By Larry Pace
R Recipes is your convenient problem-solution reference for studying and utilizing the preferred R programming language for statistics and different numerical research. filled with hundreds and hundreds of code and visible recipes, this booklet allows you to quick study the basics and discover the frontiers of programming, reading and utilizing R.
R Recipes provides textual and visible recipes for simple and efficient templates to be used and re-use on your day by day R programming and knowledge research perform. no matter if you are in finance, cloud computing, great or small info analytics, or different utilized computational and knowledge technology - R Recipes can be a staple on your code reference library.
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Extra resources for R Recipes A Problem-Solution Approach
The grades were retrieved from the course gradebook. The data represented three sections of the same online course, with 55 students in all. There were some missing data, as one might expect. Students probably committed more plagiarism than the data indicate, because I did not count suspected plagiarism, but only the specific incidents I could document. 36 c Chapter 3 ■ Data Structures Creating a Data Frame and Accessing Data Frame Elements Data frames can contain any type of data, including other data frames, but in this book, we will limit ourselves to data frames containing numbers and character strings.
Txt", "r") > readLines(connection, n = 1)  "Plastic Omnium maintains a proactive environmental protection policy at the highest levels of " > readLines(connection, n = 1)  "the company worldwide. It not only ensures compliance with the legal requirements in effect in " > Recipe 2-4. Getting Data from the Internet Problem Many datasets are located in repositories on the Internet. There are datasets like the GSS data we have discussed, and literally thousands more web-hosted datasets in economics, data science, finance, government data for the United States and many other countries, health care, machine learning, and various university data repositories.
Names gives the names of the variables in the long format that correspond to multiple variables in the wide format. We have reshaped the wide dataset into a long dataset (see Figure 4-2). Figure 4-2. names and timevar arguments. They can be used to give appropriate names to the variables. Now, let’s reverse the process. We will convert the long dataset back into a wide dataset. The first order of business is to sort the longScores dataset by student number so that each student’s data will occupy three consecutive rows.