How to use everything() in R

dplyr

dplyr everything()

Learn how to use everything() in R with practical examples. Step-by-step guide with code you can copy and run immediately.

Published

February 21, 2026

Introduction

The everything() function in dplyr is a selection helper that allows you to select all remaining columns in a dataset. It’s particularly useful when you want to reorder columns by moving specific ones to the front while keeping all others, or when you need to apply transformations to all columns at once.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Column Reordering

The Problem

You want to move the most important columns to the front of your dataset while keeping all other columns in their original order.

Step 1: Examine the original data structure

Let’s first look at the structure of our penguins dataset.

penguins |>
  head(3) |>
  glimpse()

This shows us all 8 columns in their default order, with species, island, and bill measurements first.

Step 2: Move specific columns to front

We’ll move the year and sex columns to the beginning while keeping all others.

penguins |>
  select(year, sex, everything()) |>
  head(3)

Now year and sex appear first, followed by all remaining columns in their original order.

Step 3: Reorder multiple columns strategically

Let’s prioritize the outcome variable and key predictors.

penguins |>
  select(body_mass_g, species, everything()) |>
  head(3)

The body mass (our potential outcome) and species now lead, making the data more analysis-ready.

Example 2: Practical Data Transformation

The Problem

You’re preparing data for analysis and need to standardize all numeric columns while preserving categorical variables and maintaining a logical column order for your report.

Step 1: Identify and separate column types

First, let’s move all categorical variables to the front for better data overview.

penguins_reordered <- penguins |>
  select(species, island, sex, year, everything())

penguins_reordered |> head(3)

This groups related categorical information together at the start of our dataset.

Step 2: Apply transformations using everything()

Now we’ll standardize all numeric columns while keeping categorical ones unchanged.

penguins_standardized <- penguins_reordered |>
  mutate(across(where(is.numeric), scale)) |>
  select(species, everything())

penguins_standardized |> head(3)

All numeric columns are now standardized (mean = 0, sd = 1), while categorical columns remain intact.

Step 3: Create a analysis-ready format

Let’s create a final version with the target variable first and all predictors following.

analysis_data <- penguins |>
  select(body_mass_g, everything(), -year) |>
  filter(complete.cases(.))

analysis_data |> head(3)

We now have body mass as our first column (potential dependent variable), followed by all potential predictors, with incomplete cases removed.

Step 4: Verify the transformation

Check that our reordering and filtering worked as expected.

analysis_data |>
  summarise(
    n_rows = n(),
    n_cols = ncol(.),
    first_col = names(.)[1]
  )

This confirms our data has the expected structure with body_mass_g as the first column and no missing values.

Summary

everything() selects all columns not already mentioned in your select() statement
It’s perfect for reordering columns by moving important ones to the front while preserving others
Combine it with where() and across() for powerful data transformations
Use it to create analysis-ready datasets with logical column ordering
It maintains the original order of non-specified columns, making your data transformations predictable

--- title: "How to use everything() in R" description: "Learn how to use everything() in R with practical examples. Step-by-step guide with code you can copy and run immediately." date: 2026-02-21 categories: ['dplyr', 'dplyr everything()'] format: html: code-fold: false code-tools: true --- ## Introduction The `everything()` function in dplyr is a selection helper that allows you to select all remaining columns in a dataset. It's particularly useful when you want to reorder columns by moving specific ones to the front while keeping all others, or when you need to apply transformations to all columns at once. ## Getting Started ```r library(tidyverse) library(palmerpenguins) ``` ## Example 1: Basic Column Reordering ### The Problem You want to move the most important columns to the front of your dataset while keeping all other columns in their original order. ### Step 1: Examine the original data structure Let's first look at the structure of our penguins dataset. ```r penguins |> head(3) |> glimpse() ``` This shows us all 8 columns in their default order, with species, island, and bill measurements first. ### Step 2: Move specific columns to front We'll move the year and sex columns to the beginning while keeping all others. ```r penguins |> select(year, sex, everything()) |> head(3) ``` Now year and sex appear first, followed by all remaining columns in their original order. ### Step 3: Reorder multiple columns strategically Let's prioritize the outcome variable and key predictors. ```r penguins |> select(body_mass_g, species, everything()) |> head(3) ``` The body mass (our potential outcome) and species now lead, making the data more analysis-ready. ## Example 2: Practical Data Transformation ### The Problem You're preparing data for analysis and need to standardize all numeric columns while preserving categorical variables and maintaining a logical column order for your report. ### Step 1: Identify and separate column types First, let's move all categorical variables to the front for better data overview. ```r penguins_reordered <- penguins |> select(species, island, sex, year, everything()) penguins_reordered |> head(3) ``` This groups related categorical information together at the start of our dataset. ### Step 2: Apply transformations using everything() Now we'll standardize all numeric columns while keeping categorical ones unchanged. ```r penguins_standardized <- penguins_reordered |> mutate(across(where(is.numeric), scale)) |> select(species, everything()) penguins_standardized |> head(3) ``` All numeric columns are now standardized (mean = 0, sd = 1), while categorical columns remain intact. ### Step 3: Create a analysis-ready format Let's create a final version with the target variable first and all predictors following. ```r analysis_data <- penguins |> select(body_mass_g, everything(), -year) |> filter(complete.cases(.)) analysis_data |> head(3) ``` We now have body mass as our first column (potential dependent variable), followed by all potential predictors, with incomplete cases removed. ### Step 4: Verify the transformation Check that our reordering and filtering worked as expected. ```r analysis_data |> summarise( n_rows = n(), n_cols = ncol(.), first_col = names(.)[1] ) ``` This confirms our data has the expected structure with body_mass_g as the first column and no missing values. ## Summary - `everything()` selects all columns not already mentioned in your [`select()`](/dplyr/how-to-use-select-in-r.html) statement - It's perfect for reordering columns by moving important ones to the front while preserving others - Combine it with [`where()`](/dplyr/how-to-use-where-in-r.html) and [`across()`](/dplyr/how-to-use-across-in-r.html) for powerful data transformations - Use it to create analysis-ready datasets with logical column ordering - It maintains the original order of non-specified columns, making your data transformations predictable --- ## Related Posts - [How to use select() in R](/dplyr/how-to-use-select-in-r.html) - [How to use mutate() in R](/dplyr/how-to-use-mutate-in-r.html) - [How to use pull() in R](/dplyr/how-to-use-pull-in-r.html) - [How to use separate() in R](/tidyr/how-to-use-separate-in-r.html) - [How to use separate_wider_delim() in R](/tidyr/how-to-use-separatewiderdelim-in-r.html)