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File handling in R refers to the process of reading data from external sources, storing data into files, and manipulating files for analysis. In data analysis projects, large amounts of data are usually stored in external files such as CSV files, text files, Excel sheets, or databases. R provides built-in functions that allow analysts to easily read, write, and manage these files. Efficient file handling is essential in data analysis with R and Python because it allows data scientists to import datasets, clean them, analyze them, and export the processed results. In R, file handling is mainly performed using functions that read data into R objects such as data frames, vectors, or matrices. The most common file formats used in data analysis are CSV (Comma Separated Values) files and text files. These formats are widely used because they are simple, portable, and supported by many programming languages including Python. Reading data means importing the data stored in a file into the R environment so that it can be processed and analyzed. Writing data means exporting the processed or analyzed data from R into an external file for storage, sharing, or further use. CSV files are one of the most commonly used formats in data analysis. A CSV file stores tabular data where values are separated by commas. Each row represents a record and each column represents a variable. R provides functions such as read.csv() and write.csv() to read and write CSV files. To read a CSV file in R, the read.csv() function is used. This function loads the contents of the CSV file into a data frame. A data frame is a table-like structure in R where rows represent observations and columns represent variables. Example of reading a CSV file in R: data <- read.csv("student_data.csv") In this ___ ____ ________ ___ ____.
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