So far in our Analysis Phase, we have used SQL queries to accomplish many necessary tasks. We’ve created a database in SQL Server, imported a table, altered datatype for the table’s columns, learned to use SELECT FROM WHERE queries, and used that knowledge to create new tables from the original. It’s time to use our…
In the previous post, we were able to import our dataset by using the Data Import Wizard. During the process, we wrote some simple queries to create our database and to alter the data within it. Taking these steps set up our project to where we can now begin the Analysis Phase in earnest. Our…
In our last post, we created a SQL Server database for our project and successfully imported Version 2 of our dataset into it. We did this using the Data Import Wizard that comes with SQL Server Management Studio. While the Import Wizard is convenient, it often has trouble importing the data exactly as we want…
We just did a lot of heaving lifting in our R workspace, creating new columns using several nested functions. Now that we have completed adding the three additional columns to all 12 of our spreadsheets for 2021, we can move on to combining these spreadsheets and saving them to new ones. We will combine these…
Version 2 of our dataset is really coming together now that we have added two new columns to it. We only have one more column to add before we can get our project on to the next stage. With new columns for day of the week and ride lengths added the dataset, we now need…
By this point, we should have a basic template, or “baseline”, set up in our R workspace for every spreadsheet in our dataset. We will use this baseline of our “Version 2” to start making edits to the dataset. The edits that we will be adding onto to our spreadsheets will be two new columns,…
Now that we have our R console of choice set up and have created a “Loading Page” for our R packages, we can create a new version of our dataset. From this new version, we will be able to add the new columns that will display the “Trip Distance” and “Day of Week”. Before we…
R is a great programming language that can help us to expand upon a raw dataset and extract more insightful information from it. We are going to use R to enhance our Cyclistic dataset by adding two more columns to our spreadsheets: one for the day of the week a ride took place on, and…
Now that we have a made the desired edits to our raw dataset and have created new spreadsheets that display our changes, it is time to analyze our data. This is the crux of project. During our “Analysis” phase, we will use our data to develop the key insights which will help us answer the…
The first new column that we will add onto our sheets is meant to show the amount of time that each individual ride lasted for. We will call it “ride_length”, keeping with the naming convention found in the other columns. Specifically, we will continue to use an underscore to replace spaces in the titles of…