**R – Longhow Lam's Blog**, and kindly contributed to R-bloggers)

## Introduction

Because it is Friday, another ‘*playful and frivolous**‘* data exercise

IKEA is more than a store, it is a very nice experience to go through. I can drop of my two kids at smàland, have some ‘quality time’ by walking around the store with my wife and eat some delicious Swedish meatballs. Back at home, the IKEA furniture are a good **‘relation-tester’:** try building a big wardrobe together with your wife…..

The nice thing about IKEA is that you don’t have to come to the store for nothing, you can check the availability of an item on the IKEA website.

According to the website this gets refreshed every 1,5 hour. This brought me on an idea, if I check the availability every 1,5 hour I could get an idea of the number of items sold for a particular item.

## The IKEA Billy index

Probably the **most iconic item **of IKEA is **the Billy bookcase**. Just in case you don’t know how this bookcase looks like, below is a picture, its simplicity in its most elegant way….

For every 1,5 hour over the last few months I have checked the Dutch IKEA website for the availability of this famous item for the **13 stores **in the Netherlands, and calculated the **negative difference** between consecutive values.

The data that you get from this little *playful exercise* do not necessarily represent the numbers of Billy bookcases really sold. Maybe the stock got replenished in between, maybe items were moved internally to other stores. For example, if there are **50** Billy’s in Amsterdam available and 1,5 hour later there are **45** Billy’s, maybe 5 were sold, or 6 were sold and 1 got returned? replenished? I just don’t know!

All I see are movements in availability that might have been caused by products sold. But anyway, let’s call the movements of availability of the Billy’s **the IKEA Billy index.**

## Some graphs of the Billy Index

### Trends and forecasts

Facebook released a nice R package, called **prophet**. It can be used to perform forecasting on time series, and it is used internally by Facebook across many applications. I ran the prophet forecasting algorithm on the IKEA Billy index. The graph below shows the result.

There are some high peaks end of October, and end of December. We can also clearly see the Saturday peaks that the algorithm has picked up from the historic data and projected it in its future forecasts.

### Weekday and color

The graph above showed already that on Saturdays the Billy index is high, what about the other days? The graph below shows the other days, it depicts the sum of the Ikea index per day since I started to collect this data (end of September). Wednesdays and Thursdays are less active days.

Clearly most of the Billy’s are white.

### Correlations

Does the daily Billy Index correlate with other data? I have used some Dutch **weather data** that can be downloaded from the Royal Netherlands Meteorological Institute (KNMI). The data consists of many daily weather variables. The graph below shows a correlation matrix of the IKEA Billy Index and only some of these weather variables.

The only correlation with some meaning of the IKEA Billy Index and a weather variable is the **Wind Speed** (**-0.19**). Increasing wind speeds means decreasing Billy’s.

It’s an explainable correlation of course…. You wouldn’t want to go to IKEA on (very) windy days, it is not easy to drive through strong winds with your Billy on top of your car.

Cheers, Longhow.

**leave a comment**for the author, please follow the link and comment on their blog:

**R – Longhow Lam's Blog**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...