How to Remove Single or Multiple Rows from Data Frame in R

The best and most efficient way to remove rows from a data frame is using “negative indexing”. It is a base R approach that does not require any packages.

However, there are different approaches you should be aware of, depending on the situation you are encountering.

Here are five ways:

1. Using negative indexing (For single or multiple rows)
2. Removing rows by name
3. Using subset() function
4. Using dplyr package
5. Using the na.omit() function (For removing rows with NA values)

Method 1: Using negative indexing

Negative sign (-) means exclusion. If you have a data frame df and you want to remove the first row, you just need to write df[-1, ].

For multiple rows, use df[-c(1,3,5), ], which will remove rows 1, 3, and 5. It is a removal by row number. The c() function combines the indices, and the negative sign excludes them.

It is the process of accessing a data frame without specifying some rows using a negative index. This is also called row indexing.

Syntax

df[-c(row_index_1, row_index_2),]

Removing a single row (By row number)

df <- data.frame(
   Shares = c("TCS", "Reliance", "HDFC Bank", "HUL", "KPIT"),
   Price = c(3200, 1900, 1500, 2200, 1400)
)

df_remain <- df[-c(3), ]

df_remain

Removing multiple rows (By row numbers)

To remove the second and third rows, use -c(2, 3). The negative sign before c(2, 3) tells R to exclude those rows.

df <- data.frame(
   Shares = c("TCS", "Reliance", "HDFC Bank", "HUL", KPIT),
   Price = c(3200, 1900, 1500, 2200, 1400)
)

df_remain <- df[-c(2, 3),]

df_remain

Using range

If you want to remove multiple rows, you can define them as a range. For example, -c(2:4) refers to the sequence of rows (rows 2, 3, and 4) that will be excluded from the data frame.

df <- data.frame(
  Shares = c("TCS", "Reliance", "HDFC Bank", "HUL", "KPIT"),
  Price = c(3200, 1900, 1500, 2200, 1400)
)

modified_df <- df[-c(2:4), ]

modified_df

Method 2: Removing rows by name

If you have a data frame that contains row names, you can remove the row by its name using the which() function. To get the specific row name, use the rownames() function.

df <- data.frame(
  Shares = c("TCS", "Reliance", "HDFC Bank", "HUL", "KPIT"),
  Price = c(3200, 1900, 1500, 2200, 1400)
)

rownames(df) <- c("row1", "row2", "row3", "row4", "row5")

cat("After removing 4th row", "\n")

modified_df <- df[-which(rownames(df) == "row4"), ]
modified_df

Method 3: Using the subset() function

The subset() function is helpful when you have a specific logical condition. It removes only the rows that meet that condition.

To properly use the subset() function, you must provide a logical expression that evaluates FALSE for the rows you want to remove.

df <- data.frame(
  Shares = c("TCS", "Reliance", "HDFC Bank", "HUL", "KPIT"),
  Price = c(3200, 1900, 1500, 2200, 1400)
)

df_after_removed <- subset(df, Price > 1900)

df_after_removed

Method 4: Using the dplyr package

If you already use the dplyr package in your program, then you can use the dplyr package’s filter() or slice() function.

Using dplyr::filter()

You can pass the condition using a logical expression to the filter() function, which will filter out the rows for you.

library(dplyr)

df <- data.frame(
  Shares = c("TCS", "Reliance", "HDFC Bank", "HUL", "KPIT"),
  Price = c(3200, 1900, 1500, 2200, 1400)
)

filtered_df <- df %>% filter(Price > 1400)

filtered_df

Using dplyr::slice()

Pass the indices you want to remove in the slice() function, and it will remove it from the data frame.

library(dplyr)

df <- data.frame(
  Shares = c("TCS", "Reliance", "HDFC Bank", "HUL", "KPIT"),
  Price = c(3200, 1900, 1500, 2200, 1400)
)

# Remove rows using slice()
df_sliced <- df %>%
  slice(-c(1, 4, 5)) # Exclude rows 1, 4, ands 5

print(df_sliced)

Output

   Shares     Price
1  Reliance   1900
2  HDFC Bank  1500

Removing duplicate rows

Check out removing duplicate rows from the data frame article for more information.

Method 5: Using the na.omit() function

If you want to quickly remove rows with NA values, use the built-in na.omit() function. For more targeted NA removal, I recommend using the complete.cases() or tidyr::drop_na() functions.

df <- data.frame(
  Shares = c("TCS", "Reliance", "HDFC Bank", NA, "KPIT"),
  Price = c(3200, 1900, 1500, NA, 1400)
)

df_na_removed <- na.omit(df)

df_na_removed

That’s it!