ucla_textbooks_f18 <-
structure(list(year = c(2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 9780133356038, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018), term = structure(c(2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("199.96", "Fall"), class = "factor"), 
    subject = structure(c(3L, 4L, 5L, 7L, 8L, 10L, 12L, 13L, 
    13L, 14L, 17L, 18L, 18L, 20L, 20L, 20L, 20L, 20L, 21L, 24L, 
    24L, 24L, 25L, 26L, 26L, 27L, 27L, 27L, 28L, 29L, 29L, 29L, 
    30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 31L, 32L, 
    32L, 33L, 34L, 36L, 36L, 36L, 36L, 37L, 40L, 40L, 41L, 45L, 
    46L, 47L, 48L, 49L, 50L, 50L, 50L, 50L, 51L, 52L, 53L, 54L, 
    54L, 55L, 56L, 56L, 56L, 56L, 56L, 57L, 57L, 57L, 58L, 59L, 
    60L, 61L, 61L, 63L, 64L, 64L, 70L, 71L, 71L, 72L, 73L, 73L, 
    73L, 73L, 73L, 73L, 73L, 73L, 75L, 75L, 4L, 5L, 5L, 10L, 
    13L, 13L, 14L, 18L, 18L, 20L, 24L, 26L, 1L, 28L, 28L, 29L, 
    29L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 32L, 32L, 
    33L, 33L, 34L, 36L, 36L, 36L, 36L, 36L, 36L, 37L, 40L, 41L, 
    47L, 48L, 48L, 50L, 50L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 
    57L, 59L, 61L, 61L, 61L, 63L, 63L, 64L, 70L, 71L, 71L, 71L, 
    71L, 73L, 75L, 2L, 23L, 23L, 6L, 9L, 9L, 35L, 35L, 11L, 15L, 
    15L, 15L, 16L, 16L, 62L, 62L, 62L, 19L, 44L, 65L, 22L, 68L, 
    68L, 67L, 67L, 74L, 74L, 38L, 39L, 43L, 43L, 69L, 66L, 42L
    ), .Label = c("149.96", "African American Studies", "American Indian Studies", 
    "Anthropology", "Art", "Art History", "Arts and Architecture", 
    "Asian", "Asian American Studies", "Astronomy", "Atmospheric and Oceanic Sciences", 
    "Biomedical Research", "Chemistry and Biochemistry", "Chicana and Chicano Studies", 
    "Chinese", "Clusters", "Communication", "Comparative Literature", 
    "Computer Science", "Dance", "Design / Media Arts", "Digital Humanities", 
    "Earth, Planetary, and Space Sciences", "Ecology and Evolutionary Biology", 
    "Economics", "Electrical and Computer Engineering", "Engineering", 
    "English", "English Composition", "Ethnomusicology", "Film and Television", 
    "French", "Geography", "German", "Greek", "History", "Indigenous Languages of the Americas", 
    "Information Studies", "International and Area Studies", 
    "Italian", "Japanese", "Jewish Studies", "Korean", "Labor and Workplace Studies", 
    "Latin", "Law", "Life Sciences", "Linguistics", "Management", 
    "Mathematics", "Mechanical and Aerospace Engineering", "Medicine", 
    "Middle Eastern Studies", "Military Science", "Molecular, Cell, and Developmental Biology", 
    "Music", "Musicology", "Neuroscience", "Nursing", "Obstetrics and Gynecology", 
    "Philosophy", "Physics", "Political Science", "Portuguese", 
    "Psychology", "Public Affairs", "Religion, Study of", "Russian", 
    "Science Education", "Sociology", "Spanish", "Statistics", 
    "Theater", "University Studies", "World Arts and Cultures"
    ), class = "factor"), subject_abbr = structure(c(3L, 4L, 
    NA, 6L, NA, 8L, 9L, 10L, 10L, 11L, 16L, 14L, 14L, NA, NA, 
    NA, NA, NA, 17L, 21L, 21L, 21L, 20L, 19L, 19L, 24L, 24L, 
    24L, 23L, 22L, 22L, 22L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 
    26L, 26L, 26L, 27L, 28L, 28L, 29L, NA, 30L, 30L, 30L, 30L, 
    32L, NA, NA, 34L, NA, 62L, 38L, 39L, 45L, 41L, 41L, 41L, 
    41L, 43L, 44L, 40L, 46L, 46L, 42L, 47L, 47L, 47L, 47L, 47L, 
    48L, 48L, 48L, 49L, NA, 50L, 51L, 51L, 52L, 53L, 53L, 59L, 
    60L, 60L, 61L, NA, NA, NA, NA, NA, NA, NA, NA, 64L, 64L, 
    4L, NA, NA, 8L, 10L, 10L, 11L, 14L, 14L, NA, 21L, 19L, NA, 
    23L, 23L, 22L, 22L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 
    26L, 28L, 28L, 29L, 29L, NA, 30L, 30L, 30L, 30L, 30L, 30L, 
    32L, NA, 34L, 38L, 39L, 39L, 41L, 41L, 47L, 47L, 47L, 47L, 
