How to Select DataFrame Columns by Index in R?
Last Updated :
12 Dec, 2022
In this article, we will discuss how to select columns by index from a dataframe in R programming language.
Note: The indexing of the columns in the R programming language always starts from 1.
Method 1: Select Specific Columns By Index with Base R
Here, we are going to select columns by using index with the base R in the dataframe.
Syntax:
dataframe[,c(column_indexes)]
Example:
R
# create a dataframe with 3 rows and 4 columns
data=data.frame(name=c("akash","kyathi","preethi"),
subjects=c("java","R","dbms"),
marks=c(90,98,78))
# select second and third column
print(data[,c(2,3)])
Output:
subjects marks
1 java 90
2 R 98
3 dbms 78
Method 2: Select Specific Columns In The Index Range
We can select by using the index range operator, as shown below.
Syntax:
dataframe[,column_index_start:column_index_end]
where.
- column_index_start is the first index number, and column_index_end is the second index number.
Example:
R
# create a dataframe with 3 rows and 4 columns
data=data.frame(name=c("akash","kyathi","preethi"),
subjects=c("java","R","dbms"),
marks=c(90,98,78))
# select first to third column
print(data[,1:3])
Output:
name subjects marks
1 akash java 90
2 kyathi R 98
3 preethi dbms 78
Method 3: Select Index Column By Excluding Columns Indexes
We can exclude the index columns by specifying in the c() vector with a - sign.
Syntax:
dataframe[,-c(column_indexes)]
Example:
R
# create a dataframe with 3 rows and 4 columns
data=data.frame(name=c("akash","kyathi","preethi"),
subjects=c("java","R","dbms"),
marks=c(90,98,78))
# exclude second and third column
print(data[,-c(2,3)])
Output:
[1] "akash" "kyathi" "preethi"
Method 4: Select Column Names By Index Using dplyr
The select() function from the dplyr package is used for selecting column by index.
Syntax:
dataframe %>%
select(column_numbers)
where
- %>% operator is to load into dataframe
Syntax to import and install the dplyr package:
install.packages("dplyr")
library("dplyr")
Example:
R
# loads the package
library("dplyr")
# create a dataframe with 3 rows and 4 columns
data = data.frame(name=c("akash", "kyathi", "preethi"),
subjects=c("java", "R", "dbms"),
marks=c(90, 98, 78))
# select 1 and 3 columns
data % >%
select(1, 3)
Output:
name marks
1 akash 90
2 kyathi 98
3 preethi 78
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