Understanding the Problem and Identifying the Solution

In this blog post, we will be discussing a common issue that R users encounter when trying to access data from a dataset using the $ operator. The problem lies in understanding how to select data based on character strings in R.

Background Information

R is a popular programming language for statistical computing and graphics. It has an extensive range of libraries and packages available, including data manipulation and analysis tools like dplyr, tidyr, and readr. In this post, we will be focusing on the use of sapply to apply functions to multiple datasets.

The Problem

The problem at hand is a simple one. A user has written a function pollutantmean that takes three arguments: a directory path, a pollutant name, and an ID number. The function reads in CSV files from the specified directory based on the provided ID, calculates the mean of the specified pollutant, and returns a vector containing the means.

However, when the user tries to call pollutantmean("specdata", "nitrate", 23), they get an error message indicating that data$pollutant is NULL. The error occurs because the character string "nitrate" cannot be referenced using $.

Breaking Down the Solution

The solution lies in understanding how to select data based on character strings in R. In this case, we need to replace the $ operator with square brackets ([]) when referencing the pollutant name.

Let’s take a closer look at the error message:

Error in pollutantmean(“specdata”, “nitrate”, 23) :

• attempt to apply non-function

This message indicates that R is unable to find a function named pollutantmean with the specified arguments. However, we know that this function exists and has been defined elsewhere.

The issue lies in the way we are calling this function. Let’s examine the code more closely:

results <- sapply(id, f)

In this line of code, sapply is applied to the id vector using a function f. The f function takes a single argument, num, which represents the ID number.

The f function looks like this:

f <- function(num){
  ...
}

Inside the f function, we have a block of code that reads in a CSV file based on the provided ID:

fname <- paste('specdata/00',as.character(num),".csv",sep="")
data <- read.csv(fname)
data <- data[complete.cases(data),]
return(mean(data$pollutant))

Here, we are using read.csv to read in the CSV file and assign it to the variable data. We then subset the data based on complete cases (i.e., rows with no missing values).

However, when referencing data$pollutant, we encounter the error.

Understanding Why $ Doesn’t Work

In R, when you reference a column name using $, you must ensure that it is a valid identifier. In this case, data$pollutant does not resolve to a single value because the pollutant name "nitrate" is a character string.

The reason for this behavior lies in how R handles references to columns based on character strings. When you use $, R assumes that you are referencing a column by its full name. However, when the column name contains special characters or spaces, it does not resolve correctly.

This issue can be resolved by using square brackets ([]) instead of $. The syntax for using square brackets to reference columns is:

data[["column_name"]]

For example, data[["nitrate"]] would return the values in the “nitrate” column.

Correcting the Code

To fix the issue with our code, we need to replace the $ operator with square brackets when referencing the pollutant name:

results <- sapply(id, f)

return(mean(data[[pollutant]]))

By using data[[pollutant]], we are ensuring that R knows how to reference the “pollutant” column based on its full name.

Final Answer

The final answer lies in understanding how to select data based on character strings in R. By replacing the $ operator with square brackets, we can resolve the error and get the desired output.

Here is the corrected code:

pollutantmean <- function(directory, pollutant, id = 1:332) {
  f <- function(num){
    if(num>=0 & num<=9){
      fname <- paste('specdata/00',as.character(num),".csv",sep="")
    }
    else if (num>=10 & num <=99){
      fname <- paste('specdata/0',as.character(num),".csv",sep="")
    }
    else{
      fname <- paste('specdata/',as.character(num),".csv",sep="")
    }
    data <- read.csv(fname)
    data <- data[complete.cases(data),]
    return(mean(data[[pollutant]]))
  }
  results <- sapply(id, f)
  return(results)

}

With this corrected code, we can now call pollutantmean("specdata", "nitrate", 23) without encountering any errors.

Example Use Cases

Here are some example use cases for the pollutantmean function:

# Get the mean of nitrate for ID 1
result <- pollutantmean("specdata", "nitrate", 1)
print(result)

# Get the mean of sulfate for IDs 10 and 20
results <- pollutantmean("specdata", "sulfate", c(10, 20))
print(results)

Last modified on 2025-01-18