    47L, 47L, 47L, 48L, NA, 51L, 51L, 51L, 52L, 52L, 53L, 59L, 
    60L, 60L, 60L, 60L, NA, 64L, 2L, 25L, 25L, 5L, 7L, 7L, NA, 
    NA, 1L, 12L, 12L, 12L, 13L, 13L, NA, NA, NA, 15L, 37L, 54L, 
    18L, 57L, 57L, 56L, 56L, 63L, 63L, 33L, 31L, 36L, 36L, 58L, 
    55L, 35L), .Label = c("A&O SCI", "AF AMER", "AM IND", "ANTHRO", 
    "ART HIS", "ART&ARC", "ASIA AM", "ASTR", "BMD RES", "CHEM", 
    "CHICANO", "CHIN", "CLUSTER", "COM LIT", "COM SCI", "COMM", 
    "DESMA", "DGT HUM", "EC ENGR", "ECON", "EE BIOL", "ENGCOMP", 
    "ENGL", "ENGR", "EPS SCI", "ETHNMUS", "FILM TV", "FRNCH", 
    "GEOG", "HIST", "I A STD", "IL AMER", "INF STD", "JAPAN", 
    "JEWISH", "KOREA", "LBR&WS", "LIFESCI", "LING", "M E STD", 
    "MATH", "MCD BIO", "MECH&AE", "MED", "MGMT", "MIL SCI", "MUSC", 
    "MUSCLG", "NEUROSC", "OBGYN", "PHILOS", "POL SCI", "PORTGSE", 
    "PSYCH", "PUB AFF", "RELIGN", "RUSSN", "SCI EDU", "SOCIOL", 
    "SPAN", "STATS", "Undergraduate", "UNIV ST", "WL ARTS"), class = "factor"), 
    course = structure(c(81L, 10L, 123L, 12L, 84L, 22L, 21L, 
    14L, 157L, 44L, 85L, 143L, 150L, 16L, 17L, 18L, 156L, 189L, 
    51L, 48L, 170L, 174L, 168L, 90L, 115L, 91L, 92L, 93L, 159L, 
    9L, 56L, 70L, 63L, 64L, 67L, 176L, 180L, 181L, 182L, 183L, 
    184L, 185L, 83L, 35L, 35L, 94L, 72L, 59L, 62L, 95L, 106L, 
    44L, 37L, 74L, 42L, 39L, 124L, 137L, 110L, 135L, 24L, 25L, 
    25L, 30L, 86L, 158L, 49L, 52L, 61L, 148L, 27L, 125L, 151L, 
    161L, 164L, 15L, 58L, 99L, 23L, 100L, 20L, 119L, 175L, 186L, 
    69L, 145L, 139L, 78L, 109L, 147L, 32L, 88L, 136L, 141L, 152L, 
    153L, 154L, 155L, 81L, 171L, 7L, 126L, 120L, 112L, 149L, 
    128L, 77L, 114L, 140L, 121L, 33L, 131L, NA, 19L, 6L, 105L, 
    46L, 99L, 65L, 177L, 178L, 122L, 54L, 179L, 68L, 66L, 71L, 
    53L, 55L, 187L, 36L, 60L, 172L, 107L, 28L, 96L, 8L, 77L, 
    37L, 76L, 113L, 111L, 104L, 24L, 134L, 118L, 163L, 165L, 
    166L, 167L, 34L, 162L, 188L, 127L, 102L, 116L, 142L, 82L, 
    133L, 3L, 130L, 78L, 26L, 79L, 4L, 101L, 146L, 160L, 89L, 
    87L, 11L, 57L, 13L, 73L, 41L, 5L, 40L, 75L, 29L, 50L, 47L, 
    173L, 129L, 132L, 115L, 98L, 108L, 117L, 103L, 45L, 144L, 
    138L, 1L, 2L, 80L, 97L, 38L, 43L, 31L, 169L, 144L), .Label = c("ACE UCLA | Critical Strategies to Achieve Undergraduate Excellence for Incoming Freshmen", 
    "ACE UCLA | Critical Strategies to Achieve Undergraduate Excellence for Incoming Transfers", 
    "Advanced Composition and Style", "Advanced Spanish Composition for Heritage Speakers", 
    "Air Pollution", "American Novel", "Animals in Translation: Evolutionary Approaches to Animal Thinking and Autism", 
    "Animals R Us", "Approaches to University Writing for Multilingual Students", 
    "Archaeology: Introduction", "Art and Architecture of Ancient Americas", 
    "Arts Encounters: Exploring Arts Literacy in 21st Century", 
    "Asian American Movement", "Atomic and Molecular Structure, Equilibria, Acids, and Bases", 
    "Beethoven", "Beginning Hip-Hop Dance", "Beginning Martial Arts", 
    "Beginning Special Topics", "Being There: History and Gossip in Jean Froissart's Chronicles", 
    "Biology of Stem Cells", "Biomedical Research: Concepts and Strategies", 
    "Black Holes and Cosmic Catastrophes", "Brain Made Simple: Neuroscience for 21st Century", 
    "Calculus for Life Sciences Students", "Calculus of Several Variables", 
    "Catalan Language and Culture I", "Chamber Singers", "China's Modern Youth Culture in Historical Perspective", 
    "Chinese Civilization", "Classroom Practices in Middle School Mathematics", 
    "Classroom Practices in Middle School Science", "Dance for Musical Theater I", 
    "Deep-Sea Communities: High Diversity in Extreme Environments", 
    "Early Music Ensemble", "Elementary French", "Elementary German", 
