Handling Variations in Text Files

1. Missing Values

• Use the na.strings argument to define which strings should be handled as missing values.
• Example: read.csv(“data.csv”, na.strings = c(“”, “NA”).

2. Different Separators

• Specify the separator with the sep option in read.table().
• Example: read.table(“data.txt”, sep = “”)

3 .Inconsistent Data

• Use the quote argument to define the quoting character for values that contain separators.
• Example: read.csv(“data.csv”, quote = ‘”‘).

In today’s data-driven world, collecting data from multiple sources and turning it into a structured manner is a critical responsibility for data analysts and scientists. Text files are a prominent source of data, as they frequently include useful information in plain text format. To be used successfully, this data must be translated into a structured format, such as a DataFrame, which is a two-dimensional, size-mutable, heterogeneous tabular data structure with labeled axes.