Module # 6 Visual Differences & Deviation Analysis via R programming

 


For this exercise, I created a bar chart in R using the supplied mtcars dataset. The chart compared average miles per gallon for cars with different cylinder numbers (4, 6, and 8). I plotted the differences with barplot () function, along with color coding & axis labels.

Visualization showed clear differences between groups - cars with 4 cylinders got much better fuel efficiency than 6 or 8 cylinders. This allowed me to quickly see how engine size relates to MPG which is not so obvious from the raw data table. The chart was not meant to represent deviation from a benchmark but implied that it showed how 8-cylinder cars lag behind the overall average MPG, which is a negative deviation from the norm.

Regarding Stephen Few's ideas from Chapter 9 in Now You See It, my chart is in line with his ideas about deviation and comparison analysis. Few points out that effective visuals should show patterns, differences and exceptions without being overly decorated. The simplicity of a bar chart suits that goal - it directs attention to the values being compared without distractions.

My design is based on Nathan Yau's discussion in Visualize This Chapter 7 "Spotting differences and relationships." Yau says visualizations should tell a story about the data - through intuitive comparisons and thoughtful color choice and labeling. My chart followed those guidelines with clear labels and different colors to make the group differences stand out.

One minor issue was understanding the visual output precisely. The spacing and scaling of the bars initially made exact determination of mean differences slightly difficult. Adjusting labels and adding numeric values above bars helped me communicate that message better. This made me think of Few who said that even simple charts need scaling and annotation to avoid misinterpretation.

Overall this exercise demonstrated that clarity and simplicity yield the best insights. Using professional data-visualization principles even for simple R graphics, you can make charts that convey comparisons and deviations honestly.

Comments

Post a Comment

Popular posts from this blog

Final Project

Image
  Problem Description: I wanted to explore a dataset that was rich enough to support several different visual analysis types yet simple enough to interpret. And so I selected the diamonds dataset built into R, which has over 53,000 observations and ten variables describing carat weight / cut / color / clarity / price. My research question in general was: How do physical and quality attributes of a diamond affect its price - and what patterns emerge when they are considered individually and together? From that question I sliced the analysis into several connected ideas. I wanted to know how the distribution of diamond prices and carat size varies across cut categories. I also wondered which cut categories command higher prices and if there were significant deviations from the average price across the dataset. Last but not least, I wanted to test whether price was strongly associated with carat weight and how multiple attributes like cut and color affect average price. Such small que...
Read more

Tampa Metro Area Unemployment Rate

Image
Source Url: https://fred.stlouisfed.org/series/TAMP312UR The visualization, from FRED (Federal Reserve Bank of St. Louis), shows the unemployment rate for the Tampa - St. Petersburg - Clearwater metro area. A human designer created it, not AI. The chart follows a very standardized template for FRED visualizations which includes consistent format, clear axis labels, neutral color choices, and proper sourcing from the Bureau of Labor Statistics. All these elements tells us that it was designed by a human, which makes it easier to read. AI usually produces extra styles or random designs to make it flashy which is not the case in this graph.
Read more