How To Convert Double To Numeric In R (4 Best Approaches)

In R, the process of converting a data type from double to numeric is straightforward. The double data type is a floating-point numeric data type that can store decimal values with high precision.

On the other hand, the numeric data type is a general data type that can store both integer and floating-point values. To convert a double to numeric, you can use the as.numeric() function in R. This function takes a double value as an input and returns a numeric value. The conversion process is automatic, and the precision of the resulting numeric value may be reduced.

Therefore, it is important to consider the precision requirements of your application before performing the conversion. Overall, converting a double to numeric in R is a simple process that can be accomplished using built-in functions.

Why do we need to convert double to numeric in r

We need to convert double to numeric because:

Data type consistency: In some cases, it may be necessary to ensure that all numeric values in a dataset are of the same data type. If some values are in double format and others are in numeric format, it can cause issues with calculations or statistical analyses.
Memory efficiency: Double data type occupies more memory than numeric data type. If we have large data sets, it can become more memory efficient to convert double data type to numeric data type.
Data type limitations: Some functions or packages in R may only accept numeric data types, so converting double data type to numeric data type will enable us to use those functions or packages.
Computation speed: Numeric data type is faster to process than double data type. For larger datasets or computationally intensive tasks, converting double data type to numeric data type may improve processing speed.
Visualization: Some visualization packages in R may require data to be in a specific format, such as numeric data type. In such cases, converting double data type to numeric data type can help ensure that the visualization functions work correctly.
Interoperability: Some external systems or applications may only accept numeric data type and may not recognize double data type. In such cases, converting double data type to numeric data type can ensure that the data can be used in those external systems or applications.

Ways to convert double to numeric in r

Here are some approaches for converting double to numeric :

Using the as.numeric() function
Using the round() function
Using the floor() function
Using the as.integer() function

Approaches

Approach 1: Using the as.numeric() function

In R, the “as.numeric()” function is used to convert a double data type to numeric data type. The double data type is a sub-type of numeric data type with a wider range of values and higher precision. This conversion can be necessary to ensure data type consistency, improve memory efficiency, and enable the use of certain functions and packages.

# Creating a double vector
x <- c(1.2, 2.5, 3.6, 4.9)

# Printing the class of x
class(x)

# Converting double to numeric
y <- as.numeric(x)

# Printing the class of y
class(y)
print(y)

Output:

[1] "numeric"
[1] "numeric"
[1] 1.2 2.5 3.6 4.9

Explanation:

We start by creating a double vector named “x” with four decimal values.
We print the class of “x” using the “class” function, which shows that “x” is a double.
We convert “x” to a numeric vector using the “as.numeric” function and store the result in a new variable named “y”.
We print the class of “y” using the “class” function, which shows that “y” is now a numeric vector.

Approach 2: Using the round() function

In R, the round() function in R is used to round off a given number to a certain number of decimal places. It returns the rounded value based on the number of decimal places specified in the second argument.

# Creating a double vector
x <- c(1.2345, 2.5678, 3.6789, 4.9999)

# Rounding off the decimal values in x to 2 decimal places
y <- round(x, 2)

# Converting y to a numeric vector
z <- as.numeric(y)

# Printing the class of z
class(z)
print(z)

Output:

[1] "numeric"
[1] 1.232.57 3.68 5.00

Explanation:

We start by creating a double vector named “x” with four decimal values.
We use the “round” function to round off the decimal values in “x” to 2 decimal places and store the result in a new variable named “y”.
We convert “y” to a numeric vector using the “as.numeric” function and store the result in a new variable named “z”.
We print the class of “z” using the “class” function, which shows that “z” is now a numeric vector.

Approach 3: Using the floor() function

The floor() function in R is used to round down a given number to the nearest integer value. It returns the largest integer value that is less than or equal to the input value.

