# Load the tidyverse library
library(tidyverse)
# Read in the car sales data
# Make sure the data file is in your lab_0/data/ folder
car_data <- read_csv("data/car_sales_data.csv")Lab 0: Getting Started with dplyr
Your First Data Analysis
Overview
Welcome to your first lab! In this (not graded) assignment, you’ll practice the fundamental dplyr operations I overviewed in class using car sales data. This lab will help you get comfortable with:
- Basic data exploration
- Column selection and manipulation
- Creating new variables
- Filtering data
- Grouping and summarizing
Instructions: Copy this template into your portfolio repository under a lab_0/ folder, then complete each section with your code and answers. You will write the code under the comment section in each chunk. Be sure to also copy the data folder into your lab_0 folder.
Setup
Exercise 1: Getting to Know Your Data
1.1 Data Structure Exploration
Explore the structure of your data and answer these questions:
# data structure
glimpse(car_data)Rows: 50,000
Columns: 7
$ Manufacturer <chr> "Ford", "Porsche", "Ford", "Toyota", "VW", "Ford…
$ Model <chr> "Fiesta", "718 Cayman", "Mondeo", "RAV4", "Polo"…
$ `Engine size` <dbl> 1.0, 4.0, 1.6, 1.8, 1.0, 1.4, 1.8, 1.4, 1.2, 2.0…
$ `Fuel type` <chr> "Petrol", "Petrol", "Diesel", "Hybrid", "Petrol"…
$ `Year of manufacture` <dbl> 2002, 2016, 2014, 1988, 2006, 2018, 2010, 2015, …
$ Mileage <dbl> 127300, 57850, 39190, 210814, 127869, 33603, 866…
$ Price <dbl> 3074, 49704, 24072, 1705, 4101, 29204, 14350, 30…# Variable types
var_types <- sapply(car_data, class)
var_types Manufacturer Model Engine size Fuel type
"character" "character" "numeric" "character"
Year of manufacture Mileage Price
"numeric" "numeric" "numeric" # Problematic names
problematic_names <- names(car_data)[!make.names(names(car_data)) == names(car_data)]
problematic_names[1] "Engine size" "Fuel type" "Year of manufacture"# Look at the first few rows
head(car_data)# A tibble: 6 × 7
Manufacturer Model `Engine size` `Fuel type` `Year of manufacture` Mileage
<chr> <chr> <dbl> <chr> <dbl> <dbl>
1 Ford Fiesta 1 Petrol 2002 127300
2 Porsche 718 Caym… 4 Petrol 2016 57850
3 Ford Mondeo 1.6 Diesel 2014 39190
4 Toyota RAV4 1.8 Hybrid 1988 210814
5 VW Polo 1 Petrol 2006 127869
6 Ford Focus 1.4 Petrol 2018 33603
# ℹ 1 more variable: Price <dbl>Questions to answer: - How many rows and columns does the dataset have? - What types of variables do you see (numeric, character, etc.)? - Are there any column names that might cause problems? Why?
Your answers:
Rows: 50,000 | Columns: 7
Variable types:
| Column name | Variable type |
|---|---|
| Manufacturer | character |
| Model | character |
| Engine size | numeric |
| Fuel type | character |
| Year of manufacture | numeric |
| Mileage | numeric |
| Price | numeric |
- Problematic names: Engine size, Fuel type, Year of manufacture (contain spaces)
1.2 Tibble vs Data Frame
Compare how tibbles and data frames display:
# Look at the tibble version (what we have)
car_data# A tibble: 50,000 × 7
Manufacturer Model `Engine size` `Fuel type` `Year of manufacture` Mileage
<chr> <chr> <dbl> <chr> <dbl> <dbl>
1 Ford Fiesta 1 Petrol 2002 127300
2 Porsche 718 Cay… 4 Petrol 2016 57850
3 Ford Mondeo 1.6 Diesel 2014 39190
4 Toyota RAV4 1.8 Hybrid 1988 210814
5 VW Polo 1 Petrol 2006 127869
6 Ford Focus 1.4 Petrol 2018 33603
7 Ford Mondeo 1.8 Diesel 2010 86686
8 Toyota Prius 1.4 Hybrid 2015 30663
9 VW Polo 1.2 Petrol 2012 73470
10 Ford Focus 2 Diesel 1992 262514
# ℹ 49,990 more rows
# ℹ 1 more variable: Price <dbl># Convert to regular data frame and display
car_df <- as.data.frame(car_data)Question: What differences do you notice in how they print?
Your answer:
- Tibble (car_data)
- Prints a clean preview: shows only the first 10 rows by default.
- Displays column types.
- Prints a clean preview: shows only the first 10 rows by default.
- Data frame (car_df)
- Prints the entire dataset (or up to the
max.printlimit).
- Does not show column types alongside values.
