Lecture 6: Working with Data Frames in R

Overview

• In this lecture, we’ll cover:
  • Introduction to data frames, a widely used data structure in R.
  • How to create, access, and modify data frames.
  • Practical examples of working with business datasets in data frames.

1. Introduction to Data Frames

• A data frame is a two-dimensional data structure in R where:
  • Each column represents a different variable, and columns can store different data types (numeric, character, logical, etc.).
  • Each row represents an observation or record.
• Data frames are similar to tables in a spreadsheet or SQL database, making them ideal for business data analysis.

Key Uses of Data Frames:

• Organizing datasets for analysis.
• Storing business data such as sales records, employee details, customer information, etc.
• Data cleaning and transformation for analysis.

2. Creating Data Frames

You can create a data frame using the data.frame() function, where each argument represents a column in the data frame.

Example: Creating a Sales Data Frame

# Creating a data frame for sales data
sales_data <- data.frame(
  Product = c("A", "B", "C", "D", "E"),
  Sales_Q1 = c(120, 150, 90, 100, 130),
  Sales_Q2 = c(170, 200, 140, 180, 190)
)
sales_data

  Product Sales_Q1 Sales_Q2
1       A      120      170
2       B      150      200
3       C       90      140
4       D      100      180
5       E      130      190

• Explanation: In this example, sales_data is a data frame where:
  • The Product column contains product names.
  • Sales_Q1 and Sales_Q2 columns contain sales figures for the first and second quarters.

Checking the Structure of a Data Frame

You can inspect the structure of a data frame using the str() function, which shows the types of each column.

str(sales_data)

'data.frame':   5 obs. of  3 variables:
 $ Product : chr  "A" "B" "C" "D" ...
 $ Sales_Q1: num  120 150 90 100 130
 $ Sales_Q2: num  170 200 140 180 190

• Explanation: This code reveals the structure of sales_data, showing that Product is a character column, while Sales_Q1 and Sales_Q2 are numeric columns.

3. Accessing Data in a Data Frame

Accessing Columns in a Data Frame

You can access individual columns in a data frame using the $ operator or square brackets.

Example: Accessing a Single Column

# Accessing the Sales_Q1 column
sales_data$Sales_Q1

[1] 120 150  90 100 130

• Explanation: This retrieves the Sales_Q1 column from the sales_data data frame.

Example: Accessing Multiple Columns

# Accessing the Product and Sales_Q2 columns
sales_data[, c("Product", "Sales_Q2")]

  Product Sales_Q2
1       A      170
2       B      200
3       C      140
4       D      180
5       E      190

• Explanation: This extracts the Product and Sales_Q2 columns, showing both in the output.

Accessing Rows in a Data Frame

You can access rows by specifying row indices using square brackets [row, ].

Example: Accessing a Specific Row

# Accessing the first row of the data frame
sales_data[1, ]

  Product Sales_Q1 Sales_Q2
1       A      120      170

• Explanation: This code retrieves the first row, showing all the details for Product A in the first quarter (Q1) and second quarter (Q2).

4. Modifying Data in a Data Frame

You can update specific elements, rows, or columns of a data frame by assigning new values.

Example: Modifying an Element in a Data Frame

# Changing the sales value for Product B in Q2
sales_data[2, "Sales_Q2"] <- 210
sales_data

  Product Sales_Q1 Sales_Q2
1       A      120      170
2       B      150      210
3       C       90      140
4       D      100      180
5       E      130      190

• Explanation: This updates the sales figure for Product B in the second quarter from 200 to 210.

Example: Adding a New Column

# Adding a new column for Sales in Q3
sales_data$Sales_Q3 <- c(190, 220, 150, 200, 210)
sales_data

  Product Sales_Q1 Sales_Q2 Sales_Q3
1       A      120      170      190
2       B      150      210      220
3       C       90      140      150
4       D      100      180      200
5       E      130      190      210

• Explanation: This adds a new column, Sales_Q3, to the sales_data data frame with sales figures for the third quarter.

5. Practical Example: Analyzing Sales Data

Let’s calculate the total sales across all three quarters for each product by creating a new column.

Example: Calculating Total Sales

# Creating a new column for total sales across all quarters
sales_data$Total_Sales <- sales_data$Sales_Q1 + sales_data$Sales_Q2 + sales_data$Sales_Q3
sales_data

  Product Sales_Q1 Sales_Q2 Sales_Q3 Total_Sales
1       A      120      170      190         480
2       B      150      210      220         580
3       C       90      140      150         380
4       D      100      180      200         480
5       E      130      190      210         530

• Explanation: This code adds a new column Total_Sales that sums up sales from Sales_Q1, Sales_Q2, and Sales_Q3 for each product.

6. Sorting and Filtering Data in a Data Frame

Sorting Data Frames

You can sort a data frame by one or more columns using the order() function.

Example: Sorting by Total Sales in Descending Order

# Sorting the data frame by Total_Sales in descending order
sales_data_sorted <- sales_data[order(-sales_data$Total_Sales), ]
sales_data_sorted

  Product Sales_Q1 Sales_Q2 Sales_Q3 Total_Sales
2       B      150      210      220         580
5       E      130      190      210         530
1       A      120      170      190         480
4       D      100      180      200         480
3       C       90      140      150         380

• Explanation: This code sorts the sales_data by Total_Sales in descending order, with the highest sales appearing first.

Filtering Data Frames

You can filter rows of a data frame based on specific conditions.

Example: Filtering Products with Sales Greater Than 150 in Q1

# Filtering products with Sales_Q1 greater than 150
high_sales_q1 <- sales_data[sales_data$Sales_Q1 > 150, ]
high_sales_q1

[1] Product     Sales_Q1    Sales_Q2    Sales_Q3    Total_Sales
<0 rows> (or 0-length row.names)

• Explanation: This code filters the sales_data to show only the products with sales greater than 150 in the first quarter.

Key Takeaways

• Data frames are one of the most important data structures in R for handling datasets with different types of data in columns.
• You can easily create, access, modify, sort, and filter data frames in R.
• Data frames are widely used in business analytics for organizing, analyzing, and transforming data.

Looking Forward

• In the next lecture, we’ll explore lists and factors, two other important data structures in R, and how to work with categorical data and complex collections of data.