Lab 1: Exploring Data Types Using the mpg Dataset

🎯 Lab Objectives

By completing this lab, you will be able to:

Load and explore a dataset in R
Identify qualitative and quantitative variables
Generate summary statistics using base R
Interpret numerical summaries and categorical frequencies
Connect variable types to levels of measurement

🧰 Required Tools

R or RStudio
Internet access
No prior R experience required

📁 Part 1: Load and Explore the Dataset

Step 1: Load the `mpg` dataset

library(ggplot2)
data(mpg)

Step 2: View the structure of the dataset

str(mpg)

## tibble [234 × 11] (S3: tbl_df/tbl/data.frame)
##  $ manufacturer: chr [1:234] "audi" "audi" "audi" "audi" ...
##  $ model       : chr [1:234] "a4" "a4" "a4" "a4" ...
##  $ displ       : num [1:234] 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
##  $ year        : int [1:234] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
##  $ cyl         : int [1:234] 4 4 4 4 6 6 6 4 4 4 ...
##  $ trans       : chr [1:234] "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
##  $ drv         : chr [1:234] "f" "f" "f" "f" ...
##  $ cty         : int [1:234] 18 21 20 21 16 18 18 18 16 20 ...
##  $ hwy         : int [1:234] 29 29 31 30 26 26 27 26 25 28 ...
##  $ fl          : chr [1:234] "p" "p" "p" "p" ...
##  $ class       : chr [1:234] "compact" "compact" "compact" "compact" ...

✏️ Questions

How many observations are in the dataset?
How many variables are included?
Which variables are quantitative?
Which variables are qualitative?

📊 Part 2: Overall Summary of the Dataset

Step 3: Generate summary statistics

summary(mpg)

##  manufacturer          model               displ            year     
##  Length:234         Length:234         Min.   :1.600   Min.   :1999  
##  Class :character   Class :character   1st Qu.:2.400   1st Qu.:1999  
##  Mode  :character   Mode  :character   Median :3.300   Median :2004  
##                                        Mean   :3.472   Mean   :2004  
##                                        3rd Qu.:4.600   3rd Qu.:2008  
##                                        Max.   :7.000   Max.   :2008  
##       cyl           trans               drv                 cty       
##  Min.   :4.000   Length:234         Length:234         Min.   : 9.00  
##  1st Qu.:4.000   Class :character   Class :character   1st Qu.:14.00  
##  Median :6.000   Mode  :character   Mode  :character   Median :17.00  
##  Mean   :5.889                                         Mean   :16.86  
##  3rd Qu.:8.000                                         3rd Qu.:19.00  
##  Max.   :8.000                                         Max.   :35.00  
##       hwy             fl               class          
##  Min.   :12.00   Length:234         Length:234        
##  1st Qu.:18.00   Class :character   Class :character  
##  Median :24.00   Mode  :character   Mode  :character  
##  Mean   :23.44                                        
##  3rd Qu.:27.00                                        
##  Max.   :44.00

✏️ Questions

Which variables include minimum, median, and maximum values?
Why do some variables show frequency counts instead of numerical summaries?

📈 Part 3: Summary Table for Quantitative Variables

Step 4: Select only quantitative variables

num_vars <- mpg[, sapply(mpg, is.numeric)]

Step 5: Create a summary table

summary_table <- data.frame(
  Variable = names(num_vars),
  Min = sapply(num_vars, min),
  Q1 = sapply(num_vars, quantile, 0.25),
  Median = sapply(num_vars, median),
  Mean = sapply(num_vars, mean),
  Q3 = sapply(num_vars, quantile, 0.75),
  Max = sapply(num_vars, max)
)

summary_table

##       Variable    Min     Q1 Median        Mean     Q3  Max
## displ    displ    1.6    2.4    3.3    3.471795    4.6    7
## year      year 1999.0 1999.0 2003.5 2003.500000 2008.0 2008
## cyl        cyl    4.0    4.0    6.0    5.888889    8.0    8
## cty        cty    9.0   14.0   17.0   16.858974   19.0   35
## hwy        hwy   12.0   18.0   24.0   23.440171   27.0   44

✏️ Questions

Which quantitative variable has the largest range?
Compare the mean and median of cty. What does this suggest about the distribution?
Which variable appears to have the least variability? Explain.

