A Fine Slice Of SQL Server
Steven Sanderson gets rid of rows:
As data analysts and scientists, we often find ourselves working with large datasets where data cleaning becomes a crucial step in our analysis pipeline. One common task is removing unwanted rows from our data. In this guide, we’ll explore how to efficiently remove multiple rows in R using the base R package.
Read on for a couple of ways to do this, including removing by some filter and removing by some index.
Comments closedSteven 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.
Comments closedMauricio Vargas Sepúlveda makes some tea:
Tetley tea boxes feature the following caffeine meter:
In R we can replicate this meter using ggplot2.
I enjoy this kind of thing because, even though the end result doesn’t look exactly like the chart on the tea box, it’s close enough to appreciate the effort. H/T R-Bloggers.
Comments closedSteven Sanderson joins on multiple columns:
Let’s start with a simple scenario. You have two data frames, and you want to merge them based on two columns:
IDandYear. The goal is to combine the data where theIDandYearvalues match in both data frames.
All of the examples here use the merge() function, so check them out.
Steven Sanderson is from the 5th dimension:
Visualizing similarities between data points can be tricky, especially when dealing with many features. This is where multidimensional scaling (MDS) comes in handy. It allows us to explore these relationships in a lower-dimensional space, typically 2D or 3D for easier interpretation. In R, the
cmdscale()function from base R and is a great tool for performing classical MDS.
Click through to see how this works. In case you’re curious, cmdscale() is an example of principal coordinates analysis. If you’re familiar with principal components analysis, that’s a different form of multidimensional scaling.
Rhian Davies consults the map:
I love playing with spatial data. Perhaps because I enjoy exploring the outdoors, or because I spend hours playing Geoguessr, or maybe it’s just because maps are pretty but there’s nothing more fun than tinkering with location data.
However, reading in spatial data, especially large data sets can sometimes be a pain. Here are some simple things to consider when working in spatial data in R and breaking large data sets into more manageable chunks.
Click through for three tips when dealing with spatial data. The code is in R but the tips make sense in any language.
Comments closedSteven Sanderson says, act normal:
Data normalization is a crucial preprocessing step in data analysis and machine learning workflows. It helps in standardizing the scale of numeric features, ensuring fair treatment to all variables regardless of their magnitude. In this tutorial, we’ll explore how to normalize data in R using practical examples and step-by-step explanations.
Read on for a definition of what this means and how you can do it.
Comments closedI’m very happy to announce the addition of 6 new books to the Big Book of R collection, which now stands at about 420 books in total!
Thanks to Isabella Velásquez, Emil Hvitfeldt and Metehan GÜNGÖR for their submissions :).
Read on for a link to the updates, as well as to the Big Book of R itself. H/T R-Bloggers.
Comments closedSteven Sanderson has achieved normality:
Before we dive into the code, let’s understand the concept behind quantile normalization. At its core, quantile normalization aims to equalize the distributions of multiple datasets by aligning their quantiles. This ensures that each dataset has the same distribution of values, making meaningful comparisons possible.
This is a bit different from normalizing individual data points in one dataset, as you can see in the post.
Comments closedSteven Sanderson reads the map():
In the world of data manipulation and analysis with R, efficiency and simplicity are paramount. One function that epitomizes these qualities is
map(). Whether you’re a novice or a seasoned R programmer, masteringmap()can significantly streamline your workflow and enhance your code readability. In this guide, we’ll delve into the syntax, usage, and numerous examples to help you harness the full power ofmap().
Click through for examples of how this works in R.
Comments closed