Visual Analytics Journal

 For this assignment, I used the built-in mtcars dataset in R because it includes several numerical and categorical variables that describe car performance. I wanted to explore how a car’s weight , horsepower , and number of cylinders affect its fuel efficiency (miles per gallon) . In my visualization, the x-axis represents weight , the y-axis represents miles per gallon , color encodes cylinders , point size shows horsepower , and shape indicates transmission type (automatic or manual).  The plot reveals clear relationships: heavier cars tend to have lower fuel efficiency, and those with more cylinders and higher horsepower are the least efficient. Manual cars generally achieve slightly better mileage than automatics of similar weight. These overlapping patterns demonstrate how multivariate visualization can highlight trade-offs between multiple design factors that wouldn’t be visible in a single-variable plot. When designing the graph, I applied three of Antony Hortin’s ...

 For this assignment, I explored relationships between variables in the built-in mtcars dataset using a correlation analysis. The goal was to identify which car characteristics are most related to fuel efficiency and to apply Stephen Few’s visualization design recommendations for clarity and effectiveness.   Visualization The heatmap above visualizes the correlation matrix for all numerical variables in the mtcars dataset. The color scale ranges from deep blue (strong negative correlation) to red (strong positive correlation), with white representing weak or no relationship. Patterns and Relationships Observed From the correlation matrix, several interesting relationships appear: Weight ( wt ) and miles per gallon ( mpg ) have a strong negative correlation , indicating that heavier cars are less fuel-efficient. Horsepower ( hp ) and displacement ( disp ) show a strong positive correlation , meaning that larger engines generally produce more power. Variables associa...

  Histogram of Miles per Gallon (mpg)     Reflection      For this assignment, I used the built-in mtcars dataset to explore the distribution of miles per gallon (mpg) across 32 car models. The histogram I created shows that most cars in the dataset achieve between 15 and 22 miles per gallon, while a smaller number reach over 30 mpg. This right-skewed distribution suggests that highly fuel-efficient cars were less common at the time the data was collected, which aligns with historical trends in vehicle design and fuel economy.      In designing my visualization, I followed Stephen Few’s principles for clear distribution displays by using aligned axes, minimal gridlines, and a simple color palette. The “skyblue” fill adds subtle visual appeal without distracting from the data itself. The axis labels and title make the chart easy to interpret at a glance. Following Nathan Yau’s approach, I used a histogram to highlight the shape and spr...

  For this assignment, I used RStudio and the built-in mtcars dataset to explore differences in average miles per gallon (MPG) across cars with different numbers of cylinders. Using ggplot2 , I created a simple bar chart that displays the mean MPG for 4-, 6-, and 8-cylinder cars. The chart clearly shows that 4-cylinder cars achieve the highest average fuel efficiency , followed by 6-cylinder, and then 8-cylinder vehicles with the lowest MPG. This visualization directly supports a spotting differences task, it makes the contrasts between groups immediately visible through bar height comparisons along a shared baseline. From a design perspective, I applied a minimal theme and used a clear title and axis labels to keep attention on the comparison itself. This aligns with Stephen Few’s  principle of simplifying visuals so that viewers can detect differences quickly and accurately. Likewise, Yau  emphasizes using straightforward encodings, like length along a common scale...