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There are a lot of different data visualizations out there. It's nice to see a few simple examples of what the different types look like so you can figure out what to order. These are the visualization types we offer at MergeYourData.com with a brief explanation and snapshot of what they look like. Additionally, we often post more complex visualizations we've built using public data on our blog using the tag visualization, so make sure to check those out too.
Table of Contents
Click on the titles to be directed down to that portion of the page.
- Bar Chart
- Basic Map (with zip, county, sub-regional, regional, country and continent breakdowns)
- Box and whisker
- Bubble Chart
- Candlestick Chart
- Donut Chart
- Gantt Chart
- Heat Map
- Highlight table
- Histogram Chart
- Line Chart
- Pictogram Chart
- Pie Chart
- Scatter Plot
- Waterfall Chart
- Word cloud
Basic VisualizationsThis group contains examples of basic visualization types we offer.
Bar charts (vertical or horizontal) are some of the simplest but most direct visualizations available. They're great for analyzing trends and distributions in a direct way. People are used to seeing bar charts and their variations, making it a universal and clear way of displaying comparative information.
Bar Chart - Radial
Radial bar charts can be a fun visualization for infographics and very specific use cases. They don't provide any analytical benefit in our opinion compared to other forms of visualizations, but helps switch up the look for some use cases. Below we've used 0-60 Top Speed data from production cars to compare the top 5.
Bar Chart - Single
Below, we've used Lending Club's loan rejection data from 2007-2012 to see the growth in rejections per quarter and forecast their 2013 numbers.
Bar Chart - Stacked
Stacked bar charts can offer a nice comparison of category distributions inside of each bar or column. Two common ways stacked bar charts can work is by simply showing a breakdown of the sum of each bar, or by showing the 100% distribution.
Using Lending Club rejection data from 2007-2012, we can create a stacked bar chart that shows the number of rejected applicants by employment length grouping. This gives a good view of the total number of rejected applicants increasing while showing the growth per segment as well.
Using that same data set, we also created a 100% stacked bar chart that can show how the distribution changed of rejected applicants by employment length. While this isn't a great visualization for seeing the growth in total rejected applicants, it is great for comparing the segment change over time.
As a bonus, we can even add in an additional two views. One with a second chart right below showing the percent running difference quarter to quarter. The other with that same calculation as an overlay instead.
Basic Map (with zip, county, sub-regional, regional, country and continent breakdowns)
Basic maps can be used to show a simple view of one data field and can be broken down into different modular views. Here are a few examples of different basic maps.
And here's a basic map breakdown by region, averaging the median income across all states in their respective region.
Lastly, here's a view of 2016 GDP by Country.
Box and whisker
This type of chart provides a simple and clear view of distributions. Paired with categorical breakdowns, one can both compare between categories and analyze distribution within those categories. Box and whisker plots use the median for the middle of the box, outer values for the whiskers, dots for the individual data points, and 25% quartile ranges for the composition of the box. Here we compared the distributions of personal capital expenditure in 2018 in the United States. Each region is compared by seeing the distribution of states within the region.
This visualization can be used to group and compare values by size and color. It's a quick and useful visualization for infographics. Although here we show a bubble chart without an X or Y axis, it can be combined with and X or Y axis to show a categorized comparison (such as a year to year comparison via size). For our example, we use November 2018 Personal Income Categories in billions to see where total income is coming from in the US.
To some, the candlestick and box and whisker chart look eerily similar. While they may look similar, their composition is very different. Box and Whiskers use statistical breakdowns such as quartiles, median, outliers, etc. to provide composition box and whiskers. Candlestick charts are instead used to display price data for financial charts. The color of the body portion indicates whether the price change was bearish or bullish. For bullish candlesticks, the top indicates the closing price and the bottom indicates the opening price. Bearish candlesticks are the opposite. The "wicks" on the top or bottom indicate the time period's high and low (but not necessarily the opening or closing price).
For our example, we've used some historical stock price data for GE in 2018-2019.
The donut chart offers a view communicating the same information as a pie chart. An arguable advantage of the donut chart is that it provides a better comparison of the sizes between slices. Pie charts can offer a skewed perspective due to the comparison of the area of each pie rather than the comparison of length of each slice of the donut chart.
In our example below, we take the Q3 2018 loan data from Lending Club and provide a donut chart breakdown by grade for the total number of loans in the quarter.
Sometimes it's best to be able to quickly see what the highlights are of your data. Highlight tables allow you to categorize by ranges of values, or just highlight the highest or lowest values in your data.
We took Lending Club's Q3 2018 loan data to visualize the highest number of late loans broken down by term, loan grade, and how late the loan is. This example is only analyzing the count of late loans, but if we wanted to get a more thorough analysis we could add something like percentage of total loans by grade that are late (to see if there is a riskier loan grade category than expected).
This basic type of chart provides a simple visual breakdown of the proportion each category makes up of the whole. Its usefulness is limited by the number of slices that can be included and its inability to provide a comparison between different pie charts. When multiply pie charts are combined with something like pie size indicating total value, its effectiveness can be increased.
For this example, the Q3 2018 Lending Club loan data was used again. The loan status proportion is provided excluding loans that are current.