Double-precision floating-point numbers are a crucial tool for working with complicated data and carrying out sophisticated mathematical operations in R because they offer greater precision and accuracy than normal numeric values.
It is crucial to know how to convert numeric values to doubles because many datasets and R functions may do so by default.
The purpose of converting numeric data to doubles in R is explained in this post, along with several techniques and examples. You may increase the precision, speed, and compatibility of your computations in R by learning how to convert numeric quantities to double-precision floating-point integers.
Why Convert Numeric to Double in R?
Here are a few reasons why numeric to double conversion is required:
- More Precision: Double-precision floating-point numbers offer greater accuracy than regular numeric values in calculations involving numbers.
- Interoperability: Several programming languages and mathematical libraries utilise double-precision floating-point values as their default format.
- Compatibility: Double-precision floating-point integers are required as input for several R functions and packages.
- Statistical Analysis: Double-precision floating-point values are the preferred data format for many statistical models and methods.
- Increased Performance: In some circumstances, double-precision floating-point computations can be performed more quickly and effectively than when utilising ordinary numeric values.
Various Approaches to converting numeric to double in R:
- The as.double() function
- The storage.mode() function
- The options() function
- The format() function
- The round() function
1. Using the as.double() function:
Converts a numeric vector to double precision. It is the most straightforward and widely used method of converting numeric to double.
Code :
# Creating a numeric value
x <- 1.23456789
# Converting the numeric value to a double using the as.double() function
x_double <- as.double(x)
# Printing the original and double-precision floating-point values
cat("Original numeric value:", x, "\n")
cat("Double-precision floating-point value:", x_double)
Output:
Original numeric value: 1.234568
Double-precision floating-point value: 1.23456789
Explanation:
- A numerical value is transformed into a double-precision floating-point number using the as.double() function.
- A built-in R function called as.double() accepts a numerical value as an argument and outputs a double-precision floating-point number.
- First, we construct a numerical number x in the sample code with the value 1.23456789.
- To generate a new variable x double with the same value but in double-precision format, send this value to the as.double() function.
- Using the cat() function, print the initial numeric value and the double-precision floating-point value.
2. Using the storage.mode() function:
To change the storage mode of an R object. It can be used to convert a numeric vector to double precision by setting the mode to “double”
Code :
# Creating a numeric value
x <- 1.23456789
# Changing the storage mode of the numeric value to "double"
storage.mode(x) <- "double"
# Printing the original and double-precision floating-point values
cat("Original numeric value:", x, "\n")
cat("Double-precision floating-point value:", x)
Output :
Original numeric value: 1.234568
Double-precision floating-point value: 1.234568
Explanation:
- With the storage.mode() method, a numeric value’s storage mode is changed to “double.”
- The built-in R function storage.mode() transforms a variable’s storage mode to the mode provided by a variable and a mode argument.
- A number with the value 1.23456789 called x appears in the sample code.
- After that, use the storage.mode() method to set x’s storage mode to “double.”
- This converts the variable’s precision to double-precision floating-point format.
- The cat() function can be used to output both the initial numeric value and the double-precision floating-point value.
3. Using the options() function:
This function is used to set options for the R environment. One of the options that can be set is the “digits” option, which controls the number of significant digits used in numeric calculations. By setting this option to a high value, we can increase the precision of our calculations.
Code :
# Creating a numeric value
x <- 1.23456789
# Changing the default storage mode of numeric values to "double"
options(digits = 22)
# Printing the original numeric value with higher precision
cat("Original numeric value:", x)
Output :
Original numeric value: 1.23456789Explanation:
- setting the digits option to 22 will increase the amount of digits that are shown by default for numeric values from 7 to 22.
- As a result, while showing numerical data, there is more precision. First, we construct a numerical number x in the sample code with the value 1.23456789.
- Can change the default number of digits to 22 by using the options() method.
- More precisely print the initial numeric value.
4. Using the format() function:
This function is used to format a numeric vector or other object in a specific way. It can be used to convert a numeric vector to double precision by specifying a format that includes a large number of decimal places.
Code :
# Creating a numeric value
x <- 1.23456789
# Converting the numeric value to a double using the format() function
x_double <- format(x, scientific = FALSE)
# Printing the original and double-precision floating-point values
cat("Original numeric value:", x, "\n")
cat("Double-precision floating-point value:", x_double)
Output :
Original numeric value: 1.234568
Double-precision floating-point value: 1.23456789
Explanation:
- A numerical value is transformed into a double-precision floating-point number using the format() method.
- The built-in R function format() returns a character string with the given format after accepting a value and a format parameter.
- To achieve a fixed-point notation for the double-precision number, set the scientific argument to FALSE.
- The number x has the value 1.23456789.
- Finally, create a new variable called x double with the same value but formatted in double-precision using the format() method.
- Using the cat() function, print the initial numeric value and the double-precision floating-point value.
5. Using the round() function:
This function is used to round a numeric vector to a specified number of decimal places. It can be used to convert a numeric vector to double precision by rounding to a large number of decimal places.
Code :
# Creating a numeric value
x <- 1.23456789
# Converting the numeric value to a double using the round() function
x_double <- round(x, digits = 15)
# Printing the original and double-precision floating-point values
cat("Original numeric value:", x, "\n")
cat("Double-precision floating-point value:", x_double)
Output :
Original numeric value: 1.234568
Double-precision floating-point value: 1.234568
Explanation:
- A numerical value is transformed into a double-precision floating-point number using the round() method.
- The built-in R function round() provides a result rounded to the given number of digits after accepting a value and a digits argument.
- 1.23456789 as the value of the numerical number x.
- Then, round the value of x to 15 digits using the round() method, and create a new variable called x double with the same value but in double-precision format.
- Using the cat() function, print the initial numeric value and the double-precision floating-point value.
Best Approach
The best approach for converting numeric to double in R overall is to use the as.double() function. Here are some reasons why using as.double() is the best approach:
- Precision: The as.double() function explicitly converts the numeric value to a double-precision floating-point number, which has higher precision than a numeric type.
- Code readability: The as.double() function is easy to read and requires only one line of code. It is also widely used in the R community, so it is easily recognizable by other programmers.
- Compatibility: The as.double() function is compatible with other R functions and libraries, so it can be used in a variety of contexts.
Conclusion
Converting numeric values to double in R is a crucial process that enhances the accuracy and precision of data analysis, especially in fields like finance, science, climate data analysis, medical data analysis, and statistical analysis. Numeric values can be converted to double using functions like as.double(), type.convert(), typecast(), and round().
The as.double() function is the most recommended method due to its simplicity and ease of understanding. Proper data conversion is essential in ensuring accurate and reliable data analysis, which is crucial for making informed decisions in various fields.
24×7 Live R Programming Homework Help From Expert Tutors!
