When using ggplot2, the “Error: Discrete value supplied to continuous scale” typically arises when there is a mismatch between the data type of the variable assigned to the y-axis and the scale type specified in the ggplot aesthetic. This commonly occurs when the y-axis represents categorical or discrete data, but the scale is mistakenly set to continuous.

To resolve this error, ensure that your y-axis variable is properly defined as a factor or character type in your data frame. Additionally, check that you haven’t inadvertently used a continuous scale, like scale_y_continuous(), when you should be using a discrete scale, such as scale_y_discrete().

Here’s a concise solution: verify that your y-axis variable is categorical and that you’re using the appropriate scale type. This ensures accurate representation of categorical data in ggplot2, preventing the “Discrete value supplied to continuous scale” error.

```
meltDF <- data.frame(
MW = c(
3.9, 6.4, 7.4, 8.1, 9, 9.4, 3.9, 6.4, 7.4, 8.1, 9, 9.4, 3.9, 6.4, 7.4, 8.1, 9,
9.4, 3.9, 6.4, 7.4, 8.1, 9, 9.4, 3.9, 6.4, 7.4, 8.1, 9, 9.4, 3.9, 6.4, 7.4,
8.1, 9, 9.4, 3.9, 6.4, 7.4, 8.1, 9, 9.4, 3.9, 6.4, 7.4, 8.1, 9, 9.4, 3.9, 6.4,
7.4, 8.1, 9, 9.4, 3.9, 6.4, 7.4, 8.1, 9, 9.4, 3.9, 6.4, 7.4, 8.1, 9, 9.4, 3.9,
6.4, 7.4, 8.1, 9, 9.4, 3.9, 6.4, 7.4, 8.1, 9, 9.4, 3.9, 6.4, 7.4, 8.1, 9, 9.4,
3.9, 6.4, 7.4, 8.1, 9, 9.4, 3.9, 6.4, 7.4, 8.1, 9, 9.4, 3.9, 6.4, 7.4, 8.1, 9,
9.4, 3.9, 6.4, 7.4, 8.1, 9, 9.4, 3.9, 6.4, 7.4, 8.1, 9, 9.4, 3.9, 6.4, 7.4,
8.1, 9, 9.4
),
variable = factor(
c(
"10", "10", "10", "10", "10", "10", "33.95", "33.95", "33.95",
"33.95", "33.95", "33.95", "58.66", "58.66", "58.66", "58.66",
"58.66", "58.66", "84.42", "84.42", "84.42", "84.42", "84.42",
"84.42", "110.21", "110.21", "110.21", "110.21", "110.21", "110.21",
"134.16", "134.16", "134.16", "134.16", "134.16", "134.16", "164.69",
"164.69", "164.69", "164.69", "164.69", "164.69", "199.1", "199.1",
"199.1", "199.1", "199.1", "199.1", "234.35", "234.35", "234.35",
"234.35", "234.35", "234.35", "257.19", "257.19", "257.19", "257.19",
"257.19", "257.19", "361.84", "361.84", "361.84", "361.84", "361.84",
"361.84", "432.74", "432.74", "432.74", "432.74", "432.74", "432.74",
"506.34", "506.34", "506.34", "506.34", "506.34", "506.34", "581.46",
"581.46", "581.46", "581.46", "581.46", "581.46", "651.71", "651.71",
"651.71", "651.71", "651.71", "651.71", "732.59", "732.59", "732.59",
"732.59", "732.59", "732.59", "817.56", "817.56", "817.56", "817.56",
"817.56", "817.56", "896.24", "896.24", "896.24", "896.24", "896.24",
"896.24", "971.77", "971.77", "971.77", "971.77", "971.77", "971.77",
"1038.91", "1038.91", "1038.91", "1038.91", "1038.91", "1038.91"
),
levels = c(
"10", "33.95", "58.66", "84.42", "110.21", "134.16", "164.69",
"199.1", "234.35", "257.19", "361.84", "432.74", "506.34", "581.46",
"651.71", "732.59", "817.56", "896.24", "971.77", "1038.91"
)
),
value = c(
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0
)
)
The plotting code:
## Plotting
ggplot(meltDF[meltDF$value == 1,]) + geom_point(aes(x = MW, y = variable)) +
scale_x_continuous(limits=c(0, 1200), breaks=c(0, 400, 800, 1200)) +
scale_y_continuous(limits=c(0, 1200), breaks=c(0, 400, 800, 1200))
```

## How to fix the error in R: “Discrete value supplied to continuous scale”?

The error message signals the use of a categorical variable where a continuous one is expected in R. Though R usually pinpoints the problematic variable, you haven’t specified it. Including the complete error message would aid in diagnosis. If the variable is indeed continuous but misunderstood by R, you can use the code as. numeric(variable_name). However, if it’s meant to be categorical, your approach might be flawed, possibly involving an unsuitable chart type.

