How to fix the R error – read.xlsx in r showing error object not found

A read.xlsx in R showing error object not found message tends to cause a lot of confusion when it pops up. This is due in large part to the amount of work that goes into loading an Excel file in R. When things go right it’s a seamless process. But there’s a lot going on behind the scenes. And if any of those elements go askew you might see your script fail with that error. The error itself essentially just means that your script’s attempt to load an xlsx file and reference it as an object failed. And the process of fixing it is straightforward in a sense. You simply need to locate where the problem is occurring in the loading process and fix that roadblock. But to do so you’ll need to walk through a few separate steps.

An Overview of Excel Files and Related Errors

When you load an xlsx Excel file in an office suite you may very well see something quite similar to an R data frame. This often leads people to assume that xlsx isn’t that different from a standard csv file. But that’s not quite true. Excel files can contain the same information as a csv or an R data frame. But it’s distinct enough to require quite a bit of work to fit it into the R ecosystem.

The first point to consider is the fact that an xlsx file is compressed rather than existing as simple plain text. The file format also uses XML formatting for the data stored within it. Loading an xlsx file essentially requires a chain of events. Your script needs to access the xlsx file on your hard drive. Next, it needs to decompress the file’s contents and load that information into memory. The process ends by converting the elements within the Excel sheet into a native R structure.

Keep in mind as well that these processes often depend on one or more external libraries or even full runtimes. And an error in just one of those pieces can be enough to cause a read error when your script attempts to load the Excel file. However, that just means you need to check a couple of different places to find out where the problem stems from. In finding the source of the problem you’ll almost always be able to implement a quick fix. Fixing the problem essentially means fixing whatever part of the information transfer chain is silently failing.

How To Pinpoint the Cause and Fix the Read Error

It’s best to start out with the easiest possibility to find and fix. Does your file actually exist? Just one typo is enough to essentially tell your script to load up a non-existent file. You might want to try manually copying and pasting the name of the Microsoft Excel spreadsheet into your script to ensure you’ve got it right. Also, remember to make sure that the file extension is part of the file name. While you’re at it, double-check the file path. And don’t forget that the delimiting character in your file path can change depending on what operating system you’re using. The forward and back slashes in your path can sometimes make a world of difference. Especially if you’re working with multiple operating systems.

Next, it’s a good idea to try loading up the xlsx file in Excel. That way you can verify that the file isn’t corrupted. If it is corrupted you can try using 3rd party tools to perform automatic file repair and rescue the data within it. Note that this can also explain instances of missing values from a file that seemingly had the import run smoothly. You’ve probably tried to extract data from a corrupted zip file at some point. This can result in a portion of the data being successfully decompressed while also leaving some remaining garbage. And the same can happen with an xlsx file. You might get some, but not all, of the contents if you’re loading a corrupted xlsx.

You should also double-check that the row names and column name of the Excel sheet matches up to what you have in your code. Even one tiny typo is enough to trigger this error. Likewise, make sure that you’re correctly mapping them to your data frame and properly addressing the parent-child relationship between items. For example, you’d want to specifically access elements of a loaded xlsx such as in the following example.

myNewStats <- read_xlsx(“myDocument.xlsx”)
myNewStats$initial <- myNewStats$a1

This is especially important when moving data to or from an Excel sheet and a data frame. There are some unique caveats to the MS Excel file format within R. But as a general rule, R handles most objects through a system of cascading inheritance. An “a1” row might exist after importing the data. But it only exists as a child of that newly imported data. The “a1” needs to always be referenced from that imported data, rather than as a unique entity unto itself, unless it’s copied or assigned somewhere else. Trying to directly access it independently of its parent might also result in these issues.

Note that the previous code uses a library called readxl to load the spreadsheet. R’s libraries are one of the reasons why the language is so beloved in data science. A process like this which would normally take a lot of work to set up can instead be created with just a couple lines of code. But this can also cause some issues. For example, readxl depends on the rcpp package to function. If either of those entities becomes incompatible with the other, or non-functional, then the readxl package won’t be able to read your Excel worksheet. And there’s always a chance of libraries suffering from damage, becoming out of date, etc. So if you’re seeing this error you should also make sure the xlsx package you’re using is valid. Doing a complete uninstall of the package and then installing it again should ensure that it’s in good shape.

On top of this, some Excel libraries have even more dependencies. For example, the xlsx library of the same name for R relies on Java. As such, updates to Java have a chance of causing incompatibilities that could result in a read failure. What makes these problems especially troublesome is that they tend to fail at a level where the R interpreter doesn’t really understand what’s going on. As such, error details tend to remain vague. Debugging these issues often requires you to think back to the last time your codebase was working. If your code suddenly stopped reading xlsx then it might be due to an automated process that ran without you even noticing. For example, a Java runtime that was set to automatically update itself. The fix would be to roll back these possible points of failure to previous versions.

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