Comparing Two Excel Files in R and Returning Different Titles

In this article, we will explore how to compare two Excel files using the readxl library in R. Specifically, we will focus on finding titles that have been added or removed from the second Excel sheet (Book 2) compared to the first sheet (Book 1). We’ll also discuss some potential improvements to make the output more readable and user-friendly for non-technical colleagues.

Introduction

The readxl library provides a convenient way to read Excel files into R data frames. However, when comparing two Excel sheets, it can be challenging to present the results in a clear and concise manner. In this article, we will use the tidyverse package and the anti_join function from the dplyr library to create an output table that highlights differences between the two sheets.

Installing Required Libraries

To start comparing Excel files in R, you’ll need to install the required libraries. The most essential ones are:

• readxl: for reading Excel files
• tidyverse: provides a suite of tools for data manipulation and visualization
• dplyr: offers functions for manipulating and cleaning data

You can install these libraries using the following command in your R console:

install.packages(c("readxl", "tidyverse", "dplyr"))

Loading Libraries and Reading Excel Files

Let’s load the required libraries and read our two Excel files (Book1.xlsx and Book2.xlsx) into R data frames.

# Load necessary libraries
library(readxl)
library(tidyverse)

# Read Excel files into data frames
dat <- read_xlsx("Book1.xlsx")
dat2 <- read_xlsx("Book2.xlsx")

# Check the structure of the data frames
str(dat)
str(dat2)

Comparing Data Frames and Creating Output Table

To compare dat and dat2, we can use the anti_join function from the dplyr library. This function returns a table that includes only the rows where a value does not exist in both data frames.

# Compare data frames using anti_join
book1_output <- anti_join(dat, dat2, by = "Title") %>% 
  mutate(source = "Book1")

book2_output <- anti_join(dat2, dat, by = "Title") %>% 
  mutate(source = "Book2")

final_output <- rbind(book1_output, book2_output)

In the code above:

• anti_join(dat, dat2, by = "Title") returns a table with titles that are present in either dat but not dat2, along with their corresponding studio names and types.
• The mutate(source = "Book1") function adds an additional column called “source” to the output table. This column indicates which Excel sheet each title comes from (i.e., whether it was present in Book 1 or not).
• The rbind(book1_output, book2_output) function combines the two tables into a single one, with titles that are only present in book1_output appearing above those that are also present in book2_output.

Output and Interpretation

The final output table looks something like this:

In this output, the columns represent:

• Title: the title of each movie
• Source: which Excel sheet each title comes from (Book 1 or Book 2)
• Studio: the studio name associated with each title
• Type: the genre of each title (special in this case, as it’s a specific type of film)

Improving Readability

To make the output more readable and user-friendly for non-technical colleagues, you can consider the following modifications:

1. Table Formatting

Instead of using plain text to format your table, use Markdown code to create a nicely aligned and formatted table.

| Column 1 | Column 2 | ... |
|:---------|:---------:|...:|
| Value 1  | Value 2   | ...  |
| Value 3  | Value 4   | ...  |

2. Use of Highlighting

Use the highlight shortcode to add color to your code blocks and output tables.

```markdown
{< highlight LANGUAGE >}
// Your code here
{< /highlight >}

You can use different colors for highlighting based on the programming language or syntax used in the code block.

3. Add Images

Include images within your table to help illustrate complex data points or add visual interest.

| Column 1 | ... |
|----------:|...:|
| Value 1  | <img src="image.png" alt="Description"> |

By adding these improvements, you can present the comparison results in a more visually appealing and easy-to-understand format for your non-technical colleagues.

Conclusion

In this article, we explored how to compare two Excel files using R and create an output table that highlights differences between the two sheets. By leveraging libraries such as readxl, tidyverse, and dplyr, you can easily perform data comparisons and present results in a clear and concise manner.

Remember to consider improvements like table formatting, highlighting, and adding images to make your output more readable and user-friendly for non-technical colleagues.

I hope this article has been helpful in showcasing the power of R for data analysis and comparison. If you have any questions or need further assistance, feel free to ask!

Last modified on 2024-10-07