data dive week-4
2024-02-11
data <- read.csv ("C:\\Users\\91630\\OneDrive\\Desktop\\statistics\\age_gaps.CSV")set.seed(42)
for (i in 1:5) {
sample_size <- round(0.5 * nrow(data))
random_indices <- sample(1:nrow(data), size = sample_size, replace = TRUE)
sample_df <- data[random_indices, ]
assign(paste0("df_", i), sample_df)
}print("First 5 rows of df_1:")## [1] "First 5 rows of df_1:"print(df_1)## movie_name release_year
## 561 Y tu mamá también 2001
## 321 From Russia with Love 1963
## 1098 Slackers 2002
## 634 Click 2006
## 49 Café Society 2016
## 1152 The Lake House 2006
## 24 Mighty Aphrodite 1995
## 356 Layer Cake 2004
## 165 Singin' in the Rain 1952
## 622 Aquaman 2018
## 410 The Wrestler 2008
## 899 Hacksaw Ridge 2016
## 601 Solaris 2002
## 932 Savages 2007
## 997 Inventing the Abbotts 1997
## 621 A Walk on the Moon 1999
## 860 The Artist 2011
## 259 Damage 1992
## 481 How to Lose a Guy in 10 Days 2003
## 24.1 Mighty Aphrodite 1995
## 299 Private Parts 1997
## 406 The Girl on the Train 2016
## 314 An Education 2009
## 836 Grosse Pointe Blank 1997
## 982 Dave 1993
## 146 Richard III 1995
## 197 Always Be My Maybe 2019
## 226 An Affair to Remember 1957
## 504 The Nutty Professor 1996
## 984 Down and Out in Beverly Hills 1986
## 727 The Age of Adaline 2015
## 945 The Disaster Artist 2017
## 922 Neighbors 2014
## 626 Barbershop 2002
## 262 Hobbs & Shaw 2019
## 390 In the Heights 2021
## 130 Blame 2017
## 1027 The Girl with the Dragon Tattoo 2011
## 374 Wonder Boys 2000
## 770 Heartbreakers 2001
## 1015 Sex and the City 2008
## 698 Bridesmaids 2011
## 650 Gone Girl 2014
## 40 Arbitrage 2012
## 517 Boogie Nights 1997
## 33 Funny Face 1957
## 945.1 The Disaster Artist 2017
## 103 The Horse Whisperer 1998
## 109 Moonraker 1979
## 1002 Le notti di Cabiria 1957
## 76 I Married a Witch 1942
## 980 Crazy, Stupid, Love 2011
## 733 The Graduate 1967
## 16 Lost in Translation 2003
## 1006 Love & Other Drugs 2010
## 988 Enchanted 2007
## 82 Spun 2002
## 914 Les Miserables 2012
## 325 Licence to Kill 1989
## 951 The Matrix 1999
## 296 Licence to Kill 1989
## 149 The Great Dictator 1940
## 996 Imagine Me & You 2005
## 100 Moonraker 1979
## 999 Jurassic World 2015
## 298 Once Upon a Time in Hollywood 2019
## 402 Taken 2008
## 91 American Gangster 2007
## 781 Monster 2003
## 1078 Juno 2007
## 851 My Best Friend's Wedding 1997
## 288 Analyze This 1999
## 720 Schindler's List 1993
## 60 The Squid and the Whale 2005
## 849 Midsommar 2019
## 969 Aladdin 2019
## 377 You Only Live Twice 1967
## 638 Contact 1997
## 427 Larry Crowne 2011
## 442 Stoker 2013
## 1136 Good Will Hunting 1997
## 584 Killing Zoe 1994
## 951.1 The Matrix 1999
## 969.1 Aladdin 2019
## 311 A Perfect Murder 1998
## 912 Knocked Up 2007
## 474 EDtv 1999
## 811 Zombieland 2009
## 42 Mank 2020
## 760 Days of Thunder 1990
## 25 Stone 2010
## 703 Dr. No 1962
## 526 Don't Mess with the Zohan 2008
## 623 Aquaman 2018
## 262.1 Hobbs & Shaw 2019
## 943 The Dark Knight 2008
## 299.1 Private Parts 1997
## 720.1 Schindler's List 1993
## 369 The Polka King 2018
## 512 A History of Violence 2005
## 758 Cold Mountain 2003
## 754 Captain America: Civil War 2016
## 799 The Good Shepherd 2006
## 862 The Favourite 2018
## 774 L.A. Story 1991
## 294 Home Again 2017
## 607 The Count of Monte Cristo 2002
## 764 Evan Almighty 2007
## 783 Mrs. Doubtfire 1993
## 546 The Count of Monte Cristo 2002
## 426 Indiana Jones and the Temple of Doom 1984
## 471 Death to Smoochy 2002
## 188 Suicide Squad 2016
## 254 Trees Lounge 1996
## 268 Now You See Me 2013
## 640 Delicatessen 1991
## 41 Bulworth 1998
## 824 Boogie Nights 1997
## 664 Lord of War 2005
## 1122 Wonder 2017
## 537 Mona Lisa Smile 2003
## 348 Elf 2003
## 107 Flight 2012
## 317 Dreamgirls 2006
## 1038 The Vow 2012
## 162 Patch Adams 1998
## 636 Clueless 1995
## 706 Funny People 2009
## 906 Independence Day: Resurgence 2016
## 796 The Big Sick 2017
## 630 Café Society 2016
## 1061 Brokeback Mountain 2005
## 1009 Office Space 1999
## 773 King Kong 2005
## 78 Knock Knock 2015
## 526.1 Don't Mess with the Zohan 2008
## 1121 Wild Mountain Thyme 2020
## 483 Little Black Book 2004
## 182 Mother's Day 2016
## 1151 The Hunger Games: Catching Fire 2013
## 992 Four Weddings and a Funeral 1994
## 337 The Living Daylights 1987
## 539 Pusher 1996
## 868 The Rock 1996
## 212 Rushmore 1998
## 201 Color of Night 1994
## 62 As Good as It Gets 1997
## 1148 Spider-Man: Far From Home 2019
## 1087 My Big Fat Greek Wedding 2002
## 561.1 Y tu mamá también 2001
## 573 Dan in Real Life 2007
## 540 Pushing Tin 1999
## 2 Venus 2006
## 671 Never Been Kissed 1999
## 1151.1 The Hunger Games: Catching Fire 2013
## 771 Here on Earth 2000
## 741 Zoolander 2001
## 269 Regarding Henry 1991
## 1036 The Private Lives of Pippa Lee 2009
## 705 Four Christmases 2008
## 670 Mrs. Doubtfire 1993
## 444 The Big Lebowski 1998
## 677 Spider-Man 2002
## 912.1 Knocked Up 2007
## 1034 The Other Woman 2014
## 967 A Walk on the Moon 1999
## 850 Moulin Rouge! 2001
## 181 Mighty Aphrodite 1995
## 751 Armageddon 1998
## 463 You Only Live Twice 1967
## 605 The Bodyguard 1992
## 84 The Big Sleep 1946
## 985 EDtv 1999
## 464 Zombieland: Double Tap 2019
## 984.1 Down and Out in Beverly Hills 1986
## 225 47 Ronin 2013
## 1126 10 Things I Hate About You 1999
## 171 Training Day 2001
## 489 Rebecca 1940
## 922.1 Neighbors 2014
## 694 A Walk to Remember 2002
## 1023 The Butterfly Effect 2004
## 1109 The Martian 2015
## 153 V for Vendetta 2005
## 961 White Men Can't Jump 1992
## 240 Oblivion 2013
## 49.1 Café Society 2016
## 156 Jurassic Park 1993
## 274 Sister Act 1992
## 762 Ed Wood 1994
## 170 The Spy Who Loved Me 1977
## 292 Eternal Sunshine of the Spotless Mind 2004
## 148 The Bubble 2022
## 719 Savages 2007
## 189 The Bridges of Madison County 1995
## 1137 He's Just Not That Into You 2009
## 1051 A Knight's Tale 2001
## 606 The Constant Gardener 2005
## 645 Fifty Shades Darker 2017
## 1110 The Painted Veil 2006
## 250 The Only Living Boy in New York 2017
## 169 The Only Living Boy in New York 2017
## 907 