LIS4317 Visual Analytics

Review the reading resources and post on your blog a new entry with your work with ggplot2 and time series (try yourself) and discuss the input of visualization on time series analysis. The dataset I will be using is titled "Video Game Sales" ( https://www.kaggle.com/datasets/anandshaw2001/video-game-sales ) The first plot I created deals with the amount of video game sales over the years. Using ggplot, I was able to establish a relationship of the total amount of video games sold over the course of forty years.  > ggplot(videoGameSales, aes(x = as.numeric(Year), y = Global_Sales)) + + geom_line(stat = "summary", fun = sum) + + labs(title = "Global Video Game Sales Over the Years", x = "Year", y = "Total Sales (millions)") The next plot was created with R-base graphics. This one was a little trickier without the use  of ggplot. More steps had to be incorporated such as the use of the aggregate function. I divided the code into s...

Create your own multi-variates visualization graph (RStudio, ggplot2, Adobe Illustrator, Plotly or Tableau). You can use your own data you find or use the data we employ in class. In your discussion on your blog, judge if multivariables is a good way to visualize the outcome or not and discuss how you can implement the  5 principles/rules of design to your work,  I covered in this module's presentation.  The five principles of design include Alignment, Repetition, Contrast, Proximity, and Balance.  The dataset I am using is carprise.csv. Below is a screenshot of the data. It tells us information such as car type, minimum and maximum price, the actual price, range price, mpg city and mpg highway.  For my visualization, I used a scatter bubble chart with multiple variables. I focused on mpg.highway, price, range.price and type of car. From these we are able to see various relationships. We can see that as mpg for highway increases, the price tends to stay lower. W...

Create your own visual analytics based on  correlation or regression analysis using ggplot2 . The visual will follow our textbook recommendation to use grid to enhance the comparisons between scatter plots or your variables. Download data set Post the result on your blog and express your opinion about Few's recommendation.     The first visualization that demonstrates regression analysis was a simple one that showed the relationship between Weight and MPG. Below is the code and visualization. From this simple regression analysis, we can see that as the weight of the car increases, the MPG decreases.  > ggplot(mtcars, aes(wt, mpg)) + + geom_point() + + geom_smooth(method = "lm") + + labs(title = "Weight vs. MPG Regression Analysis", + x = "Weight", + y = "Miles per Gallon (MPG)") `geom_smooth()` using formula = 'y ~ x' For the next visual representation I decided to use the package corrplot. I incorporated addCoe...

Create your own visual analytics based on Distribution analysis. The visual will follow our textbook recommendation to use grid to enhance the comparisons between scatter plots or your variables. Attached is  data set I used in my presentation. Another popular  data set is taken from R  mtcars Links to an external site. .  You can retrieve this data set from R by typing> mtcars Testing one of these two data sets regarding visual distributions as discussed in this module, and post on your blog the results and express your opinion about Few's recommendations in your testing. How to import the data to RStudio? Follow this guideline:  https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio Links to an external site. Another great resource from Distribution is the blog by Nathan Yau - the author of our textbook Visualize This. This blog posting title: How to Visualize and Compare Distributions in R. https://flowingdata.com/2012/05/15/ho...