This is where things are a little different in the project notesbooks vs hosted RStudio. P <- ggplot(aD2, aes(x=year, y=value, group=1)) +ĥ) Export the manipulated data set and chart #Filter to include only Median Gross RentĤ) Create a simple chart on one of the key indicators 'Median Gross Rent' #Create a line graph We need to create two new columns to represent the key value pair combination. Currently the metric value for every year is in it's own column. After that we need to reformat the table to easily use in a line graph. We are first going to filter down to only include the 'Median Gross Rent' KPI. Finally, it has a lot of great (and free) integrations like: SPSS, Cognos dashboards and a variety of embedded AI services like Watson Visual Recognition and Natural Language Classifier. The fact that it's hosted, means that I can access it from any website (I'm talking ipads folks). As an R user, I like it because my colleagues and I can leverage the collaboration options and work in the same project space but use different languages or tools. It allows us to integrate a variety of languages, products, techniques and data assets all within one place. Watson Studio is a hosted, full service and scalable data science platform. Even better, I can share it because the service has a free tier! Why? Well first and foremost, I use it a lot and I want to share the benefits. As such, I wanted to dedicate a whole blog to explain your R options within IBM Watson Studio. Since I try to make my blogs beginner friendly, I usually begin with a little talk about your options for running R code. I just can't help it y'all, I'm like a moth to a flame with these fancy R packages. As you may have noticed, I blog a lot about R.

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