Your code suggests the Stations are classified as class=”factor”. To resolve this, consider adjusting the classification to a numerical value.

The error “Discrete value supplied to continuous scale, Defaulting to continuous. Error: Discrete value supplied to continuous scale” hints at the need to specify the column name for the dataframe correctly. Ensure it is appropriately included in your code.

## Ggplot2 error: Discrete value supplied to a continuous scale

The ggplot2 error “Discrete value supplied to continuous scale” indicates a mismatch between the data type of a variable and the scale type specified in the ggplot aesthetic. This commonly occurs when a categorical or discrete variable is incorrectly treated as continuous in the plot.

**To resolve this issue:**

Check Data Types: Ensure that the variable mapped to the axis (probably the y-axis in this case) is of a categorical or discrete data type. In R, you can use the class() function to check the data type of a variable.

`class(your_data$your_variable)`

Use Correct Scale: Confirm that you’re using the appropriate scale for the variable. If the variable is categorical, use scale_y_discrete(); if it’s continuous, use scale_y_continuous().

Check Aesthetic Mapping: Examine your ggplot code for any mismatches in the mapping of variables to aesthetics. Ensure that the variable causing the issue is correctly specified in the aesthetic mapping.

Here’s a simple example:

```
# Incorrect: Mapping a categorical variable to y-axis with continuous scale
ggplot(data = your_data, aes(x = x_variable, y = categorical_variable)) +
geom_point() +
scale_y_continuous()
# Correct: Using discrete scale for categorical variable
ggplot(data = your_data, aes(x = x_variable, y = categorical_variable)) +
geom_point() +
scale_y_discrete()
```

By addressing these points, you should be able to resolve the “Discrete value supplied to continuous scale” error in ggplot2.

## Ggplot2 error : Discrete value supplied to continuous scale

The error “Discrete value supplied to continuous scale” in ggplot2 usually arises when there’s a mismatch between the data type of a variable and the scale type specified in the ggplot aesthetic. Here’s a step-by-step guide to troubleshoot and resolve this issue:

Check Data Types:

Ensure that the variables mapped to the aesthetics are of the correct data type. For example, if you are mapping a categorical variable to the y-axis, it should be a factor or character variable, not numeric.

```
# Incorrect: Mapping a categorical variable to a continuous scale
ggplot(data = your_data, aes(x = x_variable, y = categorical_variable)) +
geom_point() +
scale_y_continuous()
# Correct: Using a discrete scale for a categorical variable
ggplot(data = your_data, aes(x = x_variable, y = categorical_variable)) +
geom_point() +
scale_y_discrete()
```

Use Correct Scale:

Make sure you’re using the appropriate scale for the data type. For continuous variables, use scale_y_continuous(), and for categorical variables, use scale_y_discrete().

```
# Incorrect: Using continuous scale for a categorical variable
ggplot(data = your_data, aes(x = x_variable, y = categorical_variable)) +
geom_point() +
scale_y_continuous()
# Correct: Using discrete scale for a categorical variable
ggplot(data = your_data, aes(x = x_variable, y = categorical_variable)) +
geom_point() +
scale_y_discrete()
```

Verify Aesthetic Mapping:

Double-check your ggplot code for any mismatches in the mapping of variables to aesthetics. Ensure that the variables causing the issue are correctly specified in the aesthetic mapping.

```
ggplot(data = your_data, aes(x = x_variable, y = y_variable)) +
geom_point() +
scale_y_continuous() # Make sure the scale is appropriate for the data type
```

## R ggmap ggplot2 error “Error: Discrete value supplied to continuous scale”

The “Error: Discrete value supplied to continuous scale” in ggplot2 often indicates a mismatch between the data type of a variable and the scale type specified in the ggplot aesthetic. This can happen when using ggmap, which is an extension of ggplot2 for creating maps.

To resolve this issue when working with ggmap and ggplot2:

Check Data Types:

Ensure that the variables mapped to the aesthetics are of the correct data type. In the context of mapping, this commonly involves latitude and longitude values. They should be numeric (continuous) rather than factors or characters (discrete).

