Here are the vectors from last week’s WPA.Using these vectors, create a dataframe called (short for movies.sample) that contain the vectors.
m.names <- c("Baramgwa hamjje sarajida", "Sleepless in Seattle", "The Water Diviner",
"Fly Away Home", "The Three Musketeers", "Candyman: Farewell to Flesh",
"Honey I Blew Up the Kid", "Kingsman: The Secret Service", "Ajab Prem Ki Ghazab Kahani",
"A Bug's Life", "Courage Under Fire", "Dirty Pretty Things",
"In the Name of the Father", "Soul Plane", "Magnum Force", "About Time",
"House of Sand and Fog", "Bokura ga ita Zenpen", "Jackass 3D",
"Tropic Thunder - A Pirate's Tale")
boxoffice <- c(28686545, 218076024, 30864649, 35870837, 50375628, 13899536,
58662452, 404561724, 15906411, 363089431, 100748262, 14156753,
25096862, 14553807, 44680473, 89177486, 16157923, 26324268, 171685793,
191091250)
genre <- c("Action", "Romantic Comedy", "Drama", "Drama", "Adventure",
"Horror", "Comedy", "Action", "Comedy", "Adventure", "Drama",
"Drama", "Drama", "Comedy", "Action", "Romantic Comedy", "Drama",
"Drama", "Comedy", "Comedy")
time <- c(121, 100, 112, NA, NA, NA, NA, 129, NA, 96, 111, NA, NA, NA,
NA, 123, NA, 121, 93, 106)
rating <- c(NA, "PG", "R", "PG", "PG", "R", "PG", "R", NA, "G", "R",
"R", "R", "R", NA, "R", "R", NA, "R", "R")With the help of these vectors we are able to creat the sample data frame.
movies.sample <- cbind(m.names, boxoffice, genre, time, rating )
movies.sample## m.names boxoffice genre
## [1,] "Baramgwa hamjje sarajida" "28686545" "Action"
## [2,] "Sleepless in Seattle" "218076024" "Romantic Comedy"
## [3,] "The Water Diviner" "30864649" "Drama"
## [4,] "Fly Away Home" "35870837" "Drama"
## [5,] "The Three Musketeers" "50375628" "Adventure"
## [6,] "Candyman: Farewell to Flesh" "13899536" "Horror"
## [7,] "Honey I Blew Up the Kid" "58662452" "Comedy"
## [8,] "Kingsman: The Secret Service" "404561724" "Action"
## [9,] "Ajab Prem Ki Ghazab Kahani" "15906411" "Comedy"
## [10,] "A Bug's Life" "363089431" "Adventure"
## [11,] "Courage Under Fire" "100748262" "Drama"
## [12,] "Dirty Pretty Things" "14156753" "Drama"
## [13,] "In the Name of the Father" "25096862" "Drama"
## [14,] "Soul Plane" "14553807" "Comedy"
## [15,] "Magnum Force" "44680473" "Action"
## [16,] "About Time" "89177486" "Romantic Comedy"
## [17,] "House of Sand and Fog" "16157923" "Drama"
## [18,] "Bokura ga ita Zenpen" "26324268" "Drama"
## [19,] "Jackass 3D" "171685793" "Comedy"
## [20,] "Tropic Thunder - A Pirate's Tale" "191091250" "Comedy"
## time rating
## [1,] "121" NA
## [2,] "100" "PG"
## [3,] "112" "R"
## [4,] NA "PG"
## [5,] NA "PG"
## [6,] NA "R"
## [7,] NA "PG"
## [8,] "129" "R"
## [9,] NA NA
## [10,] "96" "G"
## [11,] "111" "R"
## [12,] NA "R"
## [13,] NA "R"
## [14,] NA "R"
## [15,] NA NA
## [16,] "123" "R"
## [17,] NA "R"
## [18,] "121" NA
## [19,] "93" "R"
## [20,] "106" "R"movies <- read.table("http://nathanieldphillips.com/wp-content/uploads/2015/11/movies.txt",
sep = "\t",
header = T,
stringsAsFactors = F)How many rows and columns are in the movies dataset?
