Slice() Function In R

Slice() is a function in R Programming Language that is used to manipulate data frames and datasets using a simple syntax. It is used to make subsets of data frames, it allows data manipulation. These datasets can be sliced using the slice() function.

R slice() function syntax:

Syntax : slice(.data, …, n = NULL, input = NULL)

Parameters:

1. data: it represents the data frame or dataset
2. …(three dots): it is used to specify conditions like logical conditions and row selection conditions.
3. n: It is used to denote the number of rows to select alternatively to the condition(…).
4. input: It is used as an alternative to conditions(…) to indicate several rows to be selected.

In this article, we will learn about the R slice() function with the help of multiple examples.

Importing the dplyr Package for Data Manipulation

Installation (If Not Installed):

Install the dplyr package in Rstudio using install.packages(“dplyr”) this package is essential to run slice() function.

install.packages(“dplyr”)

Loading the Package:

load the dplyr package in R using the library(dplyr). This will load the dplyr package so that we can run the slice() function.

library(dplyr)

Let’s create a simple dataset to explore various scenarios using the slice() function.

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.

R

# Creating a sample dataset
sample_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)
)
# Display the sample dataset
print("Original dataset:")
print(sample_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 in R Examples

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 ‘sample_data’ dataset. we provided the dataset and row index as arguments in the slice() function and it selected the 3rd row.

R

single_row <- slice(sample_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

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 ‘sample_data’ dataset. it will print only 1,5 and 8th rows of the dataset.

R

multiple_rows <- slice(sample_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

Range Selection

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 ‘sample_data’ dataset and print them.

R

range_selection <- slice(sample_data, 2:6)
print(range_selection)

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

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. Here we are excluding the 3rd and 7th rows and printing the remaining rows.

R

negative_indexing <- slice(sample_data, -c(3, 7))
print(negative_indexing)

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

Conditional Selection

We can use conditions to select the rows of a dataset. Here we are selecting the rows where the Age is greater than 30 using the slice() function and printing them. Which() function checks for the condition and selects the rows that satisfy the condition. Here which() function checks for the rows where the age is greater than 30 and selects those rows.

R

conditional_selection <- slice(sample_data, which(sample_data$Age > 30))
print(conditional_selection)

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

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.

R

top_n_rows <- slice_head(sample_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

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.

R

bottom_n_rows <- slice_tail(sample_data, n = 3)
print(bottom_n_rows)

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

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

R

random_rows <- slice_sample(sample_data, n = 2)
print(random_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

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.

R

alternate_rows <- slice(sample_data, seq(1, nrow(sample_data), by = 2))
print(alternate_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

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

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

R

sorted_top_3 <- sample_data %>% arrange(desc(Score1)) %>% slice_head(n = 3)
print(sorted_top_3)

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

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.

R

age_gt_25_top_2 <- sample_data %>% filter(Age > 25) %>% slice_head(n = 2)
print(age_gt_25_top_2)

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

Conclusion

In conclusion, slice() is a function in R which is a part of the dplyr package and it is used to extract subsets from a dataset. In this article, we covered the basic syntax of the slice(), explaining different parameters in the slice() function, various examples to implement the slice() function, and its most commonly used combination functions arrange() and filter(). You can refer to my Google Colab Notebook for better understanding.