Learn To Create Class Structure In C And Create An Array Of

Learn To Create Class Structure In C2 Create An Array Of Objec

Learn to create class structure in C++, create an array of objects in C++, read a CSV file containing COVID-19 data, create COVID19 objects, store them in a dataset class, and perform various search operations based on commands read from redirected input.

Paper For Above instruction

The ongoing COVID-19 pandemic has underscored the importance of effective data management and analysis to inform public health decisions. In the realm of epidemiological data, object-oriented programming offers a structured approach to handling complex datasets. This paper discusses the implementation of a C++ program that processes COVID-19 case and death data per county across the United States, emphasizing class design, data parsing from CSV files, and command-based data querying.

To efficiently manage the COVID-19 data, a class structure is essential. The COVID19 class encapsulates individual records, including date, county, state, FIPS code, cases, and deaths. Attributes such as date, county, and state are modeled as C-style strings (char pointers), while fips, cases, and deaths are integers. The class provides a default constructor, a parameterized constructor for initialization, and necessary accessor and mutator methods, along with a display function to output record details.

Building upon this, the COVID19DataSet class maintains an array of COVID19 objects, along with a count of stored records and a maximum size. It provides methods to add new records, display all data, and perform various query operations such as total cases and deaths by county or state, as well as within date ranges. These methods facilitate efficient data retrieval based on user commands.

The main program orchestrates the data processing workflow. It begins by reading an input text file via redirected input, which contains the number of records, the filename of the CSV data file, and subsequent commands. The program opens the CSV file, reads and parses each line—taking care to handle comma delimiters—creating COVID19 objects and populating a COVID19DataSet instance. Commands read from input trigger the relevant query methods, enabling the user to extract specific COVID-19 statistics, such as total cases or deaths for particular counties, states, or date ranges.

Key challenges include robust file handling, string management in C++, and accurate filtering of data based on command parameters. The program must also adhere to constraints like using only the iostream header, and ensuring output matches expected formats for automated grading.

In conclusion, this project illustrates how object-oriented programming can be leveraged for epidemiological data analysis. By designing appropriate classes, parsing CSV data effectively, and implementing command-driven querying, developers can create a flexible and extensible system for managing large-scale health datasets. Future enhancements could include dynamic memory management, more sophisticated search capabilities, and graphical data visualization.

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