We were able to use R to make visualizations of our data that were precise and customized to our liking. R is a great tool for hand-crafting graphs to suit our project, but it is a bit complex to work with. Tableau is another tool that we could use to make visualizations, and its much…
We are finally at the end of our project! With all of the data and visualizations that we have gathered and enhanced, we are now ready for the final two phases of data analysis: the Share phase and the Act phase. The Share phase is where you share your findings with the stakeholders who are…
As you’ve seen from the previous posts, R can be a pretty complex language, capable of making complex and precise graphs. It’s proven quite useful in making scatterplots with our Cyclistic dataset. In the last post, we finished making a scatterplot that compared Ride Length (Hours) and Trip Distance (Miles) for each rider across the…
At this point, we’ve gone over how to make a scatterplot in R using ggplot() and geom_point(). We have also added another function onto our code to add a title to our graph (ggtitle()). Our graph is starting to look more presentable, but it’s still going to need a lot of polish before we can…
The concepts we went over in the last post laid down the foundation for creating data visualization in R. Now we’ll dig deeper and find out ways to customized the layout, labels, and appearance of our visualizations. We’ll start by introducing an additional element to our graphs: shape. The shape element can be used in…
The last post was a light refresher on working in an R workspace and uploading data into that workspace. With our desired SQL exports loaded into R, we can now begin creating visualizations of our data. This will help produce images to represent the numbers, and will aid in our presentation of this data to…
In the previous posts, we used SQL to organize the data that had been collected across the whole of 2021. We saved particular queries that offered useful insights into the story that our dataset provides. It’s time to turn these insights into visualizations, in order to will help our stakeholders understand the trends of the…
By now, we have used SQL to organize our data in ways that begin to tell the story of our overall dataset. We’ve ran some basic queries that provided simple summary information, which can be used in a presentation of our findings. Though this data on it’s own doesn’t tell the whole story, it does…
In the last post, we went over the somewhat painstaking process of finding both the Max and Mean of our ride_length column. These are useful bits of information that we can use in our analysis and later on in our presentation . Another insightful bit of data that we can extract from our dataset is…
So far in the Analysis Phase, we’ve used SQL queries to create subsets of data that can be used to create visualizations of the data to help us understand the story that our dataset tells. We’ve also been able to collect smaller insights on our data, like how many member rides there have been in…