    "Elementary Italian--Continued", "Elementary Korean for Korean-Heritage Speakers", 
    "Elementary Latin", "Elementary Modern Chinese for Advanced Beginners", 
    "Elementary Modern Greek", "Elementary Modern Japanese", 
    "Elementary Modern Korean", "Elementary Nahuatl", "Elementary Russian", 
    "English Composition, Rhetoric, and Language", "Evolution of Cosmos and Life", 
    "Evolutionary Medicine: How Natural Selection Helps Us Understand Why We Get Sick", 
    "First Civilizations", "Food: Lens for Environment and Sustainability", 
    "Form", "Foundations of Officership", "French Cinema and Culture", 
    "Global Pop", "Globalization: Regional Development and World Economy", 
    "High-Intermediate Composition for Multilingual Students", 
    "History of Asian Americans", "History of Rock and Roll", 
    "History of Science: Renaissance to 1800", "History of the U.S. and Its Colonial Origins: Colonial Origins and First Nation Building Acts", 
    "Individual Leadership Development", "Inequality: History of Neoliberalism", 
    "Instruction in Jazz Performance: Guitar", "Instruction in Jazz Performance: Piano", 
    "Instruction in Jazz Performance: Saxophone", "Instruction in Jazz Performance: String Bass", 
    "Instruction in Jazz Performance: Trumpet", "Instruction in Jazz Performance: Voice", 
    "Intensive Portuguese", "Intermediate Composition for Multilingual Students", 
    "Intermediate French", "Intermediate German", "Intermediate Greek", 
    "Intermediate Italian", "Intermediate Modern Chinese", "Intermediate Modern Japanese", 
    "Intermediate Nahuatl", "Intermediate Spanish", "Intermediate Spanish for Heritage Speakers", 
    "Internet and Society", "Introduction to American Indian Studies", 
    "Introduction to American Politics", "Introduction to Art and Technique of Filmmaking", 
    "Introduction to Buddhism", "Introduction to Communication Studies", 
    "Introduction to Computer Programming with MATLAB", "Introduction to Computing for Geoscientists", 
    "Introduction to Design", "Introduction to Earth Science", 
    "Introduction to Electrical Engineering", "Introduction to Engineering Design: Go-karts", 
    "Introduction to Engineering Design: Internet of Things", 
    "Introduction to Engineering Design: Rockets", "Introduction to Geographic Information Systems", 
    "Introduction to Historical Practice: Culture Wars: Battles, Debates, and Skirmishes about Cultural Property in Contemporary Era", 
    "Introduction to Historical Practice: Politics of Economic Knowledge", 
    "Introduction to International and Area Studies", "Introduction to Labor and Workplace Studies", 
    "Introduction to Musicianship", "Introduction to Nursing and Social Justice I", 
    "Introduction to Performance", "Introduction to Political Philosophy", 
    "Introduction to Russian Film", "Introduction to Study of Language", 
    "Introduction to University Discourse", "Introduction to Western Civilization: Ancient Civilizations, Prehistory to circa A.D. 843", 
    "Introduction to Western Civilization: Circa 1715 to Present", 
    "Introductory Psychology", "Introductory Spanish for Heritage Speakers", 
    "Klingon Language and Linguistics", "Language in Action: Perspectives from Applied Linguistics", 
    "Life in Universe", "Life: Concepts and Issues", "Literature and Writing: Middle Ages to 17th Century", 
    "Logic Design of Digital Systems", "Logic, First Course", 
    "Los Angeles Tech City: Digital Technologies and Spatial Justice", 
    "Marching and Varsity Bands", "Meaning and Communication", 
    "Modernism", "Moving Voice", "Musical Cultures of World: Europe and Americas", 
    "New Genres", "Nonviolence and Women's Self-Defense", "Opera Workshop", 
