R is one of the most popular platforms for tasks related to data science and statistics. Everything about the language is designed around those specific needs. However, this can give rise to a number of problems. After all, a platform devoted to data science will ensure operations can’t run unless they’re properly formatted. But actually fixing those formatting errors isn’t always straightforward. For example, you might get an “invalid type (list) for variable” within code that seems syntactically valid. But you’ll soon learn how to find and fix the root cause of this particular error.
The Root Cause of an Invalid Type Error
An invalid type error in R reflects, as the name suggests, an attempt to use incompatible types in a function. The situation is most commonly encountered when passing multiple values to a function at the same time. This is somewhat similar to what you’d face if you tried to add an integer and string value together without prior conversion. But things can get a little trickier when dealing with collections such as a list. This is why an invalid type (list) error is especially common when working with analysis of variance (ANOVA) or regression analysis. The point of comparison needs to be one of R’s standard singular vectors rather than a container with multiple values. Passing a list essentially creates a missing value because the data type supplied doesn’t match the allowable formats.
If a list is passed then the list, rather than the list’s contents, is presented for analysis. And this will cause an invalid type error if the function expects a standard singular vector rather than a larger recursive vector that contains multiple elements. In short, the error typically occurs when method functionality is designed around a singular item but instead sees multiple instances in a container. If a type parameter of the incorrect format is passed we’ll typically see errors.
The errors can be frustrating when they come up. But they’re an important part of writing a function, expression, or other implementation of programming logic. Overly lenient code can lead to statistical errors further down the line. The messages help to reduce the chance of errors when working with easily confused elements. The issues can be further compounded by the fact that an R variable type instance can appear to be compatible with another at first glance while actually being quite distinct.
Taking a Closer Look at Lists and Vectors
The compatibility problem is complex in theory, but a lot easier to understand when you see it in a real-world situation. The issue is more succinctly demonstrated within the following R code.
a <- list(1,2,3,4,5)
b <- c(6,7,8,9,10)
c <- lm(b ~ a)
print(c)
We begin by creating our list, called a, that contains the numbers 1 to 5. Keep in mind that our list object is distinct from the elements contained within it. This holds true for all of the container types in R. From a matrix, to an array, or a list. Our list contains individual vector element instances. But the list is also a unique recursive vector that is distinct from its contents. A bottle containing water isn’t a liquid. And, likewise, a list remains distinct from the elements stored within it. And this comes into play when we try to fit a linear model onto a data frame on line 3.
At first glance, it seems like we’re dealing with two fully compatible containers. Each has five numeric vector instances and a single dimension. However, note that when we defined b we used the c function. This created a concatenation of the items which makes it distinct from a. You can see this more clearly by running the following code.
a <- list(1,2,3,4,5)
b <- c(6,7,8,9,10)
print(a)
print(class(a))
print(b)
print(class(b))
We can now see that a and b look similar to the human eye, but are actually quite different from each other. So when we try to pass both variable names to the liner model function it produces type errors. At this point, the path to a solution should be clear. All we need to do is convert our list to a compatible vector.
Fixing the Errors With a Simple Conversion
R even has a function that provides us with the exact functionality that we need. We want to take data in a list format and essentially “unlist” it. And to do so we can use R’s unlist function. Take a look at the following code.
a <- list(1,2,3,4,5)
print(a)
print(class(a))
b <- c(6,7,8,9,10)
print(b)
print(class(b))
a <- unlist(a)
print(a)
print(class(a))
c <- lm(b ~ a)
print(c)
print(class(c))
We begin in a similar manner to the prior example. However, to illustrate what’s happening we’ll print out the variables and their classes with each step. And this time around we’ll redefine the a variable name with the result of passing its original contents to unlist. Note that in doing so we change the class type from list to numeric. This makes it compatible with the numeric definition assigned to b. As such, there’s no longer an error when we pass a and b to lm and assign the results to c. This can be done in a more succinct manner by just using the unlist within the lm call. You can see that in effect below.
a <- list(1,2,3,4,5)
b <- c(6,7,8,9,10)
c <- lm(b ~ unlist(a))
print(c)
The order of operations converts a from the list data type before lm runs. This concept works within the larger context of R as a whole. For example, we could use it with the sum function. Insert the following code after line 1 of the previous example.
print(sum(unlist(a)))
We’d normally receive an invalid type error if we tried to run sum on a list. But because we’re using unlist, sum isn’t seeing the list. It instead only sees unlist’s output. The outward-moving design allows us to convert types within functions without needing to specifically redefine them in our code.