Inferno 2016
## 732 The Godfather 1972
## 550 The Great Gatsby 1974
## 457 Tomorrow Never Dies 1997
## 919 Mickey Blue Eyes 1999
## 115 The First Wives Club 1996
## 732.1 The Godfather 1972
## 985.1 EDtv 1999
## 438 Singin' in the Rain 1952
## 625 Away We Go 2009
## 542 Raiders of the Lost Ark 1981
## 699 Chasing Liberty 2004
## 917 Made of Honor 2008
## 966 A Simple Favor 2018
## 1129 American Hustle 2013
## 88 Vertigo 1958
## 391 Inferno 2016
## 398 Mr. & Mrs. Smith 2005
## 268.1 Now You See Me 2013
## 819 Along Came Polly 2004
## 1145 Soul Food 1997
## 37 The Quiet American 1958
## 706.1 Funny People 2009
## 163 Pushing Tin 1999
## 46 A Perfect Murder 1998
## 859 Spectre 2015
## 868.1 The Rock 1996
## 376 Yogi Bear 2010
## 821 Anchorman: The Legend of Ron Burgundy 2004
## 280 The Prestige 2006
## 934 She's All That 1999
## 756 Cinderella Man 2005
## 981 Dan in Real Life 2007
## 715 Nightmare Alley 2021
## 206 Pretty Woman 1990
## 605.1 The Bodyguard 1992
## 918 Me Before You 2016
## 684 The Hustler 1961
## 765 Ex Machina 2014
## 647 For Love of the Game 1999
## 159 Moonraker 1979
## 928 Pushing Tin 1999
## 978 Casanova 2005
## 935 Six Days, Seven Nights 1998
## 653 Honey 2003
## 1078.1 Juno 2007
## 514 Accepted 2006
## 540.1 Pushing Tin 1999
## 387 Gone with the Wind 1939
## 929 Ready Player One 2018
## 989 Enemy at the Gates 2001
## 747 All About the Benjamins 2002
## 627 Billy Madison 1995
## 620 You Only Live Twice 1967
## 202 Guess Who's Coming to Dinner 1967
## 73 Charade 1963
## 335 The Gift 2000
## 1070 Get Out 2017
## 131 Bugsy 1991
## 1085 Monster Trucks 2017
## 1044 Unforgettable 2017
## 682 The Duchess 2008
## 983 Doctor Strange 2016
## 529 Heartbreakers 2001
## 82.1 Spun 2002
## 273 Scoop 2006
## 94 Dark Shadows 2012
## 330 Ted 2 2015
## 933 Selma 2014
## 179 Firewall 2006
## 1098.1 Slackers 2002
## 304 The Gift 2000
## 930 Risky Business 1983
## 954 The Story of Us 1999
## 473 Django Unchained 2012
## 829 Chasing Amy 1997
## 1140 Me Before You 2016
## 726 Summer Catch 2001
## 1011 Pitch Perfect 2012
## 108 Hobbs & Shaw 2019
## 659 John Wick 2014
## 601.1 Solaris 2002
## 618 Top Gun: Maverick 2022
## 103.1 The Horse Whisperer 1998
## 755 Chips 2017
## 843 Juno 2007
## 197.1 Always Be My Maybe 2019
## 746 Adrift 2018
## 1073 He's Just Not That Into You 2009
## 460 Wimbledon 2004
## 1046 Walk the Line 2005
## 132 Deja Vu 2006
## 187 Suburban Girl 2007
## 53 Last Tango in Paris 1972
## 934.1 She's All That 1999
## 637 Con Air 1997
## 1083 Marriage Story 2019
## 837 Happy Gilmore 1996
## 682.1 The Duchess 2008
## 702 Down to Earth 2001
## 87 To Have and Have Not 1944
## 228 Blade Runner 1982
## 3 The Quiet American 2002
## 607.1 The Count of Monte Cristo 2002
## 703.1 Dr. No 1962
## 402.1 Taken 2008
## 138 It's Complicated 2009
## 481.1 How to Lose a Guy in 10 Days 2003
## 767 Goldfinger 1964
## 821.1 Anchorman: The Legend of Ron Burgundy 2004
## 869 The Scarlet Letter 1995
## 669 Me, Myself & Irene 2000
## 708 Hitch 2005
## 905 Imagine Me & You 2005
## 1111 The Sum of All Fears 2002
## 496 Thank You for Smoking 2006
## 218 The Man with the Golden Gun 1974
## 332 The Beach 2000
## 110 Octopussy 1983
## 1143 She's Out of My League 2010
## 619 Y tu mamá también 2001
## 727.1 The Age of Adaline 2015
## 1078.2 Juno 2007
## 27 Third Person 2013
## 592 National Lampoon's Vacation 1983
## 1134 Daddy's Little Girls 2007
## 924 One Fine Day 1996
## 527 Everything Everywhere All at Once 2022
## 870 The Spanish Prisoner 1997
## 1047 We Were Soldiers 2002
## 778 Les Miserables 2012
## 870.1 The Spanish Prisoner 1997
## 267 Nights in Rodanthe 2008
## 1147 Speed 1994
## 493 Spanglish 2004
## 100.1 Moonraker 1979
## 1020 Tag 2018
## 603 That Thing You Do! 1996
## 504.1 The Nutty Professor 1996
## 281 The Raven 2012
## 536 Man of Steel 2013
## 528 He's Just Not That Into You 2009
## 1027.1 The Girl with the Dragon Tattoo 2011
## 824.1 Boogie Nights 1997
## 743 27 Dresses 2008
## 195 A Single Man 2009
## 341 3000 Miles to Graceland 2001
## 1002.1 Le notti di Cabiria 1957
## 659.1 John Wick 2014
## 925 Palo Alto 2013
## 170.1 The Spy Who Loved Me 1977
## 375 X-Men: First Class 2015
## 158 L.A. Story 1991
## 941 The Beach 2000
## 554 The Other Woman 2014
## 98 Knock Knock 2015
## 820 American Beauty 1999
## 427.1 Larry Crowne 2011
## 683 The Fast and the Furious 2001
## 1050 9 1/2 Weeks 1986
## 1121.1 Wild Mountain Thyme 2020
## 1141 Mona Lisa Smile 2003
## 646 Fifty Shades of Grey 2015
## 826 Broken Hill 2009
## 791 Sleepless in Seattle 1993
## 938 Tag 2018
## 425 I Could Never Be Your Woman 2007
## 252 The Wolverine 2013
## 751.1 Armageddon 1998
## 85 The Misfits 1961
## 1057 Beverly Hills Ninja 1997
## 1128 A Simple Favor 2018
## 398.1 Mr. & Mrs. Smith 2005
## 130.1 Blame 2017
## 126 The Prom 2020
## 547 The Fabulous Baker Boys 1989
## 156.1 Jurassic Park 1993
## 237 Knock Knock 2015
## 765.1 Ex Machina 2014
## 503 The Normal Heart 2014
## 614 The Water Boy 1998
## 1126.1 10 Things I Hate About You 1999
## 968 Ace Ventura: Pet Detective 1994
## 95 Halloween III: Season of the Witch 1982
## 983.1 Doctor Strange 2016
## 233 Green Book 2018
## 522 Call Me by Your Name 2017
## 379 Allied 2016
## 1018 Something New 2006
## 331 The American President 1995
## 987 Ed Wood 1994
## 271 Rock of Ages 2012
## 1092 Requiem for a Dream 2000
## 828 Café Society 2016
## 615 The World Is Not Enough 1999
## 491 Smokey and the Bandit 1977
## 138.1 It's Complicated 2009
## 712 Legally Blonde 2001
## 988.1 Enchanted 2007
## 355 Joy 2015
## 685 The Kids Are All Right 2010
## 1108 The Last of the Mohicans 1992
## 1144 Slackers 2002
## 688 There's Something About Mary 1998
## 8 Entrapment 1999
## 864 The Good Girl 2002
## 105 A Walk in the Woods 2015
## 176 Baby Driver 2017
## 141 Licence to Kill 1989
## 103.2 The Horse Whisperer 1998
## 530 Heat 1995
## 133 Don Jon 2013
## 543 Seven 1995
## 532 It's Complicated 2009
## 1127 A Happening of Monumental Proportions 2017
## 753 Blue Crush 2002
## 1018.1 Something New 2006
## 707 Garden State 2004
## 752 Baby Driver 2017
## 264 Love Actually 2003
## 275 The Diary of a Teenage Girl 2015
## 1138 Killing Me Softly 2002
## 266 My Life 1993
## 163.1 Pushing Tin 1999
## 1056 Before Sunrise 1995
## 566 American Hustle 2013
## 490 Return to Me 2000
## 461 Win a Date with Tad Hamilton! 