Subsetting a Data Frame Based on Another Data Frame with Multiple Conditions Using dplyr Package in R

Subsetting a Data Frame Based on Another Data Frame with Multiple Conditions

As a data analyst or scientist, working with datasets can be a daunting task. Sometimes, you might need to filter or subset a dataset based on conditions specified in another dataset. In this article, we will explore how to achieve this using the dplyr package in R.

Introduction to Data Subsetting

Data subsetting is a crucial step in data analysis that involves selecting a subset of rows and columns from an existing dataset. This process can be performed manually or using various programming languages and libraries.

In our case, we have two datasets: betatable, which contains methylation array data, and genes_pos, which contains gene information along with their corresponding chromosome ranges. We want to subset the betatable data frame based on specific conditions specified in the genes_pos data frame.

Reproducible Example

To demonstrate this concept, let’s consider a simple example using R.

set.seed(123)
x <- data.frame(x = sample(1:100, 100, replace = TRUE), y = sample(1:100, 100, replace = TRUE), chr = sample(c("chr1", "chr2", "chr3"), 100, replace = T), Position = sample(1:10000, 100, replace = TRUE))
genes <- data.frame(gene = c("gene1", "gene2", "gene3"), chr = c("chr1", "chr2", "chr3"), rangelower = c(1, 3000, 6000), rangeupper = c(2999, 5999, 10001))

Using `dplyr::inner_join` and `filter`

We will use the dplyr package to perform an inner join between the two data frames based on the common column (chr). Afterward, we will filter the joined dataset using the specified conditions.

library(dplyr)

new_df <- x %>% 
  inner_join(genes, by = "chr") %>% 
  filter(Position < rangeupper, Position > rangelower)

Understanding `inner_join` and Filtering

Let’s break down what happens in this code:

We use the inner_join() function to combine the two data frames based on their common column (chr). This results in a new data frame that contains only the rows where there is a match between the two datasets.
The by = "chr" argument specifies the common column used for joining.

# Inner join example
inner_join_data <- data.frame(
  x = c(1, 2), 
  y = c(3, 4), 
  chr = c("chr1", "chr2")
)

genes_data <- data.frame(
  gene = c("gene1", "gene2"), 
  chr = c("chr1", "chr2"), 
  rangelower = c(1000, 2000),
  rangeupper = c(2000, 4000)
)

inner_joined_df <- inner_join(inner_join_data, genes_data, by = "chr")

Splitting the DataFrame by Gene

After obtaining the filtered data frame (new_df), we can split it into separate data frames based on the gene column.

list_dfs <- split(new_df, new_df$gene)

This will create a list of data frames where each element in the list corresponds to a specific gene.

Conclusion

Subsetting a dataset based on conditions specified in another dataset is an essential task in data analysis. In this article, we explored how to achieve this using dplyr and inner_join. We also demonstrated how to filter the joined dataset and split it into separate data frames based on the gene column.

By following these steps, you can efficiently manage large datasets by applying specific conditions or filters to extract relevant information.

References

Last modified on 2024-10-29