In today’s dashboard challenge, we embarked on an exciting data exploration adventure, armed with nothing but a URL containing a wealth of information about various aspects of SpaceX. The content varies from the company itself to rockets, ships, launches, and landings, so we had plenty of choices. After carefully observing all the data, I decided to focus on analyzing rocket launches, delving into the details of these awe-inspiring events.
The data obtained from the API was in a JSON format, all packed into a single cell. Consequently, half of my time was devoted to organizing the data into a usable format and scope that suited my analysis goals. The journey of data preparation is outlined in the image below.
To begin, I utilized a JSON parsing tool to extract key-value pairs from the consolidated column and convert them into a more structured table format. I then examined the field values in each column, gaining an understanding of their meanings and structures. The next step is to split the JSON name column, which allowed me to group the fields according to rocket using the Transform tool. I subsequently joined the rocket, launch, and launchpad data, merging them into a cohesive dataset for in-depth analysis.
With the data in its rightful place, I set my sights on the dashboard. My vision was to conduct analyses on multiple levels of detail, unearthing valuable insights about SpaceX launches. Some of the fascinating analyses I conducted included: yearly breakdown of launch costs, cost percentage of successful launches each year, time gaps between SpaceX’s first-ever launch and subsequent missions.
In essence, this endeavor not only allowed me to glean valuable insights about SpaceX rocket launches but also honed my skills in navigating APIs—an indispensable tool in the data analyst’s arsenal. Embracing the challenges of data wrangling and visualizing, I gained a deeper appreciation for the wonders of space exploration and the power of data analytics.