I have a data frame named “mydata” that looks like this this:

```
A B C D
1. 5 4 4 4
2. 5 4 4 4
3. 5 4 4 4
4. 5 4 4 4
5. 5 4 4 4
6. 5 4 4 4
7. 5 4 4 4
```

I’d like to delete row 2,4,6. For example, like this:

```
A B C D
1. 5 4 4 4
3. 5 4 4 4
5. 5 4 4 4
7. 5 4 4 4
```

### 8 Answers

The key idea is you form a set of the rows you want to remove, and keep the complement of that set.

In R, the complement of a set is given by the ‘-‘ operator.

So, assuming the `data.frame`

is called `myData`

:

```
myData[-c(2, 4, 6), ] # notice the -
```

Of course, don’t forget to “reassign” `myData`

if you wanted to drop those rows entirely—otherwise, R just prints the results.

```
myData <- myData[-c(2, 4, 6), ]
```

You can also work with a so called boolean vector, aka `logical`

:

```
row_to_keep = c(TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE)
myData = myData[row_to_keep,]
```

Note that the `!`

operator acts as a NOT, i.e. `!TRUE == FALSE`

:

```
myData = myData[!row_to_keep,]
```

This seems a bit cumbersome in comparison to @mrwab’s answer (+1 btw :)), but a logical vector can be generated on the fly, e.g. where a column value exceeds a certain value:

```
myData = myData[myData$A > 4,]
myData = myData[!myData$A > 4,] # equal to myData[myData$A <= 4,]
```

You can transform a boolean vector to a vector of indices:

```
row_to_keep = which(myData$A > 4)
```

Finally, a very neat trick is that you can use this kind of subsetting not only for extraction, but also for assignment:

```
myData$A[myData$A > 4,] <- NA
```

where column `A`

is assigned `NA`

(not a number) where `A`

exceeds 4.

### Problems with deleting by row number

For quick and dirty analyses, you can delete rows of a data.frame by number as per the top answer. I.e.,

```
newdata <- myData[-c(2, 4, 6), ]
```

However, if you are trying to write a robust data analysis script, you should generally avoid deleting rows by numeric position. This is because the order of the rows in your data may change in the future. A general principle of a data.frame or database tables is that the order of the rows should not matter. If the order does matter, this should be encoded in an actual variable in the data.frame.

For example, imagine you imported a dataset and deleted rows by numeric position after inspecting the data and identifying the row numbers of the rows that you wanted to delete. However, at some later point, you go into the raw data and have a look around and reorder the data. Your row deletion code will now delete the wrong rows, and worse, you are unlikely to get any errors warning you that this has occurred.

### Better strategy

A better strategy is to delete rows based on substantive and stable properties of the row. For example, if you had an `id`

column variable that uniquely identifies each case, you could use that.

```
newdata <- myData[ !(myData$id %in% c(2,4,6)), ]
```

Other times, you will have a formal exclusion criteria that could be specified, and you could use one of the many subsetting tools in R to exclude cases based on that rule.

Create id column in your data frame or use any column name to identify the row. Using index is not fair to delete.

Use `subset`

function to create new frame.

```
updated_myData <- subset(myData, id!= 6)
print (updated_myData)
updated_myData <- subset(myData, id %in% c(1, 3, 5, 7))
print (updated_myData)
```

**By simplified sequence :**

```
mydata[-(1:3 * 2), ]
```

**By sequence :**

```
mydata[seq(1, nrow(mydata), by = 2) , ]
```

**By negative sequence :**

```
mydata[-seq(2, nrow(mydata), by = 2) , ]
```

**Or if you want to subset by selecting odd numbers:**

```
mydata[which(1:nrow(mydata) %% 2 == 1) , ]
```

**Or if you want to subset by selecting odd numbers, version 2:**

```
mydata[which(1:nrow(mydata) %% 2 != 0) , ]
```

**Or if you want to subset by filtering even numbers out:**

```
mydata[!which(1:nrow(mydata) %% 2 == 0) , ]
```

**Or if you want to subset by filtering even numbers out, version 2:**

```
mydata[!which(1:nrow(mydata) %% 2 != 1) , ]
```

Delete Dan from employee.data – No need to manage a new data.frame.

```
employee.data <- subset(employee.data, name!="Dan")
```

For completeness, I’ll add that this can be done with `dplyr`

as well using `slice`

. The advantage of using this is that it can be part of a piped workflow.

```
df <- df %>%
.
.
slice(-c(2, 4, 6)) %>%
.
.
```

Of course, you can also use it without pipes.

```
df <- slice(df, -c(2, 4, 6))
```

The “not vector” format, `-c(2, 4, 6)`

means to get everything that is *not* at rows 2, 4 and 6. For an example using a range, let’s say you wanted to remove the first 5 rows, you could do `slice(df, 6:n())`

. For more examples, see the docs.

Here’s a quick and dirty function to remove a row by index.

```
removeRowByIndex <- function(x, row_index) {
nr <- nrow(x)
if (nr < row_index) {
print('row_index exceeds number of rows')
} else if (row_index == 1)
{
return(x[2:nr, ])
} else if (row_index == nr) {
return(x[1:(nr - 1), ])
} else {
return (x[c(1:(row_index - 1), (row_index + 1):nr), ])
}
}
```

It’s main flaw is it the row_index argument doesn’t follow the R pattern of being a vector of values. There may be other problems as I only spent a couple of minutes writing and testing it, and have only started using R in the last few weeks. Any comments and improvements on this would be very welcome!