📋 Part 4: Summary of Qualitative Variables

Step 6: Select qualitative variables

cat_vars <- mpg[, sapply(mpg, is.factor)]

Step 7: View frequency tables

lapply(cat_vars, table)

## named list()

✏️ Questions

Which manufacturer appears most frequently in the dataset?
Which vehicle class has the fewest observations?
Why are frequency tables appropriate for these variables?

📐 Part 5: Variable Types and Measurement Levels

Step 8: Create a variable classification table

variable_info <- data.frame(
  Variable = names(mpg),
  Type = ifelse(sapply(mpg, is.numeric), "Quantitative", "Qualitative"),
  Measurement_Level = c(
    "Nominal", "Nominal", "Nominal", "Nominal",
    "Ratio", "Ratio", "Ratio", "Ratio",
    "Ratio", "Ratio", "Nominal"
  )
)

variable_info

##                  Variable         Type Measurement_Level
## manufacturer manufacturer  Qualitative           Nominal
## model               model  Qualitative           Nominal
## displ               displ Quantitative           Nominal
## year                 year Quantitative           Nominal
## cyl                   cyl Quantitative             Ratio
## trans               trans  Qualitative             Ratio
## drv                   drv  Qualitative             Ratio
## cty                   cty Quantitative             Ratio
## hwy                   hwy Quantitative             Ratio
## fl                     fl  Qualitative             Ratio
## class               class  Qualitative           Nominal

✏️ Questions

Identify two nominal-level variables and explain why they are nominal.
Identify two ratio-level variables and explain what makes them ratio-level.
Why is year considered a ratio-level variable in this dataset?

🧠 Reflection

How does understanding variable types help determine which statistical methods to use?
In your own words, explain the difference between qualitative and quantitative data using examples from this dataset.

⭐ Optional Challenge

Identify one variable that could reasonably be treated as ordinal instead of nominal.
Explain your reasoning.

✅ Submission Instructions

Submit your knitted HTML file and your .Rmd file on Canvas.
Make sure all code runs without errors.
Clearly label all written responses by question number.

Lab 1: Exploring Data Types Using the mpg Dataset

General Statistics

2026-01-12

🎯 Lab Objectives

🧰 Required Tools

📁 Part 1: Load and Explore the Dataset

Step 1: Load the `mpg` dataset

Step 2: View the structure of the dataset

✏️ Questions

📊 Part 2: Overall Summary of the Dataset

Step 3: Generate summary statistics

✏️ Questions

📈 Part 3: Summary Table for Quantitative Variables

Step 4: Select only quantitative variables

Step 5: Create a summary table

✏️ Questions

📋 Part 4: Summary of Qualitative Variables

Step 6: Select qualitative variables

Step 7: View frequency tables

✏️ Questions

📐 Part 5: Variable Types and Measurement Levels

Step 8: Create a variable classification table

✏️ Questions

🧠 Reflection

⭐ Optional Challenge

✅ Submission Instructions

Lab 1: Exploring Data Types Using the mpg Dataset

General Statistics

2026-01-12

🎯 Lab Objectives

🧰 Required Tools

📁 Part 1: Load and Explore the Dataset

Step 1: Load the mpg dataset

Step 2: View the structure of the dataset

✏️ Questions

📊 Part 2: Overall Summary of the Dataset

Step 3: Generate summary statistics

✏️ Questions

📈 Part 3: Summary Table for Quantitative Variables

Step 4: Select only quantitative variables

Step 5: Create a summary table

✏️ Questions

📋 Part 4: Summary of Qualitative Variables

Step 6: Select qualitative variables

Step 7: View frequency tables

✏️ Questions

📐 Part 5: Variable Types and Measurement Levels

Step 8: Create a variable classification table

✏️ Questions

🧠 Reflection

⭐ Optional Challenge

✅ Submission Instructions

Step 1: Load the `mpg` dataset