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Indexing and Slicing Data Frames in R

Last Updated : 21 Dec, 2023
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In this article let’s discuss indexing and slicing the Data Frames or how to access elements of a data frame in R Programming Language.

What is Indexing or accessing?

The process of accessing particular data components or subsets within a vector, matrix, or data frame is called indexing. It enables us to pick, remove, or change particular values or parts of our data based on criteria.

Indexing or accessing the Data Frame in R

By indexing the Data Frame in R we will get the particular column data. Indexing can be done by specifying column names in square brackets. The syntax for indexing the data frame is-

dataframeName[“columnName”]

R




# create a data frame 
stats <- data.frame(player=c('A', 'B', 'C', 'D'),
                runs=c(100, 200, 408, NA),
                wickets=c(17, 20, NA, 5))
 
print("stats Dataframe")
stats
 
# fetch data in certain column
stats["runs"]


Output:

  player runs wickets
1 A 100 17
2 B 200 20
3 C 408 NA
4 D NA 5
stats["runs"]
runs
1 100
2 200
3 408
4 NA

In this example we create a Data Frame “stats” that contains runs scored and wickets taken by a player and perform indexing on the data frame to extract runs scored by players.

Slicing In R

The process of extracting or accessing particular subsets or sections of a vector, matrix, or data frame depending on predetermined criteria is known as slicing. Using start and end indices, we can choose sequential ranges of elements or subsets of data.

Slicing or accessing the Data Frame

Slicing the Data Frame gives the required rows and columns. This can be done by three ways. They are listed below-

  • Slicing with [ , ]
  • Slicing with logical vectors.
  • Slicing with subset().

Slicing or accessing with [ , ]

Slicing the data frame with [ , ] returns data of specified rows and columns. The syntax of this is mentioned below-

dataframeName[ fromRow : toRow , columnNumber]

R




# create a data frame 
stats <- data.frame(player=c('A', 'B', 'C', 'D'),
                runs=c(100, 200, 408, NA),
                wickets=c(17, 20, NA, 5))
 
print("stats Dataframe")
stats
 
# fetch 2,3 rows and 1,2 columns
stats[2:3,c(1,2)]
 
# fetch 1:3 rows of 1st column
cat("players - ")
stats[1:3,1]


Output

[1] "stats Dataframe"
player runs wickets
1 A 100 17
2 B 200 20
3 C 408 NA
4 D NA 5
fetch 2,3 rows and 1,2 columns
player runs
2 B 200
3 C 408
fetch 1:3 rows of 1st column
[1] "A" "B" "C"

In the below code we performed slicing on the data frame to fetch specified rows and columns.

Slicing with logical vectors

We can perform slicing on the data by specifying the logical conditions. It is used to get the filtered data.

R




# create a data frame 
stats <- data.frame(player=c('A', 'B', 'C', 'D'),
                runs=c(100, 200, 408, 23),
                wickets=c(17, 20, 3, 5))
 
print("stats Dataframe")
stats
 
# fetch player details who scores
# more than 100 runs
batsmens<-stats[stats$runs>100,]
batsmens


 Output

[1] "stats Dataframe"
player runs wickets
1 A 100 17
2 B 200 20
3 C 408 3
4 D 23 5
player runs wickets
2 B 200 20
3 C 408 3

 In this example, we fetch the records of players who scored more than 100 runs.

Slicing with subset() 

We can slice data frames using the subset() method. The subset method accepts the data, filter logic to slice and the columns to fetch. The syntax of slicing with subset is –

subset( x = dataframe, subset = filter_logic, select=c(columnNames))

R




# create a data frame 
stats <- data.frame(player=c('A', 'B', 'C', 'D'),
                runs=c(100, 200, 408, 23),
                wickets=c(17, 20, 3, 5))
 
print("stats Dataframe")
stats
 
# fetch player details who pick
# more than 5 wickets
subset(x=stats, subset=wickets>5, select=c(player,wickets))


Output

[1] "stats Dataframe"
player runs wickets
1 A 100 17
2 B 200 20
3 C 408 3
4 D 23 5
player wickets
1 A 17
2 B 20

In the below code, we fetched the players who picked more than 5 wickets from the data frame stats by slicing the data frame using the subset method.



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