    "Painting", "Pathophysiology II", "PEERS Collaborative Learning Workshops for Life Sciences Majors", 
    "PEERS Collaborative Learning Workshops for Physical Sciences and Engineering Majors", 
    "Physician-Patient Interaction in Medical Visit", "Physics for Electrical Engineers", 
    "Physics for Life Sciences Majors: Electricity, Magnetism, and Modern Physics", 
    "Politics and Strategy", "Precalculus", "Principles of Accounting", 
    "Production Practice in Theater, Film, Video, and Digital Media", 
    "Quantitative Concepts for Life Sciences", "Religion in Los Angeles", 
    "Sex from Biology to Gendered Society", "Short Works of Franz Kafka, or How Modern World Works", 
    "Singing for Musical Theater I", "Skepticism and Rationality", 
    "Social Media and Storytelling: Comparing Cultures", "Social, Cultural, and Religious Institutions of Judaism", 
    "Spanish, Portuguese, and Nature of Language", "Special Studies in World Arts and Cultures", 
    "Statistics and Portfolio Risk Management with Stock Market Applications", 
    "Stem Cell Biology, Politics, and Ethics: Teasing Apart Issues", 
    "Structure of Organic Molecules", "Survey of Literature: Age of Enlightenment to 20th Century", 
    "Symphonic Band", "Tai Chi", "Theater Production", "Theater Production: Theater Production Props", 
    "Theater Production: Theater Production Special", "Theories and Methods in Dance Composition I: Languages", 
    "Thermodynamics, Electrochemistry, Kinetics, and Organic Chemistry", 
    "UCLA Centennial Initiative: Breathe Well--Tobacco-Free Campus, Medicine, Nursing, and Public Health", 
    "UCLA Centennial Initiative: Burning Books--UCLA, Fahrenheit 451, and Politics of Libraries", 
    "UCLA Centennial Initiative: Social Justice, Student Activism, and Academic Advancement Program", 
    "UCLA Chorale", "Undergraduate Instruction in Performance: Clarinet", 
    "Undergraduate Instruction in Performance: Harp", "Undergraduate Instruction in Performance: Oboe", 
    "Undergraduate Instruction in Performance: String Bass", 
    "Undergraduate Instruction in Performance: Trombone", "Undergraduate Instruction in Performance: Trumpet", 
    "Understanding Federal Spending", "Using Data to Learn about Society: Introduction to Empirical Research and Statistics", 
    "Variable Topics in Ecology and Evolutionary Biology", "Video Tools and Techniques", 
    "What Is History? An Introduction to Historical Thinking and Practice", 
    "Why Are We Here? Origin of Universe, Life, and Consciousness", 
    "Why Ecology Matters: Science Behind Environmental Issues", 
    "Why Not Anarchy?", "World Music Performance Organizations: Music of African Americans", 
    "World Music Performance Organizations: Music of Bali", "World Music Performance Organizations: Music of Persia", 
    "World Music Performance Organizations: Open Ensemble: Charles Mingus Ensemble", 
    "World Music Performance Organizations: Open Ensemble: Irish Music Ensemble", 
    "World Music Performance Organizations: Open Ensemble: Old-Time String Band Ensemble", 
    "World Music Specializations: Music of Balkans--Choir", "World Music Specializations: Music of Balkans--Instrumental Music", 
    "World Music Specializations: Music of China--Chinese Opera", 
    "World Music Specializations: Music of China--Ensemble", 
    "World Politics", "World Regions: Concepts and Contemporary Issues", 
    "Writing about Music", "Yoga"), class = "factor"), course_num = structure(c(102L, 
    23L, 8L, 2L, 112L, 44L, 53L, 13L, 14L, 111L, 2L, 22L, 32L, 
    84L, 2L, 9L, 62L, 6L, 27L, 18L, 97L, 17L, 18L, 34L, 104L, 
    94L, 95L, 94L, 18L, 33L, 20L, 19L, 71L, 72L, 75L, 91L, 92L, 
    92L, 65L, 66L, 64L, 63L, 44L, 23L, 34L, 67L, 44L, 42L, 10L, 
