Understanding Climate Change Through Data Visualization

  Comparing Base R, Lattice, and ggplot2 I used the Guns dataset to explore how concealed carry laws relate to crime rates across US states from 1977-1999. Syntax Differences Base R uses simple commands like  plot()  and  boxplot()  but requires multiple steps to customize visualizations. Lattice introduced a formula syntax that made creating panel plots much easier, especially with the  |  operator for conditioning. ggplot2 uses layered grammar with  +  to build plots, which felt more logical once I got used to it. Which System Works Best ggplot2 produced the cleanest output with less code. The default styling looked professional and adding features like smoothing lines was straightforward. Lattice worked well for conditioned plots but felt limited outside that use case. Base R gave me direct control but required more manual work for decent results. Challenges Switching between systems was harder than expected. Each has different logic for t...

 I used the mtcars dataset to examine how vehicle weight and engine configuration affect fuel economy. The visualization displays three variables: weight (x-axis), miles per gallon (y-axis), and cylinder count shown through color-coded regression lines. The data reveals a clear pattern: heavier cars consume more fuel across all engine types, but 8-cylinder vehicles consistently perform worse than 4-cylinder cars at any given weight. The parallel slopes demonstrate that both weight and engine size independently impact efficiency. Multivariate visualization effectively uncovered relationships that single-variable plots would miss. I applied three design principles: contrast using intuitive colors (green for efficient engines, red for inefficient ones), hierarchy through a descriptive title that presents the main finding upfront, and simplicity by removing distracting elements like horsepower encoding. The result is a clear, focused visualization that tells a complete story about how ...

 This assignment shows how to load data, calculate averages by groups, find specific patterns in text, and save results to files. Part 1: Load the Data library(plyr) > student6 = read.table(file.choose(), header = TRUE, stringsAsFactors = FALSE) > str(student6) 'data.frame': 20 obs. of  1 variable:  $ Name.Age.Sex.Grade: chr  "Raul,25,Male,80" "Booker,18,Male,83" "Lauri,21,Female,90" "Leonie,21,Female,91" ... > head(student6)     Name.Age.Sex.Grade 1      Raul,25,Male,80 2    Booker,18,Male,83 3   Lauri,21,Female,90 4  Leonie,21,Female,91 5 Sherlyn,22,Female,85 6 Mikaela,20,Female,69 What it does:  Loads the student data file into R and checks that it imported correctly with 20 students and their information. Part 2: Calculate Average Grades  #Step 2 > student6 = read.table(file.choose(), header = TRUE, stringsAsFactors = FALSE, sep = ",") > str(student6) 'data.frame': 20 obs. of  4 variab...

  What patterns or relationships did you observe? The mtcars dataset revealed clear patterns about fuel efficiency. Weight had the strongest negative correlation with mpg at -0.87, meaning heavier cars consistently get worse gas mileage. Cylinders (-0.85), displacement (-0.85), and horsepower (-0.78) showed similar negative relationships. The regression analysis confirmed these patterns. A simple model using just weight explained 75% of the MPG variation, while adding horsepower and displacement increased this to 83%. Interestingly, displacement became statistically insignificant in the multiple regression model, suggesting these variables overlap in measuring vehicle "bigness." How did your use of grid layout enhance interpretation? The 2x2 grid layout made comparing relationships much easier than viewing plots separately. I could immediately see that all four variables showed downward-sloping patterns, confirming negative relationships with mpg. The grid also revealed diffe...

What I Did   This assignment helped me learn how R's generic functions work and the difference between S3 and S4 objects. I used the mtcars dataset to see how functions change behavior based on what kind of object they're working with.   Loading the Dataset   First, I loaded mtcars data(" mtcars ") head( mtcars ) str( mtcars )   This dataset has 11 columns with things like miles per gallon, horsepower, Etc . How Generic Functions Work   I tested some basic R functions to see how they adapt to different objects:   summary( mtcars ) plot( mtcars )   lm_model = lm (mpg ~ wt + hp, data = mtcars ) summary( lm_model )   What I noticed: when you run summary( ) on a data frame, you get min/max/mean values. But when you run it on a linear model, you get totally different outputs, like coefficients and p-values. Same function name, different behavior!   Creating S3 Objects   S3 is the simpler object system. Here's how I made one:   s3...