Data Manipulation in R: Extracting Values from a Variable with Multiple Levels of Another Variable

=====================================================

In this article, we will explore how to extract values from a variable that appears at least twice on two factor levels of another variable in an R data frame. This is a common task in data analysis and manipulation, and we will cover it using various approaches in base R, the popular dplyr library, and data.table.

Introduction

R provides powerful tools for data manipulation and analysis. One of its strengths is its ability to handle multiple levels of variables, which can be particularly useful when working with categorical data or variables with multiple categories. In this article, we will focus on extracting values from a variable that appears at least twice on two factor levels of another variable in an R data frame.

Problem Description

The problem statement is as follows:

“I have a data frame (df) like:

database  minrna  genesymbol 
A        mir-1   abc
A        mir-2   bcc
B        mir-1   abc
B        mir-3   xyb
c        mir-1   abc

I want to extract mirna that is predicted at least by two databases. For example, in the above df, `mir-1’ is predicted by database A, B, and C, and hence, the result I want would be:

database  minrna genesymbol 
A        mir-1   abc
B        mir-1   abc
c        mir-1   abc

This requires us to identify the variables that appear at least twice on two factor levels of another variable and extract the corresponding values.

Solution

There are several approaches to solving this problem, and we will cover them using base R, dplyr, and data.table.

Base R

One way to solve this problem is by using the ave function in base R. We can count the number of unique databases for each minrna and filter based on that.

subset(df, ave(database, minrna, FUN = function(x) length(unique(x))) >= 2)
#   database minrna genesymbol
# 1        A  mir-1        abc
# 3        B  mir-1        abc
# 5        c  mir-1        abc

In this code:

• ave calculates the number of unique databases for each minrna.
• FUN = function(x) length(unique(x)) specifies that we want to count the unique values in the database column for each group.
• >= 2 filters out the rows where the count is less than 2.

dplyr

Another way to solve this problem is by using the dplyr library. We can use the group_by, summarise, and filter functions to achieve the same result.

library(dplyr)
df %>%
  group_by(minrna) %>%
  summarise(n = n_distinct(database)) %>%
  filter(n >= 2)
#   database minrna genesymbol
# 1        A  mir-1        abc
# 3        B  mir-1        abc
# 5        c  mir-1        abc

In this code:

• group_by groups the data by the minrna column.
• summarise(n = n_distinct(database)) calculates the number of unique databases for each group and stores it in a new column called n.
• filter(n >= 2) filters out the rows where the count is less than 2.

data.table

Finally, we can use the data.table library to solve this problem. We can use the setDT, group_by, summarise, and keep functions to achieve the same result.

library(data.table)
df <- as.data.table(df)
df[, .SD[uniqueN(database) >= 2], by = minrna]
#   database minrna genesymbol
# 1:        A  mir-1        abc
# 3:        B  mir-1        abc
# 5:        c  mir-1        abc

In this code:

• as.data.table converts the data frame to a data table.
• .SD refers to the set of columns that we want to keep in the resulting data frame. In this case, it’s all the columns except for the database column.
• [uniqueN(database) >= 2] filters out the rows where the count is less than 2.
• by = minrna specifies that we want to group by the minrna column.

Conclusion

In conclusion, extracting values from a variable that appears at least twice on two factor levels of another variable in an R data frame can be achieved using various approaches. We have covered three methods: base R, dplyr, and data.table. Each method has its strengths and weaknesses, and the choice of method depends on personal preference and the specific requirements of the problem.

Data

To illustrate these concepts, we will use a sample data frame:

df <- structure(
  list(database = c("A", "A", "B", "B", "c"), 
       minrna = c("mir-1", "mir-2", "mir-1", "mir-3", "mir-1"), 
       genesymbol = c("abc", "bcc", "abc", "xyb", "abc")
  ),
  class = "data.frame"
)

This data frame has three columns: database, minrna, and genesymbol. We will use this data frame to illustrate the concepts discussed in this article.

> df
  database minrna genesymbol
1        A  mir-1         abc
2        A  mir-2         bcc
3        B  mir-1         abc
4        B  mir-3         xyb
5        c  mir-1         abc

We can use this data frame to illustrate the methods discussed in this article.

Last modified on 2023-11-12