Filtering Filtering in R refers to the process of selecting subsets of data based on specific conditions. This is essential for data analysis as it allows you to work only with parts of the data that meet certain criteria. Filtering can be performed on vectors, matrices, data frames, and lists. Filtering with Vectors Filtering vectors […]
Filtering with R Lire la suite »
NA and NULL Values In R, NA (Not Available) and NULL are two special types of values used to represent missing or undefined data. They are crucial for handling incomplete data, performing data cleaning, and conducting statistical analysis. NA (Not Available) NA is used to represent missing or undefined data within vectors, matrices, and data
NA and NULL Values with R Lire la suite »
Vector In, Matrix Out The concept “Vector In, Matrix Out” refers to functions in R that accept vectors as inputs and produce matrices as outputs. This can be useful in various scenarios, such as reshaping data, creating matrices from vectors, or performing operations that involve matrix structures. Basic Matrix Creation from Vectors One of the
Vector In, Matrix Out with R Lire la suite »
Vector In, Vector Out The “Vector In, Vector Out” concept in R refers to the capability of functions to accept vectors as inputs and return vectors as outputs. This feature is fundamental to data manipulation in R, enabling efficient and consistent application of functions across data sets without the need for explicit loops. Basic Functionality
Vector In, Vector Out with R Lire la suite »
Vectorized Operations Vectorized operations are a key feature of R and contribute significantly to its efficiency and ease of use. In R, operations are applied to entire vectors (or matrices) at once rather than using explicit loops to iterate over individual elements. This approach simplifies code, improves performance, and aligns with R’s design philosophy. Basics
Vectorized Operations with R Lire la suite »
Using all() and any() The all() and any() functions in R are used to test logical conditions across elements of a vector. They are particularly useful for evaluating whether all or any of the elements of a vector satisfy a given condition. all() The all() function checks if all elements of a logical vector are
Using all() and any() with R Lire la suite »
Repeating Vector Constants with rep() The rep() function in R is used to repeat elements of a vector. It is particularly useful when you need to create vectors where certain elements repeat a specific number of times or follow a particular pattern. This function provides flexibility for generating repetitive vectors in various contexts. Basic Syntax
Repeating Vector Constants with rep() in R Lire la suite »
Generating Vector Sequences with seq() The seq() function in R is a versatile tool for creating sequences of numbers. It provides more control than the : operator, allowing you to specify the start, end, step size, and even the number of elements in the sequence. Basic Syntax The basic syntax of the seq() function is:
Generating Vector Sequences with seq() in R Lire la suite »
Generating Useful Vectors with the Operator: The operator : in R is a simple and efficient method for generating sequences of numbers. It is particularly useful for creating vectors of consecutive integers. This section explores how to use this operator to generate vectors and its various behaviors. Creating Sequences The : operator allows for straightforward
Generating Useful Vectors with the Operator with R Lire la suite »
Vector Indexing Vector indexing in R allows you to access or modify specific elements within a vector. R uses 1-based indexing, meaning that the index of the first element is 1. This section will cover the various ways to index and subset vectors, including positive and negative indexing, logical indexing, and more. Positive Indexing You
Vector Indexing with R Lire la suite »