How to Subset Data in R: Select, Filter, and Extract Data

Selecting rows from a specific column

One behavior to watch for is the simplification that happens when you select a single column. Selecting the first three rows of just the payment column simplifies the result into a vector.

debt[1:3, 2]

100 200 150

The 2 here refers to the second column of debt, which is payment. Because we selected a single column, R drops the data frame structure and returns a plain numeric vector.

Dataframe formatting

To keep it as a dataframe, just add drop = FALSE as shown below: 

debt[1:3, 2, drop = FALSE]

    payment
1       100
2       200
3       150

Selecting a specific column

You can also select a single column with the $ operator by writing dataframe$column. The following example extracts the payment column from debt. R returns the result as a vector rather than a data frame.

debt$payment

100 200 150 50 75 100

Using the subset() Function

When looking to create more complex subsets or a subset based on a condition, the next step up is to use the subset() function. For example, what if you wanted to look at debt from someone named Dan. You could just use the brackets to select their debt and total it up, but it isn't a very robust way of doing things, especially with potential changes to the dataset.

# This works, but is not informative
debt[1:3, ]

subset() on a categorical variable

A better way to do this is to use the subset() function to select the rows where the name column is equal to Dan. Notice that there needs to be a double equals sign (==), known as a relational operator.

# This works, but is not informative nor robust
debt[1:3, ]

# Much more informative!
subset(debt, name == "Dan")

      name     payment
1      Dan         100
2      Dan         200
3      Dan         150

The result contains only the three rows where name equals "Dan". Notice that the original row numbers (1, 2, 3) are preserved, which helps you trace results back to the source data frame.

subset() on a numeric variable

We can also subset on numeric columns. If we wanted to see rows where payments equal $100, you would do the following:

subset(debt, payment == 100)

      name  payment
1      Dan      100
6      Rob      100

Two rows match the condition: row 1 (Dan) and row 6 (Rob) both have payments of exactly $100. This shows that subset() scans every row regardless of the name, returning all matches.

Accessing and Subsetting Dataframes

What if you need only the cash flows from company A? Here the subset() function filters rows from a different data frame, cash:

subset(cash, company == "A")

      company  cash_flow  year
1           A       1000     1
2           A       4000     3
3           A        550     4

Remember:

• The first argument you pass to subset() is the name of your dataframe, cash.

• Notice that you shouldn't put company in quotes!

• The == is the equality operator. It tests to find where two things are equal and returns a logical vector.

Interactive Example of the subset() Method

In the following example, we use subset() to select rows of cash corresponding to company B, then filter for cash flows due in 1 year.

# Rows about company B
subset(cash, company == "B")

# Rows with cash flows due in 1 year
subset(cash, year == 1)

When you run the above code, it produces the following result:

  company cash_flow year
4       B      1500    1
5       B      1100    2
6       B       750    4
7       B      6000    5

  company cash_flow year
1       A      1000    1
4       B      1500    1

Try it for yourself in our Introduction to R for Finance course.

This content is taken from DataCamp’s Introduction to R for Finance course by Lore Dirick.

Subsetting with dplyr

The tidyverse package dplyr provides a modern alternative for subsetting data frames. Its filter() function selects rows and select() picks columns, both using a pipe-based syntax that chains operations together.

library(dplyr)

# Filter rows where name is "Dan"
debt %>% filter(name == "Dan")

# Select only the payment column (stays as a data frame)
debt %>% select(payment)

# Combine: filter rows and select columns in one pipeline
debt %>%
  filter(payment >= 100) %>%
  select(name, payment)

Unlike subset(), dplyr functions work reliably inside other functions and handle grouped operations, joins, and summaries in the same pipeline. If you're building data analysis workflows rather than running one-off queries, dplyr is the standard tool.

Comparison of Subsetting Methods

Let's compare the different subsetting methods in R in a table:

Final Thoughts

R gives you several ways to subset data: bracket notation [] for full control, subset() for readable interactive filtering, and dplyr::filter() for data pipelines. Brackets work on any R object, subset() reads clearly for one-off exploration, and dplyr scales well when you're chaining multiple operations.

To go deeper with data manipulation in R, I recommend the Data Manipulation with dplyr course or the Introduction to the Tidyverse course on DataCamp.