@drsimonj here with a quick share on making great use of the secondary y axis with ggplot2 – super helpful if you’re plotting groups of time series! Here’s an example of what I want to show you how to create (pay attention to the numbers of the ...

@drsimonj here with a quick share on making great use of the secondary y axis with ggplot2 – super helpful if you’re plotting groups of time series! Here’s an example of what I want to show you how to create (pay attention to the numbers of the ...

Seeing a racist outburst made me wonder whether the Australian Government unfairly supports people based on their background. Using data from the Australian Government and Bureau of Statistics, I couldn’t find compelling evidence of this being true. Don’t believe me? Read on and see what you make of ...

@drsimonj here to help you embark on git repo analyses! Ever wondered who contributes to git repos? How their contributions have changed over time? What sort of conventions different authors use in their commit messages? Maybe you were inspired by Mara Averick to contribute to tidyverse packages and wonder how ...

@drsimonj here to share how I create and reuse corporate color palettes for ggplot2. You’ve started work as a data scientist at “drsimonj Inc” (congratulations, by the way) and PR have asked that all your Figures use the corporate colours. They send you the image below (coincidentally the Metro ...

@drsimonj here with five simple tricks I find myself sharing all the time with fellow R users to improve their code! This post was originally published on DataCamp’s community as one of their top 10 articles in 2017  1. More fun to sequence from 1 Next time you use the colon operator to ...

@drsimonj here to share a ggplot2-based function for plotting path analysis/structural equation models (SEM) fitted with Yves Rosseel’s lavaan package.  Background SEM and its related methods (path analysis, confirmatory factor analysis, etc.) can be visualized as Directed Acyclic Graphs with nodes representing variables (observed or latent), and ...

@drsimonj here to show you how to go from data in a data.frame to a tidy data.frame of model output by combining twidlr and broom in a single, tidy model pipeline.  The problem Different model functions take different types of inputs (data.frames, matrices, etc) and produce different ...

@drsimonj here to make pretty scatter plots of correlated variables with ggplot2! We’ll learn how to create plots that look like this:  Data In a data.frame d, we’ll simulate two correlated variables a and b of length n: set.seed(170513) n 2 0.9133158 0.21116682 #__ 3 1.4516084 0.69060249 #__ 4 0.5264596 0.22471694 #__ 5 -1.9412516 -1.70890512 #__ 6 1.4198574 0.30805526  Basic scatter plot Using ...

@drsimonj here to make pretty histograms with ggplot2! In this post you’ll learn how to create histograms like this:  The data Let’s simulate data for a continuous variable x in a data frame d: set.seed(070510) d x #__ 1 1.3681661 #__ 2 -0.0452337 #__ 3 0.0290572 #__ 4 -0.8717429 #__ 5 0.9565475 #__ 6 -0.5521690  Basic Histogram Create the basic ggplot2 histogram ...

@drsimonj here to introduce my latest tidy-modelling package for R, “twidlr”. twidlr wraps model and predict functions you already know and love with a consistent data.frame-based API! All models wrapped by twidlr can be fit to data and used to make predictions as follows: library(twidlr) fit

@drsimonj here to show you how to conduct ridge regression (linear regression with L2 regularization) in R using the glmnet package, and use simulations to demonstrate its relative advantages over ordinary least squares regression.  Ridge regression Ridge regression uses L2 regularisation to weight/penalise residuals when the parameters of a ...

@drsimonj here to show you how to do leave-one-out cross validation using pipelearner.  Leave-one-out cross validation Leave-one-out is a type of cross validation whereby the following is done for each observation in the data: Run model on all other observations Use model to predict value for observation This means that ...

@drsimonj here to show you how to use ggraph and corrr to create correlation network plots like these:  ggraph and corrr The ggraph package by Thomas Lin Pedersen, has just been published on CRAN and it’s so hot right now! What does it do? “ggraph is an extension of ...

@drsimonj here to show you how to use xgboost (extreme gradient boosting) models in pipelearner.  Why a post on xgboost and pipelearner? xgboost is one of the most powerful machine-learning libraries, so there’s a good reason to use it. pipelearner helps to create machine-learning pipelines that make it easy ...

@drsimonj here to share my big takeaways from rstudio::conf 2017. My aim here is to share the broad data science opinions and challenges that I feel bring together the R community right now, and perhaps offer some guidance to anyone wanting to get into the R community. DISCLAIMER: this is ...

@drsimonj here to introduce pipelearner – a package I’m developing to make it easy to create machine learning pipelines in R – and to spread the word in the hope that some readers may be interested in contributing or testing it. This post will demonstrate some examples of what pipeleaner can ...