When working with data from e.g. stores or social media sources, it's essential to follow a structured approach to ensure data quality and to understand its characteristics.

Below is a general approach to inspecting data in both R and Python, describing each step.

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R

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Python

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Description of Each Step

1. Read the Data: This step involves reading the data from a CSV file into a variable. In R, the read.csv function is used, while in Python, pandas.read_csv is utilized.
2. Convert to Data Frame: Ensuring the data is in a data frame format, which is essential for data manipulation and analysis. In R, this might require an explicit conversion using as.data.frame.
3. Examine Dimensions: Checking the dimensions (number of rows and columns) of the data to verify it matches expectations.
4. Inspect the Data: Viewing the first few and last few rows of the data to get a sense of its structure and content. The head and tail functions are commonly used in both R and Python.
5. Random Sample of Data: Extracting a random sample of rows provides a quick overview of the data, which can help in spotting any anomalies or unexpected values.
6. Check Structure: Using functions like str in R and info in Python to check the structure of the data, including data types and the presence of null values.
7. Convert Variable Types: Converting data types as necessary, especially converting columns to factor types (in R) or category types (in Python) for categorical data.
8. Summary Statistics: Generating summary statistics for each column, such as mean, median, min, and max values, to understand the distribution and detect any anomalies.
9. Detailed Descriptive Statistics: Using more detailed statistical functions to get a deeper understanding of the data, including measures like trimmed mean and skewness. This is done using the psych package in R and scipy.stats in Python.

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