programming assignment 1 visualize data using a chart

Sunday, August 2, 2015

Programming assignment 1: visualize data using a chart, no comments :, post a comment.

This first programming assignment will give you a quick overview of various basic visualization techniques while allowing you to become more familiar with a visualization library. You will work with a tabular dataset and visualize it in different ways. Since this is the first assignment, your visualizations will be rather simple and no interactivity is required.

The dataset for this project is the classical old cars dataset, which lists several characteristics of various cars built between 1970 and 1982. This is a typical tabular dataset where the items (or keys) correspond to the individual car models and the attributes correspond to the available characteristics. This type of dataset is non-trivial to visualize because several attributes (in this case 6) need to be represented simultaneously. Here, we will mostly circumvent this challenge by considering different subsets of the data.

Practically, the dataset indicates the number of cylinders, the engine volume, the vehicle weight, the year, the geographic origin, the horsepower, and the gas mileage of 398 different models. The dataset is provided as a csv file.

Task 1: Grouped Bar Chart

Visualize with a grouped bar chart (see above) the gas mileage distribution of all the models by geographic origin. In other words, you will create a chart in which each gas mileage value is associated with 3 bars (one for each origin: US, Europe, Japan) and show how the distributions of the gas mileage values compare across regions. For that you will discretize the range of possible gas mileage values in 2 mpg increments.

Deliverable: p1_grouped.py

Task 2: Line Chart

Visualize for each geographic origin the temporal evolution of the gas mileage from 1970 to 1982 using a line chart. Each data point will correspond to the annual average of the gas mileage for a given origin and each curve will comprise 13 points. Assign a different color to each curve.

Deliverable: p1_line.py

Task 3: Scatter Plot

So far, we have only visualized a single attribute ( ie. the gas mileage). For this third task, visualize the relationship between horsepower and gas mileage in a scatter plot. The horizontal axis should correspond to the horsepower, the vertical axis to the gas mileage and each data point to a particular car. Apply to the scatter plot a color coding indicating the year.

Deliverable: p1_scatter.py

Task 4: Scatter Plot Matrix

Visualize gas mileage, weight, horsepower, and engine size in a scatter plot matrix. Color code the individual data points by country of origin.

Deliverable: p1_matrix.py

Submit your solution for this project on Brightspace before September 10, 2020 at 11:59 pm . Refer to the instructions below.

• Include all the requested python files : p1_grouped.py, p1_line.py, p1_scatter.py, and p1_matrix.py. Alternatively, include html files if you are using D3 .
• Use Python 3 in your implementation as well as an up-to-date version of the visualization library of your choice (e.g., Bokeh 2.2)
• Include all submitted files in a single directory named <me>_p1 , where <me> is your Purdue login. Do not include the CSV data file in your submission.
• Do not use an absolute path to access the dataset. Assume instead that it will be present in the same directory as your python files.

• Programming Assignment 1: Visualize Data Using a Chart
• by Parichay Kishore
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The Increasing Global Temperature

Coursera data visualization project 1, created by jean pan.

This is a simple data visualization exercise from Coursera Data Visualization course.

The data used in this assignment is GISTEMP data from NASA.

The visualization tool I'm using is D3.js .

Explanation

This graph visualizes the GISTEMP data for the Globe and the North and South Hemispheres through all the given years ( 1880 - 2014 ). The Blue line is for the Globe, the Orange line describes the data for the Northern Hemisphere and the Green for the South Hemisphere.

From the resulting graph, although there is a little decreasing during 19th century, we can see that the overall trend of global temperature is increasing. Both north and south follow the same trend as the global, but we can find the south increases smoother than the north.

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This pandas tutorial is divided into 10 modules which progressively teach you all techniques for data visualization using Pandas Library. Here, You will learn basic plots like line plots, histograms, pie charts, etc., and how to customize them. You will also learn advanced plotting techniques like plots and subplots, 3D plotting, grouped plots, etc. At last, we will go through the best practices for plotting, study real-world examples and use cases, and test our skills with Pandas data visualization exercises.

