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Svbtle Menu is writing on the Svbtle network. r-bloggers.com @drsimonj say hello rss feed about svbtle sign up Walkthroughs and projects using R for data science. @drsimonj say hi r-bloggers.com Read this first Label line ends in time series with ggplot2 @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 right): Setup To setup we’ll need the tidyverse package and the Orange data set that comes with R. This tracks the circumference growth of five orange trees over time. library(tidyverse) d <- Orange head(d) > Grouped Data: circumference ~ age | Tree > Tree age circumference > 1 1 118 30 > 2 1 484 58 > 3 1 664 87 > 4 1 1004 115 > 5 1 1231 120 > 6 1 1372 142 Template code To create the basic case where the numbers appear at the end of your time series lines, your code might look something like this: You have a data set with: - GROUP colum - X colum ... Continue reading → Aug 21, 2018 Exploring correlations in R with corrr @drsimonj here to share a (sort of) readable version of my presentation at the amst-R-dam meetup on 14 August, 2018: “Exploring correlations in R with corrr”. Those who attended will know that I changed the topic of the talk, originally advertised as “R from academia to commerical business”. For anyone who’s interested, I gave that talk at useR! 2018 and, thanks to the R consortium, you can watch it here. I also gave a “Wrangling data in the Tidyverse” tutorial that you can follow at Part 1 and Part 2. The story of corrr Moving to corrr — the first package I ever created. It started when I was a postgrad student studying individual differences in decision making. My research data was responses to test batteries. My statistical bread and butter was regression-based techniques like multiple regression, path analysis, factor analysis (EFA and CFA), and structural equation modelling. I... Continue reading → May 30, 2018 Does financial support in Australia favour residents born elsewhere? Responding to racism with data 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 the data. Australian racism goes viral, again Australian racism went viral again this year when a man was filmed abusing staff at Centrelink, which delivers social security payments and services to Australians (see story here). The man yells that he didn’t vote for multiculturalism and that Centrelink is supporting everyone except “Australians”. It is distressing to watch, especially as someone whose ancestors found a home in Australia having escaped persecution. He can’t take it back, but the man did publically apologise and may be suffering from mental illness (see story here). This... Continue reading → Mar 26, 2018 Guide to tidy git analysis @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 you fit in? This post – intended for intermediate R users – will help you answer these sorts of questions using tidy R tools. Install and load these packages to follow along: Parts 1 and 2 library(tidyverse) library(glue) library(stringr) library(forcats) Part 3 library(tidygraph) library(ggraph) library(tidytext) Part 1: Git repo to a tidy data frame Get a git repo We’ll explore the open-source ggplot2 repo by copying it to our local machine with git clone , typically run on a command-line like: git clone <repository_url> <directory> Find the <repository_url> for... Continue reading → Feb 26, 2018 Creating corporate colour palettes for ggplot2 @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 UI colors on color-hex.com): You want to use these colours with ggplot2 while also making your code reusable and flexible. Outline and setup We’re going to create the following: Named vector of hex codes for the corporate colors Function to access hex codes (in 1) Named list of corporate color palettes (combinations of colors via 2) Function to access palettes (in 3) ggplot2-compatible scale functions that use the corporate palettes (via 4) Load the ggplot2 package and set a default theme to setup: library(ggplot2) theme_set(theme_minimal()) Start with color Everything starts... Continue reading → Dec 30, 2017 Five tips to improve your R code @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 create a sequence from 1 like 1:n , try seq() . Sequence a vector x <- runif(10) seq(x) > [1] 1 2 3 4 5 6 7 8 9 10 Sequence an integer seq(nrow(mtcars)) > [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 > [24] 24 25 26 27 28 29 30 31 32 The colon operator can produce unexpected results that can create all sorts of problems without you noticing! Take a look at what happens when you want to sequence the length of an empty vector: Empty vector x <- c() 1:length(x) > [1] 1 0 seq(x) > integer(0) You’ll also notice that this saves you from using functions like len ... Continue reading → Oct 2, 2017 ggplot2 SEM models with tidygraph and ggraph @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 edges representing the specified relationships between them. For this reason, we will use Thomas Lin Pedersen’s tidygraph and ggraph packages. These packages work together to work with relational structures in a tidy format and plot them using ggplot2. The function Below is a function ggsem() , which takes a fitted lavaan object and returns a ggplot2 object representing the nodes, edges, and parameter values. It handles regression paths, correlations, latent factors, and factor loadings. library(tidyverse) library(tidygraph) library(ggraph) ... Continue reading → Aug 14, 2017 Big Data Solutions: A/B t test @drsimonj here to share my code for using Welch’s t -test to compare group means using summary statistics. Motivation I’ve just started working with A/B tests that use big data. Where once I’d whimsically run t.test() , now my data won’t fit into memory! I’m sharing my solution here in the hope that it might help others. In-memory data As a baseline, let’s start with an in-memory case by comparing whether automatic and manual cars have different Miles Per Gallon ratings on average (using the mtcars data set). t.test(mpg ~ am, data = mtcars) > > Welch Two Sample t-test > > data: mpg by am > t = -3.7671, df = 18.332, p-value = 0.001374 > alternative hypothesis: true difference in means is not equal to 0 > 95 percent confidence interval: > -11.280194 -3.209684 > sample estimates: > mean in group 0 mean in group 1 > 17.14737 24.39231 Well… that was easy! Big Data ... Continue reading → Jun 1, 2017 A tidy model pipeline with twidlr and broom @drsimonj here ...