# Creating a double vector
x <- c(1.2345, 2.5678, 3.6789, 4.9999)

# Truncating the decimal part of x using floor() function
y <- floor(x)

# Converting y to a numeric vector
z <- as.numeric(y)

# Printing the class of z
class(z)
print(z)

Output:

[1] "numeric"
[1] 1 2 3 4

Explanation:

We start by creating a double vector named “x” with four decimal values.
We use the “floor” function to truncate the decimal part of the double values in “x” and store the result in a new variable named “y”.
We convert “y” to a numeric vector using the “as.numeric” function and store the result in a new variable named “z”.
We print the class of “z” using the “class” function, which shows that “z” is now a numeric vector.

Approach 4: Using the as.integer() function

In R, as.integer is a function that is used to convert a numeric value or a vector of numeric values to an integer value or a vector of integer values.

The as.integer function takes a single argument, which can be either a numeric value or a vector of numeric values. If the argument is a numeric value, it is converted to an integer value. If the argument is a vector of numeric values, each element in the vector is converted to an integer value, and a new vector of integers is returned.

# define a numeric vector
x <- c(1.23, 4.56, 7.89)

# convert numeric vector to integer using as.integer
y <- as.integer(x)

# print the original and converted vectors
class(y)
print(y)

Output:

[1]"integer"
[1]1 4 7

Explanation:

In the code above, we first define a numeric vector numeric_vector with three decimal values.
We then use the as.integer function to convert the numeric vector to an integer vector, and store the result in a new variable integer_vector.
Finally, we print both the original and converted vectors using the print function.

Best Approaches

Best approach out of all approaches is the as.numeric method in R. Here are the reasons why:

Usability: as.numeric is a built-in function in R, which means it is part of the base R package and does not require any additional installation or setup.
Optimization: as.numeric is designed specifically for converting objects to numeric data types, so it is optimized for this task and will generally be faster and more accurate than other approaches.
Accessibility: as.numeric has built-in error handling, so it will raise an error if the input is not convertible to numeric, which can help catch potential issues early in the data analysis process.
Easibility: as.numeric can handle a wide range of input types, including character strings, factors, and dates, which makes it very versatile.
Conversion: as.numeric can be easily combined with other functions in R, such as apply, to convert entire vectors or data frames to numeric data types.
Support: as.numeric is well-documented and widely used in the R community, so it is easy to find help and support if you encounter any issues while using it.

Sample Question

Sample Problem 1:

Write an R Code that takes a list of the number of chocolates packed each day which converts double to numeric using as.numeric.

Solution:

We start by defining a sample list of doubles called chocolates_packed.
We then use the as.numeric() function to convert the list to a numeric data type and store the result in a new variable called chocolates_packed_numeric.
The as.numeric() function takes the list of doubles as its argument and returns a new list with each element converted to numeric data type.
The resulting list chocolates_packed_numeric can now be used for further mathematical operations or statistical analysis.

# Sample list of doubles
chocolates_packed <- c(23.5, 17.2, 19.8, 21.0, 16.3)

# Convert to numeric data type
chocolates_packed_numeric <- as.numeric(chocolates_packed)

# Print the input and output vectors to compare them
print(chocolates_packed_numeric)
class(chocolates_packed_numeric)

Output:

[1]23.517.219.821.016.3
[1]"numeric"

Sample Problem 2:

Given a data frame df with a column called products and count of each product, write R code to convert this column to a numeric data type. Store the result in a new column called final count.

Solution:

We start by creating a sample data frame called df with two columns: “products” and “count”.
We then use the round() function to convert the “count” column to a numeric data type and store the result in a new column called “final_count”.
The round() function takes two arguments: the first is the column we want to round (in this case, “count”), and the second is the number of digits we want to round to (in this case, 2).
We assign the rounded values to the new column “final_count” using the $ operator to reference the column in our data frame.
Finally, we print the resulting data frame to confirm that the “count” column has been converted to a numeric data type and rounded to two digits.

