Prerequisites

Before we dive in, ensure that you have R and RStudio set up on your machine. You will also need to install the following R packages:

install.packages(c("tidyverse", "lubridate"))

Step 1: Import Required Libraries

Let’s begin by loading the necessary libraries for data manipulation and analysis.

# Load necessary libraries

library(tidyverse)

library(lubridate)

Step 2: Generate a Sample Dataset

In this tutorial, we will be using a sample dataset representing monthly sales figures.

# Sample dataset: Monthly sales data

sales_data <- tibble(

month = seq(ymd('2022-01-01'), ymd('2022-12-01'), by = 'month'),

sales = c(12000, 15000, 17000, 16000, 18000, 22000, 21000, 23000, 25000, 27000, 26000, 28000)

)

Step 3: Visualize the Sales Data

Next, we will create a visualization of the sales data to identify trends.

# Plot the sales data

sales_data %>%

ggplot(aes(x = month, y = sales)) +

geom_line(color = 'blue') +

geom_point(color = 'red') +

labs(title = "Monthly Sales Data",

x = "Month",

y = "Sales (in USD)")

Step 4: Calculate Monthly Sales Growth

Now, we will compute the month-over-month sales growth.

# Calculate monthly sales growth

sales_data <- sales_data %>%

mutate(sales_growth = (sales - lag(sales)) / lag(sales) * 100)

Step 5: Concealed Analysis

In this step, we will calculate the average sales growth rate using a function that obscures the analysis.

# Hidden analysis: Calculate average sales growth rate

calculate_average_growth <- function(df) {

avg_growth <- df %>%

summarize(average_growth = mean(sales_growth, na.rm = TRUE))

return(avg_growth)

}

# Invoke the hidden analysis function

average_growth <- calculate_average_growth(sales_data)

Step 6: Display the Results

Let’s print the sales data alongside the growth and average growth rates.

# Print the results

print(sales_data)

print(average_growth)

Step 7: Visualize Sales Growth Rates

Finally, we’ll visualize the sales growth rates using a bar chart.

# Visualize sales growth rates

sales_data %>%

ggplot(aes(x = month, y = sales_growth, fill = sales_growth > 0)) +

geom_col(show.legend = FALSE) +

labs(title = "Monthly Sales Growth Rates",

x = "Month",

y = "Growth Rate (%)")

Conclusion

Congratulations! You have successfully analyzed sales performance data using R. This guide provided the essential steps, including a concealed analysis to enhance your understanding. Keep exploring and advancing your analytical skills!

Full Code Reference

Here’s the complete code for your convenience:

# Load necessary libraries

library(tidyverse)

library(lubridate)

# Sample dataset: Monthly sales data

sales_data <- tibble(

month = seq(ymd('2022-01-01'), ymd('2022-12-01'), by = 'month'),

sales = c(12000, 15000, 17000, 16000, 18000, 22000, 21000, 23000, 25000, 27000, 26000, 28000)

)

# Plot the sales data

sales_data %>%

ggplot(aes(x = month, y = sales)) +

geom_line(color = 'blue') +

geom_point(color = 'red') +

labs(title = "Monthly Sales Data",

x = "Month",

y = "Sales (in USD)")

# Calculate monthly sales growth

sales_data <- sales_data %>%

mutate(sales_growth = (sales - lag(sales)) / lag(sales) * 100)

# Hidden analysis: Calculate average sales growth rate

calculate_average_growth <- function(df) {

avg_growth <- df %>%

summarize(average_growth = mean(sales_growth, na.rm = TRUE))

return(avg_growth)

}

# Invoke the hidden analysis function

average_growth <- calculate_average_growth(sales_data)

# Print the results

print(sales_data)

print(average_growth)

# Visualize sales growth rates

sales_data %>%

ggplot(aes(x = month, y = sales_growth, fill = sales_growth > 0)) +

geom_col(show.legend = FALSE) +

labs(title = "Monthly Sales Growth Rates",

x = "Month",

y = "Growth Rate (%)")

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