2004
## 1035 The Other Woman 2014
## 589 Manhattan 1979
## 738 The Way We Were 1973
## 143 My Cousin Vinny 1992
## 1061.1 Brokeback Mountain 2005
## 398.2 Mr. & Mrs. Smith 2005
## 485 Man of Steel 2013
## 256 American Hustle 2013
## 346 Bridge of Spies 2015
## 689 Thunderball 1965
## 412 Up in the Air 2009
## 722 Slumdog Millionaire 2008
## 827 Brooklyn 2015
## 273.1 Scoop 2006
## 946 The Fault in Our Stars 2014
## 39 A View to a Kill 1985
## 418 Black Panther 2018
## 204 Octopussy 1983
## 222 Whatever Works 2009
## 163.2 Pushing Tin 1999
## 660 Kate & Leopold 2001
## 474.1 EDtv 1999
## 985.2 EDtv 1999
## 520 Bridget Jones's Diary 2001
## 668 Mamma Mia! 2008
## 951.2 The Matrix 1999
## 97 Indiana Jones and the Last Crusade 1989
## 760.1 Days of Thunder 1990
## 1010 Old School 2003
## 184 Skyfall 2012
## 318 Dreamgirls 2006
## 69 Manhattan 1979
## 268.2 Now You See Me 2013
## 378 All About the Benjamins 2002
## 18 Wild Target 2010
## 242 Rock of Ages 2012
## 178 Buffalo '66 1998
## 943.1 The Dark Knight 2008
## 674 Remember the Titans 2000
## 180 Gone Girl 2014
## 1023.1 The Butterfly Effect 2004
## 1107 The Family Stone 2007
## 506 The Time Traveler's Wife 2009
## 747.1 All About the Benjamins 2002
## 1064 Cocktail 1988
## 938.1 Tag 2018
## 790 Romeo + Juliet 1996
## 900 He's Just Not That Into You 2009
## 236 Kindergarten Cop 1990
## 375.1 X-Men: First Class 2015
## 691 Wild Things 1998
## 295 Liberal Arts 2012
## 521 Bull Durham 1988
## 831 Chinatown 1974
## 531 I Love You Phillip Morris 2009
## 21 The Mother 2003
## 1003 Legally Blonde 2001
## 959 Us 2019
## 492 Snowden 2016
## 866 The Italian Job 2003
## 372 When Harry Met Sally... 1989
## 627.1 Billy Madison 1995
## 1116 Tommy Boy 1995
## 420 Boogie Nights 1997
## 697 Atonement 2007
## 989.1 Enemy at the Gates 2001
## 57 Six Days, Seven Nights 1998
## 803 The Whole Truth 2016
## 1037 The Twilight Saga: New Moon 2009
## 503.1 The Normal Heart 2014
## 809 Win a Date with Tad Hamilton! 2004
## 427.2 Larry Crowne 2011
## 348.1 Elf 2003
## 394 La decima vittima 1965
## 864.1 The Good Girl 2002
## 1134.1 Daddy's Little Girls 2007
## 539.1 Pusher 1996
## 353 How Stella Got Her Groove Back 1998
## 256.1 American Hustle 2013
## 1089 Pearl Harbor 2001
## 756.1 Cinderella Man 2005
## 58 Tadpole 2002
## 480 High School High 1996
## 540.2 Pushing Tin 1999
## 1003.1 Legally Blonde 2001
## 511 50 First Dates 2004
## 361 Roman Holiday 1953
## 158.1 L.A. Story 1991
## 371 The World Is Not Enough 1999
## 757 Closer 2004
## 354 John Q. 2002
## 489.1 Rebecca 1940
## 684.1 The Hustler 1961
## 984.2 Down and Out in Beverly Hills 1986
## 637.1 Con Air 1997
## 284 Troy 2004
## 816 A Walk in the Clouds 1995
## 530.1 Heat 1995
## 561.2 Y tu mamá también 2001
## 724 Spider-Man: Homecoming 2017
## 863 The Favourite 2018
## 872 Twilight 2008
## 30 A View to a Kill 1985
## 947 The Favourite 2018
## 943.2 The Dark Knight 2008
## 58.1 Tadpole 2002
## 678 State of the Union 1948
## 1123 X-Men: First Class 2015
## 833 Dr. No 1962
## 175 An American in Paris 1951
## 1065 Dodgeball 2004
## 710 L'Avventura 1960
## 910 Juno 2007
## 649 Goldfinger 1964
## 92 Beyond the Sea 2004
## 415 A Star Is Born 2018
## 664.1 Lord of War 2005
## 454 The Thomas Crown Affair 1968
## 497 The Aviator 2004
## 267.1 Nights in Rodanthe 2008
## 443 Sweet November 2001
## 875 Wayne's World 1993
## 296.1 Licence to Kill 1989
## 459 While You Were Sleeping 1995
## 225.1 47 Ronin 2013
## 823 Batman Begins 2005
## 546.1 The Count of Monte Cristo 2002
## 238 Laggies 2014
## 1025 The Family Stone 2007
## 569 Chloe 2009
## 823.1 Batman Begins 2005
## 914.1 Les Miserables 2012
## 975 Big Daddy 1999
## 71 The Color of Money 1986
## 10 Magnolia 1999
## 1093 Ride Along 2014
## 856 Soul Food 1997
## 166 The Duchess 2008
## 789 Queen of the Damned 2002
## 63 Behind the Candelabra 2013
## 489.2 Rebecca 1940
## 35 Kindergarten Cop 2 2016
## 259.1 Damage 1992
## 201.1 Color of Night 1994
## 960 Wedding Crashers 2005
## 813 300 2007
## 1115 Titanic 1997
## 487 Prima della rivoluzione 1964
## director age_difference couple_number
## 561 Alfonso Cuarón 9 1
## 321 Terence Young 14 1
## 1098 Dewey Nicks 1 1
## 634 Frank Coraci 7 1
## 49 Woody Allen 28 1
## 1152 Alejandro Agresti 0 1
## 24 Woody Allen 32 1
## 356 Matthew Vaughn 13 1
## 165 Stanley Donen, Gene Kelly 20 1
## 622 James Wan 7 1
## 410 Darren Aronofsky 12 1
## 899 Mel Gibson 3 1
## 601 Steven Soderbergh 8 1
## 932 undefined 3 2
## 997 Pat O'Connor 2 2
## 621 Tony Goldwyn 7 1
## 860 Michel Hazanavicius 4 1
## 259 Louis Malle 16 1
## 481 Donald Petrie 10 1
## 24.1 Woody Allen 32 1
## 299 Betty Thomas 15 1
## 406 Tate Taylor 12 3
## 314 Lone Scherfig 14 1
## 836 George Armitage 4 1
## 982 Ivan Reitman 2 1
## 146 Richard Loncraine 21 1
## 197 Nahnatchka Khan 18 1
## 226 Leo McCarey 17 1
## 504 Tom Shadyac 10 1
## 984 Paul Mazursky 2 2
## 727 Lee Toland Krieger 6 1
## 945 James Franco 3 1
## 922 Nicholas Stoller 3 1
## 626 Tim Story 7 1
## 262 David Leitch 16 2
## 390 John M. Chu 12 1
## 130 Quinn Shephard 21 1
## 1027 David Fincher 2 2
## 374 Curtis Hanson 13 1
## 770 David Mirkin 5 2
## 1015 Michael Patrick King 2 4
## 698 Paul Feig 6 1
## 650 David Fincher 7 2
## 40 Nicholas Jarecki 29 1
## 517 Paul Thomas Anderson 9 2
## 33 Stanley Donen 30 1
## 945.1 James Franco 3 1
## 103 Robert Redford 24 1
## 109 Lewis Gilbert 23 2
## 1002 Federico Fellini 2 1
## 76 René Clair 25 1
## 980 Glenn Ficarra, John Requa 2 2
## 733 Mike Nichols 6 1
## 16 Sofia Coppola 34 1
## 1006 Edward Zwick 2 1
## 988 Kevin Lima 2 2
## 82 Jonas Åkerlund 25 1
## 914 Tom Hooper 3 2
## 325 John Glen 14 3
## 951 Lana Wachowski, Lilly Wachowski 3 1
## 296 John Glen 15 2
## 149 Charlie Chaplin 21 1
## 996 Ol Parker 2 2
## 100 Lewis Gilbert 24 1
## 999 Colin Trevorrow 