Often you may want to convert a continuous variable to a categorical variable in R.
One of the easiest ways to do so is by using the discretize() function from the arules package in R, which can be used to perform this exact task.
The discretize() function uses the following syntax:
discretize(x, method=’frequency’, breaks=3, labels=NULL, include.lowest=TRUE, right=FALSE, …)
where:
- x: Name of data frame
- method: Method to use for discretization
- breaks: Number of categories or a vector with boundaries
- labels: Labels for resulting categories
- include.lowest: Whether the first interval should be closed to the left
- right: Whether the intervals should be closed on the right
The following example shows how to use the discretize() function in practice.
Note: Before using the discretize() function, you may need to first install the arules package. You can use the following syntax to do so:
install.packages('arules')
Once the arules package has been installed, you can proceed to use the discretize() function.
Example: How to Use the discretize() Function in R
Suppose that we create a vector named my_values that contains 15 numeric values.
Suppose that we would like to discretize the vector so that each value in the vector is placed into one of three bins in which each bin has the same frequency.
We can use the discretize() function with the following syntax to do so:
library(arules)
#create vector of values
my_values <- c(3, 3, 4, 4, 7, 8, 10, 11, 13, 14, 15, 19, 22, 22, 28)
#discretize values in vector
discretize(my_values)
[1] [3,7.67) [3,7.67) [3,7.67) [3,7.67) [3,7.67) [7.67,14.3)
[7] [7.67,14.3) [7.67,14.3) [7.67,14.3) [7.67,14.3) [14.3,28] [14.3,28]
[13] [14.3,28] [14.3,28] [14.3,28]
attr(,"discretized:breaks")
[1] 3.000000 7.666667 14.333333 28.000000
attr(,"discretized:method")
[1] frequency
Levels: [3,7.67) [7.67,14.3) [14.3,28]
We can see that each of the values in the original vector have been placed into one of the following categories:
- [3, 7.67)
- [7.67, 14.3)
- [14.3, 28)
Notice that there are five values in each of these categories. This is because the default method for the discretize() function is ‘frequency’ , which ensures that each category has the same frequency.
By using this method, there is no guarantee that each category actually has the same width. We can see from the category boundaries that indeed the first bin has the smallest width while the last category has the largest width.
Suppose instead that we specified method=’interval’ to ensure that each category has the same width:
library(arules)
#create vector of values
my_values <- c(3, 3, 4, 4, 7, 8, 10, 11, 13, 14, 15, 19, 22, 22, 28)
#discretize values in vector
discretize(my_values, method='interval')
[1] [3,11.3) [3,11.3) [3,11.3) [3,11.3) [3,11.3) [3,11.3)
[7] [3,11.3) [3,11.3) [11.3,19.7) [11.3,19.7) [11.3,19.7) [11.3,19.7)
[13] [19.7,28] [19.7,28] [19.7,28]
attr(,"discretized:breaks")
[1] 3.00000 11.33333 19.66667 28.00000
attr(,"discretized:method")
[1] interval
Levels: [3,11.3) [11.3,19.7) [19.7,28]
This method places each of the values in the original vector into one of the following categories:
- [3, 11.3)
- [11.3, 19.7)
- [19.7, 28]
Notice that the width of each of these categories is the exact same but there is not an equal frequency of values that fall into each of these categories.
The first category contains nine values, the second category contains three values and the third category also contains three values.
Feel free to use whichever method you prefer depending on how you would like your individual values to be placed into categories.
Additional Resources
The following tutorials explain how to perform other common tasks in R:
How to Create a Frequency Table by Group in R
How to Create Relative Frequency Tables in R
How to Use the describe() Function in R
How to Use the describeBy() Function in R
Thanks..
I need more to learn R-language, and how to solve problems when the error occur in the running.
Hi Farah…To effectively learn R and troubleshoot errors during runtime, here’s a structured approach you can follow:
### 1. **Getting Started with R**
– **Installation:** If you haven’t installed R and RStudio (a popular IDE for R), do so first. RStudio provides a user-friendly interface to write and execute R scripts.
– Download R: [CRAN R Project](https://cran.r-project.org/)
– Download RStudio: [RStudio](https://www.rstudio.com/products/rstudio/download/)
– **Basic Syntax:** Start by learning the basic syntax and data types in R.
– **Variables and Data Types:** Learn how to create and manipulate variables. Get familiar with data types like vectors, lists, matrices, data frames, and factors.
– **Control Structures:** Understand how to use `if`, `else`, `for`, `while`, and `apply` functions.
– **Core Functions:** Familiarize yourself with commonly used functions in R.
– **Reading Data:** Learn how to import data using `read.csv()`, `read.table()`, `read_excel()` (from the `readxl` package), etc.
– **Data Manipulation:** Master data manipulation using packages like `dplyr` and `tidyr`.
– **Visualization:** Start with basic plots using `plot()`, then move to more advanced visualizations with `ggplot2`.
### 2. **Learning Resources**
– **Books:**
– *R for Data Science* by Hadley Wickham and Garrett Grolemund
– *Advanced R* by Hadley Wickham
– **Online Courses:**
– [Coursera – R Programming by Johns Hopkins University](https://www.coursera.org/learn/r-programming)
– [Datacamp – Introduction to R](https://www.datacamp.com/courses/free-introduction-to-r)
– **YouTube Channels:**
– **FreeCodeCamp**: They offer comprehensive tutorials on R.
– **StatQuest with Josh Starmer**: Great for statistical concepts explained in R.
### 3. **Troubleshooting Errors**
– **Understand Error Messages:**
– **Read the Error:** When you encounter an error, carefully read the error message. It usually gives a hint about what went wrong.
– **Use `traceback()`:** After an error occurs, run `traceback()` to see the sequence of function calls that led to the error. This can help pinpoint where the error occurred.
– **Use `debug()` and `browser()`:** For more complex debugging, you can use `debug()` to step through a function or `browser()` to stop the execution and inspect the current environment.
– **Common Errors:**
– **Syntax Errors:** Check for missing parentheses, unmatched quotes, or incorrect use of operators.
– **Data Type Issues:** Ensure that the data types of your variables match the expected types for the functions you’re using.
– **Missing Packages:** If a function is not found, make sure the package containing it is installed and loaded using `install.packages()` and `library()`.
– **Online Resources:**
– **Stack Overflow:** Search or ask about specific R errors.
– **RStudio Community:** Participate in discussions about R-related issues.
– **Cheat Sheets:** RStudio provides cheat sheets for various R packages and functions. These can be incredibly helpful for quick references.
### 4. **Practical Problem-Solving**
– **Start with Simple Projects:** Begin with small projects like data exploration, cleaning, and simple visualizations.
– **Analyze Datasets:** Use public datasets from sources like Kaggle to practice data manipulation, visualization, and modeling in R.
– **Work Through Examples:** Follow along with examples in the books or online tutorials, modifying them slightly to see how the code changes impact the output.
### 5. **Regular Practice**
– **Daily Coding:** Spend at least 30 minutes a day coding in R.
– **Project-Based Learning:** Apply what you learn to projects. This could be anything from analyzing your personal data to contributing to open-source R projects.
– **Community Involvement:** Engage with the R community by participating in forums, attending R meetups, or contributing to discussions on social platforms.
By following this approach, you’ll gradually build a strong foundation in R and develop the skills necessary to troubleshoot errors effectively.