Lab | Exploring the sample_superstore Dataset

In this lab, you will work with the sample_superstore dataset to practice creating, inspecting, and manipulating dataframes. Follow the steps below to complete the tasks.

Step 1: Importing the Dataset

• Load the sample_superstore dataset into a dataframe. You can find this dataset in the datasets package or download it as a CSV file from online sources.

• Save the dataframe as superstore.

  Hint: Use the read.csv() or read_excel() function to import the dataset.

Step 2: Inspecting the Dataframe

• Display the first 10 rows of the dataframe using the head() function.
• Use the str() function to inspect the structure of the dataframe. What are the data types of the columns?
• Use the summary() function to get a summary of the dataframe. What insights can you gather from the summary?

Step 3: Accessing Data

• Extract the Sales column as a vector using the $ operator.
• Subset the first 15 rows and the columns Order ID, Customer Name, and Sales.
• Use the nrow() and ncol() functions to determine the number of rows and columns in the dataframe.

Step 4: Filtering Data

• Filter the dataframe to show only rows where the Profit is greater than 100.
• Filter the dataframe to show only rows where the Category is "Furniture" and the Sales are greater than 500.
• Filter the dataframe to show only rows where the Region is "West" and the Quantity is greater than 5.

Step 5: Adding and Modifying Columns

• Add a new column called Profit Margin that calculates the profit margin as (Profit / Sales) * 100.
• Modify the Sales column by rounding the values to 2 decimal places.
• Remove the Postal Code column from the dataframe using the subset() or select() function.

Step 6: Handling Missing Data

• Check for missing values in the dataframe using the is.na() function. Are there any missing values?
• If there are missing values, remove rows with missing data using the na.omit() function.
• Replace any missing values in the Sales column with the mean of the Sales column using the na.fill() function.

Step 7: Advanced Analysis (Optional)

• Group the dataframe by Region and calculate the total Sales and Profit for each region.
• Create a new column called Discount Level that categorizes the Discount column into:
  • "Low" (0-0.2)
  • "Medium" (0.2-0.5)
  • "High" (0.5-1)
• Sort the dataframe by Sales in descending order.

Deliverables

• Submitted notebook (or file) with your responses to each of the exercises.

Submission

• Upon completion, add your deliverables to git.
• Then commit git and push your branch to the remote.
• Make a pull request and paste the PR link in the submission field in the Student Portal.

Good luck!