dim(movies)## [1] 5000 14What are the column names?
names(movies)## [1] "name" "boxoffice.total"
## [3] "boxoffice.domestic" "boxoffice.international"
## [5] "dvd.domestic" "budget"
## [7] "rating" "genre"
## [9] "creative.type" "time"
## [11] "year" "production.method"
## [13] "sequel" "boxoffice.domestic.inflationadj"Look at the first few rows of the dataset using the head() function.
head(movies)## name boxoffice.total boxoffice.domestic
## 1 Avatar 2783918982 760507625
## 2 Titanic 2207615668 658672302
## 3 Jurassic World 1665443635 651443635
## 4 The Avengers 1519479547 623279547
## 5 Furious 7 1516246709 351032910
## 6 The Avengers: Age of Ultron 1404705868 459005868
## boxoffice.international dvd.domestic budget rating genre
## 1 2023411357 230915507 425000000 PG-13 Action
## 2 1548943366 NA 200000000 PG-13 Thriller/Suspense
## 3 1014000000 NA 215000000 PG-13 Action
## 4 896200000 109515497 225000000 PG-13 Adventure
## 5 1165213799 14947559 190000000 PG-13 Action
## 6 945700000 7312791 250000000 PG-13 Action
## creative.type time year production.method sequel
## 1 Science Fiction 162 2009 Animation/Live Action 0
## 2 Historical Fiction 194 1997 Live Action 0
## 3 Science Fiction 124 2015 Live Action 1
## 4 Super Hero 143 2012 Animation/Live Action 0
## 5 Contemporary Fiction 137 2014 Live Action 1
## 6 Super Hero 141 2015 Live Action 1
## boxoffice.domestic.inflationadj
## 1 826198130
## 2 1139182838
## 3 651443635
## 4 655383136
## 5 351032910
## 6 459005868Look at the last few rows using the tail() function.
tail(movies)## name boxoffice.total boxoffice.domestic
## 4995 Idlewild 12571185 12571185
## 4996 Hellraiser III: Hell on Earth 12525537 12525537
## 4997 Sorority Boys 12516222 10198766
## 4998 Love Jones 12514138 12514138
## 4999 Klute 12512637 12512637
## 5000 Breakin' All the Rules 12512317 12232382
## boxoffice.international dvd.domestic budget rating genre
## 4995 0 8753109 15000000 R Musical
## 4996 0 NA 0 R Horror
## 4997 2317456 NA 12000000 R Comedy
## 4998 0 NA 10000000 R Drama
## 4999 0 NA 0 <NA> <NA>
## 5000 279935 NA 9000000 PG-13 Romantic Comedy
## creative.type time year production.method sequel
## 4995 Historical Fiction NA 2006 Live Action 0
## 4996 Fantasy NA 1992 Live Action 1
## 4997 Contemporary Fiction NA 2002 Live Action 0
## 4998 Contemporary Fiction NA 1997 Live Action 0
## 4999 <NA> NA 1971 <NA> 0
## 5000 Contemporary Fiction NA 2004 Live Action 0
## boxoffice.domestic.inflationadj
## 4995 16064248
## 4996 12525537
## 4997 14692539
## 4998 22819901
## 4999 63473192
## 5000 16487126Look at rows 20 through 30.
movies[c(20:30),]## name boxoffice.total
