Rectangular Text Data
- In R, to manipulate and analyze data, reading and writing rectangular text data is an important activity. Rows and columns of rectangular text data get divided in a tabular layout and are frequently separated by commas or tabs.
- Rectangular text data is ordered as rows and columns of structured data, commonly stored in text files. Every observation is represented by a row, and every variable or field is represented by a column.
To Read Rectangular Text Data, it’s important to define the delimiter, data types, and whether the first row contains column names. Saving time and memory through efficient reading is possible.
To Write Rectangular Text Data, export your R data structures, such as data frames, to text files. The output file’s location can be specified, along with the delimiter and character string quoting options.
There are various packages for reading and writing rectangular text data. Let’s talk about the two major packages that support CSV file, txt file, etc.
Different Packages To Read and Write Rectangular Text Data
data.table The data.table package generates a powerful and fast data manipulation framework in R. It is a reliable option when speed and performance are required because of its well-known efficiency in processing massive information. It is only for both CSV and txt files.
Functions to Use
- Fread – To read data, use the fread function and the file location.
- Fwrite – To write data, use the fwrite function and give the data and output file paths.
readr – The readr package is a component of the tidyverse ecosystem, a collection of R packages focused to simplify and speed up data manipulation and analysis. Readr also specializes in quickly and accurately reading rectangular text data sources. It is only for CSV files.
Functions to use
Read and Write Rectangular Text Data Quickly using R
Reading and writing rectangular text data quickly in R Programming Language can be achieved using various packages and functions, depending on your specific needs and the data format. Two commonly used packages for this purpose are readr and data. table. Here’s how you can do it with these packages.