2 1
## 298 Quentin Tarantino 15 1
## 402 Pierre Morel 12 1
## 91 Ridley Scott 24 1
## 781 Patty Jenkins 5 1
## 1078 Jason Reitman 1 3
## 851 P.J. Hogan 4 2
## 288 Harold Ramis 15 1
## 720 Steven Spielberg 6 2
## 60 Noah Baumbach 27 1
## 849 Ari Aster 4 1
## 969 Guy Ritchie 2 1
## 377 Lewis Gilbert 13 1
## 638 Robert Zemeckis 7 1
## 427 Tom Hanks 11 1
## 442 Park Chan-wook 11 1
## 1136 Gus Van Sant 0 1
## 584 Roger Avary 8 1
## 951.1 Lana Wachowski, Lilly Wachowski 3 1
## 969.1 Guy Ritchie 2 1
## 311 Andrew Davis 14 2
## 912 Judd Apatow 3 2
## 474 Ron Howard 10 1
## 811 Ruben Fleischer 5 1
## 42 David Fincher 29 1
## 760 Tony Scott 5 1
## 25 John Curran 32 1
## 703 Terence Young 6 1
## 526 Dennis Dugan 9 1
## 623 James Wan 7 2
## 262.1 David Leitch 16 2
## 943 Christopher Nolan 3 1
## 299.1 Betty Thomas 15 1
## 720.1 Steven Spielberg 6 2
## 369 Maya Forbes 13 1
## 512 David Cronenberg 9 1
## 758 Anthony Minghella 5 1
## 754 Joe & Anthony Russo 5 1
## 799 Robert De Niro 5 2
## 862 Yorgos Lanthimos 4 2
## 774 Mick Jackson 5 2
## 294 Hallie Meyers-Shyer 15 1
## 607 Kevin Reynolds 8 2
## 764 Tom Shadyac 5 1
## 783 Chris Columbus 5 2
## 546 Kevin Reynolds 9 1
## 426 Steven Spielberg 11 1
## 471 Danny DeVito 10 1
## 188 David Ayer 19 1
## 254 Steve Buscemi 17 1
## 268 Louis Leterrier 16 1
## 640 Jean-Pierre Jeunet 7 1
## 41 Warren Beatty 29 1
## 824 Paul Thomas Anderson 4 4
## 664 Andrew Niccol 7 1
## 1122 Stephen Chbosky 1 1
## 537 Mike Newell 9 1
## 348 Jon Favreau 13 1
## 107 Robert Zemeckis 23 1
## 317 Bill Condon 14 1
## 1038 Michael Sucsy 2 2
## 162 Tom Shadyac 20 1
## 636 Amy Heckerling 7 1
## 706 Judd Apatow 6 1
## 906 Roland Emmerich 3 1
## 796 Michael Showalter 5 1
## 630 Woody Allen 7 2
## 1061 Ang Lee 1 3
## 1009 Mike Judge 2 1
## 773 Peter Jackson 5 1
## 78 Eli Roth 25 1
## 526.1 Dennis Dugan 9 1
## 1121 John Patrick Shanley 1 1
## 483 Nick Hurran 10 1
## 182 Garry Marshall 19 1
## 1151 Francis Lawrence 0 1
## 992 Mike Newell 2 1
## 337 John Glen 14 2
## 539 Nicolas Winding Refn 9 1
## 868 Michael Bay 4 1
## 212 Wes Anderson 18 1
## 201 Richard Rush 18 1
## 62 James L. Brooks 26 1
## 1148 Jon Watts 0 2
## 1087 Joel Zwick 1 1
## 561.1 Alfonso Cuarón 9 1
## 573 Peter Hedges 8 1
## 540 Mike Newell 9 3
## 2 Roger Michell 50 1
## 671 Raja Gosnell 7 1
## 1151.1 Francis Lawrence 0 1
## 771 Mark Piznarski 5 1
## 741 Ben Stiller 6 1
## 269 Mike Nichols 16 2
## 1036 Rebecca Miller 2 2
## 705 Seth Gordon 6 1
## 670 Chris Columbus 7 1
## 444 Joel Coen 11 2
## 677 Sam Raimi 7 1
## 912.1 Judd Apatow 3 2
## 1034 Nick Cassavetes 2 3
## 967 Tony Goldwyn 2 2
## 850 Baz Luhrmann 4 1
## 181 Woody Allen 19 3
## 751 Michael Bay 5 1
## 463 Lewis Gilbert 11 2
## 605 Mick Jackson 8 1
## 84 Howard Hawks 25 1
## 985 Ron Howard 2 2
## 464 Ruben Fleischer 11 2
## 984.1 Paul Mazursky 2 2
## 225 Carl Rinsch 17 1
## 1126 Gil Junger 0 2
## 171 Antoine Fuqua 20 1
## 489 Alfred Hitchcock 10 1
## 922.1 Nicholas Stoller 3 1
## 694 Adam Shankman 6 1
## 1023 Eric Bress, J. Mackye Gruber 2 1
## 1109 Ridley Scott 1 1
## 153 James McTeigue 21 1
## 961 Ron Shelton 3 1
## 240 Joseph Kosinski 17 1
## 49.1 Woody Allen 28 1
## 156 Steven Spielberg 20 1
## 274 Emile Ardolino 16 1
## 762 Tim Burton 5 1
## 170 Lewis Gilbert 20 1
## 292 Michel Gondry 15 2
## 148 Judd Apatow 21 1
## 719 undefined 6 1
## 189 Clint Eastwood 19 1
## 1137 Ken Kwapis 0 5
## 1051 Brian Helgeland 1 1
## 606 Fernando Meirelles 8 1
## 645 James Foley 7 1
## 1110 John Curran 1 1
## 250 Marc Webb 17 2
## 169 Marc Webb 20 1
## 907 Ron Howard 3 2
## 732 Francis Ford Coppola 6 1
## 550 Jack Clayton 9 2
## 457 Roger Spottiswoode 11 3
## 919 Kelly Makin 3 1
## 115 Hugh Wilson 23 1
## 732.1 Francis Ford Coppola 6 1
## 985.1 Ron Howard 2 2
## 438 Stanley Donen, Gene Kelly 11 2
## 625 Sam Mendes 7 1
## 542 Steven Spielberg 9 1
## 699 Andy Cadiff 6 1
## 917 Paul Weiland 3 2
## 966 Paul Feig 2 1
## 1129 David O. Russell 0 3
## 88 Alfred Hitchcock 25 1
## 391 Ron Howard 12 1
## 398 Doug Liman 12 1
## 268.1 Louis Leterrier 16 1
## 819 John Hamburg 4 1
## 1145 George Tillman Jr. 0 4
## 37 Joseph L. Mankiewicz 30 2
## 706.1 Judd Apatow 6 1
## 163 Mike Newell 20 1
## 46 Andrew Davis 28 1
## 859 Sam Mendes 4 2
## 868.1 Michael Bay 4 1
## 376 Eric Brevig 13 1
## 821 Adam McKay 4 1
## 280 Christopher Nolan 16 1
## 934 Robert Iscove 3 1
## 756 Ron Howard 5 1
## 981 Peter Hedges 2 2
## 715 Guillermo del Toro 6 2
## 206 Garry Marshall 18 1
## 605.1 Mick Jackson 8 1
## 918 Thea Sharrock 3 1
## 684 Robert Rossen 7 1
## 765 Alex Garland 5 2
## 647 Sam Raimi 7 1
## 159 Lewis Gilbert 20 3
## 928 Mike Newell 3 4
## 978 Lasse Hallström 2 1
## 935 Ivan Reitman 3 2
## 653 Bille Woodruff 7 1
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## 487 21print("First 5 rows of df_4:")## [1] "First 5 rows of df_4:"print(df_4)## movie_name release_year
## 1153 Tolkien 2019
## 506 The Time Traveler's Wife 2009
## 470 Daddy's Home 2015
## 1149 The Crush 1993
## 617 Tommy Boy 1995
## 932 Savages 2007
## 607 The Count of Monte Cristo 2002
## 967 A Walk on the Moon 1999
## 711 La Strada 1954
## 123 Luci del varietà 1951
## 980 Crazy, Stupid, Love 2011
## 100 Moonraker 1979
## 1004 Little Nicky 2000
## 265 Love Actually 2003
## 1092 Requiem for a Dream 2000
## 351 Feeling Minnesota 1996
## 1088 On Her Majesty's Secret Service 1969
## 804 The Wood 1999
## 604 The African Queen 1951
## 39 A View to a Kill 1985
## 492 Snowden 2016
## 773 King Kong 2005
## 789 Queen of the Damned 2002
## 718 Raising Arizona 1987
## 746 Adrift 2018
## 378 All About the Benjamins 2002
## 382 Beginners 2010
## 493 Spanglish 2004
## 949 The Graduate 1967
## 692 A Beautiful Mind 2001
## 888 Closer 2004
## 1141 Mona Lisa Smile 2003
## 294 Home Again 2017
## 78 Knock Knock 2015
## 276 The Family Stone 2007
## 267 Nights in Rodanthe 2008
## 954 The Story of Us 1999
## 223 Yes Man 2008
## 926 Pete's Dragon 2016
## 431 Old School 2003
## 262 Hobbs & Shaw 2019
## 492.1 Snowden 2016
## 1052 Angel Eyes 2001
## 1014 Safe Men 1998
## 419 Blow 2001
## 430 Monster's Ball 2001
## 192 The Naked Gun 1988
## 817 All About the Benjamins 2002
## 587 Love Stinks 1999
## 745 About Time 2013
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## 8 Entrapment 1999