## 20 Star Wars Ep. I: The Phantom Menace 1027044677
## 21 Alice in Wonderland 1025491110
## 22 The Hobbit: An Unexpected Journey 1017003568
## 23 The Dark Knight 1002891358
## 24 The Lion King 987480140
## 25 Despicable Me 2 974873764
## 26 Harry Potter and the Sorcerer\xd5s Stone 974755371
## 27 Pirates of the Caribbean: At World's End 963420425
## 28 The Hobbit: The Desolation of Smaug 960366855
## 29 Harry Potter and the Deathly Hallows: Part I 959301070
## 30 The Hobbit: The Battle of the Five Armies 955119788
## boxoffice.domestic boxoffice.international dvd.domestic budget
## 20 474544677 552500000 NA 115000000
## 21 334191110 691300000 82297116 200000000
## 22 303003568 714000000 34191124 250000000
## 23 533345358 469546000 282134660 185000000
## 24 422780140 564700000 86246006 79300000
## 25 368065385 606808379 124381409 76000000
## 26 317575550 657179821 NA 125000000
## 27 309420425 654000000 312228146 300000000
## 28 258366855 702000000 40247146 250000000
## 29 295001070 664300000 94385569 125000000
## 30 255119788 700000000 22439417 250000000
## rating genre creative.type time year production.method
## 20 PG Adventure Science Fiction 133 1999 Animation/Live Action
## 21 PG Adventure Fantasy 108 2010 Animation/Live Action
## 22 PG-13 Adventure Fantasy 169 2012 Animation/Live Action
## 23 PG-13 Action Super Hero 152 2008 Live Action
## 24 G Adventure Kids Fiction 87 1994 Hand Animation
## 25 PG Comedy Kids Fiction 98 2013 Digital Animation
## 26 PG Adventure Fantasy 152 2001 Animation/Live Action
## 27 PG-13 Adventure Historical Fiction 167 2007 Live Action
## 28 PG-13 Adventure Fantasy 201 2013 Animation/Live Action
## 29 PG-13 Adventure Fantasy 146 2010 Animation/Live Action
## 30 PG-13 Adventure Fantasy 144 2014 Live Action
## sequel boxoffice.domestic.inflationadj
## 20 1 755899504
## 21 0 354522122
## 22 1 317749322
## 23 1 621624466
## 24 0 756219975
## 25 1 378930222
## 26 0 468612897
## 27 1 376431535
## 28 1 265846191
## 29 1 312886528
## 30 1 260520067Show me all the data for Harry Potter and the Chamber of Secrets.
movies[movies$name == "Harry Potter and the Chamber of Secrets",]## name boxoffice.total
## 40 Harry Potter and the Chamber of Secrets 878979634
## boxoffice.domestic boxoffice.international dvd.domestic budget
## 40 261987880 616991754 NA 100000000
## rating genre creative.type time year production.method sequel
## 40 PG Adventure Fantasy 161 2002 Animation/Live Action 1
## boxoffice.domestic.inflationadj
## 40 376584346What was the rating of Harry Potter and the Chamber of Secrets?
movies[movies$name == "Harry Potter and the Chamber of Secrets", names(movies) == "rating"]## [1] "PG"What was the boxoffice total of Harry Potter and the Deathly Hallows?
movies[movies$name == "Harry Potter and the Deathly Hallows: Part I", names(movies) == "boxoffice.total"]## [1] 959301070What was the running time of Harry Potter and the Order of the Phoenix?
movies[movies$name == "Harry Potter and the Order of the Phoenix", names(movies) == "time"]## [1] 138It’s easy to sort rows in a dataframe according to a column: just use the following code structure.
Using this logic, sort the movies dataframe by year (with the oldest movies at the top of the dataframe).
index <- order(movies$year, decreasing = F)
movies.order.year <- movies[index,]