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The guide made learning Python data visualization super easy with its step-by-step instructions and fun examples. It changed the scary stuff into something I actually enjoy doing!

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The tutorial's simple explanations and hands-on examples turned tricky data concepts into a piece of cake. It's like my secret weapon in the coding world! 🚀💻🔍

The user-friendly vibe and real-world applications were a total win for me. Now, I'm confidently navigating the world of data representation in Python, thanks to this tutorial.

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The tutorial's thorough approach perfectly met all my learning needs. It goes beyond just graphs and charts; it's about acquiring skills that are valuable in both academic and professional settings.

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Assignment 1: Visualization Design

In this assignment, you will design a visualization for a small data set and provide a rigorous rationale for your design choices. You should in theory be ready to explain the contribution of every pixel in the display. You are free to use any graphics or charting tool you please - including drafting it by hand. (See Resources for a list of possible visualization tools.)

Data Set: Monthly Hours of Sunshine in Major U.S. Cities

The climate of a place can have a tremendous impact on people's lived experience, ranging from personal moods to how an entire region defines itself. Here, you will examine a set of average monthly climate measurements for six major U.S. cities, roughly covering the edges of the continental United States.

For more information about the dataset, including download links for CSV and JSON formats, see https://observablehq.com/@uwdata/hours-of-sunshine .

Your task is to design a static (i.e., single image) visualization that you believe effectively communicates the data and provide a short write-up (no more than 4 paragraphs) describing your design rationale. Start by choosing a question you'd like to answer. Design your visualization to answer that question, and use the question as the title of your graphic.

While you must use the data set given, note that you are free to transform the data as you see fit. Such transforms may include (but are not limited to) log transformation, computing percentages or averages, grouping elements into new categories, or removing unnecessary variables or records. You may also incorporate external data. Your chart image should be interpretable as a stand-alone graphic, without recourse to your short write-up. Do not forget to include an appropriate subtitle, axis labels, or legends as needed!

As different visualizations can emphasize different aspects of a data set, your write-up should document what aspects of the data you are attempting to most effectively communicate. In short, what story are you trying to tell? Just as important, your write-up should also note which aspects of the data might be obscured due to your visualization design.

In your write-up, you should provide a rigorous rationale for your design decisions. Document the visual encodings you used and why they are appropriate for the data and your specific question. These decisions include the choice of visualization type, size, color, scale, and other visual elements, as well as the use of sorting or other data transformations. How do these decisions facilitate effective communication and help to answer your proposed question?

The assignment score is out of a maximum of 10 points. Historically, the median score on this assignment has been 8.5, which corresponds to an A-. We will determine scores by judging both the soundness of your visualization design and the quality of the write-up. We will also look for consideration of audience, message, and intended task (e.g., what question you are trying to answer). Here are examples of aspects that may lead to point deductions:

We will reward entries that go above and beyond the assignment requirements to produce effective graphics. Examples may include outstanding visual design, meaningful incorporation of external data to reveal important trends, demonstrating exceptional creativity, or effective annotations and other narrative devices.

Submission Details

This is an individual assignment. You may not work in groups. Your completed assignment is due on Mon 1/11, by 11:59pm on Canvas . We will be discussing submissions in class this week, so be sure to avoid a late submission .

You must submit your assignment using Canvas . Please upload a single zip file named using the pattern "uwnetid_a1.zip" (replacing "uwnetid" with your UW network login - this is the same as your @uw email address, not a numeric id number). The zip archive should contain two files: a plain text file named "readme.txt" and a PNG or JPG image file of your visualization design named "uwnetid_a1.png" or "uwnetid_a1.jpg".

Please use the correct file extension for your image (either .png or .jpg) and be sure your image is sized for a reasonable viewing experience. Viewers should not have to zoom or scroll in order to effectively view your submission!

The readme.txt file should contain your write-up, as described above. Please be sure to include your name and UW net id in your readme.

If you are on the waiting list for the class do not have access to the Canvas site, please email your submission to us at [email protected] .

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