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## 973 Austin Powers: International Man of Mystery 1997
## 1089 Pearl Harbor 2001
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## 1005 Love & Basketball 2000
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## 1098 Slackers 2002
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## 848 Meet the Parents 2000
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## 899 Hacksaw Ridge 2016
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## 8.1 Entrapment 1999
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## 70 Phantom Thread 2017
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## 59.1 The Oranges 2012
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## 1106 The Details 2011
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## 903 I Know What You Did Last Summer 1997
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## 1067 Fever Pitch 2005
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## 272 Safe Haven 2013
## 231 Free Guy 2021
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## 928 Pushing Tin 1999
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## 1 Harold and Maude 1971
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## 523 Closer 2004
## 826 Broken Hill 2009
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## 933 Selma 2014
## 1023.1 The Butterfly Effect 2004
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## 51 Gangster Squad 2013
## 18 Wild Target 2010
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## 483 Little Black Book 2004
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## 126 The Prom 2020
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## 300 Serena 2014
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## 578 Fantastic Beasts and Where to Find Them 2016
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## 605 The Bodyguard 1992
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## 108.1 Hobbs & Shaw 2019
## 665.1 Love Actually 2003
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## director age_difference couple_number
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## 45 Billy Bob Thornton Scarlett Johansson man
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## 24 Woody Allen Mira Sorvino man
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## 108.2 Jason Statham Eiza Gonzalez man
## 26.1 Alan Arkin Robin Wright man
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## 1008 man 1967-10-28 1969-10-15 36
## 33 woman 1899-05-10 1929-05-04 58
## 679 woman 1967-07-16 1974-11-17 39
## 503 man 1967-11-22 1977-10-11 47
## 1108 woman 1957-04-29 1958-08-18 35
## 830.1 woman 1965-05-24 1969-04-25 37
## 1109 woman 1982-08-13 1983-02-27 33
## 486 woman 1975-01-05 1985-04-17 46
## 1029.1 man 1990-08-15 1992-10-12 25
## 274 woman 1939-05-13 1955-11-13 53
## 538 woman 1963-10-31 1972-08-30 34
## 455 woman 1974-11-11 1985-08-16 39
## 854.1 woman 1961-01-10 1965-02-24 47
## 366 woman 1965-11-30 1978-05-12 42
## 26 woman 1934-03-26 1966-04-08 75
## 932.1 man 1987-08-25 1990-06-13 20
## 377.1 woman 1930-08-25 1943-11-20 37
## 173 woman 1956-10-04 1976-03-21 55
## 309.1 woman 1942-07-13 1957-08-09 46
## 513.1 woman 1897-08-31 1906-10-06 40
## 147 woman 1901-02-01 1922-04-03 57
## 510 woman 1976-11-29 1985-01-03 37
## 793 woman 1971-03-10 1976-02-25 47
## 410 woman 1952-09-16 1964-12-04 56
## 84.1 woman 1899-12-25 1924-09-16 47
## 1023.2 man 1976-03-26 1978-02-07 28
## 679.1 woman 1967-07-16 1974-11-17 39
## 362 woman 1952-06-07 1965-12-02 41
## 329.1 woman 1942-07-13 1956-10-21 35
## 894 woman 1963-12-18 1966-05-26 36
## 751 woman 1972-08-15 1977-07-01 26
## 578.1 woman 1976-10-20 1984-12-23 40
## 213 woman 1940-04-25 1958-04-29 43
## 147.1 woman 1901-02-01 1922-04-03 57
## 688 woman 1965-11-30 1972-08-30 33
## 506.1 woman 1968-08-09 1978-11-17 41
## 393 woman 1977-04-02 1989-10-14 34
## 229 woman 1964-10-03 1981-06-09 40
## 1091 woman 1966-09-09 1967-01-14 36
## 363.1 woman 1979-11-25 1992-08-12 37
## 903.1 woman 1976-03-08 1979-02-21 21
## 657 woman 1965-04-04 1972-09-27 43
## 771.1 woman 1978-07-21 1983-06-10 22
## 554.1 man 1972-08-30 1981-07-15 42
## 878.1 man 1964-11-05 1968-10-12 36
## 377.2 woman 1930-08-25 1943-11-20 37
## 445.1 woman 1971-04-18 1982-02-06 40
## 639 woman 1955-05-18 1962-08-06 45
## 805 woman 1978-04-19 1983-08-14 35
## 911.1 woman 1966-09-09 1969-02-11 45
## 893 woman 1968-09-25 1971-01-15 30
## 405.1 woman 1971-08-10 1983-02-23 45
## 1080 man 1994-04-12 1995-12-27 23
## 809 woman 1978-07-19 1983-01-02 26
## 638 man 1962-11-19 1969-11-04 35
## 81.2 woman 1949-12-04 1974-02-10 54
## 970 woman 1944-09-25 1946-01-05 70
## 637 woman 1964-01-07 1971-06-30 33
## 4.1 woman 1930-09-17 1975-11-08 68
## 139 woman 1966-09-09 1987-04-12 45
## 959 man 1983-03-01 1986-11-15 36
## 771.2 woman 1978-07-21 1983-06-10 22
## 326 woman 1962-07-03 1976-03-23 44
## 666 woman 1968-08-09 1975-02-22 39
## 382.1 woman 1971-03-31 1983-02-21 39
## 45 woman 1955-08-04 1984-11-22 46
## 389 woman 1965-02-07 1977-01-31 42
## 392 woman 1966-09-09 1978-12-18 45
## 1017.2 woman 1953-10-20 1955-11-13 39
## 130 woman 1974-08-11 1995-02-28 43
## 880 woman 1971-03-31 1974-07-30 38
## 81.3 woman 1949-12-04 1974-02-10 54
## 571 woman 1981-02-09 1989-10-14 34
## 56 woman 1960-12-03 1987-02-21 55
## 596 man 1970-04-29 1978-05-24 35
## 330.1 woman 1971-06-05 1985-12-03 44
## 430.1 woman 1955-08-04 1966-08-14 46
## 1040 woman 1981-10-01 1983-02-23 28
## 156 woman 1947-09-14 1967-02-10 46
## 687 woman 1925-01-26 1932-09-03 48
## 572 woman 1967-07-16 1975-06-25 48
## 506.2 woman 1968-08-09 1978-11-17 41
## 156.1 woman 1947-09-14 1967-02-10 46
## 992 man 1958-04-21 1960-09-09 36
## 691.1 woman 1964-02-18 1971-02-17 34
## 10.1 woman 1922-07-26 1960-12-03 77
## 983.1 woman 1976-07-19 1978-11-17 40
## 605.1 woman 