Software Development
Getting Started with R Programming
Final Exam: Getting Started with R Programming
it_fedawr_01_enus
R Programming for Beginners: Exploring R Vectors
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R Programming for Beginners: Getting Started
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R Programming for Beginners: Leveraging R with Matrices, Arrays, & Lists
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R Programming for Beginners: Understanding Data Frames, Factors, & Strings
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Final Exam: Getting Started with R Programming
Lesson Objectives
Final Exam: Getting Started with R Programming
- combine two data frames using the rbind() and cbind() functions
- create arrays and store multi-dimensional data in them
- create data frames to store data as indexed rows and columns
- create variables using different techniques
- create vectors and explore the nuances of vectors
- filter data frames to view rows that satisfy a condition
- generate vectors using the c() and vector() functions
- implement logical and relational operations on vectors
- implement matrix multiplication using the %*% operator
- index into the individual values stored in cells in data frames
- install the R kernel on Jupyter Notebook on Windows
- label the rows in data frames and view statistics
- name dimensions in matrices and index into them using names
- perform cell-wise math operations on data stored in matrices
- perform indexing, slicing, and dicing operations with vectors
- perform vectorized or element-wise operations
- set up the R kernel on Jupyter Notebook on macOS
- use matrices to store 2-dimensional data and index into them
- use the ? operator to view docs for functions
- use various built-in functions such as print() and abs()
Overview/Description
Final Exam: Getting Started with R Programming will test your knowledge and application of the topics presented throughout the Getting Started with R Programming track of the Skillsoft Aspire Data Analysis with R Journey.
Target
Prerequisites: none
R Programming for Beginners: Exploring R Vectors
Lesson Objectives
R Programming for Beginners: Exploring R Vectors
- discover the key concepts covered in this course
- generate vectors using the c() and vector() functions
- create vectors and explore the nuances of vectors
- perform indexing, slicing, and dicing operations with vectors
- perform vectorized or element-wise operations
- implement logical and relational operations on vectors
- create vectors containing data stored as name-value pairs
- use recycling for operations with two different sized vectors
- perform logical and filter operations on elements in vectors
- filter vectors based on logical conditions
- summarize the key concepts covered in this course
Overview/Description
Vectors are the easiest type of data structures in R. However, to use them successfully, it's important to appreciate their restrictions, recognize the types available, and identify their members - or components as they're officially called in R. This course shows you how to create and generate vectors using the c() and vector() functions, respectively. You'll perform vectorized operations on elements in vectors. Practice filtering and slicing vectors. And use the which(), any(), and all() functions on vectors. Furthermore, you'll perform naming and indexing operations on vectors and work with different length vectors using vector recycling. On completing this course, you'll have the knowledge and know-how to utilize vectors for their intended purpose.
Vectors are the easiest type of data structures in R. However, to use them successfully, it's important to appreciate their restrictions, recognize the types available, and identify their members - or components as they're officially called in R. This course shows you how to create and generate vectors using the c() and vector() functions, respectively. You'll perform vectorized operations on elements in vectors. Practice filtering and slicing vectors. And use the which(), any(), and all() functions on vectors. Furthermore, you'll perform naming and indexing operations on vectors and work with different length vectors using vector recycling. On completing this course, you'll have the knowledge and know-how to utilize vectors for their intended purpose.
Target
Prerequisites: none
R Programming for Beginners: Getting Started
Lesson Objectives
R Programming for Beginners: Getting Started
- discover the key concepts covered in this course
- install and set up the R kernel on Jupyter Notebook on macOS
- install and set up the R kernel on Jupyter Notebook on Windows
- use the ? operator to view docs for functions
- invoke the help() function and create and use variables
- illustrate the use of reserved words and the <- and = assignment operators
- perform math operations on variables
- perform arithmetic operations using operators such as + and -
- create variables using different techniques
- use various built-in functions such as print() and abs()
- execute numeric built-in functions such as seq()
- recall the different basic or atomic data types
- summarize the key concepts covered in this course
Overview/Description
The free and robust statistical package R has been decades in the making and is worth learning for serious statistical operations, such as conducting new medical data analysis. This course teaches you everything you need to know to get started with R, from installing R to running R from the command line. You'll grasp how to invoke basic functions and view the documentation on those. You'll create variables in R and explore various reserved words and the = and <- operators. You'll then perform basic arithmetic operations on variables, invoke built-in functions, and work with various atomic data types, such as character, integer, double, logical, complex, real, and raw. By the end of this course, you'll have the skills you need to get working with R.
