Assignment: Using ChatGPT to Generate and Share Visualizations

 # Load required library

library(ggplot2)


# Create histogram of mpg from mtcars

ggplot(mtcars, aes(x = mpg)) +

  geom_histogram(binwidth = 2, fill = "steelblue", color = "black") +

  labs(

    title = "Distribution of Miles Per Gallon (mpg) in mtcars",

    x = "Miles Per Gallon (mpg)",

    y = "Count"

  ) +

  theme_minimal()

To save the image:

  r

Copy code

ggsave("mtcars_mpg_histogram.png", width = 6, height = 4)

Reflection:

For this assignment, I used ChatGPT to help me create a histogram of the mpg variable in the mtcars dataset using ggplot2 in R. I asked it for example code, and it generated a clean and workable starting point. I copied the code into RStudio, ran it, and the plot worked right away, which made the process smooth. I then made a few changes, like adjusting the bin width and theme, to make the chart easier to read. The only real challenge was deciding what visual adjustments looked best, but trying different options helped me understand the code better. Overall, ChatGPT felt useful it's a good tool that helped me get started faster and focus on improving the final result.

Comments

Post a Comment

Popular posts from this blog

Final Project LIS4317

Image
LIS4317 Final Project – Diamonds Dataset Analysis   Kyla Garcia Platform: Tableau Dataset: Diamonds (R built-in, 53,940 observations, 10 variables) Date: November 26, 2025   1. Introduction The prices of diamonds differ according to its Four Cs: Cut, Clarity, Color and Carat. This project discusses the impact of each factor on price and the predictors with the highest strengths. Research Question: To what extent do diamond characteristics predict and influence diamond price?   2. Dataset Overview Variable Description Carat Weight of the diamond Cut Cut quality (Fair, Good, Very Good, Premium, Ideal) Color D–J, from colorless to near-colorless Clarity I1 → IF (low to high clarity) Depth Total depth % Table Width ratio Price Price in USD x, y, z Dimens...
Read more
 I had trouble figuring out how to use adobe illustrator. It wasn't that the site was difficult to read it that my file from last week wouldn't work properly when trying to upload. I ended up having to make a map with the same data and using that. Last week's map will look different then this weeks because of that. I chose to go with pretty standard colors nothing too bold just clear to understand. By having red and greens I think it makes it bright enough for people to see. To enhance the map further, I added vector icons to show where the most libraries are and a few simple lines to show boundaries. These additions provided helpful reference points. Gestalt principles helped when deciding where to put labels to help people understand more, Overall the design could use some work, but I feel like it's clear with colors and labels.  https://assets.adobe.com/id/urn:aaid:sc:US:7445a4bf-3c0f-403c-bde1-f8fa220dcefe?view=published
Read more

Module 11 assignment

Image
  # Step 1: Install packages install.packages(c(   "CarletonStats", "devtools", "epanetReader", "fmsb", "ggplot2",    "ggthemes", "latticeExtra", "MASS", "PerformanceAnalytics",    "psych", "plyr", "prettyR", "plotrix", "proto",    "RCurl", "reshape", "reshape2" )) # Step 2: Load packages library(ggplot2) library(ggthemes) library(latticeExtra) library(MASS) library(PerformanceAnalytics) library(psych) library(plyr) install.packages("ggExtra") library(ggExtra) # Create a sample dataset set.seed(123) df <- data.frame(   x = rnorm(200, mean = 5, sd = 1.5),   y = rnorm(200, mean = 5, sd = 1) ) # Base scatter plot p <- ggplot(df, aes(x = x, y = y)) +   geom_point(color = "steelblue", alpha = 0.7) +   theme_minimal() +   labs(     title = "Marginal Histogram Scatter Plot",     x = "V...
Read more