How to Remove the First Row of DataFrame in R

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Removing first row from Data Frame

When we attempt to remove the first row of a data frame, we are essentially selecting all the rows except for the first one. This means we are excluding the first row and retrieving all other rows in the data frame.

DataFrames are like tables with rows and columns. To remove rows, you can use indexing, and the rows are the first dimension in the square brackets. Like this: df [row_index, ]. The index of rows starts from 1.

Here are three ways:

  1. Using negative indexing
  2. Using dplyr::slice()
  3. Using tail()

Method 1: Using negative indexing

Negative indexing is a way to exclude specific rows based on your requirements. Since our requirement is to remove the first row, we can use df[-1, ], where -1 means to exclude the first row.

df <- data.frame(
  age = c(20, 21, 19, 22, 21),
  gender = c("Male", "Female", "Male", "Female", "Male"),
  score = c(85, 90, 88, 78, 92)
)

df[-1, ]

Output

Using negative indexing to remove the first row of R data frame

The above output screenshot shows that the first row with row index 1 is removed from the data frame. We are removing a row by its position.

It’s important to note that the row indices have not been reset, meaning that the row index starts at 2 instead of 1.

Data Frame with One Row

When you have a data frame containing only one row, df[-1, ] will return an empty data frame. It won’t throw any error. If you don’t use drop = FALSE, the result might inadvertently convert to a vector (but with 0 rows, it stays as a DataFrame).

df <- data.frame(
  age = c(20),
  gender = c("Male"),
  score = c(85)
)

df[-1, , drop = FALSE]

# [1] age gender score
# <0 rows> (or 0-length row.names)

Empty Data Frame

If the data frame is already empty and you try to remove the first row, it will still return an empty data frame.

df <- data.frame()

df[-1, , drop = FALSE]

# data frame with 0 columns and 0 rows

Check row count before removal

Sometimes, you have an empty data frame and try to remove rows, which results in an expected error. We can prevent this by checking if it contains any rows before removal.

Use nrow() to avoid unintended empty results. Let’s create a reusable function to handle edge cases.

df <- data.frame()

safe_remove_first_row <- function(df) {
  if (nrow(df) == 0) {
    return(df)
  }
  df[-1, , drop = FALSE]
}

safe_remove_first_row(df)

# data frame with 0 columns and 0 rows

Pros

  1. It is a direct and simple method.
  2. You don’t need to install any third-party packages.

Cons

  1. It retains its original row numbers. Use rownames(df) <- NULL to reset.
  2. If the result is a single row, it converts to a vector unless drop = FALSE is added (e.g., df[-1, , drop = FALSE]).

Method 2: Using dplyr::slice()

The dplyr slice() function selects rows from a data frame, and using slice(df, -1), we can remove the first row. However, you must ensure that dplyr is installed and loaded in your environment.

library(dplyr)

df <- data.frame(
  age = c(20, 21, 19, 22, 21),
  gender = c("Male", "Female", "Male", "Female", "Male"),
  score = c(85, 90, 88, 78, 92)
)

df <- df %>% slice(-1)
df

Output

Using dplyr slice() function to remove the first row

After removing the first row, you’ll notice that the row index has been reset, starting again at 1. 

If the data frame has only one row and you remove that row, it will return an empty data frame. Unlike base R, it always preserves the DataFrame structure.

library(dplyr)

df <- data.frame(
  age = c(20),
  gender = c("Male"),
  score = c(85)
)

df <- df %>% slice(-1)
df

# data frame with 0 columns and 0 rows

If your data frame is empty and you use the slice(-1) method, it will still return an empty data frame and will not give any error.

library(dplyr)

df <- data.frame()

df <- df %>% slice(-1)
df

# data frame with 0 columns and 0 rows

Pros

  1. It seamlessly works with the %>% pipe operator and other dplyr verbs.
  2. It returns a Data Frame (no accidental vector conversion).
  3. It automatically renumbers rows (e.g., rows become 1, 2, …).

Cons

  1. It requires installing dplyr, which is a third-party package.

Method 3: Using tail()

The base tail() function returns the last five rows of the data frame, but if you use a tail() function with n = -1, it excludes the first row.

df <- data.frame(
  age = c(20, 21, 19, 22, 21),
  gender = c("Male", "Female", "Male", "Female", "Male"),
  score = c(85, 90, 88, 78, 92)
)

df <- tail(df, n = -1)

df

Output

Using tail() with n=-1 to remove the first row

The above output screenshot shows that after the removal, the row index does not reset. It starts from row 2.

If the data frame has a single row, it will return an empty data frame.

df <- data.frame(
  age = c(20),
  gender = c("Male"),
  score = c(85)
)

df <- tail(df, n = -1)
df

# <0 rows> (or 0-length row.names)

If the data frame is already empty, it will return an empty data frame without any errors.

df <- data.frame()

df <- tail(df, n = -1)
df

# data frame with 0 columns and 0 rows

Pros

  1. It avoids explicit row indexing.
  2. It clearly expresses its intent to remove the first row just by its syntax.

Cons

  1. It retains original row labels. So, you have to reset manually.
  2. Like df[-1, ], it converts single-row results to vectors.

That’s it!

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