Robert Dees LIS4273

 For this week I worked with ggplot2 and messed around with time series data to see how it actually looks over time. The hot dog contest example was kinda cool because you can clearly see which years had record performances instead of just looking at numbers. Then with the economics dataset, the line graphs made it way easier to see how things like unemployment change over time and how different variables relate to each other. I feel like visualization makes a big difference with time series data because you can instantly spot trends, patterns, or anything weird going on. Without graphs it would honestly just be a bunch of numbers that are harder to understand. Using ggplot2 also makes everything look cleaner and more organized, so it just makes the data way easier to read and interpret.

  For this visualization I used the mtcars dataset in R because it has multiple variables about different cars that make it good for a multivariate plot. In my graph I compared vehicle weight and miles per gallon to see how fuel efficiency changes, while also using color to show the number of cylinders, point size to represent horsepower, and faceting to separate cars by the number of gears. The plot shows that heavier cars generally have lower MPG, while lighter cars tend to be more fuel efficient, and cars with more cylinders and higher horsepower usually have worse fuel economy. Using a multivariate visualization was helpful because it lets you see several relationships in the data at the same time instead of looking at multiple separate charts. I also tried to apply some of the design principles from the module like contrast with different colors for cylinders, alignment with clearly labeled axes, and balance by keeping the layout simple so the relationships in the data are...

 library(ggplot2) # use built-in dataset data(mtcars) # scatter plot with regression line ggplot(mtcars, aes(x = hp, y = mpg)) +   geom_point(color = "steelblue", size = 3) +   stat_smooth(method = "lm", se = FALSE, color = "black") +   labs(     title = "Relationship Between Horsepower and Fuel Efficiency",     x = "Horsepower",     y = "Miles Per Gallon"   ) +   theme_minimal() I used the built-in mtcars dataset in R to explore the relationship between horsepower and miles per gallon using a scatter plot with a regression line. The visualization shows a clear negative relationship, meaning cars with higher horsepower tend to have lower fuel efficiency. Using a simple layout with clear labels and minimal colors follows Few’s design recommendations by making the relationship between variables easy to interpret.

 For this assignment I created a histogram in R using the built in mtcars dataset to show the distribution of miles per gallon (mpg). The visualization makes it easy to see that most cars fall between about 15 and 25 mpg, with only a few vehicles above 30 mpg. This fits with Few’s and Yau’s discussion on basic descriptive visualization because it focuses on clarity and simplicity rather than decoration. The chart communicates one clear message about the spread of the data and avoids unnecessary visual elements. Overall, it works well as a basic example of descriptive analysis in R.

 I used the provided dataset with Average Position and Time to apply both Ranking and Part to Whole design frameworks. I created an ordered bar chart to rank the Average Position values, which makes it easy to see differences and compare performance across time points. Then I used a donut chart to show how each time value contributes to the total. The ranking chart clearly shows order and comparison, while the Part to Whole chart shows overall distribution. One limitation of the donut chart is that smaller segments are harder to compare when there are many categories. Overall, the two frameworks highlight different insights from the same data. I preferred the bar chart because I thought it was easier to read.

  I used a dataset of Florida gym locations downloaded from GymsListHQ: https://gymslistshq.com/b2b-database/country/united-states/list-of-gyms-in-florida  I created a geographic map of fitness centers in Florida using Tableau. Each point on the map represents a gym location, and the color of each point reflects the rating of the gym. This map allows viewers to see both where gyms are located and how they are rated. I noticed that many gyms are clustered in more populated areas such as Central and South Florida. Cities like Orlando, Tampa, and areas near Miami show a higher concentration of fitness centers, while fewer locations appear in the Panhandle and more rural parts of the state. This shows how gym locations are often connected to population density and demand. One challenge I faced was making sure Tableau correctly recognized the geographic data. I also had to make sure the dataset I downloaded worked properly in Tableau. Another challenge was adjusting the map, so the...