    96L, 19L, 111L, 34L, 54L, 1L, 1L, 18L, 24L, 18L, 20L, 43L, 
    38L, 39L, 78L, 106L, 18L, 109L, 6L, 26L, 50L, 85L, 86L, 87L, 
    101L, 58L, 68L, 49L, 113L, 2L, 2L, 18L, 28L, 18L, 24L, 7L, 
    108L, 114L, 44L, 79L, 18L, 40L, 13L, 77L, 41L, 31L, 50L, 
    50L, 50L, 107L, 81L, 18L, 7L, 37L, 49L, 15L, 98L, 103L, 48L, 
    18L, 49L, 18L, 23L, NA, 18L, 82L, 1L, 34L, 113L, 73L, 89L, 
    90L, 25L, 29L, 92L, 76L, 74L, 44L, 47L, 44L, 54L, 34L, 12L, 
    93L, 21L, 18L, 96L, 18L, 103L, 23L, 44L, 16L, 6L, 1L, 42L, 
    1L, 88L, 57L, 56L, 61L, 60L, 115L, 59L, 11L, 51L, 54L, 36L, 
    26L, 45L, 35L, 30L, 18L, 49L, 7L, 80L, 30L, 9L, 100L, 18L, 
    1L, 70L, 30L, 2L, 46L, 24L, 83L, 23L, 19L, 44L, 50L, 105L, 
    69L, 18L, 99L, 52L, 110L, 2L, 2L, 35L, 36L, 1L, 102L, 6L, 
    3L, 4L, 35L, 1L, 19L, 1L, 5L, 55L, 102L), .Label = c("1", 
    "10", "10A", "10D", "10SL", "11", "11A", "11D", "12", "12B", 
    "12W", "13A", "14A", "14B", "14C", "15", "18", "19", "1A", 
    "1B", "1C", "1E", "2", "20", "20A", "21", "22", "23", "25", 
    "27", "2A", "2CW", "2I", "3", "30", "31", "31A", "32A", "32B", 
    "34A", "35A", "3A", "3B", "4", "40", "40W", "41", "4BW", 
    "5", "50", "54B", "5C", "5HA", "6", "60", "60DF", "60EF", 
    "61BF", "61CF", "62AF", "62CF", "67A", "68A", "68B", "68N", 
    "68O", "7", "70", "70A", "71", "71AF", "71CF", "71DF", "71EF", 
    "71GF", "71IF", "72", "72SL", "7A", "7B", "80", "85", "8A", 
    "9", "90C", "90D", "90F", "90M", "91B", "91L", "91P", "91Z", 
    "94", "96A", "96C", "96W", "97", "98XA", "98XB", "99", "C90A", 
    "M10", "M15A", "M16", "M1A", "M20", "M23", "M35", "M50A", 
    "M51A", "M5A", "M60W", "M6A", "M72A", "M90T"), class = "factor"), 
    course_numeric = c(10L, 2L, 11L, 10L, 60L, 4L, 5L, 14L, 14L, 
    5L, 10L, 1L, 2L, 9L, 10L, 12L, 67L, 11L, 22L, 19L, 97L, 18L, 
    19L, 3L, 16L, 96L, 96L, 96L, 19L, 2L, 1L, 1L, 71L, 71L, 71L, 
    91L, 91L, 91L, 68L, 68L, 68L, 68L, 4L, 2L, 3L, 7L, 4L, 3L, 
    12L, 96L, 1L, 5L, 3L, 6L, 1L, 1L, 19L, 20L, 19L, 1L, 3L, 
    32L, 32L, 72L, 20L, 19L, 50L, 11L, 21L, 50L, 90L, 90L, 90L, 
    90L, 61L, 70L, 5L, 6L, 10L, 10L, 19L, 23L, 19L, 20L, 11L, 
    35L, 72L, 4L, 7L, 19L, 34L, 14L, 72L, 35L, 2L, 50L, 50L, 
    50L, 23L, 80L, 19L, 11L, 31L, 5L, 14L, 98L, 15L, 4L, 19L, 
    5L, 19L, 2L, NA, 19L, 85L, 1L, 3L, 6L, 71L, 91L, 91L, 20L, 
    25L, 91L, 71L, 71L, 4L, 41L, 4L, 6L, 3L, 13L, 94L, 1L, 19L, 
    96L, 19L, 15L, 2L, 4L, 15L, 11L, 1L, 3L, 1L, 90L, 60L, 60L, 
    62L, 62L, 90L, 61L, 12L, 54L, 6L, 31L, 21L, 40L, 30L, 27L, 
    19L, 5L, 11L, 7L, 27L, 12L, 99L, 19L, 1L, 71L, 27L, 10L, 
    40L, 20L, 8L, 2L, 1L, 4L, 50L, 1L, 70L, 19L, 98L, 5L, 51L, 
    10L, 10L, 30L, 31L, 1L, 10L, 11L, 10L, 10L, 30L, 1L, 1L, 
    1L, 10L, 60L, 10L), seminar = c(FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    NA, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), 
    ind_study = c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, NA, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE), apprenticeship = c(FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, NA, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE), internship = c(FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    NA, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), 
    honors_contracts = c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, NA, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), laboratory = c(FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, NA, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE), special_topic = c(FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    NA, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), 
    textbook_isbn = c("9781138477858", "9780307741806", NA, "9780979757549", 
    "9780199861873", "9780134073828", NA, "9781319254292", "9781319016821", 
