This tutorial will discuss how to drop the rows or columns in the R programming language. If you are interested in this topic, continue reading until the end to find the right solution.
What is the drop in R?
The subset() function in R can be used to drop the row or column using conditions. For example, let’s look at how to drop rows with several conditions. In R, you can drop the rows or columns by row index (row number) and row name.
Ways to drop in R
Here, we will create a data frame that is used for all examples as follows:
# Create a data frame
df <- data.frame(
name = c(
"Marnie W. Vivanco", "Augustine D. Salinas", "Amanda R. Goldstein",
"Clarence E. Almodovar", "Alan T. Feuerstein", "Emma J. Lawrence"
),
ID = c(1:6),
Math = c(8, 9, 6, 10, 8, 7),
English = c(8, 10, 10, 8, 9, 9),
Class = c("A", "B", "A", "B", "A", "A")
)
# View a data frame
dfOutput:
name ID Math English Class
1 Marnie W. Vivanco 1 8 8 A
2 Augustine D. Salinas 2 9 10 B
3 Amanda R. Goldstein 3 6 10 A
4 Clarence E. Almodovar 4 10 8 B
5 Alan T. Feuerstein 5 8 9 A
6 Emma J. Lawrence 6 7 9 ADrop rows don’t use function
Now, we will drop the rows in a data frame without using the function as below:
# Create a data frame
df <- data.frame(
Name = c(
"Marnie W. Vivanco", "Augustine D. Salinas", "Amanda R. Goldstein",
"Clarence E. Almodovar", "Alan T. Feuerstein", "Emma J. Lawrence"
),
ID = c(1:6),
Math = c(8, 9, 6, 10, 8, 7),
English = c(8, 10, 10, 8, 9, 9),
Class = c("A", "B", "A", "B", "A", "A")
)
# Drop
df1 <- df[df$Name != "Augustine D. Salinas", ]
df1Output:
Name ID Math English Class
1 Marnie W. Vivanco 1 8 8 A
3 Amanda R. Goldstein 3 6 10 A
4 Clarence E. Almodovar 4 10 8 B
5 Alan T. Feuerstein 5 8 9 A
6 Emma J. Lawrence 6 7 9 ADrop rows or columns with the subset() function
In the example below, we will use the subset() function to drop the rows.
# Create a data frame
df <- data.frame(
Name = c(
"Marnie W. Vivanco", "Augustine D. Salinas", "Amanda R. Goldstein",
"Clarence E. Almodovar", "Alan T. Feuerstein", "Emma J. Lawrence"
),
ID = c(1:6),
Math = c(8, 9, 6, 10, 8, 7),
English = c(8, 10, 10, 8, 9, 9),
Class = c("A", "B", "A", "B", "A", "A")
)
# Drop
df1 <- subset(df, Name != "Augustine D. Salinas" & Name != "Alan T. Feuerstein")
df1Output:
Name ID Math English Class
1 Marnie W. Vivanco 1 8 8 A
3 Amanda R. Goldstein 3 6 10 A
4 Clarence E. Almodovar 4 10 8 B
6 Emma J. Lawrence 6 7 9 AFor another example, we will drop the columns with the subset() function as the following:
# Create a data frame
df <- data.frame(
Name = c(
"Marnie W. Vivanco", "Augustine D. Salinas", "Amanda R. Goldstein",
"Clarence E. Almodovar", "Alan T. Feuerstein", "Emma J. Lawrence"
),
ID = c(1:6),
Math = c(8, 9, 6, 10, 8, 7),
English = c(8, 10, 10, 8, 9, 9),
Class = c("A", "B", "A", "B", "A", "A")
)
# Drop
df1 <- subset(df, select = -c(Math, English))
df1Output:
Name ID Class
1 Marnie W. Vivanco 1 A
2 Augustine D. Salinas 2 B
3 Amanda R. Goldstein 3 A
4 Clarence E. Almodovar 4 B
5 Alan T. Feuerstein 5 A
6 Emma J. Lawrence 6 ABesides, we can drop the columns by index. See the code example:
# Create a data frame
df <- data.frame(
Name = c(
"Marnie W. Vivanco", "Augustine D. Salinas", "Amanda R. Goldstein",
"Clarence E. Almodovar", "Alan T. Feuerstein", "Emma J. Lawrence"
),
ID = c(1:6),
Math = c(8, 9, 6, 10, 8, 7),
English = c(8, 10, 10, 8, 9, 9),
Class = c("A", "B", "A", "B", "A", "A")
)
# Drop
df1 <- subset(df, select = -c(3, 5))
df1Output:
Name ID English
1 Marnie W. Vivanco 1 8
2 Augustine D. Salinas 2 10
3 Amanda R. Goldstein 3 10
4 Clarence E. Almodovar 4 8
5 Alan T. Feuerstein 5 9
6 Emma J. Lawrence 6 9Drop rows or columns with missing value
Here, we will create a data frame with a missing value.
# Create a data frame
df <- data.frame(
Name = c(
"Marnie W. Vivanco", "Augustine D. Salinas", "Amanda R. Goldstein",
"Clarence E. Almodovar", "Alan T. Feuerstein", "Emma J. Lawrence"
),
ID = c(1:6),
Math = c(8, 9, NA, 10, 8, NA),
English = c(8, 10, 10, 8, NA, 9),
Class = c("A", "B", "A", "B", "A", "A")
)
# View a data frame
dfOutput:
Name ID Math English Class
1 Marnie W. Vivanco 1 8 8 A
2 Augustine D. Salinas 2 9 10 B
3 Amanda R. Goldstein 3 NA 10 A
4 Clarence E. Almodovar 4 10 8 B
5 Alan T. Feuerstein 5 8 NA A
6 Emma J. Lawrence 6 NA 9 ANow, we will drop the rows which have missing values in this data frame.
# Create a data frame
df <- data.frame(Name=c("Marnie W. Vivanco", "Augustine D. Salinas", "Amanda R. Goldstein",
"Clarence E. Almodovar", "Alan T. Feuerstein", "Emma J. Lawrence"),
ID=c(1:6),
Math=c(8, 9, NA, 10, 8, NA),
English=c(8, 10, 10, 8, NA, 9),
Class=c("A", "B", "A", "B", "A", "A"))
# Drop the rows have missing value
df1 <- df[!(is.na(df$Math) | is.na(df$English)),]
df1Output:
Name ID Math English Class
1 Marnie W. Vivanco 1 8 8 A
2 Augustine D. Salinas 2 9 10 B
4 Clarence E. Almodovar 4 10 8 BSummary
This tutorial will demonstrate how to drop the rows or columns in the R programming language. If you have any questions, please leave a comment below.
Have a great day!
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