Clear Explanation of each step is very important. Section 1:…

Clear Explanation of each step is very important. Section 1: Data Analysis (R Studio – R language – Library: example, mosaic) Describe the fields of the dataset. (Use the data detail file for assistance) Create a summary of stats for the dataset. Discuss the Min, Max, Median, and Mean of the continuous fields. Discuss the Counts and Percentages of the categorical fields. (ex. Pie Chart) Discuss any missing data elements and/or issues/concerns with the dataset. Section 2: Data Visualizations (R Studio – R language – Library:  example, ggplot2) Graphs: Bar Plot, Box Plot, Scatter Plot, and Histogram Label: X-Axis, Y-Axis, and Titles Discuss: Plot, Breakout, Drilldown, and Position (only Bar Plot) Findings: What story is presented in the visualizations? Minimum: 3

Section 1: Data Analysis

In this section, we will perform an analysis of the dataset using R Studio with the R language, specifically utilizing the mosaic library. We will describe the fields of the dataset, create a summary of statistics, and discuss any missing data elements or issues.

First, let’s describe the fields of the dataset. The dataset provided should include information about various variables or attributes. The data detail file will help us in understanding the variables present in the dataset. It will provide information such as the variable names, their definitions, and their types (categorical or continuous).

Next, we will create a summary of statistics for the dataset. We will use the summary() function in R to generate descriptive statistics for the dataset. This summary will include measures such as minimum (Min), maximum (Max), median, and mean for the continuous fields. These statistics will provide us with insights into the range and central tendencies of the continuous variables in the dataset.

For the categorical fields, we will discuss the counts and percentages. We can use various visualization techniques, such as a pie chart, to help us understand the distribution of the different categories within each categorical field. This will allow us to identify the proportions of each category and their relative importance within the dataset.

Furthermore, we will also review the dataset for any missing data elements or potential issues and concerns. Missing data can lead to biased or inaccurate analysis, so it is crucial to identify and address any missing values. We will examine the dataset for any missing values and assess their impact on the analysis. Additionally, we will consider any issues or concerns related to data quality, consistency, or integrity that may affect the reliability of our analysis.

Section 2: Data Visualizations

In this section, we will create various visualizations using R Studio with the R language, specifically utilizing the ggplot2 library. We will generate a bar plot, box plot, scatter plot, and histogram. For each visualization, we will label the x-axis, y-axis, and provide appropriate titles.

The bar plot will represent the categorical variables, displaying the frequencies or relative proportions of each category. The box plot will illustrate the distribution and variability of the continuous variables, providing information on the quartiles, median, and outliers. The scatter plot will allow us to visualize the relationship between two continuous variables, helping us identify any patterns or trends. Finally, the histogram will provide a graphical representation of the distribution of a single continuous variable.

We will discuss each plot, describing its purpose and the insights it provides. We will also consider the concept of breakout, which involves examining subsets of the data based on certain criteria. This allows us to explore patterns or differences within specific groups. Additionally, we will discuss drilldown, which involves digging deeper into the data by adding more variables or dimensions to the visualization. Lastly, for the bar plot, we will discuss the concept of position, which refers to the arrangement and ordering of the bars to emphasize certain patterns or comparisons.

Overall, our goal for the visualizations is to identify any patterns, trends, or relationships present in the dataset. These visual representations will help us gain a better understanding of the data and convey the story it tells. We aim to provide at least three key findings or insights based on our analysis of the visualizations.