The free and robust statistical package R has been decades in the making and is worth learning for serious statistical operations, such as conducting new medical data analysis. This course teaches you everything you need to know to get started with R, from installing R to running R from the command line. You'll grasp how to invoke basic functions and view the documentation on those. You'll create variables in R and explore various reserved words and the = and <- operators. You'll then perform basic arithmetic operations on variables, invoke built-in functions, and work with various atomic data types, such as character, integer, double, logical, complex, real, and raw. By the end of this course, you'll have the skills you need to get working with R.
Target
Prerequisites: none
R Programming for Beginners: Leveraging R with Matrices, Arrays, & Lists
Lesson Objectives
R Programming for Beginners: Leveraging R with Matrices, Arrays, & Lists
- discover the key concepts covered in this course
- use matrices to store 2-dimensional data and index into them
- name dimensions in matrices and index into them using names
- perform cell-wise math operations on data stored in matrices
- implement matrix multiplication using the %*% operator
- join and rearrange matrices using rbind(), dim(), and columnbind()
- perform indexing and math operations on matrices
- create arrays and store multi-dimensional data in them
- index into arrays using both the indices and the index labels
- create lists and perform indexing operations
- use lists with name-value pairs
- add, edit, and remove the names and values in lists
- create lists containing data of different types
- summarize the key concepts covered in this course
Overview/Description
Vectors are a great basic data structure in R, but they have important limitations on the dimensions and types of data they contain. Matrices, arrays, and lists are powerful R structures that mitigate these limitations. This course will help you distinguish each of these three elements' purpose and show you how to use them. You'll start by using matrices to store two-dimensional data. You'll then differentiate between row-major and column-major matrices. You'll learn how to use arrays and how you can easily create three-dimensional arrays as you can two-dimensional arrays. You'll then move on to the use of lists and how they differ from vectors. After taking this course, you'll be able to identify when and how to use a matrix, a list, and an array.
Vectors are a great basic data structure in R, but they have important limitations on the dimensions and types of data they contain. Matrices, arrays, and lists are powerful R structures that mitigate these limitations. This course will help you distinguish each of these three elements' purpose and show you how to use them. You'll start by using matrices to store two-dimensional data. You'll then differentiate between row-major and column-major matrices. You'll learn how to use arrays and how you can easily create three-dimensional arrays as you can two-dimensional arrays. You'll then move on to the use of lists and how they differ from vectors. After taking this course, you'll be able to identify when and how to use a matrix, a list, and an array.
Target
Prerequisites: none
R Programming for Beginners: Understanding Data Frames, Factors, & Strings
Lesson Objectives
R Programming for Beginners: Understanding Data Frames, Factors, & Strings
- discover the key concepts covered in this course
- create data frames to store data as indexed rows and columns
- label the rows in data frames and view statistics
- index into the individual values stored in cells in data frames
- filter data frames to view rows that satisfy a condition
- combine two data frames using the rbind() and cbind() functions
- implement various join operations on data frames using merge()
- use factors to limit the allowed values in a variable
- recall how string values can be set to be factors in data frames
- create factors and filter data and use the tapply() and split() functions
- use tables to view counts of rows with specific values for fields
- perform operations on strings, such as combining and splitting strings
- format strings using formatC() and print data with placeholders using sprintf()
- summarize the key concepts covered in this course
Overview/Description
Data frames are an R abstraction for tabular data similar to that contained in spreadsheet files or database tables. Data frames can work directly with files in the CSV, JSON, and Excel format, all common formats used to store data. This course outlines the characteristics of data frames in the R programming language and demonstrates how to use them. You'll learn to create basic R data frames from multiple vectors. You'll use factors - similar to enums or enumerated types in other programming languages and great for categorical variables. You'll also learn how to perform various string manipulation operations, such as splitting and joining strings and changing case. You'll then practice the important topic of printing precisely formatted strings with placeholders for variable values. When you're done, you'll be able to use data frames, factors, and strings professionally in your R programming projects.
Data frames are an R abstraction for tabular data similar to that contained in spreadsheet files or database tables. Data frames can work directly with files in the CSV, JSON, and Excel format, all common formats used to store data. This course outlines the characteristics of data frames in the R programming language and demonstrates how to use them. You'll learn to create basic R data frames from multiple vectors. You'll use factors - similar to enums or enumerated types in other programming languages and great for categorical variables. You'll also learn how to perform various string manipulation operations, such as splitting and joining strings and changing case. You'll then practice the important topic of printing precisely formatted strings with placeholders for variable values. When you're done, you'll be able to use data frames, factors, and strings professionally in your R programming projects.
Target
Prerequisites: none