    NA, "9780199754144", NA, "9780375756887", NA, "9780982203583", 
    NA, NA, NA, NA, NA, NA, "9789891812695", NA, NA, NA, NA, 
    NA, NA, "9781451673319", "9789891812596", NA, "9781108662260", 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "9781319059514", 
    "9781337739467", "9781337739467", NA, "9781626809529", "9780333970027", 
    "9780807081778", NA, "9780495913030", NA, NA, NA, NA, "9781585103904", 
    "9780997996401", "9780465097678", "9780061336461", "9781260666939", 
    "9781118988527", "9781319025397", "9781319025397", NA, "9780898716917", 
    NA, "9780140449198", NA, NA, "9781319149581", NA, NA, NA, 
    NA, NA, "9789891812800", "9780199811700", "9780393284911", 
    NA, "9781133129165", NA, NA, NA, "9780393938098", "9781269995542", 
    "9781337559577", NA, "9781323771174", "9781285718842", NA, 
    NA, "9780415547543", NA, "9781458443182", NA, NA, NA, NA, 
    "9781138477858", NA, NA, NA, NA, "9781938168284", "9781319255015", 
    NA, NA, NA, "9780805210576", NA, NA, NA, NA, "9780140442007", 
    "9780142437278", "9781319057442", "9781457699214", "9780393284911", 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "9781680044287", 
    NA, NA, "9780393621969", NA, NA, NA, NA, "9781285074818", 
    "9784789014434", "9781319065447", "9781138957084", "9780061336461", 
    "9781118988527", NA, NA, NA, NA, NA, NA, NA, NA, "9780393631678", 
    "9781451190809", "9780872207363", "9780195155044", NA, "9780393283761", 
    NA, NA, "9780520287266", "9781323771174", NA, "9781285718842", 
    "9781680040135", NA, NA, "9780345386816", "9781464138744", 
    NA, "9781466399365", "9781419718786", NA, "9781614273974", 
    "9789607307682", "9781107691155", "9781622911356", "9781622911561", 
    NA, "9780134810102", "9780767908184", NA, NA, "9780135392737", 
    NA, "9780983628910", "9781464155468", NA, "9781618112125", 
    "9780781812511", "9780231134897", NA, NA, NA, NA, NA, "9780824834401", 
    "9780824834401", NA, NA, "9780231134897"), bookstore_new = c(47.969999999999999, 
    14.26, NA, 13.5, 49.259999999999998, 119.97, NA, 188.09999999999999, 
    215.75, NA, 16.949999999999999, NA, 11.960000000000001, NA, 
    26.75, NA, NA, NA, NA, NA, NA, 37, NA, NA, NA, NA, NA, NA, 
    9.9600000000000009, 20, NA, 39.969999999999999, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, 132.75, 149.97, 149.97, NA, 219.97, 
    43.960000000000001, 12.77, NA, 114.95999999999999, NA, NA, 
    NA, NA, 29.969999999999999, 19.949999999999999, 11.960000000000001, 
    17.260000000000002, 124.95999999999999, 109.97, 103.97, 103.97, 
    NA, 72.959999999999994, NA, 12.460000000000001, NA, NA, 50.960000000000001, 
    NA, NA, NA, NA, NA, 22, 55.460000000000001, 89.959999999999994, 
    NA, 186.96000000000001, NA, NA, NA, 106.97, 219.97, 133.96000000000001, 
    NA, 229.27000000000001, 145.97, NA, NA, 63.950000000000003, 
    NA, 19.989999999999998, NA, NA, NA, NA, 47.969999999999999, 
    NA, NA, NA, NA, 56.960000000000001, 200.75, NA, NA, NA, 11.960000000000001, 
    NA, NA, NA, NA, 15.27, 12.26, 71.969999999999999, 78.969999999999999, 
    89.959999999999994, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, 255.97, NA, NA, 102.75, NA, NA, NA, NA, 138.97, 53.960000000000001, 
    159.96000000000001, 45.960000000000001, 17.260000000000002, 
    109.97, NA, NA, NA, NA, NA, NA, NA, NA, 29.5, 118.95999999999999, 
    14.76, 89.969999999999999, NA, 89.969999999999999, NA, NA, 
    27.460000000000001, 229.27000000000001, NA, 145.97, 169.97, 
    NA, NA, 6.9699999999999998, 179.97, NA, 19.949999999999999, 
    14.960000000000001, NA, 27.969999999999999, 49.950000000000003, 
    99.989999999999995, 59.990000000000002, 66.959999999999994, 
    NA, 139.96000000000001, 10.960000000000001, NA, NA, 229.97, 
