The way it works in cowplot is that, we have assign our individual ggplot-plots as an R object (which is by default of type ggplot). These objects are finally used by cowplot to produce a unified single plot.
In the below code, We will build three different histograms using the R’s in-built dataset iris and then assign one by one to an R object. Finally, we will use cowplot function
plot_grid()to combine the two plots of our interest.
The only thing that disappointed me with
cowplot is that its name has nothing to do with cattle.
Even more common than grouping columns is probably grouping data by rows. The htmlTable allows you to do this by
tspanner. The most common approach is by using
rgroupas the first row-grouping element but with larger tables you frequently want to separate concepts into separate sections. Here’s a more complex example. This has previously been a little cumbersome to to counting the rows of each tspanner but now you’re able to (1) leave out the last row, (2) specify the number of rgroups instead of the number of rows. The latter is convenient as the
n.tspannermust align with the underlying rgroup.
I haven’t used this package before, but it does look interesting. H/T R-bloggers
While this commit was done in the autumn 2017, nothing further happened until I decided to make gganimate the center of my useR 2018 keynote, at which point I was forced (by myself) to have some sort of package ready by the summer of 2018.
A fair amount of users have shown displeasure in the breaking changes this history has resulted in. Many blog posts have already been written focusing on the old API, as well as code on numerous computers that will no longer work. I understand this frustration, of course, but both me and David agreed that doing it this way was for the best in the end. I’m positive that the new API has already greatly exceeded the mind-share of the old API and given a year the old API will be all but a distant memory…
Read on for information on these breaking changes, and how the changes will make life easier in the long run. And stay for the fireworks. H/T R-Bloggers
The Small World of Words project focuses on word associations. You can try it out for yourself to see how it works, but the general idea is that the participant is presented with a word (from “telephone” to “journalist” to “yoga”) and is then asked to give their immediate association with that word. The project has collected more than 15 million responses to date, and is still collecting data. You can check out some pre-built visualizations the researchers have put together to explore the dataset, or you can download the data for yourself.
It’s an interesting analysis of the data set, mixed in with some good R code.
Plotting univariate (sampled) normal data
Well, that’s obvious.
d %>% ggplot(aes(x = X1)) + geom_density()
It gets much less obvious from there. It was also interesting learning about
ggplotly, a function which translates ggplot2 visuals to plotly visuals.
Note that there are some R/Python visuals and currently, R/Python visuals are not available on “Publish to Web”. Hence, I have just used a checkbox on the top of the report to show the images wherever R visuals are used (can be identified by the colorful border around the image). However, you can download the source file and then publish it to your tenant, and see the actual R visuals there in a browser by unselecting the checkbox. You can also look at the pbix file and see the source code behind the visuals.
Definitely check this out. Jason did a great job.
Most of the people swap UI with UX and that’s the most common mistake. Let’s understand their difference.
UI mainly focuses on the look of your application. It is the process of improving the interactivity and presentation of your web or mobile app. Being a UI designer you need to have creative and convergent thinking, so you can improve its look and contribute to better user interaction with the application. With the unique visualization of UI designer, we can have every screen page, buttons and other visual elements of the app look intuitive. UI Designer must also have basic knowledge of the tools in order to create a better app UI plus keeping in mind the user’s requirements. Tools that designer basically use are Adobe XD, Adobe Photoshop, Illustrator and sketch.
Whereas UX is all about creating the basic skeleton of any application. It works on wireframing of an application and structuring all its components appropriately to create the user flow. The thought process of a UX Designer must be both a mix of critical and creative thinking. UX design is more of a human-centric design an enhancement of user’s experience is the main goal here. User’s needs and research play a significant role here. Usability testing must also be done frequently after the basic skeleton of the app has been prepared because that helps in cross-checking all the components.
Read the whole thing.
For those of you who have been following along with issue #51 in the ggmap repo, you’ll notice that there have been a number of changes in the Google Maps Static API service. Unfortunately these have caused some breakage in previous ggmap functionality.
If you used this package prior to July 2018, you may were likely able to do so without signing up for the Google Static Map API service yourself. As indicated on the the ggmap github repo – “Google has recently changed its API requirements, and ggmap users are now required to provide an API key and enable billing. The billing enablement especially is a bit of a downer, but you can use the free tier without incurring charges. Also, the service being exposed through an easy to use r package that extends ggplot2 is pretty great so I’ll allow it.
This recent API change hurts. But click through for the tutorial, which doesn’t hurt.
The logic is to create a table with the DAX function UNION. Each Table expression in UNION function represents a value of slicer. Apart from that slicer related value, all the rest of the values are blanks. It is key to have them as blanks than zero’s, we don’t see any data.
In other words, pivoting the table to turn one measure with several different category values into one measure per category. If you know the number of categories (4 in this case), this solution can work well for you.
One simple way is to plot several facets according to the grouping variable:
d %>% ggplot() + aes(x = hp, y = mpg) + geom_point() + facet_wrap(~ cyl)
Faceting is great, but it’s good to know the other technique as well.