Based on the requirement of the user, sometimes you may need to produce an output of different data types. You might need to perform a numeric calculation inside the function, but an output needs to display as a text or sometimes, the input is numeric but the output is a customized graph. There are several types of output that can be produced by creating customized function; also, the values of the output can be controlled. In this recipe, you will write a new function that can produce diffeent types of output.
- 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
Producing different output types and return values
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