Turning kindle notes into a tidy data
Posted on May 8, 2017
(Last modified on November 9, 2022)
| 12 minutes
| 2380 words
| Roel M. Hogervorst
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
Posted on March 14, 2017
(Last modified on November 9, 2022)
| 7 minutes
| 1378 words
| Roel M. Hogervorst
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. **
This tutorial explains how to link Zotero (a reference manager) to your project folder and how to easily add citations.
[Read More]Generate text using Markov Chains (sort of)
Posted on January 21, 2017
(Last modified on November 9, 2022)
| 6 minutes
| 1267 words
| Roel M. Hogervorst
Inspired by the hilarious podcast The Greatest Generation, I have worked again with all the lines from all the episode scripts of TNG. Today I will make a clunky bot (although it does nothing and is absolutely not useful) that talks like Captain Picard. I actually wanted to use a Markov Chain to generate text. A Markov Chain has a specific property. It doesn’t care what happened before, it only looks at probabilities from the current state to a next state.
[Read More]Plotting a map with ggplot2, color by tile
Posted on January 6, 2017
(Last modified on November 9, 2022)
| 11 minutes
| 2274 words
| Roel M. Hogervorst
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]Submitting your first package to CRAN, my experience
Posted on July 9, 2016
(Last modified on November 9, 2022)
| 5 minutes
| 1034 words
| Roel M. Hogervorst
I recently published my first R package to The Comprehensive R Archive Network (CRAN). It was very exciting and also quite easy. Let me walk you through my process.
First a description of my brand new package: badgecreatr, then a description of steps to take for submission.
Package description When you go around github looking at projects you often see these interesting images in the readme
The ones you see above are from ggplot2.
[Read More]Introducing Badgecreatr, a package that places badges in your readme
Posted on July 8, 2016
(Last modified on November 9, 2022)
| 2 minutes
| 369 words
| Roel M. Hogervorst
Introducing Badgecreatr, a package to create and place badges in your readme.Rmd file on Github.
Badgecreatr will create the following badges (aka shields):
Installation Install the package with install.packages("badgecreatr")
How do you use badgecreatr? Badgecreatr has one main function: badgeplacer().
The most simple command is:
badgecreatr::badgeplacer( githubaccount = "yourgithubname",githubrepo = "yourpackagename", branch = "master") If your project is in its infancy and you don’t want people to use it yet:
[Read More]Non-standard-evaluation and standard evaluation in dplyr
Posted on June 13, 2016
(Last modified on November 9, 2022)
| 6 minutes
| 1069 words
| Roel M. Hogervorst
THIS POST IS NO LONGER ENTIRELY RELEVANT. DPLYR 0.7 HAS A SLIGHTLY DIFFERENT (AND SLIGHTLY MORE INTUITIVE) WAY OF WORKING WITH NON-STANDARD EVALUATION. EDIT: IT IS COMPLETELY DIFFERENT NOW. AND I RECOMMEND https://dplyr.tidyverse.org/.
I love the dplyr package with all of its functions, however if you use normal dplyr in functions in your package r-cmd-check will give you a warning: R CMD check NOTE: No visible binding for global variable NAME OF YOUR VARIABLE 1.
[Read More]Your most valuable collaborator, future-you
You don’t want future-you to curse past-you
Posted on May 26, 2016
(Last modified on November 9, 2022)
| 9 minutes
| 1824 words
| Roel M. Hogervorst
I was recently at a R users meetup where Hadley Wickham talked about data wrangling. He showed some interesting stuff! Did you know that you can put a data frame into a data frame? You can make a list of data frames and add that list to your data frame. Very cool, and more useful then I thought, but that is not what I wanted to talk about.
I would like to give you some tips about working with someone you will probably work with in the future.
[Read More]Introduction to R projects
Posted on April 10, 2016
(Last modified on November 9, 2022)
| 2 minutes
| 252 words
| Roel M. Hogervorst
It often makes sense to separate your projects. And since space is cheap you are probably creating separate folders on your computer. In RStudio you can create different projects that live in their own folder.
When you start a different project the files of that project work independently from other projects. And the standard locations of your workspace and other things are also separated from the rest.
In my case, for example, I have several projects and the last 10 or so are displayed in the dropdown menu:
[Read More]From spss to R, part 4
Posted on April 4, 2016
(Last modified on November 9, 2022)
| 15 minutes
| 3088 words
| Roel M. Hogervorst
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]