Everybody loves a chart or graph! Data visualization tells a story about complex data in a simple format that can be instantly understood. But behind every chart, there is data that has been collected, curated, and prepared for visualization. Most of the time, this is probably done using a spreadsheet such as Microsoft Excel or Google Sheets.
But, what if you have a REALLY big data set? Data visualization becomes a much harder problem if you can't easily open the source data. Microsoft Excel is limited to one million rows, and Google sheets is limited to 10 million cells, which is basically the equivalent limit (imagine a 10 column file, limited to 1 million rows). Therefore, you will need to locate other big data visualization tools. And let's face it, not everyone knows how to set up a database and use SQL to summarize data.
Enter Gigasheet, the big data spreadsheet that can handle up to a billion rows of data without installing any software. Additionally, Gigasheet is a full-fledged data science workbench with all tools you will ever need for data analysis and visualization. Simply upload your file and start analyzing.
In this blog, we will explore how easy it is to explore huge files and perform big data visualization with Gigasheet. So, read on.
For our analysis and visualization, we are using a dataset with information regarding the artist and the songs on the Spotify global weekly chart. The dataset includes columns for artist name, song title, position in the chart, number of streams, and much more.
As mentioned, we will use Gigasheet to convert data to charts from our Spotify weekly chart dataset. We will explore different visualization options, including pie charts, line graphs, bar graphs, and more. We will also use features like groups, filters, etc., for data analysis. Let’s get started.
Before we can convert data to charts, we need to upload our data set to Gigasheet. So, first, we will head to gigasheet.com and sign in. (You can sign up for Gigasheet here for free!)
Once we are in, we have the Gigasheet Library on our screens which stores all our files. Now, we will click on New and choose our Spotify global weekly chart dataset.
Our dataset is a CSV file, but you can upload large datasets in many popular formats, including XLSX, JSON, LOG, ZIP, and more. Now we will click on our dataset, and Gigasheet will open it in tabular form.
Below we will explore several different charts that can be created from the data set.
Our Spotify dataset has details of the top 200 songs, so let us see which songs had more streams in the week. We will click on Filter in the menu bar and filter rows with Pos (position) less than or equal to 20, as shown below.
Now for data visualization, we will select all the cells in the Title and Streams columns. From here, there are 2 ways to create a chart:
Once highlighted, create a visualization with the click of a button.
If you want to change the chart type, mouse over the chart are and select the triangle to choose a different chart type. Here, I convert it to a pie chart.
Here I choose, Chart Range > Column > Grouped.
And there we have the top 20 songs and the number they did in the week.
Now, let us see, out of all the artists on the Spotify weekly chart, which ones have the most songs featured on the list? First, we will click on Group in the menu bar and group rows by Artist column.
Then, we will sort the Title column in descending order by clicking on the header row, as shown below.
Since many artists have multiple songs on the list, we need to calculate the sum of their weekly streams. Therefore, we will bring up the drop menu and select Sum.
We will use a pie chart to visualize data for the top 10 artists with the most songs. And select cells in Title column for the top 10 artists, and select Chart Range > Pie.
Bad Bunny is the top artist with the most songs on the Spotify weekly chart and the most combined streams in the week. So let us deep dive into the performance of the artist’s songs and convert data to charts. First, we will filter data for Bad Bunny using the filter as shown in the snapshot.
Not all songs saw boosted streams; some songs had static numbers, and some even had fewer streams from the previous week. We will select Title, Streams, and Stream+ columns to visualize this trend and right-click > Chart Range > Line.
We also get plenty of options to customize charts to create data visualizations tailored to their specific needs.
As you saw above, Gigasheet has many graphs and charts, but you can switch from one type to another with one click. If the bar graph does not work best with your dataset? Just expand the chart settings and select the chart type you prefer.
In Gigasheet, you can add titles to graph and labels to axis, legends, and data in Format section of graph settings.
Working with a color scheme on your data analysis report? Gigasheet allows you to customize the look of graphs as well.
You can download the charts you prepare, download them to your device to add charts in Excel or any tool you work on.
Anyone can easily visualize data and extract meaningful insights, thanks to Gigasheet. You can quickly create charts and graphs from their data without coding or specialized software. Additionally, it provides users with a massive selection of editable templates and options to customize their data visualization to suit their particular requirements. And Gigasheet is completely free-to-use, so sign up today!