CategoriesRegression Models Tags ggplot2 Machine Learning Prediction R Programming In this post, the failure pressure will be predicted for a pipeline containing a defect based solely on burst test results and learning machine models. For this purpose, various Machine Learning models will be fitted to test data under R using the caret package, and in the process compare the accuracy of the models in Related...
This post will go through 10 different Linux commands and their R alternatives. If you’re interested in learning more R functions for working with files like some of those below, also check out this post. How to list all the files in a directory Linux R What does it do? ls list.files() Lists all the The post 10 R...
After living for more than a year on GitHub, we are pleased to announce that the {remedy} package is now on CRAN. A package for easier Markdown writing About {remedy} Have you ever been frustrated about having to manually add markdown tags to your Rmd? If you are an avid markdown user, no doubt you’ve been complaining about what...
We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier...
Searching for Speed Everyone who has ever used R has, at one time or another, wished for an increase in R’s speed. If you haven’t, you’re not using R hard enough! Recently, as part of some research on credibility, I was calculating layer loss costs for millions of simulated loss observations. As I progressed, the Read the full article... The...
It’s always nice to be able to speed things up a bit. My first blog post ever described an approach using Rcpp to make huge improvements in a particularly intensive computational process. Here, I want to show how simple it is to speed things up by using the R package parallel and its function mclapply. I’ve been using this...
This is the second post of a series on the concept of “network centrality” with applications in R and the package netrankr. The first part briefly introduced the concept itself, relevant R package, and some reoccurring issues for applications. This post will discuss some theoretical foundations and common properties of indices, specifically the neighborhood-inclusion preorder and what we can learn from them. library(igraph) library(ggraph) library(tidyverse) library(netrankr) Introduction When looking...
”“Data scientists … spend from 50 percent to 80 percent of their time … collecting and preparing unruly digital data, before it can be explored for useful nuggets.” (— New York Times). The chartbookR package allows the convenient creation of economic and financial data chartbooks. It handles most of the data wrangling and can thus create large chartbooks with few...
I will run an 8-hour course on R and Data Mining at Black Mountain, CSIRO, Australia on 10 & 13 December 2018. The course materials, incl. slides, R scripts and datasets, are available at http://www.rdatamining.com/training/course. Below is outline of the … Continue reading →
New minor release of the R/exams package to CRAN, containing a range of smaller improvements and bug fixes. Notably scanning of written NOPS exams is enhanced and made more reliable and a new exercise template demonstrates how to use advanced...
A few month ago I joined the R/Pharma conference in Cambridge, MA. As a take away I thought of my project and how I can improve, with solutions others provided. Mainly solutions in R are R-packages. So I’m a R-Shiny programmer in a regulated environment, so the list of the solutions I took are mainly helping you, if you are...
I taught my Data Visualization seminar in Philadelphia this past Friday and Saturday. It covers most of the content of my book, including a unit on making maps. The examples in the book are from the United States. But what about other places? Two of the participants were from Canada, and so here’s an example that walks through the...
CategoriesProgramming Tags R Programming RMarkdown Time Series In this article, you learn how to make Automatic Dashboard visualizations with Time series visualizations in R. First you need to install the `rmarkdown` package into your R library. Assuming that you installed the `rmarkdown`, next you create a new `rmarkdown` script in R. After this you type the following code in order to create Related Post Automated...
Motivation Column operations Add Modify Remove Benchmark Summary Motivation The dplyr functions select and mutate nowadays are commonly applied to perform data.frame column operations, frequently combined with magrittrs forward %__% pipe. While working well interactively, however, these methods often would require additional checking if used in “serious” code, for example, to catch column name clashes. In principle, the container package provides a dict-class...
We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier...
Continuing on with my “slight?” obsession with colours… I love colours in “Material Colour Palette”. There various website that will lets you grab the colours by clicking, such as this one, but I just wanted to have little handy cheet sheet for myself, so I’ve decided I’ll do that using R & my favourite ggplot2. Getting colours out of image...
Once again, I gives me great pleasure to announce a new package has joined CRAN. transformr is the spatial brother of tweenr and as with the tweenr update a few months ago, this package is very much driven by the infrastructural needs of gganimate. It is probably the last piece needed before I can begin preparing gganimate for CRAN, so if you are...
Exploratory data analysis is important, everybody knows that. With R, it is also easy. Below you see three lines of code that allow you to interactively explore the Preston Curve, the prominent association of country level real income per capita with life expectancy. install.packages("ExPanDaR") library(ExPanDaR) wb
Exercise template for computing the 2-sided confidence interval (with extended Moodle processing) for the mean based on a random sample. Name: confint3 Type: cloze Related: confint2 Description: Computing the 2-sided confidence interval at 95% level for...
… this week that I made a first cameo in the debian/changelog for the Debian R package: r-base (0.63.1-1) unstable; urgency=low New upstream release Linked html directory to /usr/doc/r-base/doc/html (Dirk Eddelbuettel) – Douglas Bates [email protected]
This note is a comment on some of the timings shared in the dplyr-0.8.0 pre-release announcement. The original published timings were as follows: With performance metrics: measurements are marketing. So let’s dig in the above a bit. These timings are of the kind of small task large number of repetition breed that Matt Dowle writes … Continue reading Timing...
CategoriesProgramming Tags Data Visualisation R Markdown R Programming In this article, you learn how to make Automated Dashboard visualizations with distribution in R. First you need to install the `rmarkdown` package into your R library. Assuming that you installed the `rmarkdown`, next you create a new `rmarkdown` script in R. After this you type the following code in order to create a dashboard Related...
Introduction In this post in the R:case4base series we will examine sorting (ordering) data in base R. We will learn to sort our data based on one or multiple columns, with ascending or descending order and as always look at alternatives to base R, namely the tidyverse’s dplyr and data.table to show how we can achieve the same results. It is...
We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier...
TLDR: Begin with N of 10, increase by 10 until p __ 0.05 or max N reached. This design has inflated type-I error. Lower p-value threshold needed to ensure specified type-I error rate. The number of interim analyses and max N affect the type-I error rate. Threshold can be identified using simulation. A recent Facebook … Continue reading "Increase...
CategoriesProgramming Tags Data Visualisation R Markdown R Programming In this article, you learn how to make Automated Dashboard Visualizations with Ranking in R. First you need to install the `rmarkdown` package into your R library. Assuming that you installed the `rmarkdown`, next you create a new `rmarkdown` script in R. After this you type the following code in order to create a dashboard Related...
Many of our projects involve getting doctors, nurses, and medical students to collect data on the patients they are looking after. We want to involve many of them in data analysis, without the requirement for coding experience or access to statistical software. To achieve this we have built Shinyfit, a shiny app for linear, logistic, … Continue reading "Shinyfit:...
Time really does fly. It’s been 5 months since Delhi NCR useR group had come into being and our first meetup. It was a successful event which included sessions featuring an R-core member and a veteran data scientist. More importantly, …
We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier...
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