A Fine Slice Of SQL Server
Steven Sanderson can’t stop at one filter:
In data analysis with R, subsetting data frames based on multiple conditions is a common task. It allows us to extract specific subsets of data that meet certain criteria. In this blog post, we will explore how to subset a data frame using three different methods: base R’s
subset()function, dplyr’sfilter()function, and the data.table package.
Click through for examples.
Comments closedSteven Sanderson renames factor levels of a categorical variable:
Before we jump into renaming factor levels, let’s quickly recap what factors are and why they’re useful. Factors are used to represent categorical data in R. They store both the values of the categorical variables and their corresponding levels. Each level represents a unique category within the variable.
Click through for three methods you can use to pull this off.
Comments closedSteven Sanderson explains and does:
Before we dive into the how, let’s briefly discuss why you might want to set a column as the index in your data frame. By doing so, you essentially designate that column as the unique identifier for each row in your data. This can be particularly useful when dealing with time-series data, categorical variables, or any other column that serves as a natural identifier.
Setting a column as the index offers several advantages:
Read on to see those advantages.
Comments closedMaelle Salmon looks at a good package in R
Does your package need the user to provide secrets, like API tokens, to work? Have you considered telling your package users about the keyring package, or even forcing them to use it?
The keyring package maintained by Gábor Csárdi is a package that accesses the system credential store from R: each operating system has a special place for storing secrets securely, that keyring knows how to interact with. The credential store can hold several keyrings, each keyring can be protected by a specific password and can hold several keys which are the secrets.
Read on for several advantages of using the keyring package.
Comments closedSteven Sanderson performs a melt():
The melt() function in the data.table package is an extremely useful tool for reshaping datasets in R. However, for beginners, understanding how to use melt() can be tricky. In this post, I’ll walk through several examples to demonstrate how to use melt() to move from wide to long data formats.
“Melting,” by the way, is the R term for unpivoting data.
Comments closedSebastian Sauer shows off some code. There’s pretty much nothing to excerpt here, as it’s all code and images, so the gist of what Sebastian shows off is how to use the vjust parameter on ggplot2 labels to force where those labels show up.
Steven Sanderson doesn’t need all of the data:
Ah, data! The lifeblood of many an analysis, but sometimes it can feel like you’re lost in a tangled jungle. Thankfully, R offers powerful tools to navigate this data wilderness, and filtering is one of the most essential skills in your arsenal. Today, we’ll explore how to filter both data.tables and data.frames, making your data exploration a breeze!
Click through for ways to filter two popular constructs in R.
Comments closedSteven Sanderson talks data types:
Imagine your data as a diverse collection of individuals. Some might be numbers (like age or weight), while others might be text (like names or addresses). These different categories are called data types, and R recognizes several key ones:
Click through for that list. It’s a bit different from what you’d expect if you come at this from a SQL or C-based programming language background. But they all make good sense when you remember that R is a domain-specific language for statistics, so it’s going to emphasize the things that make the most sense for statisticians and data scientists.
Comments closedSteven Sanderson changes the script:
Before we dive into the code, let’s quickly review what superscripts and subscripts are.
- Superscripts: These are smaller-sized characters or numbers that appear above the baseline of the text. They are often used to denote exponents or indices.
- Subscripts: On the other hand, subscripts are smaller-sized characters or numbers that appear below the baseline of the text. They are commonly used in mathematical expressions or chemical formulas.
Read on to see how you can generate these in R visuals.
Comments closedSteven Sanderson talks about a series of functions:
Welcome, fellow R warriors! Today, we delve into the heart of vectorized operations with R’s “apply” family:
apply(),lapply(),sapply(), andtapply(). These functions are your secret weapons for efficiency and elegance, so buckle up and prepare to be amazed!But first, the “why”: Loops are great, but for repetitive tasks on data structures, vectorization reigns supreme. It’s faster, cleaner, and lets you focus on the “what” instead of the “how” of your analysis. Enter the apply family, each member offering a unique twist on applying functions to your data.
The trickiest part about the apply() series is remembering which one does what. This is where purrr’s map() function does a better job, I think.