Introduction to Dynamic Range Definition in R

In this article, we will explore how to dynamically define the range of values for a given condition in R. We’ll be using two dataframes, one with samples organized by group and time, and another that defines for each group a stage defined by start (beg) and end (end) times.

Understanding the Problem

We have two dataframes, df1 and df2. df1 contains samples organized by group and time, while df2 defines for each group a stage defined by start (beg) and end (end) times. We want to add to df1 the stage from df2, based on the values of group and time.

Desired Output

The desired output is to add to df1 the stage from df2 for each group and time that falls within the defined range.

Approach Using fuzzyjoin Package

One way to achieve this is by using the fuzzyjoin package. This package allows us to perform fuzzy joins, which are a type of join operation where we can specify a tolerance or a fuzzy match between two columns.

Understanding Fuzzy Join

A fuzzy join is a type of join operation that allows us to specify a tolerance or a fuzzy match between two columns. In our case, we want to find the rows in df2 where the group and time columns match with the corresponding values in df1. However, since the data types are not exactly matching (one is integer and one is character), we need to use a fuzzy match.

Using Fuzzy Join

To use the fuzzyjoin package, we first need to define our dataframes as follows:

# Load the required libraries
library(fuzzyjoin)
library(dplyr)

# Define the dataframes
df1 <- data.frame(sample = c("Oct", "Feb", "Nov", "May", "Jun"), group = c("B", "A", "A", "A", "A"), time = c(10, 15, 7, 5, 0))
df2 <- data.frame(group = c("A", "A", "A", "B", "B", "C"), stage = c("I", "II", "III", "I", "II", "I"), beg = c(4, 9, 13, 3, 13, 2), end = c(8, 12, 20, 12, 18, 6))

Performing Fuzzy Join

Next, we can perform the fuzzy join using the fuzzy_left_join function. We specify the columns to match on, including the group and time columns from both dataframes.

# Perform the fuzzy left join
df_joined <- fuzzy_left_join(df1, df2, 
                             by = c('group', 'time' = 'beg', 'time' = 'end'), 
                             match_fun = c(`==`, `&gt;=`, `&lt;=`))

Understanding the Match Function

The match function is used to specify how we want to match the values in our columns. In this case, we use a vector of functions that includes:

• == for exact equality
• >= for values greater than or equal to
• <= for values less than or equal to

Result

The result of the fuzzy join is a new dataframe (df_joined) that contains all the rows from both dataframes, where the group and time columns match with the corresponding values in both dataframes.

# Print the joined dataframe
print(df_joined)

This will give us the desired output:

Conclusion

In this article, we explored how to dynamically define the range of values for a given condition in R using the fuzzyjoin package. We defined two dataframes, one with samples organized by group and time, and another that defines for each group a stage defined by start (beg) and end (end) times. We then used the fuzzy_left_join function to perform a fuzzy join between these two dataframes, resulting in the desired output.

Last modified on 2025-01-05