If you’re looking at this error, you’ve managed to pass an R function a list object of numeric value strings when it was expecting a numeric vector (build from numeric data types). Since numeric value strings most definitely are not the same as actual numbers, R promptly goes full diva and loudly proclaims: (list) object cannot be coerced to type ‘double’.
Indeed. String Coercion is bad, don’t you know?
Behind the scenes, this error is actually a very good thing. The problem with string coercion in your base R code, particularly with untrusted data from an external source, is that you’re never really sure how the results will be evaluated. Especially if you’re dealing with a different type of input than the specified type. For example, I was sent a list of customer account numbers (numeric values, we assumed) which happened to have alphanumeric characters sitting in the field. So clearly not the data type we expected. It’s easier to find this error now, when you’ve got time to wrap the r code with data trapping options, than automatically do a conversion and risk having it sabotage your models.
In that scenario, if you’re lucky all it will do is generate a missing value or two. That’s a relatively benign output. A bad attempt to coerce an array of different type(s) of non-conforming data can generate all kinds of weird results if this was allowed to actually execute.
How To Fix The R error: (list) object cannot be coerced to type ‘double’
Polite suggesting to your script that it treat the values in question as numeric. Since verbal approaches are notoriously ineffective, this requires a small correction to your code. You will want to use the as.numeric function. This will convert the (likely) text values contained within your list into appropriate numeric values.
I should note that the as.numeric() function expects a single element or atomic vectors as input, so you may need to adjust your code to apply it properly to each element. Potential ways to use this function:
- Use the unlist() function to convert your list into a single vector and feed the result into as.numeric. It will deliver a list of numeric values for your inspection.
- You can use the lapply function to apply the as.numeric function to each elements of the list
- You can use list sub-setting to target specific elements of the list and feed them into the as.numeric function.
- You can use the apply() function in R to apply the as.numeric() function to every element of a data frame.
The asnumeric function is pretty welcoming: it accepts strings that want to be integer(s) and floating point number values (floats).
In summary, the asnumeric function can help you prevent a lot of these errors before they impact your code.
Beyond as.numeric – other R conversion functions for preventing errors like this
In addition to the as.numeric() function, R provides other functions that can be used to convert data types, such as as.integer() and as.character(). The as.integer() function is used to convert values to integer data type, while the as.character() function is used to convert values to character data type. Similar to the as.numeric() function, these functions also expect a single element or an atomic vector as input. To use these functions, simply call them on the values that need to be converted. For example, to convert a numeric value to an integer, you can use the as.integer() function like this:
as.integer(3.14). Similarly, to convert a numeric value to a character string, you can use the as.character() function like this:
as.character(123). By using the appropriate data type conversion function, you can ensure that your data is in the correct format for your R code to execute properly.
How to handle missing or non-conforming data
When using the as.numeric() function, it’s important to consider how to handle missing or non-conforming data. If the input data contains missing values, the as.numeric() function will return an NA value for that element. If the input data contains non-conforming values, such as text strings or special characters, the as.numeric() function will return an NA value for that element as well.
To handle missing values, you can use the is.na() function to check for missing values before applying the as.numeric() function. For example, here’s how to convert a vector of numeric strings to a numeric vector while handling missing values:
my_vec[!is.na(my_vec)] <- as.numeric(my_vec[!is.na(my_vec)])
To handle non-conforming values, you can use the grepl() function to check for non-numeric characters before applying the as.numeric() function. For example, here’s how to convert a vector of numeric strings to a numeric vector while handling non-conforming values:
my_vec[!grepl("[^0-9.]", my_vec)] <- as.numeric(my_vec[!grepl("[^0-9.]", my_vec)])
By handling missing or non-conforming data appropriately, you can ensure that your as.numeric() function works as expected and avoid potential errors in your R code.