Online Shopping Data

How many people around the world shop online? What can that tell us?

Introduction

In this page, we'll explore some data about online shopping rates around the world. We'll look at what we can learn from the data, and some interesting things that we can take away. The data on this page comes from The World Bank. Before we continue, take a look at the raw dataset. Click Here to view the raw dataset.

Key Terms

Indicator
This data is from the World Bank's dataset. They define data in categories of different indicators. The indicator for this data set is: "Used a mobile phone or the internet to buy something online"
Raw/Cleaned Data
Raw data is data that you get directly from the data source. In this case, the data contained several missing values, and irrelevant rows. By cleaning data, you format it how you want, and remove everything that's unnessesary.
TIME_PERIOD
In the raw data, the year is defined as TIME_PERIOD. It's important to be able to understand what each of the terms in the raw data mean so you can understand them when cleaning the data.
OBS_VALUE
In the raw data, the OBS_VALUE column means "Observed Value." In this case, we're looking at online shopping rates, so the observed value is the percentage at which people in that group used a mobile phone or the internet to buy something online.
REF_AREA_LABEL
REF_AREA_LABEL refers to the area that the value was observed in. In this case, it's usually the country, but this dataset includes some areas too, like "North America," or places that are disagreed upon if they are countries. For example, the country of Taiwan is represented as "Taiwan, China."

Introduction to the Dataset

On the right, you can see a dataset with some data. As described above, this was cleaned (a lot), and doesn't contain a lot of information. The original raw dataset contained 37 columns and 3,556 rows, for a total of over 130,000 data points! The cleaned data is visible in the table to the right. Scroll down while in the visualization window, and you can view the entire table.

To continue, keep scrolling down.

Change Over Time

Now, on the right, you can see a smaller sub-table where some data was taken out. Specifically, the rows have been merged and the individual years have been moved to be columns.

We can see that for most countries, the rate of online shopping increased between 2021 and 2024. However, for China and The West Bank and Gaza, the observed rates actually decreased during that time period.

Split by Gender

Finally, we'll take a look at how the data breaks down by gender using this third table on the right. In this table, you can see the Total, Male, and Female observed rates for online shopping. One thing that you might notice is that China doesn't have any data in the breakdown of the separate genders. This is because in that first data table, the breakdown for Male/Female was from the year 2021, and not from 2024. Sometimes, when visualizing data, it's important to pay attention to small things like this and missing data to prevent incorrect outcomes.

For Indonesia and the data from 2021 for China, there's a higher rate of online shopping for Females than males, but not by many percentage points. However, for the other countries, Males have a higher online shopping rate, with The West Bank and Gaza with the largest difference. When looking at this data, think about technology access and how that might differ across genders.