# Create sample data frame
df <- data.frame(products = c("Product A", "Product B", "Product C"),
                 count = c(23.456, 17.891, 19.123))

# Use round() function to convert count to numeric and store in final_count column
df$final_count <- round(df$count, digits = 2)

# Print resulting data frame
df

Output:

products  count final_count
1 Product A 23.456       23.46
2 Product B 17.891       17.89
3 Product C 19.123       19.12

Sample Problem 3:

Given a list of length 5 containing the doubles 1.1, 2.2, 3.3, 4.4, and 5.5, write R code to convert this vector to a numeric data type.

Solution:

The code defines an example vector of doubles called x with five values.
The floor() function is used to round down each value in the vector to the nearest integer.
The resulting integers are stored in a new vector called x_numeric.
The original and new vectors are printed using the print() function to compare the values.

# Define an example vector of doubles
x <- c(1.1, 2.2, 3.3, 4.4, 5.5)

# Convert the vector to a numeric data type using floor() function
x_numeric <- floor(x)

# Print the original and new vectors to compare them
print(x)
print(x_numeric)

Output:

[1] 1.1 2.2 3.3 4.4 5.5
[1] 1 2 3 4 5

Sample Problem 4:

Suppose you have a data frame df with a column called height containing doubles representing the height of individuals in meters. Write R code to convert this column to a numeric data type and then print the mean height using the mean function.

Solution:

The code generates an example data frame called df with a column of doubles called height representing the height of individuals in meters.
The as.integer() function is used to convert the height column to a numeric data type by truncating the decimal places and keeping only the integer part of the values.
The resulting integers are stored in a new column called height_numeric.
The mean() function is used to calculate the mean height from the height_numeric column.
The resulting mean height is stored in the variable mean_height.
The mean height is printed using the print() function.

# Generate example data frame with a column of doubles representing height
df <- data.frame(height = c(1.65, 1.72, 1.85, 1.78, 1.63))

# Convert column height to a numeric data type using as.integer() function
df$height_numeric <- as.integer(df$height)

# Calculate and print the mean height using the mean() function
mean_height <- mean(df$height_numeric)
print(df)
print(mean_height)

Output:

height height_numeric
1   1.65              1
2   1.72              1
3   1.85              1
4   1.78              1
5   1.63              1
[1] 1

Conclusion

In conclusion, converting double to numeric in R is a simple process that can be performed using the as.numeric() function. It is important to understand the differences between these two data types, as the precision of the resulting numeric value may be reduced. If you need to perform mathematical operations that require high precision, it is advisable to use the double data type.

On the other hand, if your application requires general numeric values that can store both integers and floating-point numbers, the numeric data type is suitable. Understanding the data types in R and their respective conversion functions is essential for data analysis and programming in R.

24×7 Live R Programming Homework Help From Expert Tutors! Get Help Now

Author:

Balaraj Gulabal

Hello there! My name is Balaraj B Gulabal. I’m excited to tell you a bit about myself. First and foremost, I am passionate about coding in Java. I love the challenge and creativity that comes with programming, and the feeling of satisfaction when a project is completed successfully. In addition to my technical skills, I have knowledge of finance and money. This understanding of financial concepts is useful when working on projects that involve financial data, and it helps me to think critically about money-related decisions in my personal life. In my free time, I enjoy watching movies and TV shows. I love getting lost in a good story and exploring new worlds and perspectives through visual media. I also stay active with physical workouts to maintain my health and well-being. Staying fit and healthy allows me to maintain my focus and energy, both in my work and my personal life. When I need to unwind, I enjoy listening to jazz. The smooth rhythms and melodies help me to relax and clear my mind. And of course, as you may remember, I also love chicken! It’s one of my favourite foods. Overall, I’m a hard worker and a fast learner. I’m always eager to take on new challenges and learn new skills. I’m confident in my ability to tackle any task or problem that comes my way, and I’m excited to see where my interests and abilities will take me in the future. Follow: