How do I select the last 3 rows in R?

Answered by James Kissner

To select the last 3 rows in R, you can use the built-in function called `tail()`. This function allows you to extract the last few rows from a data frame. It is quite handy when you want to quickly check the last few observations in your dataset or perform any specific operations on them.

I remember using this function extensively when I was working on a project analyzing stock market data. I had a large dataset with daily stock prices, and I often needed to look at the most recent observations to identify any patterns or trends. The `tail()` function made it easy for me to extract the last few rows and analyze them without having to go through the entire dataset.

The syntax for using `tail()` is quite simple. You just need to pass the name of your data frame and specify the number of rows you want to extract. In this case, since we want the last 3 rows, you would specify `n = 3`. Here’s an example:

“`R
# Create a sample data frame
Df <- data.frame( Id = 1:10, Value = rnorm(10) ) # Use tail() to select the last 3 rows Last_three_rows <- tail(df, n = 3)

# Print the selected rows Print(last_three_rows) ``` When you run this code, you will see that the output only contains the last 3 rows of the data frame `df`. The `id` and `value` columns for those rows will be displayed. This can be particularly useful when you are dealing with large datasets and want to focus on the most recent observations. In addition to selecting the last few rows, the `tail()` function also allows you to specify other parameters, such as the number of columns to display or the number of characters to show for each column. This can be helpful when you have wide datasets and want to limit the output to a specific number of columns or control the display of text. The `tail()` function in R is a handy tool for selecting the last few rows of a data frame. It provides a quick way to extract and analyze the most recent observations in your dataset without having to go through the entire structure. Whether you are working with stock market data, time series data, or any other type of data where the order matters, `tail()` can be a useful function to have in your R toolkit.