ggplot– You can spot one from a mile away, which is great! And when you do it’s a silent fist bump. But sometimes you want more than the standard theme.
Fonts can breathe new life into your plots, helping to match the theme of your presentation, poster or report. This is always a second thought for me and
needto work out how to do it again, hence the post .
Read on to see how to use each of these packages. H/T R-bloggers
As you can see, there are data labels for each subcategory (means gender and education), but no data label showing the total of each education category. for example, we want to know how much was the total sales in the High School category. Now that you know the problem, let’s see a way to fix it.
Read on for Reza’s solution to the problem. In general, if people might care about the total, do them a favor and show the total.
If there is one thing of general utility lacking in ggplot2 it is probably the ability to annotate data cleanly. Sure, there’s
geom_label()but using them requires a fair bit of fiddling to get the best placement and further, they are mainly relevant for labeling and not longer text.
ggrepelhas improved immensely on the fiddling part, but the lack of support for longer text annotation as well as annotating whole areas is still an issue.
In order to at least partly address this, ggforce includes a family of geoms under the
geom_mark_*()moniker. They all behaves equivalently except for how they encircle the given area(s).
There are some really interesting features in the
ggforce package, so check them out.
The Lines section of the Data Visualization Checklist helps us enhance reader interpretability by handling a lot of the junk, or what Edward Tufte called the “noise” in the graph. I’m referring to all of the parts of the graph that don’t actually display data or assist reader cognition. Create more readability by deleting unnecessary lines.
The default chart, on the left, has black gridlines. These stand out quite a bit because of how well black contrasts against the white chart background. But the gridlines shouldn’t be standing out so much because they are not the most important part of the graph
I like that Stephanie keeps the gridlines. I’ve seen Tufte advocate removing them altogether but there’s a lot of value in keeping them in; just don’t make them the sharpest focus color.
2. feather instead of csv
The app relied on some pre-wrangled csv files; these have been replaced by files stored using the .feather format, which makes for a signficant performance improvement.
Martin has made a significant number of changes and it’s cool to see the full list of changes. H/T R-bloggers
Edward Tufte recommended use of soft colors that do not tire the eyes. I’ve actually never read his books (yet), but a former boss of mine was a devout disciple and produced some beautifully soft color palettes.
Stephen Few, in “Show Me the Numbers,” reiterated Tufte’s color theories and recommended three sets of hues:
Light – for large shapes, e.g. bars
Medium – for small shapes, e.g. points
Dark/Bright – for calling attention to data
Click through for more including where you can get this Power BI theme. I’m not exactly the world’s biggest fan of the default palette so I’ll have to check this one out.
I have given many presentations and talks about Data Visualization, and still, I am amazed by how many visualizations I see which is not following the basic rules. In this article, I want to focus on table visual. A table is a visual that most of us are using it on many occasions, in fact, many users, like to see the data in table format. However, a table can be visualized in a way that is not readable. In this article, I’m showing you the most common style of a table which many report developers use, and then challenge it with a better style. The mystery is of course in conditional formatting. Like all my other articles, this article is demonstrating this technique in Power BI. If you like to learn more about Power BI, read Power BI book from Rookie to Rock Star.
Some of these formats are better than others, but you do have the power to do quite a bit with it in Power BI.
The Tooltips can display a string with multiple lines. This is useful for the DumpFilters measure that creates a new line for every column with a filter. You might wonder why the DumpFilters measure is required considering that Power BI can already display any filters and slicers affecting a visual. The reason is that the DumpFilters measure isolates the filters of a single cell and can show the effects of filters that are not visible in the standard visualization provided by Power BI.
This is interesting reading and a good way of sharing to users how they got to the current view of data.
Since some time, there’s a wrapper for
ggplot2available, bundled in the package
ggformula. One nice thing is that in that it plays nicely with the popular R package
mosaicprovides some useful functions for modeling along with a tamed and consistent syntax. In this post, we will discuss some “ornaments”, that is, some details of beautification of a plot. I confess that every one will deem it central, but in some cases in comes in handy to know how to “refine” a plot using
Note that this “refinement” is primarily controlled via the function
gf_lab()(for labs), and
gf_lims()(for axis limits). Themes can be adjusted using
Click through for several examples.
Today Gartner released the 2019 magic quadrant for Business Intelligence. As expected (by me at least), Microsoft is continuing its trail blazing and now has a clear lead over Tableau in both ability to execute and completeness of vision. I thought it would be interesting to see a trend over time for the last 5 years, as this is the time period that I have been a professional Power BI Consultant. I needed some way to extract the numerical data points from the images I had collected. This article shows you how to do that. Here is the final output – a scatter chart with a play axis in Power BI of course.
I was just commenting the other day about how somebody should do this and Matt went and did it.