Understanding the rollmedian Function and Its Limitation
The rollmedian function in R is used to calculate the median of a vector with a specified window size (k). However, this function has a limitation when it comes to handling data frames with more rows than columns. In this section, we will delve into the technical details behind rollmedian and explore why it fails when trying to add an additional column to a data frame.
What is rollmedian?
The rollmedian function calculates the median of a vector using a specified window size (k). The function takes two main arguments: the input vector, and the window size. It returns a new vector containing the median values for each position in the original vector, with a window size of k.
How Does rollmedian Work?
The rollmedian function works by iterating over the input vector and calculating the median of the current and previous k-1 elements. The median is calculated using the formula:
median(x) = (x[1] + x[2] + ... + x[k]) / k
Where x[i] represents the i-th element in the input vector.
The Limitation of rollmedian
The main limitation of rollmedian is that it assumes the input vector has at least k elements. If the input vector has fewer than k elements, the function will raise an error. This is because the median calculation requires a minimum of k-1 elements to calculate the first median value.
The Case of Data Frames
When working with data frames, each column represents a separate vector. In the given example, we want to add an additional column calculating the median of the current and previous 2 values (k=3). However, when we try to apply rollmedian to the entire data frame using dataframe$Value,k=3, R throws an error.
This is because the data frame has only 8 rows, but rollmedian requires at least 6 elements to calculate the first median value (k-1 = 2). The additional column will have fewer than 6 elements, causing the function to fail.
A Solution: Handling Out-of-Bounds Cases
To avoid this limitation, we can use a simple trick. Instead of applying rollmedian directly to the entire data frame, we can create a custom function that handles out-of-bounds cases. This function will return NA for the first k-1 elements and then apply rollmedian to the remaining elements.
Creating a Custom Function
We can create a simple function called med.fun that takes three arguments: the variable name, the data frame, and the window size (k). The function returns a new column containing the median values for each position in the original vector.
med.fun <- function(var, data, k){
# Note: variable name must be in quotes
return(c(rep(NA, k-1), with(data, rollmedian(get(var), k=k))))
}
Using the Custom Function
We can use the custom function med.fun to create a new column calculating the median of the current and previous 2 values. We pass the variable name "Value", the data frame dataframe, and the window size k=3 as arguments.
dataframe$medianval <- med.fun("Value", dataframe, 5)
Output
The output of the custom function will be a new column containing the median values for each position in the original vector. The first two elements will be NA, and then the median values will start being calculated.
dataframe
# Date Value medianval
# 1 21/07/2016 14.8 NA
# 2 22/07/2016 14.9 NA
# 3 23/07/2016 15.8 14.9
# 4 24/07/2016 15.0 15.0
# 5 25/07/2016 15.7 15.7
# 6 26/07/2016 15.6 15.6
# 7 27/07/2016 16.1 15.7
# 8 28/07/2016 16.1 16.1
Conclusion
In this article, we explored the limitation of the rollmedian function when working with data frames and added an additional column to calculate the median of the current and previous 2 values. We created a custom function called med.fun that handles out-of-bounds cases and applies rollmedian to the remaining elements. This approach allows us to extend the functionality of rollmedian to handle more complex scenarios.
Additional Resources
For further reading, we recommend checking out the following resources:
Note: The code blocks in this article are written using Hugo’s highlight shortcode to ensure proper syntax highlighting.
Last modified on 2024-02-09