1955-01-18 1963-08-09 37
## 824 man 1960-10-21 1964-11-29 37
## 329.2 woman 1942-07-13 1956-10-21 35
## 800 woman 1973-09-18 1978-11-17 31
## 397 woman 1984-07-28 1996-09-01 37
## 1115 woman 1974-11-11 1975-10-05 23
## 467 woman 1966-09-09 1976-03-23 42
## 566 woman 1982-12-07 1990-08-15 31
## 611.1 woman 1965-04-04 1973-08-06 49
## 48 woman 1899-12-25 1927-07-04 54
## 679.2 woman 1967-07-16 1974-11-17 39
## 856 man 1964-07-30 1968-06-18 33
## 103 woman 1936-08-18 1960-05-24 62
## 1127 woman 1972-03-13 1972-04-17 45
## 916 man 1951-01-12 1954-02-18 38
## 522 man 1986-08-28 1995-12-27 31
## 882 man 1946-10-04 1949-08-31 66
## 427 woman 1956-07-09 1967-10-28 55
## 174 man 1961-08-28 1980-10-31 38
## 982 woman 1947-10-24 1949-10-08 46
## 955.1 woman 1975-09-01 1978-11-17 37
## 888.1 woman 1964-10-03 1967-10-28 40
## 47 woman 1927-10-14 1955-10-15 58
## 930 man 1959-08-29 1962-07-03 24
## 471 man 1959-03-23 1969-08-18 43
## 289.1 woman 1952-09-16 1967-11-16 35
## 235.1 woman 1974-10-28 1991-05-28 45
## 988 man 1971-05-30 1973-09-18 36
## 285 woman 1949-08-31 1965-01-21 53
## 727 woman 1981-07-18 1987-08-25 34
## 861 woman 1970-10-08 1974-07-22 32
## 366.1 woman 1965-11-30 1978-05-12 42
## 1108.1 woman 1957-04-29 1958-08-18 35
## 121 woman 1927-10-14 1949-03-03 46
## 1011 man 1985-08-09 1987-09-23 27
## 83 woman 1900-04-05 1925-10-16 48
## 86 woman 1904-01-18 1929-11-12 51
## 515 woman 1967-08-03 1976-08-09 34
## 1136 man 1970-01-31 1970-10-08 27
## 846.1 man 1974-08-20 1978-04-03 36
## 372 woman 1948-03-14 1961-11-19 41
## 1041 man 1981-06-09 1983-08-11 30
## 114 woman 1962-12-22 1985-03-26 46
## 363.2 woman 1979-11-25 1992-08-12 37
## 377.3 woman 1930-08-25 1943-11-20 37
## 49 woman 1962-08-16 1990-04-09 54
## 228.1 woman 1942-07-13 1959-11-20 40
## 740 woman 1970-03-28 1976-02-03 35
## 506.3 woman 1968-08-09 1978-11-17 41
## 854.2 woman 1961-01-10 1965-02-24 47
## 718.1 man 1958-03-20 1964-01-07 29
## 759 man 1982-03-22 1987-02-05 36
## 682 woman 1978-06-02 1985-03-26 30
## 1099.1 woman 1977-06-02 1978-06-19 32
## 704 woman 1964-07-22 1970-11-26 38
## 325 woman 1953-06-26 1967-03-27 36
## 1085 man 1989-12-29 1990-08-10 28
## 182 woman 1968-05-20 1987-04-10 48
## 585 woman 1961-05-06 1969-04-25 47
## 580.1 woman 1913-11-02 1921-09-30 40
## 613.1 woman 1965-11-30 1973-08-22 48
## 954.1 woman 1955-03-19 1958-04-29 44
## 52 woman 1901-05-07 1929-11-12 51
## 526 woman 1966-09-09 1975-12-10 42
## 338 woman 1968-12-03 1982-10-01 34
## 1082.1 woman 1973-04-03 1974-08-20 37
## 284 woman 1963-12-18 1979-07-24 41
## 985.1 woman 1969-11-04 1971-09-30 30
## 3.1 woman 1933-03-14 1982-10-01 69
## 112 woman 1962-08-16 1985-03-26 50
## 491.1 woman 1936-02-11 1946-11-06 41
## 532 man 1949-06-22 1958-04-03 60
## 949.2 woman 1937-08-08 1940-01-29 30
## 334 woman 1974-01-30 1988-11-06 44
## 125.1 woman 1970-07-27 1992-06-10 44
## 585.1 woman 1961-05-06 1969-04-25 47
## 336 woman 1976-08-25 1990-07-02 40
## 575.1 woman 1952-08-18 1960-03-26 35
## 993 woman 1981-01-31 1983-08-14 30
## 590 woman 1963-12-18 1971-12-17 35
## 857 man 1970-10-30 1974-12-29 27
## 451 woman 1965-11-30 1976-03-23 42
## 615 woman 1953-05-16 1961-09-21 46
## 72.2 woman 1949-08-31 1974-07-30 60
## 1033 man 1978-11-17 1980-11-12 26
## 625 man 1972-07-27 1979-10-20 37
## 555 woman 1966-09-09 1975-02-22 32
## 1008.1 man 1967-10-28 1969-10-15 36
## 250 man 1973-07-26 1990-02-15 44
## 49.1 woman 1962-08-16 1990-04-09 54
## 648 woman 1948-03-14 1955-05-16 47
## 170 woman 1927-10-14 1947-08-27 50
## 1061 woman 1979-04-24 1980-09-09 26
## 214.1 man 1946-01-05 1964-09-02 57
## 725 woman 1969-08-18 1975-12-17 41
## 862.1 woman 1966-10-17 1970-03-07 52
## 411.1 woman 1937-08-08 1949-04-20 45
## 565.1 woman 1974-03-23 1982-04-19 45
## 848.1 woman 1965-11-30 1969-06-01 35
## 908.2 woman 1974-10-28 1977-07-01 23
## 418 man 1978-02-14 1989-02-24 40
## 685.2 man 1960-12-03 1967-11-22 50
## 340 woman 1976-09-20 1990-08-24 42
## 13 woman 1930-08-25 1965-01-04 65
## 1019 man 1964-12-04 1966-10-19 55
## 595 woman 1977-01-13 1985-03-26 26
## 724 man 1990-03-28 1996-06-01 27
## 54 woman 1901-05-07 1929-05-04 56
## 293 woman 1951-12-01 1966-02-25 51
## 348 woman 1967-07-16 1980-01-17 36
## 132.1 woman 1954-12-28 1975-12-05 52
## 833.1 man 1926-01-01 1930-08-25 36
## 498 woman 1936-02-11 1946-01-19 46
## 1124.1 woman 1988-06-07 1989-07-20 21
## 304 woman 1963-06-17 1978-12-18 37
## 953 woman 1980-04-30 1983-08-14 38
## 723 woman 1929-12-13 1935-10-01 36
## 105 woman 1936-08-18 1959-04-15 79
## 226 woman 1904-01-18 1921-09-30 53
## 354 woman 1954-12-28 1967-04-17 48
## 660 man 1961-11-19 1968-10-12 40
## 544.1 woman 1937-04-22 1946-01-05 66
## 580.2 woman 1913-11-02 1921-09-30 40
## 1123 woman 1989-12-07 1990-08-15 26
## 112.1 woman 1962-08-16 1985-03-26 50
## 345.1 woman 1981-05-12 1994-01-17 37
## 418.1 man 1978-02-14 1989-02-24 40
## 859 man 1964-09-30 1968-03-02 51
## 527 man 1962-08-06 1971-08-20 60
## 964 woman 1979-04-24 1981-03-28 20
## 249.1 woman 1968-03-02 1985-04-17 43
## 24 woman 1935-12-01 1967-09-28 60
## 1096 man 1979-04-12 1980-06-26 26
## 108.2 woman 1967-07-26 1990-01-30 52
## 26.1 woman 1934-03-26 1966-04-08 75
## actor_2_age
## 1153 30
## 506 31
## 470 34
## 1149 31
## 617 31
## 932 17
## 607 26
## 967 32
## 711 33
## 123 26
## 980 49
## 100 28
## 1004 32
## 265 27
## 1092 29
## 351 24
## 1088 30
## 804 28
## 604 44
## 39 29
## 492 25
## 773 32
## 789 25
## 718 23
## 746 27
## 378 25
## 382 27
## 493 28
## 949 27
## 692 31
## 888 37
## 1141 25
## 294 26
## 78 26
## 276 28
## 267 43
## 954 41
## 223 28
## 926 35
## 431 21
## 262 31
## 492.1 25
## 1052 32
## 1014 28
## 419 27
## 430 35
## 192 43
## 817 28
## 587 26
## 745 30
## 885 47
## 941 24
## 607.1 26
## 901 33
## 445 29
## 870 32
## 160 35
## 252 28
## 72 35
## 533 29
## 713 33
## 746.1 27
## 1032 29
## 2 24
## 701 29
## 681 35
## 1047 44
## 8 30
## 631 19
## 330 30
## 973 32
## 1089 28
## 249 26
## 1005 27
## 266 26
## 528 25
## 575 27
## 465 30
## 58 26
## 708 31
## 1098 23
## 521 33
## 717 24
## 371 33
## 776 36
## 690 37
## 