There are several ways to select data frame columns in R by using the R base and dplyr package. In this article, I will explain how to select columns by using the select() function from the dplyr package, R base bracket notation df[]. Using these I will cover examples like selecting a specific column/multiple columns from the data frame by name/position, and many more.
Sometimes you may need to change the column names, if so read rename data frame columns in r.
Key Points -
- Use df[] notation to select columns by index or name in R base, allowing for flexibility in specifying columns.
- Select columns by their index position using df[, c(1, 2, 3)] or by a range like df[, 2:4].
- Use df[, "column_name"] to select specific columns by their names, or df[, c("col1", "col2")] for multiple columns.
- Utilize the select() function from the dplyr package for more streamlined column selection operations.
- With select(), choose columns by name or index, or even perform operations like excluding columns using negative notation.
- Use starts_with(), and ends_with() within select() to choose columns based on specific naming patterns.
- Leverage the %>% operator from Magrittr to perform sequential column selection operations in a readable and efficient manner.
- Use negative indexing to exclude specific columns from the selection, e.g., df[, -c(2, 4)].
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1. Quick Examples of Selecting Columns from the Data Frame
Following are quick examples of how to select data frame columns in R.
# Quick Examples of selecting columns
# Example 1: R base - Select columns by name
df[,"name"]
# Example 2: R base - Select columns from list
df[,c("name","gender")]
# Example 3: R base - Select columns by index position
df[,c(2,3)]
# Example 4: Load dplyr
library('dplyr')
# Example 5: dplyr - Select columns by list of index or position
df %>% select(c(2,3))
# Example 6: Select columns by index range
df %>% select(2:3)
# Example 6: dplyr - Select columns by label name & gender
df %>% select('name','gender')
df %>% select(c('name','gender'))
# Example 7: dplyr - Select columns except name & gender
df %>% select(-c('name','gender'))
# Example 8: dplyr - Select columns between name and state
df %>% select('name':'state')
# Example 9: dplyr - Select columns starts with a string
df %>% select(starts_with('gen'))
# Example 10: dplyr - Select columns not start with a string
df %>% select(-starts_with('gen'))
# Example 11: dplyr - Select columns that ends with a string
df %>% select(ends_with('e'))
# Example 12: dplyr - Select columns that contains
df %>% select(contains('a'))
# Example 13: dplyr - Select all numeric columns
df %>% select_if(is.numeric)
First, create an R DataFrame using the data.frame() function.
# Create DataFrame
df <- data.frame(
id = c(10,11),
name = c('sai','ram'),
gender = c('M','M'),
dob = as.Date(c('1990-10-02','1981-3-24')),
state = c('CA','NY'),
row.names=c('r1','r2')
)
df
Yields below output.
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2. Get Columns using the R base
To select columns from a data frame in R, we can use the R base df[] bracket notation. In R, when working with a data.frame, we usually use the $ symbol to refer to the column name along with the data frame object. However, this notation can be confusing and make the R code harder to read. Thus, the use of bracket notation is recommended as an alternative.
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2.1 Select by Column Index
The df[] notation takes syntax df[rows,columns], so when using this notation to select columns in R, you can specify the column indexes/labels on the right after the comma. To select single/multiple columns by index, or range of column indexes using starting_position:end_position or by a list of index positions.
# R base - select specific column by index
df[, 2]
# Output:
# [1] "sai" "ram"
# R base - by list of positions
df[,c(2,3)]
# R base - by range
df[,2:3]
Yields below output.
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2.2 Select by Name
Alternatively, to select columns by name in R you can use this notation. Simply, pass the specified column name that you want to get from a data frame, into df[] notation. It will return all the values of the specified column.
# R base - Select columns by name
df[,"name"]
# Output
# [1] "sai" "ram"
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2.3 Select Columns from List
Sometimes when we want to select multiple columns at a time from a data frame, you can use df[] notation. To specify these column names using vector within a notation. It will return the data frame with specified columns.
# R base - Select columns from list
df[,c("name","gender")]
# Output
# name gender
# r1 sai M
# r2 ram M
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2.4 Select a column Using the $ Operator
You can use the $ operator to select a specific column by name. For example,
# Select specific column by name using $
df2 <- df$name
df2
# Output
# [1] "sai" "ram"
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3. Select Columns using the dplyr Package
You can use select() function from the dplyr package to get specified single/multiple columns of the data frame. This function allows the data frame as a first argument and the column position of single/multiple is the second argument.
