Category: R

Steven Sanderson wants to check for row existence:

Hello, fellow R users! Today, we’re going to explore a common scenario you might encounter when working with data frames: checking if a row from one data frame exists in another. This is a handy skill that can help you compare datasets and verify data integrity.

There are several ways to do it. Click through for two of those ways.

Steven Sanderson takes a slice:

Explanation: – We use nrow(my_df) to get the total number of rows in the data frame. – Then, we use indexing ([nrow(my_df), ]) to extract the last row.

Read on for a simple example of getting the last row using base R, dplyr, and data.table. Then, we kick it up a notch to get the second-to-last row, with the idea being that you could substitute “second-to-last” with any arbitrary number.

Steven Sanderson grabs some rows:

Imagine you want to analyze the fuel efficiency (miles per gallon) of a particular car. Here’s how to grab a single row by its index (row number):

Read on for that example, as well as examples covering two other cases: multiple rows based on index value, and ranges of indices.

Art Steinmetz runs a comparison:

As we start working with larger and larger datasets, the basic tools of the tidyverse start to look a little slow. In the last few years several packages more suited to large datasets have emerged. Some of these are column, rather than row, oriented. Some use parallel processing. Some are vector optimized. Speedy databases that have made their way into the R ecosystem are data.table, arrow, polars and duckdb. All of these are available for Python as well. Each of these carries with it its own interface and learning curve. duckdb, for example is a dialect of SQL, an entirely different language so our dplyr code above has to look like this in SQL:

Read on for a detailed comparison. Your mileage may vary, etc., but I’m pleasantly surprised with the results, given that I like the Tidyverse for its ease of use compared to base R and other alternatives like raw data.table. H/T R-Bloggers.

Steven Sanderson shows us three ways:

Handling missing values is a crucial aspect of data preprocessing in R. Often, datasets contain missing values, which can adversely affect the analysis or modeling process. One common task is to remove rows containing missing values entirely. In this tutorial, we’ll explore different methods to accomplish this task in R, catering to scenarios where we want to remove rows with either some or all missing values.

Click through for three ways to do this.