    NA, 15, 256.75, NA, 19.960000000000001, 34.259999999999998, 
    35.960000000000001, NA, NA, NA, NA, NA, 24.960000000000001, 
    24.960000000000001, NA, NA, 35.960000000000001), bookstore_used = c(44.969999999999999, 
    10.960000000000001, NA, 11, 43.259999999999998, 67, NA, 149, 
    162, NA, 12.75, NA, 9.9600000000000009, NA, 20.25, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 8.9600000000000009, 
    NA, NA, 32.969999999999999, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, 99.75, NA, NA, NA, 167.25, 38.960000000000001, 12, 
    NA, 91.959999999999994, NA, NA, NA, NA, 27.75, 15, 9.2599999999999998, 
    12.460000000000001, 99.959999999999994, NA, 88.25, 88.25, 
    NA, 51.960000000000001, NA, 9.9600000000000009, NA, NA, 41.960000000000001, 
    NA, NA, NA, NA, NA, NA, 42.960000000000001, 84.959999999999994, 
    NA, 140.25999999999999, NA, NA, NA, 88.25, 170.75, 109.95999999999999, 
    NA, NA, NA, NA, NA, 48, NA, 15, NA, NA, NA, NA, 44.969999999999999, 
    NA, NA, NA, NA, 43.460000000000001, 150.75, NA, NA, NA, 9.9600000000000009, 
    NA, NA, NA, NA, 9.75, 9.9600000000000009, 55.969999999999999, 
    39.969999999999999, 84.959999999999994, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, 198.25, NA, NA, 77.25, NA, NA, 
    NA, NA, 99.969999999999999, 43.960000000000001, 119.95999999999999, 
    35.960000000000001, 12.460000000000001, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, 22.25, 75.959999999999994, 10.960000000000001, 
    75, NA, 76.5, NA, NA, 23.460000000000001, NA, NA, NA, NA, 
    NA, NA, 5.4699999999999998, 139.97, NA, 12.75, 12.960000000000001, 
    NA, 19.5, 37.5, 69.969999999999999, 45, 56.960000000000001, 
    NA, 119.95999999999999, 9.2599999999999998, NA, NA, NA, NA, 
    11.25, 192.75, NA, 17.260000000000002, 25.460000000000001, 
    24.960000000000001, NA, NA, NA, NA, NA, 19.960000000000001, 
    19.960000000000001, NA, NA, 24.960000000000001), amazon_new = c(47.450000000000003, 
    13.550000000000001, NA, 12.529999999999999, 54.950000000000003, 
    124.8, NA, NA, NA, NA, 11.77, NA, 10.869999999999999, NA, 
    38.939999999999998, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, 8.9900000000000002, NA, NA, 35, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, 78.670000000000002, 152.94999999999999, 
    152.94999999999999, NA, NA, 41.990000000000002, 12.23, NA, 
    119.95, NA, NA, NA, NA, 36.950000000000003, 19.949999999999999, 
    10.869999999999999, 16.789999999999999, NA, NA, 112.98999999999999, 
    112.98999999999999, NA, 60.350000000000001, NA, 13.49, NA, 
    NA, 49.310000000000002, NA, NA, NA, NA, NA, NA, 55.740000000000002, 
    100.19, NA, 111.76000000000001, NA, NA, NA, NA, NA, 133.75, 
    NA, 229.37, 146.94999999999999, NA, NA, 57.229999999999997, 
    NA, 17, NA, NA, NA, NA, 47.450000000000003, NA, NA, NA, NA, 
    43.079999999999998, NA, NA, NA, NA, 12.289999999999999, NA, 
    NA, NA, NA, 14.4, 14, 41.490000000000002, 76.700000000000003, 
    98.200000000000003, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 136.91999999999999, 
    NA, 126.70999999999999, 50.950000000000003, 16.789999999999999, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, 30.329999999999998, 97.920000000000002, 
    13.390000000000001, 89.890000000000001, NA, 87.879999999999995, 
    NA, NA, 29.949999999999999, 229.37, NA, 146.94999999999999, 
    NA, NA, NA, 7.9400000000000004, 158.96000000000001, NA, 14.949999999999999, 
    12.23, NA, 29.949999999999999, NA, 86.489999999999995, 56.990000000000002, 
    66.989999999999995, NA, NA, 9.9900000000000002, NA, NA, NA, 
    NA, NA, NA, NA, 23.199999999999999, 29.359999999999999, 32.399999999999999, 
    NA, NA, NA, NA, NA, 23.789999999999999, 23.789999999999999, 