To perform sequential operations within a dplyr package you can use the infix operator %>% from magrittr. which is %>% is known as the pipe operator. It pipes the data frame df into the next function. Whatever is on the left side of %>% is passed as the first argument to the function on the right side.
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3.1 Select columns by Column Number
The select() function of dplyr package also supports selecting columns by index from the R data frame. Use this function if you want to select the data frame columns by index or position. The following example returns columns 2 and 3 from the data frame.
# Load dplyr
library('dplyr')
# Select columns
df %>% select(2,3)
# Select columns by list of index or position
df %>% select(c(2,3))
# Select columns by index range
df %>% select(2:3)
Yields below output.
# Output
name gender
r1 sai M
r2 ram M
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3.2 Select columns by Name using dplyr
You can also select data frame columns by name, select multiple columns, and all columns in the list (contains in the list) using the dplyr package. The first example from the following selects the specified columns that are supplied to the select() function with a comma separator. The second example selects all columns from the list.
# Select columns by label name & gender
df %>% select('name','gender')
df %>% select(c('name','gender'))
# Output
# name gender
# r1 sai M
# r2 ram M
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3.3. Get Columns of Not specified
To use the select() function from dplyr for column selection, simply pass the list of column names(don't want to get) specifying by negative vector. It will drop specified columns from the DataFrame by Name and return the remaining columns of the data frame.
# Select columns except name & gender
df %>% select(-c('name','gender'))
# Output
# id dob state
# r1 10 1990-10-02 CA
# r2 11 1981-03-24 NY
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3.4. Select All Columns Between 2 Columns
You can also get the particular portion of columns of the data frame by using the range operator(:) within the select() function of the dplyr package. You can specify the range within a select() function with starting point and ending point. This will return all columns between the starting position and the ending position, including them.
# Select columns between name and state
df %>% select('name':'state')
# Output
# name gender dob state
# r1 sai M 1990-10-02 CA
# r2 ram M 1981-03-24 NY
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3.5. Get Selected Columns Use starts_with()
Use starts_with() function within a select() function to get the columns based on certain criteria. In this case, it selects columns whose names start with the specified prefix. This will check for column names that start with the specified prefix.
# Select columns starts with a string
df %>% select(starts_with('gen'))
# Output
# gender
# r1 M
# r2 M
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3.6. Get Selected Columns Use ends_with()
Use ends_with() function within a select() function to get the columns based on certain criteria. In this case, it selects columns whose names end with the specified suffix. This will check for column names that end with the specified suffix.
# Select columns that ends with a string
df %>% select(ends_with('e'))
# Output
# name state
# r1 sai CA
# r2 ram NY
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3.7. Get Columns Containing character
In case you want to select all columns that contain a character or string use contains(). The following example selects all columns that contain a character a.
# Select columns that contains
df %>% select(contains('a'))
# Output
# name state
# r1 sai CA
# r2 ram NY
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3.8. Select All Numeric Columns
Selecting all numeric columns is one of the most used operations. If you have a data frame with columns with strings and integers, performing certain statistical operations on the entire data frame results in error hence, first you need to select all numeric columns using is.numeric input to select_if() and operate on the result of it. Use is.character to select columns of character type.
# Select all numeric columns
df %>% select_if(is.numeric)
# Output
# id
# r1 10
# r2 11
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Frequently Asked Questions of Select Columns in R
You can use the R base df[] notation to select specific columns from the data frame by column index/column label. For example, df[, c('col1', 'col2', 'col3')] or df[, c(col_index1, col_index3)].
To select columns by index number you can use the the R base df[] notation. for example, df[, c(col_index1, col_index3)]
You can use negative indexing to exclude specific columns. For example, df <- df[, -c(2, 4)]
Use the dplyr package, which provides the %>% pipe operator and functions like select()to select columns from the data frame very efficiently. For example, df %>% select(col1, col2)
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5. Conclusion
In this article, you have learned how to select single/multiple columns/range of columns using the R base bracket notation df[] and the select() method from the dplyr package, by column index/ column label with multiple examples.
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