Optimizing Code: Passing df Twice in 1 Plot & Months for Financial Data Visualization Using R's dplyr Library

Optimizing Code: Passing df Twice in 1 Plot & Months

In this blog post, we’ll explore a common issue when working with data visualization in R, specifically when dealing with dates and months. We’ll examine the challenges of passing data twice to create a plot and discuss how to optimize this process using R’s dplyr library.

Introduction

When creating plots for financial data, it’s essential to consider the month and year columns separately. However, in some cases, these columns can be used interchangeably to simplify calculations. In this article, we’ll look at an example where a user passes the df (data frame) twice in one plot, once for months and once for actual values.

Challenges of Passing df Twice

The given R code uses ggplot2 for data visualization. The user has passed the df twice to create two separate plots:

# Create a sequence from 1 to the current month - 1
monthseq <- seq(1, current_month - 1)

# Convert these numbers to actual months in the current year
current_year <- format(Sys.Date(), "%Y")
dates <- as.Date(paste(current_year, monthseq, "01", sep = "-"), "%Y-%m-%d")

# Format these dates to get abbreviated month names
month_list <- format(dates, "%b")

final_financials[is.na(final_financials)] <- 0 # changing all NA's to 0

report <- final_financials %>% 
  filter(Month %in% month_list & Segment == "Revenues") 

png(filename = "figs/ActualVBudget.png")
report %>%
  ggplot(aes(Month, Actuals)) + geom_col() + geom_crossbar(
    data = report,
    aes(x = Month),
    y = report$Budget,
    ymin = report$Budget,
    ymax = report$Budget,
    color = "red"
)
dev.off()

However, this approach has several issues:

The Month column is used for both the x-axis and filtering data. This can lead to unexpected results when using different scales for these two features.
Passing df twice in one plot can make the code harder to read and maintain.

Simplifying the Code

To optimize this process, we can use R’s dplyr library to simplify the code and avoid passing df twice. We’ll create a new data frame with only the necessary columns and then filter it for the desired months.

# Load required libraries
library(tidyverse)

# Made-up financials
report <- 
  tribble(
    ~Month, ~Actuals, ~Budget, ~Segment,
    "Jan", 10, 9, "Revenues",
    "Feb", 11, 11, "Revenues",
    "Mar", 12, NA, "Revenues",
    "Apr", 12, 13, "Revenues"
  )

# Create a sequence from 1 to the current month - 1
months <- seq(
  ymd("2024-01-01"), 
  floor_date(today(), unit = "month") - months(1), 
  by = "month"
)

# Filter data for desired months and Segment
report <- report %>% 
  filter(Month %in% months, Segment == "Revenues")

# Calculate the current month as a numeric value
current_month <- as.numeric(format(Sys.Date(), "%m"))

# Create a sequence from 1 to the current month - 1
monthseq <- seq(1, current_month - 1)

# Convert these numbers to actual months in the current year
current_year <- format(Sys.Date(), "%Y")
dates <- as.Date(paste(current_year, monthseq, "01", sep = "-"), "%Y-%m-%d")

# Format these dates to get abbreviated month names
month_list <- format(dates, "%b")

Optimized Plot

Now that we have a simplified version of the data frame, let’s create an optimized plot using ggplot2. We’ll use geom_crossbar for the budget visualization and avoid passing df twice in one plot.

# Create the optimized plot
report %>%
  ggplot(aes(Month, Actuals)) +
  geom_col() +
  geom_crossbar(
    aes(y = Budget, ymin = Budget, ymax = Budget), 
    color = "red"
  )

Saving the Plot

Finally, let’s save the optimized plot using ggsave.

# Save the plot to a file
ggsave("ActualVBudget.png")
#> Saving 7 x 5 in image

Conclusion

In this article, we explored how passing df twice in one plot can be optimized using R’s dplyr library. By simplifying the code and avoiding unnecessary operations, we can create more efficient data visualization solutions.

We also discussed some key challenges when working with dates and months, including the importance of considering these features separately to avoid unexpected results.

Last modified on 2024-01-27