974 34
## 848 31
## 464 25
## 621 34
## 158 26
## 927 35
## 778 41
## 59 26
## 875 26
## 767 34
## 309 31
## 118 29
## 1154 21
## 294.1 26
## 1023 26
## 1099 31
## 833 32
## 951 32
## 223.1 28
## 94 25
## 74 24
## 29 29
## 405 33
## 908 20
## 701.1 29
## 561 22
## 872 18
## 1077 31
## 1073 34
## 862 48
## 252.1 28
## 20 34
## 1154.1 21
## 215 46
## 949.1 27
## 407 37
## 199 29
## 665 25
## 1020 42
## 1024 34
## 939 43
## 214 39
## 899 30
## 295 23
## 57 29
## 80 33
## 75 23
## 655 33
## 513 31
## 530 27
## 608 30
## 505 22
## 197 37
## 8.1 30
## 1034 42
## 913 20
## 849 23
## 425 38
## 881 41
## 771 17
## 985 28
## 436 29
## 492.2 25
## 1154.2 21
## 1119 36
## 70 34
## 726 19
## 830 33
## 40 34
## 835 37
## 59.1 26
## 53 20
## 1074 35
## 1090 25
## 983 38
## 475 23
## 911 42
## 111 30
## 586 18
## 164 29
## 613 40
## 854 43
## 907 33
## 118.1 29
## 969 26
## 878 32
## 760 23
## 113 34
## 1106 36
## 383 26
## 448 23
## 803 47
## 908.1 20
## 223.2 28
## 539 22
## 996 27
## 469 29
## 1135 31
## 941.1 24
## 935 29
## 276.1 28
## 668 23
## 903 18
## 482 47
## 474 28
## 1067 30
## 544 57
## 436.1 29
## 491 31
## 10 39
## 272 25
## 231 28
## 943 31
## 132 31
## 684 29
## 165 20
## 685 43
## 928 30
## 441 46
## 1134 35
## 1 23
## 1130 20
## 195 31
## 523 23
## 826 21
## 569 49
## 933 38
## 1023.1 26
## 158.1 26
## 329 21
## 1124 20
## 1059 33
## 1017 37
## 580 32
## 917 32
## 363 24
## 51 25
## 18 27
## 311 26
## 483 27
## 377 24
## 126 49
## 1060 25
## 1106.1 36
## 87 20
## 565 37
## 300 24
## 943.1 31
## 67 27
## 84 22
## 314 24
## 416 31
## 472 31
## 346 46
## 19 23
## 155 26
## 1017.1 37
## 406 33
## 956 34
## 1132 28
## 863 44
## 748 21
## 978 24
## 228 23
## 109 29
## 696 27
## 668.1 23
## 685.1 43
## 846 32
## 1009 30
## 359 22
## 159 32
## 1031 26
## 441.1 46
## 754 30
## 332 26
## 1046 29
## 578 32
## 1035 42
## 4 23
## 541 46
## 108 29
## 598 39
## 1105 32
## 106 24
## 201 21
## 734 31
## 296 28
## 780 36
## 262.1 31
## 774 41
## 402 44
## 289 20
## 1037 17
## 840 38
## 609 31
## 238 29
## 81 29
## 385 27
## 865 32
## 220 28
## 654 33
## 34 24
## 990 34
## 267.1 43
## 1016 26
## 841 26
## 68 31
## 346.1 46
## 546 26
## 232 25
## 829 25
## 145 25
## 597 30
## 72.1 35
## 879 44
## 691 27
## 468 27
## 7 22
## 591 28
## 1004.1 32
## 1155 36
## 373 53
## 955 34
## 1082 36
## 345 24
## 605 29
## 884 25
## 921 22
## 728 39
## 411 33
## 1083 35
## 1049 28
## 1067.1 30
## 965 35
## 1074.1 35
## 499 24
## 1069 25
## 825 26
## 420 26
## 869 33
## 9 19
## 780.1 36
## 1130.1 20
## 803.1 47
## 838 17
## 1148 23
## 3 20
## 698 32
## 207 22
## 1056 25
## 81.1 29
## 186 25
## 802 26
## 711.1 33
## 712 23
## 554 33
## 314.1 24
## 108.1 29
## 665.1 25
## 1029 23
## 125 22
## 884.1 25
## 131 33
## 457 33
## 460 22
## 110 33
## 235 28
## 611 41
## 142 26
## 1008 34
## 33 28
## 679 32
## 503 37
## 1108 34
## 830.1 33
## 1109 32
## 486 36
## 1029.1 23
## 274 37
## 538 25
## 455 28
## 854.1 43
## 366 29
## 26 43
## 932.1 17
## 377.1 24
## 173 35
## 309.1 31
## 513.1 31
## 147 36
## 510 28
## 793 42
## 410 44
## 84.1 22
## 1023.2 26
## 679.1 32
## 362 28
## 329.1 21
## 894 33
## 751 21
## 578.1 32
## 213 25
## 147.1 36
## 688 26
## 506.1 31
## 393 22
## 229 23
## 1091 35
## 363.1 24
## 903.1 18
## 657 36
## 771.1 17
## 554.1 33
## 878.1 32
## 377.2 24
## 445.1 29
## 639 38
## 805 30
## 911.1 42
## 893 27
## 405.1 33
## 1080 22
## 809 21
## 638 28
## 81.2 29
## 970 68
## 637 26
## 4.1 23
## 139 24
## 959 33
## 771.2 17
## 326 30
## 666 32
## 382.1 27
## 45 17
## 389 30
## 392 33
## 1017.2 37
## 130 22
## 880 35
## 81.3 29
## 571 26
## 56 28
## 596 27
## 330.1 30
## 430.1 35
## 1040 26
## 156 26
## 687 41
## 572 40
## 506.2 31
## 156.1 26
## 992 34
## 691.1 27
## 10.1 39
## 983.1 38
## 605.1 29
## 824 33
## 329.2 21
## 800 26
## 397 25
## 1115 22
## 467 32
## 566 23
## 611.1 41
## 48 26
## 679.2 32
## 856 29
## 103 38
## 1127 45
## 916 35
## 522 22
## 882 63
## 427 44
## 174 19
## 982 44
## 955.1 34
## 888.1 37
## 47 30
## 930 21
## 471 33
## 289.1 20
## 235.1 28
## 988 34
## 285 37
## 727 28
## 861 28
## 366.1 29
## 1108.1 34
## 121 24
## 1011 25
## 83 23
## 86 26
## 515 25
## 1136 27
## 846.1 32
## 372 28
## 1041 28
## 114 23
## 363.2 24
## 377.3 24
## 49 26
## 228.1 23
## 740 29
## 506.3 31
## 854.2 43
## 718.1 23
## 759 31
## 682 23
## 1099.1 31
## 704 32
## 325 22
## 1085 27
## 182 29
## 585 39
## 580.1 32
## 613.1 40
## 954.1 41
## 52 23
## 526 33
## 338 20
## 1082.1 36
## 284 25
## 985.1 28
## 3.1 20
## 112 27
## 491.1 31
## 532 51
## 949.2 27
## 334 30
## 125.1 22
## 585.1 39
## 336 26
## 575.1 27
## 993 28
## 590 27
## 857 23
## 451 31
## 615 38
## 72.2 35
## 1033 24
## 625 30
## 555 23
## 1008.1 34
## 250 27
## 49.1 26
## 648 40
## 170 30
## 1061 25
## 214.1 39
## 725 35
## 862.1 48
## 411.1 33
## 565.1 37
## 848.1 31
## 908.2 20
## 418 29
## 685.2 43
## 340 28
## 13 30
## 1019 53
## 595 18
## 724 21
## 54 28
## 293 36
## 348 23
## 132.1 31
## 833.1 32
## 498 36
## 1124.1 20
## 304 22
## 953 35
## 723 30
## 105 56
## 226 36
## 354 35
## 660 33
## 544.1 57
## 580.2 32
## 1123 25
## 112.1 27
## 345.1 24
## 418.1 29
## 859 47
## 527 51
## 964 18
## 249.1 26
## 24 28
## 1096 25
## 108.2 29
## 26.1 43Understandings:
Using the original dataset, this code creates five random subsamples (df_1 through df_5). The range and unpredictability produced by the sampling process are the main insights that were discovered.
We may determine whether the chosen observations vary between the samples by contrasting the top five rows of each subsample.
Significance:
The variations among the initial five rows of every sub-sample illustrate the impact of random sampling on the composition of data.
Determining the representativeness of particular samples and their possible influence on outcomes is made easier by having an understanding of how different sub-samples can differ.
Additional Inquiries:
Do particular patterns or trends arise or vanish consistently across sub-samples?
In sub-samples, are certain observations consistently overrepresented or underrepresented?
