# PART 1: WHY/HOW TO USE RSTUDIO FOR DATA JOURNALISM?

Whether you are new to data journalism or a full blown pro in computer assisted reporting, you might be aware of the fact that we tell more sophisticated data stories as we move on. Why? Possibly because there is so much more opportunity to do it, more and more complex data can be found to work with or maybe it is just because the competition in data journalism increases in general and everyone is keen to present something increasingly better.

Whatever it may be, to meet the demand to work with new data, we increasingly have the option to apply new data visualisations techniques for the web, such as D3 or other new javascript libraries. But what about the analysis part. Tools like Matlab and SPSS are great tools, but can also become extremely expensive and may offer just not the right environment for the fast paced world of data journalism of today. In addition, many tools, inclduing some of the newbies around (e.g. Tableau Software) may either offer too little to do the high level analysis work, or if they really do, simply won't pay off.So what's the alternative?

# R

I mostly don't know much about the interesting bits and bytes in the data until I have a gone through a base level analysis, find outliers, or some weird or questionable trends in the data. Yes, MS Excel can help, but how about something that is not crashing all the time and giving you a headache, and can handle much more data while providing a sophisticated environemnt scrape, analysis and output data?

One of the cheapest, and in my opinion, most eloquent ways to discover data for people like data journalists is using R. R is a programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. That's the offical labeling. Here we will misuse it for data journalism.

Interest in "RStudio", and "Data Science" follows a similar trend, and a comparable upwards curve: Since 2011 on Google Search, did the area of data science and number crunching become trendy?

I found R liberating to have such a powerful and well documented open source solution at hand. This short tutorial and resource blog should give people like me, a quick intro into the opportunities of R.

I regret that I have not had the chance to use R to its full extent. As for now, that I do many more data stories for clients, I thought I might as well write a small tutorial how to use R for some finance stock data - and maybe more importantly for our online journalists in the virtual room - how to use it to publish interactive charts on the web. For the latter, we will use plotly, but more on it in the course of the tutorial.

## How to Setup? Download R-Studio

Every good tutorial starts with a detailed description on how to download the system, the requirements and other boring setup stuff. Well, first off, I never promised you that this is a good tutorial in the first place, so we will skip over this section rather quickly. If you have already R installed (if you haven't, go to the open source cran.r-project), get the latest version on Rstudio.com and download it for your system. Find a tutorial how to install RStudio also here. If you wonder what the difference is: RStudio is a free and open source integrated development environment (IDE) for R while R is a programming language for statistical computing and graphics.

At the end, I will post some tutorials, guides, additional resources, lists and all kinds of stuff that may help data journalists to get going. Also, I must warm you. The R community, forums and the documentation for R is rather extensive. For me, I felt quite overwhelmed at first. But have no fear. The community is welcoming newbies like you, and help you quickly to learn the dark arts of R. So, let's get started with some data.