Understanding pmin and Pattern Matching in R: Unlocking Data Insights with Efficient Code

Understanding pmin and Pattern Matching in R

R is a popular programming language for statistical computing and graphics. It provides an extensive set of libraries and tools for data manipulation, analysis, and visualization. In this article, we’ll delve into the world of R’s pmin function, explore its capabilities, and discuss how to apply pattern matching to find minimum values in columns with specific names.

Introduction to pmin

The pmin function in R returns the smallest value from a list of numeric vectors. It is an element-wise operation, meaning it compares corresponding elements from each input vector and returns the smaller value. This function is commonly used for data cleaning, filtering, and analysis.

# Basic usage of pmin
x <- c(10, 20, 30)
y <- c(15, 25, 35)

pmin(x, y)  # Output: [1] 10 15 20

In this example, pmin compares corresponding elements from vectors x and y, returning the smaller value.

Pattern Matching in R

Pattern matching is a powerful feature in R that allows you to select rows or columns based on specific conditions. In the context of the original question, pattern matching enables us to identify columns with names starting “gwas_p”.

R provides several functions for pattern matching, including grepl, stringr::str_detect, and starts_with. The most relevant function in this scenario is starts_with.

# Using starts_with to select columns based on name pattern
library(dplyr)

df <- structure(list(Suggested.Symbol = c("CCT4", "DHRS2", "PMS2", 
                                        "FARSB", "RPL31", "ASNS"), gwas_p.onset = c(0.9378, 0.5983, 7.674e-10, 
                                                                       0.09781, 0.5495, 0.7841), gwas_p.dc14 = c(0.3975, 0.3707, 6.117e-17, 
                                                                      0.2975, 0.4443, 0.7661), gwas_p.tfc6 = c(0.2078, 0.896, 7.388e-19, 
14596.5, 30430.5, 66960.6)), row.names = c(NA, 6L), class = "data.frame")

df %>%
  select(starts_with("gwas_p"))  # Select columns with names starting "gwas_p"

In this example, starts_with is used to select all columns whose names start with the prefix “gwas_p”.

Finding Minimum Values Using pmin and Pattern Matching

Now that we’ve explored pattern matching in R, let’s discuss how to use pmin along with pattern matching to find minimum values in columns with specific names.

The original question asked how to find the minimum value in certain columns by name like so:

df <- df %>% 
  mutate(p.min = pmin(p_onset, p_dc14))

However, this approach only applies to two specified column names. To find the minimum value for all columns with names starting “gwas_p”, we need to use pmin with pattern matching.

Here’s an example of how you can achieve this:

# Using do.call and pmin with pattern matching
transform(df, p.min = do.call(pmin, df[starts_with("gwas_p")]))

In this code snippet:

We use starts_with to select columns whose names start with the prefix “gwas_p”.
We pass these selected columns to do.call, which applies the pmin function element-wise.
The result is assigned to a new column named “p.min” in the original dataframe.

Example Walkthrough

Let’s walk through an example to illustrate how this code works:

Suppose we have the following dataframe:

structure(list(Suggested.Symbol = c("CCT4", "DHRS2", "PMS2", 
                                    "FARSB", "RPL31", "ASNS"), gwas_p.onset = c(0.9378, 0.5983, 7.674e-10, 
               0.09781, 0.5495, 0.7841), gwas_p.dc14 = c(0.3975, 0.3707, 6.117e-17, 
               0.2975, 0.4443, 0.7661), gwas_p.tfc6 = c(0.2078, 0.896, 7.388e-19, 
               5.896e-01, 3.043e-01, 6.696e-01)), row.names = c(NA, 6L), class = "data.frame")

If we apply the transformation:

transform(df, p.min = do.call(pmin, df[starts_with("gwas_p")]))

The resulting dataframe will have an additional column named “p.min” containing the minimum values for columns with names starting “gwas_p”.

Conclusion

In this article, we explored how to use R’s pmin function in combination with pattern matching to find minimum values in columns with specific names. We discussed various pattern-matching functions available in R and demonstrated their usage using examples.

By leveraging these techniques, you can effectively manipulate your data in R to extract insights and perform complex analyses.

Additional Resources

Last modified on 2024-05-02