Web Reference: Aug 17, 2025 · Missing data is one of the most common challenges in data analysis and statistical modeling. Whether the data originates from surveys, administrative registers, or clinical trials, it is almost inevitable that some values are absent. Apr 1, 2024 · It's crucial for researchers and analysts to recognize the types of missing data, understand the mechanisms behind them, and apply appropriate methods for handling them. We first need to identify where and how data is missing in our dataset. When importing your data, be aware of values that should be classified as missing. For example, 99, 999, “Missing”, blank cells (““), or cells with an empty space (” “). You can convert these to NA (R’s version of missing data) during the data import command.
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