Error Continuous Value Supplied to Discrete Scale

The error: continuous value supplied to discrete scale pops up when the grouping variable isn't provided as a factor. The issue is quite simple and can be solved with a slight change in the code.

Read on to know more about the cause and fix it at the earliest.

Contents

• Why Is Error: Continuous Value Supplied To Discrete Scale Occurring?
  • – The Grouping Variable Isn't a Factor
• How To Resolve the Continuous Value Error in R?
  • – Convert the Grouping Variable To Fix Error: Continuous Value Supplied To Discrete Scale
• FAQ
  • – What Does the Error: Discrete Value Supplied To Continuous Scale Informs?
  • – What Are Discrete Values?
  • – What do You Mean By Continuous Values?
  • – What Are the Examples of Discrete and Continuous Data?
  • – How To Convert Discrete Data Into Continuous in Tableau?
  • – Why Continuous Data Is Better Than Discrete Data?
• Conclusion

Why Is Error: Continuous Value Supplied To Discrete Scale Occurring?

The stated error is occurring because you haven't converted the grouping variable or variables into factors. You can read the cause description below.

– The Grouping Variable Isn't a Factor

You will get the continuous value vupplied to discrete scale error when the grouping variable doesn't belong to the factor class. The following code snippet shows the erroneous code:

ggplot(data, aes(x, group = group, color = group)) +
geom_boxplot() +
scale_color_manual(values = c("pink", "green", "purple", "yellow", "black"))

How To Resolve the Continuous Value Error in R?

You can resolve the given error by putting into use the following solution.

– Convert the Grouping Variable To Fix Error: Continuous Value Supplied To Discrete Scale

All that you need to do is to convert the grouping variable by passing the same to the factor() function. Here, you can use the factor() function like "as.factor()" or "factor()."

This code block has been provided to help you in resolving the error:

ggplot(data, aes(x, group = group, color = factor(group))) +
geom_boxplot() +
scale_color_manual(values = c("pink", "green", "purple", "yellow", "black"))

FAQ

You can get the answers to your queries in this section. Please read below to ensure that you don't miss out on some important information.

– What Does the Error: Discrete Value Supplied To Continuous Scale Informs?

The given error points towards the incorrect usage of the scale__continuous() function while using the ggplot() function. It informs that you are creating a continuous scale for a non-numeric or discrete value axis as seen here:

> a = data.frame(x = 1:20, y = c("rose", "sunflower", "tulip", "bluebell", "snowdrop"))
> ggplot(a, aes(x, y)) + geom_point() + scale_y_continuous(limits = c(0, 15))

You can solve the said error by changing the axis in the scale__continuous() function as shown below:

> a = data.frame(x = 1:10, y = c("rose", "sunflower", "tulip", "bluebell", "snowdrop"))
> ggplot(a, aes(x, y)) + geom_point() + scale_x_continuous(limits = c(0, 15))

– What Are Discrete Values?

Discrete values contain integers that can't be subdivided into multiple parts. You will find the numeric, non-negative, finite, and countable integers under the given category.

– What do You Mean By Continuous Values?

Continuous values include the infinite number of values that lie between any two realistic points. The numeric and date/time values fall under this category.

– What Are the Examples of Discrete and Continuous Data?

The total count of planets in the Solar System is an example of discrete data. It is because the planets can be counted easily. On the other hand, the quantity of stars in the sky is an example of continuous data because it's quite difficult to count the same.

– How To Convert Discrete Data Into Continuous in Tableau?

You can convert discrete data into continuous by clicking on the field and choosing continuous. However, if you want to perform the same action from the Data pane, then right-click on the required field and select "Convert to Continuous." As a result, you'll notice that the color of the field has changed.

Remember that the green fields represent continuous values while the blue fields represent discrete data.

– Why Continuous Data Is Better Than Discrete Data?

Continuous data is better than discrete data because the former involves an infinite number of values that ensure more accuracy. Furthermore, the continuous data helps in finding out how the variables change with the passing time.

Conclusion

Certainly, adding only a single function to your program makes things work as if there wasn't any issue in the first place. Here is the one-liner that you should never forget to stay right with your R projects:

• Eror: continuous value supplied to discrete scale issue happens because you haven't converted the grouping variable or variables into factors.
• Converting the grouping variable into a factor can help you in dealing with the continuous value error.
• You can use the factor() function like "as.factor()" or "factor()." to solve the error.

Remember, not all errors need complex solutions but some only demand a slight change before they leave your program to never come back.

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