R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

R.version
##                _                           
## platform       aarch64-apple-darwin20      
## arch           aarch64                     
## os             darwin20                    
## system         aarch64, darwin20           
## status                                     
## major          4                           
## minor          5.2                         
## year           2025                        
## month          10                          
## day            31                          
## svn rev        88974                       
## language       R                           
## version.string R version 4.5.2 (2025-10-31)
## nickname       [Not] Part in a Rumble
required_packages <- c(
  "tidyverse", "rmarkdown", "knitr", "ggplot2", "dplyr",
  "car", "lmtest", "sandwich", "stargazer", "broom"
)

installed <- sapply(required_packages, require, character.only = TRUE)
## Loading required package: tidyverse
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.6
## ✔ forcats   1.0.1     ✔ stringr   1.6.0
## ✔ ggplot2   4.0.1     ✔ tibble    3.3.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.2
## ✔ purrr     1.2.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## Loading required package: rmarkdown
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## Loading required package: knitr
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## Loading required package: car
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## Loading required package: carData
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## Attaching package: 'car'
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## The following object is masked from 'package:dplyr':
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## The following object is masked from 'package:purrr':
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##     some
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## Loading required package: lmtest
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## Loading required package: zoo
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## Attaching package: 'zoo'
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## The following objects are masked from 'package:base':
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##     as.Date, as.Date.numeric
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## Loading required package: sandwich
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## Loading required package: stargazer
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## 
## Please cite as: 
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## 
##  Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
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##  R package version 5.2.3. https://CRAN.R-project.org/package=stargazer 
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## Loading required package: broom
installed
## tidyverse rmarkdown     knitr   ggplot2     dplyr       car    lmtest  sandwich 
##      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE 
## stargazer     broom 
##      TRUE      TRUE
install.packages(c(
  "tidyverse", "rmarkdown", "knitr", "ggplot2", "dplyr",
  "car", "lmtest", "sandwich", "stargazer", "broom"
))
dir.create("data", showWarnings = FALSE)
dir.create("scripts", showWarnings = FALSE)
dir.create("outputs", showWarnings = FALSE)
dir.create("figures", showWarnings = FALSE)

list.files()
##  [1] "Applications"                        
##  [2] "Beru_Isaac_Setup_Checklist.Rmd .Rmd" 
##  [3] "Beru_Isaac_Setup_Checklist.Rmd-.html"
##  [4] "Beru_Isaac_Setup_Checklist.Rmd-.Rmd" 
##  [5] "data"                                
##  [6] "Desktop"                             
##  [7] "Documents"                           
##  [8] "Downloads"                           
##  [9] "figures"                             
## [10] "Library"                             
## [11] "Movies"                              
## [12] "Music"                               
## [13] "outputs"                             
## [14] "Pictures"                            
## [15] "Public"                              
## [16] "scripts"                             
## [17] "University at Albany - SUNY"
library(ggplot2)
data(mtcars)

ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(
    title = "Miles Per Gallon vs Weight",
    x = "Weight (1000 lbs)",
    y = "Miles Per Gallon"
  )
## `geom_smooth()` using formula = 'y ~ x'

model <- lm(mpg ~ wt, data = mtcars)
summary(model)
## 
## Call:
## lm(formula = mpg ~ wt, data = mtcars)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5432 -2.3647 -0.1252  1.4096  6.8727 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  37.2851     1.8776  19.858  < 2e-16 ***
## wt           -5.3445     0.5591  -9.559 1.29e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.046 on 30 degrees of freedom
## Multiple R-squared:  0.7528, Adjusted R-squared:  0.7446 
## F-statistic: 91.38 on 1 and 30 DF,  p-value: 1.294e-10

I ran a mini-analysis using the mtcars dataset by creating a scatterplot of mpg vs wt and fitting a linear regression model (mpg ~ wt). The results indicate a negative relationship: heavier cars tend to have lower miles per gallon.

This checklist confirmed my R/RStudio setup is working, my required packages load correctly, and I can knit an R Markdown file to HTML. I also created a basic project folder structure and practiced a simple analysis workflow that I’ll use throughout the semester.