Average Sales per Weekday with ggplot2: A Step-by-Step Guide

Average Sales per Weekday with ggplot2

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In this article, we’ll explore how to calculate and visualize the average sales per weekday using the popular R programming language and the ggplot2 graphics system.

Introduction to ggplot2

ggplot2 is a powerful data visualization library in R that provides a consistent and efficient way to create high-quality visualizations. It’s based on the concept of “grammar” of graphics, which means that it uses a specific syntax to define the structure and appearance of the plot.

Understanding the Data

The problem statement provides a sample dataset with three columns: date, weekday, and salesval. The date column contains dates in the format YYYY-MM-DD, while the weekday column indicates the day of the week (e.g., “Mi” for Monday). The salesval column stores the sales values for each transaction.

Calculating Average Sales per Weekday

To calculate the average sales per weekday, we need to aggregate the data by grouping it by the weekday column and then calculating the mean of the salesval column for each group. In R, this can be achieved using the aggregate() function from the dplyr package.

However, in the provided code snippet, the author is trying to achieve a similar result using the stat_summary() function, but with a twist: they’re trying to calculate the average total sales per weekday instead of just the average sales value per transaction per weekday. This approach won’t work as expected because it only considers one row for each group, whereas we need to sum up all the sales values for each weekday.

Solution using Aggregate Function

To solve this problem, we can use the aggregate() function to aggregate the data by grouping it by the weekday column and then calculating the mean of the salesval column for each group. Here’s the code snippet:

ggplot(data = aggregate(df$salesval, list(df$weekday), mean), aes(Group.1, x)) +
    geom_col()

In this code, aggregate() takes three arguments:

The data frame df
The grouping variable salesval (in this case, the weekday column)
A function to apply for aggregation (mean, in this case)

The resulting output is a grouped bar chart where each bar represents the average sales value per weekday.

Understanding the Code

Let’s break down the code snippet:

ggplot(): This creates a new ggplot object.
data = aggregate(...): The aggregate() function aggregates the data by grouping it by the weekday column and calculating the mean of the salesval column for each group. The result is passed to the data argument of the ggplot() function.
list(df$weekday): This specifies the grouping variable, which in this case is the weekday column from the df data frame.
mean(): This specifies the aggregation function, which calculates the mean of the salesval column for each group.
aes(Group.1, x): The aes() function maps the aesthetic attributes to the geometric layer. In this case, we’re mapping the Group.1 (i.e., the group label, in this case, the weekday) to the x-axis and x (i.e., the actual values of the sales) is not needed here because it’s calculated by the aggregate function.
geom_col(): This adds a bar layer to the plot.

Output

The output of this code snippet is a grouped bar chart where each bar represents the average sales value per weekday. The x-axis shows the weekdays, and the y-axis shows the average sales values for each day.

Example Use Case

Let’s consider an example where we have a data frame df with the following structure:

## A tibble: 12 x 3
   date       weekday salesval
   <date>      <chr>     <dbl>
 1 2003-10-31 Mi        425.36
 2 2003-10-31 Mi       1504.50
 3 2003-10-31 Mi        170.14
 4 2002-03-12 Mo       -215.80
 5 2002-02-08 Mi          0.00
 6 2002-04-17 Do        215.80
 7 2003-11-01 Tu        300.00
 8 2003-11-01 Tu        400.00
 9 2002-03-13 We       -100.00
10 2002-02-09 Th         0.00
11 2003-10-30 Fr       500.00
12 2003-10-30 Fr       600.00

Running the code snippet above will produce a grouped bar chart where each bar represents the average sales value per weekday.

Conclusion

In this article, we explored how to calculate and visualize the average sales per weekday using R’s dplyr package and ggplot2 graphics system. We discussed the importance of aggregating data by grouping it by relevant variables and calculated the mean of the values for each group. Finally, we provided an example code snippet that demonstrates how to achieve this result.

Last modified on 2024-07-21