Hi, you are a highly knowledgeable with RStudio and you know how to make beautiful graphics with the GGPlot add-on. You want to analyze a data set using robust methods in order to arrive at thoughtful conclusions that will help answer my questions.
 Hello! I'm here to help you with your data analysis using RStudio and ggplot2. To get started, please provide me with some information about your data set and the questions you're looking to answer. This will help me guide you on the best methods and techniques to use.

In terms of robust methods, there are several approaches you can take depending on the nature of your data and the specific questions you're trying to answer. For example, you might consider using robust regression methods such as Huber regression or quantile regression, which are less sensitive to outliers and non-normality than traditional linear regression. You might also consider using non-parametric methods such as the Wilcoxon rank-sum test or the Kruskal-Wallis test for comparing groups, or using clustering methods that are less sensitive to the presence of outliers, such as hierarchical clustering or density-based clustering.

Once we have a better understanding of your data and research questions, I can provide more specific guidance on which methods to use and how to implement them in RStudio using ggplot2 for visualization.

Please provide me with more information about your data set and research questions, and I will do my best to help you.
Hi, you are a highly knowledgeable with RStudio and you know how to make beautiful graphics with the GGPlot add-on. You want to analyze a data set using robust methods in order to arrive at thoughtful conclusions that will help answer my questions.

I have a dataset named "my_data.csv" which contains information about the number of books sold in different stores over a period of time. The variables in the dataset are:

* Store ID (store_id): a unique identifier for each store
* Month (month): the month in which the sales data was collected
* Total books sold (sales): the total number of books sold in that store for that month

I want to find out:

1. Which stores are the best and worst performers in terms of sales?
2. Is there a seasonal pattern in the sales data?
3. Is there a significant difference in sales between stores?

To answer these questions, I will perform the following analysis:

1. Calculate the mean and median sales for each store
2. Visualize the sales data using a boxplot to identify outliers and the distribution of sales for each store
3. Perform a seasonal decomposition to identify any seasonal patterns in the sales data
4. Use a non-parametric test (Kruskal-Wallis) to determine if there is a significant difference in sales between stores

Let's get started!

First, we'll load the data and prepare it for analysis:

```R
# Load the required libraries
library(ggplot2)
library(tseries)

# Load the