- Prints the entire dataset (or up to the
Exercise 2: Basic Column Operations
2.1 Selecting Columns
Practice selecting different combinations of columns:
library(dplyr)
# Select just Model and Mileage columns
car_data %>%
select(Model, Mileage)# A tibble: 50,000 × 2
Model Mileage
<chr> <dbl>
1 Fiesta 127300
2 718 Cayman 57850
3 Mondeo 39190
4 RAV4 210814
5 Polo 127869
6 Focus 33603
7 Mondeo 86686
8 Prius 30663
9 Polo 73470
10 Focus 262514
# ℹ 49,990 more rows# Select Manufacturer, Price, and Fuel type
car_data %>%
select(Manufacturer, Price, `Fuel type`)# A tibble: 50,000 × 3
Manufacturer Price `Fuel type`
<chr> <dbl> <chr>
1 Ford 3074 Petrol
2 Porsche 49704 Petrol
3 Ford 24072 Diesel
4 Toyota 1705 Hybrid
5 VW 4101 Petrol
6 Ford 29204 Petrol
7 Ford 14350 Diesel
8 Toyota 30297 Hybrid
9 VW 9977 Petrol
10 Ford 1049 Diesel
# ℹ 49,990 more rows# Challenge: Select all columns EXCEPT Engine size
car_data %>%
select(-`Engine size`)# A tibble: 50,000 × 6
Manufacturer Model `Fuel type` `Year of manufacture` Mileage Price
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Ford Fiesta Petrol 2002 127300 3074
2 Porsche 718 Cayman Petrol 2016 57850 49704
3 Ford Mondeo Diesel 2014 39190 24072
4 Toyota RAV4 Hybrid 1988 210814 1705
5 VW Polo Petrol 2006 127869 4101
6 Ford Focus Petrol 2018 33603 29204
7 Ford Mondeo Diesel 2010 86686 14350
8 Toyota Prius Hybrid 2015 30663 30297
9 VW Polo Petrol 2012 73470 9977
10 Ford Focus Diesel 1992 262514 1049
# ℹ 49,990 more rows2.2 Renaming Columns
Let’s fix a problematic column name:
# Rename 'Year of manufacture' to year
car_data <- car_data %>%
rename(year = `Year of manufacture`)
# Check that it worked
names(car_data)[1] "Manufacturer" "Model" "Engine size" "Fuel type" "year"
[6] "Mileage" "Price" Question: Why did we need backticks around Year of manufacture but not around year?
Your answer: Because the column name contains spaces, which makes it an invalid R identifier. And backticks tell R to treat it as a literal name. In contrast, year is a valid R variable name (no spaces or special characters), so it can be used directly without backticks.
Exercise 3: Creating New Columns
3.1 Calculate Car Age
# Create an 'age' column (2025 minus year of manufacture)
# Create a mileage_per_year column
car_data <- car_data %>%
mutate(
age = 2025 - year,
mileage_per_year = Mileage / age
)
# Look at your new columns
car_data %>%
select(Model, year, age, Mileage, mileage_per_year) %>%
head()# A tibble: 6 × 5
Model year age Mileage mileage_per_year
<chr> <dbl> <dbl> <dbl> <dbl>
1 Fiesta 2002 23 127300 5535.
2 718 Cayman 2016 9 57850 6428.
3 Mondeo 2014 11 39190 3563.
4 RAV4 1988 37 210814 5698.
5 Polo 2006 19 127869 6730.
6 Focus 2018 7 33603 4800.3.2 Categorize Cars
# Create a price_category column where if price is < 15000, its is coded as budget, between 15000 and 30000 is midrange and greater than 30000 is mid-range (use case_when)
car_data <- car_data %>%
mutate(
price_category = case_when(
Price < 15000 ~ "budget",
Price >= 15000 & Price <= 30000 ~ "midrange",
Price > 30000 ~ "mid-range"
)
)
# Check your categories select the new column and show it
car_data %>%
select(price_category) %>%
head()# A tibble: 6 × 1
price_category
<chr>
1 budget
2 mid-range
3 midrange
4 budget
5 budget
6 midrange Exercise 4: Filtering Practice
4.1 Basic Filtering
# Find all Toyota cars
toyota_cars <- car_data %>%
filter(Manufacturer == "Toyota")
# Find cars with mileage less than 30,000
low_mileage_cars <- car_data %>%
filter(Mileage < 30000)
# Find luxury cars (from price category) with low mileage
luxury_low_mileage <- car_data %>%
filter(price_category == "mid-range", Mileage < 30000)
head(toyota_cars)# A tibble: 6 × 10
Manufacturer Model `Engine size` `Fuel type` year Mileage Price age
<chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 Toyota RAV4 1.8 Hybrid 1988 210814 1705 37
2 Toyota Prius 1.4 Hybrid 2015 30663 30297 10
