Apprendre Introduction to Data Cleaning and Wrangling | Data Cleaning and Wrangling Essentials

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Definition

Data cleaning is the process of detecting and correcting (or removing) inaccurate, incomplete, or irrelevant data from a dataset. Data wrangling, also known as data munging, refers to transforming and mapping raw data into a more useful format for analysis. Both are crucial steps that ensure your data is accurate, consistent, and ready for meaningful analysis.

You often encounter real-world data that contains inconsistencies, errors, or missing values. Common data issues include:

Duplicated records;
Misspelled entries;
Inconsistent formats (such as dates or capitalization);
Missing or outlier values.

These problems can lead to incorrect conclusions if not addressed. Cleaning your data before analysis is necessary to ensure that your results are trustworthy and actionable.


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# Simulating a messy dataset
messy_data <- data.frame(
  Name = c("Alice", "bob", "Charlie", NA, "Eve", "Bob"),
  Age = c(25, NA, 30, 22, 29, 25),
  Gender = c("F", "m", "M", "F", NA, "m"),
  Score = c("85", "90", "eighty", NA, "95", "90")
)

# A cleaned version might look like:
cleaned_data <- data.frame(
  Name = c("Alice", "Bob", "Charlie", "Eve"),
  Age = c(25, 25, 30, 29),
  Gender = c("F", "M", "M", "F"),
  Score = c(85, 90, 80, 95)
)

The data cleaning and wrangling workflow typically begins by identifying issues within your dataset, such as missing or inconsistent values. Next, you clean the data by handling these issues — removing duplicates, correcting formats, and filling in or removing missing values. After cleaning, you transform and prepare the data for analysis by reshaping, aggregating, or merging datasets as needed. This systematic process ensures that your data is suitable for accurate analysis and visualization.


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# Simulate a new dataset
sample_data <- data.frame(
  ID = 1:5,
  Value = c(10, NA, 30, 25, 40),
  Category = c("A", "B", "B", "A", "C")
)

# Display structure
str(sample_data)

# Display summary statistics
summary(sample_data)

You should use data cleaning and wrangling whenever you are preparing real-world data for analysis, especially when the data source is external or uncontrolled. For example, cleaning survey responses, merging sales records from multiple sources, or preparing healthcare data for research all require these steps. Proper cleaning and wrangling help you avoid misleading results and ensure your insights are based on high-quality data.

1. What is the primary goal of data cleaning?

2. Why is it important to inspect your data before analysis?

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Section 1. Chapitre 1

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Section 1. Chapitre 1