During input validation you could generate customized messages so that anyone can see what was wrong. The exception handling has been discussed in detail into the recipe Handling exceptions and error messages in this chapter. There are functions such as warning(), stop(), tryCatch(), and so on which have been discussed with proper examples.
- Preface
- Installing and Configuring R and its Libraries
- Introduction
- Installing and configuring base R in Windows
- Installing and configuring base R in Linux
- Installing and configuring RStudio IDE in Windows
- Installing and configuring RStudio IDE in Linux
- Installing and configuring R tools for Visual Studio in Windows
- Installing R libraries from various sources
- Installing a specific version of R library
- Data Structures in R
- Introduction
- Creating a vector and accessing its properties
- Creating a matrix and accessing its properties
- Creating a data frame and accessing its properties
- Creating an array and accessing its properties
- Creating a list from a combination of vector, matrix, and data frame
- Converting a matrix to a data frame and a data frame to a matrix
- Writing Customized Functions
- Conditional and Iterative Operations
- Introduction
- The use of the if conditional statement
- The use of the if…else conditional operator
- The use of the ifelse vectorised conditional operator
- Writing a function using the switch operator
- Comparing the performance of switch and series of the if…else statements
- Using for loop for iterations
- Vectorised operation versus for loop
- R Objects and Classes
- Querying, Filtering, and Summarizing
- Introduction
- Using the pipe operator for data processing
- Efficient and fast summarization using the dplyr verbs
- Using the customized function within the dplyr verbs
- Using the select verb for data processing
- Using the filter verb for data processing
- Using the arrange verb for data processing
- Using mutate for data processing
- Using summarise to summarize dataset
- R for Text Processing
- Introduction
- Extracting unstructured text data from a plain web page
- Extracting text data from an HTML page
- Extracting text data from an HTML page using the XML library
- Extracting text data from PubMed
- Importing unstructured text data from a plain text file
- Importing plain text data from a PDF file
- Pre-processing text data for topic modeling and sentiment analysis
- Creating a word cloud to explore unstructured text data
- Using regular expression in text processing
- R and Databases
- Parallel Processing in R
There’s more…
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.