3 Toyota RAV4 2.2 Petrol 2007 79393 16026 18
4 Toyota Yaris 1.4 Petrol 1998 97286 4046 27
5 Toyota RAV4 2.4 Hybrid 2003 117425 11667 22
6 Toyota Yaris 1.2 Petrol 1992 245990 720 33
# ℹ 2 more variables: mileage_per_year <dbl>, price_category <chr>head(low_mileage_cars)# A tibble: 6 × 10
Manufacturer Model `Engine size` `Fuel type` year Mileage Price age
<chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 Toyota RAV4 2 Hybrid 2018 28381 52671 7
2 VW Golf 2 Petrol 2020 18985 36387 5
3 BMW M5 4 Petrol 2017 22759 97758 8
4 Toyota RAV4 2.4 Petrol 2018 24588 49125 7
5 VW Golf 2 Hybrid 2018 25017 36957 7
6 Porsche 718 Cayman 2.4 Petrol 2021 14070 69526 4
# ℹ 2 more variables: mileage_per_year <dbl>, price_category <chr>head(luxury_low_mileage)# A tibble: 6 × 10
Manufacturer Model `Engine size` `Fuel type` year Mileage Price age
<chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 Toyota RAV4 2 Hybrid 2018 28381 52671 7
2 VW Golf 2 Petrol 2020 18985 36387 5
3 BMW M5 4 Petrol 2017 22759 97758 8
4 Toyota RAV4 2.4 Petrol 2018 24588 49125 7
5 VW Golf 2 Hybrid 2018 25017 36957 7
6 Porsche 718 Cayman 2.4 Petrol 2021 14070 69526 4
# ℹ 2 more variables: mileage_per_year <dbl>, price_category <chr>4.2 Multiple Conditions
# Find cars that are EITHER Honda OR Nissan
honda_nissan <- car_data %>%
filter(Manufacturer %in% c("Honda", "Nissan"))
# Find cars with price between $20,000 and $35,000
mid_price <- car_data %>%
filter(between(Price, 20000, 35000))
# Find diesel cars less than 10 years old
diesel_recent <- car_data %>%
filter(`Fuel type` == "Diesel", 2025 - year < 10) %>%
count()
head(honda_nissan)# A tibble: 0 × 10
# ℹ 10 variables: Manufacturer <chr>, Model <chr>, Engine size <dbl>,
# Fuel type <chr>, year <dbl>, Mileage <dbl>, Price <dbl>, age <dbl>,
# mileage_per_year <dbl>, price_category <chr>head(mid_price)# A tibble: 6 × 10
Manufacturer Model `Engine size` `Fuel type` year Mileage Price age
<chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 Ford Mondeo 1.6 Diesel 2014 39190 24072 11
2 Ford Focus 1.4 Petrol 2018 33603 29204 7
3 Toyota Prius 1.4 Hybrid 2015 30663 30297 10
4 Toyota Prius 1.4 Hybrid 2016 43893 29946 9
5 Toyota Prius 1.4 Hybrid 2016 43130 30085 9
6 VW Passat 1.6 Petrol 2016 64344 23641 9
# ℹ 2 more variables: mileage_per_year <dbl>, price_category <chr>head(diesel_recent)# A tibble: 1 × 1
n
<int>
1 2040Question: How many diesel cars are less than 10 years old?
Your answer: 2,040
Exercise 5: Grouping and Summarizing
5.1 Basic Summaries
# Calculate average price by manufacturer
avg_price_by_brand <- car_data %>%
group_by(Manufacturer) %>%
summarize(avg_price = mean(Price, na.rm = TRUE))
avg_price_by_brand# A tibble: 5 × 2
Manufacturer avg_price
<chr> <dbl>
1 BMW 24429.
2 Ford 10672.
3 Porsche 29104.
4 Toyota 14340.
5 VW 10363.# Calculate average mileage by fuel type
avg_mileage_by_fuel <- car_data %>%
group_by(`Fuel type`) %>%
summarize(avg_mileage = mean(Mileage, na.rm = TRUE))
# Count cars by manufacturer
cars_by_manufacturer <- car_data %>%
count(Manufacturer)
avg_mileage_by_fuel# A tibble: 3 × 2
`Fuel type` avg_mileage
<chr> <dbl>
1 Diesel 112667.
2 Hybrid 111622.
3 Petrol 112795.cars_by_manufacturer# A tibble: 5 × 2
Manufacturer n
<chr> <int>
1 BMW 4965
2 Ford 14959
3 Porsche 2609
4 Toyota 12554
5 VW 149135.2 Categorical Summaries
# Frequency table for price categories
price_freq <- car_data %>%
count(price_category)
price_freq# A tibble: 3 × 2
price_category n
<chr> <int>
1 budget 34040
2 mid-range 6178
3 midrange 9782Submission Notes
To submit this lab: 1. Make sure your code runs without errors 2. Fill in all the “[YOUR ANSWER]” sections and complete all of the empty code! 3. Save this file in your portfolio’s lab_0/ folder 4. Commit and push to GitHub 5. Check that it appears on your GitHub Pages portfolio site
Questions? Post on the canvas discussion board or come to office hours!