head(movies.order.year)## name boxoffice.total boxoffice.domestic
## 3798 The Big Parade 22000000 11000000
## 682 Snow White and the Seven Dwarfs 184925485 184925485
## 217 Gone with the Wind 390525192 198680470
## 2918 The Wizard of Oz 33711566 33711566
## 1490 Pinocchio 84300000 84300000
## 1504 Fantasia 83320000 83320000
## boxoffice.international dvd.domestic budget rating genre
## 3798 11000000 NA 245000 <NA> Drama
## 682 0 NA 1488000 G Musical
## 217 191844722 NA 3900000 G Drama
## 2918 0 NA 2777000 PG-13 Musical
## 1490 0 NA 0 G Adventure
## 1504 0 NA 2280000 G Musical
## creative.type time year production.method sequel
## 3798 <NA> NA 1925 Live Action 0
## 682 Fantasy 83 1937 Hand Animation 0
## 217 Historical Fiction 220 1939 Live Action 0
## 2918 Fantasy 101 1939 Live Action 0
## 1490 Kids Fiction NA 1940 Hand Animation 0
## 1504 Multiple Creative Types NA 1940 Hand Animation 0
## boxoffice.domestic.inflationadj
## 3798 0
## 682 6729679608
## 217 2033987841
## 2918 1031987972
## 1490 2927763484
## 1504 2893727800What are the names of the 5 oldest movies in the dataset?
movies.order.year$name[c(1:5)]## [1] "The Big Parade" "Snow White and the Seven Dwarfs"
## [3] "Gone with the Wind" "The Wizard of Oz"
## [5] "Pinocchio"Now, sort the dataframe back into its original order (by boxoffice). The first movie in the dataframe should be Avatar!
index2 <- order(movies$boxoffice.total, decreasing = T)
movies.order.boxoffice.total <- movies[index2,]
head(movies.order.boxoffice.total)## name boxoffice.total boxoffice.domestic
## 1 Avatar 2783918982 760507625
## 2 Titanic 2207615668 658672302
## 3 Jurassic World 1665443635 651443635
## 4 The Avengers 1519479547 623279547
## 5 Furious 7 1516246709 351032910
## 6 The Avengers: Age of Ultron 1404705868 459005868
## boxoffice.international dvd.domestic budget rating genre
## 1 2023411357 230915507 425000000 PG-13 Action
## 2 1548943366 NA 200000000 PG-13 Thriller/Suspense
## 3 1014000000 NA 215000000 PG-13 Action
## 4 896200000 109515497 225000000 PG-13 Adventure
## 5 1165213799 14947559 190000000 PG-13 Action
## 6 945700000 7312791 250000000 PG-13 Action
## creative.type time year production.method sequel
## 1 Science Fiction 162 2009 Animation/Live Action 0
## 2 Historical Fiction 194 1997 Live Action 0
## 3 Science Fiction 124 2015 Live Action 1
## 4 Super Hero 143 2012 Animation/Live Action 0
## 5 Contemporary Fiction 137 2014 Live Action 1
## 6 Super Hero 141 2015 Live Action 1
## boxoffice.domestic.inflationadj
## 1 826198130
## 2 1139182838
## 3 651443635
## 4 655383136
## 5 351032910
## 6 459005868Add a new column called “boxoffice.millions” that has the box-office values in millions of dollars. For example, a value of 1000000 in the original boxoffice vector should be 1 in boxoffice.millions
boxoffice.millions <- movies$boxoffice.total / 1000000
movies2 <- cbind(movies, boxoffice.millions)
head(movies2)## name boxoffice.total boxoffice.domestic
## 1 Avatar 2783918982 760507625
## 2 Titanic 2207615668 658672302
## 3 Jurassic World 1665443635 651443635
## 4 The Avengers 1519479547 623279547
## 5 Furious 7 1516246709 351032910
## 6 The Avengers: Age of Ultron 1404705868 459005868
## boxoffice.international dvd.domestic budget rating genre
## 1 2023411357 230915507 425000000 PG-13 Action
## 2 1548943366 NA 200000000 PG-13 Thriller/Suspense
## 3 1014000000 NA 215000000 PG-13 Action
## 4 896200000 109515497 225000000 PG-13 Adventure
## 5 1165213799 14947559 190000000 PG-13 Action
## 6 945700000 7312791 250000000 PG-13 Action
## creative.type time year production.method sequel
## 1 Science Fiction 162 2009 Animation/Live Action 0
## 2 Historical Fiction 194 1997 Live Action 0
## 3 Science Fiction 124 2015 Live Action 1
## 4 Super Hero 143 2012 Animation/Live Action 0
## 5 Contemporary Fiction 137 2014 Live Action 1
## 6 Super Hero 141 2015 Live Action 1
## boxoffice.domestic.inflationadj boxoffice.millions
## 1 826198130 2783.919
## 2 1139182838 2207.616
## 3 651443635 1665.444
## 4 655383136 1519.480
## 5 351032910 1516.247
## 6 459005868 1404.706What is the mean box-office totals of all movies?
median(movies$time, na.rm = T)## [1] 108What is the median running length of the movies?