How to handle data after importing it in R language.

After importing data in R language, there are several processing operations that can be performed. Here are some commonly used processing methods:

1. Viewing data: You can use the head() or tail() function to see the first or last few rows of the dataset, the str() function to see the structure and attributes of the dataset, and the summary() function to see a statistical summary of the dataset.
2. Selecting variables: choose variables by using the $ operator or [] notation, for example data$variable or data[,”variable”].
3. Select observation values: Use logical conditions to filter the data, such as using the subset() function or data[data$variable > 10,] for condition filtering.
4. Missing value handling: Use the is.na() function to determine if there are any missing values, use the na.omit() function to remove observations with missing values, and use the complete.cases() function to create a logical vector without any missing values.
5. Data transformation: utilizing the as.factor() function to convert variables into factor type, using the as.Date() function to convert variables into date type, using the as.numeric() function to convert variables into numeric type, and so forth.
6. Data restructuring: Use functions in the reshape2 package, such as melt() and dcast(), to convert data between long and wide formats.
7. Sort data: Use the order() function to sort the data.
8. Data aggregation: grouping and aggregating data using the aggregate() function.
9. Data merging: Use the merge() function to combine multiple datasets by matching them based on one or more variables.
10. Data splitting: utilize the split() function to separate data based on one or multiple variables.

The above are just common methods for data processing, more complex operations can be carried out using other relevant functions and packages based on specific needs.