One of the biggest complaints from developers about RDBMSs is that they are inflexible and hard to change. This has led to many developers in the past using existing fields for a variety of purposes, sometimes putting data from multiple places into one field. In more recent times, this has led to adding or migrating data to new data stores, with the idea that the system can adapt easily to changing requirements.

I ran across a post on system flexibility that somewhat illustrates the issue. The writing is a little hard to understand, but the author is thinking about the process of ordering food and how a system might need to handle some of the exceptions or additional changes. While the employees in a store might deal with this in an ad hoc manner, those of us building software need to think through the possible issues and account for (at least) some of them in code.

Software typically has much more flexibility than we do at the database level, since anyone building software is usually building on top of the database system. However, those of us that design schemas need to ensure that we take care to consider the different ways that our data store will serve the needs of the end user and design in the proper structures needed to support the data that will be stored.

In my experience, it's important for the database developer to question the requirements of the customer. Just as a software developer might try and tease out additional features and functions that are needed, the database developer needs to carefully query the customer to ensure that there won't be additional data that the customer assumes is being captured. We need to be aware of relationships and cardinality between the data elements, in order to better design a system that meets the current, and potential future, needs.

Building and designing a database isn't that hard, but it does take some attention to detail and a meticulous nature to think through how some real world situation is modeled. While I think small, frequent DevOps releases are a way to move forward, I'd also like to ensure that we capture as much detail as early as possible to minimize disruptions. After all, refactoring and changing the schema is harder in a database system than in an application software class.

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To filter a set of rows out of the data frame, we want to use the df[] syntax, using criteria for the rows in the place before the comma. In this case, we would use:

This gives us all rows where the CompPrcnt >= 100, our criteria. However, we only want the player name, team, and attempts. To get these columns only, we need a vector of columns. In this case, c(2,3,10) gives us these columns.

Ref: Filtering a dataframe - click here

Filtering Data - click here

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