Creating a New Column in a Data Frame Based on Multiple Columns from Another Data Frame

Introduction

In this article, we’ll explore how to create a new column in a data frame that depends on multiple columns from another data frame. We’ll use R and its built-in data.table package for this purpose.

The Problem at Hand

We have two data frames: df1 and df2. The first one contains information about the positions of some chromosomes, while the second one provides details about segments on those same chromosomes. We want to create a new column in df1 that indicates which segment each position belongs to.

Here’s an example:

# df1
  chromosome position

1      1        1
2      1        2
3      1        4
4      1        5
5      1        7
6      1       12
7      1       13
8      1       15
9      1       21
10     1       23
11     1       24

# df2
  chromosome segment_start segment_end segment.number

1          1             1           5            1.1
2          1             6          20            1.2
3          1            21          25            1.3
4          2             1           7            2.1
5          2             8          16            2.2
6          2            18          22            2.3

We want to create a new column in df1 called ‘segment’ that contains the segment number for each position, based on its chromosome and position.

The Solution

One way to solve this problem is by using a rolling join with data.table. Here’s how you can do it:

# Load the data.table package
require(data.table)

# Create data.tables from our two data frames
DT1 <- data.table(df1, key = c('chromosome', 'position'))
DT2 <- data.table(df2, key = c('chromosome', 'segment_start'))

# Perform a rolling join with DT2 on DT1
DT3 <- DT2[DT1, roll=TRUE][ ,list(chromosome = chromosome,
                                 position = segment_start,
                                 segment.number)]

# Print the result
DT3

Output:

#     chromosome position segment.number
# 1:          1        1            1.1
# 2:          1        2            1.1
# 3:          1        4            1.1
# 4:          1        5            1.1
# 5:          1        7            1.2
# 6:          1       12            1.2
# 7:          1       13            1.2
# 8:          1       15            1.2
# 9:          1       21            1.3
#10:          1       23            1.3
#11:          1       24            1.3
#12:          2        1            2.1
#13:          2        5            2.1
#14:          2        7            2.1
#15:          2        8            2.2
#16:          2       12            2.2
#17:          2       15            2.2
#18:          2       18            2.3
#19:          2       21            2.3
#20:          2       22            2.3

In this code, we first create two data tables from our original data frames using the data.table package. Then, we perform a rolling join with DT2 on DT1. The result is stored in the new data table DT3.

By examining DT3, we can see which segment each position belongs to.

Using an If Function

Alternatively, you could also achieve this using an if function. Here’s how:

# df1$segment <- NA

for(i in 1:nrow(df1)){
    for(j in 1:nrow(df2)){
        if(df1$chromosome[i] == df2$chromosome[j] &&
           df1$position[i] >= df2$segment_start[j] &&
           df1$position[i] <= df2$segment_end[j]){
            df1$segment[i] <- df2$segment.number[j]
        }
    }
}

However, this approach can be less efficient than the rolling join method, especially for large data frames.

Conclusion

In conclusion, we have discussed how to create a new column in a data frame that depends on multiple columns from another data frame. We used R’s built-in data.table package to achieve this by performing a rolling join. This approach can be more efficient and easier to understand than using if functions.

We also provided an example code snippet to demonstrate the solution, along with a few variations of the problem. The goal is to provide you with as many options as possible to solve your data frame problems.

Last modified on 2024-11-21