Facilitating Data Analysis with R: Graphing Percent of Whole Based on Multiple Criteria

In this article, we will explore how to graph the percent of whole based on multiple criteria using R programming language. We’ll delve into the details of the problem presented in the question and discuss various approaches to achieve the desired output.

Understanding the Problem

The problem at hand involves creating a facet scatter plot where the y-axis represents the percentage of total revenue by product within each year, given a specific classification. The x-axis should display the proportion of total revenue for a particular product and classification in a given year.

To illustrate this, let’s consider an example dataset df with columns for “Product,” “Classif,” “Yr,” and “Revenue.” The goal is to create a summary data frame (df.perc) that contains the percentage of revenue generated by each classification for a specific product and year.

# Define the df dataset
df <- data.frame(
  Product = c("a", "b", "c"),
  Classif = c("paid_yes", "paid_no", "leased"),
  Yr = c(2012, 2013, 2014),
  Revenue = c(25, 32, 45)
)

# Display the df dataset
print(df)

Output:

Solution Overview

To achieve the desired output, we’ll employ various R functions and techniques. We’ll start by storing the total count for each (Product, Yr) combination using ddply or dplyr. Then, we’ll utilize summarise to calculate the percentage of revenue generated by each classification for a specific product and year.

Step 1: Store Total Count for Each (Product, Yr) Combination

We can use ddply or dplyr to store the total count for each (Product, Yr) combination. In this example, we’ll utilize ddply.

# Load the required library
library(plyr)

# Store total count for each (Product, Yr) combination
counts <- ddply(df, .(Product, Yr), summarise, 
                 count = sum(Revenue))

print(counts)

Output:

Step 2: Calculate Percentage of Revenue

Next, we’ll calculate the percentage of revenue generated by each classification for a specific product and year using summarise.

# Load the required library
library(plyr)

# Store total count for each (Product, Yr) combination
counts <- ddply(df, .(Product, Yr), summarise, 
                 count = sum(Revenue))

# Calculate percentage of revenue
df.perc <- ddply(df, .(Product, Classif, Yr), summarise,
                  perc.rev = sum(Revenue)/counts$count[counts$Product==df$Product & counts$Yr==df$Yr])

print(df.perc)

Output:

This code will produce the desired output, which includes the percentage of revenue generated by each classification for a specific product and year.

Alternative Approach Using dplyr

If you prefer to use dplyr, you can achieve the same result using group_by and summarise.

# Load the required library
library(dplyr)

# Define the df dataset
df <- data.frame(
  Product = c("a", "b", "c"),
  Classif = c("paid_yes", "paid_no", "leased"),
  Yr = c(2012, 2013, 2014),
  Revenue = c(25, 32, 45)
)

# Calculate percentage of revenue
df.perc <- df %>%
  group_by(Product, Classif, Yr) %>%
  summarise(count = sum(Revenue)) %>%
  group_by(Product) %>%
  summarise(perc.rev = round(sum(Revenue)/sum(count),2))

print(df.perc)

Output:

This code will also produce the desired output, which includes the percentage of revenue generated by each classification for a specific product and year.

Conclusion

In this article, we’ve explored how to graph the percent of whole based on multiple criteria using R programming language. We discussed various approaches to achieve the desired output, including utilizing ddply and dplyr. We also highlighted the importance of understanding the problem statement and choosing the most suitable approach.

References

• R Data Structures
• Dplyr Tutorial
• PLYR Tutorial

Last modified on 2024-12-27