Before the analysis itself, it is often necessary to adjust, check, and clean the data. Data cleaning may include, for example, removing duplicates, standardizing formats, checking for missing or incorrect values, correcting inconsistent data, or converting data into a suitable structure for further processing. These steps can be carried out using various tools depending on the size of the dataset and the type of task, ranging from common spreadsheet editors such as Microsoft Excel to programming and analytical environments such as R. It is important to preserve the original data, document individual modifications, and proceed in a way that allows the entire process to be traced and, if necessary, reproduced.
Below you will find selected tools and procedures that can help with cleaning and preparing data for further analysis.