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Variables and Data Types in R
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In R, a variable is a name used to store a value or a set of values in memory. Variables allow you to save data and use it later in calculations, analysis, or visualizations. Unlike some other programming languages, R does not require you to declare the type of a variable before assigning a value. The type is automatically determined based on the data you assign.
To create a variable in R, you use the assignment operator <-. For example, writing x <- 10 creates a variable named x and stores the numeric value 10 in it. You can also use the equals sign = for assignment, but <- is the standard and preferred method in R programming.
R supports several basic data types. The most common one is the numeric type, which is used for numbers such as integers and decimal values. For example, age <- 25 or price <- 199.99 are numeric variables. Another important type is the character type, which stores text. Character values must be written inside quotes, such as name <- "Ravi" or city <- "Mumbai".
Logical data types are used to represent true or false values. These are written as TRUE or FALSE in capital letters. For example, isStudent <- TRUE stores a logical value. Logical variables are often used in conditions and decision-making statements.
R also has a special data type called a factor, which is used to store categorical data. Categorical data represents groups or categories, such as gender, colors, or product types. For example, gender <- factor("Male") creates a factor variable.
Another important concept is vectors, which are collections of values of the same data type. In R, even a single value is treated as a vector of length one. You can create a vector using the c() function. For example, numbers <- c(1, 2, 3, 4, 5) creates a numeric vector, while fruits <- c("Apple", "Banana", "Mango") creates a character vector.
Understanding variables and data types is essential because every operation in R depends on how data is stored and handled. Choosing the correct data type helps in performing accurate calculations, creating meaningful visualizations, and building efficient data analysis workflows.
