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Category: Visualization

ggplot2 Coordinate Systems

Lea Waniek walks us through coordinate systems in ggplot2:

The coordinate system can be manipulated by adding one of ggplot’s different coordinate systems. When you are imagining a coordinate system, you are most likely thinking of a Cartesian one. The Cartesian coordinate system combines x and y dimension orthogonally and is ggplots default (coord_cartesian).

There also are several varaitions of the familiar Cartesian coordinate system in ggplot, namely coord_fixedcoord_flip and coord_trans. For all of them, the displayed section of the data can be specified by defining the maximal value depicted on the x (xlim =) and y (ylim =) axis. This allows to “zoom in” or “zoom out” of a plot. It is a great advantage, that all manipulations of the coordinate system only alter the depiction of the data but not the data itself.

I tend to avoid polar coordinates, but that’s mostly because I don’t work in a space which benefits from it.

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Limitations Of Mapping In Power BI

David Stelfox points out a limitation in Power BI and tries to circumvent it with R to some limited effect:

This results in a row per ride and visualises pretty well in SSMS. If you are familiar with the geography of London you can make out the river Thames toward the centre of the image and Regents Park towards the top left:

This could be overlaid on a shape file of London or a map from another provider such as Google Maps or Mapbox.

However, when you try to load the dataset into Power BI, you find that Power BI does not natively support Geography data types. There is an idea you can vote on here to get them supported: https://ideas.powerbi.com/forums/265200-power-bi-ideas/suggestions/12257955-support-sql-server-geometry-geography-data-types-i

Hit up that idea link if you want to see geography type support within Power BI.

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Demos Using Amazon QuickSight

Karthik Kumar Odapally and Pranabesh Mandal have several example visuals that you can generate using Amazon QuickSight:

Typical Amazon QuickSight workflow

When you create an analysis, the typical workflow is as follows:

  1. Connect to a data source, and then create a new dataset or choose an existing dataset.

  2. (Optional) If you created a new dataset, prepare the data (for example, by changing field names or data types).

  3. Create a new analysis.

  4. Add a visual to the analysis by choosing the fields to visualize. Choose a specific visual type, or use AutoGraph and let Amazon QuickSight choose the most appropriate visual type, based on the number and data types of the fields that you select.

  5. (Optional) Modify the visual to meet your requirements (for example, by adding a filter or changing the visual type).

  6. (Optional) Add more visuals to the analysis.

  7. (Optional) Add scenes to the default story to provide a narrative about some aspect of the analysis data.

  8. (Optional) Publish the analysis as a dashboard to share insights with other users.

It’s interesting to see how Amazon is trying to move this functionality from third-party tools (Power BI, Tableau, etc.) and notebooks right into the set of AWS offerings.  Contrast this with the way that Microsoft is building in Jupyter with Azure Notebooks.

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Creating Seaborn Plots With R

Abdul Majed Raja shows how to call Python from R and build plots using the Seaborn Python package:

The reticulate package provides a comprehensive set of tools for interoperability between Python and R. The package includes facilities for:

  • Calling Python from R in a variety of ways including R Markdown, sourcing Python scripts, importing Python modules, and using Python interactively within an R session.
  • Translation between R and Python objects (for example, between R and Pandas data frames, or between R matrices and NumPy arrays).
  • Flexible binding to different versions of Python including virtual environments and Conda environments.

Reticulate embeds a Python session within your R session, enabling seamless, high-performance interoperability.

The more common use of reticulate I’ve seen is running TensorFlow neural networks from R.

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Creating Map Plots With ggmap

Laura Ellis shows how to use the ggmap package to create choropleth maps in R:

In the last map, it was a bit tricky to see the density of the incidents because all the graphed points were sitting on top of each other.  In this scenario, we are going to make the data all one color and we are going to set the alpha variable which will make the dots transparent.  This helps display the density of points plotted.

Also note, we can re-use the base map created in the first step “p” to plot the new map.

Check it out.  This is an introduction to creating choropleths, making it a good start.

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Faceting With R And SQL Server ML Services

Marlon Ribunal has a quick example showing how to build faceted plots with SQL Server ML Services and ggplot2:

In my previous post, I have demonstrated how easy it is to create a bar graph in SQL Server 2017 In-Database Machine Learning using  R.

We’re going to build upon that basic graph.

Sometimes doing data analysis would require us to look at an overview of our data across specific partitions, say a year. For example, we want to see how our product groups fare on month-to-month basis across the last 4 years.

In a data analytics perspective, there are quite a handful of data points in this requirement – data aggregate (quantity), monthly periods, and year partitions.

One of the approaches to handle such requirement is by using a facet. Faceting is a way of plotting subsets of data into a matrix of panels based on one or more variables – or facets.

Click through for the example and code.  Facets are quite useful, but they run the risk of misleading if you squeeze too many onto the screen.  The same line can look quite different with a “tall” facet versus a “wide” facet, and that can change how people interpret your visual.

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Building Forest Plots With ggplot2

Faisal Atakora shows how to build a forest plot using ggplot2:

To build a Forest Plot often the forestplot package is used in R. However, I find the ggplot2 to have more advantages in making Forest Plots, such as enable inclusion of several variables with many categories in a lattice form. You can also use any scale of your choice such as log scale etc. In this post, I will introduce how to plot Risk Ratios and their Confidence Intervals of several conditions.

Click through for the script.  You might also want to compare it to the forestplot package to see how these differ.

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Dynamically Showing Or Hiding Columns In SSRS With Parameters

Sander Stad shows how to show or hide columns at runtime in SQL Server Reporting Services reports using parameters:

Regularly I have reports that have an extensive amount of columns.
Because the amount of columns, reports tend to become inefficient and have too much information we don’t always need. The users may want to select certain columns and to make the report easier to read.

Hiding and showing columns in SSRS reports using parameters is a solution to make reports more dynamic and easier to use.

At the time of writing of this article, SQL Server Reporting Services did not yet have the possibility to use checkbox parameters. Instead we will be using a multi-value text parameter to show or hide our columns.

Click through to see how to do this.

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Line Drawing And The Traveling Salesman Problem

Antonio Sanchez Chinchon builds a shortest-path portrait generator:

In this experiment I apply an heuristic algorithm to solve the TSP to draw a portrait. The idea is pretty simple:

  • Load a photo

  • Convert it to black and white

  • Choose a sample of black points

  • Solve the TSP to calculate a route among the points

  • Plot the route

Click through for the code.  This is an interesting application of the traveling salesman problem.

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Contrasting Plotly And Seaborn

Natasha Sharma contrasts the Seaborn and Plotly libraries for visualizing data:

It was important to use a library which can provide easy and high-class interactivity. Before embedding the plots into my website code, I tested a few different libraries like Matplotlib and Seaborn in order to visualize the results and to see how different they can look. After few trials, I came across Plotly library and found it valuable for my project because of its inbuilt functionality which gives user a high class interactivity.

In this post, I am going to compare Seaborn and Plotly using – Bar Chart and Heatmap diagram. I will be using Breast cancer dataset to visualize these plots. But before jumping into the comparison, the dataset I used needed preprocessing like data cleaning so, I followed steps.

In this case, the contrast is mostly lines of code versus visual quality; read on for more.

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