Understanding the Error and Fixing it with dplyr in R

As a data scientist, working with datasets can be challenging, especially when dealing with different libraries like dplyr. In this article, we’ll dive into an error that users of the dplyr library might encounter, and explore how to fix it.

Introduction to dplyr

dplyr is a popular R package used for data manipulation. It provides various functions that help in organizing, filtering, and analyzing datasets. One of its key features is the use of pipes (%>%) which simplify the code by allowing users to chain operations together in a straightforward way.

The Error: `Error in filter_impl(.data, dots) : invalid argument to unary operator`

The error Error in filter_impl(.data, dots) : invalid argument to unary operator occurs when trying to use the filter() function with an invalid argument. This can happen due to several reasons such as incorrect data type or missing values.

In the provided R code snippet, the user is trying to find the lowest mortality rate of a hospital given the state and outcome name (e.g., heart attack). The code snippet uses the dplyr library for data manipulation.

Understanding the Code

best <- function(state_input, oc_name) {
    outcome <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
    
    if (oc_name == "heart attack") {
        return_outcome <- outcome %>% 
            select(State, Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack) %>% 
            filter(State == state_input) %>% 
            arrange(Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack) %>% 
            top_n(1, -Hospital.30.Day.Death..Mortality..Rates.from.Heart Attacker)
    }
}

This function reads a CSV file named “outcome-of-care-measures.csv” into the outCOME variable and filters it based on the state provided in the state_input argument. The result is then sorted by the specified column (Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack) in descending order.

Understanding the Problem

However, this function throws an error with a cryptic message:

Error in filter_impl(.data, dots) : invalid argument to unary operator

Fixing the Error

The issue arises from the fact that colClasses = "character" is used when reading the CSV file. This tells R to treat all columns as character strings. When trying to compare a column with another using %>% filter(), it expects a numeric or factor type, not character.

Solution 1: Fixing Character Columns

Instead of treating all columns as character strings, we need to specify which ones are of the correct data type. We can use stringsAsFactors = FALSE when reading the CSV file to avoid this issue:

outcome <- read.csv("outcome-of-care-measures.csv", stringsAsFactors = FALSE)

This tells R not to convert character columns into factors by default, which is what we want.

Alternative Solution 2: Removing Conditional Statement

If you plan to use the same function for different outcomes (e.g., heart attack and stroke), consider removing the conditional statement. Instead, load your data outside of the function call and handle it separately:

best_heart_attack <- function(state_input) {
    return_outcome <- read.csv("outcome-of-care-measures.csv", stringsAsFactors = FALSE) %>% 
        select(State, Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack) %>% 
        filter(State == state_input) %>% 
        arrange(Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack) %>% 
        top_n(1, -Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attacker)
    
    return(return_outcome)
}

In this solution, you can call the function with just the state and it will return the desired result.

Additional Considerations

Make sure to handle missing values appropriately. The filter() function ignores NA values by default.
Use meaningful column names instead of abbreviations (e.g., “Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack” could be renamed to something more understandable).
Always verify the data types of your variables and ensure that they match what you’re expecting.

Example Use Cases

Here’s an example of how you might use these functions:

# Create a sample dataset (not actually reading from a CSV file)
sample_data <- data.frame(State = c("TX", "NY", "FL"), 
                          Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack = c(100, 50, 200))

# Find the lowest mortality rate in Texas using the original function
original_function <- function(state_input, oc_name) {
    # ... (same code as above)
}

best(original_function("TX", "heart attack"), sample_data)

# Find the lowest mortality rate in New York using the alternative solution
alternative_solution <- function(state_input) {
    # ... (same code as above, but without conditional statement)
}

alternative_solution("NY")

In this example, we create a sample dataset to demonstrate how you can use these functions. We call original_function with state “TX” and “heart attack”, and alternative_solution with state “NY”.

Last modified on 2024-08-04