In an earlier post I gave a gentle introduction to tidy evaluation in the R tidyverse using simple examples. I covered quoting with enquo and unquoting with !! in brief dplyr and ggplot2 snippets. Today, I aim to build a collection of more complex use cases involving additional tools. Those are ...

In an earlier post I gave a gentle introduction to tidy evaluation in the R tidyverse using simple examples. I covered quoting with enquo and unquoting with !! in brief dplyr and ggplot2 snippets. Today, I aim to build a collection of more complex use cases involving additional tools. Those are ...

Meeting Hadley! Last Monday, I had the pleasure of attending a talk given by Hadley Wickham at LondonR, which was held at one of their usual venues at the UCL Darwin Lecture Theatre. For most readers of this blog, Hadley needs no introduction: it is...

In this post, we will develop a framework for always-valid inference based on the paper Always Valid Inference: Continuous Monitoring of A/B Tests (2019 Johari, Pekelis, Walsh). Using an always-valid p-value allows us to continuously monitor A/B tests, and potentially stop the test early in a valid way1. In ...

In this post, I present something I am currently investigating (feedback welcome!) and that I am implementing in my new package {bigutilsr}. This package can be used to detect outlier samples in Principal Component Analysis (PCA). remotes::install_github("privefl/bigutilsr") library(bigutilsr) I present three different statistics of outlierness ...

Topic Extraction is an integral part of IE (Information Extraction) from Corpus of Text to understand what are all the key things the corpus is talking about. While this can be achieved naively using unigrams and bigrams, a more intelligent way of doing it with an algorithm called RAKE is ...

What are driving factors behind the gas price? With freely accessible data we are goging to find out if the brand, the location and more have any impact on the price! Der Beitrag Which Factors Influence Gas Prices? Do Gas Companies Narratives Hold True? erschien zuerst auf STATWORX.

Introduction This workbook outlines key concepts and functions related to map projections — also referred to as coordinate reference systems (CRSs) — and transformation of geographic data from one projection to another. It is based on the open source book Geocomputation with R, and Chapter 6 in particular. It was developed for the ‘...

This will be Part 1 of what I hope to be a multi-part series of plotting soccer event-level data with R! This is more of a tutorial blog post rather than a deep analytical piece but I will give some context to the examples to set ...

I recently became an RStudio Certified Trainer, and thought that it might interest the broader R Community to learn about this new program. For those who don’t know, RStudio has recently put together a process to independently verify that R trainers (a) are proficient with the Tidyverse and (b) ...

I hope you will give me 7 minutes to convince you why CSS is a good thing, and why you should be using it in your projects. In this post I’ll give you a little overview of CSS and I’ll discuss adding CSS to R Shiny. In my future ...

Creating an effective, informative, and aesthetically appealing report to showcase your data can be tedious: it’s often difficult to display your data and your plots together in an uncluttered manner, and even harder to implement interactivity between the individual elements. Dash for R facilitates this task, providing an intuitive ...

Background AUC is an important metric in machine learning for classification. It is often used as a measure of a model’s performance. In effect, AUC is a measure between 0 and 1 of a model’s performance that rank-orders predictions from a model. For a detailed explanation of AUC, see this ...

Finding prediction intervals (for future observations) is something different than finding confidence intervals (for unknown population parameters). Here, I demonstrate one approach to doing so. First we load the library and simulate some data:

library(mgcv)
set.seed(1)
dat <- gamSim(eg = 1, n = 400, dist = "normal", scale = 2)
## Gu & Wahba 4 term additive model

The simulated in dat contains the “truth” in the f variables:

str(dat)
## 'data.frame':    400 obs. of  10 variables:
##  $ y : num  3.3407 -0.0758 10.6832 8.7291 14.9911 ...
##  $ x0: num  0.266 0.372 0.573 0.908 0.202 ...
##  $ x1: num  0.659 0.185 0.954 0.898 0.944 ...
##  $ x2: num  0.8587 0.0344 0.971 0.7451 0.2733 ...
##  $ x3: num  0.367 0.741 0.934 0.673 0.701 ...
##  $ f : num  5.51 3.58 8.69 8.75 16.19 ...
##  $ f0: num  1.481 1.841 1.948 0.569 1.184 ...
##  $ f1: num  3.74 1.45 6.74 6.02 6.6 ...
##  $ f2: num  2.98e-01 2.88e-01 8.61e-05 2.16 8.40 ...
##  $ f3: num  0 0 0 0 0 0 0 0 0 0 ...
fit_lm <- lm(f ~ f0 + f1 + f2 + f3, data = dat)
plot(dat$f, predict(fit_lm))
abline(0, 1)

And what can ...