Slice() Function In R

The slice() function in R is a very useful function to manipulate and subset data frames. it allows you to pick individual rows or a range of rows from a dataset with simple syntax This function is part of the dplyr package, which is essential for data manipulation.

Syntax

slice(.data, ..., n = NULL, input = NULL)

Parameters:

• data: Data frame or dataset.
• ... : Used to state conditions such as logical conditions or row indices.
• n: Puts a certain number of rows.
• input: Another for ., to be used to specify rows to select explicitly.

Installing and Loading dplyr Package

Before using the slice() function, make sure the dplyr package is installed and loaded.

install.packages("dplyr")
library(dplyr)

Example: Creating a Sample Dataset

We have created a data frame 'sample_data' consisting of ID, Age, Gender, Score1, Score2, Status, and Income columns for six students. Now we will run some examples on this dataset to understand the slice() function.

s_data <- tibble(
  ID = c(1:10),
  Age = c(25, 30, 35, 28, 22, 40, 33, 26, 38, 29),
  Gender = c("Male", "Female", "Male", "Female", "Male", "Male", "Female", "Male", "Female", "Male"),
  Score1 = c(85, 70, 60, 75, 90, 80, 92, 78, 65, 88),
  Score2 = c(75, 82, 88, 95, 70, 68, 80, 85, 77, 93),
  Status = c("Active", "Inactive", "Active", "Active", "Inactive", "Active", "Inactive", "Active", "Inactive", "Active"),
  Income = c(50000, 60000, 75000, 55000, 80000, 90000, 72000, 65000, 82000, 70000)
)

print("Original dataset:")
print(s_data)

Output:

[1] "Original dataset:"
A tibble: 10 × 7
      ID   Age Gender Score1 Score2 Status   Income
   <int><dbl><chr><dbl><dbl><chr><dbl>
 1     1    25 Male       85     75 Active    50000
 2     2    30 Female     70     82 Inactive  60000
 3     3    35 Male       60     88 Active    75000
 4     4    28 Female     75     95 Active    55000
 5     5    22 Male       90     70 Inactive  80000
 6     6    40 Male       80     68 Active    90000
 7     7    33 Female     92     80 Inactive  72000
 8     8    26 Male       78     85 Active    65000
 9     9    38 Female     65     77 Inactive  82000
10    10    29 Male       88     93 Active    70000

Slice() Function Examples

1. Select a Single Row:

We can select a single row of a dataset by just passing the index of the row you want to select. Here is an example of selecting a single 3rd row of the 's_data' dataset.

single_row <- slice(s_data, 3)
print(single_row)

Output:

A tibble: 1 × 7
     ID   Age Gender Score1 Score2 Status Income
  <int><dbl><chr><dbl><dbl><chr><dbl>
1     3    35 Male       60     88 Active  75000

2. Multiple Row Selection

We can select multiple rows in a dataset by passing the indexes of the rows in the c() function. Here is an example of selecting 1,5 and 8th rows of the 's_data' dataset. it will print only 1,5 and 8th rows of the dataset.

multiple_rows <- slice(s_data, c(1, 5, 8))
print(multiple_rows)

Output:

A tibble: 3 × 7
     ID   Age Gender Score1 Score2 Status   Income
  <int><dbl><chr><dbl><dbl><chr><dbl>
1     1    25 Male       85     75 Active    50000
2     5    22 Male       90     70 Inactive  80000
3     8    26 Male       78     85 Active    65000

3. Select a Range of Rows

We can select a range of rows by passing the index range. Here is an example of selecting a range of rows, we select from the 2nd row to the 6th row of the 's_data' dataset and print them.

range_sele <- slice(s_data, 2:6)
print(range_sele)

Output:

A tibble: 5 × 7
     ID   Age Gender Score1 Score2 Status   Income
  <int><dbl><chr><dbl><dbl><chr><dbl>
1     2    30 Female     70     82 Inactive  60000
2     3    35 Male       60     88 Active    75000
3     4    28 Female     75     95 Active    55000
4     5    22 Male       90     70 Inactive  80000
5     6    40 Male       80     68 Active    90000

4. Negative Indexing

We can exclude the rows from the selection by using negative indexing. It is done by passing the indexes we don't need in the c() function with a '-' symbol before it. It will exclude the rows in the c() function and select the remaining rows.

negative_ind <- slice(s_data, -c(3, 7))
print(negative_ind)

Output:

A tibble: 8 × 7
     ID   Age Gender Score1 Score2 Status   Income
  <int><dbl><chr><dbl><dbl><chr><dbl>
1     1    25 Male       85     75 Active    50000
2     2    30 Female     70     82 Inactive  60000
3     4    28 Female     75     95 Active    55000
4     5    22 Male       90     70 Inactive  80000
5     6    40 Male       80     68 Active    90000
6     8    26 Male       78     85 Active    65000
7     9    38 Female     65     77 Inactive  82000
8    10    29 Male       88     93 Active    70000

Here we have excluded the 3rd and 7th rows and printing the remaining rows.

