M04 Reflection Essay Advanced Data Visualization
1 M04 Reflection Essay Advanced Data Visualization
This report summarizes key concepts from the workshop and videos, then connects them to how I will design clearer and more effective visualizations.
2 Prompt 1: Summarize what you learned from the presentation by Thomas Lin Pedersen on ggplot2 workshop Part 2.
2.0.1 Summary
In part 2 of Thomas Lin Pedersen’s workshop, I learned a multitude of effective data visualization techniques. Pedersen explained a wide variety of parts about graphics and their vocabulary, such as scales, guides, coordinate systems, and themes. They are structural decisions that influence interpretation of data. Ever ggplot is built from varying layers that takes data and makes it easier to visualize using geometric shapes and transformations. The workshop provided clarity of the many aspects that goes towards the data visualization of data.
3 Prompt 2: Scales and Transformations video
3.0.1 Summary (1)
The video focuses on how raw data is taken and translated visually. More specifically, I learned that “scales” control how data values formatting, such as positioning, color, and size. Transformations, on the other hand, presents different ways these patterns can be perceived, through areas such as log scales, percentage formatting, and custom axis breaks.
3.0.2 How it fits grammar of graphics (2)
This fits into the grammer of graphics as scales and transformations introduce the framework for graphics introduced in Step 1. Raw data is taken and translated into perceivable visual values. Thus, carefully choosing scales and aesthetic planning is important to avoid misleading or confusing viewers.
4 Prompt 3: List three new functions not taught previously that will be helpful, and explain what each can do.
glm(): This function allows me to model relationships between variables when the outcome is not normally distributed for continuous. Helps model relationshipp in data
geom_line(): A function that creates line charts in ggplot2 by connecting data points in order. Visualizes trends and patterns.
inner_join():A function from dplyr that merges two data sets together based on a shared key. Helps prepare and combine data
These functions from the video help me understand different ways to integrate data wrangling, modeling, and visual communication of data.
4.0.1 Prompt 4: What did you like about gt and gtExtra? How do they complement charts? When would you prefer tables over charts?
What I liked about the gt and gtExtra packages was of how they can transform basic tables into polished and ready displays. With the ability to add color gradients, inline bar charts, and formatting makes tables visually engaging. They complement charts because they provide numerical detail that visually support summaries of data. I would prefer to use charts as they can easily highlight patterns and trends at a quick glance.
5 Prompt 5: Pick three tips and tricks and explain why you liked them.
The use of color palettes seemed useful as using color schemes can improve readability and visual clutter when comparing data.
Annotating, labeling, and adding captions can also make visualization more clearer and concise for viewers.
Adjusting margins, spaces, and theme elements can improve visualization balance. There should not be a lot of white space, but instead we can use it for organization and spacing to make data less cluttered.
6 Prompt 6: Run the codes and reflect. What impressed you most about the visualization approach or tools demonstrated in the codebook?
What impressed me the most about the visualization approach was of the level of customization possible within ggplot2. The ability to customize themes, adjust coordinate systems, as well as manipulate scales demonstrates the level of flexibility when creating graphics.
In particular, I think it is interesting that by modifying a graphic’s theme or elements can significantly elevate its functionality and readability.
7 What challenges do you face in mastering data visualization skills? How will you overcome them?
A challenge that I had faced when trying to master data visualization skills was of the technical complexity of typing all the codes. Although I never had any experience in coding this way, I still believe that mastering these set of skills in important in the world of business data analytics. In order to present data, you need to be able to organize it and make it presentable to others. To overcome my challenges of remembering and typing data visualization codes, I repeatedly practiced to strengthen my knowledge and muscle memory.
The more you practice your data visualization skills, the better you will become overtime!