Easy error handling in R with purrr’s possibly

The video tutorial covers error handling in data processing using R. It begins with the challenges of importing CSV and Excel files with inconsistent data types. The instructor demonstrates creating a function to process these files and handle errors using the 'possibly' function. The tutorial shows how to identify problematic files, revise functions to accommodate data type variations, and ensure smooth data processing without interruptions.

What is a common issue when running code over multiple objects?

• The code runs too fast.

• The code stops due to an error in one object.

• The code produces too much output.

• The code requires too much memory.

What function is used to import files in R?

• list.files

• load.files

• read.files

• import.files

Which function is used to transform data types in R?

• mutate

• transmute

• convert

• transform

What does the 'possibly' function in R do?

• It skips the file with an error and continues processing others.

• It automatically corrects errors in the data.

• It speeds up the data processing.

• It stops the code when an error occurs.

Why is the 'map' function used instead of 'map_df' with 'possibly'?

• Because 'map' uses less memory.

• Because 'map' is more accurate.

• Because 'map' returns a list, which is needed for error handling.

• Because 'map' is faster.

How can you identify which file caused an error?

• By checking the file size.

• By using a debugger.

• By looking at the file name in the list.

• By opening each file manually.

What is the purpose of using 'ifelse' in the revised function?

• To increase the accuracy of the data.

• To speed up the processing.

• To handle different data types in the 'value' column.

• To reduce memory usage.