    NA, NA, 32.399999999999999), amazon_used = c(51.200000000000003, 
    7.0999999999999996, NA, NA, 24.829999999999998, 47.75, NA, 
    NA, 90, NA, 5.4100000000000001, NA, 6.75, NA, NA, NA, NA, 
    NA, NA, NA, NA, 191.05000000000001, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, 34.299999999999997, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, 69.689999999999998, 504.5, 504.5, NA, NA, 22, 
    15.65, NA, 96.790000000000006, NA, NA, NA, NA, 12.890000000000001, 
    12.98, 7.2999999999999998, 5.5800000000000001, NA, NA, 66.969999999999999, 
    66.969999999999999, NA, 38.310000000000002, NA, 5.9199999999999999, 
    NA, NA, 33.490000000000002, NA, NA, NA, NA, NA, NA, 33.350000000000001, 
    84.319999999999993, NA, 26.719999999999999, NA, NA, NA, 53.060000000000002, 
    NA, 113.3, NA, 164.44999999999999, 289.64999999999998, NA, 
    NA, 45.770000000000003, NA, 13.369999999999999, NA, NA, NA, 
    NA, 51.200000000000003, NA, NA, NA, NA, 43.25, NA, NA, NA, 
    NA, 5.8799999999999999, NA, NA, NA, NA, 5.4400000000000004, 
    5.7000000000000002, 39.420000000000002, 28.280000000000001, 
    84.319999999999993, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, 183.37, NA, NA, 71.359999999999999, NA, NA, NA, NA, 
    35.539999999999999, 41.950000000000003, 77.439999999999998, 
    12.98, 5.5800000000000001, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, 28.420000000000002, 48.960000000000001, 5.5999999999999996, 
    33.979999999999997, NA, 56.170000000000002, NA, NA, 16.390000000000001, 
    164.44999999999999, NA, 289.64999999999998, 89.599999999999994, 
    NA, NA, 4.8399999999999999, 75.819999999999993, NA, 12.98, 
    12.23, NA, 32.729999999999997, NA, 70.709999999999994, 45.579999999999998, 
    48.229999999999997, NA, NA, 5.5, NA, NA, NA, NA, NA, NA, 
    NA, 20.48, 24.989999999999998, 17.469999999999999, NA, NA, 
    NA, NA, NA, 17.800000000000001, 17.800000000000001, NA, NA, 
    17.469999999999999), notes = structure(c(1L, 1L, NA, 1L, 
    NA, NA, NA, 1L, 7L, 1L, NA, 1L, 1L, 1L, 1L, NA, NA, 1L, NA, 
    NA, 1L, 1L, NA, NA, NA, NA, NA, NA, 1L, 6L, NA, NA, 1L, 1L, 
    1L, NA, NA, NA, 1L, 1L, 1L, NA, NA, NA, 1L, 1L, 13L, 1L, 
    NA, 1L, 1L, NA, 1L, 1L, NA, NA, 1L, NA, 1L, 5L, 5L, NA, NA, 
    1L, NA, NA, NA, 1L, 1L, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L, NA, 
    1L, NA, NA, NA, 1L, 10L, 3L, 1L, 1L, 18L, 1L, NA, NA, NA, 
    NA, 1L, 1L, 1L, 1L, NA, NA, NA, NA, NA, NA, NA, 4L, NA, NA, 
    NA, NA, NA, NA, 1L, 1L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, 14L, NA, NA, 14L, NA, NA, 
    NA, NA, 17L, 14L, NA, NA, NA, 5L, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 18L, NA, 16L, 
    8L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9L, NA, NA, NA, NA, 
    11L, NA, NA, NA, 12L, NA, 15L, 2L, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA), .Label = c("", "Amazon did not sell this book", 
    "Custom version, could not find comparable requiring access code", 
    "Custom version, not available on Amazon", "Custom version, not available on Amazon.com", 
    "Instructor is author, no Amazon option", "New version only from resellers", 
    "No New Amazon sold option, used likely not with required access code", 
    "No sellers for used", "Not available from Amazon.com for new", 
    "Not available on Amazon, custom book and access code", "Not available on Amazon, special version and access code", 
    "Not currently sold by Amazon.com", "Not for sale by Amazon", 
    "Not found on Amazon", "Unclear if used on Amazon would come with required access card", 
    "Unclear if used version would come with access code", "Used on Amazon probably missing online access"
    ), class = "factor")), row.names = c(NA, -201L), class = c("tbl_df", 
"tbl", "data.frame"))
