The arrange() verb takes the first input argument as a data frame and then, the names of variables to organize the rows in ascending order. In the code, it takes USAairlineData2016 as input data and the ORIGIN and DEST variables for organizing the rows in ascending order. To do the descending arrangement, the desc() function has been used. The arrange() verb works relatively slower than other verbs in dplyr, but it works faster than the base R order() function.
- 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
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