This tutorial overviews the basics of working with Flourish Studio, a powerful online data visualization tool. This software can be used to produce a wide array of both static and dynamic visualizations, including scatter plots, basic charts, projection maps, network graphs, bar chart races, and more.
Flourish offers both free and paid options. Flourish Public is free to use, but your data and visualizations are publicly accessible. As such, if using Flourish Public, consider redacting or removing any sensitive information, and be sure you have rights and permissions to upload the data you're using. If you find you need the confidentiality of a paid personal plan, contact Flourish regarding their academic discount plans.
To get started with Flourish Public, you'll need to register a new account. As Flourish Studio is browser-based, you won't need to install any software.
Today we'll be working with a cleaned subset of the Detroit Open Data Portal's Detroit Police Department Citizen Complaints: 2016-April 2019 data set. The "Detroit 2018 Citizen Complaints By Month_Type" data set can be found here. To begin, make a copy of the data set, then download and save to your desktop.
Creating Basic Visualizations and Importing Data
Once you've signed in to Flourish for the first time, you'll see a home page with your projects. As you create visualizations, this page is where you'll be able to go back and view or edit them.
Next, choose a template for the type of visualization you'd like to create. Types of visualizations are grouped by headings. Once you've selected a visualization type, you'll be able to change that visualization to any under that heading. For this first exercise, we'll focus on the Line, Bar, and Pie Charts heading. Select the Column Chart (stacked).
Now that we've selected Column Chart (stacked), we'll see a sample visualization, pre-populated with an example data set. All Flourish visualizations will come preloaded with these examples--be sure to take a close look at them prior to loading your data set. Consider, is your data formatted similarly? Are there changes you can make before loading data that might make the process easier?
Toggle to 'Data' to inspect the sample data set. Flourish will read the first row of your data set as headings--in this example column A is labeled 'Year', and columns B-E are labeled numerically. On the right side of your screen you can select columns to visualize. Depending on the visualization, the options shown here will change. For our column chart, we have the options of Labels, Values, and Charts Grid.
The Labels option is for selecting your X-axis and usually consists of a column of name, date, or time entries. The Values option selects one or more columns of numbers and will be your Y-axis variables. For this sample visualization, our X-axis Labels entry will be column A (years), and our Y-axis Values entry will be columns B-E. As we have selected a stacked column chart, each of our Y-axis values will be stacked on top of each other. Try changing your Values entry to just B-D and see how your visualization changes.
Select the Detroit 2018 Citizen Complaints by Month_Type.xlsx document you downloaded earlier and select 'Import Publicly'. We'll notice that the same column selections we'd had previously are still active. As such, we'll want to change our columns to visualize to match our new data set. For Labels, we'll select column A (month). For Values, select columns B-I, representing all forms of complaints that were submitted to the Detroit Police Department in 2018. Switch back to Preview your visualization.
Notice how each complaint format column is shown in a different color, with Walk-In in green, Telephone in orange, etc. Which complaint formats were most popular? What times of year had the most complaints? Is there anything more about the data you can tell from the visualization?
Although we have a complete data visualization at this point, what if we want more? Flourish Studio gives us the possibility to significantly modify our visualizations without having to reload or modify our data set. For this next part of our activity, we'll first look at changing to different visualization types, then at labeling and preparing our visualization for export.
First, let's go back to the chart type category. Instead of a Column Chart (stacked), let's try a couple other options. First, let's switch to a Line Chart. When choosing a chart type, consider--what type of data are you hoping to highlight, and does this visualization accurately represent your data set?
Examining our line chart results, we notice this type of graphic specifically highlights the high number of walk-in and telephone complaints the Detroit Police received in 2018. Maybe we're more interested in total complaints per month though, so let's try switching to a completely different type of chart all together, the Heirarchical Chart.
To create a Heirarchical Chart, click on the back arrow in the top left of the page to return to your Flourish homepage. From here, we'll click on New visualization. Scroll down to the Heirarchy category of visualizations. We'll choose the Heirarchical Bars option. Changing from the Preview to the Data tab, notice that Film, Genre, and Film Studio are currently nested. In Heirarchical visualizations, nesting groups specific categories, allowing you to create a cumulative graphic while still subdividing. As we're exclusively interested in the monthly differences between each different type of complaint, we won't be nesting our data. Consider though—how would changes to the format of your dataset impact the way the visualization appears? Would there be a way to represent all types of complaints divided heirarchically by month?
Moving forward, select Import your data and upload the "Detroit 2018 Citizen Complaints By Month_Type" dataset. As we're going to divide our visualization by month, select Column A, Month, for Nesting. Next, select Columns B-I for Size By. As we're choosing multiple numerical columns to size by, this will create a dropdown menu within our visualization to allow you to choose which complaint type to subdivide by.
Switching back to the Preview tab, notice that your graph has been subdivided by each individual complaint type. Try changing to 'Telephone', 'Outside Agency', or some of the other tabs to notice the changes. Consider—when would a visualization like this be useful? Is it an honest and accurate portrayal of your data set? How would you need to differently clean or structure your data to convey a different meaning?
As with previous visualizations, the tabs along the right side allow us to modify this visualization's appearance. We'd like to make clear that this is only showing a subdivided section of our data set, so let's add a header. Click on the Header tab and add a descriptive Title and Subtitle, as shown below. Try adjusting some of the other settings along the right side tabs to help your visualization best convey your desired meaning.
