Exercise 1

Exercise 1: Login to RStudio Pro

1. Go to course website → Computing → Using the RStudio Server
2. Follow login instructions
3. Log in with your credentials

Check: You should see the RStudio interface with 4 panes.

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Exercise 2: Create RStudio Project

1. File → New Project → New Directory → New Project
2. Name: ex-1
3. Click Create Project

Check: Project name appears in top-right corner.

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Exercise 3: Create Quarto Document

1. File → New File → Quarto Document
2. Title: “My First Analysis”
3. Save as analysis.qmd

Check: New .qmd file is open and saved.

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Exercise 4: Install ISLR Package and Load Data

Run this code in the console ONCE:

install.packages("ISLR")

Then run this:

library(ISLR)
library(ggplot2)
# Test it works
data(Auto)
head(Auto)

  mpg cylinders displacement horsepower weight acceleration year origin
1  18         8          307        130   3504         12.0   70      1
2  15         8          350        165   3693         11.5   70      1
3  18         8          318        150   3436         11.0   70      1
4  16         8          304        150   3433         12.0   70      1
5  17         8          302        140   3449         10.5   70      1
6  15         8          429        198   4341         10.0   70      1
                       name
1 chevrolet chevelle malibu
2         buick skylark 320
3        plymouth satellite
4             amc rebel sst
5               ford torino
6          ford galaxie 500

Check: You see the Auto dataset.

Now run this in the console:

?Auto

Question: What is in this dataset?

Action: Add the code below to your .qmd file along with a short description of the data.

library(ISLR)
library(ggplot2)
data(Auto)

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Exercise 5: Linear Model and Plot

Add the following code to your .qmd file. Try running it.

# Fit linear model
model1 <- lm(mpg ~ horsepower, data = Auto)
summary(model1)

Call:
lm(formula = mpg ~ horsepower, data = Auto)

Residuals:
     Min       1Q   Median       3Q      Max 
-13.5710  -3.2592  -0.3435   2.7630  16.9240 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 39.935861   0.717499   55.66   <2e-16 ***
horsepower  -0.157845   0.006446  -24.49   <2e-16 ***
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Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.906 on 390 degrees of freedom
Multiple R-squared:  0.6059,    Adjusted R-squared:  0.6049 
F-statistic: 599.7 on 1 and 390 DF,  p-value: < 2.2e-16

# Create plot
ggplot(Auto, aes(x = horsepower, y = mpg)) +
  geom_point() +
  geom_smooth(method = "lm", formula = "y ~ x") +
  labs(title = "Horsepower vs MPG",
       x = "Horsepower", 
       y = "Miles per Gallon")

Check: You have model output and a scatter plot with trend line.

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Finish

Click “Render” to create your HTML report. Upload your .html file in Canvas under Exercise 1