Programs to Try Analysis and Visualization Dataset- Assignment 13

 Problem Statements

1. Analysis and Visualization of the mtcars Dataset

Using the built-in mtcars dataset, perform the following tasks:

  • Display the first few records and the structure of the dataset.
  • Calculate the average mileage (mpg) and horsepower (hp).
  • Identify the car with the highest mileage and the car with the highest horsepower.
  • Classify cars based on the number of cylinders.
  • Create appropriate visualizations such as bar charts, histograms, scatter plots, and box plots to study the relationships among variables.

2. Analysis and Visualization of Student Data from a CSV File

A file stud.csv contains the fields:

Rno, Name, M1, M2, M3

Perform the following tasks:

  • Read the CSV file into R.
  • Calculate the total marks and average marks of each student.
  • Identify the topper and students scoring above 80 average marks.
  • Sort the students in descending order of total marks.
  • Create suitable visualizations including bar plots, histograms, box plots, pie charts, and scatter plots.
  • Write the sorted list to a new CSV file named ranklist.csv.

3. Analysis and Visualization of Employee Salary Data from an Excel File

An Excel file employee.xlsx contains the fields:

EmpID, Name, Department, Experience, Salary

Perform the following tasks:

  • Read the Excel file into R.
  • Find the average salary of employees in each department.
  • Identify the employee receiving the highest salary.
  • Display employees having more than 10 years of experience.
  • Create suitable visualizations to study salary distribution and department-wise salary variations.
  • Write the department-wise sorted employee list to a new Excel file named salary_report.xlsx.

4. Analysis and Visualization of Sales Data from a CSV or Excel File

A file sales.csv (or sales.xlsx) contains the following fields:

ProductID, ProductName, Category, QuantitySold, UnitPrice

Perform the following tasks:

  • Read the file into R.
  • Calculate the sales amount of each product.
  • Find the product generating the maximum revenue.
  • Compute category-wise total sales.
  • Sort products in descending order of revenue.
  • Create suitable visualizations such as bar plots, pie charts, histograms, box plots, and scatter plots to analyze sales patterns.
  • Export the sorted data to a new file named sales_report.csv (or sales_report.xlsx).

5.Analysis and Visualization of the Air Quality Dataset

Using the built-in airquality dataset in R, perform the following tasks:

  • Load the dataset and display its structure and summary statistics.
  • Determine the number of missing values present in each column.
  • Calculate the average values of Ozone, Solar Radiation, Wind, and Temperature.
  • Identify the day with the maximum temperature and the day with the highest ozone concentration.
  • Find the month having the highest average temperature.
  • Create suitable visualizations such as:
    • Histogram of temperature values.
    • Box plot of ozone levels.
    • Scatter plot showing the relationship between temperature and ozone concentration.
    • Bar chart showing the average temperature for each month.
    • Pie chart representing the proportion of observations belonging to different months.
  • Remove records containing missing values and compare the summary statistics before and after cleaning.
  • Identify days where temperature exceeds 90°F and ozone concentration exceeds the overall average ozone level.
  • Write the cleaned and processed data to a new CSV file named airquality_cleaned.csv.
  • Interpret the results obtained from the statistical analysis and visualizations

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