When working with data in R, you will often need to move your data between different sessions or share it with others. A common workflow is to export your data to a CSV file and then later import that same file back into R. This process is especially useful when you want to save your progress, transfer data between projects, or ensure that your data is in a widely supported format. After exporting a data frame to a CSV file, you can quickly reload it into your R environment and continue your analysis without losing any information.

data_csv_imported <- read.csv("my_data.csv")

The read.csv() function is designed to read comma-separated values (CSV) files into R as data frames. In the example above, the function reads the contents of "my_data.csv" and assigns the resulting data frame to the variable data_csv_imported. This workflow—exporting data with write.csv() and then importing it with read.csv()—ensures that your data's structure and values are preserved between sessions. The main parameter for read.csv() is the file name or path, which tells R where to find your CSV file. You can also adjust other parameters, such as whether the first row contains headers or how missing values are handled, but the default settings are typically sufficient when working with files exported from R itself. This smooth import and export process allows you to efficiently manage your data in R, making it easy to pick up your work right where you left off.