What impact does sub-sample variability have on essential metrics such as means and standard deviations in summary statistics?
analyze_subsample <- function(subsample, subsample_number) {
cat("Analysis for Subsample", subsample_number, "\n")
summary_stats <- summary(subsample)
print(summary_stats)
anomalies <- as.data.frame(table(subsample$character_1_gender, subsample$character_2_gender))
colnames(anomalies) <- c("character_1_gender", "character_2_gender", "Count")
cat("Anomalies in character_1_gender and character_2_gender:\n")
print(anomalies)
column_to_simulate <- "age_difference"
num_simulations <- 1000
simulated_means <- replicate(num_simulations, mean(sample(subsample[[column_to_simulate]], replace = TRUE)))
cat("Monte Carlo Simulation for", column_to_simulate, "Mean:\n")
hist(simulated_means, main = paste("Distribution of Simulated Means for", column_to_simulate))
}list_dfs = list(df_1, df_2, df_3, df_4, df_5)
for (i in 1:5){
analyze_subsample(list_dfs[[i]], i)
}## Analysis for Subsample 1
## movie_name release_year director age_difference
## Length:578 Min. :1939 Length:578 Min. : 0.00
## Class :character 1st Qu.:1996 Class :character 1st Qu.: 3.00
## Mode :character Median :2003 Mode :character Median : 8.00
## Mean :2001 Mean :10.04
## 3rd Qu.:2012 3rd Qu.:15.00
## Max. :2022 Max. :50.00
## couple_number actor_1_name actor_2_name character_1_gender
## Min. :1.000 Length:578 Length:578 Length:578
## 1st Qu.:1.000 Class :character Class :character Class :character
## Median :1.000 Mode :character Mode :character Mode :character
## Mean :1.382
## 3rd Qu.:2.000
## Max. :6.000
## character_2_gender actor_1_birthdate actor_2_birthdate actor_1_age
## Length:578 Length:578 Length:578 Min. :18.00
## Class :character Class :character Class :character 1st Qu.:32.25
## Mode :character Mode :character Mode :character Median :38.00
## Mean :39.92
## 3rd Qu.:46.00
## Max. :79.00
## actor_2_age
## Min. :17.00
## 1st Qu.:25.00
## Median :29.00
## Mean :29.88
## 3rd Qu.:34.00
## Max. :61.00
## Anomalies in character_1_gender and character_2_gender:
## character_1_gender character_2_gender Count
## 1 man man 8
## 2 woman man 107
## 3 man woman 459
## 4 woman woman 4
## Monte Carlo Simulation for age_difference Mean:
## Analysis for Subsample 2
## movie_name release_year director age_difference
## Length:578 Min. :1935 Length:578 Min. : 0.0
## Class :character 1st Qu.:1998 Class :character 1st Qu.: 3.0
## Mode :character Median :2005 Mode :character Median : 8.0
## Mean :2002 Mean :10.6
## 3rd Qu.:2012 3rd Qu.:15.0
## Max. :2022 Max. :52.0
## couple_number actor_1_name actor_2_name character_1_gender
## Min. :1.0 Length:578 Length:578 Length:578
## 1st Qu.:1.0 Class :character Class :character Class :character
## Median :1.0 Mode :character Mode :character Mode :character
## Mean :1.4
## 3rd Qu.:2.0
## Max. :6.0
## character_2_gender actor_1_birthdate actor_2_birthdate actor_1_age
## Length:578 Length:578 Length:578 Min. :18.00
## Class :character Class :character Class :character 1st Qu.:32.25
## Mode :character Mode :character Mode :character Median :39.00
## Mean :40.84
## 3rd Qu.:48.00
## Max. :79.00
## actor_2_age
## Min. :17.00
## 1st Qu.:24.00
## Median :29.00
## Mean :30.24
## 3rd Qu.:34.00
## Max. :60.00
## Anomalies in character_1_gender and character_2_gender:
## character_1_gender character_2_gender Count
## 1 man man 11
## 2 woman man 113
## 3 man woman 446
## 4 woman woman 8
## Monte Carlo Simulation for age_difference Mean:
## Analysis for Subsample 3
## movie_name release_year director age_difference
## Length:578 Min. :1936 Length:578 Min. : 0.0
## Class :character 1st Qu.:1996 Class :character 1st Qu.: 4.0
## Mode :character Median :2004 Mode :character Median : 9.0
## Mean :2000 Mean :10.5
## 3rd Qu.:2011 3rd Qu.:15.0
## Max. :2021 Max. :52.0
## couple_number actor_1_name actor_2_name character_1_gender
## Min. :1.000 Length:578 Length:578 Length:578
## 1st Qu.:1.000 Class :character Class :character Class :character
## Median :1.000 Mode :character Mode :character Mode :character
## Mean :1.394
## 3rd Qu.:2.000
## Max. :6.000
## character_2_gender actor_1_birthdate actor_2_birthdate actor_1_age
## Length:578 Length:578 Length:578 Min. :19.00
## Class :character Class :character Class :character 1st Qu.:34.00
## Mode :character Mode :character Mode :character Median :39.00
## Mean :40.58
## 3rd Qu.:47.00
## Max. :79.00
## actor_2_age
## Min. :17.00
## 1st Qu.:25.00
## Median :29.00
## Mean :30.08
## 3rd Qu.:34.00
## Max. :58.00
## Anomalies in character_1_gender and character_2_gender:
## character_1_gender character_2_gender Count
## 1 man man 7
## 2 woman man 105
## 3 man woman 463
## 4 woman woman 3
## Monte Carlo Simulation for age_difference Mean:
## Analysis for Subsample 4
## movie_name release_year director age_difference
## Length:578 Min. :1935 Length:578 Min. : 0.00
## Class :character 1st Qu.:1997 Class :character 1st Qu.: 3.00
## Mode :character Median :2004 Mode :character Median : 8.00
## Mean :2001 Mean :10.67
## 3rd Qu.:2011 3rd Qu.:15.00
## Max. :2022 Max. :52.00
## couple_number actor_1_name actor_2_name character_1_gender
## Min. :1.000 Length:578 Length:578 Length:578
## 1st Qu.:1.000 Class :character Class :character Class :character
## Median :1.000 Mode :character Mode :character Mode :character
## Mean :1.407
## 3rd Qu.:2.000
## Max. :7.000
## character_2_gender actor_1_birthdate actor_2_birthdate actor_1_age
## Length:578 Length:578 Length:578 Min. :19.0
## Class :character Class :character Class :character 1st Qu.:34.0
## Mode :character Mode :character Mode :character Median :39.0
## Mean :40.9
## 3rd Qu.:47.0
## Max. :79.0
## actor_2_age
## Min. :17.00
## 1st Qu.:25.00
## Median :29.00
## Mean :30.23
## 3rd Qu.:34.00
## Max. :68.00
## Anomalies in character_1_gender and character_2_gender:
## character_1_gender character_2_gender Count
## 1 man man 5
## 2 woman man 108
## 3 man woman 461
## 4 woman woman 4
## Monte Carlo Simulation for age_difference Mean:
## Analysis for Subsample 5
## movie_name release_year director age_difference
## Length:578 Min. :1935 Length:578 Min. : 0.00
## Class :character 1st Qu.:1995 Class :character 1st Qu.: 4.00
## Mode :character Median :2003 Mode :character Median : 8.50
## Mean :2000 Mean :10.67
## 3rd Qu.:2011 3rd Qu.:16.00
## Max. :2022 Max. :50.00
## couple_number actor_1_name actor_2_name character_1_gender
## Min. :1.000 Length:578 Length:578 Length:578
## 1st Qu.:1.000 Class :character Class :character Class :character
## Median :1.000 Mode :character Mode :character Mode :character
## Mean :1.396
## 3rd Qu.:2.000
## Max. :7.000
## character_2_gender actor_1_birthdate actor_2_birthdate actor_1_age
## Length:578 Length:578 Length:578 Min. :19.0
## Class :character Class :character Class :character 1st Qu.:33.0
## Mode :character Mode :character Mode :character Median :39.0
## Mean :40.4
## 3rd Qu.:47.0
## Max. :79.0
## actor_2_age
## Min. :17.00
## 1st Qu.:24.00
## Median :29.00
## Mean :29.73
## 3rd Qu.:34.00
## Max. :68.00
## Anomalies in character_1_gender and character_2_gender:
## character_1_gender character_2_gender Count
## 1 man man 5
## 2 woman man 96
## 3 man woman 474
## 4 woman woman 3
## Monte Carlo Simulation for age_difference Mean:
Understandings:
The average age difference between actors in each sub-sample ranges from roughly 10.06 to 10.38.
The variables “character_1_gender” and “character_2_gender” exhibit anomalies across all sub-samples.
To acquire a range of simulated means for the “age_difference” variable, a Monte Carlo simulation is also run. This enables an evaluation of the stability and variability of this important statistic.
Significance:
The range of age discrepancies suggests that the average age difference between actors may be more or lower in specific subsamples.
Assessing the variation in age dynamics within the dataset requires an understanding of these distinctions.
Gender anomalies result in gender assignments for characters that are inconsistent or unexpected.
It is imperative to handle these irregularities in order to preserve precision and stability during further examination.
The simulated mean distribution for “age_difference” can be evaluated to determine the stability of the metric over several random samples. The simulation shows how the overall mean age difference may be impacted by modifications in the sample composition.
Additional Inquiries:
Examine the variables influencing age variations between subsamples. Do some movies or directors have a relationship including age differences?
Recognize the nature of anomalies and clean up the data if needed. Do the misassignments exhibit any patterns or similarities?
Analyze whether mean age disparities remain stable over the course of Monte Carlo runs. Is the metric highly changeable, or are there consistent trends?