5. Conditional Row Selection

We can use the which() function with slice() to choose rows based on conditions. For instance, to choose rows where Age is more than 30, which() verifies the condition and chooses the matching rows.

con_sele <- slice(s_data, which(s_data$Age > 30))
print(con_sele)

Output:

A tibble: 4 × 7
     ID   Age Gender Score1 Score2 Status   Income
  <int><dbl><chr><dbl><dbl><chr><dbl>
1     3    35 Male       60     88 Active    75000
2     6    40 Male       80     68 Active    90000
3     7    33 Female     92     80 Inactive  72000
4     9    38 Female     65     77 Inactive  82000

6. Select Top N Rows

We can select the top rows by using the slice_head() function. We pass the dataset and number of rows as arguments. Here we are selecting the top 4 (n=4) rows of the dataset.

top_n_rows <- slice_head(s_data, n = 4)
print(top_n_rows)

Output:

A tibble: 4 × 7
     ID   Age Gender Score1 Score2 Status   Income
  <int><dbl><chr><dbl><dbl><chr><dbl>
1     1    25 Male       85     75 Active    50000
2     2    30 Female     70     82 Inactive  60000
3     3    35 Male       60     88 Active    75000
4     4    28 Female     75     95 Active    55000

7. Select Bottom N Rows

We can select the bottom rows by using the slice_tail() function. We pass the dataset and number of rows as arguments. Here we are selecting the bottom 3 (n=3) rows of the dataset.

bottom_nrows <- slice_tail(s_data, n = 3)
print(bottom_nrows)

Output:

A tibble: 3 × 7
     ID   Age Gender Score1 Score2 Status   Income
  <int><dbl><chr><dbl><dbl><chr><dbl>
1     8    26 Male       78     85 Active    65000
2     9    38 Female     65     77 Inactive  82000
3    10    29 Male       88     93 Active    70000

8 Random Row Selection

We can select rows randomly by using the slice_sample() function and pass the dataset, and number of rows to be selected as arguments. Here we are selecting two random rows from the dataset

ran_rows <- slice_sample(s_data, n = 2)
print(ran_rows)

Output:

A tibble: 2 × 7
     ID   Age Gender Score1 Score2 Status   Income
  <int><dbl><chr><dbl><dbl><chr><dbl>
1     9    38 Female     65     77 Inactive  82000
2     1    25 Male       85     75 Active    50000

9. Alternate Row Selection

We can select alternate rows of a dataset using the slice() function and seq() function starting from the 1st index and incrementing by 2 up to number of rows of the dataset.

alt_rows <- slice(s_data, seq(1, nrow(s_data), by = 2))
print(alt_rows)

Output:

A tibble: 5 × 7
     ID   Age Gender Score1 Score2 Status   Income
  <int><dbl><chr><dbl><dbl><chr><dbl>
1     1    25 Male       85     75 Active    50000
2     3    35 Male       60     88 Active    75000
3     5    22 Male       90     70 Inactive  80000
4     7    33 Female     92     80 Inactive  72000
5     9    38 Female     65     77 Inactive  82000

Combining slice() with Other Functions

The slice () function can combine with other functions and the most common functions that are combined with the slice() function are arranged () and filter().

1. arrange() function

In R, the arrange() is a function used to arrange the data frame based on one or more variables. It is mainly used to sort and arrange the data in a data frame. here we are arranging the dataset in descending order and printing the top 3 rows of the sorted dataset

sort <- s_data %>% arrange(desc(Score1)) %>% slice_head(n = 3)
print(sort)

Output:

A tibble: 3 × 7
     ID   Age Gender Score1 Score2 Status   Income
  <int><dbl><chr><dbl><dbl><chr><dbl>
1     7    33 Female     92     80 Inactive  72000
2     5    22 Male       90     70 Inactive  80000
3    10    29 Male       88     93 Active    70000

2. filter function()

In R, the filter() function is used to filter the rows based on the conditions. here we are filtering the dataset by a condition where the Age is greater than 25 and printing the top two rows of the dataset.

age <- s_data %>% filter(Age > 25) %>% slice_head(n = 2)
print(age)

Output:

A tibble: 2 × 7
     ID   Age Gender Score1 Score2 Status   Income
  <int><dbl><chr><dbl><dbl><chr><dbl>
1     2    30 Female     70     82 Inactive  60000
2     3    35 Male       60     88 Active    75000

Related Articles:

• R Programming Language – Introduction
• R – Data Frames