Reputation: 3207
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
Upvotes: 315
Views: 1936380
Reputation: 420
As we all know, in R there are plenty ways to solve one task. Below are some examples how to use the available dplyr functions to subset a dataframe.
Using slice()
function as explained in a previous answer by Ryan H.
Using the filter
-based solution with the row_number()
:
X<-rep(4,7) data.frame(A=rep(5,7), B=X, C=X, D=X) %>% filter(!row_number() %in% c(2,4,6))
This will output the 1st, 3rd, 5th and 7th rows but also the row numbers will be changed to 1-4:
A B C D
1 5 4 4 4
2 5 4 4 4
3 5 4 4 4
4 5 4 4 4
To keep the row numbers you can add rowid_to_column()
and then use the id-column for the subsetting:
X<-rep(4,7)
data.frame(A=rep(5,7), B=X, C=X, D=X) %>%
rowid_to_column("N") %>%
filter(!N %in% c(2,4,6))
N A B C D
1 1 5 4 4 4
2 3 5 4 4 4
3 5 5 4 4 4
4 7 5 4 4 4
anti_join()
.anti_join
is applied to two datasets and removes the rows which they both have in common. So, to remove the rows [2, 4, 6]
from your dataset you can do the following:
DF %>% rowid_to_column("N") %>%
anti_join(data.frame(N=c(2,4,6)), by="N")
Upvotes: 4
Reputation: 21
Another approach when working with Unique IDs is to subset data: *This came from an actual report where I wanted to remove the chemical standard
Chem.Report<-subset(Chem.Report, Chem_ID!="Standard")
Chem_ID is the column name. The ! is important for excluding
Upvotes: 1
Reputation: 21
To identify by a name:
Code:
Rows<-which(grepl("unique ID", DF$Column))
DF2<-DF[-c(Rows),]
DF2
Upvotes: 1
Reputation: 7844
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.
Upvotes: 15
Reputation: 193507
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), ]
Upvotes: 474
Reputation: 11
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!
Upvotes: 1
Reputation: 101
Delete Dan from employee.data - No need to manage a new data.frame.
employee.data <- subset(employee.data, name!="Dan")
Upvotes: 10
Reputation: 342
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) , ]
Upvotes: 15
Reputation: 34907
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.
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.
Upvotes: 85
Reputation: 195
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)
Upvotes: 17
Reputation: 60924
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.
Upvotes: 103