Running an R script on heroku

Automate alllll the things!

In this post I will show you how to run an R script on heroku every day. This is a continuation of my previous post on tweeting a death from wikidata. Why would I want to run a script on heroku? It is extremely simple, you donโ€™t need to spin up a machine in the cloud on AWS, Google, Azure or Nerdalize. You can just run the script and it works. [Read More]

Tweeting daily famous deaths from wikidata to twitter with R and docker

A tweet a day keeps the insanity at bay

In this explainer I walk you through the steps I took to create a twitter bot that tweets daily about people who died on that date. I created a script that queries wikidata, takes that information and creates a sentence. That sentence is then tweeted. For example: A tweet I literally just send out from the docker container I hope you are has excited as I am about this project. [Read More]

Reading in an epub (ebook) file with the pubcrawl package

In this tutorial I show how to read in a epub file (f.i. from your ebook collection on you computer) into R with the pubcrawl package. In emoji speak: ๐Ÿบ๐Ÿ“–๐Ÿ“ฆ . I will show the reading in part, (one line of code) and some other actions you might want to perform on textfiles before they are ready for text analysis. After you read in your epub file you can do some cool analyses on it, but that is part of the next blogpost. [Read More]

Turning kindle notes into a tidy data

It is my dream to do everything with R. And we aRe almost there. We can write blogs in blogdown or bookdown, write reports in RMarkdown (thank you Yihui Xie!) create interactive webpages with Shiny (thank you Winston Chang). Control our lifx lights with lifxr (great work Carl!) and use emoticons everywhere with the emo package. There is even a novel of my vision! I recently found chapter 40 of A Dr. [Read More]

Writing manuscripts in Rstudio, easy citations

Intro and setup This is a simple explanation of how to write a manuscript in RStudio. Writing a manuscript in RStudio is not ideal, but it has gotten better over time. It is now relatively easy to add citations to documents in RStudio. **The goal is not think about formatting, and citations, but to write the manuscript and add citations on the fly with a nice visual help. ** [Read More]

Plotting a map with ggplot2, color by tile

Introduction Last week I was playing with creating maps using R and GGPLOT2. As I was learning I realized information about creating maps in ggplot is scattered over the internet. So here I combine all that knowledge. So if something is absolutely wrong/ ridiculous / stupid / slightly off or not clear, contact me or open an issue on the github page. When you search for plotting examples you will often encounter the packages maps and mapdata. [Read More]

From spss to R, part 4

This is the second part of working with ggplot. We will combine the packages dplyr and ggplot to improve our workflow. When you make a visualisation you often experiment with different versions of your plot. Our workflow will be dynamic, in stead of saving every version of the plot you created, we will recreate the plot untill it looks the way you want it. In the previous lesson we worked with some build in datasets. [Read More]

Creating a package for your data set

Turning your dataset into a package is very useful for reproducable research. This tutorial is for you, even if you’ve never created a package in r. Why would you turn your dataset into a package? very easy to share easy to load (library(name) is easier then load("path/to/file") or data<-read.csv("path/to/file") etc.) documentation is part of the package and will never separate from data attributes of file remain nice and easy introduction to package building What do you need to do to create a dataset package: [Read More]

From spss to R, part 3

In this post we will start with a build-in dataset and some basic ggplot graphics. In the next post we will combine dplyr and ggplot to do awesome stuff with the Dutch University student counts from the previous lessons. We will work with the build-in dataset mtcars. There are many datasets in r library(help = "datasets") but in many examples online you will see the iris and mtcars examples. Find more information about the dataset with ? [Read More]

Tidying your data

Introduction To make analyses work we often need to change the way files look. Sometimes information is recorded in a way that was very efficient for input but not workable for your analyses. In other words, the data is messy and we need to make it tidy. Tidy data means 1: Each variable forms a column. Each observation forms a row. Each type of observational unit forms a table. [Read More]