tidyverse

Set-up Getting the tweets Are world leaders actually bots? Set-up Given that I do quite like twitter, I thought it would be a good idea to right about R’s interface to the twitter API; {rtweet}. As usual, we can grab the package in the usual way. We’re also going to need the {tidyverse} for the analysis, {rvest} for some initial webscraping of twitter names, {lubridate} for some date manipulation and {stringr} for some minor text mining.

Hi all, so given our logo here at Jumping Rivers is a set of lines designed to look like a Gaussian Process, we thought it would be a neat idea to recreate this image in R. To do so we’re going to need a couple packages. We do the usual install.packages() dance (remember this step can be performed in parallel). install.packages(c("ggplot2", "ggalt", "readr")) We’re also going to need the data containing the points for the lines and which set of points belongs to which line.

Let’s get something straight, there isn’t really any trouble with tibbles. I’m hoping you’ve noticed this is a play on 1967 Star Trek episode, “The Trouble with Tribbles”. I’ve recently got myself a job as a Data Scientist, here, at Jumping Rivers. Having never come across tibbles until this point, I now find myself using them in nearly every R script I compose. Be that your timeless standard R script, your friendly Shiny app or an analytical Markdown document.

The {plotly} package. A godsend for interactive documents, dashboard and presentations. For such documents, there is no doubt that anyone would prefer a plot created in {plotly} rather than {ggplot2}. Why? Using {plotly} gives you neat and crucially interactive options at the top, whereas {ggplot2} objects are static. In an app we have been developing here at Jumping Rivers, we found ourselves asking the question would it be quicker to use plot_ly() or wrapping a {ggplot2} object in ggplotly()?

Can’t be bothered reading, tell me now A simple one line tweak can significantly speed up package installation and updates. The wonder of CRAN One of the best features of R is CRAN. When a package is submitted to CRAN, not only is it checked under three versions of R R-past, R-release and R-devel but also three different operating systems Windows, Linux and Mac (with multiple flavours of each) CRAN also checks that the updated package doesn’t break existing packages.