Assignment #9: Visualization in R – Base Graphics, Lattice, and ggplot2

 For this assignment, I used the airquality dataset from the datasets package in R. I chose this dataset because it includes several quantitative variables such as Ozone, Temperature, and Wind, along with a grouping variable, Month, which made it a good fit for comparing different graphing systems in R. After loading the dataset, I removed missing values and converted the Month variable into a factor so it would work better in grouped and faceted plots.

Using base R, I created a scatter plot of Temperature versus Ozone and a histogram of Wind. Base R was straightforward and easy to use for simple plots, but it required more manual setup for labels and appearance. With lattice, I created a conditioned scatter plot of Ozone versus Temperature by Month and a boxplot of Wind by Month. Lattice was useful for grouped displays and small multiples, and it made conditioning by category very simple. Finally, with ggplot2, I created a scatter plot with a regression line and a faceted histogram by Month. ggplot2 felt the most flexible and polished because it uses layering, which makes it easier to build complex visuals step by step.

The syntax and workflow were noticeably different across the three systems. Base R uses separate functions and direct variable references, which works well for quick graphs but can feel limited for more advanced visualizations. Lattice is especially strong for multivariate and conditioned plots, but the syntax can feel less intuitive at first. ggplot2 provided the most control and produced the most publication-quality output with relatively little code once the structure was set up. The biggest challenge when switching between systems was remembering that each one handles grouping and customization differently. Overall, ggplot2 was my favorite because it was easier to create clean, layered, and professional-looking graphics.

Comments

Post a Comment

Popular posts from this blog

ABC vs. CBS: Comparing Fictional Presidential Poll Data in R

 # Assignment 3 - Fictional 2016 Presidential Poll Data # Note: This data is made up and does NOT reflect real election results # Candidate names Name <- c("Jeb", "Donald", "Ted", "Marco", "Carly", "Hillary", "Bernie") # Poll results from ABC and CBS ABC_poll <- c(4, 62, 51, 21, 2, 14, 15) CBS_poll <- c(12, 75, 43, 19, 1, 21, 19) # Combine data into a data frame poll_data <- data.frame(Name, ABC_poll, CBS_poll) # Add a column to compare differences between polls poll_data$Difference <- CBS_poll - ABC_poll # View the data print(poll_data) # Optional: Simple bar plot comparison barplot(   t(as.matrix(poll_data[, c("ABC_poll", "CBS_poll")])),   beside = TRUE,   names.arg = poll_data$Name,   col = c("gray70", "gray40"),   legend.text = c("ABC Poll", "CBS Poll"),   main = "Fictional 2016 Presidential Poll Results",   ylab = "Poll Per...
Read more

Assignment #10: Building Your Own R Package

 # Proposal for My R Package: Friedman For this project, I am proposing an R package called **Friedman**. The purpose of this package is to provide beginner-friendly tools for simple data analysis and visualization in R. Many students and new R users struggle with repetitive tasks such as cleaning datasets, calculating summary statistics, and creating clear visualizations. This package will help make those tasks easier by grouping useful functions into one simple package. The main audience for this package is students, beginner analysts, and anyone who wants a more straightforward way to explore data in R. Instead of writing long code repeatedly, users will be able to call functions from Friedman to quickly summarize and visualize their data. This makes the package especially useful for class assignments, small research projects, and practice with R programming. Some of the key functions I plan to implement include: - `clean_data()` – removes missing values or standardizes column n...
Read more

Module # 4 Programming structure assignment

 # ------------------------------------------------------------ # hospital_analysis.R # Hospital patient intake dataset: plots + brief summaries # ------------------------------------------------------------ # ---- 1) Create the dataset ---- Freq <- c(0.6, 0.3, 0.4, 0.4, 0.2, 0.6, 0.3, 0.4, 0.9, 0.2) BP   <- c(103, 87, 32, 42, 59, 109, 78, 205, 135, 176) # first: bad = 1, good = 0, NA = missing First <- c(1, 1, 1, 1, 0, 0, 0, 0, NA, 1) # second: low = 0, high = 1 Second <- c(0, 0, 1, 1, 0, 0, 1, 1, 1, 1) # finaldecision: low = 0, high = 1 FinalDecision <- c(0, 1, 0, 1, 0, 1, 0, 1, 1, 1) hospital_data <- data.frame(Freq, BP, First, Second, FinalDecision) # Print the dataset print("Hospital dataset:") print(hospital_data) # ---- 2) Boxplot: Blood Pressure by Final Decision ---- # Side-by-side comparison of BP for Low vs High final decision boxplot(   BP ~ FinalDecision,   data = hospital_data,   names = c("Low Priority (0)", "High Priority (...
Read more