Steps to implement Slicing in R

Now, let us know the steps to implement this slice() method in R.

Step 1: Install and load the packakges

The first step is to install and load the dplyr package which has this function into the environment.

install.packages(‘dplyr’)

library(dplyr)

Step 2: Data preparation

In this step, we now need data to slice. So, this step is data preparation.

df <- data.frame(
  id = c(101, 102, 103, 104, 105),
  name = c('Madhu', 'Ram', 'Krishna', 'Radha', 'Lakshmi'),
  gender = c('F', 'M', 'M', 'F', 'F'),
  dob = as.Date(c('1992-05-15', '1988-12-31', '1995-07-20', '1990-03-10', '1987-11-05')),
  state = c('CA', 'NY', 'TX', 'FL', 'WA'),
  stringsAsFactors = FALSE
)

The above code shows how to create a basic dataframe with multiple rows and columns in R. We used data.frame() to create it. We can either create our own dataframe or else, we can use an already existing dataset and work on it.

Step 3: Slice operation

Now it is time to perform slice operation on the data frame.

df2 <- df %>% slice(2,3)

In this case the operation in slice(), after which the result is stored in new variable df2. The parameters inside the slice() function denotes the start and end indexes for slicing, where both are included.

Note: In the above code, we used an operator called pipe (%>%) in dplyr package. The operator takes the input from the left hand dataframe and performs the operation on the right side.

These were the steps involved in using the slice() function. Now, let us dive into types of slice() functions in dplyr package which make data analysis much more simpler.

With so much data around us in today’s world, dealing with them becomes tough. In this case, the Dplyr data frame package from R acts as a lifesaver and that package stands out as a powerful and versatile tool. for data manipulation. In R Programming Language package has many functions and among them, slice() is particularly useful for extracting specific rows from any data frame based on their indexes (positions).

In this article, we will look at the details of this slice() function and explore how can it help in the data manipulation process.