How does R handle tabular data?

There are several common methods that can be used in R language to process tabular data.

1. To read table data, you can use the functions read.table() or read.csv() to read the data and store it as a data frame object.
2. Viewing Data: You can use the head() function to look at the first few rows of the data frame, and use the str() function to check its structure and types.
3. Filtering data: You can use logical operators (such as ==, >, <, etc.) and conditional statements to filter data. For example, you can use the subset() function to filter data based on conditions.
4. Extracting data: Specific columns or rows can be extracted from a data frame using either an index or variable name. For example, you can use the $ symbol or [[]] symbol to extract a particular column.
5. Convert data types: You can use functions such as as.numeric() and as.character() to change data into different types. For example, you can convert character data into numeric data.
6. Missing value handling: You can use the is.na() function to check for missing values in a data frame, and use the na.omit() or complete.cases() function to remove rows containing missing values.
7. Sorting Data: You can use the order() function to sort a data frame. For example, you can sort the data in ascending or descending order based on the values in a specific column.
8. Summarize data: You can use the aggregate() function to summarize data. For example, you can calculate statistics such as the sum, mean, and maximum value of a certain column.
9. Combine data: You can use the merge() function to merge two or more data frames based on a common variable. For example, you can merge two data frames with the same column based on the same column.
10. Create charts: You can use various plotting functions (such as plot(), barplot(), hist(), etc.) to visually analyze and display tabular data.

The above are just some commonly used methods for processing table data. R language also has many other powerful functions and packages that can be used to handle and analyze table data.