Sebastian 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.
Category: R
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.
Steven Sanderson isn’t satisfied with a single date:
In the world of data analysis and manipulation, working with dates is a common and crucial task. Whether you’re analyzing financial data, tracking trends over time, or forecasting future events, understanding how to generate date sequences efficiently is essential. In this blog post, we’ll explore three powerful R packages—lubridate, timetk, and base R—that make working with dates a breeze. By the end of this guide, you’ll be equipped with the knowledge to generate date sequences effortlessly and efficiently in R.
Click through for several ways to generate date sequences, including weekly sequences.
Comments closedSteven Sanderson is looking for a date:
As an R programmer, you may often encounter datasets where you need to determine whether a column contains date values. This task is crucial for data cleaning, manipulation, and analysis. In this blog post, we’ll explore various methods to check if a column is a date in R, with a focus on using the
lubridatepackage and thets_is_date_class()function from thehealthyR.tspackage.
Click through to see how, using lubridate and healthyR.
Comments closedSteven Sanderson checks the week:
When working with dates in R, you may need to extract the week number for any given date. This can be useful for doing time series analysis or visualizations by week.
In this post, I’ll demonstrate how to get the week number from dates in R using both base R and the lubridate package. I’ll provide simple examples so you can try it yourself.
Steven also makes a good point about ISO weeks (which are common in Europe) versus calendar weeks.
Comments closedSteven Sanderson reads an Excel file:
Have you ever battled with Excel’s quirky date formats in your R projects? If so, you’re not alone! Those cryptic numbers can be a real headache, but fear not, fellow R warriors! Today, we’ll conquer this challenge and transform those numbers into beautiful, usable dates.
This is a common pain point in a lot of libraries and Steven shows how to solve it in R using a pair of functions.
Comments closedJohn MacKintosh takes us through what’s new in R’s data.table package:
The newest version of data.table has hit CRAN, and there are lots of great new features.
Among them, a
%notin%function, a newletfunction that can be used instead of:=( I wasn’t too fussed about this originally but have tried it a few times today and I may well adopt it – although I do like that:=really stands out in my code when assigning / updating variables).
Read on for the big changes. H/T R-Bloggers.
Comments closed