# 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
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:
# Use glimpse() to see the data structure
# Check the column names
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…# 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: 4x doubles, 3xchr types - Problematic names: Engine size, Fuel type, Year of manufacture
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)
# car_dfQuestion: What differences do you notice in how they print?
Your answer: data frame will render every single row within the dataset.
Exercise 2: Basic Column Operations
2.1 Selecting Columns
Practice selecting different combinations of columns:
# Select just Model and Mileage columns
Model_Mileage <- select(car_data, Model, Mileage)
# Select Manufacturer, Price, and Fuel type
Manu_price_Fuel <- select(car_data,`Year of manufacture`, Price, `Fuel type`)
# Challenge: Select all columns EXCEPT Engine Size
no_engine <- select(car_data, -`Engine size`)2.2 Renaming Columns
Let’s fix a problematic column name:
# Rename 'Year of manufacture' to year
car_data <- rename(car_data, 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: spaces
Exercise 3: Creating New Columns
3.1 Calculate Car Age
# Create an 'age' column (2025 minus year of manufacture)
car_data <- car_data %>%
mutate(
age = 2025 - year, # compute age
)
# Create a mileage_per_year column
car_data <- car_data %>%
mutate(
mileage_per_year = Mileage / age # compute mileage per year
)
# Look at your new columns
select(car_data, Model, year, age, Mileage, mileage_per_year)# A tibble: 50,000 × 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.
7 Mondeo 2010 15 86686 5779.
8 Prius 2015 10 30663 3066.
9 Polo 2012 13 73470 5652.
10 Focus 1992 33 262514 7955.
# ℹ 49,990 more rows3.2 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 ~ "luxury"
)
)
# Check your categories select the new column and show it
select(car_data, Model, Price, price_category)# A tibble: 50,000 × 3
Model Price price_category
<chr> <dbl> <chr>
1 Fiesta 3074 budget
2 718 Cayman 49704 luxury
3 Mondeo 24072 midrange
4 RAV4 1705 budget
5 Polo 4101 budget
6 Focus 29204 midrange
7 Mondeo 14350 budget
8 Prius 30297 luxury
9 Polo 9977 budget
10 Focus 1049 budget
# ℹ 49,990 more rowsExercise 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 <- car_data %>%
filter(Mileage < 30000)
# Find luxury cars (from price category) with low mileage
luxury_cars <- car_data %>%
filter(price_category == "luxury")
toyota_cars# A tibble: 12,554 × 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
7 Toyota RAV4 2 Hybrid 2018 28381 52671 7
8 Toyota Prius 1 Hybrid 2003 115291 6512 22
9 Toyota Prius 1 Hybrid 1990 238571 961 35
10 Toyota Prius 1.8 Hybrid 2017 31958 38961 8
# ℹ 12,544 more rows
# ℹ 2 more variables: mileage_per_year <dbl>, price_category <chr>low_mileage# A tibble: 5,402 × 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
7 Ford Focus 1.8 Petrol 2020 22371 40336 5
8 Ford Mondeo 1.6 Diesel 2015 21834 28435 10
9 VW Passat 1.6 Diesel 2018 22122 36634 7
10 VW Passat 1.4 Diesel 2020 21413 39310 5
# ℹ 5,392 more rows
# ℹ 2 more variables: mileage_per_year <dbl>, price_category <chr>luxury_cars# A tibble: 6,178 × 10
Manufacturer Model `Engine size` `Fuel type` year Mileage Price age
<chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 Porsche 718 Cayman 4 Petrol 2016 57850 49704 9
2 Toyota Prius 1.4 Hybrid 2015 30663 30297 10
3 Toyota RAV4 2 Hybrid 2018 28381 52671 7
4 Porsche 911 2.6 Petrol 2009 66273 41963 16
5 Toyota Prius 1.8 Hybrid 2017 31958 38961 8
6 VW Golf 2 Petrol 2020 18985 36387 5
7 BMW M5 4 Petrol 2017 22759 97758 8
8 Toyota RAV4 2.4 Petrol 2018 24588 49125 7
9 Porsche Cayenne 2.6 Diesel 2015 33693 54037 10
10 VW Golf 2 Hybrid 2018 25017 36957 7
# ℹ 6,168 more rows
# 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 == "Honda" | Manufacturer == "Nissan")
# Find cars with price between $20,000 and $35,000
price_range <- car_data %>%
filter(Price >= 20000, Price <= 35000)
# Find diesel cars less than 10 years old
diesel_recent <- car_data %>%
filter(`Fuel type` == "Diesel", age < 10)
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>price_range# A tibble: 7,301 × 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
7 Ford Mondeo 1.6 Diesel 2015 21834 28435 10
8 BMW M5 4.4 Petrol 2008 109941 31711 17
9 BMW Z4 2.2 Petrol 2014 61332 26084 11
10 Porsche 911 3.5 Petrol 2003 107705 24378 22
# ℹ 7,291 more rows
# ℹ 2 more variables: mileage_per_year <dbl>, price_category <chr>diesel_recent# A tibble: 2,040 × 10
Manufacturer Model `Engine size` `Fuel type` year Mileage Price age
<chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 Ford Fiesta 1 Diesel 2017 38370 16257 8
2 VW Passat 1.6 Diesel 2018 22122 36634 7
3 VW Passat 1.4 Diesel 2020 21413 39310 5
4 BMW X3 2 Diesel 2018 27389 44018 7
5 Ford Mondeo 2 Diesel 2016 51724 28482 9
6 Porsche Cayenne 2.6 Diesel 2019 20147 76182 6
7 VW Polo 1.2 Diesel 2018 37411 19649 7
8 Ford Mondeo 1.8 Diesel 2016 29439 30886 9
9 Ford Mondeo 1.4 Diesel 2020 18929 37720 5
10 Ford Mondeo 1.4 Diesel 2018 42017 28904 7
# ℹ 2,030 more rows
# ℹ 2 more variables: mileage_per_year <dbl>, price_category <chr>Question: 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_price_by_brand <- car_data %>%
group_by(`Fuel type`) %>%
summarize(avg_mileage = mean(Mileage, na.rm = TRUE))
avg_price_by_brand# A tibble: 3 × 2
`Fuel type` avg_mileage
<chr> <dbl>
1 Diesel 112667.
2 Hybrid 111622.
3 Petrol 112795.# Count cars by manufacturer
car_counts <- car_data %>%
count(Manufacturer)
car_counts# 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
freq_cat <- car_data %>%
count(price_category, name = "frequency") %>%
mutate(proportion = frequency / sum(frequency))
freq_cat# A tibble: 3 × 3
price_category frequency proportion
<chr> <int> <dbl>
1 budget 34040 0.681
2 luxury 6178 0.124
3 midrange 9782 0.196Submission 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!