Data Cleaning (Example)
First, we had downloaded the raw .txt file from the Hanguksa Yeongu Hwibo website and imported it into Excel.
Removing irrelevant parts
Since the original format of the dataset is a continuous text string on one column of the worksheet, we need to separate it into individual columns based on the category. In order to do that, we split this text data by using a comma delimiter character (e.g., comma, tab, space, or semi-colon) in Excel. You can use the Excel ribbon under the Data tab and click on the Text to Columns icon in the Data Tools group of the Excel ribbon. Select Delimited on the option buttons to split text into different columns in Excel.
Arranging data order
& Checking errors
We extracted relevant data needed for our analysis (e.g., material ID, classification number, period, author(s), title, periodical (with volume), publisher, publication date, and URL).
After getting rid of unnecessary data values, we sort the list of data by the material ID and organized the inaccurate format in the correct order.
Simplifying data
For our analysis, we will only need publication years. Thus, we converted the publication date to year format.
Well-done, it looks much better! Now, our dataset is ready to be analyzed.