Use Correct Scale:

Confirm that you are using the appropriate scale for latitude and longitude. In ggplot2, these are typically treated as continuous variables, so you should use scale_x_continuous() and scale_y_continuous().

Verify Aesthetic Mapping:

Examine the aesthetic mappings in your ggplot code. Ensure that the latitude and longitude variables are correctly specified and that there are no unintended mappings to continuous scales.

Here’s a basic example:

```
# Incorrect: Mapping a factor variable to longitude with continuous scale
ggplot(data = your_data, aes(x = factor_variable, y = longitude_variable)) +
geom_point() +
scale_y_continuous()
# Correct: Using continuous scale for latitude and longitude
ggplot(data = your_data, aes(x = longitude_variable, y = latitude_variable)) +
geom_point() +
scale_x_continuous() +
scale_y_continuous()
```

By addressing these points, you should be able to resolve the “Discrete value supplied to continuous scale” error when using ggmap and ggplot2 for mapping.

## R ggplot geom_bar Error: Discrete value supplied to continuous scale

The error “Error: Discrete value supplied to continuous scale” in ggplot with geom_bar typically occurs when there is a mismatch between the data type of a variable and the scale type specified in the ggplot aesthetic. Here are steps to troubleshoot and resolve the issue:

Check Data Types:

Ensure that the variable mapped to the y-axis in geom_bar is of a numeric or continuous data type. If it is a categorical variable, you should use geom_bar(stat=”count”) instead.

```
# Incorrect: Using a categorical variable on the y-axis
ggplot(data = your_data, aes(x = x_variable, y = categorical_variable)) +
geom_bar()
# Correct: Using count with a categorical variable
ggplot(data = your_data, aes(x = x_variable)) +
geom_bar(stat = "count", aes(fill = categorical_variable))
```

Use Correct Scale:

Confirm that you are using the appropriate scale for the variable on the y-axis. If it’s continuous, use scale_y_continuous(); if it’s categorical, use scale_y_discrete().

```
# Incorrect: Using continuous scale with a categorical variable
ggplot(data = your_data, aes(x = x_variable, y = categorical_variable)) +
geom_bar() +
scale_y_continuous()
# Correct: Using discrete scale with a categorical variable
ggplot(data = your_data, aes(x = x_variable, y = categorical_variable)) +
geom_bar() +
scale_y_discrete()
```

Verify Aesthetic Mapping:

Double-check that the aesthetic mappings in your ggplot call are correctly specified and that there are no unintended mappings to continuous scales.

By addressing these points, you should be able to identify and resolve the “Discrete value supplied to continuous scale” error when using geom_bar in ggplot.

## FAQs

### Why am I getting the “Error: Discrete value supplied to continuous scale” in ggplot2?

This error arises when there’s a mismatch between the data type of the variable on the y-axis and the scale type specified. It commonly occurs when using a categorical (discrete) variable on the y-axis with a continuous scale.

### How can I identify the variable causing the issue?

Look for the specific variable mentioned in the error message. Ensure it’s intended to be categorical, and if so, check your ggplot code for appropriate scale types.

### What if the variable is continuous but misinterpreted by R?

If the variable is continuous but R misunderstands it, use as.numeric(variable_name) to clarify its nature.

### Could my chart type be causing the issue?

Yes, inappropriate chart types for categorical data might trigger this error. Verify that you’re using a suitable chart for your data.

### I noticed my Stations are labeled as “factor”. How can I resolve this?

Modify the classification of the Stations to a numerical value if they are meant to be continuous.

### Any specific considerations for the ggplot code?

Ensure that your ggplot code accurately includes the column names from your dataframe, addressing any potential issues related to column selection.

### Are there common pitfalls leading to this error?

Yes, using continuous scales for categorical data or mismatching data types can cause this error. Double-check your data and ggplot specifications.

### What’s the significance of the error message “Defaulting to continuous”?

This indicates that ggplot2 is attempting to interpret the data as continuous due to the error. Resolve the underlying issue to ensure correct representation of your categorical data.

## Conclusion

The “Discrete value supplied to continuous scale” error in ggplot2 is a common issue that arises when there is a mismatch between the data type of a variable and the scale type specified in the ggplot aesthetic. By ensuring that categorical variables are correctly identified as factors or characters and using the appropriate scale type, such as scale_y_discrete(), you can address this error.

It’s crucial to include the complete error message for better diagnosis, and if needed, consider converting variables with the as.numeric() function. Paying attention to data types and making adjustments accordingly will lead to accurate representation and visualization of your data in ggplot2.