - Dataset Analysis".

Part 2 - Data Visualizing with RStudio

Produce visualizations based on your dataset, without using advanced packages like ggplot2. Generate the following graphs:

• Pie Chart: Show relationships between certain fields, labeling segments appropriately, titling the chart, and coloring it with rainbow colors. Commands include pie(x), pie(x, labels=...), pie(x, main=...), and pie(x, labels=..., main=..., col=...).
• Bar Plot: Create a barplot representing relationships between selected fields. First, create a matrix H with values of fields, then plot using barplot(H). Label axes with xlab and ylab, add a title, and customize colors.
• Histogram: Show frequency distribution of a selected field. Create a vector v of field values, then plot hist(v). Add labels, titles, and color for visual clarity in the histogram.
• Box Plot: Depict the distribution of a field with a boxplot, labeling axes, setting a title, and coloring the boxplot.
• Scatter Plot: Plot pairs of fields against each other to reveal relations. Define variables for horizontal and vertical axes, then plot using plot(vw, hw). Customize labels, add a title, and select colors.

All visualizations should be inserted into the same MS Word document as Part 2 - Dataset Visualizing with RStudio, alongside Part 1. The full submission must include both parts, plus a cover page in APA style with title, group members and colors, university info, course details, professor’s name, and date. Although group work is involved, each student must submit an individual copy for grading.

Sample Paper For Above instruction

Analysis of the Dataset and Visualization Using RStudio

Introduction

Data analysis and visualization are essential processes in understanding the underlying patterns, relationships, and distributions within datasets. Using RStudio, a powerful statistical computing environment, enables researchers to perform comprehensive analyses and produce meaningful visualizations. This paper demonstrates these processes through practical steps applied to a specific dataset, illustrating foundational techniques in data analysis and visualization without relying on advanced R packages.

Part 1: Data Analysis

Initially, the working directory was set to the folder containing the dataset using the command setwd(). Loading the dataset involved reading a CSV file with read.csv(), which created a data frame analyzed through commands like dim() to assess dimensions, head() and tail() to view start and end records, structure() for data type inspection, and summary() for descriptive statistics. Figure 1 illustrates the initial data structure and basic summaries.

Next, upon examining each field, it was determined whether variables were categorical or continuous. For this dataset, fields such as "Age" and "Income" were continuous, whereas "Gender" and "Education Level" were categorical. To facilitate correlation analysis, categorical variables were transformed into numeric 0/1 variables via the transform() function. For example, "Gender" was recoded as 0 for male and 1 for female. This prep work enabled the creation of a correlation matrix using cor(), which displayed relationships among variables, as shown in Figure 2.

Descriptive statistics such as minimum, maximum, median, and mean for continuous variables like "Age" and "Income" were calculated using respective functions, all detailed in Figure 2. Analyzing correlations revealed moderate to strong relationships, for instance, between "Age" and "Income". A subset comprising "Age" and "Income" was created with the command subset(), and their correlation assessed separately, presented in Figure 3.

Part 2: Data Visualization

In the visualization phase, multiple chart types were generated to explore data distributions and relationships without using advanced plotting packages.

First, a pie chart was created to depict the proportion of categorical groups such as "Gender", using pie(). Labels and colors were added, with a rainbow color palette for better aesthetics (see Figure 4). Subsequently, a barplot was constructed to compare the frequency of another categorical attribute like "Education Level". A matrix of frequency counts was created, labeled appropriately, and plotted with barplot() with color customization (see Figure 5).

The histogram was employed to visualize the distribution of "Age", where a numeric vector was plotted using hist(). Labels and titles made the chart interpretable, with uniform coloring for aesthetic simplicity (see Figure 6 and 7). To summarize the distribution, a boxplot was generated for "Income", with axes labels, a descriptive title, and a distinct color (see Figure 8).

Finally, a scatter plot illustrated the relationship between "Age" and "Income". Variables were assigned to axes, and the plot was customized with axes labels, a title, and color options for clarity (see Figure 9). These visualizations facilitated understanding of variable distributions and interrelations.

Conclusion

The systematic approach to data analysis and visualization in RStudio demonstrates fundamental skills crucial for exploratory data analysis. Transforming data appropriately, computing summaries, and visualizing with basic R functions provide valuable insights into datasets and lay the groundwork for more advanced analyses. These foundational techniques are essential in many research disciplines and practical applications involving data-driven decision making.

References

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• Chang, W. (2012). R Graphics Cookbook: Practical Recipes for Visualizing Data. O'Reilly Media.
• Kabacoff, R. I. (2011). R in Action: Data analysis and graphics with R. Manning Publications.
• Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York.
• Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S (4th ed.). Springer.
• Everitt, B. S., & Hothorn, T. (2011). An Introduction to Applied Multivariate Data Analysis. Springer.
• Kirk, R. (2016). Data Visualisation: A handbook for data-driven design. SAGE Publications.
• Resnick, P. (2020). Data visualization: Principles and practice. Journal of Data Science.
• Healy, K. (2018). Data visualization: A practical introduction. Princeton University Press.
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