In addition to the charts we've tried out, Flourish Studio allows for many types of advanced visualizations, including complex maps, network graphs and dynamic charts. Each of these will follow the same base structure we've explored, with the addition of more data types and categories. Ensuring you have adequately cleaned and prepared your data set is crucial when working with an advanced visualization.
Alongside these other visualizations, Flourish offers a variety of mapping options. The most basic of map visualizations Flourish offers are projection and marker maps, basic maps respectively showing region shading or points of interest. Following a similar design process as these maps, Flourish allows for various other more complex offerings, including arc maps, point maps, and region maps.
Begin by navigating back to your homepage, creating a new visualization, and selecting the 'Category dot map' under the Marker Map section of templates. As with our earlier templates, a visualization pre-loaded with data will open, this time showing the position of various fruit trees around London. Tabbing over from the 'Preview' to the 'data' section, you'll notice your data is pulling from three separate spreadsheets. Columns to visualize are pulling from all three.
The first spreadsheet, 'Categories', provides information on our marker types. We'll be uploading the 'Detroit PD Dataset Categories' spreadsheet to replace this one. The second spreadsheet, 'Data', provides information on our marker locations. We'll be replacing this spreadsheet with the 'Detroit PD Motor Vehicle Arrests 2017-19' spreadsheet. The final spreadsheet, 'Regions', provides information for the inset map. For our exercise, we'll be hiding the inset map, so we can ignore this final spreadsheet.
Let's begin uploading our data in the 'Categories' spreadsheet. Notice that this spreadsheet is by default pulling information for Category, Icon, Icon size, and Color. Although not enabled by default, Opacity will allow you to vary the transparency of each icon. After examining how each spreadsheet column is currently being referenced, select 'Import your data', upload the 'Detroit PD Dataset Categories' spreadsheet, and verify your new columns match up with the visualization categories.
Next, we'll update the 'Data' spreadsheet. This spreadsheet is currently pulling information for the category and latitude/longitude location, but notice the ability to add addition data types. Here you can add names, marker images, and other descriptors to provide detail for your visualization. Upload the 'Detroit PD Motor Vehicle Arrests 2017-19' spreadsheet and select column B, 'offense_description' for the Category selection. Likewise, select the proper columns for longitude and latitude. Take care here, lat/long can be easily mixed up!
Now that we've got our data entered in, flip over to the preview tab to see how your data is looking. Not to worry if it's still focusing elsewhere (as shown below)—we'll address that next!
Looking at our visualization, we see the bulk of our data showing up in the Detroit area, with one outlying 'Larceny - Personal Property From Motor Vehicle' point showing in the Pacific ocean. Let's investigate what's happening with this point!
Clicking on the data point we see that it's a point meant for the 1000 Block of Bellevue Street in Detroit, MI. Let's navigate to this data point by switching to the Data tab and searching for "1000 Block of Bellevue Street". Finding one result, we see that the Latitude and Longitude information is listed incorrectly. Let's update this data point to the correct coordinates for the 1000 Block Bellevue St.: Latitude of 42.3486, Longitude of -83.0084. With this is updated, let's switch back to our preview tab. Notice how our visualization's focus has changed!
Now that we've fully cleaned our data, let's focus on revising the rest of our visualization. As we earlier decided against having an inset map, let's toggle off the display map in the inset map tab. Similarly, we can adjust the look of our map by toggling on or off many of the available options.
Let's first start within the Base Map tab. Select Map style and try changing the map template. As we'll be looking at data points on specific streets, I'll choose Klokantech Basic (shown above), which pulls from Open Street Map data. Next, let's limit the ability to zoom in-and-out so our visualization stays focused on the greater Detroit area. Let's set Min zoom to 10.25. Likewise, our visualization is showing at an angle—let's straighten it out and center it with the Pitch and Bearing controls. Pitch will change the tilt angle of the image, while Bearing will change the rotation. Let's change Pitch to 25 and Bearing to 20.
Try changing some of the other settings to customize your visualization. Perhaps add a title to your legend or add a header. Vizualizations are meant to convey meaning, so carefully consider—what are you trying to say with this graphic?
Exporting Your Visualization
Now that you've created a great visualization, it's time to export it for use in your project or website. In order to share or embed, projects must be published. As with our earlier disclaimer on publicly uploading sensitive data, be aware that all published visualizations using the free version of Flourish Studio are made public.
To export your visualization, click the Export & Publish icon in the top right of your screen. Then click Publish to share and embed. This will prompt you with a verification box. Click Publish to display your visualization.
Once we've clicked publish, you'll see your visualization is now displaying as publicly visible and the Export and publish box is now displaying green. If you make further changes to your visualization, you'll need to republish for the changes to take effect. Here you'll also see a link for your visualization, as well as an embed code to add this visualization to your website. An example embedded visualization of our mapping exercise is shown below.
For static visualizations like those we made at the start of this tutorial, Flourish will additionally provide the ability to download as an image. Dynamic visualizations, like our later mapping example, will not download as an image.
Although we've explored a few types of visualizations, Flourish Studio offers many more alternative and advance visualizations not covered in this tutorial. For more information on specific visualizations and features, visit the Flourish help page and blog. For inspiration for your visualizations, also be sure to visit Flourish's examples gallery and Twitter profile.
Lastly, always feel free to reach out to me with any questions. Happy visualizing!